Welcome to kwcoco’s documentation!¶

class kwcoco.cli.coco_conform.CocoConformCLI[source]¶

class CLIConfig(data=None, default=None, cmdline=False)[source]¶

Make the COCO file conform to the spec.

Populates inferable information such as image size, annotation area, etc.

epilog = Multiline-String[source]¶

Show Value

        Example Usage:
            kwcoco conform --help
            kwcoco conform --src=special:shapes8 --dst conformed.json

default[source]¶

name = conform[source]¶

classmethod main(cls, cmdline=True, **kw)[source]¶

Example

>>> # xdoctest: +SKIP
>>> kw = {'src': 'special:shapes8'}
>>> cmdline = False
>>> cls = CocoConformCLI
>>> cls.main(cmdline, **kw)

kwcoco.cli.coco_conform._CLI[source]¶

`kwcoco.cli.coco_eval`¶

Wraps the logic in kwcoco/coco_evaluator.py with a command line script

Module Contents¶

Classes¶

`CocoEvalCLIConfig`	Base class for custom configuration objects
`CocoEvalCLI`

Functions¶

main(cmdline=True, **kw)

Todo

[X] should live in kwcoco.cli.coco_eval

Attributes¶

class kwcoco.cli.coco_eval.CocoEvalCLIConfig(data=None, default=None, cmdline=False)[source]¶

Base class for custom configuration objects

A configuration that can be specified by commandline args, a yaml config file, and / or a in-code dictionary. To use, define a class variable named “default” and assing it to a dict of default values. You can also use special Value classes to denote types. You can also define a method normalize, to postprocess the arguments after this class receives them.

Usage:

Create a class that herits from this class.

Assign the “default” class-level variable as a dictionary of options

The keys of this dictionary must be command line friendly strings.

The values of the “defaults dictionary” can be literal values or instances of the scriptconfig.Value class, which allows for specification of default values, type information, help strings, and aliases.

You may also implement normalize (function with that takes no args and has no return) to postprocess your results after initialization.

When creating an instance of the class the defaults variable is used to make a dictionary-like object. You can override defaults by specifying the data keyword argument to either a file path or another dictionary. You can also specify cmdline=True to allow the contents of sys.argv to influence the values of the new object.

An instance of the config class behaves like a dictinary, except that you cannot set keys that do not already exist (as specified in the defaults dict).

Key Methods:

dump - dump a json representation to a file

dumps - dump a json representation to a string

argparse - create the argparse object associated with this config

argparse - create an argparse.ArgumentParser object that
is defined by the defaults of this config.

load - rewrite the values based on a filepath, dictionary, or
command line contents.

Variables

_data – this protected variable holds the raw state of the config object and is accessed by the dict-like
_default – this protected variable maintains the default values for this config.

Example

>>> # Inherit from `Config` and assign `default`
>>> import scriptconfig as scfg
>>> class MyConfig(scfg.Config):
>>>     default = {
>>>         'option1': scfg.Value((1, 2, 3), tuple),
>>>         'option2': 'bar',
>>>         'option3': None,
>>>     }
>>> # You can now make instances of this class
>>> config1 = MyConfig()
>>> config2 = MyConfig(default=dict(option1='baz'))

__doc__[source]¶

default[source]¶

class kwcoco.cli.coco_eval.CocoEvalCLI[source]¶

name = eval[source]¶

CLIConfig[source]¶

classmethod main(cls, cmdline=True, **kw)[source]¶

Example

>>> # xdoctest: +REQUIRES(module:kwplot)
>>> import ubelt as ub
>>> from kwcoco.cli.coco_eval import *  # NOQA
>>> from os.path import join
>>> import kwcoco
>>> dpath = ub.ensure_app_cache_dir('kwcoco/tests/eval')
>>> true_dset = kwcoco.CocoDataset.demo('shapes8')
>>> from kwcoco.demo.perterb import perterb_coco
>>> kwargs = {
>>>     'box_noise': 0.5,
>>>     'n_fp': (0, 10),
>>>     'n_fn': (0, 10),
>>> }
>>> pred_dset = perterb_coco(true_dset, **kwargs)
>>> true_dset.fpath = join(dpath, 'true.mscoco.json')
>>> pred_dset.fpath = join(dpath, 'pred.mscoco.json')
>>> true_dset.dump(true_dset.fpath)
>>> pred_dset.dump(pred_dset.fpath)
>>> draw = False  # set to false for faster tests
>>> CocoEvalCLI.main(
>>>     true_dataset=true_dset.fpath,
>>>     pred_dataset=pred_dset.fpath,
>>>     draw=draw)

kwcoco.cli.coco_eval.main(cmdline=True, **kw)[source]¶

Todo

[X] should live in kwcoco.cli.coco_eval

CommandLine

# Generate test data
xdoctest -m kwcoco.cli.coco_eval CocoEvalCLI.main

kwcoco eval \
    --true_dataset=$HOME/.cache/kwcoco/tests/eval/true.mscoco.json \
    --pred_dataset=$HOME/.cache/kwcoco/tests/eval/pred.mscoco.json \
    --out_dpath=$HOME/.cache/kwcoco/tests/eval/out \
    --force_pycocoutils=False \
    --area_range=all,0-4096,4096-inf

nautilus $HOME/.cache/kwcoco/tests/eval/out

kwcoco.cli.coco_eval._CLI[source]¶

`kwcoco.cli.coco_grab`¶

Module Contents¶

Classes¶

CocoGrabCLI

Attributes¶

class kwcoco.cli.coco_grab.CocoGrabCLI[source]¶

class CLIConfig(data=None, default=None, cmdline=False)[source]¶

Grab standard datasets.

Example

kwcoco grab cifar10 camvid

default[source]¶

name = grab[source]¶

classmethod main(cls, cmdline=True, **kw)[source]¶

kwcoco.cli.coco_grab._CLI[source]¶

`kwcoco.cli.coco_modify_categories`¶

Module Contents¶

Classes¶

CocoModifyCatsCLI

Remove, rename, or coarsen categories.

Attributes¶

class kwcoco.cli.coco_modify_categories.CocoModifyCatsCLI[source]¶

Remove, rename, or coarsen categories.

class CLIConfig(data=None, default=None, cmdline=False)[source]¶

Rename or remove categories

epilog = Multiline-String[source]¶

Show Value

        Example Usage:
            kwcoco modify_categories --help
            kwcoco modify_categories --src=special:shapes8 --dst modcats.json
            kwcoco modify_categories --src=special:shapes8 --dst modcats.json --rename eff:F,star:sun
            kwcoco modify_categories --src=special:shapes8 --dst modcats.json --remove eff,star
            kwcoco modify_categories --src=special:shapes8 --dst modcats.json --keep eff,

            kwcoco modify_categories --src=special:shapes8 --dst modcats.json --keep=[] --keep_annots=True

default[source]¶

name = modify_categories[source]¶

classmethod main(cls, cmdline=True, **kw)[source]¶

Example

>>> # xdoctest: +SKIP
>>> kw = {'src': 'special:shapes8'}
>>> cmdline = False
>>> cls = CocoModifyCatsCLI
>>> cls.main(cmdline, **kw)

kwcoco.cli.coco_modify_categories._CLI[source]¶

`kwcoco.cli.coco_reroot`¶

Module Contents¶

Classes¶

CocoRerootCLI

Attributes¶

class kwcoco.cli.coco_reroot.CocoRerootCLI[source]¶

class CLIConfig(data=None, default=None, cmdline=False)[source]¶

Reroot image paths onto a new image root.

Modify the root of a coco dataset such to either make paths relative to a new root or make paths absolute.

Todo

[ ] Evaluate that all tests cases work

epilog = Multiline-String[source]¶

Show Value

        Example Usage:
            kwcoco reroot --help
            kwcoco reroot --src=special:shapes8 --dst rerooted.json
            kwcoco reroot --src=special:shapes8 --new_prefix=foo --check=True --dst rerooted.json

default[source]¶

name = reroot[source]¶

classmethod main(cls, cmdline=True, **kw)[source]¶

Example

>>> # xdoctest: +SKIP
>>> kw = {'src': 'special:shapes8'}
>>> cmdline = False
>>> cls = CocoRerootCLI
>>> cls.main(cmdline, **kw)

kwcoco.cli.coco_reroot._CLI[source]¶

`kwcoco.cli.coco_show`¶

Module Contents¶

Classes¶

CocoShowCLI

Attributes¶

class kwcoco.cli.coco_show.CocoShowCLI[source]¶

class CLIConfig(data=None, default=None, cmdline=False)[source]¶

Visualize a COCO image using matplotlib or opencv, optionally writing it to disk

epilog = Multiline-String[source]¶

Show Value

        Example Usage:
            kwcoco show --help
            kwcoco show --src=special:shapes8 --gid=1
            kwcoco show --src=special:shapes8 --gid=1 --dst out.png

default[source]¶

name = show[source]¶

classmethod main(cls, cmdline=True, **kw)[source]¶

Todo

[ ] Visualize auxiliary data

Example

>>> # xdoctest: +SKIP
>>> kw = {'src': 'special:shapes8'}
>>> cmdline = False
>>> cls = CocoShowCLI
>>> cls.main(cmdline, **kw)

kwcoco.cli.coco_show._CLI[source]¶

`kwcoco.cli.coco_split`¶

Module Contents¶

Classes¶

CocoSplitCLI

Attributes¶

class kwcoco.cli.coco_split.CocoSplitCLI[source]¶

Bases: object

class CLIConfig(data=None, default=None, cmdline=False)[source]¶

Split a single COCO dataset into two sub-datasets.

default[source]¶

epilog = Multiline-String[source]¶

Show Value

        Example Usage:
            kwcoco split --src special:shapes8 --dst1=learn.mscoco.json --dst2=test.mscoco.json --factor=3 --rng=42

name = split[source]¶

classmethod main(cls, cmdline=True, **kw)[source]¶

Example

>>> kw = {'src': 'special:shapes8',
>>>       'dst1': 'train.json', 'dst2': 'test.json'}
>>> cmdline = False
>>> cls = CocoSplitCLI
>>> cls.main(cmdline, **kw)

kwcoco.cli.coco_split._CLI[source]¶

`kwcoco.cli.coco_stats`¶

Module Contents¶

Classes¶

CocoStatsCLI

Attributes¶

class kwcoco.cli.coco_stats.CocoStatsCLI[source]¶

class CLIConfig(data=None, default=None, cmdline=False)[source]¶

Compute summary statistics about a COCO dataset

default[source]¶

epilog = Multiline-String[source]¶

Show Value

        Example Usage:
            kwcoco stats --src=special:shapes8
            kwcoco stats --src=special:shapes8 --boxes=True

name = stats[source]¶

classmethod main(cls, cmdline=True, **kw)[source]¶

Example

>>> kw = {'src': 'special:shapes8'}
>>> cmdline = False
>>> cls = CocoStatsCLI
>>> cls.main(cmdline, **kw)

kwcoco.cli.coco_stats._CLI[source]¶

`kwcoco.cli.coco_subset`¶

Module Contents¶

Classes¶

CocoSubsetCLI

Functions¶

query_subset(dset, config)

Example

Attributes¶

class kwcoco.cli.coco_subset.CocoSubsetCLI[source]¶

Bases: object

class CLIConfig(data=None, default=None, cmdline=False)[source]¶

Take a subset of this dataset and write it to a new file

default[source]¶

epilog = Multiline-String[source]¶

Show Value

        Example Usage:
            kwcoco subset --src special:shapes8 --dst=foo.kwcoco.json

            # Take only the even image-ids
            kwcoco subset --src special:shapes8 --dst=foo-even.kwcoco.json --select_images '.id % 2 == 0'

            # Take only the videos where the name ends with 2
            kwcoco subset --src special:vidshapes8 --dst=vidsub.kwcoco.json --select_videos '.name | endswith("2")'

name = subset[source]¶

classmethod main(cls, cmdline=True, **kw)[source]¶

Example

>>> kw = {'src': 'special:shapes8',
>>>       'dst': 'subset.json', 'include_categories': 'superstar'}
>>> cmdline = False
>>> cls = CocoSubsetCLI
>>> cls.main(cmdline, **kw)

kwcoco.cli.coco_subset.query_subset(dset, config)[source]¶

Example

>>> # xdoctest: +REQUIRES(module:jq)
>>> from kwcoco.cli.coco_subset import *  # NOQA
>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo()
>>> assert dset.n_images == 3
>>> #
>>> config = CocoSubsetCLI.CLIConfig({'select_images': '.id < 3'})
>>> new_dset = query_subset(dset, config)
>>> assert new_dset.n_images == 2
>>> #
>>> config = CocoSubsetCLI.CLIConfig({'select_images': '.file_name | test(".*.png")'})
>>> new_dset = query_subset(dset, config)
>>> assert all(n.endswith('.png') for n in new_dset.images().lookup('file_name'))
>>> assert new_dset.n_images == 2
>>> #
>>> config = CocoSubsetCLI.CLIConfig({'select_images': '.file_name | test(".*.png") | not'})
>>> new_dset = query_subset(dset, config)
>>> assert not any(n.endswith('.png') for n in new_dset.images().lookup('file_name'))
>>> assert new_dset.n_images == 1
>>> #
>>> config = CocoSubsetCLI.CLIConfig({'select_images': '.id < 3 and (.file_name | test(".*.png"))'})
>>> new_dset = query_subset(dset, config)
>>> assert new_dset.n_images == 1
>>> #
>>> config = CocoSubsetCLI.CLIConfig({'select_images': '.id < 3 or (.file_name | test(".*.png"))'})
>>> new_dset = query_subset(dset, config)
>>> assert new_dset.n_images == 3

Example

>>> # xdoctest: +REQUIRES(module:jq)
>>> from kwcoco.cli.coco_subset import *  # NOQA
>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes8')
>>> assert dset.n_videos == 8
>>> assert dset.n_images == 16
>>> config = CocoSubsetCLI.CLIConfig({'select_videos': '.name == "toy_video_3"'})
>>> new_dset = query_subset(dset, config)
>>> assert new_dset.n_images == 2
>>> assert new_dset.n_videos == 1

kwcoco.cli.coco_subset._CLI[source]¶

`kwcoco.cli.coco_toydata`¶

Module Contents¶

Classes¶

CocoToyDataCLI

Attributes¶

class kwcoco.cli.coco_toydata.CocoToyDataCLI[source]¶

Bases: object

class CLIConfig(data=None, default=None, cmdline=False)[source]¶

Create COCO toydata for demo and testing purposes.

default[source]¶

epilog = Multiline-String[source]¶

Show Value

        Example Usage:
            kwcoco toydata --key=shapes8 --dst=toydata.kwcoco.json

            kwcoco toydata --key=shapes8 --bundle_dpath=my_test_bundle_v1
            kwcoco toydata --key=shapes8 --bundle_dpath=my_test_bundle_v1

            kwcoco toydata                 --key=shapes8                 --dst=./shapes8.kwcoco/dataset.kwcoco.json

        TODO:
            - [ ] allow specification of images directory

name = toydata[source]¶

classmethod main(cls, cmdline=True, **kw)[source]¶

Example

>>> kw = {'key': 'shapes8', 'dst': 'test.json'}
>>> cmdline = False
>>> cls = CocoToyDataCLI
>>> cls.main(cmdline, **kw)

kwcoco.cli.coco_toydata._CLI[source]¶

`kwcoco.cli.coco_union`¶

Module Contents¶

Classes¶

CocoUnionCLI

Attributes¶

class kwcoco.cli.coco_union.CocoUnionCLI[source]¶

Bases: object

class CLIConfig(data=None, default=None, cmdline=False)[source]¶

Combine multiple COCO datasets into a single merged dataset.

default[source]¶

epilog = Multiline-String[source]¶

Show Value

        Example Usage:
            kwcoco union --src special:shapes8 special:shapes1 --dst=combo.mscoco.json

name = union[source]¶

classmethod main(cls, cmdline=True, **kw)[source]¶

Example

>>> kw = {'src': ['special:shapes8', 'special:shapes1']}
>>> cmdline = False
>>> cls = CocoUnionCLI
>>> cls.main(cmdline, **kw)

kwcoco.cli.coco_union._CLI[source]¶

`kwcoco.cli.coco_validate`¶

Module Contents¶

Classes¶

CocoValidateCLI

Attributes¶

class kwcoco.cli.coco_validate.CocoValidateCLI[source]¶

class CLIConfig(data=None, default=None, cmdline=False)[source]¶

http://ai.bu.edu/M3SDA/#dataset

Validate that a coco file conforms to the json schema, that assets exist, and potentially fix corrupted assets by removing them.

default[source]¶

epilog = Multiline-String[source]¶

Show Value

        Example Usage:
            kwcoco toydata --dst foo.json --key=special:shapes8
            kwcoco validate --src=foo.json --corrupted=True

name = validate[source]¶

classmethod main(cls, cmdline=True, **kw)[source]¶

Example

>>> from kwcoco.cli.coco_validate import *  # NOQA
>>> kw = {'src': 'special:shapes8'}
>>> cmdline = False
>>> cls = CocoValidateCLI
>>> cls.main(cmdline, **kw)

kwcoco.cli.coco_validate._CLI[source]¶

`kwcoco.data`¶

Submodules¶

`kwcoco.data.grab_camvid`¶

Downloads the CamVid data if necessary, and converts it to COCO.

Module Contents¶

Functions¶

`_devcheck_sample_full_image`()
`_devcheck_load_sub_image`()
`grab_camvid_train_test_val_splits`(coco_dset, mode='segnet')
`grab_camvid_sampler`()	Grab a kwcoco.CocoSampler object for the CamVid dataset.
`grab_coco_camvid`()	Example
`grab_raw_camvid`()	Grab the raw camvid data.
`rgb_to_cid`(r, g, b)
`cid_to_rgb`(cid)
`convert_camvid_raw_to_coco`(camvid_raw_info)	Converts the raw camvid format to an MSCOCO based format, ( which lets use
`_define_camvid_class_hierarcy`(dset)
`main`()	Dump the paths to the coco file to stdout

kwcoco.data.grab_camvid._devcheck_sample_full_image()[source]¶

kwcoco.data.grab_camvid._devcheck_load_sub_image()[source]¶

kwcoco.data.grab_camvid.grab_camvid_train_test_val_splits(coco_dset, mode='segnet')[source]¶

kwcoco.data.grab_camvid.grab_camvid_sampler()[source]¶

Grab a kwcoco.CocoSampler object for the CamVid dataset.

Returns: sampler
Return type: kwcoco.CocoSampler

Example

>>> # xdoctest: +REQUIRES(--download)
>>> sampler = grab_camvid_sampler()
>>> print('sampler = {!r}'.format(sampler))
>>> # sampler.load_sample()
>>> for gid in ub.ProgIter(sampler.image_ids, desc='load image'):
>>>     img = sampler.load_image(gid)

kwcoco.data.grab_camvid.grab_coco_camvid()[source]¶

Example

>>> # xdoctest: +REQUIRES(--download)
>>> dset = grab_coco_camvid()
>>> print('dset = {!r}'.format(dset))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> plt = kwplot.autoplt()
>>> plt.clf()
>>> dset.show_image(gid=1)

kwcoco.data.grab_camvid.grab_raw_camvid()[source]¶: Grab the raw camvid data.

kwcoco.data.grab_camvid.rgb_to_cid(r, g, b)[source]¶

kwcoco.data.grab_camvid.cid_to_rgb(cid)[source]¶

kwcoco.data.grab_camvid.convert_camvid_raw_to_coco(camvid_raw_info)[source]¶

Converts the raw camvid format to an MSCOCO based format, ( which lets use use kwcoco’s COCO backend).

Example

>>> # xdoctest: +REQUIRES(--download)
>>> camvid_raw_info = grab_raw_camvid()
>>> # test with a reduced set of data
>>> del camvid_raw_info['img_paths'][2:]
>>> del camvid_raw_info['mask_paths'][2:]
>>> dset = convert_camvid_raw_to_coco(camvid_raw_info)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> plt = kwplot.autoplt()
>>> kwplot.figure(fnum=1, pnum=(1, 2, 1))
>>> dset.show_image(gid=1)
>>> kwplot.figure(fnum=1, pnum=(1, 2, 2))
>>> dset.show_image(gid=2)

kwcoco.data.grab_camvid._define_camvid_class_hierarcy(dset)[source]¶

kwcoco.data.grab_camvid.main()[source]¶

Dump the paths to the coco file to stdout

By default these will go to in the path:: ~/.cache/kwcoco/camvid/camvid-master
The four files will be:: ~/.cache/kwcoco/camvid/camvid-master/camvid-full.mscoco.json ~/.cache/kwcoco/camvid/camvid-master/camvid-train.mscoco.json ~/.cache/kwcoco/camvid/camvid-master/camvid-vali.mscoco.json ~/.cache/kwcoco/camvid/camvid-master/camvid-test.mscoco.json

`kwcoco.data.grab_cifar`¶

Downloads and converts CIFAR 10 and CIFAR 100 to kwcoco format

Module Contents¶

Functions¶

`_convert_cifar_x`(dpath, cifar_dset, cifar_name, classes)
`convert_cifar10`(dpath=None)
`convert_cifar100`(dpath=None)
`main`()

kwcoco.data.grab_cifar._convert_cifar_x(dpath, cifar_dset, cifar_name, classes)[source]¶

kwcoco.data.grab_cifar.convert_cifar10(dpath=None)[source]¶

kwcoco.data.grab_cifar.convert_cifar100(dpath=None)[source]¶

kwcoco.data.grab_cifar.main()[source]¶

`kwcoco.data.grab_datasets`¶

[ ] UCF101 - Action Recognition Data Set - https://www.crcv.ucf.edu/data/UCF101.php
[ ] HMDB: a large human motion database - https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/
[ ] https://paperswithcode.com/dataset/imagenet
[ ] https://paperswithcode.com/dataset/coco
[ ] https://paperswithcode.com/dataset/fashion-mnist
[ ] https://paperswithcode.com/dataset/visual-question-answering
[ ] https://paperswithcode.com/dataset/lfw
[ ] https://paperswithcode.com/dataset/lsun
[ ] https://paperswithcode.com/dataset/ava
[ ] https://paperswithcode.com/dataset/activitynet
[ ] https://paperswithcode.com/dataset/clevr

`kwcoco.data.grab_domainnet`¶

References

Module Contents¶

Functions¶

grab_domain_net()

kwcoco.data.grab_domainnet.grab_domain_net()[source]¶

Todo

[ ] Allow the user to specify the download directory, generalize this

pattern across the data grab scripts.

`kwcoco.data.grab_spacenet`¶

References

https://medium.com/the-downlinq/the-spacenet-7-multi-temporal-urban-development-challenge-algorithmic-baseline-4515ec9bd9fe https://arxiv.org/pdf/2102.11958.pdf https://spacenet.ai/sn7-challenge/

Module Contents¶

Functions¶

grab_spacenet7(data_dpath)

References

convert_spacenet_to_kwcoco(extract_dpath, coco_fpath)

Converts the raw SpaceNet7 dataset to kwcoco

main()

kwcoco.data.grab_spacenet.grab_spacenet7(data_dpath)[source]¶

References

https://spacenet.ai/sn7-challenge/

Requires:: awscli

kwcoco.data.grab_spacenet.convert_spacenet_to_kwcoco(extract_dpath, coco_fpath)[source]¶

Converts the raw SpaceNet7 dataset to kwcoco

Note

The “train” directory contains 60 “videos” representing a region over time.
Each “video” directory contains :
- images - unmasked images
- images_masked - images with masks applied
- labels - geojson polys in wgs84?
- labels_match - geojson polys in wgs84 with track ids?
- labels_match_pix - geojson polys in pixels with track ids?
- UDM_masks - unusable data masks (binary data corresponding with an image, may not exist)

File names appear like:: “global_monthly_2018_01_mosaic_L15-1538E-1163N_6154_3539_13”

kwcoco.data.grab_spacenet.main()[source]¶

`kwcoco.data.grab_voc`¶

Module Contents¶

Functions¶

`__torrent_voc`()	Requires:
`convert_voc_to_coco`(dpath=None)
`_convert_voc_split`(devkit_dpath, classes, split, year, root)	split, year = 'train', 2012
`_read_split_paths`(devkit_dpath, split, year)	split = 'train'
`ensure_voc_data`(dpath=None, force=False, years=[2007, 2012])	Download the Pascal VOC data if it does not already exist.
`ensure_voc_coco`(dpath=None)	Download the Pascal VOC data and convert it to coco, if it does exit.
`main`()

kwcoco.data.grab_voc.__torrent_voc()[source]¶

Requires:: pip install deluge pip install python-libtorrent-bin

References

https://academictorrents.com/details/f6ddac36ac7ae2ef79dc72a26a065b803c9c7230

Todo

[ ] Is there a pythonic way to download a torrent programatically?

kwcoco.data.grab_voc.convert_voc_to_coco(dpath=None)[source]¶

kwcoco.data.grab_voc._convert_voc_split(devkit_dpath, classes, split, year, root)[source]¶: split, year = ‘train’, 2012 split, year = ‘train’, 2007

kwcoco.data.grab_voc._read_split_paths(devkit_dpath, split, year)[source]¶: split = ‘train’ self = VOCDataset(‘test’) year = 2007 year = 2012

kwcoco.data.grab_voc.ensure_voc_data(dpath=None, force=False, years=[2007, 2012])[source]¶

Download the Pascal VOC data if it does not already exist.

Note

[ ] These URLS seem to be dead

Example

>>> # xdoctest: +REQUIRES(--download)
>>> devkit_dpath = ensure_voc_data()

kwcoco.data.grab_voc.ensure_voc_coco(dpath=None)[source]¶

Download the Pascal VOC data and convert it to coco, if it does exit.

Parameters

dpath (str) – download directory. Defaults to “~/data/VOC”.

Returns

mapping from dataset tags to coco file paths.: The original datasets have keys prefixed with underscores. The standard splits keys are train, vali, and test.

Return type

Dict[str, str]

kwcoco.data.grab_voc.main()[source]¶

`kwcoco.demo`¶

Submodules¶

`kwcoco.demo.boids`¶

Module Contents¶

Classes¶

Boids

Efficient numpy based backend for generating boid positions.

Functions¶

`clamp_mag`(vec, mag, axis=None)	vec = np.random.rand(10, 2)
`triu_condense_multi_index`(multi_index, dims, symetric=False)	Like np.ravel_multi_index but returns positions in an upper triangular
`_spatial_index_scratch`()
`closest_point_on_line_segment`(pts, e1, e2)	Finds the closet point from p on line segment (e1, e2)
`_pygame_render_boids`()	Fast and responsive BOID rendering. This is an easter egg.
`_yeah_boid`()

class kwcoco.demo.boids.Boids(num, dims=2, rng=None, **kwargs)[source]¶

Bases: ubelt.NiceRepr

Efficient numpy based backend for generating boid positions.

BOID = bird-oid object

References

https://www.youtube.com/watch?v=mhjuuHl6qHM https://medium.com/better-programming/boids-simulating-birds-flock-behavior-in-python-9fff99375118 https://en.wikipedia.org/wiki/Boids

Example

>>> from kwcoco.demo.boids import *  # NOQA
>>> num_frames = 10
>>> num_objects = 3
>>> rng = None
>>> self = Boids(num=num_objects, rng=rng).initialize()
>>> paths = self.paths(num_frames)
>>> #
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> plt = kwplot.autoplt()
>>> from mpl_toolkits.mplot3d import Axes3D  # NOQA
>>> ax = plt.gca(projection='3d')
>>> ax.cla()
>>> #
>>> for path in paths:
>>>     time = np.arange(len(path))
>>>     ax.plot(time, path.T[0] * 1, path.T[1] * 1, ',-')
>>> ax.set_xlim(0, num_frames)
>>> ax.set_ylim(-.01, 1.01)
>>> ax.set_zlim(-.01, 1.01)
>>> ax.set_xlabel('time')
>>> ax.set_ylabel('u-pos')
>>> ax.set_zlabel('v-pos')
>>> kwplot.show_if_requested()

import xdev _ = xdev.profile_now(self.compute_forces)() _ = xdev.profile_now(self.update_neighbors)()

Example

>>> # Test determenism
>>> from kwcoco.demo.boids import *  # NOQA
>>> num_frames = 2
>>> num_objects = 1
>>> rng = 4532
>>> self = Boids(num=num_objects, rng=rng).initialize()
>>> #print(ub.hash_data(self.pos))
>>> #print(ub.hash_data(self.vel))
>>> #print(ub.hash_data(self.acc))
>>> tocheck = []
>>> for i in range(100):
>>>     self = Boids(num=num_objects, rng=rng).initialize()
>>>     self.step()
>>>     self.step()
>>>     self.step()
>>>     tocheck.append(self.pos.copy())
>>> assert ub.allsame(list(map(ub.hash_data, tocheck)))

__nice__(self)[source]¶

initialize(self)[source]¶

update_neighbors(self)[source]¶

compute_forces(self)[source]¶

boundary_conditions(self)[source]¶

step(self)[source]¶: Update positions, velocities, and accelerations

paths(self, num_steps)[source]¶

kwcoco.demo.boids.clamp_mag(vec, mag, axis=None)[source]¶: vec = np.random.rand(10, 2) mag = 1.0 axis = 1 new_vec = clamp_mag(vec, mag, axis) np.linalg.norm(new_vec, axis=axis)

kwcoco.demo.boids.triu_condense_multi_index(multi_index, dims, symetric=False)[source]¶

Like np.ravel_multi_index but returns positions in an upper triangular condensed square matrix

Examples

multi_index (Tuple[ArrayLike]):: indexes for each dimension into the square matrix
dims (Tuple[int]):: shape of each dimension in the square matrix (should all be the same)
symetric (bool):: if True, converts lower triangular indices to their upper triangular location. This may cause a copy to occur.

References

https://stackoverflow.com/a/36867493/887074 https://numpy.org/doc/stable/reference/generated/numpy.ravel_multi_index.html#numpy.ravel_multi_index

Examples

>>> dims = (3, 3)
>>> symetric = True
>>> multi_index = (np.array([0, 0, 1]), np.array([1, 2, 2]))
>>> condensed_idxs = triu_condense_multi_index(multi_index, dims, symetric=symetric)
>>> assert condensed_idxs.tolist() == [0, 1, 2]

>>> n = 7
>>> symetric = True
>>> multi_index = np.triu_indices(n=n, k=1)
>>> condensed_idxs = triu_condense_multi_index(multi_index, [n] * 2, symetric=symetric)
>>> assert condensed_idxs.tolist() == list(range(n * (n - 1) // 2))
>>> from scipy.spatial.distance import pdist, squareform
>>> square_mat = np.zeros((n, n))
>>> conden_mat = squareform(square_mat)
>>> conden_mat[condensed_idxs] = np.arange(len(condensed_idxs)) + 1
>>> square_mat = squareform(conden_mat)
>>> print('square_mat =\n{}'.format(ub.repr2(square_mat, nl=1)))

>>> n = 7
>>> symetric = True
>>> multi_index = np.tril_indices(n=n, k=-1)
>>> condensed_idxs = triu_condense_multi_index(multi_index, [n] * 2, symetric=symetric)
>>> assert sorted(condensed_idxs.tolist()) == list(range(n * (n - 1) // 2))
>>> from scipy.spatial.distance import pdist, squareform
>>> square_mat = np.zeros((n, n))
>>> conden_mat = squareform(square_mat, checks=False)
>>> conden_mat[condensed_idxs] = np.arange(len(condensed_idxs)) + 1
>>> square_mat = squareform(conden_mat)
>>> print('square_mat =\n{}'.format(ub.repr2(square_mat, nl=1)))

kwcoco.demo.boids._spatial_index_scratch()[source]¶

kwcoco.demo.boids.closest_point_on_line_segment(pts, e1, e2)[source]¶

Finds the closet point from p on line segment (e1, e2)

Parameters

pts (ndarray) – xy points [Nx2]
e1 (ndarray) – the first xy endpoint of the segment
e2 (ndarray) – the second xy endpoint of the segment

Returns

pt_on_seg - the closest xy point on (e1, e2) from ptp

Return type

ndarray

References

http://en.wikipedia.org/wiki/Distance_from_a_point_to_a_line http://stackoverflow.com/questions/849211/shortest-distance-between-a-point-and-a-line-segment

Example

>>> # ENABLE_DOCTEST
>>> from kwcoco.demo.boids import *  # NOQA
>>> verts = np.array([[ 21.83012702,  13.16987298],
>>>                   [ 16.83012702,  21.83012702],
>>>                   [  8.16987298,  16.83012702],
>>>                   [ 13.16987298,   8.16987298],
>>>                   [ 21.83012702,  13.16987298]])
>>> rng = np.random.RandomState(0)
>>> pts = rng.rand(64, 2) * 20 + 5
>>> e1, e2 = verts[0:2]
>>> closest_point_on_line_segment(pts, e1, e2)

kwcoco.demo.boids._pygame_render_boids()[source]¶

Fast and responsive BOID rendering. This is an easter egg.

Requirements:: pip install pygame

CommandLine

python -m kwcoco.demo.boids
pip install pygame kwcoco -U && python -m kwcoco.demo.boids

kwcoco.demo.boids._yeah_boid()[source]¶

`kwcoco.demo.perterb`¶

Module Contents¶

Functions¶

`perterb_coco`(coco_dset, **kwargs)	Perterbs a coco dataset
`_demo_construct_probs`(pred_cxs, pred_scores, classes, rng, hacked=1)	Constructs random probabilities for demo data

kwcoco.demo.perterb.perterb_coco(coco_dset, **kwargs)[source]¶

Perterbs a coco dataset

Parameters

rng (int, default=0)
box_noise (int, default=0)
cls_noise (int, default=0)
null_pred (bool, default=False)
with_probs (bool, default=False)
score_noise (float, default=0.2)
hacked (int, default=1)

Example

>>> from kwcoco.demo.perterb import *  # NOQA
>>> from kwcoco.demo.perterb import _demo_construct_probs
>>> import kwcoco
>>> coco_dset = true_dset = kwcoco.CocoDataset.demo('shapes8')
>>> kwargs = {
>>>     'box_noise': 0.5,
>>>     'n_fp': 3,
>>>     'with_probs': 1,
>>>     'with_heatmaps': 1,
>>> }
>>> pred_dset = perterb_coco(true_dset, **kwargs)
>>> pred_dset._check_json_serializable()
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> gid = 1
>>> canvas = true_dset.delayed_load(gid).finalize()
>>> canvas = true_dset.annots(gid=gid).detections.draw_on(canvas, color='green')
>>> canvas = pred_dset.annots(gid=gid).detections.draw_on(canvas, color='blue')
>>> kwplot.imshow(canvas)

kwcoco.demo.perterb._demo_construct_probs(pred_cxs, pred_scores, classes, rng, hacked=1)[source]¶

Constructs random probabilities for demo data

Example

>>> import kwcoco
>>> import kwarray
>>> rng = kwarray.ensure_rng(0)
>>> classes = kwcoco.CategoryTree.coerce(10)
>>> hacked = 1
>>> pred_cxs = rng.randint(0, 10, 10)
>>> pred_scores = rng.rand(10)
>>> probs = _demo_construct_probs(pred_cxs, pred_scores, classes, rng, hacked)
>>> probs.sum(axis=1)

`kwcoco.demo.toydata`¶

Generates “toydata” for demo and testing purposes.

Note

The implementation of demodata_toy_img and demodata_toy_dset should be redone using the tools built for random_video_dset, which have more extensible implementations.

Module Contents¶

Functions¶

`random_single_video_dset`(image_size=(600, 600), num_frames=5, num_tracks=3, tid_start=1, gid_start=1, video_id=1, anchors=None, rng=None, render=False, dpath=None, autobuild=True, verbose=3, aux=None, multispectral=False, max_speed=0.01, channels=None, multisensor=False, **kwargs)	Create the video scene layout of object positions.
`random_video_dset`(num_videos=1, num_frames=2, num_tracks=2, anchors=None, image_size=(600, 600), verbose=3, render=False, aux=None, multispectral=False, multisensor=False, rng=None, dpath=None, max_speed=0.01, channels=None, **kwargs)	Create a toy Coco Video Dataset
`demodata_toy_dset`(image_size=(600, 600), n_imgs=5, verbose=3, rng=0, newstyle=True, dpath=None, bundle_dpath=None, aux=None, use_cache=True, **kwargs)	Create a toy detection problem
`demodata_toy_img`(anchors=None, image_size=(104, 104), categories=None, n_annots=(0, 50), fg_scale=0.5, bg_scale=0.8, bg_intensity=0.1, fg_intensity=0.9, gray=True, centerobj=None, exact=False, newstyle=True, rng=None, aux=None, **kwargs)	Generate a single image with non-overlapping toy objects of available

kwcoco.demo.toydata.random_single_video_dset(image_size=(600, 600), num_frames=5, num_tracks=3, tid_start=1, gid_start=1, video_id=1, anchors=None, rng=None, render=False, dpath=None, autobuild=True, verbose=3, aux=None, multispectral=False, max_speed=0.01, channels=None, multisensor=False, **kwargs)[source]¶

Create the video scene layout of object positions.

Note

Does not render the data unless specified.

Parameters

image_size (Tuple[int, int]) – size of the images
num_frames (int) – number of frames in this video
num_tracks (int) – number of tracks in this video
tid_start (int, default=1) – track-id start index
gid_start (int, default=1) – image-id start index
video_id (int, default=1) – video-id of this video
anchors (ndarray | None) – base anchor sizes of the object boxes we will generate.
rng (RandomState) – random state / seed
render (bool | dict) – if truthy, does the rendering according to provided params in the case of dict input.
autobuild (bool, default=True) – prebuild coco lookup indexes
verbose (int) – verbosity level
aux (bool | List[str]) – if specified generates auxiliary channels
multispectral (bool) – if specified simulates multispectral imagry This is similar to aux, but has no “main” file.
max_speed (float) – max speed of movers
channels (str | None | ChannelSpec) – if specified generates multispectral images with dummy channels
multisensor (bool) –

if True, generates demodata from “multiple sensors”, in
other words, observations may have different “bands”.
**kwargs – used for old backwards compatible argument names gsize - alias for image_size

Todo

[ ] Need maximum allowed object overlap measure
[ ] Need better parameterized path generation

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> anchors = np.array([ [0.3, 0.3],  [0.1, 0.1]])
>>> dset = random_single_video_dset(render=True, num_frames=10, num_tracks=10, anchors=anchors, max_speed=0.2)
>>> # xdoctest: +REQUIRES(--show)
>>> # Show the tracks in a single image
>>> import kwplot
>>> kwplot.autompl()
>>> annots = dset.annots()
>>> tids = annots.lookup('track_id')
>>> tid_to_aids = ub.group_items(annots.aids, tids)
>>> paths = []
>>> track_boxes = []
>>> for tid, aids in tid_to_aids.items():
>>>     boxes = dset.annots(aids).boxes.to_cxywh()
>>>     path = boxes.data[:, 0:2]
>>>     paths.append(path)
>>>     track_boxes.append(boxes)
>>> import kwplot
>>> plt = kwplot.autoplt()
>>> ax = plt.gca()
>>> ax.cla()
>>> #
>>> import kwimage
>>> colors = kwimage.Color.distinct(len(track_boxes))
>>> for i, boxes in enumerate(track_boxes):
>>>     color = colors[i]
>>>     path = boxes.data[:, 0:2]
>>>     boxes.draw(color=color, centers={'radius': 0.01}, alpha=0.5)
>>>     ax.plot(path.T[0], path.T[1], 'x-', color=color)

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> anchors = np.array([ [0.2, 0.2],  [0.1, 0.1]])
>>> gsize = np.array([(600, 600)])
>>> print(anchors * gsize)
>>> dset = random_single_video_dset(render=True, num_frames=10,
>>>                             anchors=anchors, num_tracks=10,
>>>                             image_size='random')
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> plt = kwplot.autoplt()
>>> plt.clf()
>>> gids = list(dset.imgs.keys())
>>> pnums = kwplot.PlotNums(nSubplots=len(gids))
>>> for gid in gids:
>>>     dset.show_image(gid, pnum=pnums(), fnum=1, title=False)
>>> pnums = kwplot.PlotNums(nSubplots=len(gids))

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> dset = random_single_video_dset(num_frames=10, num_tracks=10, aux=True)
>>> assert 'auxiliary' in dset.imgs[1]
>>> assert dset.imgs[1]['auxiliary'][0]['channels']
>>> assert dset.imgs[1]['auxiliary'][1]['channels']

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> multispectral = True
>>> dset = random_single_video_dset(num_frames=1, num_tracks=1, multispectral=True)
>>> dset._check_json_serializable()
>>> dset.dataset['images']
>>> assert dset.imgs[1]['auxiliary'][1]['channels']
>>> # test that we can render
>>> render_toy_dataset(dset, rng=0, dpath=None, renderkw={})

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> dset = random_single_video_dset(num_frames=4, num_tracks=1, multispectral=True, multisensor=True, image_size='random', rng=2338)
>>> dset._check_json_serializable()
>>> assert dset.imgs[1]['auxiliary'][1]['channels']
>>> # Print before and after render
>>> #print('multisensor-images = {}'.format(ub.repr2(dset.dataset['images'], nl=-2)))
>>> #print('multisensor-images = {}'.format(ub.repr2(dset.dataset, nl=-2)))
>>> print(ub.hash_data(dset.dataset))
>>> # test that we can render
>>> render_toy_dataset(dset, rng=0, dpath=None, renderkw={})
>>> #print('multisensor-images = {}'.format(ub.repr2(dset.dataset['images'], nl=-2)))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> from kwcoco.demo.toydata_video import _draw_video_sequence  # NOQA
>>> gids = [1, 2, 3, 4]
>>> final = _draw_video_sequence(dset, gids)
>>> print('dset.fpath = {!r}'.format(dset.fpath))
>>> kwplot.imshow(final)

kwcoco.demo.toydata.random_video_dset(num_videos=1, num_frames=2, num_tracks=2, anchors=None, image_size=(600, 600), verbose=3, render=False, aux=None, multispectral=False, multisensor=False, rng=None, dpath=None, max_speed=0.01, channels=None, **kwargs)[source]¶

Create a toy Coco Video Dataset

Parameters

num_videos (int) – number of videos
num_frames (int) – number of images per video
num_tracks (int) – number of tracks per video
image_size (Tuple[int, int]) – The width and height of the generated images
render (bool | dict) – if truthy the toy annotations are synthetically rendered. See render_toy_image() for details.
rng (int | None | RandomState) – random seed / state
dpath (str) – only used if render is truthy, place to write rendered images.
verbose (int, default=3) – verbosity mode
aux (bool) – if True generates dummy auxiliary channels
multispectral (bool) – similar to aux, but does not have the concept of a “main” image.
max_speed (float) – max speed of movers
channels (str) – experimental new way to get MSI with specific band distributions.
**kwargs – used for old backwards compatible argument names gsize - alias for image_size

SeeAlso:: random_single_video_dset

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> dset = random_video_dset(render=True, num_videos=3, num_frames=2,
>>>                          num_tracks=5, image_size=(128, 128))
>>> # xdoctest: +REQUIRES(--show)
>>> dset.show_image(1, doclf=True)
>>> dset.show_image(2, doclf=True)

>>> from kwcoco.demo.toydata_video import *  # NOQA
dset = random_video_dset(render=False, num_videos=3, num_frames=2,
    num_tracks=10)
dset._tree()
dset.imgs[1]

dset = random_single_video_dset() dset._tree() dset.imgs[1]

from kwcoco.demo.toydata_video import * # NOQA dset = random_video_dset(render=True, num_videos=3, num_frames=2,

num_tracks=10)

print(dset.imgs[1]) print(‘dset.bundle_dpath = {!r}’.format(dset.bundle_dpath)) dset._tree()

import xdev globals().update(xdev.get_func_kwargs(random_video_dset)) num_videos = 2

kwcoco.demo.toydata.demodata_toy_dset(image_size=(600, 600), n_imgs=5, verbose=3, rng=0, newstyle=True, dpath=None, bundle_dpath=None, aux=None, use_cache=True, **kwargs)[source]¶

Create a toy detection problem

Parameters

image_size (Tuple[int, int]) – The width and height of the generated images
n_imgs (int) – number of images to generate
rng (int | RandomState, default=0) – random number generator or seed
newstyle (bool, default=True) – create newstyle kwcoco data
dpath (str) – path to the directory that will contain the bundle, (defaults to a kwcoco cache dir). Ignored if bundle_dpath is given.
bundle_dpath (str) – path to the directory that will store images. If specified, dpath is ignored. If unspecified, a bundle will be written inside dpath.
aux (bool) – if True generates dummy auxiliary channels
verbose (int, default=3) – verbosity mode
use_cache (bool, default=True) – if True caches the generated json in the dpath.
**kwargs – used for old backwards compatible argument names gsize - alias for image_size

Return type

kwcoco.CocoDataset

SeeAlso:: random_video_dset

CommandLine

xdoctest -m kwcoco.demo.toydata_image demodata_toy_dset --show

Todo

[ ] Non-homogeneous images sizes

Example

>>> from kwcoco.demo.toydata_image import *
>>> import kwcoco
>>> dset = demodata_toy_dset(image_size=(300, 300), aux=True, use_cache=False)
>>> # xdoctest: +REQUIRES(--show)
>>> print(ub.repr2(dset.dataset, nl=2))
>>> import kwplot
>>> kwplot.autompl()
>>> dset.show_image(gid=1)
>>> ub.startfile(dset.bundle_dpath)

dset._tree()

>>> from kwcoco.demo.toydata_image import *
>>> import kwcoco

dset = demodata_toy_dset(image_size=(300, 300), aux=True, use_cache=False) print(dset.imgs[1]) dset._tree()

dset = demodata_toy_dset(image_size=(300, 300), aux=True, use_cache=False,: bundle_dpath=’test_bundle’)

print(dset.imgs[1]) dset._tree()

dset = demodata_toy_dset(: image_size=(300, 300), aux=True, use_cache=False, dpath=’test_cache_dpath’)

kwcoco.demo.toydata.demodata_toy_img(anchors=None, image_size=(104, 104), categories=None, n_annots=(0, 50), fg_scale=0.5, bg_scale=0.8, bg_intensity=0.1, fg_intensity=0.9, gray=True, centerobj=None, exact=False, newstyle=True, rng=None, aux=None, **kwargs)[source]¶

Generate a single image with non-overlapping toy objects of available categories.

Todo

DEPRECATE IN FAVOR OF: random_single_video_dset + render_toy_image

Parameters

anchors (ndarray) – Nx2 base width / height of boxes
gsize (Tuple[int, int]) – width / height of the image
categories (List[str]) – list of category names
n_annots (Tuple | int) – controls how many annotations are in the image. if it is a tuple, then it is interpreted as uniform random bounds
fg_scale (float) – standard deviation of foreground intensity
bg_scale (float) – standard deviation of background intensity
bg_intensity (float) – mean of background intensity
fg_intensity (float) – mean of foreground intensity
centerobj (bool) – if ‘pos’, then the first annotation will be in the center of the image, if ‘neg’, then no annotations will be in the center.
exact (bool) – if True, ensures that exactly the number of specified annots are generated.
newstyle (bool) – use new-sytle kwcoco format
rng (RandomState) – the random state used to seed the process
aux – if specified builds auxiliary channels
**kwargs – used for old backwards compatible argument names. gsize - alias for image_size

CommandLine

xdoctest -m kwcoco.demo.toydata_image demodata_toy_img:0 --profile
xdoctest -m kwcoco.demo.toydata_image demodata_toy_img:1 --show

Example

>>> from kwcoco.demo.toydata_image import *  # NOQA
>>> img, anns = demodata_toy_img(image_size=(32, 32), anchors=[[.3, .3]], rng=0)
>>> img['imdata'] = '<ndarray shape={}>'.format(img['imdata'].shape)
>>> print('img = {}'.format(ub.repr2(img)))
>>> print('anns = {}'.format(ub.repr2(anns, nl=2, cbr=True)))
>>> # xdoctest: +IGNORE_WANT
img = {
    'height': 32,
    'imdata': '<ndarray shape=(32, 32, 3)>',
    'width': 32,
}
anns = [{'bbox': [15, 10, 9, 8],
  'category_name': 'star',
  'keypoints': [],
  'segmentation': {'counts': '[`06j0000O20N1000e8', 'size': [32, 32]},},
 {'bbox': [11, 20, 7, 7],
  'category_name': 'star',
  'keypoints': [],
  'segmentation': {'counts': 'g;1m04N0O20N102L[=', 'size': [32, 32]},},
 {'bbox': [4, 4, 8, 6],
  'category_name': 'superstar',
  'keypoints': [{'keypoint_category': 'left_eye', 'xy': [7.25, 6.8125]}, {'keypoint_category': 'right_eye', 'xy': [8.75, 6.8125]}],
  'segmentation': {'counts': 'U4210j0300O01010O00MVO0ed0', 'size': [32, 32]},},
 {'bbox': [3, 20, 6, 7],
  'category_name': 'star',
  'keypoints': [],
  'segmentation': {'counts': 'g31m04N000002L[f0', 'size': [32, 32]},},]

Example

>>> # xdoctest: +REQUIRES(--show)
>>> img, anns = demodata_toy_img(image_size=(172, 172), rng=None, aux=True)
>>> print('anns = {}'.format(ub.repr2(anns, nl=1)))
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(img['imdata'], pnum=(1, 2, 1), fnum=1)
>>> auxdata = img['auxiliary'][0]['imdata']
>>> kwplot.imshow(auxdata, pnum=(1, 2, 2), fnum=1)
>>> kwplot.show_if_requested()

Example

>>> # xdoctest: +REQUIRES(--show)
>>> img, anns = demodata_toy_img(image_size=(172, 172), rng=None, aux=True)
>>> print('anns = {}'.format(ub.repr2(anns, nl=1)))
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(img['imdata'], pnum=(1, 2, 1), fnum=1)
>>> auxdata = img['auxiliary'][0]['imdata']
>>> kwplot.imshow(auxdata, pnum=(1, 2, 2), fnum=1)
>>> kwplot.show_if_requested()

`kwcoco.demo.toydata_image`¶

Generates “toydata” for demo and testing purposes.

Loose image version of the toydata generators.

Note

The implementation of demodata_toy_img and demodata_toy_dset should be redone using the tools built for random_video_dset, which have more extensible implementations.

Module Contents¶

Functions¶

`demodata_toy_dset`(image_size=(600, 600), n_imgs=5, verbose=3, rng=0, newstyle=True, dpath=None, bundle_dpath=None, aux=None, use_cache=True, **kwargs)	Create a toy detection problem
`demodata_toy_img`(anchors=None, image_size=(104, 104), categories=None, n_annots=(0, 50), fg_scale=0.5, bg_scale=0.8, bg_intensity=0.1, fg_intensity=0.9, gray=True, centerobj=None, exact=False, newstyle=True, rng=None, aux=None, **kwargs)	Generate a single image with non-overlapping toy objects of available

Attributes¶

`profile`
`TOYDATA_IMAGE_VERSION`

kwcoco.demo.toydata_image.profile[source]¶

kwcoco.demo.toydata_image.TOYDATA_IMAGE_VERSION = 20[source]¶

kwcoco.demo.toydata_image.demodata_toy_dset(image_size=(600, 600), n_imgs=5, verbose=3, rng=0, newstyle=True, dpath=None, bundle_dpath=None, aux=None, use_cache=True, **kwargs)[source]¶

Create a toy detection problem

Parameters

image_size (Tuple[int, int]) – The width and height of the generated images
n_imgs (int) – number of images to generate
rng (int | RandomState, default=0) – random number generator or seed
newstyle (bool, default=True) – create newstyle kwcoco data
dpath (str) – path to the directory that will contain the bundle, (defaults to a kwcoco cache dir). Ignored if bundle_dpath is given.
bundle_dpath (str) – path to the directory that will store images. If specified, dpath is ignored. If unspecified, a bundle will be written inside dpath.
aux (bool) – if True generates dummy auxiliary channels
verbose (int, default=3) – verbosity mode
use_cache (bool, default=True) – if True caches the generated json in the dpath.
**kwargs – used for old backwards compatible argument names gsize - alias for image_size

Return type

kwcoco.CocoDataset

SeeAlso:: random_video_dset

CommandLine

xdoctest -m kwcoco.demo.toydata_image demodata_toy_dset --show

Todo

[ ] Non-homogeneous images sizes

Example

>>> from kwcoco.demo.toydata_image import *
>>> import kwcoco
>>> dset = demodata_toy_dset(image_size=(300, 300), aux=True, use_cache=False)
>>> # xdoctest: +REQUIRES(--show)
>>> print(ub.repr2(dset.dataset, nl=2))
>>> import kwplot
>>> kwplot.autompl()
>>> dset.show_image(gid=1)
>>> ub.startfile(dset.bundle_dpath)

dset._tree()

>>> from kwcoco.demo.toydata_image import *
>>> import kwcoco

dset = demodata_toy_dset(image_size=(300, 300), aux=True, use_cache=False) print(dset.imgs[1]) dset._tree()

dset = demodata_toy_dset(image_size=(300, 300), aux=True, use_cache=False,: bundle_dpath=’test_bundle’)

print(dset.imgs[1]) dset._tree()

dset = demodata_toy_dset(: image_size=(300, 300), aux=True, use_cache=False, dpath=’test_cache_dpath’)

kwcoco.demo.toydata_image.demodata_toy_img(anchors=None, image_size=(104, 104), categories=None, n_annots=(0, 50), fg_scale=0.5, bg_scale=0.8, bg_intensity=0.1, fg_intensity=0.9, gray=True, centerobj=None, exact=False, newstyle=True, rng=None, aux=None, **kwargs)[source]¶

Generate a single image with non-overlapping toy objects of available categories.

Todo

DEPRECATE IN FAVOR OF: random_single_video_dset + render_toy_image

Parameters

anchors (ndarray) – Nx2 base width / height of boxes
gsize (Tuple[int, int]) – width / height of the image
categories (List[str]) – list of category names
n_annots (Tuple | int) – controls how many annotations are in the image. if it is a tuple, then it is interpreted as uniform random bounds
fg_scale (float) – standard deviation of foreground intensity
bg_scale (float) – standard deviation of background intensity
bg_intensity (float) – mean of background intensity
fg_intensity (float) – mean of foreground intensity
centerobj (bool) – if ‘pos’, then the first annotation will be in the center of the image, if ‘neg’, then no annotations will be in the center.
exact (bool) – if True, ensures that exactly the number of specified annots are generated.
newstyle (bool) – use new-sytle kwcoco format
rng (RandomState) – the random state used to seed the process
aux – if specified builds auxiliary channels
**kwargs – used for old backwards compatible argument names. gsize - alias for image_size

CommandLine

xdoctest -m kwcoco.demo.toydata_image demodata_toy_img:0 --profile
xdoctest -m kwcoco.demo.toydata_image demodata_toy_img:1 --show

Example

>>> from kwcoco.demo.toydata_image import *  # NOQA
>>> img, anns = demodata_toy_img(image_size=(32, 32), anchors=[[.3, .3]], rng=0)
>>> img['imdata'] = '<ndarray shape={}>'.format(img['imdata'].shape)
>>> print('img = {}'.format(ub.repr2(img)))
>>> print('anns = {}'.format(ub.repr2(anns, nl=2, cbr=True)))
>>> # xdoctest: +IGNORE_WANT
img = {
    'height': 32,
    'imdata': '<ndarray shape=(32, 32, 3)>',
    'width': 32,
}
anns = [{'bbox': [15, 10, 9, 8],
  'category_name': 'star',
  'keypoints': [],
  'segmentation': {'counts': '[`06j0000O20N1000e8', 'size': [32, 32]},},
 {'bbox': [11, 20, 7, 7],
  'category_name': 'star',
  'keypoints': [],
  'segmentation': {'counts': 'g;1m04N0O20N102L[=', 'size': [32, 32]},},
 {'bbox': [4, 4, 8, 6],
  'category_name': 'superstar',
  'keypoints': [{'keypoint_category': 'left_eye', 'xy': [7.25, 6.8125]}, {'keypoint_category': 'right_eye', 'xy': [8.75, 6.8125]}],
  'segmentation': {'counts': 'U4210j0300O01010O00MVO0ed0', 'size': [32, 32]},},
 {'bbox': [3, 20, 6, 7],
  'category_name': 'star',
  'keypoints': [],
  'segmentation': {'counts': 'g31m04N000002L[f0', 'size': [32, 32]},},]

Example

>>> # xdoctest: +REQUIRES(--show)
>>> img, anns = demodata_toy_img(image_size=(172, 172), rng=None, aux=True)
>>> print('anns = {}'.format(ub.repr2(anns, nl=1)))
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(img['imdata'], pnum=(1, 2, 1), fnum=1)
>>> auxdata = img['auxiliary'][0]['imdata']
>>> kwplot.imshow(auxdata, pnum=(1, 2, 2), fnum=1)
>>> kwplot.show_if_requested()

Example

>>> # xdoctest: +REQUIRES(--show)
>>> img, anns = demodata_toy_img(image_size=(172, 172), rng=None, aux=True)
>>> print('anns = {}'.format(ub.repr2(anns, nl=1)))
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(img['imdata'], pnum=(1, 2, 1), fnum=1)
>>> auxdata = img['auxiliary'][0]['imdata']
>>> kwplot.imshow(auxdata, pnum=(1, 2, 2), fnum=1)
>>> kwplot.show_if_requested()

`kwcoco.demo.toydata_video`¶

Generates “toydata” for demo and testing purposes.

This is the video version of the toydata generator and should be prefered to the loose image version in toydata_image.

Module Contents¶

Functions¶

`random_video_dset`(num_videos=1, num_frames=2, num_tracks=2, anchors=None, image_size=(600, 600), verbose=3, render=False, aux=None, multispectral=False, multisensor=False, rng=None, dpath=None, max_speed=0.01, channels=None, **kwargs)	Create a toy Coco Video Dataset
`random_single_video_dset`(image_size=(600, 600), num_frames=5, num_tracks=3, tid_start=1, gid_start=1, video_id=1, anchors=None, rng=None, render=False, dpath=None, autobuild=True, verbose=3, aux=None, multispectral=False, max_speed=0.01, channels=None, multisensor=False, **kwargs)	Create the video scene layout of object positions.
`_draw_video_sequence`(dset, gids)	Helper to draw a multi-sensor sequence
`render_toy_dataset`(dset, rng, dpath=None, renderkw=None, verbose=0)	Create toydata_video renderings for a preconstructed coco dataset.
`render_toy_image`(dset, gid, rng=None, renderkw=None)	Modifies dataset inplace, rendering synthetic annotations.
`render_foreground`(imdata, chan_to_auxinfo, dset, annots, catpats, with_sseg, with_kpts, dims, newstyle, gray, rng)	Renders demo annoations on top of a demo background
`render_background`(img, rng, gray, bg_intensity, bg_scale)
`false_color`(twochan)	TODO: the function ensure_false_color will eventually be ported to kwimage
`random_multi_object_path`(num_objects, num_frames, rng=None, max_speed=0.01)
`random_path`(num, degree=1, dimension=2, rng=None, mode='boid')	Create a random path using a somem ethod curve.

Attributes¶

`profile`
`TOYDATA_VIDEO_VERSION`

kwcoco.demo.toydata_video.profile[source]¶

kwcoco.demo.toydata_video.TOYDATA_VIDEO_VERSION = 21[source]¶

kwcoco.demo.toydata_video.random_video_dset(num_videos=1, num_frames=2, num_tracks=2, anchors=None, image_size=(600, 600), verbose=3, render=False, aux=None, multispectral=False, multisensor=False, rng=None, dpath=None, max_speed=0.01, channels=None, **kwargs)[source]¶

Create a toy Coco Video Dataset

Parameters

num_videos (int) – number of videos
num_frames (int) – number of images per video
num_tracks (int) – number of tracks per video
image_size (Tuple[int, int]) – The width and height of the generated images
render (bool | dict) – if truthy the toy annotations are synthetically rendered. See render_toy_image() for details.
rng (int | None | RandomState) – random seed / state
dpath (str) – only used if render is truthy, place to write rendered images.
verbose (int, default=3) – verbosity mode
aux (bool) – if True generates dummy auxiliary channels
multispectral (bool) – similar to aux, but does not have the concept of a “main” image.
max_speed (float) – max speed of movers
channels (str) – experimental new way to get MSI with specific band distributions.
**kwargs – used for old backwards compatible argument names gsize - alias for image_size

SeeAlso:: random_single_video_dset

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> dset = random_video_dset(render=True, num_videos=3, num_frames=2,
>>>                          num_tracks=5, image_size=(128, 128))
>>> # xdoctest: +REQUIRES(--show)
>>> dset.show_image(1, doclf=True)
>>> dset.show_image(2, doclf=True)

>>> from kwcoco.demo.toydata_video import *  # NOQA
dset = random_video_dset(render=False, num_videos=3, num_frames=2,
    num_tracks=10)
dset._tree()
dset.imgs[1]

dset = random_single_video_dset() dset._tree() dset.imgs[1]

from kwcoco.demo.toydata_video import * # NOQA dset = random_video_dset(render=True, num_videos=3, num_frames=2,

num_tracks=10)

print(dset.imgs[1]) print(‘dset.bundle_dpath = {!r}’.format(dset.bundle_dpath)) dset._tree()

import xdev globals().update(xdev.get_func_kwargs(random_video_dset)) num_videos = 2

kwcoco.demo.toydata_video.random_single_video_dset(image_size=(600, 600), num_frames=5, num_tracks=3, tid_start=1, gid_start=1, video_id=1, anchors=None, rng=None, render=False, dpath=None, autobuild=True, verbose=3, aux=None, multispectral=False, max_speed=0.01, channels=None, multisensor=False, **kwargs)[source]¶

Create the video scene layout of object positions.

Note

Does not render the data unless specified.

Parameters

image_size (Tuple[int, int]) – size of the images
num_frames (int) – number of frames in this video
num_tracks (int) – number of tracks in this video
tid_start (int, default=1) – track-id start index
gid_start (int, default=1) – image-id start index
video_id (int, default=1) – video-id of this video
anchors (ndarray | None) – base anchor sizes of the object boxes we will generate.
rng (RandomState) – random state / seed
render (bool | dict) – if truthy, does the rendering according to provided params in the case of dict input.
autobuild (bool, default=True) – prebuild coco lookup indexes
verbose (int) – verbosity level
aux (bool | List[str]) – if specified generates auxiliary channels
multispectral (bool) – if specified simulates multispectral imagry This is similar to aux, but has no “main” file.
max_speed (float) – max speed of movers
channels (str | None | ChannelSpec) – if specified generates multispectral images with dummy channels
multisensor (bool) –

if True, generates demodata from “multiple sensors”, in
other words, observations may have different “bands”.
**kwargs – used for old backwards compatible argument names gsize - alias for image_size

Todo

[ ] Need maximum allowed object overlap measure
[ ] Need better parameterized path generation

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> anchors = np.array([ [0.3, 0.3],  [0.1, 0.1]])
>>> dset = random_single_video_dset(render=True, num_frames=10, num_tracks=10, anchors=anchors, max_speed=0.2)
>>> # xdoctest: +REQUIRES(--show)
>>> # Show the tracks in a single image
>>> import kwplot
>>> kwplot.autompl()
>>> annots = dset.annots()
>>> tids = annots.lookup('track_id')
>>> tid_to_aids = ub.group_items(annots.aids, tids)
>>> paths = []
>>> track_boxes = []
>>> for tid, aids in tid_to_aids.items():
>>>     boxes = dset.annots(aids).boxes.to_cxywh()
>>>     path = boxes.data[:, 0:2]
>>>     paths.append(path)
>>>     track_boxes.append(boxes)
>>> import kwplot
>>> plt = kwplot.autoplt()
>>> ax = plt.gca()
>>> ax.cla()
>>> #
>>> import kwimage
>>> colors = kwimage.Color.distinct(len(track_boxes))
>>> for i, boxes in enumerate(track_boxes):
>>>     color = colors[i]
>>>     path = boxes.data[:, 0:2]
>>>     boxes.draw(color=color, centers={'radius': 0.01}, alpha=0.5)
>>>     ax.plot(path.T[0], path.T[1], 'x-', color=color)

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> anchors = np.array([ [0.2, 0.2],  [0.1, 0.1]])
>>> gsize = np.array([(600, 600)])
>>> print(anchors * gsize)
>>> dset = random_single_video_dset(render=True, num_frames=10,
>>>                             anchors=anchors, num_tracks=10,
>>>                             image_size='random')
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> plt = kwplot.autoplt()
>>> plt.clf()
>>> gids = list(dset.imgs.keys())
>>> pnums = kwplot.PlotNums(nSubplots=len(gids))
>>> for gid in gids:
>>>     dset.show_image(gid, pnum=pnums(), fnum=1, title=False)
>>> pnums = kwplot.PlotNums(nSubplots=len(gids))

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> dset = random_single_video_dset(num_frames=10, num_tracks=10, aux=True)
>>> assert 'auxiliary' in dset.imgs[1]
>>> assert dset.imgs[1]['auxiliary'][0]['channels']
>>> assert dset.imgs[1]['auxiliary'][1]['channels']

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> multispectral = True
>>> dset = random_single_video_dset(num_frames=1, num_tracks=1, multispectral=True)
>>> dset._check_json_serializable()
>>> dset.dataset['images']
>>> assert dset.imgs[1]['auxiliary'][1]['channels']
>>> # test that we can render
>>> render_toy_dataset(dset, rng=0, dpath=None, renderkw={})

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> dset = random_single_video_dset(num_frames=4, num_tracks=1, multispectral=True, multisensor=True, image_size='random', rng=2338)
>>> dset._check_json_serializable()
>>> assert dset.imgs[1]['auxiliary'][1]['channels']
>>> # Print before and after render
>>> #print('multisensor-images = {}'.format(ub.repr2(dset.dataset['images'], nl=-2)))
>>> #print('multisensor-images = {}'.format(ub.repr2(dset.dataset, nl=-2)))
>>> print(ub.hash_data(dset.dataset))
>>> # test that we can render
>>> render_toy_dataset(dset, rng=0, dpath=None, renderkw={})
>>> #print('multisensor-images = {}'.format(ub.repr2(dset.dataset['images'], nl=-2)))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> from kwcoco.demo.toydata_video import _draw_video_sequence  # NOQA
>>> gids = [1, 2, 3, 4]
>>> final = _draw_video_sequence(dset, gids)
>>> print('dset.fpath = {!r}'.format(dset.fpath))
>>> kwplot.imshow(final)

kwcoco.demo.toydata_video._draw_video_sequence(dset, gids)[source]¶: Helper to draw a multi-sensor sequence

kwcoco.demo.toydata_video.render_toy_dataset(dset, rng, dpath=None, renderkw=None, verbose=0)[source]¶

Create toydata_video renderings for a preconstructed coco dataset.

Parameters

dset (CocoDataset) – A dataset that contains special “renderable” annotations. (e.g. the demo shapes). Each image can contain special fields that influence how an image will be rendered.

Currently this process is simple, it just creates a noisy image with the shapes superimposed over where they should exist as indicated by the annotations. In the future this may become more sophisticated.

Each item in dset.dataset[‘images’] will be modified to add the “file_name” field indicating where the rendered data is writen.
rng (int | None | RandomState) – random state
dpath (str) – The location to write the images to. If unspecified, it is written to the rendered folder inside the kwcoco cache directory.
renderkw (dict) – See render_toy_image() for details. Also takes imwrite keywords args only handled in this function. TODO better docs.

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> import kwarray
>>> rng = None
>>> rng = kwarray.ensure_rng(rng)
>>> num_tracks = 3
>>> dset = random_video_dset(rng=rng, num_videos=3, num_frames=5,
>>>                          num_tracks=num_tracks, image_size=(128, 128))
>>> dset = render_toy_dataset(dset, rng)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> plt = kwplot.autoplt()
>>> plt.clf()
>>> gids = list(dset.imgs.keys())
>>> pnums = kwplot.PlotNums(nSubplots=len(gids), nRows=num_tracks)
>>> for gid in gids:
>>>     dset.show_image(gid, pnum=pnums(), fnum=1, title=False)
>>> pnums = kwplot.PlotNums(nSubplots=len(gids))

kwcoco.demo.toydata_video.render_toy_image(dset, gid, rng=None, renderkw=None)[source]¶

Modifies dataset inplace, rendering synthetic annotations.

This does not write to disk. Instead this writes to placeholder values in the image dictionary.

Parameters

dset (CocoDataset) – coco dataset with renderable anotations / images
gid (int) – image to render
rng (int | None | RandomState) – random state
renderkw (dict) – rendering config gray (boo): gray or color images fg_scale (float): foreground noisyness (gauss std) bg_scale (float): background noisyness (gauss std) fg_intensity (float): foreground brightness (gauss mean) bg_intensity (float): background brightness (gauss mean) newstyle (bool): use new kwcoco datastructure formats with_kpts (bool): include keypoint info with_sseg (bool): include segmentation info

Returns

the inplace-modified image dictionary

Return type

Dict

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> image_size=(600, 600)
>>> num_frames=5
>>> verbose=3
>>> rng = None
>>> import kwarray
>>> rng = kwarray.ensure_rng(rng)
>>> aux = 'mx'
>>> dset = random_single_video_dset(
>>>     image_size=image_size, num_frames=num_frames, verbose=verbose, aux=aux, rng=rng)
>>> print('dset.dataset = {}'.format(ub.repr2(dset.dataset, nl=2)))
>>> gid = 1
>>> renderkw = {}
>>> render_toy_image(dset, gid, rng, renderkw=renderkw)
>>> img = dset.imgs[gid]
>>> canvas = img['imdata']
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(canvas, doclf=True, pnum=(1, 2, 1))
>>> dets = dset.annots(gid=gid).detections
>>> dets.draw()

>>> auxdata = img['auxiliary'][0]['imdata']
>>> aux_canvas = false_color(auxdata)
>>> kwplot.imshow(aux_canvas, pnum=(1, 2, 2))
>>> _ = dets.draw()

>>> # xdoctest: +REQUIRES(--show)
>>> img, anns = demodata_toy_img(image_size=(172, 172), rng=None, aux=True)
>>> print('anns = {}'.format(ub.repr2(anns, nl=1)))
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(img['imdata'], pnum=(1, 2, 1), fnum=1)
>>> auxdata = img['auxiliary'][0]['imdata']
>>> kwplot.imshow(auxdata, pnum=(1, 2, 2), fnum=1)
>>> kwplot.show_if_requested()

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> multispectral = True
>>> dset = random_single_video_dset(num_frames=1, num_tracks=1, multispectral=True)
>>> gid = 1
>>> dset.imgs[gid]
>>> rng = kwarray.ensure_rng(0)
>>> renderkw = {'with_sseg': True}
>>> img = render_toy_image(dset, gid, rng=rng, renderkw=renderkw)

kwcoco.demo.toydata_video.render_foreground(imdata, chan_to_auxinfo, dset, annots, catpats, with_sseg, with_kpts, dims, newstyle, gray, rng)[source]¶: Renders demo annoations on top of a demo background

kwcoco.demo.toydata_video.render_background(img, rng, gray, bg_intensity, bg_scale)[source]¶

kwcoco.demo.toydata_video.false_color(twochan)[source]¶: TODO: the function ensure_false_color will eventually be ported to kwimage use that instead.

kwcoco.demo.toydata_video.random_multi_object_path(num_objects, num_frames, rng=None, max_speed=0.01)[source]¶

kwcoco.demo.toydata_video.random_path(num, degree=1, dimension=2, rng=None, mode='boid')[source]¶

Create a random path using a somem ethod curve.

Parameters

num (int) – number of points in the path
degree (int, default=1) – degree of curvieness of the path
dimension (int, default=2) – number of spatial dimensions
mode (str) – can be boid, walk, or bezier
rng (RandomState, default=None) – seed

References

https://github.com/dhermes/bezier

Example

>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> num = 10
>>> dimension = 2
>>> degree = 3
>>> rng = None
>>> path = random_path(num, degree, dimension, rng, mode='boid')
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> plt = kwplot.autoplt()
>>> kwplot.multi_plot(xdata=path[:, 0], ydata=path[:, 1], fnum=1, doclf=1, xlim=(0, 1), ylim=(0, 1))
>>> kwplot.show_if_requested()

Example

>>> # xdoctest: +REQUIRES(--3d)
>>> # xdoctest: +REQUIRES(module:bezier)
>>> import kwarray
>>> import kwplot
>>> plt = kwplot.autoplt()
>>> #
>>> num= num_frames = 100
>>> rng = kwarray.ensure_rng(0)
>>> #
>>> from kwcoco.demo.toydata_video import *  # NOQA
>>> paths = []
>>> paths.append(random_path(num, degree=3, dimension=3, mode='bezier'))
>>> paths.append(random_path(num, degree=2, dimension=3, mode='bezier'))
>>> paths.append(random_path(num, degree=4, dimension=3, mode='bezier'))
>>> #
>>> from mpl_toolkits.mplot3d import Axes3D  # NOQA
>>> ax = plt.gca(projection='3d')
>>> ax.cla()
>>> #
>>> for path in paths:
>>>     time = np.arange(len(path))
>>>     ax.plot(time, path.T[0] * 1, path.T[1] * 1, 'o-')
>>> ax.set_xlim(0, num_frames)
>>> ax.set_ylim(-.01, 1.01)
>>> ax.set_zlim(-.01, 1.01)
>>> ax.set_xlabel('x')
>>> ax.set_ylabel('y')
>>> ax.set_zlabel('z')

`kwcoco.demo.toypatterns`¶

Module Contents¶

Classes¶

CategoryPatterns

Example

Rasters

Functions¶

star(a, dtype=np.uint8)

Generates a star shaped structuring element.

Attributes¶

benchmark_large_hyperspectral()

kwcoco.demo.toypatterns.profile[source]¶

class kwcoco.demo.toypatterns.CategoryPatterns(categories=None, fg_scale=0.5, fg_intensity=0.9, rng=None)[source]¶

Bases: object

Example

>>> from kwcoco.demo.toypatterns import *  # NOQA
>>> self = CategoryPatterns.coerce()
>>> chip = np.zeros((100, 100, 3))
>>> offset = (20, 10)
>>> dims = (160, 140)
>>> info = self.random_category(chip, offset, dims)
>>> print('info = {}'.format(ub.repr2(info, nl=1)))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(info['data'], pnum=(1, 2, 1), fnum=1, title='chip-space')
>>> kpts = kwimage.Points._from_coco(info['keypoints'])
>>> kpts.translate(-np.array(offset)).draw(radius=3)
>>> #####
>>> mask = kwimage.Mask.coerce(info['segmentation'])
>>> kwplot.imshow(mask.to_c_mask().data, pnum=(1, 2, 2), fnum=1, title='img-space')
>>> kpts.draw(radius=3)
>>> kwplot.show_if_requested()

_default_categories[source]¶

_default_keypoint_categories[source]¶

_default_catnames = ['star', 'eff', 'superstar'][source]¶

classmethod coerce(CategoryPatterns, data=None, **kwargs)[source]¶

Construct category patterns from either defaults or only with specific categories. Can accept either an existig category pattern object, a list of known catnames, or mscoco category dictionaries.

Example

>>> data = ['superstar']
>>> self = CategoryPatterns.coerce(data)

__len__(self)[source]¶

__getitem__(self, index)[source]¶

__iter__(self)[source]¶

index(self, name)[source]¶

get(self, index, default=ub.NoParam)[source]¶

random_category(self, chip, xy_offset=None, dims=None, newstyle=True, size=None)[source]¶

Example

>>> from kwcoco.demo.toypatterns import *  # NOQA
>>> self = CategoryPatterns.coerce(['superstar'])
>>> chip = np.random.rand(64, 64)
>>> info = self.random_category(chip)

render_category(self, cname, chip, xy_offset=None, dims=None, newstyle=True, size=None)[source]¶

Example

>>> from kwcoco.demo.toypatterns import *  # NOQA
>>> self = CategoryPatterns.coerce(['superstar'])
>>> chip = np.random.rand(64, 64)
>>> info = self.render_category('superstar', chip, newstyle=True)
>>> print('info = {}'.format(ub.repr2(info, nl=-1)))
>>> info = self.render_category('superstar', chip, newstyle=False)
>>> print('info = {}'.format(ub.repr2(info, nl=-1)))

Example

>>> from kwcoco.demo.toypatterns import *  # NOQA
>>> self = CategoryPatterns.coerce(['superstar'])
>>> chip = None
>>> dims = (64, 64)
>>> info = self.render_category('superstar', chip, newstyle=True, dims=dims, size=dims)
>>> print('info = {}'.format(ub.repr2(info, nl=-1)))

_todo_refactor_geometric_info(self, cname, xy_offset, dims)[source]¶

This function is used to populate kpts and sseg information in the autogenerated coco dataset before rendering. It is redundant with other functionality.

TODO: rectify with _from_elem

Example

>>> self = CategoryPatterns.coerce(['superstar'])
>>> dims = (64, 64)
>>> cname = 'superstar'
>>> xy_offset = None
>>> self._todo_refactor_geometric_info(cname, xy_offset, dims)

_package_info(self, cname, data, mask, kpts, xy_offset, dims, newstyle)[source]¶: packages data from _from_elem into coco-like annotation

_from_elem(self, cname, chip, size=None)[source]¶

Example

>>> # hack to allow chip to be None
>>> chip = None
>>> size = (32, 32)
>>> cname = 'superstar'
>>> self = CategoryPatterns.coerce()
>>> self._from_elem(cname, chip, size)

kwcoco.demo.toypatterns.star(a, dtype=np.uint8)[source]¶

Generates a star shaped structuring element.

Much faster than skimage.morphology version

class kwcoco.demo.toypatterns.Rasters[source]¶

static superstar()[source]¶: test data patch

static eff()[source]¶: test data patch

`kwcoco.examples`¶

Submodules¶

`kwcoco.examples.bench_large_hyperspectral`¶

Module Contents¶

Functions¶

kwcoco.examples.bench_large_hyperspectral.benchmark_large_hyperspectral()[source]¶

`kwcoco.examples.draw_gt_and_predicted_boxes`¶

Module Contents¶

Functions¶

draw_true_and_pred_boxes(true_fpath, pred_fpath, gid, viz_fpath)

How do you generally visualize gt and predicted bounding boxes together?

kwcoco.examples.draw_gt_and_predicted_boxes.draw_true_and_pred_boxes(true_fpath, pred_fpath, gid, viz_fpath)[source]¶

How do you generally visualize gt and predicted bounding boxes together?

Example

>>> import kwcoco
>>> import ubelt as ub
>>> from os.path import join
>>> from kwcoco.demo.perterb import perterb_coco
>>> # Create a working directory
>>> dpath = ub.ensure_app_cache_dir('kwcoco/examples/draw_true_and_pred_boxes')
>>> # Lets setup some dummy true data
>>> true_dset = kwcoco.CocoDataset.demo('shapes2')
>>> true_dset.fpath = join(dpath, 'true_dset.kwcoco.json')
>>> true_dset.dump(true_dset.fpath, newlines=True)
>>> # Lets setup some dummy predicted data
>>> pred_dset = perterb_coco(true_dset, box_noise=100, rng=421)
>>> pred_dset.fpath = join(dpath, 'pred_dset.kwcoco.json')
>>> pred_dset.dump(pred_dset.fpath, newlines=True)
>>> #
>>> # We now have our true and predicted data, lets visualize
>>> true_fpath = true_dset.fpath
>>> pred_fpath = pred_dset.fpath
>>> print('dpath = {!r}'.format(dpath))
>>> print('true_fpath = {!r}'.format(true_fpath))
>>> print('pred_fpath = {!r}'.format(pred_fpath))
>>> # Lets choose an image id to visualize and a path to write to
>>> gid = 1
>>> viz_fpath = join(dpath, 'viz_{}.jpg'.format(gid))
>>> # The answer to the question is in the logic of this function
>>> draw_true_and_pred_boxes(true_fpath, pred_fpath, gid, viz_fpath)

`kwcoco.examples.faq`¶

These are answers to the questions: How do I?

Module Contents¶

Functions¶

get_images_with_videoid()

Q: How would you recommend querying a kwcoco file to get all of the images

kwcoco.examples.faq.get_images_with_videoid()[source]¶: Q: How would you recommend querying a kwcoco file to get all of the images associated with a video id?

`kwcoco.examples.getting_started_existing_dataset`¶

Module Contents¶

Functions¶

getting_started_existing_dataset()

If you want to start using the Python API. Just open IPython and try:

the_core_dataset_backend()

demo_vectorize_interface()

>>> import kwcoco

kwcoco.examples.getting_started_existing_dataset.getting_started_existing_dataset()[source]¶: If you want to start using the Python API. Just open IPython and try:

kwcoco.examples.getting_started_existing_dataset.the_core_dataset_backend()[source]¶

kwcoco.examples.getting_started_existing_dataset.demo_vectorize_interface()[source]¶

>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes2')
>>> #
>>> aids = [1, 2, 3, 4]
>>> annots = dset.annots(aids)
...
>>> print('annots = {!r}'.format(annots))
annots = <Annots(num=4) at ...>

>>> annots.lookup('bbox')
[[346.5, 335.2, 33.2, 99.4],
 [344.5, 327.7, 48.8, 111.1],
 [548.0, 154.4, 57.2, 62.1],
 [548.7, 151.0, 59.4, 80.5]]

>>> gids = annots.lookup('image_id')
>>> print('gids = {!r}'.format(gids))
gids = [1, 2, 1, 2]

>>> images = dset.images(gids)
>>> list(zip(images.lookup('width'), images.lookup('height')))
[(600, 600), (600, 600), (600, 600), (600, 600)]

`kwcoco.examples.loading_multispectral_data`¶

Module Contents¶

Functions¶

demo_load_msi_data()

kwcoco.examples.loading_multispectral_data.demo_load_msi_data()[source]¶

`kwcoco.examples.modification_example`¶

Module Contents¶

Functions¶

`dataset_modification_example_via_copy`()	Say you are given a dataset as input and you need to add your own
`dataset_modification_example_via_construction`()	Alternatively you can make a new dataset and copy over categories / images

kwcoco.examples.modification_example.dataset_modification_example_via_copy()[source]¶: Say you are given a dataset as input and you need to add your own annotation “predictions” to it. You could copy the existing dataset, remove all the annotations, and then add your new annotations.

kwcoco.examples.modification_example.dataset_modification_example_via_construction()[source]¶: Alternatively you can make a new dataset and copy over categories / images as needed

`kwcoco.examples.simple_kwcoco_torch_dataset`¶

This example demonstrates how to use kwcoco to write a very simple torch dataset. This assumes the dataset will be single-image RGB inputs. This file is intended to talk the reader through what we are doing and why.

This example aims for clairity over being concise. There are APIs exposed by kwcoco (and its sister module ndsampler) that can perform the same tasks more efficiently and with fewer lines of code.

If you run the doctest, it will produce a visualization that shows the images with boxes drawn on it, running it multiple times will let you see the augmentations. This can be done with the following command:

xdoctest -m kwcoco.examples.simple_kwcoco_torch_dataset KWCocoSimpleTorchDataset –show

Or just copy the doctest into IPython and run it.

Module Contents¶

Classes¶

KWCocoSimpleTorchDataset

A simple torch dataloader where each image is considered a single item.

Attributes¶

DatasetBase

kwcoco.examples.simple_kwcoco_torch_dataset.DatasetBase[source]¶

class kwcoco.examples.simple_kwcoco_torch_dataset.KWCocoSimpleTorchDataset(coco_dset, input_dims=None, antialias=False, rng=None)[source]¶

Bases: DatasetBase

A simple torch dataloader where each image is considered a single item.

Parameters

coco_dset (kwcoco.CocoDataset | str) – something coercable to a kwcoco dataset, this could either be a kwcoco.CocoDataset object, a path to a kwcoco manifest on disk, or a special toydata code. See kwcoco.CocoDataset.coerce() for more details.
input_dims (Tuple[int, int]) – These are the (height, width) dimensions that the image will be resized to.
antialias (bool, default=False) – If true, we will antialias before downsampling.
rng (RandomState | int | None) – an existing random number generator or a random seed to produce deterministic augmentations.

Example

>>> # xdoctest: +REQUIRES(module:torch)
>>> from kwcoco.examples.simple_kwcoco_torch_dataset import *  # NOQA
>>> import kwcoco
>>> coco_dset = kwcoco.CocoDataset.demo('shapes8')
>>> input_dims = (384, 384)
>>> self = torch_dset = KWCocoSimpleTorchDataset(coco_dset, input_dims=input_dims)
>>> index = len(self) // 2
>>> item = self[index]
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.figure(doclf=True, fnum=1)
>>> kwplot.autompl()
>>> canvas = item['inputs']['rgb'].numpy().transpose(1, 2, 0)
>>> # Construct kwimage objects for batch item visualization
>>> dets = kwimage.Detections(
>>>     boxes=kwimage.Boxes(item['labels']['cxywh'], 'cxywh'),
>>>     class_idxs=item['labels']['class_idxs'],
>>>     classes=self.classes,
>>> ).numpy()
>>> # Overlay annotations on the image
>>> canvas = dets.draw_on(canvas)
>>> kwplot.imshow(canvas)
>>> kwplot.show_if_requested()

__len__(self)[source]¶: Return the number of items in the Dataset

__getitem__(self, index)[source]¶: Construct a batch item to be used in training

`kwcoco.examples.vectorized_interface`¶

Module Contents¶

Functions¶

demo_vectorized_interface()

This demonstrates how to use the kwcoco vectorized interface for images /

kwcoco.examples.vectorized_interface.demo_vectorized_interface()[source]¶: This demonstrates how to use the kwcoco vectorized interface for images / categories / annotations.

`kwcoco.metrics`¶

mkinit kwcoco.metrics -w –relative

Submodules¶

`kwcoco.metrics.assignment`¶

[ ] _fast_pdist_priority: Look at absolute difference in sibling entropy
when deciding whether to go up or down in the tree.
[ ] medschool applications true-pred matching (applicant proposing) fast
algorithm.
[ ] Maybe looping over truth rather than pred is faster? but it makes you
have to combine pred score / ious, which is weird.
[x] preallocate ndarray and use hstack to build confusion vectors?
- doesn’t help
[ ] relevant classes / classes / classes-of-interest we care about needs
to be a first class member of detection metrics.
[ ] Add parameter that allows one prediction to “match” to more than one
truth object. (example: we have a duck detector problem and all the ducks in a row are annotated as separate object, and we only care about getting the group)

Module Contents¶

Functions¶

`_assign_confusion_vectors`(true_dets, pred_dets, bg_weight=1.0, iou_thresh=0.5, bg_cidx=-1, bias=0.0, classes=None, compat='all', prioritize='iou', ignore_classes='ignore', max_dets=None)	Create confusion vectors for detections by assigning to ground true boxes
`_critical_loop`(true_dets, pred_dets, iou_lookup, isvalid_lookup, cx_to_matchable_txs, bg_weight, prioritize, iou_thresh_, pdist_priority, cx_to_ancestors, bg_cidx, ignore_classes, max_dets)
`_fast_pdist_priority`(classes, prioritize, _cache={})	Custom priority computation. Needs some vetting.
`_filter_ignore_regions`(true_dets, pred_dets, ioaa_thresh=0.5, ignore_classes='ignore')	Determine which true and predicted detections should be ignored.

Attributes¶

USE_NEG_INF

kwcoco.metrics.assignment.USE_NEG_INF = True[source]¶

kwcoco.metrics.assignment._assign_confusion_vectors(true_dets, pred_dets, bg_weight=1.0, iou_thresh=0.5, bg_cidx=- 1, bias=0.0, classes=None, compat='all', prioritize='iou', ignore_classes='ignore', max_dets=None)[source]¶

Create confusion vectors for detections by assigning to ground true boxes

Given predictions and truth for an image return (y_pred, y_true, y_score), which is suitable for sklearn classification metrics

Parameters

true_dets (Detections) – groundtruth with boxes, classes, and weights
pred_dets (Detections) – predictions with boxes, classes, and scores
iou_thresh (float, default=0.5) – bounding box overlap iou threshold required for assignment
bias (float, default=0.0) – for computing bounding box overlap, either 1 or 0
gids (List[int], default=None) – which subset of images ids to compute confusion metrics on. If not specified all images are used.
compat (str, default=’all’) – can be (‘ancestors’ | ‘mutex’ | ‘all’). determines which pred boxes are allowed to match which true boxes. If ‘mutex’, then pred boxes can only match true boxes of the same class. If ‘ancestors’, then pred boxes can match true boxes that match or have a coarser label. If ‘all’, then any pred can match any true, regardless of its category label.
prioritize (str, default=’iou’) – can be (‘iou’ | ‘class’ | ‘correct’) determines which box to assign to if mutiple true boxes overlap a predicted box. if prioritize is iou, then the true box with maximum iou (above iou_thresh) will be chosen. If prioritize is class, then it will prefer matching a compatible class above a higher iou. If prioritize is correct, then ancestors of the true class are preferred over descendents of the true class, over unreleated classes.
bg_cidx (int, default=-1) – The index of the background class. The index used in the truth column when a predicted bounding box does not match any true bounding box.
classes (List[str] | kwcoco.CategoryTree) – mapping from class indices to class names. Can also contain class heirarchy information.
ignore_classes (str | List[str]) – class name(s) indicating ignore regions
max_dets (int) – maximum number of detections to consider

Todo

[ ] This is a bottleneck function. An implementation in C / C++ /

Cython would likely improve the overall system.

[ ] Implement crowd truth. Allow multiple predictions to match any
truth objet marked as “iscrowd”.

Returns

with relevant confusion vectors. This keys of this dict can be: interpreted as columns of a data frame. The txs / pxs columns represent the indexes of the true / predicted annotations that were assigned as matching. Additionally each row also contains the true and predicted class index, the predicted score, the true weight and the iou of the true and predicted boxes. A txs value of -1 means that the predicted box was not assigned to a true annotation and a pxs value of -1 means that the true annotation was not assigne to any predicted annotation.

Return type

Example

>>> # xdoctest: +REQUIRES(module:pandas)
>>> import pandas as pd
>>> import kwimage
>>> # Given a raw numpy representation construct Detection wrappers
>>> true_dets = kwimage.Detections(
>>>     boxes=kwimage.Boxes(np.array([
>>>         [ 0,  0, 10, 10], [10,  0, 20, 10],
>>>         [10,  0, 20, 10], [20,  0, 30, 10]]), 'tlbr'),
>>>     weights=np.array([1, 0, .9, 1]),
>>>     class_idxs=np.array([0, 0, 1, 2]))
>>> pred_dets = kwimage.Detections(
>>>     boxes=kwimage.Boxes(np.array([
>>>         [6, 2, 20, 10], [3,  2, 9, 7],
>>>         [3,  9, 9, 7],  [3,  2, 9, 7],
>>>         [2,  6, 7, 7],  [20,  0, 30, 10]]), 'tlbr'),
>>>     scores=np.array([.5, .5, .5, .5, .5, .5]),
>>>     class_idxs=np.array([0, 0, 1, 2, 0, 1]))
>>> bg_weight = 1.0
>>> compat = 'all'
>>> iou_thresh = 0.5
>>> bias = 0.0
>>> import kwcoco
>>> classes = kwcoco.CategoryTree.from_mutex(list(range(3)))
>>> bg_cidx = -1
>>> y = _assign_confusion_vectors(true_dets, pred_dets, bias=bias,
>>>                               bg_weight=bg_weight, iou_thresh=iou_thresh,
>>>                               compat=compat)
>>> y = pd.DataFrame(y)
>>> print(y)  # xdoc: +IGNORE_WANT
   pred  true  score  weight     iou  txs  pxs
0     1     2 0.5000  1.0000  1.0000    3    5
1     0    -1 0.5000  1.0000 -1.0000   -1    4
2     2    -1 0.5000  1.0000 -1.0000   -1    3
3     1    -1 0.5000  1.0000 -1.0000   -1    2
4     0    -1 0.5000  1.0000 -1.0000   -1    1
5     0     0 0.5000  0.0000  0.6061    1    0
6    -1     0 0.0000  1.0000 -1.0000    0   -1
7    -1     1 0.0000  0.9000 -1.0000    2   -1

Example

>>> # xdoctest: +REQUIRES(module:pandas)
>>> import pandas as pd
>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(nimgs=1, nclasses=8,
>>>                              nboxes=(0, 20), n_fp=20,
>>>                              box_noise=.2, cls_noise=.3)
>>> classes = dmet.classes
>>> gid = 0
>>> true_dets = dmet.true_detections(gid)
>>> pred_dets = dmet.pred_detections(gid)
>>> y = _assign_confusion_vectors(true_dets, pred_dets,
>>>                               classes=dmet.classes,
>>>                               compat='all', prioritize='class')
>>> y = pd.DataFrame(y)
>>> print(y)  # xdoc: +IGNORE_WANT
>>> y = _assign_confusion_vectors(true_dets, pred_dets,
>>>                               classes=dmet.classes,
>>>                               compat='ancestors', iou_thresh=.5)
>>> y = pd.DataFrame(y)
>>> print(y)  # xdoc: +IGNORE_WANT

kwcoco.metrics.assignment._critical_loop(true_dets, pred_dets, iou_lookup, isvalid_lookup, cx_to_matchable_txs, bg_weight, prioritize, iou_thresh_, pdist_priority, cx_to_ancestors, bg_cidx, ignore_classes, max_dets)[source]¶

kwcoco.metrics.assignment._fast_pdist_priority(classes, prioritize, _cache={})[source]¶

Custom priority computation. Needs some vetting.

This is the priority used when deciding which prediction to assign to which truth.

Todo

[ ] Look at absolute difference in sibling entropy when deciding
whether to go up or down in the tree.

kwcoco.metrics.assignment._filter_ignore_regions(true_dets, pred_dets, ioaa_thresh=0.5, ignore_classes='ignore')[source]¶

Determine which true and predicted detections should be ignored.

Parameters

true_dets (Detections)
pred_dets (Detections)
ioaa_thresh (float) – intersection over other area thresh for ignoring a region.

Returns

flags indicating which true and predicted: detections should be ignored.

Return type

Tuple[ndarray, ndarray]

Example

>>> from kwcoco.metrics.assignment import *  # NOQA
>>> from kwcoco.metrics.assignment import _filter_ignore_regions
>>> import kwimage
>>> pred_dets = kwimage.Detections.random(classes=['a', 'b', 'c'])
>>> true_dets = kwimage.Detections.random(
>>>     segmentations=True, classes=['a', 'b', 'c', 'ignore'])
>>> ignore_classes = {'ignore', 'b'}
>>> ioaa_thresh = 0.5
>>> print('true_dets = {!r}'.format(true_dets))
>>> print('pred_dets = {!r}'.format(pred_dets))
>>> flags1, flags2 = _filter_ignore_regions(
>>>     true_dets, pred_dets, ioaa_thresh=ioaa_thresh, ignore_classes=ignore_classes)
>>> print('flags1 = {!r}'.format(flags1))
>>> print('flags2 = {!r}'.format(flags2))

>>> flags3, flags4 = _filter_ignore_regions(
>>>     true_dets, pred_dets, ioaa_thresh=ioaa_thresh,
>>>     ignore_classes={c.upper() for c in ignore_classes})
>>> assert np.all(flags1 == flags3)
>>> assert np.all(flags2 == flags4)

`kwcoco.metrics.clf_report`¶

Module Contents¶

Functions¶

`classification_report`(y_true, y_pred, target_names=None, sample_weight=None, verbose=False, remove_unsupported=False, log=None, ascii_only=False)	Computes a classification report which is a collection of various metrics
`ovr_classification_report`(mc_y_true, mc_probs, target_names=None, sample_weight=None, metrics=None, verbose=0, remove_unsupported=False, log=None)	One-vs-rest classification report

Attributes¶

ASCII_ONLY

kwcoco.metrics.clf_report.ASCII_ONLY[source]¶

kwcoco.metrics.clf_report.classification_report(y_true, y_pred, target_names=None, sample_weight=None, verbose=False, remove_unsupported=False, log=None, ascii_only=False)[source]¶

Computes a classification report which is a collection of various metrics commonly used to evaulate classification quality. This can handle binary and multiclass settings.

Note that this function does not accept probabilities or scores and must instead act on final decisions. See ovr_classification_report for a probability based report function using a one-vs-rest strategy.

This emulates the bm(cm) Matlab script written by David Powers that is used for computing bookmaker, markedness, and various other scores.
References:
https://csem.flinders.edu.au/research/techreps/SIE07001.pdf https://www.mathworks.com/matlabcentral/fileexchange/5648-bm-cm-?requestedDomain=www.mathworks.com Jurman, Riccadonna, Furlanello, (2012). A Comparison of MCC and CEN

Error Measures in MultiClass Prediction

Args:
y_true (array): true labels for each item y_pred (array): predicted labels for each item target_names (List): mapping from label to category name sample_weight (ndarray): weight for each item verbose (False): print if True log (callable): print or logging function remove_unsupported (bool, default=False): removes categories that have

no support.

ascii_only (bool, default=False): if True dont use unicode characters.
if the environ ASCII_ONLY is present this is forced to True and cannot be undone.

Example:
>>> # xdoctest: +IGNORE_WANT
>>> # xdoctest: +REQUIRES(module:sklearn)
>>> # xdoctest: +REQUIRES(module:pandas)
>>> y_true = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3]
>>> y_pred = [1, 2, 1, 3, 1, 2, 2, 3, 2, 2, 3, 3, 2, 3, 3, 3, 1, 3]
>>> target_names = None
>>> sample_weight = None
>>> report = classification_report(y_true, y_pred, verbose=0, ascii_only=1)
>>> print(report['confusion'])
pred  1  2  3  Σr
real
1     3  1  1   5
2     0  4  1   5
3     1  1  6   8
Σp    4  6  8  18
>>> print(report['metrics'])
metric    precision  recall    fpr  markedness  bookmaker    mcc  support
class
1            0.7500  0.6000 0.0769      0.6071     0.5231 0.5635        5
2            0.6667  0.8000 0.1538      0.5833     0.6462 0.6139        5
3            0.7500  0.7500 0.2000      0.5500     0.5500 0.5500        8
combined     0.7269  0.7222 0.1530      0.5751     0.5761 0.5758       18
Example:
>>> # xdoctest: +IGNORE_WANT
>>> # xdoctest: +REQUIRES(module:sklearn)
>>> # xdoctest: +REQUIRES(module:pandas)
>>> from kwcoco.metrics.clf_report import *  # NOQA
>>> y_true = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3]
>>> y_pred = [1, 2, 1, 3, 1, 2, 2, 3, 2, 2, 3, 3, 2, 3, 3, 3, 1, 3]
>>> target_names = None
>>> sample_weight = None
>>> logs = []
>>> report = classification_report(y_true, y_pred, verbose=1, ascii_only=True, log=logs.append)
>>> print('

‘.join(logs))

Ignore:

>>> size = 100
>>> rng = np.random.RandomState(0)
>>> p_classes = np.array([.90, .05, .05][0:2])
>>> p_classes = p_classes / p_classes.sum()
>>> p_wrong   = np.array([.03, .01, .02][0:2])
>>> y_true = testdata_ytrue(p_classes, p_wrong, size, rng)
>>> rs = []
>>> for x in range(17):
>>>     p_wrong += .05
>>>     y_pred = testdata_ypred(y_true, p_wrong, rng)
>>>     report = classification_report(y_true, y_pred, verbose='hack')
>>>     rs.append(report)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> import pandas as pd
>>> df = pd.DataFrame(rs).drop(['raw'], axis=1)
>>> delta = df.subtract(df['target'], axis=0)
>>> sqrd_error = np.sqrt((delta ** 2).sum(axis=0))
>>> print('Error')
>>> print(sqrd_error.sort_values())
>>> ys = df.to_dict(orient='list')
>>> kwplot.multi_plot(ydata_list=ys)

kwcoco.metrics.clf_report.ovr_classification_report(mc_y_true, mc_probs, target_names=None, sample_weight=None, metrics=None, verbose=0, remove_unsupported=False, log=None)[source]¶

One-vs-rest classification report

Parameters

mc_y_true (ndarray[int]) – multiclass truth labels (integer label format). Shape [N].
mc_probs (ndarray) – multiclass probabilities for each class. Shape [N x C].
target_names (Dict[int, str] – mapping from int label to string name
sample_weight (ndarray) – weight for each item. Shape [N].
metrics (List[str]) – names of metrics to compute

Example

>>> # xdoctest: +IGNORE_WANT
>>> # xdoctest: +REQUIRES(module:sklearn)
>>> # xdoctest: +REQUIRES(module:pandas)
>>> from kwcoco.metrics.clf_report import *  # NOQA
>>> y_true = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0]
>>> y_probs = np.random.rand(len(y_true), max(y_true) + 1)
>>> target_names = None
>>> sample_weight = None
>>> verbose = True
>>> report = ovr_classification_report(y_true, y_probs)
>>> print(report['ave'])
auc     0.6541
ap      0.6824
kappa   0.0963
mcc     0.1002
brier   0.2214
dtype: float64
>>> print(report['ovr'])
     auc     ap  kappa    mcc  brier  support  weight
0 0.6062 0.6161 0.0526 0.0598 0.2608        8  0.4444
1 0.5846 0.6014 0.0000 0.0000 0.2195        5  0.2778
2 0.8000 0.8693 0.2623 0.2652 0.1602        5  0.2778

`kwcoco.metrics.confusion_measures`¶

Classes that store accumulated confusion measures (usually derived from confusion vectors).

For each chosen threshold value:

thresholds[i] - the i-th threshold value

The primary data we manipulate are arrays of “confusion” counts, i.e.

tp_count[i] - true positives at the i-th threshold

fp_count[i] - false positives at the i-th threshold

fn_count[i] - false negatives at the i-th threshold

tn_count[i] - true negatives at the i-th threshold

Module Contents¶

Classes¶

`Measures`	Holds accumulated confusion counts, and derived measures
`PerClass_Measures`
`MeasureCombiner`	Helper to iteravely combine binary measures generated by some process
`OneVersusRestMeasureCombiner`	Helper to iteravely combine ovr measures generated by some process

Functions¶

`_combine_threshold`(tocombine_thresh, thresh_bins, growth, precision)	Logic to take care of combining thresholds in the case bins are not given
`reversable_diff`(arr, assume_sorted=1, reverse=False)	Does a reversable array difference operation.
`populate_info`(info)	Given raw accumulated confusion counts, populated secondary measures like

Attributes¶

kwcoco.metrics.confusion_measures.profile[source]¶

class kwcoco.metrics.confusion_measures.Measures(info)[source]¶

Bases: ubelt.NiceRepr, kwcoco.metrics.util.DictProxy

Holds accumulated confusion counts, and derived measures

Example

>>> from kwcoco.metrics.confusion_vectors import BinaryConfusionVectors  # NOQA
>>> binvecs = BinaryConfusionVectors.demo(n=100, p_error=0.5)
>>> self = binvecs.measures()
>>> print('self = {!r}'.format(self))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> self.draw(doclf=True)
>>> self.draw(key='pr',  pnum=(1, 2, 1))
>>> self.draw(key='roc', pnum=(1, 2, 2))
>>> kwplot.show_if_requested()

property catname(self)[source]¶

__nice__(self)[source]¶

reconstruct(self)[source]¶

classmethod from_json(cls, state)[source]¶

__json__(self)[source]¶

Example

>>> from kwcoco.metrics.confusion_vectors import BinaryConfusionVectors  # NOQA
>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> binvecs = BinaryConfusionVectors.demo(n=10, p_error=0.5)
>>> self = binvecs.measures()
>>> info = self.__json__()
>>> print('info = {}'.format(ub.repr2(info, nl=1)))
>>> populate_info(info)
>>> print('info = {}'.format(ub.repr2(info, nl=1)))
>>> recon = Measures.from_json(info)

summary(self)[source]¶

maximized_thresholds(self)[source]¶: Returns thresholds that maximize metrics.

counts(self)[source]¶

draw(self, key=None, prefix='', **kw)[source]¶

Example

>>> # xdoctest: +REQUIRES(module:kwplot)
>>> # xdoctest: +REQUIRES(module:pandas)
>>> from kwcoco.metrics.confusion_vectors import ConfusionVectors  # NOQA
>>> cfsn_vecs = ConfusionVectors.demo()
>>> ovr_cfsn = cfsn_vecs.binarize_ovr(keyby='name')
>>> self = ovr_cfsn.measures()['perclass']
>>> self.draw('mcc', doclf=True, fnum=1)
>>> self.draw('pr', doclf=1, fnum=2)
>>> self.draw('roc', doclf=1, fnum=3)

summary_plot(self, fnum=1, title='', subplots='auto')[source]¶

Example

>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> from kwcoco.metrics.confusion_vectors import ConfusionVectors  # NOQA
>>> cfsn_vecs = ConfusionVectors.demo(n=3, p_error=0.5)
>>> binvecs = cfsn_vecs.binarize_classless()
>>> self = binvecs.measures()
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> self.summary_plot()
>>> kwplot.show_if_requested()

classmethod demo(cls, **kwargs)[source]¶

Create a demo Measures object for testing / demos

Parameters: **kwargs – passed to BinaryConfusionVectors.demo(). some valid keys are: n, rng, p_rue, p_error, p_miss.

classmethod combine(cls, tocombine, precision=None, growth=None, thresh_bins=None)[source]¶

Combine binary confusion metrics

Parameters

tocombine (List[Measures]) – a list of measures to combine into one
precision (int | None) – If specified rounds thresholds to this precision which can prevent a RAM explosion when combining a large number of measures. However, this is a lossy operation and will impact the underlying scores. NOTE: use growth instead.
growth (int | None) – if specified this limits how much the resulting measures are allowed to grow by. If None, growth is unlimited. Otherwise, if growth is ‘max’, the growth is limited to the maximum length of an input. We might make this more numerical in the future.
thresh_bins (int) – Force this many threshold bins.

Returns

Measures

Example

>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> measures1 = Measures.demo(n=15)
>>> measures2 = measures1
>>> tocombine = [measures1, measures2]
>>> new_measures = Measures.combine(tocombine)
>>> new_measures.reconstruct()
>>> print('new_measures = {!r}'.format(new_measures))
>>> print('measures1 = {!r}'.format(measures1))
>>> print('measures2 = {!r}'.format(measures2))
>>> print(ub.repr2(measures1.__json__(), nl=1, sort=0))
>>> print(ub.repr2(measures2.__json__(), nl=1, sort=0))
>>> print(ub.repr2(new_measures.__json__(), nl=1, sort=0))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.figure(fnum=1)
>>> new_measures.summary_plot()
>>> measures1.summary_plot()
>>> measures1.draw('roc')
>>> measures2.draw('roc')
>>> new_measures.draw('roc')

Example

>>> # Demonstrate issues that can arrise from choosing a precision
>>> # that is too low  when combining metrics. Breakpoints
>>> # between different metrics can get muddled, but choosing a
>>> # precision that is too high can overwhelm memory.
>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> base = ub.map_vals(np.asarray, {
>>>     'tp_count':   [ 1,  1,  2,  2,  2,  2,  3],
>>>     'fp_count':   [ 0,  1,  1,  2,  3,  4,  5],
>>>     'fn_count':   [ 1,  1,  0,  0,  0,  0,  0],
>>>     'tn_count':   [ 5,  4,  4,  3,  2,  1,  0],
>>>     'thresholds': [.0, .0, .0, .0, .0, .0, .0],
>>> })
>>> # Make tiny offsets to thresholds
>>> rng = kwarray.ensure_rng(0)
>>> n = len(base['thresholds'])
>>> offsets = [
>>>     sorted(rng.rand(n) * 10 ** -rng.randint(4, 7))[::-1]
>>>     for _ in range(20)
>>> ]
>>> tocombine = []
>>> for offset in offsets:
>>>     base_n = base.copy()
>>>     base_n['thresholds'] += offset
>>>     measures_n = Measures(base_n).reconstruct()
>>>     tocombine.append(measures_n)
>>> for precision in [6, 5, 2]:
>>>     combo = Measures.combine(tocombine, precision=precision).reconstruct()
>>>     print('precision = {!r}'.format(precision))
>>>     print('combo = {}'.format(ub.repr2(combo, nl=1)))
>>>     print('num_thresholds = {}'.format(len(combo['thresholds'])))
>>> for growth in [None, 'max', 'log', 'root', 'half']:
>>>     combo = Measures.combine(tocombine, growth=growth).reconstruct()
>>>     print('growth = {!r}'.format(growth))
>>>     print('combo = {}'.format(ub.repr2(combo, nl=1)))
>>>     print('num_thresholds = {}'.format(len(combo['thresholds'])))
>>>     #print(combo.counts().pandas())

Example

>>> # Test case: combining a single measures should leave it unchanged
>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> measures = Measures.demo(n=40, p_true=0.2, p_error=0.4, p_miss=0.6)
>>> df1 = measures.counts().pandas().fillna(0)
>>> print(df1)
>>> tocombine = [measures]
>>> combo = Measures.combine(tocombine)
>>> df2 = combo.counts().pandas().fillna(0)
>>> print(df2)
>>> assert np.allclose(df1, df2)

>>> combo = Measures.combine(tocombine, thresh_bins=2)
>>> df3 = combo.counts().pandas().fillna(0)
>>> print(df3)

>>> # I am NOT sure if this is correct or not
>>> thresh_bins = 20
>>> combo = Measures.combine(tocombine, thresh_bins=thresh_bins)
>>> df4 = combo.counts().pandas().fillna(0)
>>> print(df4)

>>> combo = Measures.combine(tocombine, thresh_bins=np.linspace(0, 1, 20))
>>> df4 = combo.counts().pandas().fillna(0)
>>> print(df4)

assert np.allclose(combo[‘thresholds’], measures[‘thresholds’]) assert np.allclose(combo[‘fp_count’], measures[‘fp_count’]) assert np.allclose(combo[‘tp_count’], measures[‘tp_count’]) assert np.allclose(combo[‘tp_count’], measures[‘tp_count’])

globals().update(xdev.get_func_kwargs(Measures.combine))

Example

>>> # Test degenerate case
>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> tocombine = [
>>>     {'fn_count': [0.0], 'fp_count': [359980.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [7747.0]},
>>>     {'fn_count': [0.0], 'fp_count': [360849.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [424.0]},
>>>     {'fn_count': [0.0], 'fp_count': [367003.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [991.0]},
>>>     {'fn_count': [0.0], 'fp_count': [367976.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [1017.0]},
>>>     {'fn_count': [0.0], 'fp_count': [676338.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [7067.0]},
>>>     {'fn_count': [0.0], 'fp_count': [676348.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [7406.0]},
>>>     {'fn_count': [0.0], 'fp_count': [676626.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [7858.0]},
>>>     {'fn_count': [0.0], 'fp_count': [676693.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [10969.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677269.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [11188.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677331.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [11734.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677395.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [11556.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677418.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [11621.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677422.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [11424.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677648.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [9804.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677826.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [2470.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677834.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [2470.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677835.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [2470.0]},
>>>     {'fn_count': [11123.0, 0.0], 'fp_count': [0.0, 676754.0], 'thresholds': [0.0002442002442002442, 0.0], 'tn_count': [676754.0, 0.0], 'tp_count': [2.0, 11125.0]},
>>>     {'fn_count': [7738.0, 0.0], 'fp_count': [0.0, 676466.0], 'thresholds': [0.0002442002442002442, 0.0], 'tn_count': [676466.0, 0.0], 'tp_count': [0.0, 7738.0]},
>>>     {'fn_count': [8653.0, 0.0], 'fp_count': [0.0, 676341.0], 'thresholds': [0.0002442002442002442, 0.0], 'tn_count': [676341.0, 0.0], 'tp_count': [0.0, 8653.0]},
>>> ]
>>> thresh_bins = np.linspace(0, 1, 4)
>>> combo = Measures.combine(tocombine, thresh_bins=thresh_bins).reconstruct()
>>> print('tocombine = {}'.format(ub.repr2(tocombine, nl=2)))
>>> print('thresh_bins = {!r}'.format(thresh_bins))
>>> print(ub.repr2(combo.__json__(), nl=1))
>>> for thresh_bins in [4096, 1]:
>>>     combo = Measures.combine(tocombine, thresh_bins=thresh_bins).reconstruct()
>>>     print('thresh_bins = {!r}'.format(thresh_bins))
>>>     print('combo = {}'.format(ub.repr2(combo, nl=1)))
>>>     print('num_thresholds = {}'.format(len(combo['thresholds'])))
>>> for precision in [6, 5, 2]:
>>>     combo = Measures.combine(tocombine, precision=precision).reconstruct()
>>>     print('precision = {!r}'.format(precision))
>>>     print('combo = {}'.format(ub.repr2(combo, nl=1)))
>>>     print('num_thresholds = {}'.format(len(combo['thresholds'])))
>>> for growth in [None, 'max', 'log', 'root', 'half']:
>>>     combo = Measures.combine(tocombine, growth=growth).reconstruct()
>>>     print('growth = {!r}'.format(growth))
>>>     print('combo = {}'.format(ub.repr2(combo, nl=1)))
>>>     print('num_thresholds = {}'.format(len(combo['thresholds'])))

kwcoco.metrics.confusion_measures._combine_threshold(tocombine_thresh, thresh_bins, growth, precision)[source]¶

Logic to take care of combining thresholds in the case bins are not given

This can be fairly slow and lead to unnecessary memory usage

kwcoco.metrics.confusion_measures.reversable_diff(arr, assume_sorted=1, reverse=False)[source]¶

Does a reversable array difference operation.

This will be used to find positions where accumulation happened in confusion count array.

class kwcoco.metrics.confusion_measures.PerClass_Measures(cx_to_info)[source]¶

Bases: ubelt.NiceRepr, kwcoco.metrics.util.DictProxy

__nice__(self)[source]¶

summary(self)[source]¶

classmethod from_json(cls, state)[source]¶

__json__(self)[source]¶

draw(self, key='mcc', prefix='', **kw)[source]¶

Example

>>> # xdoctest: +REQUIRES(module:kwplot)
>>> from kwcoco.metrics.confusion_vectors import ConfusionVectors  # NOQA
>>> cfsn_vecs = ConfusionVectors.demo()
>>> ovr_cfsn = cfsn_vecs.binarize_ovr(keyby='name')
>>> self = ovr_cfsn.measures()['perclass']
>>> self.draw('mcc', doclf=True, fnum=1)
>>> self.draw('pr', doclf=1, fnum=2)
>>> self.draw('roc', doclf=1, fnum=3)

draw_roc(self, prefix='', **kw)[source]¶

draw_pr(self, prefix='', **kw)[source]¶

summary_plot(self, fnum=1, title='', subplots='auto')[source]¶

CommandLine

python ~/code/kwcoco/kwcoco/metrics/confusion_measures.py PerClass_Measures.summary_plot --show

Example

>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> from kwcoco.metrics.detect_metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     n_fp=(0, 1), n_fn=(0, 3), nimgs=32, nboxes=(0, 32),
>>>     classes=3, rng=0, newstyle=1, box_noise=0.7, cls_noise=0.2, score_noise=0.3, with_probs=False)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> ovr_cfsn = cfsn_vecs.binarize_ovr(keyby='name', ignore_classes=['vector', 'raster'])
>>> self = ovr_cfsn.measures()['perclass']
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> import seaborn as sns
>>> sns.set()
>>> self.summary_plot(title='demo summary_plot ovr', subplots=['pr', 'roc'])
>>> kwplot.show_if_requested()
>>> self.summary_plot(title='demo summary_plot ovr', subplots=['mcc', 'acc'], fnum=2)

class kwcoco.metrics.confusion_measures.MeasureCombiner(precision=None, growth=None, thresh_bins=None)[source]¶

Helper to iteravely combine binary measures generated by some process

Example

>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> from kwcoco.metrics.confusion_vectors import BinaryConfusionVectors
>>> rng = kwarray.ensure_rng(0)
>>> bin_combiner = MeasureCombiner(growth='max')
>>> for _ in range(80):
>>>     bin_cfsn_vecs = BinaryConfusionVectors.demo(n=rng.randint(40, 50), rng=rng, p_true=0.2, p_error=0.4, p_miss=0.6)
>>>     bin_measures = bin_cfsn_vecs.measures()
>>>     bin_combiner.submit(bin_measures)
>>> combined = bin_combiner.finalize()
>>> print('combined = {!r}'.format(combined))

property queue_size(self)[source]¶

submit(self, other)[source]¶

combine(self)[source]¶

finalize(self)[source]¶

class kwcoco.metrics.confusion_measures.OneVersusRestMeasureCombiner(precision=None, growth=None, thresh_bins=None)[source]¶

Helper to iteravely combine ovr measures generated by some process

Example

>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> from kwcoco.metrics.confusion_vectors import OneVsRestConfusionVectors
>>> rng = kwarray.ensure_rng(0)
>>> ovr_combiner = OneVersusRestMeasureCombiner(growth='max')
>>> for _ in range(80):
>>>     ovr_cfsn_vecs = OneVsRestConfusionVectors.demo()
>>>     ovr_measures = ovr_cfsn_vecs.measures()
>>>     ovr_combiner.submit(ovr_measures)
>>> combined = ovr_combiner.finalize()
>>> print('combined = {!r}'.format(combined))

submit(self, other)[source]¶

_summary(self)[source]¶

combine(self)[source]¶

finalize(self)[source]¶

kwcoco.metrics.confusion_measures.populate_info(info)[source]¶: Given raw accumulated confusion counts, populated secondary measures like AP, AUC, F1, MCC, etc..

`kwcoco.metrics.confusion_vectors`¶

Classes that store raw confusion vectors, which can be accumulated into confusion measures.

Module Contents¶

Classes¶

`ConfusionVectors`	Stores information used to construct a confusion matrix. This includes
`OneVsRestConfusionVectors`	Container for multiple one-vs-rest binary confusion vectors
`BinaryConfusionVectors`	Stores information about a binary classification problem.

Functions¶

_stabalize_data(y_true, y_score, sample_weight, npad=7)

Adds ideally calibrated dummy values to curves with few positive examples.

Attributes¶

OneVsRestConfusionVectors

kwcoco.metrics.confusion_vectors.profile[source]¶

class kwcoco.metrics.confusion_vectors.ConfusionVectors(cfsn_vecs, data, classes, probs=None)[source]¶

Bases: ubelt.NiceRepr

Stores information used to construct a confusion matrix. This includes corresponding vectors of predicted labels, true labels, sample weights, etc…

Variables

data (kwarray.DataFrameArray) – should at least have keys true, pred, weight
classes (Sequence | CategoryTree) – list of category names or category graph
probs (ndarray, optional) – probabilities for each class

Example

>>> # xdoctest: IGNORE_WANT
>>> # xdoctest: +REQUIRES(module:pandas)
>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=10, nboxes=(0, 10), n_fp=(0, 1), classes=3)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> print(cfsn_vecs.data._pandas())
     pred  true   score  weight     iou  txs  pxs  gid
0       2     2 10.0000  1.0000  1.0000    0    4    0
1       2     2  7.5025  1.0000  1.0000    1    3    0
2       1     1  5.0050  1.0000  1.0000    2    2    0
3       3    -1  2.5075  1.0000 -1.0000   -1    1    0
4       2    -1  0.0100  1.0000 -1.0000   -1    0    0
5      -1     2  0.0000  1.0000 -1.0000    3   -1    0
6      -1     2  0.0000  1.0000 -1.0000    4   -1    0
7       2     2 10.0000  1.0000  1.0000    0    5    1
8       2     2  8.0020  1.0000  1.0000    1    4    1
9       1     1  6.0040  1.0000  1.0000    2    3    1
..    ...   ...     ...     ...     ...  ...  ...  ...
62     -1     2  0.0000  1.0000 -1.0000    7   -1    7
63     -1     3  0.0000  1.0000 -1.0000    8   -1    7
64     -1     1  0.0000  1.0000 -1.0000    9   -1    7
65      1    -1 10.0000  1.0000 -1.0000   -1    0    8
66      1     1  0.0100  1.0000  1.0000    0    1    8
67      3    -1 10.0000  1.0000 -1.0000   -1    3    9
68      2     2  6.6700  1.0000  1.0000    0    2    9
69      2     2  3.3400  1.0000  1.0000    1    1    9
70      3    -1  0.0100  1.0000 -1.0000   -1    0    9
71     -1     2  0.0000  1.0000 -1.0000    2   -1    9

>>> # xdoctest: +REQUIRES(--show)
>>> # xdoctest: +REQUIRES(module:pandas)
>>> import kwplot
>>> kwplot.autompl()
>>> from kwcoco.metrics.confusion_vectors import ConfusionVectors
>>> cfsn_vecs = ConfusionVectors.demo(
>>>     nimgs=128, nboxes=(0, 10), n_fp=(0, 3), n_fn=(0, 3), classes=3)
>>> cx_to_binvecs = cfsn_vecs.binarize_ovr()
>>> measures = cx_to_binvecs.measures()['perclass']
>>> print('measures = {!r}'.format(measures))
measures = <PerClass_Measures({
    'cat_1': <Measures({'ap': 0.227, 'auc': 0.507, 'catname': cat_1, 'max_f1': f1=0.45@0.47, 'nsupport': 788.000})>,
    'cat_2': <Measures({'ap': 0.288, 'auc': 0.572, 'catname': cat_2, 'max_f1': f1=0.51@0.43, 'nsupport': 788.000})>,
    'cat_3': <Measures({'ap': 0.225, 'auc': 0.484, 'catname': cat_3, 'max_f1': f1=0.46@0.40, 'nsupport': 788.000})>,
}) at 0x7facf77bdfd0>
>>> kwplot.figure(fnum=1, doclf=True)
>>> measures.draw(key='pr', fnum=1, pnum=(1, 3, 1))
>>> measures.draw(key='roc', fnum=1, pnum=(1, 3, 2))
>>> measures.draw(key='mcc', fnum=1, pnum=(1, 3, 3))
...

__nice__(cfsn_vecs)[source]¶

__json__(self)[source]¶

Serialize to json

Example

>>> # xdoctest: +REQUIRES(module:pandas)
>>> from kwcoco.metrics import ConfusionVectors
>>> self = ConfusionVectors.demo(n_imgs=1, classes=2, n_fp=0, nboxes=1)
>>> state = self.__json__()
>>> print('state = {}'.format(ub.repr2(state, nl=2, precision=2, align=1)))
>>> recon = ConfusionVectors.from_json(state)

classmethod from_json(cls, state)[source]¶

classmethod demo(cfsn_vecs, **kw)[source]¶

Parameters: **kwargs – See kwcoco.metrics.DetectionMetrics.demo()
Returns: ConfusionVectors

Example

>>> cfsn_vecs = ConfusionVectors.demo()
>>> print('cfsn_vecs = {!r}'.format(cfsn_vecs))
>>> cx_to_binvecs = cfsn_vecs.binarize_ovr()
>>> print('cx_to_binvecs = {!r}'.format(cx_to_binvecs))

classmethod from_arrays(ConfusionVectors, true, pred=None, score=None, weight=None, probs=None, classes=None)[source]¶

Construct confusion vector data structure from component arrays

Example

>>> # xdoctest: +REQUIRES(module:pandas)
>>> import kwarray
>>> classes = ['person', 'vehicle', 'object']
>>> rng = kwarray.ensure_rng(0)
>>> true = (rng.rand(10) * len(classes)).astype(int)
>>> probs = rng.rand(len(true), len(classes))
>>> cfsn_vecs = ConfusionVectors.from_arrays(true=true, probs=probs, classes=classes)
>>> cfsn_vecs.confusion_matrix()
pred     person  vehicle  object
real
person        0        0       0
vehicle       2        4       1
object        2        1       0

confusion_matrix(cfsn_vecs, compress=False)[source]¶

Builds a confusion matrix from the confusion vectors.

Parameters

compress (bool, default=False) – if True removes rows / columns with no entries

Returns

cmthe labeled confusion matrix

(Note: we should write a efficient replacement for: this use case. #remove_pandas)

Return type

pd.DataFrame

CommandLine

xdoctest -m kwcoco.metrics.confusion_vectors ConfusionVectors.confusion_matrix

Example

>>> # xdoctest: +REQUIRES(module:pandas)
>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=10, nboxes=(0, 10), n_fp=(0, 1), n_fn=(0, 1), classes=3, cls_noise=.2)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> cm = cfsn_vecs.confusion_matrix()
...
>>> print(cm.to_string(float_format=lambda x: '%.2f' % x))
pred        background  cat_1  cat_2  cat_3
real
background        0.00   1.00   2.00   3.00
cat_1             3.00  12.00   0.00   0.00
cat_2             3.00   0.00  14.00   0.00
cat_3             2.00   0.00   0.00  17.00

coarsen(cfsn_vecs, cxs)[source]¶

Creates a coarsened set of vectors

Returns: ConfusionVectors

binarize_classless(cfsn_vecs, negative_classes=None)[source]¶

Creates a binary representation useful for measuring the performance of detectors. It is assumed that scores of “positive” classes should be high and “negative” clases should be low.

Parameters: negative_classes (List[str | int]) – list of negative class names or idxs, by default chooses any class with a true class index of -1. These classes should ideally have low scores.
Returns: BinaryConfusionVectors

Note

The “classlessness” of this depends on the compat=”all” argument being used when constructing confusion vectors, otherwise it becomes something like a macro-average because the class information was used in deciding which true and predicted boxes were allowed to match.

Example

>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=10, nboxes=(0, 10), n_fp=(0, 1), n_fn=(0, 1), classes=3)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> class_idxs = list(dmet.classes.node_to_idx.values())
>>> binvecs = cfsn_vecs.binarize_classless()

binarize_ovr(cfsn_vecs, mode=1, keyby='name', ignore_classes={'ignore'}, approx=0)[source]¶

Transforms cfsn_vecs into one-vs-rest BinaryConfusionVectors for each category.

Parameters

mode (int, default=1) – 0 for heirarchy aware or 1 for voc like. MODE 0 IS PROBABLY BROKEN
keyby (int | str) – can be cx or name
ignore_classes (Set[str]) – category names to ignore
approx (bool, default=0) – if True try and approximate missing scores otherwise assume they are irrecoverable and use -inf

Returns

which behaves like: Dict[int, BinaryConfusionVectors]: cx_to_binvecs

Return type

Example

>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> cfsn_vecs = ConfusionVectors.demo()
>>> print('cfsn_vecs = {!r}'.format(cfsn_vecs))
>>> catname_to_binvecs = cfsn_vecs.binarize_ovr(keyby='name')
>>> print('catname_to_binvecs = {!r}'.format(catname_to_binvecs))

cfsn_vecs.data.pandas() catname_to_binvecs.cx_to_binvecs[‘class_1’].data.pandas()

Note

classification_report(cfsn_vecs, verbose=0)[source]¶

Build a classification report with various metrics.

Example

>>> # xdoctest: +REQUIRES(module:pandas)
>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> cfsn_vecs = ConfusionVectors.demo()
>>> report = cfsn_vecs.classification_report(verbose=1)

class kwcoco.metrics.confusion_vectors.OneVsRestConfusionVectors(cx_to_binvecs, classes)[source]¶

Bases: ubelt.NiceRepr

Container for multiple one-vs-rest binary confusion vectors

Variables

cx_to_binvecs –
classes –

Example

>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=10, nboxes=(0, 10), n_fp=(0, 1), classes=3)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> self = cfsn_vecs.binarize_ovr(keyby='name')
>>> print('self = {!r}'.format(self))

__nice__(self)[source]¶

classmethod demo(cls)[source]¶

Parameters: **kwargs – See kwcoco.metrics.DetectionMetrics.demo()
Returns: ConfusionVectors

keys(self)[source]¶

__getitem__(self, cx)[source]¶

measures(self, stabalize_thresh=7, fp_cutoff=None, monotonic_ppv=True, ap_method='pycocotools')[source]¶

Creates binary confusion measures for every one-versus-rest category.

Parameters

stabalize_thresh (int, default=7) – if fewer than this many data points inserts dummy stabilization data so curves can still be drawn.
fp_cutoff (int, default=None) – maximum number of false positives in the truncated roc curves. None is equivalent to float('inf')
monotonic_ppv (bool, default=True) – if True ensures that precision is always increasing as recall decreases. This is done in pycocotools scoring, but I’m not sure its a good idea.

SeeAlso:: BinaryConfusionVectors.measures()

Example

>>> self = OneVsRestConfusionVectors.demo()
>>> thresh_result = self.measures()['perclass']

abstract ovr_classification_report(self)[source]¶

class kwcoco.metrics.confusion_vectors.BinaryConfusionVectors(data, cx=None, classes=None)[source]¶

Bases: ubelt.NiceRepr

Stores information about a binary classification problem. This is always with respect to a specific class, which is given by cx and classes.

The data DataFrameArray must contain: is_true - if the row is an instance of class classes[cx] pred_score - the predicted probability of class classes[cx], and weight - sample weight of the example

Example

>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> self = BinaryConfusionVectors.demo(n=10)
>>> print('self = {!r}'.format(self))
>>> print('measures = {}'.format(ub.repr2(self.measures())))

>>> self = BinaryConfusionVectors.demo(n=0)
>>> print('measures = {}'.format(ub.repr2(self.measures())))

>>> self = BinaryConfusionVectors.demo(n=1)
>>> print('measures = {}'.format(ub.repr2(self.measures())))

>>> self = BinaryConfusionVectors.demo(n=2)
>>> print('measures = {}'.format(ub.repr2(self.measures())))

classmethod demo(cls, n=10, p_true=0.5, p_error=0.2, p_miss=0.0, rng=None)[source]¶

Create random data for tests

Parameters

n (int) – number of rows
p_true (int) – fraction of real positive cases
p_error (int) – probability of making a recoverable mistake
p_miss (int) – probability of making a unrecoverable mistake
rng (int | RandomState) – random seed / state

Returns

BinaryConfusionVectors

Example

>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> cfsn = BinaryConfusionVectors.demo(n=1000, p_error=0.1, p_miss=0.1)
>>> measures = cfsn.measures()
>>> print('measures = {}'.format(ub.repr2(measures, nl=1)))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.figure(fnum=1, pnum=(1, 2, 1))
>>> measures.draw('pr')
>>> kwplot.figure(fnum=1, pnum=(1, 2, 2))
>>> measures.draw('roc')

property catname(self)[source]¶

__nice__(self)[source]¶

__len__(self)[source]¶

measures(self, stabalize_thresh=7, fp_cutoff=None, monotonic_ppv=True, ap_method='pycocotools')[source]¶

Get statistics (F1, G1, MCC) versus thresholds

Parameters

stabalize_thresh (int, default=7) – if fewer than this many data points inserts dummy stabalization data so curves can still be drawn.
fp_cutoff (int, default=None) – maximum number of false positives in the truncated roc curves. None is equivalent to float('inf')
monotonic_ppv (bool, default=True) – if True ensures that precision is always increasing as recall decreases. This is done in pycocotools scoring, but I’m not sure its a good idea.

Example

>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> self = BinaryConfusionVectors.demo(n=0)
>>> print('measures = {}'.format(ub.repr2(self.measures())))
>>> self = BinaryConfusionVectors.demo(n=1, p_true=0.5, p_error=0.5)
>>> print('measures = {}'.format(ub.repr2(self.measures())))
>>> self = BinaryConfusionVectors.demo(n=3, p_true=0.5, p_error=0.5)
>>> print('measures = {}'.format(ub.repr2(self.measures())))

>>> self = BinaryConfusionVectors.demo(n=100, p_true=0.5, p_error=0.5, p_miss=0.3)
>>> print('measures = {}'.format(ub.repr2(self.measures())))
>>> print('measures = {}'.format(ub.repr2(ub.odict(self.measures()))))

References

https://en.wikipedia.org/wiki/Confusion_matrix https://en.wikipedia.org/wiki/Precision_and_recall https://en.wikipedia.org/wiki/Matthews_correlation_coefficient

_binary_clf_curves(self, stabalize_thresh=7, fp_cutoff=None)[source]¶

Compute TP, FP, TN, and FN counts for this binary confusion vector.

Code common to ROC, PR, and threshold measures, computes the elements of the binary confusion matrix at all relevant operating point thresholds.

Parameters

stabalize_thresh (int) – if fewer than this many data points insert stabalization data.
fp_cutoff (int) – maximum number of false positives

Example

>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> self = BinaryConfusionVectors.demo(n=1, p_true=0.5, p_error=0.5)
>>> self._binary_clf_curves()

>>> self = BinaryConfusionVectors.demo(n=0, p_true=0.5, p_error=0.5)
>>> self._binary_clf_curves()

>>> self = BinaryConfusionVectors.demo(n=100, p_true=0.5, p_error=0.5)
>>> self._binary_clf_curves()

draw_distribution(self)[source]¶

_3dplot(self)[source]¶

Example

>>> # xdoctest: +REQUIRES(module:kwplot)
>>> # xdoctest: +REQUIRES(module:pandas)
>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> from kwcoco.metrics.detect_metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     n_fp=(0, 1), n_fn=(0, 2), nimgs=256, nboxes=(0, 10),
>>>     bbox_noise=10,
>>>     classes=1)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> self = bin_cfsn = cfsn_vecs.binarize_classless()
>>> #dmet.summarize(plot=True)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.figure(fnum=3)
>>> self._3dplot()

kwcoco.metrics.confusion_vectors._stabalize_data(y_true, y_score, sample_weight, npad=7)[source]¶

Adds ideally calibrated dummy values to curves with few positive examples. This acts somewhat like a Bayesian prior and smooths out the curve.

Example

y_score = np.array([0.5, 0.6]) y_true = np.array([1, 1]) sample_weight = np.array([1, 1]) npad = 7 _stabalize_data(y_true, y_score, sample_weight, npad=npad)

`kwcoco.metrics.detect_metrics`¶

Module Contents¶

Classes¶

DetectionMetrics

Object that computes associations between detections and can convert them

Functions¶

`_demo_construct_probs`(pred_cxs, pred_scores, classes, rng, hacked=1)	Constructs random probabilities for demo data
`pycocotools_confusion_vectors`(dmet, evaler, iou_thresh=0.5, verbose=0)	Example
`eval_detections_cli`(**kw)	DEPRECATED USE kwcoco eval instead
`_summarize`(self, ap=1, iouThr=None, areaRngLbl='all', maxDets=100)
`pct_summarize2`(self)

class kwcoco.metrics.detect_metrics.DetectionMetrics(dmet, classes=None)[source]¶

Bases: ubelt.NiceRepr

Object that computes associations between detections and can convert them into sklearn-compatible representations for scoring.

Variables

gid_to_true_dets (Dict) – maps image ids to truth
gid_to_pred_dets (Dict) – maps image ids to predictions
classes (CategoryTree) – category coder

Example

>>> dmet = DetectionMetrics.demo(
>>>     nimgs=100, nboxes=(0, 3), n_fp=(0, 1), classes=8, score_noise=0.9, hacked=False)
>>> print(dmet.score_kwcoco(bias=0, compat='mutex', prioritize='iou')['mAP'])
...
>>> # NOTE: IN GENERAL NETHARN AND VOC ARE NOT THE SAME
>>> print(dmet.score_voc(bias=0)['mAP'])
0.8582...
>>> #print(dmet.score_coco()['mAP'])

score_coco[source]¶

clear(dmet)[source]¶

__nice__(dmet)[source]¶

classmethod from_coco(DetectionMetrics, true_coco, pred_coco, gids=None, verbose=0)[source]¶

Create detection metrics from two coco files representing the truth and predictions.

Parameters

true_coco (kwcoco.CocoDataset)
pred_coco (kwcoco.CocoDataset)

Example

>>> import kwcoco
>>> from kwcoco.demo.perterb import perterb_coco
>>> true_coco = kwcoco.CocoDataset.demo('shapes')
>>> perterbkw = dict(box_noise=0.5, cls_noise=0.5, score_noise=0.5)
>>> pred_coco = perterb_coco(true_coco, **perterbkw)
>>> self = DetectionMetrics.from_coco(true_coco, pred_coco)
>>> self.score_voc()

_register_imagename(dmet, imgname, gid=None)[source]¶

add_predictions(dmet, pred_dets, imgname=None, gid=None)[source]¶

Register/Add predicted detections for an image

Parameters

pred_dets (Detections) – predicted detections
imgname (str) – a unique string to identify the image
gid (int, optional) – the integer image id if known

add_truth(dmet, true_dets, imgname=None, gid=None)[source]¶

Register/Add groundtruth detections for an image

Parameters

true_dets (Detections) – groundtruth
imgname (str) – a unique string to identify the image
gid (int, optional) – the integer image id if known

true_detections(dmet, gid)[source]¶: gets Detections representation for groundtruth in an image

pred_detections(dmet, gid)[source]¶: gets Detections representation for predictions in an image

confusion_vectors(dmet, iou_thresh=0.5, bias=0, gids=None, compat='mutex', prioritize='iou', ignore_classes='ignore', background_class=ub.NoParam, verbose='auto', workers=0, track_probs='try', max_dets=None)[source]¶

Assigns predicted boxes to the true boxes so we can transform the detection problem into a classification problem for scoring.

Parameters

iou_thresh (float | List[float], default=0.5) – bounding box overlap iou threshold required for assignment if a list, then return type is a dict
bias (float, default=0.0) – for computing bounding box overlap, either 1 or 0
gids (List[int], default=None) – which subset of images ids to compute confusion metrics on. If not specified all images are used.
compat (str, default=’all’) – can be (‘ancestors’ | ‘mutex’ | ‘all’). determines which pred boxes are allowed to match which true boxes. If ‘mutex’, then pred boxes can only match true boxes of the same class. If ‘ancestors’, then pred boxes can match true boxes that match or have a coarser label. If ‘all’, then any pred can match any true, regardless of its category label.
prioritize (str, default=’iou’) – can be (‘iou’ | ‘class’ | ‘correct’) determines which box to assign to if mutiple true boxes overlap a predicted box. if prioritize is iou, then the true box with maximum iou (above iou_thresh) will be chosen. If prioritize is class, then it will prefer matching a compatible class above a higher iou. If prioritize is correct, then ancestors of the true class are preferred over descendents of the true class, over unreleated classes.
ignore_classes (set, default={‘ignore’}) – class names indicating ignore regions
background_class (str, default=ub.NoParam) – Name of the background class. If unspecified we try to determine it with heuristics. A value of None means there is no background class.
verbose (int, default=’auto’) – verbosity flag. In auto mode, verbose=1 if len(gids) > 1000.
workers (int, default=0) – number of parallel assignment processes
track_probs (str, default=’try’) – can be ‘try’, ‘force’, or False. if truthy, we assume probabilities for multiple classes are available.

Returns

ConfusionVectors | Dict[float, ConfusionVectors]

Example

>>> dmet = DetectionMetrics.demo(nimgs=30, classes=3,
>>>                              nboxes=10, n_fp=3, box_noise=10,
>>>                              with_probs=False)
>>> iou_to_cfsn = dmet.confusion_vectors(iou_thresh=[0.3, 0.5, 0.9])
>>> for t, cfsn in iou_to_cfsn.items():
>>>     print('t = {!r}'.format(t))
...     print(cfsn.binarize_ovr().measures())
...     print(cfsn.binarize_classless().measures())

score_kwant(dmet, iou_thresh=0.5)[source]¶: Scores the detections using kwant

score_kwcoco(dmet, iou_thresh=0.5, bias=0, gids=None, compat='all', prioritize='iou')[source]¶: our scoring method

score_voc(dmet, iou_thresh=0.5, bias=1, method='voc2012', gids=None, ignore_classes='ignore')[source]¶

score using voc method

Example

>>> dmet = DetectionMetrics.demo(
>>>     nimgs=100, nboxes=(0, 3), n_fp=(0, 1), classes=8,
>>>     score_noise=.5)
>>> print(dmet.score_voc()['mAP'])
0.9399...

_to_coco(dmet)[source]¶

Convert to a coco representation of truth and predictions

with inverse aid mappings

score_pycocotools(dmet, with_evaler=False, with_confusion=False, verbose=0, iou_thresholds=None)[source]¶

score using ms-coco method

Returns: dictionary with pct info
Return type: Dict

Example

>>> # xdoctest: +REQUIRES(module:pycocotools)
>>> from kwcoco.metrics.detect_metrics import *
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=10, nboxes=(0, 3), n_fn=(0, 1), n_fp=(0, 1), classes=8, with_probs=False)
>>> pct_info = dmet.score_pycocotools(verbose=1,
>>>                                   with_evaler=True,
>>>                                   with_confusion=True,
>>>                                   iou_thresholds=[0.5, 0.9])
>>> evaler = pct_info['evaler']
>>> iou_to_cfsn_vecs = pct_info['iou_to_cfsn_vecs']
>>> for iou_thresh in iou_to_cfsn_vecs.keys():
>>>     print('iou_thresh = {!r}'.format(iou_thresh))
>>>     cfsn_vecs = iou_to_cfsn_vecs[iou_thresh]
>>>     ovr_measures = cfsn_vecs.binarize_ovr().measures()
>>>     print('ovr_measures = {}'.format(ub.repr2(ovr_measures, nl=1, precision=4)))

Note

by default pycocotools computes average precision as the literal average of computed precisions at 101 uniformly spaced recall thresholds.

pycocoutils seems to only allow predictions with the same category as the truth to match those truth objects. This should be the same as calling dmet.confusion_vectors with compat = mutex

pycocoutils does not take into account the fact that each box often has a score for each category.

pycocoutils will be incorrect if any annotation has an id of 0

a major difference in the way kwcoco scores versus pycocoutils is the calculation of AP. The assignment between truth and predicted detections produces similar enough results. Given our confusion vectors we use the scikit-learn definition of AP, whereas pycocoutils seems to compute precision and recall — more or less correctly — but then it resamples the precision at various specified recall thresholds (in the accumulate function, specifically how pr is resampled into the q array). This can lead to a large difference in reported scores.

pycocoutils also smooths out the precision such that it is monotonic decreasing, which might not be the best idea.

pycocotools area ranges are inclusive on both ends, that means the “small” and “medium” truth selections do overlap somewhat.

classmethod demo(cls, **kwargs)[source]¶

Creates random true boxes and predicted boxes that have some noisy offset from the truth.

Kwargs:

classes (int, default=1): class list or the number of foreground: classes.

nimgs (int, default=1): number of images in the coco datasts. nboxes (int, default=1): boxes per image. n_fp (int, default=0): number of false positives. n_fn (int, default=0): number of false negatives. box_noise (float, default=0): std of a normal distribution used to

perterb both box location and box size.

cls_noise (float, default=0): probability that a class label will: change. Must be within 0 and 1.

anchors (ndarray, default=None): used to create random boxes null_pred (bool, default=0):

if True, predicted classes are returned as null, which means only localization scoring is suitable.

with_probs (bool, default=1):: if True, includes per-class probabilities with predictions

CommandLine

xdoctest -m kwcoco.metrics.detect_metrics DetectionMetrics.demo:2 --show

Example

>>> kwargs = {}
>>> # Seed the RNG
>>> kwargs['rng'] = 0
>>> # Size parameters determine how big the data is
>>> kwargs['nimgs'] = 5
>>> kwargs['nboxes'] = 7
>>> kwargs['classes'] = 11
>>> # Noise parameters perterb predictions further from the truth
>>> kwargs['n_fp'] = 3
>>> kwargs['box_noise'] = 0.1
>>> kwargs['cls_noise'] = 0.5
>>> dmet = DetectionMetrics.demo(**kwargs)
>>> print('dmet.classes = {}'.format(dmet.classes))
dmet.classes = <CategoryTree(nNodes=12, maxDepth=3, maxBreadth=4...)>
>>> # Can grab kwimage.Detection object for any image
>>> print(dmet.true_detections(gid=0))
<Detections(4)>
>>> print(dmet.pred_detections(gid=0))
<Detections(7)>

Example

>>> # Test case with null predicted categories
>>> dmet = DetectionMetrics.demo(nimgs=30, null_pred=1, classes=3,
>>>                              nboxes=10, n_fp=3, box_noise=0.1,
>>>                              with_probs=False)
>>> dmet.gid_to_pred_dets[0].data
>>> dmet.gid_to_true_dets[0].data
>>> cfsn_vecs = dmet.confusion_vectors()
>>> binvecs_ovr = cfsn_vecs.binarize_ovr()
>>> binvecs_per = cfsn_vecs.binarize_classless()
>>> measures_per = binvecs_per.measures()
>>> measures_ovr = binvecs_ovr.measures()
>>> print('measures_per = {!r}'.format(measures_per))
>>> print('measures_ovr = {!r}'.format(measures_ovr))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> measures_ovr['perclass'].draw(key='pr', fnum=2)

Example

>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> from kwcoco.metrics.detect_metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     n_fp=(0, 1), n_fn=(0, 1), nimgs=32, nboxes=(0, 16),
>>>     classes=3, rng=0, newstyle=1, box_noise=0.5, cls_noise=0.0, score_noise=0.3, with_probs=False)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> summary = dmet.summarize(plot=True, title='DetectionMetrics summary demo', with_ovr=True, with_bin=False)
>>> summary['bin_measures']
>>> kwplot.show_if_requested()

summarize(dmet, out_dpath=None, plot=False, title='', with_bin='auto', with_ovr='auto')[source]¶

Example

>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> from kwcoco.metrics.detect_metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     n_fp=(0, 128), n_fn=(0, 4), nimgs=512, nboxes=(0, 32),
>>>     classes=3, rng=0)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> dmet.summarize(plot=True, title='DetectionMetrics summary demo')
>>> kwplot.show_if_requested()

kwcoco.metrics.detect_metrics._demo_construct_probs(pred_cxs, pred_scores, classes, rng, hacked=1)[source]¶: Constructs random probabilities for demo data

kwcoco.metrics.detect_metrics.pycocotools_confusion_vectors(dmet, evaler, iou_thresh=0.5, verbose=0)[source]¶

Example

>>> # xdoctest: +REQUIRES(module:pycocotools)
>>> from kwcoco.metrics.detect_metrics import *
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=10, nboxes=(0, 3), n_fn=(0, 1), n_fp=(0, 1), classes=8, with_probs=False)
>>> coco_scores = dmet.score_pycocotools(with_evaler=True)
>>> evaler = coco_scores['evaler']
>>> cfsn_vecs = pycocotools_confusion_vectors(dmet, evaler, verbose=1)

kwcoco.metrics.detect_metrics.eval_detections_cli(**kw)[source]¶

DEPRECATED USE kwcoco eval instead

CommandLine

xdoctest -m ~/code/kwcoco/kwcoco/metrics/detect_metrics.py eval_detections_cli

kwcoco.metrics.detect_metrics._summarize(self, ap=1, iouThr=None, areaRngLbl='all', maxDets=100)[source]¶

kwcoco.metrics.detect_metrics.pct_summarize2(self)[source]¶

`kwcoco.metrics.drawing`¶

Module Contents¶

Functions¶

`draw_perclass_roc`(cx_to_info, classes=None, prefix='', fnum=1, fp_axis='count', **kw)	Parameters cx_to_info (PerClass_Measures \| Dict)
`demo_format_options`()
`concice_si_display`(val, eps=1e-08, precision=2, si_thresh=4)	Display numbers in scientific notation if above a threshold
`_realpos_label_suffix`(info)	Creates a label suffix that indicates the number of real positive cases
`draw_perclass_prcurve`(cx_to_info, classes=None, prefix='', fnum=1, **kw)	Parameters cx_to_info (PerClass_Measures \| Dict)
`draw_perclass_thresholds`(cx_to_info, key='mcc', classes=None, prefix='', fnum=1, **kw)	Parameters cx_to_info (PerClass_Measures \| Dict)
`draw_roc`(info, prefix='', fnum=1, **kw)	Parameters info (Measures \| Dict)
`draw_prcurve`(info, prefix='', fnum=1, **kw)	Draws a single pr curve.
`draw_threshold_curves`(info, keys=None, prefix='', fnum=1, **kw)	Parameters info (Measures \| Dict)

kwcoco.metrics.drawing.draw_perclass_roc(cx_to_info, classes=None, prefix='', fnum=1, fp_axis='count', **kw)[source]¶

Parameters

cx_to_info (PerClass_Measures | Dict)
fp_axis (str) – can be count or rate

kwcoco.metrics.drawing.demo_format_options()[source]¶

kwcoco.metrics.drawing.concice_si_display(val, eps=1e-08, precision=2, si_thresh=4)[source]¶

Display numbers in scientific notation if above a threshold

Parameters

eps (float) – threshold to be formated as an integer if other integer conditions hold.
precision (int) – maximum significant digits (might print less)
si_thresh (int) – If the number is less than 10^{si_thresh}, then it will be printed as an integer if it is within eps of an integer.

References

https://docs.python.org/2/library/stdtypes.html#string-formatting-operations

Example

>>> grid = {
>>>     'sign': [1, -1],
>>>     'exp': [1, -1],
>>>     'big_part': [0, 32132e3, 4000000032],
>>>     'med_part': [0, 0.5, 0.9432, 0.000043, 0.01, 1, 2],
>>>     'small_part': [0, 1321e-3, 43242e-11],
>>> }
>>> for kw in ub.named_product(grid):
>>>     sign = kw.pop('sign')
>>>     exp = kw.pop('exp')
>>>     raw = (sum(map(float, kw.values())))
>>>     val = sign * raw ** exp if raw != 0 else sign * raw
>>>     print('{:>20} - {}'.format(concice_si_display(val), val))
>>> from kwcoco.metrics.drawing import *  # NOQA
>>> print(concice_si_display(40000000432432))
>>> print(concice_si_display(473243280432890))
>>> print(concice_si_display(473243284289))
>>> print(concice_si_display(473243289))
>>> print(concice_si_display(4739))
>>> print(concice_si_display(473))
>>> print(concice_si_display(0.432432))
>>> print(concice_si_display(0.132432))
>>> print(concice_si_display(1.0000043))
>>> print(concice_si_display(01.00000000000000000000000000043))

kwcoco.metrics.drawing._realpos_label_suffix(info)[source]¶

Creates a label suffix that indicates the number of real positive cases versus the total amount of cases considered for an evaluation curve.

Parameters: info (Dict) – with keys, nsuppert, realpos_total

Example

>>> from kwcoco.metrics.drawing import *  # NOQA
>>> info = {'nsupport': 10, 'realpos_total': 10}
>>> _realpos_label_suffix(info)
10/10
>>> info = {'nsupport': 10.0, 'realpos_total': 10.0}
>>> _realpos_label_suffix(info)
10/10
>>> info = {'nsupport': 10.3333, 'realpos_total': 10.22222}
>>> _realpos_label_suffix(info)
10.22/10.33
>>> info = {'nsupport': 10.000000001, 'realpos_total': None}
>>> _realpos_label_suffix(info)
10
>>> info = {'nsupport': 10.009}
>>> _realpos_label_suffix(info)
10.01
>>> info = {'nsupport': 3331033334342.432, 'realpos_total': 1033334342.432}
>>> _realpos_label_suffix(info)
1e9/3.3e12
>>> info = {'nsupport': 0.007, 'realpos_total': 0.0000893}
>>> _realpos_label_suffix(info)
8.9e-5/0.007

kwcoco.metrics.drawing.draw_perclass_prcurve(cx_to_info, classes=None, prefix='', fnum=1, **kw)[source]¶

Parameters: cx_to_info (PerClass_Measures | Dict)

Example

>>> # xdoctest: +REQUIRES(module:kwplot)
>>> from kwcoco.metrics.drawing import *  # NOQA
>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=3, nboxes=(0, 10), n_fp=(0, 3), n_fn=(0, 2), classes=3, score_noise=0.1, box_noise=0.1, with_probs=False)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> print(cfsn_vecs.data.pandas())
>>> classes = cfsn_vecs.classes
>>> cx_to_info = cfsn_vecs.binarize_ovr().measures()['perclass']
>>> print('cx_to_info = {}'.format(ub.repr2(cx_to_info, nl=1)))
>>> import kwplot
>>> kwplot.autompl()
>>> draw_perclass_prcurve(cx_to_info, classes)
>>> # xdoctest: +REQUIRES(--show)
>>> kwplot.show_if_requested()

kwcoco.metrics.drawing.draw_perclass_thresholds(cx_to_info, key='mcc', classes=None, prefix='', fnum=1, **kw)[source]¶

Parameters: cx_to_info (PerClass_Measures | Dict)

Note

Each category is inspected independently of one another, there is no notion of confusion.

Example

>>> # xdoctest: +REQUIRES(module:kwplot)
>>> from kwcoco.metrics.drawing import *  # NOQA
>>> from kwcoco.metrics import ConfusionVectors
>>> cfsn_vecs = ConfusionVectors.demo()
>>> classes = cfsn_vecs.classes
>>> ovr_cfsn = cfsn_vecs.binarize_ovr(keyby='name')
>>> cx_to_info = ovr_cfsn.measures()['perclass']
>>> import kwplot
>>> kwplot.autompl()
>>> key = 'mcc'
>>> draw_perclass_thresholds(cx_to_info, key, classes)
>>> # xdoctest: +REQUIRES(--show)
>>> kwplot.show_if_requested()

kwcoco.metrics.drawing.draw_roc(info, prefix='', fnum=1, **kw)[source]¶

Parameters: info (Measures | Dict)

Note

There needs to be enough negative examples for using ROC to make any sense!

Example

>>> # xdoctest: +REQUIRES(module:kwplot, module:seaborn)
>>> from kwcoco.metrics.drawing import *  # NOQA
>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(nimgs=30, null_pred=1, classes=3,
>>>                              nboxes=10, n_fp=10, box_noise=0.3,
>>>                              with_probs=False)
>>> dmet.true_detections(0).data
>>> cfsn_vecs = dmet.confusion_vectors(compat='mutex', prioritize='iou', bias=0)
>>> print(cfsn_vecs.data._pandas().sort_values('score'))
>>> classes = cfsn_vecs.classes
>>> info = ub.peek(cfsn_vecs.binarize_ovr().measures()['perclass'].values())
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> draw_roc(info)
>>> kwplot.show_if_requested()

kwcoco.metrics.drawing.draw_prcurve(info, prefix='', fnum=1, **kw)[source]¶

Draws a single pr curve.

Parameters: info (Measures | Dict)

Example

>>> # xdoctest: +REQUIRES(module:kwplot)
>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=10, nboxes=(0, 10), n_fp=(0, 1), classes=3)
>>> cfsn_vecs = dmet.confusion_vectors()

>>> classes = cfsn_vecs.classes
>>> info = cfsn_vecs.binarize_classless().measures()
>>> import kwplot
>>> kwplot.autompl()
>>> draw_prcurve(info)
>>> # xdoctest: +REQUIRES(--show)
>>> kwplot.show_if_requested()

kwcoco.metrics.drawing.draw_threshold_curves(info, keys=None, prefix='', fnum=1, **kw)[source]¶

Parameters: info (Measures | Dict)

Example

>>> # xdoctest: +REQUIRES(module:kwplot)
>>> import sys, ubelt
>>> sys.path.append(ubelt.expandpath('~/code/kwcoco'))
>>> from kwcoco.metrics.drawing import *  # NOQA
>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=10, nboxes=(0, 10), n_fp=(0, 1), classes=3)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> info = cfsn_vecs.binarize_classless().measures()
>>> keys = None
>>> import kwplot
>>> kwplot.autompl()
>>> draw_threshold_curves(info, keys)
>>> # xdoctest: +REQUIRES(--show)
>>> kwplot.show_if_requested()

`kwcoco.metrics.functional`¶

Module Contents¶

Functions¶

`fast_confusion_matrix`(y_true, y_pred, n_labels, sample_weight=None)	faster version of sklearn confusion matrix that avoids the
`_truncated_roc`(y_df, bg_idx=-1, fp_cutoff=None)	Computes truncated ROC info
`_pr_curves`(y)	Compute a PR curve from a method
`_average_precision`(tpr, ppv)	Compute average precision of a binary PR curve. This is simply the area

kwcoco.metrics.functional.fast_confusion_matrix(y_true, y_pred, n_labels, sample_weight=None)[source]¶

faster version of sklearn confusion matrix that avoids the expensive checks and label rectification

Parameters

y_true (ndarray[int]) – ground truth class label for each sample
y_pred (ndarray[int]) – predicted class label for each sample
n_labels (int) – number of labels
sample_weight (ndarray[int|float]) – weight of each sample

Returns

matrix where rows represent real and cols represent pred and the value at each cell is the total amount of weight

Return type

ndarray[int64|float64, dim=2]

Example

>>> y_true = np.array([0, 0, 0, 0, 1, 1, 1, 0,  0, 1])
>>> y_pred = np.array([0, 0, 0, 0, 0, 0, 0, 1,  1, 1])
>>> fast_confusion_matrix(y_true, y_pred, 2)
array([[4, 2],
       [3, 1]])
>>> fast_confusion_matrix(y_true, y_pred, 2).ravel()
array([4, 2, 3, 1])

kwcoco.metrics.functional._truncated_roc(y_df, bg_idx=- 1, fp_cutoff=None)[source]¶: Computes truncated ROC info

kwcoco.metrics.functional._pr_curves(y)[source]¶

Compute a PR curve from a method

Parameters: y (pd.DataFrame | DataFrameArray) – output of detection_confusions
Returns: Tuple[float, ndarray, ndarray]

Example

>>> # xdoctest: +REQUIRES(module:sklearn)
>>> import pandas as pd
>>> y1 = pd.DataFrame.from_records([
>>>     {'pred': 0, 'score': 10.00, 'true': -1, 'weight': 1.00},
>>>     {'pred': 0, 'score':  1.65, 'true':  0, 'weight': 1.00},
>>>     {'pred': 0, 'score':  8.64, 'true': -1, 'weight': 1.00},
>>>     {'pred': 0, 'score':  3.97, 'true':  0, 'weight': 1.00},
>>>     {'pred': 0, 'score':  1.68, 'true':  0, 'weight': 1.00},
>>>     {'pred': 0, 'score':  5.06, 'true':  0, 'weight': 1.00},
>>>     {'pred': 0, 'score':  0.25, 'true':  0, 'weight': 1.00},
>>>     {'pred': 0, 'score':  1.75, 'true':  0, 'weight': 1.00},
>>>     {'pred': 0, 'score':  8.52, 'true':  0, 'weight': 1.00},
>>>     {'pred': 0, 'score':  5.20, 'true':  0, 'weight': 1.00},
>>> ])
>>> import kwcoco as nh
>>> import kwarray
>>> y2 = kwarray.DataFrameArray(y1)
>>> _pr_curves(y2)
>>> _pr_curves(y1)

kwcoco.metrics.functional._average_precision(tpr, ppv)[source]¶

Compute average precision of a binary PR curve. This is simply the area under the curve.

Parameters

tpr (ndarray) – true positive rate - aka recall
ppv (ndarray) – positive predictive value - aka precision

`kwcoco.metrics.sklearn_alts`¶

Faster pure-python versions of sklearn functions that avoid expensive checks and label rectifications. It is assumed that all labels are consecutive non-negative integers.

Module Contents¶

Functions¶

`confusion_matrix`(y_true, y_pred, n_labels=None, labels=None, sample_weight=None)	faster version of sklearn confusion matrix that avoids the
`global_accuracy_from_confusion`(cfsn)
`class_accuracy_from_confusion`(cfsn)
`_binary_clf_curve2`(y_true, y_score, pos_label=None, sample_weight=None)	MODIFIED VERSION OF SCIKIT-LEARN API

Attributes¶

[1] http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCdevkit_18-May-2011.tar

kwcoco.metrics.sklearn_alts.profile[source]¶

kwcoco.metrics.sklearn_alts.confusion_matrix(y_true, y_pred, n_labels=None, labels=None, sample_weight=None)[source]¶

faster version of sklearn confusion matrix that avoids the expensive checks and label rectification

Runs in about 0.7ms

Returns: matrix where rows represent real and cols represent pred
Return type: ndarray

Example

>>> y_true = np.array([0, 0, 0, 0, 1, 1, 1, 0,  0, 1])
>>> y_pred = np.array([0, 0, 0, 0, 0, 0, 0, 1,  1, 1])
>>> confusion_matrix(y_true, y_pred, 2)
array([[4, 2],
       [3, 1]])
>>> confusion_matrix(y_true, y_pred, 2).ravel()
array([4, 2, 3, 1])

Benchmarks:

import ubelt as ub y_true = np.random.randint(0, 2, 10000) y_pred = np.random.randint(0, 2, 10000)

n = 1000 for timer in ub.Timerit(n, bestof=10, label=’py-time’):

sample_weight = [1] * len(y_true) confusion_matrix(y_true, y_pred, 2, sample_weight=sample_weight)

for timer in ub.Timerit(n, bestof=10, label=’np-time’):: sample_weight = np.ones(len(y_true), dtype=int) confusion_matrix(y_true, y_pred, 2, sample_weight=sample_weight)

kwcoco.metrics.sklearn_alts.global_accuracy_from_confusion(cfsn)[source]¶

kwcoco.metrics.sklearn_alts.class_accuracy_from_confusion(cfsn)[source]¶

kwcoco.metrics.sklearn_alts._binary_clf_curve2(y_true, y_score, pos_label=None, sample_weight=None)[source]¶

MODIFIED VERSION OF SCIKIT-LEARN API

Calculate true and false positives per binary classification threshold.

Parameters

y_true (array, shape = [n_samples]) – True targets of binary classification
y_score (array, shape = [n_samples]) – Estimated probabilities or decision function
pos_label (int or str, default=None) – The label of the positive class
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

fps (array, shape = [n_thresholds]) – A count of false positives, at index i being the number of negative samples assigned a score >= thresholds[i]. The total number of negative samples is equal to fps[-1] (thus true negatives are given by fps[-1] - fps).
tps (array, shape = [n_thresholds <= len(np.unique(y_score))]) – An increasing count of true positives, at index i being the number of positive samples assigned a score >= thresholds[i]. The total number of positive samples is equal to tps[-1] (thus false negatives are given by tps[-1] - tps).
thresholds (array, shape = [n_thresholds]) – Decreasing score values.

Example

>>> y_true  = [      1,   1,   1,   1,   1,   1,   0]
>>> y_score = [ np.nan, 0.2, 0.3, 0.4, 0.5, 0.6, 0.3]
>>> sample_weight = None
>>> pos_label = None
>>> fps, tps, thresholds = _binary_clf_curve2(y_true, y_score)

`kwcoco.metrics.util`¶

Module Contents¶

Classes¶

DictProxy

Allows an object to proxy the behavior of a dict attribute

class kwcoco.metrics.util.DictProxy[source]¶

Bases: scriptconfig.dict_like.DictLike

Allows an object to proxy the behavior of a dict attribute

__getitem__(self, key)[source]¶

__setitem__(self, key, value)[source]¶

keys(self)[source]¶

__json__(self)[source]¶

`kwcoco.metrics.voc_metrics`¶

Module Contents¶

Classes¶

VOC_Metrics

API to compute object detection scores using Pascal VOC evaluation method.

Functions¶

`_pr_curves`(y, method='voc2012')	Compute a PR curve from a method
`_voc_eval`(lines, recs, classname, iou_thresh=0.5, method='voc2012', bias=1.0)	VOC AP evaluation for a single category.
`_voc_ave_precision`(rec, prec, method='voc2012')	Compute AP from precision and recall

class kwcoco.metrics.voc_metrics.VOC_Metrics(classes=None)[source]¶

Bases: ubelt.NiceRepr

API to compute object detection scores using Pascal VOC evaluation method.

To use, add true and predicted detections for each image and then run the VOC_Metrics.score() function.

Variables

recs (Dict[int, List[dict]) – true boxes for each image. maps image ids to a list of records within that image. Each record is a tlbr bbox, a difficult flag, and a class name.
cx_to_lines (Dict[int, List]) –
VOC formatted prediction preditions. mapping from class index to all predictions for that category. Each “line” is a list of [

[<imgid>, <score>, <tl_x>, <tl_y>, <br_x>, <br_y>]].

__nice__(self)[source]¶

add_truth(self, true_dets, gid)[source]¶

add_predictions(self, pred_dets, gid)[source]¶

score(self, iou_thresh=0.5, bias=1, method='voc2012')[source]¶

Compute VOC scores for every category

Example

>>> from kwcoco.metrics.detect_metrics import DetectionMetrics
>>> from kwcoco.metrics.voc_metrics import *  # NOQA
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=1, nboxes=(0, 100), n_fp=(0, 30), n_fn=(0, 30), classes=2, score_noise=0.9)
>>> self = VOC_Metrics(classes=dmet.classes)
>>> self.add_truth(dmet.true_detections(0), 0)
>>> self.add_predictions(dmet.pred_detections(0), 0)
>>> voc_scores = self.score()
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.figure(fnum=1, doclf=True)
>>> voc_scores['perclass'].draw()

kwplot.figure(fnum=2) dmet.true_detections(0).draw(color=’green’, labels=None) dmet.pred_detections(0).draw(color=’blue’, labels=None) kwplot.autoplt().gca().set_xlim(0, 100) kwplot.autoplt().gca().set_ylim(0, 100)

kwcoco.metrics.voc_metrics._pr_curves(y, method='voc2012')[source]¶

Compute a PR curve from a method

Parameters: y (pd.DataFrame | DataFrameArray) – output of detection_confusions
Returns: Tuple[float, ndarray, ndarray]

Example

>>> import pandas as pd
>>> y1 = pd.DataFrame.from_records([
>>>     {'pred': 0, 'score': 10.00, 'true': -1, 'weight': 1.00},
>>>     {'pred': 0, 'score':  1.65, 'true':  0, 'weight': 1.00},
>>>     {'pred': 0, 'score':  8.64, 'true': -1, 'weight': 1.00},
>>>     {'pred': 0, 'score':  3.97, 'true':  0, 'weight': 1.00},
>>>     {'pred': 0, 'score':  1.68, 'true':  0, 'weight': 1.00},
>>>     {'pred': 0, 'score':  5.06, 'true':  0, 'weight': 1.00},
>>>     {'pred': 0, 'score':  0.25, 'true':  0, 'weight': 1.00},
>>>     {'pred': 0, 'score':  1.75, 'true':  0, 'weight': 1.00},
>>>     {'pred': 0, 'score':  8.52, 'true':  0, 'weight': 1.00},
>>>     {'pred': 0, 'score':  5.20, 'true':  0, 'weight': 1.00},
>>> ])
>>> import kwarray
>>> y2 = kwarray.DataFrameArray(y1)
>>> _pr_curves(y2)
>>> _pr_curves(y1)

kwcoco.metrics.voc_metrics._voc_eval(lines, recs, classname, iou_thresh=0.5, method='voc2012', bias=1.0)[source]¶

VOC AP evaluation for a single category.

Parameters

lines (List[list]) – VOC formatted predictions. Each “line” is a list of [[<imgid>, <score>, <tl_x>, <tl_y>, <br_x>, <br_y>]].
recs (Dict[int, List[dict]) – true boxes for each image. maps image ids to a list of records within that image. Each record is a tlbr bbox, a difficult flag, and a class name.
classname (str) – the category to evaluate.
method (str) – code for how the AP is computed.
bias (float) – either 1.0 or 0.0.

Returns

info about the evaluation containing AP. Contains fp, tp, prec, rec,

Return type

Dict

Note

Raw replication of matlab implementation of creating assignments and the resulting PR-curves and AP. Based on MATLAB code [1].

References

kwcoco.metrics.voc_metrics._voc_ave_precision(rec, prec, method='voc2012')[source]¶

Compute AP from precision and recall Based on MATLAB code in 1, 2, and 3.

Parameters

rec (ndarray) – recall
prec (ndarray) – precision
method (str) – either voc2012 or voc2007

Returns

ap: average precision

Return type

float

References

1: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCdevkit_18-May-2011.tar
2: https://github.com/rbgirshick/voc-dpm/blob/master/test/pascal_eval.m
3: https://github.com/rbgirshick/voc-dpm/blob/c0b88564bd668bcc6216bbffe96cb061613be768/utils/bootstrap/VOCevaldet_bootstrap.m

Package Contents¶

Classes¶

`DetectionMetrics`	Object that computes associations between detections and can convert them
`BinaryConfusionVectors`	Stores information about a binary classification problem.
`ConfusionVectors`	Stores information used to construct a confusion matrix. This includes
`Measures`	Holds accumulated confusion counts, and derived measures
`OneVsRestConfusionVectors`	Container for multiple one-vs-rest binary confusion vectors
`PerClass_Measures`

Functions¶

eval_detections_cli(**kw)

DEPRECATED USE kwcoco eval instead

class kwcoco.metrics.DetectionMetrics(dmet, classes=None)[source]¶

Bases: ubelt.NiceRepr

Object that computes associations between detections and can convert them into sklearn-compatible representations for scoring.

Variables

gid_to_true_dets (Dict) – maps image ids to truth
gid_to_pred_dets (Dict) – maps image ids to predictions
classes (CategoryTree) – category coder

Example

>>> dmet = DetectionMetrics.demo(
>>>     nimgs=100, nboxes=(0, 3), n_fp=(0, 1), classes=8, score_noise=0.9, hacked=False)
>>> print(dmet.score_kwcoco(bias=0, compat='mutex', prioritize='iou')['mAP'])
...
>>> # NOTE: IN GENERAL NETHARN AND VOC ARE NOT THE SAME
>>> print(dmet.score_voc(bias=0)['mAP'])
0.8582...
>>> #print(dmet.score_coco()['mAP'])

score_coco¶

clear(dmet)¶

__nice__(dmet)¶

classmethod from_coco(DetectionMetrics, true_coco, pred_coco, gids=None, verbose=0)¶

Create detection metrics from two coco files representing the truth and predictions.

Parameters

true_coco (kwcoco.CocoDataset)
pred_coco (kwcoco.CocoDataset)

Example

>>> import kwcoco
>>> from kwcoco.demo.perterb import perterb_coco
>>> true_coco = kwcoco.CocoDataset.demo('shapes')
>>> perterbkw = dict(box_noise=0.5, cls_noise=0.5, score_noise=0.5)
>>> pred_coco = perterb_coco(true_coco, **perterbkw)
>>> self = DetectionMetrics.from_coco(true_coco, pred_coco)
>>> self.score_voc()

_register_imagename(dmet, imgname, gid=None)¶

add_predictions(dmet, pred_dets, imgname=None, gid=None)¶

Register/Add predicted detections for an image

Parameters

pred_dets (Detections) – predicted detections
imgname (str) – a unique string to identify the image
gid (int, optional) – the integer image id if known

add_truth(dmet, true_dets, imgname=None, gid=None)¶

Register/Add groundtruth detections for an image

Parameters

true_dets (Detections) – groundtruth
imgname (str) – a unique string to identify the image
gid (int, optional) – the integer image id if known

true_detections(dmet, gid)¶: gets Detections representation for groundtruth in an image

pred_detections(dmet, gid)¶: gets Detections representation for predictions in an image

confusion_vectors(dmet, iou_thresh=0.5, bias=0, gids=None, compat='mutex', prioritize='iou', ignore_classes='ignore', background_class=ub.NoParam, verbose='auto', workers=0, track_probs='try', max_dets=None)¶

Assigns predicted boxes to the true boxes so we can transform the detection problem into a classification problem for scoring.

Parameters

iou_thresh (float | List[float], default=0.5) – bounding box overlap iou threshold required for assignment if a list, then return type is a dict
bias (float, default=0.0) – for computing bounding box overlap, either 1 or 0
gids (List[int], default=None) – which subset of images ids to compute confusion metrics on. If not specified all images are used.
compat (str, default=’all’) – can be (‘ancestors’ | ‘mutex’ | ‘all’). determines which pred boxes are allowed to match which true boxes. If ‘mutex’, then pred boxes can only match true boxes of the same class. If ‘ancestors’, then pred boxes can match true boxes that match or have a coarser label. If ‘all’, then any pred can match any true, regardless of its category label.
prioritize (str, default=’iou’) – can be (‘iou’ | ‘class’ | ‘correct’) determines which box to assign to if mutiple true boxes overlap a predicted box. if prioritize is iou, then the true box with maximum iou (above iou_thresh) will be chosen. If prioritize is class, then it will prefer matching a compatible class above a higher iou. If prioritize is correct, then ancestors of the true class are preferred over descendents of the true class, over unreleated classes.
ignore_classes (set, default={‘ignore’}) – class names indicating ignore regions
background_class (str, default=ub.NoParam) – Name of the background class. If unspecified we try to determine it with heuristics. A value of None means there is no background class.
verbose (int, default=’auto’) – verbosity flag. In auto mode, verbose=1 if len(gids) > 1000.
workers (int, default=0) – number of parallel assignment processes
track_probs (str, default=’try’) – can be ‘try’, ‘force’, or False. if truthy, we assume probabilities for multiple classes are available.

Returns

ConfusionVectors | Dict[float, ConfusionVectors]

Example

>>> dmet = DetectionMetrics.demo(nimgs=30, classes=3,
>>>                              nboxes=10, n_fp=3, box_noise=10,
>>>                              with_probs=False)
>>> iou_to_cfsn = dmet.confusion_vectors(iou_thresh=[0.3, 0.5, 0.9])
>>> for t, cfsn in iou_to_cfsn.items():
>>>     print('t = {!r}'.format(t))
...     print(cfsn.binarize_ovr().measures())
...     print(cfsn.binarize_classless().measures())

score_kwant(dmet, iou_thresh=0.5)¶: Scores the detections using kwant

score_kwcoco(dmet, iou_thresh=0.5, bias=0, gids=None, compat='all', prioritize='iou')¶: our scoring method

score_voc(dmet, iou_thresh=0.5, bias=1, method='voc2012', gids=None, ignore_classes='ignore')¶

score using voc method

Example

>>> dmet = DetectionMetrics.demo(
>>>     nimgs=100, nboxes=(0, 3), n_fp=(0, 1), classes=8,
>>>     score_noise=.5)
>>> print(dmet.score_voc()['mAP'])
0.9399...

_to_coco(dmet)¶

Convert to a coco representation of truth and predictions

with inverse aid mappings

score_pycocotools(dmet, with_evaler=False, with_confusion=False, verbose=0, iou_thresholds=None)¶

score using ms-coco method

Returns: dictionary with pct info
Return type: Dict

Example

>>> # xdoctest: +REQUIRES(module:pycocotools)
>>> from kwcoco.metrics.detect_metrics import *
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=10, nboxes=(0, 3), n_fn=(0, 1), n_fp=(0, 1), classes=8, with_probs=False)
>>> pct_info = dmet.score_pycocotools(verbose=1,
>>>                                   with_evaler=True,
>>>                                   with_confusion=True,
>>>                                   iou_thresholds=[0.5, 0.9])
>>> evaler = pct_info['evaler']
>>> iou_to_cfsn_vecs = pct_info['iou_to_cfsn_vecs']
>>> for iou_thresh in iou_to_cfsn_vecs.keys():
>>>     print('iou_thresh = {!r}'.format(iou_thresh))
>>>     cfsn_vecs = iou_to_cfsn_vecs[iou_thresh]
>>>     ovr_measures = cfsn_vecs.binarize_ovr().measures()
>>>     print('ovr_measures = {}'.format(ub.repr2(ovr_measures, nl=1, precision=4)))

Note

by default pycocotools computes average precision as the literal average of computed precisions at 101 uniformly spaced recall thresholds.

pycocoutils seems to only allow predictions with the same category as the truth to match those truth objects. This should be the same as calling dmet.confusion_vectors with compat = mutex

pycocoutils does not take into account the fact that each box often has a score for each category.

pycocoutils will be incorrect if any annotation has an id of 0

a major difference in the way kwcoco scores versus pycocoutils is the calculation of AP. The assignment between truth and predicted detections produces similar enough results. Given our confusion vectors we use the scikit-learn definition of AP, whereas pycocoutils seems to compute precision and recall — more or less correctly — but then it resamples the precision at various specified recall thresholds (in the accumulate function, specifically how pr is resampled into the q array). This can lead to a large difference in reported scores.

pycocoutils also smooths out the precision such that it is monotonic decreasing, which might not be the best idea.

pycocotools area ranges are inclusive on both ends, that means the “small” and “medium” truth selections do overlap somewhat.

classmethod demo(cls, **kwargs)¶

Creates random true boxes and predicted boxes that have some noisy offset from the truth.

Kwargs:

classes (int, default=1): class list or the number of foreground: classes.

nimgs (int, default=1): number of images in the coco datasts. nboxes (int, default=1): boxes per image. n_fp (int, default=0): number of false positives. n_fn (int, default=0): number of false negatives. box_noise (float, default=0): std of a normal distribution used to

perterb both box location and box size.

cls_noise (float, default=0): probability that a class label will: change. Must be within 0 and 1.

anchors (ndarray, default=None): used to create random boxes null_pred (bool, default=0):

if True, predicted classes are returned as null, which means only localization scoring is suitable.

with_probs (bool, default=1):: if True, includes per-class probabilities with predictions

CommandLine

xdoctest -m kwcoco.metrics.detect_metrics DetectionMetrics.demo:2 --show

Example

>>> kwargs = {}
>>> # Seed the RNG
>>> kwargs['rng'] = 0
>>> # Size parameters determine how big the data is
>>> kwargs['nimgs'] = 5
>>> kwargs['nboxes'] = 7
>>> kwargs['classes'] = 11
>>> # Noise parameters perterb predictions further from the truth
>>> kwargs['n_fp'] = 3
>>> kwargs['box_noise'] = 0.1
>>> kwargs['cls_noise'] = 0.5
>>> dmet = DetectionMetrics.demo(**kwargs)
>>> print('dmet.classes = {}'.format(dmet.classes))
dmet.classes = <CategoryTree(nNodes=12, maxDepth=3, maxBreadth=4...)>
>>> # Can grab kwimage.Detection object for any image
>>> print(dmet.true_detections(gid=0))
<Detections(4)>
>>> print(dmet.pred_detections(gid=0))
<Detections(7)>

Example

>>> # Test case with null predicted categories
>>> dmet = DetectionMetrics.demo(nimgs=30, null_pred=1, classes=3,
>>>                              nboxes=10, n_fp=3, box_noise=0.1,
>>>                              with_probs=False)
>>> dmet.gid_to_pred_dets[0].data
>>> dmet.gid_to_true_dets[0].data
>>> cfsn_vecs = dmet.confusion_vectors()
>>> binvecs_ovr = cfsn_vecs.binarize_ovr()
>>> binvecs_per = cfsn_vecs.binarize_classless()
>>> measures_per = binvecs_per.measures()
>>> measures_ovr = binvecs_ovr.measures()
>>> print('measures_per = {!r}'.format(measures_per))
>>> print('measures_ovr = {!r}'.format(measures_ovr))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> measures_ovr['perclass'].draw(key='pr', fnum=2)

Example

>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> from kwcoco.metrics.detect_metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     n_fp=(0, 1), n_fn=(0, 1), nimgs=32, nboxes=(0, 16),
>>>     classes=3, rng=0, newstyle=1, box_noise=0.5, cls_noise=0.0, score_noise=0.3, with_probs=False)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> summary = dmet.summarize(plot=True, title='DetectionMetrics summary demo', with_ovr=True, with_bin=False)
>>> summary['bin_measures']
>>> kwplot.show_if_requested()

summarize(dmet, out_dpath=None, plot=False, title='', with_bin='auto', with_ovr='auto')¶

Example

>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> from kwcoco.metrics.detect_metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     n_fp=(0, 128), n_fn=(0, 4), nimgs=512, nboxes=(0, 32),
>>>     classes=3, rng=0)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> dmet.summarize(plot=True, title='DetectionMetrics summary demo')
>>> kwplot.show_if_requested()

kwcoco.metrics.eval_detections_cli(**kw)[source]¶

DEPRECATED USE kwcoco eval instead

CommandLine

xdoctest -m ~/code/kwcoco/kwcoco/metrics/detect_metrics.py eval_detections_cli

class kwcoco.metrics.BinaryConfusionVectors(data, cx=None, classes=None)[source]¶

Bases: ubelt.NiceRepr

Stores information about a binary classification problem. This is always with respect to a specific class, which is given by cx and classes.

The data DataFrameArray must contain: is_true - if the row is an instance of class classes[cx] pred_score - the predicted probability of class classes[cx], and weight - sample weight of the example

Example

>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> self = BinaryConfusionVectors.demo(n=10)
>>> print('self = {!r}'.format(self))
>>> print('measures = {}'.format(ub.repr2(self.measures())))

>>> self = BinaryConfusionVectors.demo(n=0)
>>> print('measures = {}'.format(ub.repr2(self.measures())))

>>> self = BinaryConfusionVectors.demo(n=1)
>>> print('measures = {}'.format(ub.repr2(self.measures())))

>>> self = BinaryConfusionVectors.demo(n=2)
>>> print('measures = {}'.format(ub.repr2(self.measures())))

classmethod demo(cls, n=10, p_true=0.5, p_error=0.2, p_miss=0.0, rng=None)¶

Create random data for tests

Parameters

n (int) – number of rows
p_true (int) – fraction of real positive cases
p_error (int) – probability of making a recoverable mistake
p_miss (int) – probability of making a unrecoverable mistake
rng (int | RandomState) – random seed / state

Returns

BinaryConfusionVectors

Example

>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> cfsn = BinaryConfusionVectors.demo(n=1000, p_error=0.1, p_miss=0.1)
>>> measures = cfsn.measures()
>>> print('measures = {}'.format(ub.repr2(measures, nl=1)))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.figure(fnum=1, pnum=(1, 2, 1))
>>> measures.draw('pr')
>>> kwplot.figure(fnum=1, pnum=(1, 2, 2))
>>> measures.draw('roc')

property catname(self)¶

__nice__(self)¶

__len__(self)¶

measures(self, stabalize_thresh=7, fp_cutoff=None, monotonic_ppv=True, ap_method='pycocotools')¶

Get statistics (F1, G1, MCC) versus thresholds

Parameters

stabalize_thresh (int, default=7) – if fewer than this many data points inserts dummy stabalization data so curves can still be drawn.
fp_cutoff (int, default=None) – maximum number of false positives in the truncated roc curves. None is equivalent to float('inf')
monotonic_ppv (bool, default=True) – if True ensures that precision is always increasing as recall decreases. This is done in pycocotools scoring, but I’m not sure its a good idea.

Example

>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> self = BinaryConfusionVectors.demo(n=0)
>>> print('measures = {}'.format(ub.repr2(self.measures())))
>>> self = BinaryConfusionVectors.demo(n=1, p_true=0.5, p_error=0.5)
>>> print('measures = {}'.format(ub.repr2(self.measures())))
>>> self = BinaryConfusionVectors.demo(n=3, p_true=0.5, p_error=0.5)
>>> print('measures = {}'.format(ub.repr2(self.measures())))

>>> self = BinaryConfusionVectors.demo(n=100, p_true=0.5, p_error=0.5, p_miss=0.3)
>>> print('measures = {}'.format(ub.repr2(self.measures())))
>>> print('measures = {}'.format(ub.repr2(ub.odict(self.measures()))))

References

https://en.wikipedia.org/wiki/Confusion_matrix https://en.wikipedia.org/wiki/Precision_and_recall https://en.wikipedia.org/wiki/Matthews_correlation_coefficient

_binary_clf_curves(self, stabalize_thresh=7, fp_cutoff=None)¶

Compute TP, FP, TN, and FN counts for this binary confusion vector.

Code common to ROC, PR, and threshold measures, computes the elements of the binary confusion matrix at all relevant operating point thresholds.

Parameters

stabalize_thresh (int) – if fewer than this many data points insert stabalization data.
fp_cutoff (int) – maximum number of false positives

Example

>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> self = BinaryConfusionVectors.demo(n=1, p_true=0.5, p_error=0.5)
>>> self._binary_clf_curves()

>>> self = BinaryConfusionVectors.demo(n=0, p_true=0.5, p_error=0.5)
>>> self._binary_clf_curves()

>>> self = BinaryConfusionVectors.demo(n=100, p_true=0.5, p_error=0.5)
>>> self._binary_clf_curves()

draw_distribution(self)¶

_3dplot(self)¶

Example

>>> # xdoctest: +REQUIRES(module:kwplot)
>>> # xdoctest: +REQUIRES(module:pandas)
>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> from kwcoco.metrics.detect_metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     n_fp=(0, 1), n_fn=(0, 2), nimgs=256, nboxes=(0, 10),
>>>     bbox_noise=10,
>>>     classes=1)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> self = bin_cfsn = cfsn_vecs.binarize_classless()
>>> #dmet.summarize(plot=True)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.figure(fnum=3)
>>> self._3dplot()

class kwcoco.metrics.ConfusionVectors(cfsn_vecs, data, classes, probs=None)[source]¶

Bases: ubelt.NiceRepr

Stores information used to construct a confusion matrix. This includes corresponding vectors of predicted labels, true labels, sample weights, etc…

Variables

data (kwarray.DataFrameArray) – should at least have keys true, pred, weight
classes (Sequence | CategoryTree) – list of category names or category graph
probs (ndarray, optional) – probabilities for each class

Example

>>> # xdoctest: IGNORE_WANT
>>> # xdoctest: +REQUIRES(module:pandas)
>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=10, nboxes=(0, 10), n_fp=(0, 1), classes=3)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> print(cfsn_vecs.data._pandas())
     pred  true   score  weight     iou  txs  pxs  gid
0       2     2 10.0000  1.0000  1.0000    0    4    0
1       2     2  7.5025  1.0000  1.0000    1    3    0
2       1     1  5.0050  1.0000  1.0000    2    2    0
3       3    -1  2.5075  1.0000 -1.0000   -1    1    0
4       2    -1  0.0100  1.0000 -1.0000   -1    0    0
5      -1     2  0.0000  1.0000 -1.0000    3   -1    0
6      -1     2  0.0000  1.0000 -1.0000    4   -1    0
7       2     2 10.0000  1.0000  1.0000    0    5    1
8       2     2  8.0020  1.0000  1.0000    1    4    1
9       1     1  6.0040  1.0000  1.0000    2    3    1
..    ...   ...     ...     ...     ...  ...  ...  ...
62     -1     2  0.0000  1.0000 -1.0000    7   -1    7
63     -1     3  0.0000  1.0000 -1.0000    8   -1    7
64     -1     1  0.0000  1.0000 -1.0000    9   -1    7
65      1    -1 10.0000  1.0000 -1.0000   -1    0    8
66      1     1  0.0100  1.0000  1.0000    0    1    8
67      3    -1 10.0000  1.0000 -1.0000   -1    3    9
68      2     2  6.6700  1.0000  1.0000    0    2    9
69      2     2  3.3400  1.0000  1.0000    1    1    9
70      3    -1  0.0100  1.0000 -1.0000   -1    0    9
71     -1     2  0.0000  1.0000 -1.0000    2   -1    9

>>> # xdoctest: +REQUIRES(--show)
>>> # xdoctest: +REQUIRES(module:pandas)
>>> import kwplot
>>> kwplot.autompl()
>>> from kwcoco.metrics.confusion_vectors import ConfusionVectors
>>> cfsn_vecs = ConfusionVectors.demo(
>>>     nimgs=128, nboxes=(0, 10), n_fp=(0, 3), n_fn=(0, 3), classes=3)
>>> cx_to_binvecs = cfsn_vecs.binarize_ovr()
>>> measures = cx_to_binvecs.measures()['perclass']
>>> print('measures = {!r}'.format(measures))
measures = <PerClass_Measures({
    'cat_1': <Measures({'ap': 0.227, 'auc': 0.507, 'catname': cat_1, 'max_f1': f1=0.45@0.47, 'nsupport': 788.000})>,
    'cat_2': <Measures({'ap': 0.288, 'auc': 0.572, 'catname': cat_2, 'max_f1': f1=0.51@0.43, 'nsupport': 788.000})>,
    'cat_3': <Measures({'ap': 0.225, 'auc': 0.484, 'catname': cat_3, 'max_f1': f1=0.46@0.40, 'nsupport': 788.000})>,
}) at 0x7facf77bdfd0>
>>> kwplot.figure(fnum=1, doclf=True)
>>> measures.draw(key='pr', fnum=1, pnum=(1, 3, 1))
>>> measures.draw(key='roc', fnum=1, pnum=(1, 3, 2))
>>> measures.draw(key='mcc', fnum=1, pnum=(1, 3, 3))
...

__nice__(cfsn_vecs)¶

__json__(self)¶

Serialize to json

Example

>>> # xdoctest: +REQUIRES(module:pandas)
>>> from kwcoco.metrics import ConfusionVectors
>>> self = ConfusionVectors.demo(n_imgs=1, classes=2, n_fp=0, nboxes=1)
>>> state = self.__json__()
>>> print('state = {}'.format(ub.repr2(state, nl=2, precision=2, align=1)))
>>> recon = ConfusionVectors.from_json(state)

classmethod from_json(cls, state)¶

classmethod demo(cfsn_vecs, **kw)¶

Parameters: **kwargs – See kwcoco.metrics.DetectionMetrics.demo()
Returns: ConfusionVectors

Example

>>> cfsn_vecs = ConfusionVectors.demo()
>>> print('cfsn_vecs = {!r}'.format(cfsn_vecs))
>>> cx_to_binvecs = cfsn_vecs.binarize_ovr()
>>> print('cx_to_binvecs = {!r}'.format(cx_to_binvecs))

classmethod from_arrays(ConfusionVectors, true, pred=None, score=None, weight=None, probs=None, classes=None)¶

Construct confusion vector data structure from component arrays

Example

>>> # xdoctest: +REQUIRES(module:pandas)
>>> import kwarray
>>> classes = ['person', 'vehicle', 'object']
>>> rng = kwarray.ensure_rng(0)
>>> true = (rng.rand(10) * len(classes)).astype(int)
>>> probs = rng.rand(len(true), len(classes))
>>> cfsn_vecs = ConfusionVectors.from_arrays(true=true, probs=probs, classes=classes)
>>> cfsn_vecs.confusion_matrix()
pred     person  vehicle  object
real
person        0        0       0
vehicle       2        4       1
object        2        1       0

confusion_matrix(cfsn_vecs, compress=False)¶

Builds a confusion matrix from the confusion vectors.

Parameters

compress (bool, default=False) – if True removes rows / columns with no entries

Returns

cmthe labeled confusion matrix

(Note: we should write a efficient replacement for: this use case. #remove_pandas)

Return type

pd.DataFrame

CommandLine

xdoctest -m kwcoco.metrics.confusion_vectors ConfusionVectors.confusion_matrix

Example

>>> # xdoctest: +REQUIRES(module:pandas)
>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=10, nboxes=(0, 10), n_fp=(0, 1), n_fn=(0, 1), classes=3, cls_noise=.2)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> cm = cfsn_vecs.confusion_matrix()
...
>>> print(cm.to_string(float_format=lambda x: '%.2f' % x))
pred        background  cat_1  cat_2  cat_3
real
background        0.00   1.00   2.00   3.00
cat_1             3.00  12.00   0.00   0.00
cat_2             3.00   0.00  14.00   0.00
cat_3             2.00   0.00   0.00  17.00

coarsen(cfsn_vecs, cxs)¶

Creates a coarsened set of vectors

Returns: ConfusionVectors

binarize_classless(cfsn_vecs, negative_classes=None)¶

Creates a binary representation useful for measuring the performance of detectors. It is assumed that scores of “positive” classes should be high and “negative” clases should be low.

Parameters: negative_classes (List[str | int]) – list of negative class names or idxs, by default chooses any class with a true class index of -1. These classes should ideally have low scores.
Returns: BinaryConfusionVectors

Note

The “classlessness” of this depends on the compat=”all” argument being used when constructing confusion vectors, otherwise it becomes something like a macro-average because the class information was used in deciding which true and predicted boxes were allowed to match.

Example

>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=10, nboxes=(0, 10), n_fp=(0, 1), n_fn=(0, 1), classes=3)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> class_idxs = list(dmet.classes.node_to_idx.values())
>>> binvecs = cfsn_vecs.binarize_classless()

binarize_ovr(cfsn_vecs, mode=1, keyby='name', ignore_classes={'ignore'}, approx=0)¶

Transforms cfsn_vecs into one-vs-rest BinaryConfusionVectors for each category.

Parameters

mode (int, default=1) – 0 for heirarchy aware or 1 for voc like. MODE 0 IS PROBABLY BROKEN
keyby (int | str) – can be cx or name
ignore_classes (Set[str]) – category names to ignore
approx (bool, default=0) – if True try and approximate missing scores otherwise assume they are irrecoverable and use -inf

Returns

which behaves like: Dict[int, BinaryConfusionVectors]: cx_to_binvecs

Return type

OneVsRestConfusionVectors

Example

>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> cfsn_vecs = ConfusionVectors.demo()
>>> print('cfsn_vecs = {!r}'.format(cfsn_vecs))
>>> catname_to_binvecs = cfsn_vecs.binarize_ovr(keyby='name')
>>> print('catname_to_binvecs = {!r}'.format(catname_to_binvecs))

cfsn_vecs.data.pandas() catname_to_binvecs.cx_to_binvecs[‘class_1’].data.pandas()

Note

classification_report(cfsn_vecs, verbose=0)¶

Build a classification report with various metrics.

Example

>>> # xdoctest: +REQUIRES(module:pandas)
>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> cfsn_vecs = ConfusionVectors.demo()
>>> report = cfsn_vecs.classification_report(verbose=1)

class kwcoco.metrics.Measures(info)¶

Bases: ubelt.NiceRepr, kwcoco.metrics.util.DictProxy

Holds accumulated confusion counts, and derived measures

Example

>>> from kwcoco.metrics.confusion_vectors import BinaryConfusionVectors  # NOQA
>>> binvecs = BinaryConfusionVectors.demo(n=100, p_error=0.5)
>>> self = binvecs.measures()
>>> print('self = {!r}'.format(self))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> self.draw(doclf=True)
>>> self.draw(key='pr',  pnum=(1, 2, 1))
>>> self.draw(key='roc', pnum=(1, 2, 2))
>>> kwplot.show_if_requested()

property catname(self)¶

__nice__(self)¶

reconstruct(self)¶

classmethod from_json(cls, state)¶

__json__(self)¶

Example

>>> from kwcoco.metrics.confusion_vectors import BinaryConfusionVectors  # NOQA
>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> binvecs = BinaryConfusionVectors.demo(n=10, p_error=0.5)
>>> self = binvecs.measures()
>>> info = self.__json__()
>>> print('info = {}'.format(ub.repr2(info, nl=1)))
>>> populate_info(info)
>>> print('info = {}'.format(ub.repr2(info, nl=1)))
>>> recon = Measures.from_json(info)

summary(self)¶

maximized_thresholds(self)¶: Returns thresholds that maximize metrics.

counts(self)¶

draw(self, key=None, prefix='', **kw)¶

Example

>>> # xdoctest: +REQUIRES(module:kwplot)
>>> # xdoctest: +REQUIRES(module:pandas)
>>> from kwcoco.metrics.confusion_vectors import ConfusionVectors  # NOQA
>>> cfsn_vecs = ConfusionVectors.demo()
>>> ovr_cfsn = cfsn_vecs.binarize_ovr(keyby='name')
>>> self = ovr_cfsn.measures()['perclass']
>>> self.draw('mcc', doclf=True, fnum=1)
>>> self.draw('pr', doclf=1, fnum=2)
>>> self.draw('roc', doclf=1, fnum=3)

summary_plot(self, fnum=1, title='', subplots='auto')¶

Example

>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> from kwcoco.metrics.confusion_vectors import ConfusionVectors  # NOQA
>>> cfsn_vecs = ConfusionVectors.demo(n=3, p_error=0.5)
>>> binvecs = cfsn_vecs.binarize_classless()
>>> self = binvecs.measures()
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> self.summary_plot()
>>> kwplot.show_if_requested()

classmethod demo(cls, **kwargs)¶

Create a demo Measures object for testing / demos

Parameters: **kwargs – passed to BinaryConfusionVectors.demo(). some valid keys are: n, rng, p_rue, p_error, p_miss.

classmethod combine(cls, tocombine, precision=None, growth=None, thresh_bins=None)¶

Combine binary confusion metrics

Parameters

tocombine (List[Measures]) – a list of measures to combine into one
precision (int | None) – If specified rounds thresholds to this precision which can prevent a RAM explosion when combining a large number of measures. However, this is a lossy operation and will impact the underlying scores. NOTE: use growth instead.
growth (int | None) – if specified this limits how much the resulting measures are allowed to grow by. If None, growth is unlimited. Otherwise, if growth is ‘max’, the growth is limited to the maximum length of an input. We might make this more numerical in the future.
thresh_bins (int) – Force this many threshold bins.

Returns

Measures

Example

>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> measures1 = Measures.demo(n=15)
>>> measures2 = measures1
>>> tocombine = [measures1, measures2]
>>> new_measures = Measures.combine(tocombine)
>>> new_measures.reconstruct()
>>> print('new_measures = {!r}'.format(new_measures))
>>> print('measures1 = {!r}'.format(measures1))
>>> print('measures2 = {!r}'.format(measures2))
>>> print(ub.repr2(measures1.__json__(), nl=1, sort=0))
>>> print(ub.repr2(measures2.__json__(), nl=1, sort=0))
>>> print(ub.repr2(new_measures.__json__(), nl=1, sort=0))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.figure(fnum=1)
>>> new_measures.summary_plot()
>>> measures1.summary_plot()
>>> measures1.draw('roc')
>>> measures2.draw('roc')
>>> new_measures.draw('roc')

Example

>>> # Demonstrate issues that can arrise from choosing a precision
>>> # that is too low  when combining metrics. Breakpoints
>>> # between different metrics can get muddled, but choosing a
>>> # precision that is too high can overwhelm memory.
>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> base = ub.map_vals(np.asarray, {
>>>     'tp_count':   [ 1,  1,  2,  2,  2,  2,  3],
>>>     'fp_count':   [ 0,  1,  1,  2,  3,  4,  5],
>>>     'fn_count':   [ 1,  1,  0,  0,  0,  0,  0],
>>>     'tn_count':   [ 5,  4,  4,  3,  2,  1,  0],
>>>     'thresholds': [.0, .0, .0, .0, .0, .0, .0],
>>> })
>>> # Make tiny offsets to thresholds
>>> rng = kwarray.ensure_rng(0)
>>> n = len(base['thresholds'])
>>> offsets = [
>>>     sorted(rng.rand(n) * 10 ** -rng.randint(4, 7))[::-1]
>>>     for _ in range(20)
>>> ]
>>> tocombine = []
>>> for offset in offsets:
>>>     base_n = base.copy()
>>>     base_n['thresholds'] += offset
>>>     measures_n = Measures(base_n).reconstruct()
>>>     tocombine.append(measures_n)
>>> for precision in [6, 5, 2]:
>>>     combo = Measures.combine(tocombine, precision=precision).reconstruct()
>>>     print('precision = {!r}'.format(precision))
>>>     print('combo = {}'.format(ub.repr2(combo, nl=1)))
>>>     print('num_thresholds = {}'.format(len(combo['thresholds'])))
>>> for growth in [None, 'max', 'log', 'root', 'half']:
>>>     combo = Measures.combine(tocombine, growth=growth).reconstruct()
>>>     print('growth = {!r}'.format(growth))
>>>     print('combo = {}'.format(ub.repr2(combo, nl=1)))
>>>     print('num_thresholds = {}'.format(len(combo['thresholds'])))
>>>     #print(combo.counts().pandas())

Example

>>> # Test case: combining a single measures should leave it unchanged
>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> measures = Measures.demo(n=40, p_true=0.2, p_error=0.4, p_miss=0.6)
>>> df1 = measures.counts().pandas().fillna(0)
>>> print(df1)
>>> tocombine = [measures]
>>> combo = Measures.combine(tocombine)
>>> df2 = combo.counts().pandas().fillna(0)
>>> print(df2)
>>> assert np.allclose(df1, df2)

>>> combo = Measures.combine(tocombine, thresh_bins=2)
>>> df3 = combo.counts().pandas().fillna(0)
>>> print(df3)

>>> # I am NOT sure if this is correct or not
>>> thresh_bins = 20
>>> combo = Measures.combine(tocombine, thresh_bins=thresh_bins)
>>> df4 = combo.counts().pandas().fillna(0)
>>> print(df4)

>>> combo = Measures.combine(tocombine, thresh_bins=np.linspace(0, 1, 20))
>>> df4 = combo.counts().pandas().fillna(0)
>>> print(df4)

assert np.allclose(combo[‘thresholds’], measures[‘thresholds’]) assert np.allclose(combo[‘fp_count’], measures[‘fp_count’]) assert np.allclose(combo[‘tp_count’], measures[‘tp_count’]) assert np.allclose(combo[‘tp_count’], measures[‘tp_count’])

globals().update(xdev.get_func_kwargs(Measures.combine))

Example

>>> # Test degenerate case
>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> tocombine = [
>>>     {'fn_count': [0.0], 'fp_count': [359980.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [7747.0]},
>>>     {'fn_count': [0.0], 'fp_count': [360849.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [424.0]},
>>>     {'fn_count': [0.0], 'fp_count': [367003.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [991.0]},
>>>     {'fn_count': [0.0], 'fp_count': [367976.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [1017.0]},
>>>     {'fn_count': [0.0], 'fp_count': [676338.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [7067.0]},
>>>     {'fn_count': [0.0], 'fp_count': [676348.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [7406.0]},
>>>     {'fn_count': [0.0], 'fp_count': [676626.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [7858.0]},
>>>     {'fn_count': [0.0], 'fp_count': [676693.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [10969.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677269.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [11188.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677331.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [11734.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677395.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [11556.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677418.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [11621.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677422.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [11424.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677648.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [9804.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677826.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [2470.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677834.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [2470.0]},
>>>     {'fn_count': [0.0], 'fp_count': [677835.0], 'thresholds': [0.0], 'tn_count': [0.0], 'tp_count': [2470.0]},
>>>     {'fn_count': [11123.0, 0.0], 'fp_count': [0.0, 676754.0], 'thresholds': [0.0002442002442002442, 0.0], 'tn_count': [676754.0, 0.0], 'tp_count': [2.0, 11125.0]},
>>>     {'fn_count': [7738.0, 0.0], 'fp_count': [0.0, 676466.0], 'thresholds': [0.0002442002442002442, 0.0], 'tn_count': [676466.0, 0.0], 'tp_count': [0.0, 7738.0]},
>>>     {'fn_count': [8653.0, 0.0], 'fp_count': [0.0, 676341.0], 'thresholds': [0.0002442002442002442, 0.0], 'tn_count': [676341.0, 0.0], 'tp_count': [0.0, 8653.0]},
>>> ]
>>> thresh_bins = np.linspace(0, 1, 4)
>>> combo = Measures.combine(tocombine, thresh_bins=thresh_bins).reconstruct()
>>> print('tocombine = {}'.format(ub.repr2(tocombine, nl=2)))
>>> print('thresh_bins = {!r}'.format(thresh_bins))
>>> print(ub.repr2(combo.__json__(), nl=1))
>>> for thresh_bins in [4096, 1]:
>>>     combo = Measures.combine(tocombine, thresh_bins=thresh_bins).reconstruct()
>>>     print('thresh_bins = {!r}'.format(thresh_bins))
>>>     print('combo = {}'.format(ub.repr2(combo, nl=1)))
>>>     print('num_thresholds = {}'.format(len(combo['thresholds'])))
>>> for precision in [6, 5, 2]:
>>>     combo = Measures.combine(tocombine, precision=precision).reconstruct()
>>>     print('precision = {!r}'.format(precision))
>>>     print('combo = {}'.format(ub.repr2(combo, nl=1)))
>>>     print('num_thresholds = {}'.format(len(combo['thresholds'])))
>>> for growth in [None, 'max', 'log', 'root', 'half']:
>>>     combo = Measures.combine(tocombine, growth=growth).reconstruct()
>>>     print('growth = {!r}'.format(growth))
>>>     print('combo = {}'.format(ub.repr2(combo, nl=1)))
>>>     print('num_thresholds = {}'.format(len(combo['thresholds'])))

class kwcoco.metrics.OneVsRestConfusionVectors(cx_to_binvecs, classes)[source]¶

Bases: ubelt.NiceRepr

Container for multiple one-vs-rest binary confusion vectors

Variables

cx_to_binvecs –
classes –

Example

>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=10, nboxes=(0, 10), n_fp=(0, 1), classes=3)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> self = cfsn_vecs.binarize_ovr(keyby='name')
>>> print('self = {!r}'.format(self))

__nice__(self)¶

classmethod demo(cls)¶

Parameters: **kwargs – See kwcoco.metrics.DetectionMetrics.demo()
Returns: ConfusionVectors

keys(self)¶

__getitem__(self, cx)¶

measures(self, stabalize_thresh=7, fp_cutoff=None, monotonic_ppv=True, ap_method='pycocotools')¶

Creates binary confusion measures for every one-versus-rest category.

Parameters

stabalize_thresh (int, default=7) – if fewer than this many data points inserts dummy stabilization data so curves can still be drawn.
fp_cutoff (int, default=None) – maximum number of false positives in the truncated roc curves. None is equivalent to float('inf')
monotonic_ppv (bool, default=True) – if True ensures that precision is always increasing as recall decreases. This is done in pycocotools scoring, but I’m not sure its a good idea.

SeeAlso:: BinaryConfusionVectors.measures()

Example

>>> self = OneVsRestConfusionVectors.demo()
>>> thresh_result = self.measures()['perclass']

abstract ovr_classification_report(self)¶

class kwcoco.metrics.PerClass_Measures(cx_to_info)¶

Bases: ubelt.NiceRepr, kwcoco.metrics.util.DictProxy

__nice__(self)¶

summary(self)¶

classmethod from_json(cls, state)¶

__json__(self)¶

draw(self, key='mcc', prefix='', **kw)¶

Example

>>> # xdoctest: +REQUIRES(module:kwplot)
>>> from kwcoco.metrics.confusion_vectors import ConfusionVectors  # NOQA
>>> cfsn_vecs = ConfusionVectors.demo()
>>> ovr_cfsn = cfsn_vecs.binarize_ovr(keyby='name')
>>> self = ovr_cfsn.measures()['perclass']
>>> self.draw('mcc', doclf=True, fnum=1)
>>> self.draw('pr', doclf=1, fnum=2)
>>> self.draw('roc', doclf=1, fnum=3)

draw_roc(self, prefix='', **kw)¶

draw_pr(self, prefix='', **kw)¶

summary_plot(self, fnum=1, title='', subplots='auto')¶

CommandLine

python ~/code/kwcoco/kwcoco/metrics/confusion_measures.py PerClass_Measures.summary_plot --show

Example

>>> from kwcoco.metrics.confusion_measures import *  # NOQA
>>> from kwcoco.metrics.detect_metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     n_fp=(0, 1), n_fn=(0, 3), nimgs=32, nboxes=(0, 32),
>>>     classes=3, rng=0, newstyle=1, box_noise=0.7, cls_noise=0.2, score_noise=0.3, with_probs=False)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> ovr_cfsn = cfsn_vecs.binarize_ovr(keyby='name', ignore_classes=['vector', 'raster'])
>>> self = ovr_cfsn.measures()['perclass']
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> import seaborn as sns
>>> sns.set()
>>> self.summary_plot(title='demo summary_plot ovr', subplots=['pr', 'roc'])
>>> kwplot.show_if_requested()
>>> self.summary_plot(title='demo summary_plot ovr', subplots=['mcc', 'acc'], fnum=2)

`kwcoco.util`¶

mkinit ~/code/kwcoco/kwcoco/util/__init__.py -w

Submodules¶

`kwcoco.util.dict_like`¶

Module Contents¶

Classes¶

DictLike

An inherited class must specify the getitem, setitem, and

class kwcoco.util.dict_like.DictLike[source]¶

Bases: ubelt.NiceRepr

An inherited class must specify the getitem, setitem, and: keys methods.

A class is dictionary like if it has:

__iter__, __len__, __contains__, __getitem__, items, keys, values, get,

and if it should be writable it should have: __delitem__, __setitem__, update,

And perhaps: copy,

__iter__, __len__, __contains__, __getitem__, items, keys, values, get,

and if it should be writable it should have: __delitem__, __setitem__, update,

And perhaps: copy,

asdict[source]¶

abstract getitem(self, key)[source]¶

abstract setitem(self, key, value)[source]¶

abstract delitem(self, key)[source]¶

abstract keys(self)[source]¶

__len__(self)[source]¶

__iter__(self)[source]¶

__contains__(self, key)[source]¶

__delitem__(self, key)[source]¶

__getitem__(self, key)[source]¶

__setitem__(self, key, value)[source]¶

items(self)[source]¶

values(self)[source]¶

copy(self)[source]¶

to_dict(self)[source]¶

update(self, other)[source]¶

get(self, key, default=None)[source]¶

`kwcoco.util.jsonschema_elements`¶

Functional interface into defining jsonschema structures.

See mixin classes for details.

Example

>>> from kwcoco.util.jsonschema_elements import *  # NOQA
>>> elem = SchemaElements()
>>> for base in SchemaElements.__bases__:
>>>     print('\n\n====\nbase = {!r}'.format(base))
>>>     attrs = [key for key in dir(base) if not key.startswith('_')]
>>>     for key in attrs:
>>>         value = getattr(elem, key)
>>>         print('{} = {}'.format(key, value))

Module Contents¶

Classes¶

`Element`	A dictionary used to define an element of a JSON Schema.
`ScalarElements`	Single-valued elements
`QuantifierElements`	Quantifier types
`ContainerElements`	Types that contain other types
`SchemaElements`	Functional interface into defining jsonschema structures.

Attributes¶

`elem`
`ALLOF`
`ANY`
`ANYOF`
`ARRAY`
`BOOLEAN`
`INTEGER`
`NOT`
`NULL`
`NUMBER`
`OBJECT`
`ONEOF`
`STRING`

class kwcoco.util.jsonschema_elements.Element(base, options={}, _magic=None)[source]¶

Bases: dict

A dictionary used to define an element of a JSON Schema.

The exact keys/values for the element will depend on the type of element being described. The SchemaElements defines exactly what these are for the core elements. (e.g. OBJECT, INTEGER, NULL, ARRAY, ANYOF)

Example

>>> from kwcoco.coco_schema import *  # NOQA
>>> self = Element(base={'type': 'demo'}, options={'opt1', 'opt2'})
>>> new = self(opt1=3)
>>> print('self = {}'.format(ub.repr2(self, nl=1, sort=1)))
>>> print('new = {}'.format(ub.repr2(new, nl=1, sort=1)))
>>> print('new2 = {}'.format(ub.repr2(new(), nl=1, sort=1)))
>>> print('new3 = {}'.format(ub.repr2(new(title='myvar'), nl=1, sort=1)))
>>> print('new4 = {}'.format(ub.repr2(new(title='myvar')(examples=['']), nl=1, sort=1)))
>>> print('new5 = {}'.format(ub.repr2(new(badattr=True), nl=1, sort=1)))
self = {
    'type': 'demo',
}
new = {
    'opt1': 3,
    'type': 'demo',
}
new2 = {
    'opt1': 3,
    'type': 'demo',
}
new3 = {
    'opt1': 3,
    'title': 'myvar',
    'type': 'demo',
}
new4 = {
    'examples': [''],
    'opt1': 3,
    'title': 'myvar',
    'type': 'demo',
}
new5 = {
    'opt1': 3,
    'type': 'demo',
}

__generics__[source]¶

__call__(self, *args, **kw)[source]¶

validate(self, instance=ub.NoParam)[source]¶

If instance is given, validates that that dictionary conforms to this schema. Otherwise validates that this is a valid schema element.

Parameters: instance (dict) – a dictionary to validate

__or__(self, other)[source]¶

Syntax for making an anyOf relationship

Example

>>> from kwcoco.util.jsonschema_elements import *  # NOQA
>>> obj1 = OBJECT(dict(opt1=NUMBER()))
>>> obj2 = OBJECT(dict(opt2=STRING()))
>>> obj3 = OBJECT(dict(opt3=ANY()))
>>> any_v1 = obj1 | obj2
>>> any_v2 = ANYOF(obj1, obj2)
>>> assert any_v1 == any_v2
>>> any_v3 = any_v1 | obj3
>>> any_v4 = ANYOF(obj1, obj2, obj3)
>>> assert any_v3 == any_v4

class kwcoco.util.jsonschema_elements.ScalarElements[source]¶

Bases: object

Single-valued elements

property NULL(self)[source]¶: https://json-schema.org/understanding-json-schema/reference/null.html

property BOOLEAN(self)[source]¶: https://json-schema.org/understanding-json-schema/reference/null.html

property STRING(self)[source]¶: https://json-schema.org/understanding-json-schema/reference/string.html

property NUMBER(self)[source]¶: https://json-schema.org/understanding-json-schema/reference/numeric.html#number

property INTEGER(self)[source]¶: https://json-schema.org/understanding-json-schema/reference/numeric.html#integer

class kwcoco.util.jsonschema_elements.QuantifierElements[source]¶

Bases: object

Quantifier types

https://json-schema.org/understanding-json-schema/reference/combining.html#allof

Example

>>> from kwcoco.util.jsonschema_elements import *  # NOQA
>>> elem.ANYOF(elem.STRING, elem.NUMBER).validate()
>>> elem.ONEOF(elem.STRING, elem.NUMBER).validate()
>>> elem.NOT(elem.NULL).validate()
>>> elem.NOT(elem.ANY).validate()
>>> elem.ANY.validate()

property ANY(self)[source]¶

ALLOF(self, *TYPES)[source]¶

ANYOF(self, *TYPES)[source]¶

ONEOF(self, *TYPES)[source]¶

NOT(self, TYPE)[source]¶

class kwcoco.util.jsonschema_elements.ContainerElements[source]¶

Types that contain other types

Example

>>> from kwcoco.util.jsonschema_elements import *  # NOQA
>>> print(elem.ARRAY().validate())
>>> print(elem.OBJECT().validate())
>>> print(elem.OBJECT().validate())
{'type': 'array', 'items': {}}
{'type': 'object', 'properties': {}}
{'type': 'object', 'properties': {}}

ARRAY(self, TYPE={}, **kw)[source]¶

https://json-schema.org/understanding-json-schema/reference/array.html

Example

>>> from kwcoco.util.jsonschema_elements import *  # NOQA
>>> ARRAY(numItems=3)
>>> schema = ARRAY(minItems=3)
>>> schema.validate()
{'type': 'array', 'items': {}, 'minItems': 3}

OBJECT(self, PROPERTIES={}, **kw)[source]¶

https://json-schema.org/understanding-json-schema/reference/object.html

Example

>>> import jsonschema
>>> schema = elem.OBJECT()
>>> jsonschema.validate({}, schema)
>>> #
>>> import jsonschema
>>> schema = elem.OBJECT({
>>>     'key1': elem.ANY(),
>>>     'key2': elem.ANY(),
>>> }, required=['key1'])
>>> jsonschema.validate({'key1': None}, schema)
>>> #
>>> import jsonschema
>>> schema = elem.OBJECT({
>>>     'key1': elem.OBJECT({'arr': elem.ARRAY()}),
>>>     'key2': elem.ANY(),
>>> }, required=['key1'], title='a title')
>>> schema.validate()
>>> print('schema = {}'.format(ub.repr2(schema, sort=1, nl=-1)))
>>> jsonschema.validate({'key1': {'arr': []}}, schema)
schema = {
    'properties': {
        'key1': {
            'properties': {
                'arr': {'items': {}, 'type': 'array'}
            },
            'type': 'object'
        },
        'key2': {}
    },
    'required': ['key1'],
    'title': 'a title',
    'type': 'object'
}

class kwcoco.util.jsonschema_elements.SchemaElements[source]¶

Bases: ScalarElements, QuantifierElements, ContainerElements

Functional interface into defining jsonschema structures.

See mixin classes for details.

References

https://json-schema.org/understanding-json-schema/

Todo

[ ] Generics: title, description, default, examples

CommandLine

xdoctest -m /home/joncrall/code/kwcoco/kwcoco/util/jsonschema_elements.py SchemaElements

Example

>>> from kwcoco.util.jsonschema_elements import *  # NOQA
>>> elem = SchemaElements()
>>> elem.ARRAY(elem.ANY())
>>> schema = OBJECT({
>>>     'prop1': ARRAY(INTEGER, minItems=3),
>>>     'prop2': ARRAY(STRING, numItems=2),
>>>     'prop3': ARRAY(OBJECT({
>>>         'subprob1': NUMBER,
>>>         'subprob2': NUMBER,
>>>     }))
>>> })
>>> print('schema = {}'.format(ub.repr2(schema, nl=2, sort=1)))
schema = {
    'properties': {
        'prop1': {'items': {'type': 'integer'}, 'minItems': 3, 'type': 'array'},
        'prop2': {'items': {'type': 'string'}, 'maxItems': 2, 'minItems': 2, 'type': 'array'},
        'prop3': {'items': {'properties': {'subprob1': {'type': 'number'}, 'subprob2': {'type': 'number'}}, 'type': 'object'}, 'type': 'array'},
    },
    'type': 'object',
}

>>> TYPE = elem.OBJECT({
>>>     'p1': ANY,
>>>     'p2': ANY,
>>> }, required=['p1'])
>>> import jsonschema
>>> inst = {'p1': None}
>>> jsonschema.validate(inst, schema=TYPE)
>>> #jsonschema.validate({'p2': None}, schema=TYPE)

kwcoco.util.jsonschema_elements.elem[source]¶

kwcoco.util.jsonschema_elements.ALLOF[source]¶

kwcoco.util.jsonschema_elements.ANY[source]¶

kwcoco.util.jsonschema_elements.ANYOF[source]¶

kwcoco.util.jsonschema_elements.ARRAY[source]¶

kwcoco.util.jsonschema_elements.BOOLEAN[source]¶

kwcoco.util.jsonschema_elements.INTEGER[source]¶

kwcoco.util.jsonschema_elements.NOT[source]¶

kwcoco.util.jsonschema_elements.NULL[source]¶

kwcoco.util.jsonschema_elements.NUMBER[source]¶

kwcoco.util.jsonschema_elements.OBJECT[source]¶

kwcoco.util.jsonschema_elements.ONEOF[source]¶

kwcoco.util.jsonschema_elements.STRING[source]¶

`kwcoco.util.lazy_frame_backends`¶

Ducktyped interfaces for loading subregions of images with standard slice syntax

Module Contents¶

Classes¶

`LazySpectralFrameFile`	Potentially faster than GDAL for HDR formats.
`LazyRasterIOFrameFile`	fpath = '/home/joncrall/.cache/kwcoco/demo/large_hyperspectral/big_img_128.bsq'
`LazyGDalFrameFile`

Functions¶

`_have_gdal`()
`_have_rasterio`()
`_have_spectral`()
`_demo_geoimg_with_nodata`()	Example
`_rectify_slice_dim`(part, D)
`_validate_nonzero_data`(file)	Test to see if the image is all black.
`_read_envi_header`(file)	USAGE: hdr = _read_envi_header(file)

Attributes¶

`profile`
`_GDAL_DTYPE_LUT`

kwcoco.util.lazy_frame_backends.profile[source]¶

kwcoco.util.lazy_frame_backends._have_gdal()[source]¶

kwcoco.util.lazy_frame_backends._have_rasterio()[source]¶

kwcoco.util.lazy_frame_backends._have_spectral()[source]¶

kwcoco.util.lazy_frame_backends._GDAL_DTYPE_LUT[source]¶

class kwcoco.util.lazy_frame_backends.LazySpectralFrameFile(fpath)[source]¶

Bases: ubelt.NiceRepr

Potentially faster than GDAL for HDR formats.

_ds(self)[source]¶

classmethod available(self)[source]¶: Returns True if this backend is available

property ndim(self)[source]¶

property shape(self)[source]¶

property dtype(self)[source]¶

__nice__(self)[source]¶

__getitem__(self, index)[source]¶

class kwcoco.util.lazy_frame_backends.LazyRasterIOFrameFile(fpath)[source]¶

Bases: ubelt.NiceRepr

fpath = ‘/home/joncrall/.cache/kwcoco/demo/large_hyperspectral/big_img_128.bsq’ lazy_rio = LazyRasterIOFrameFile(fpath) ds = lazy_rio._ds

classmethod available(self)[source]¶: Returns True if this backend is available

_ds(self)[source]¶

property ndim(self)[source]¶

property shape(self)[source]¶

property dtype(self)[source]¶

__nice__(self)[source]¶

__getitem__(self, index)[source]¶

kwcoco.util.lazy_frame_backends._demo_geoimg_with_nodata()[source]¶

Example

from kwcoco.util.lazy_frame_backends import * # NOQA fpath = _demo_geoimg_with_nodata() self = LazyGDalFrameFile.demo()

class kwcoco.util.lazy_frame_backends.LazyGDalFrameFile(fpath, nodata=None)[source]¶

Bases: ubelt.NiceRepr

Todo

[ ] Move to its own backend module
[ ] When used with COCO, allow the image metadata to populate the
height, width, and channels if possible.

Example

>>> # xdoctest: +REQUIRES(module:osgeo)
>>> self = LazyGDalFrameFile.demo()
>>> print('self = {!r}'.format(self))
>>> self[0:3, 0:3]
>>> self[:, :, 0]
>>> self[0]
>>> self[0, 3]

>>> # import kwplot
>>> # kwplot.imshow(self[:])

Parameters

nodata
masking_method

Example

>>> # See if we can reproduce the INTERLEAVE bug

data = np.random.rand(128, 128, 64) import kwimage import ubelt as ub from os.path import join dpath = ub.ensure_app_cache_dir(‘kwcoco/tests/reader’) fpath = join(dpath, ‘foo.tiff’) kwimage.imwrite(fpath, data, backend=’skimage’) recon1 = kwimage.imread(fpath) recon1.shape

self = LazyGDalFrameFile(fpath) self.shape self[:]

classmethod available(self)[source]¶: Returns True if this backend is available

_ds(self)[source]¶

classmethod demo(cls, key='astro', dsize=None)[source]¶

property ndim(self)[source]¶

property shape(self)[source]¶

property dtype(self)[source]¶

__nice__(self)[source]¶

__getitem__(self, index)[source]¶

References

https://gis.stackexchange.com/questions/162095/gdal-driver-create-typeerror

Example

>>> # Test nodata works correctly
>>> # xdoctest: +REQUIRES(module:osgeo)
>>> from kwcoco.util.lazy_frame_backends import *  # NOQA
>>> from kwcoco.util.lazy_frame_backends import _demo_geoimg_with_nodata
>>> fpath = _demo_geoimg_with_nodata()
>>> self = LazyGDalFrameFile(fpath, nodata='auto')
>>> imdata = self[:]
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> import kwarray
>>> kwplot.autompl()
>>> imdata = kwimage.normalize_intensity(imdata)
>>> imdata = np.nan_to_num(imdata)
>>> kwplot.imshow(imdata)

__array__(self)[source]¶

Allow this object to be passed to np.asarray

References

https://numpy.org/doc/stable/user/basics.dispatch.html

kwcoco.util.lazy_frame_backends._rectify_slice_dim(part, D)[source]¶

kwcoco.util.lazy_frame_backends._validate_nonzero_data(file)[source]¶

Test to see if the image is all black.

May fail on all-black images

Example

>>> # xdoctest: +REQUIRES(module:osgeo)
>>> import kwimage
>>> gpath = kwimage.grab_test_image_fpath()
>>> file = LazyGDalFrameFile(gpath)
>>> _validate_nonzero_data(file)

kwcoco.util.lazy_frame_backends._read_envi_header(file)[source]¶

USAGE: hdr = _read_envi_header(file)

Reads an ENVI “.hdr” file header and returns the parameters in a dictionary as strings. Header field names are treated as case insensitive and all keys in the dictionary are lowercase.

Modified from spectral/io/envi.py

References

https://github.com/spectralpython/spectral

`kwcoco.util.util_archive`¶

Module Contents¶

Classes¶

Archive

Abstraction over zipfile and tarfile

Functions¶

unarchive_file(archive_fpath, output_dpath='.', verbose=1, overwrite=True)

class kwcoco.util.util_archive.Archive(fpath=None, mode='r', backend=None, file=None)[source]¶

Bases: object

Abstraction over zipfile and tarfile

Todo

see if we can use one of these other tools instead

SeeAlso:: https://github.com/RKrahl/archive-tools https://pypi.org/project/arlib/

Example

>>> from os.path import join
>>> dpath = ub.ensure_app_cache_dir('ubelt', 'tests', 'archive')
>>> ub.delete(dpath)
>>> dpath = ub.ensure_app_cache_dir(dpath)
>>> import pathlib
>>> dpath = pathlib.Path(dpath)
>>> #
>>> #
>>> mode = 'w'
>>> self1 = Archive(str(dpath / 'demo.zip'), mode=mode)
>>> self2 = Archive(str(dpath / 'demo.tar.gz'), mode=mode)
>>> #
>>> open(dpath / 'data_1only.txt', 'w').write('bazbzzz')
>>> open(dpath / 'data_2only.txt', 'w').write('buzzz')
>>> open(dpath / 'data_both.txt', 'w').write('foobar')
>>> #
>>> self1.add(dpath / 'data_both.txt')
>>> self1.add(dpath / 'data_1only.txt')
>>> #
>>> self2.add(dpath / 'data_both.txt')
>>> self2.add(dpath / 'data_2only.txt')
>>> #
>>> self1.close()
>>> self2.close()
>>> #
>>> self1 = Archive(str(dpath / 'demo.zip'), mode='r')
>>> self2 = Archive(str(dpath / 'demo.tar.gz'), mode='r')
>>> #
>>> extract_dpath = ub.ensuredir(str(dpath / 'extracted'))
>>> extracted1 = self1.extractall(extract_dpath)
>>> extracted2 = self2.extractall(extract_dpath)
>>> for fpath in extracted2:
>>>     print(open(fpath, 'r').read())
>>> for fpath in extracted1:
>>>     print(open(fpath, 'r').read())

classmethod _open(cls, fpath, mode)[source]¶

__iter__(self)[source]¶

classmethod coerce(cls, data)[source]¶: Either open an archive file path or coerce an existing ZipFile or tarfile structure into this wrapper class

add(self, fpath, arcname=None)[source]¶

close(self)[source]¶

__enter__(self)[source]¶

__exit__(self, *args)[source]¶

extractall(self, output_dpath='.', verbose=1, overwrite=True)[source]¶

kwcoco.util.util_archive.unarchive_file(archive_fpath, output_dpath='.', verbose=1, overwrite=True)[source]¶

`kwcoco.util.util_delayed_poc`¶

This module is ported from ndsampler, and will likely eventually move to kwimage and be refactored using pymbolic

The classes in this file represent a tree of delayed operations.

Proof of concept for delayed chainable transforms in Python.

There are several optimizations that could be applied.

This is similar to GDAL’s virtual raster table, but it works in memory and I think it is easier to chain operations.

SeeAlso:: ../../dev/symbolic_delayed.py

Warning

As the name implies this is a proof of concept, and the actual implementation was hacked together too quickly. Serious refactoring will be necessary.

Concepts:

Each class should be a layer that adds a new transformation on top of underlying nested layers. Adding new layers should be quick, and there should always be the option to “finalize” a stack of layers, chaining the transforms / operations and then applying one final efficient transform at the end.

Todo

[ ] Need to handle masks / nodata values when warping. Might need to
rely more on gdal / rasterio for this.

Conventions:

dsize = (always in width / height), no channels are present

shape for images is always (height, width, channels)

channels are always the last dimension of each image, if no channel dim is specified, finalize will add it.

Videos must be the last process in the stack, and add a leading
time dimension to the shape. dsize is still width, height, but shape is now: (time, height, width, chan)

Example

>>> # Example demonstrating the modivating use case
>>> # We have multiple aligned frames for a video, but each of
>>> # those frames is in a different resolution. Furthermore,
>>> # each of the frames consists of channels in different resolutions.
>>> # Create raw channels in some "native" resolution for frame 1
>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> f1_chan1 = DelayedIdentity.demo('astro', chan=0, dsize=(300, 300))
>>> f1_chan2 = DelayedIdentity.demo('astro', chan=1, dsize=(200, 200))
>>> f1_chan3 = DelayedIdentity.demo('astro', chan=2, dsize=(10, 10))
>>> # Create raw channels in some "native" resolution for frame 2
>>> f2_chan1 = DelayedIdentity.demo('carl', dsize=(64, 64), chan=0)
>>> f2_chan2 = DelayedIdentity.demo('carl', dsize=(260, 260), chan=1)
>>> f2_chan3 = DelayedIdentity.demo('carl', dsize=(10, 10), chan=2)
>>> #
>>> # Delayed warp each channel into its "image" space
>>> # Note: the images never actually enter this space we transform through it
>>> f1_dsize = np.array((3, 3))
>>> f2_dsize = np.array((2, 2))
>>> f1_img = DelayedChannelConcat([
>>>     f1_chan1.delayed_warp(kwimage.Affine.scale(f1_dsize / f1_chan1.dsize), dsize=f1_dsize),
>>>     f1_chan2.delayed_warp(kwimage.Affine.scale(f1_dsize / f1_chan2.dsize), dsize=f1_dsize),
>>>     f1_chan3.delayed_warp(kwimage.Affine.scale(f1_dsize / f1_chan3.dsize), dsize=f1_dsize),
>>> ])
>>> f2_img = DelayedChannelConcat([
>>>     f2_chan1.delayed_warp(kwimage.Affine.scale(f2_dsize / f2_chan1.dsize), dsize=f2_dsize),
>>>     f2_chan2.delayed_warp(kwimage.Affine.scale(f2_dsize / f2_chan2.dsize), dsize=f2_dsize),
>>>     f2_chan3.delayed_warp(kwimage.Affine.scale(f2_dsize / f2_chan3.dsize), dsize=f2_dsize),
>>> ])
>>> # Combine frames into a video
>>> vid_dsize = np.array((280, 280))
>>> vid = DelayedFrameConcat([
>>>     f1_img.delayed_warp(kwimage.Affine.scale(vid_dsize / f1_img.dsize), dsize=vid_dsize),
>>>     f2_img.delayed_warp(kwimage.Affine.scale(vid_dsize / f2_img.dsize), dsize=vid_dsize),
>>> ])
>>> vid.nesting
>>> print('vid.nesting = {}'.format(ub.repr2(vid.__json__(), nl=-2)))
>>> final = vid.finalize(interpolation='nearest')
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(final[0], pnum=(1, 2, 1), fnum=1)
>>> kwplot.imshow(final[1], pnum=(1, 2, 2), fnum=1)

Example

>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> delayed = dset.delayed_load(1)
>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> astro = DelayedLoad.demo('astro')
>>> print('MSI = ' + ub.repr2(delayed.__json__(), nl=-3, sort=0))
>>> print('ASTRO = ' + ub.repr2(astro.__json__(), nl=2, sort=0))

>>> subchan = delayed.take_channels('B1|B8')
>>> subcrop = subchan.delayed_crop((slice(10, 80), slice(30, 50)))
>>> #
>>> subcrop.nesting()
>>> subchan.nesting()
>>> subchan.finalize()
>>> subcrop.finalize()
>>> #
>>> msi_crop = delayed.delayed_crop((slice(10, 80), slice(30, 50)))
>>> msi_warp = msi_crop.delayed_warp(kwimage.Affine.scale(3), dsize='auto')
>>> subdata = msi_warp.take_channels('B11|B1')
>>> final = subdata.finalize()
>>> assert final.shape == (210, 60, 2)

Example

>>> # test case where an auxiliary image does not map entirely on the image.
>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> import kwimage
>>> from os.path import join
>>> dpath = ub.ensure_app_cache_dir('kwcoco/tests/delayed_poc')
>>> chan1_fpath = join(dpath, 'chan1.tiff')
>>> chan2_fpath = join(dpath, 'chan2.tiff')
>>> chan3_fpath = join(dpath, 'chan2.tiff')
>>> chan1_raw = np.random.rand(128, 128, 1)
>>> chan2_raw = np.random.rand(64, 64, 1)
>>> chan3_raw = np.random.rand(256, 256, 1)
>>> kwimage.imwrite(chan1_fpath, chan1_raw)
>>> kwimage.imwrite(chan2_fpath, chan2_raw)
>>> kwimage.imwrite(chan3_fpath, chan3_raw)
>>> #
>>> c1 = channel_spec.FusedChannelSpec.coerce('c1')
>>> c2 = channel_spec.FusedChannelSpec.coerce('c2')
>>> c3 = channel_spec.FusedChannelSpec.coerce('c2')
>>> aux1 = DelayedLoad(chan1_fpath, dsize=chan1_raw.shape[0:2][::-1], channels=c1, num_bands=1)
>>> aux2 = DelayedLoad(chan2_fpath, dsize=chan2_raw.shape[0:2][::-1], channels=c2, num_bands=1)
>>> aux3 = DelayedLoad(chan3_fpath, dsize=chan3_raw.shape[0:2][::-1], channels=c3, num_bands=1)
>>> #
>>> img_dsize = (128, 128)
>>> transform1 = kwimage.Affine.coerce(scale=0.5)
>>> transform2 = kwimage.Affine.coerce(theta=0.5, shearx=0.01, offset=(-20, -40))
>>> transform3 = kwimage.Affine.coerce(offset=(64, 0)) @ kwimage.Affine.random(rng=10)
>>> part1 = aux1.delayed_warp(np.eye(3), dsize=img_dsize)
>>> part2 = aux2.delayed_warp(transform2, dsize=img_dsize)
>>> part3 = aux3.delayed_warp(transform3, dsize=img_dsize)
>>> delayed = DelayedChannelConcat([part1, part2, part3])
>>> #
>>> delayed_crop = delayed.crop((slice(0, 10), slice(0, 10)))
>>> delayed_final = delayed_crop.finalize()
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> final = delayed.finalize()
>>> kwplot.imshow(final, fnum=1, pnum=(1, 2, 1))
>>> kwplot.imshow(delayed_final, fnum=1, pnum=(1, 2, 2))

comp = delayed_crop.components[2]

comp.sub_data.finalize()

data = np.array([[0]]).astype(np.float32) kwimage.warp_affine(data, np.eye(3), dsize=(32, 32)) kwimage.warp_affine(data, np.eye(3))

kwimage.warp_affine(data[0:0], np.eye(3))

transform = kwimage.Affine.coerce(scale=0.1) data = np.array([[0]]).astype(np.float32)

data = np.array([[]]).astype(np.float32) kwimage.warp_affine(data, transform, dsize=(0, 2), antialias=True)

data = np.array([[]]).astype(np.float32) kwimage.warp_affine(data, transform, dsize=(10, 10))

data = np.array([[0]]).astype(np.float32) kwimage.warp_affine(data, transform, dsize=(0, 2), antialias=True)

data = np.array([[0]]).astype(np.float32) kwimage.warp_affine(data, transform, dsize=(10, 10))

cv2.warpAffine(: kwimage.grab_test_image(dsize=(1, 1)), kwimage.Affine.coerce(scale=0.1).matrix[0:2], dsize=(0, 1),

)

Module Contents¶

Classes¶

`DelayedVisionOperation`	Base class for nodes in a tree of delayed computer-vision operations
`DelayedVideoOperation`	Base class for nodes in a tree of delayed computer-vision operations
`DelayedImageOperation`	Operations that pertain only to images
`DelayedIdentity`	Noop leaf that does nothing. Can be used to hold raw data.
`DelayedNans`	Constructs nan channels as needed
`DelayedLoad`	A load operation for a specific sub-region and sub-bands in a specified
`DelayedFrameConcat`	Represents multiple frames in a video
`DelayedChannelConcat`	Represents multiple channels in an image that could be concatenated
`DelayedWarp`	POC for chainable transforms
`DelayedCrop`	Represent a delayed crop operation

Functions¶

`dequantize`(quant_data, quantization)	Helper for dequantization
`_compute_leaf_subcrop`(root_region_bounds, tf_leaf_to_root)	Given a region in a "root" image and a trasnform between that "root" and
`_largest_shape`(shapes)	Finds maximum over all shapes
`_devcheck_corner`()
`_auto_dsize`(transform, sub_dsize)

Attributes¶

`xr`
`profile`

kwcoco.util.util_delayed_poc.xr[source]¶

kwcoco.util.util_delayed_poc.profile[source]¶

class kwcoco.util.util_delayed_poc.DelayedVisionOperation[source]¶

Bases: ubelt.NiceRepr

Base class for nodes in a tree of delayed computer-vision operations

__nice__(self)[source]¶

abstract finalize(self, **kwargs)[source]¶

abstract children(self)[source]¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

_optimize_paths(self, **kwargs)[source]¶

Iterate through the leaf nodes, which are virtually transformed into the root space.

This returns some sort of hueristically optimized leaf repr wrt warps.

__json__(self)[source]¶

nesting(self)[source]¶

warp(self, *args, **kwargs)[source]¶: alias for delayed_warp, might change to this API in the future

crop(self, *args, **kwargs)[source]¶: alias for delayed_crop, might change to this API in the future

class kwcoco.util.util_delayed_poc.DelayedVideoOperation[source]¶

Base class for nodes in a tree of delayed computer-vision operations

class kwcoco.util.util_delayed_poc.DelayedImageOperation[source]¶

Operations that pertain only to images

delayed_crop(self, region_slices)[source]¶

Create a new delayed image that performs a crop in the transformed “self” space.

Parameters: region_slices (Tuple[slice, slice]) – y-slice and x-slice.

Note

Returns a heuristically “simplified” tree. In the current implementation there are only 3 operations, cat, warp, and crop. All cats go at the top, all crops go at the bottom, all warps are in the middle.

Returns: lazy executed delayed transform
Return type: DelayedWarp

Example

>>> dsize = (100, 100)
>>> tf2 = kwimage.Affine.affine(scale=3).matrix
>>> self = DelayedWarp(np.random.rand(33, 33), tf2, dsize)
>>> region_slices = (slice(5, 10), slice(1, 12))
>>> delayed_crop = self.delayed_crop(region_slices)
>>> print(ub.repr2(delayed_crop.nesting(), nl=-1, sort=0))
>>> delayed_crop.finalize()

Example

>>> chan1 = DelayedLoad.demo('astro')
>>> chan2 = DelayedLoad.demo('carl')
>>> warped1a = chan1.delayed_warp(kwimage.Affine.scale(1.2).matrix)
>>> warped2a = chan2.delayed_warp(kwimage.Affine.scale(1.5))
>>> warped1b = warped1a.delayed_warp(kwimage.Affine.scale(1.2).matrix)
>>> warped2b = warped2a.delayed_warp(kwimage.Affine.scale(1.5))
>>> #
>>> region_slices = (slice(97, 677), slice(5, 691))
>>> self = warped2b
>>> #
>>> crop1 = warped1b.delayed_crop(region_slices)
>>> crop2 = warped2b.delayed_crop(region_slices)
>>> print(ub.repr2(warped1b.nesting(), nl=-1, sort=0))
>>> print(ub.repr2(warped2b.nesting(), nl=-1, sort=0))
>>> # Notice how the crop merges the two nesting layers
>>> # (via the hueristic optimize step)
>>> print(ub.repr2(crop1.nesting(), nl=-1, sort=0))
>>> print(ub.repr2(crop2.nesting(), nl=-1, sort=0))
>>> frame1 = crop1.finalize(dsize=(500, 500))
>>> frame2 = crop2.finalize(dsize=(500, 500))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(frame1, pnum=(1, 2, 1), fnum=1)
>>> kwplot.imshow(frame2, pnum=(1, 2, 2), fnum=1)

delayed_warp(self, transform, dsize=None)[source]¶

Delayed transform the underlying data.

Note

this deviates from kwimage warp functions because instead of “output_dims” (specified in c-style shape) we specify dsize (w, h).

Returns: new delayed transform a chained transform
Return type: DelayedWarp

abstract take_channels(self, channels)[source]¶

class kwcoco.util.util_delayed_poc.DelayedIdentity(sub_data, dsize=None, channels=None, quantization=None)[source]¶

Noop leaf that does nothing. Can be used to hold raw data.

Typically used to just hold raw data.

DelayedIdentity.demo(‘astro’, chan=0, dsize=(32, 32))

Example

>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> sub_data = np.random.rand(31, 37, 3)
>>> self = DelayedIdentity(sub_data)
>>> self = DelayedIdentity(sub_data, channels='L|a|b')

>>> # test with quantization
>>> rng = kwarray.ensure_rng(32)
>>> sub_data_quant = (rng.rand(31, 37, 3) * 1000).astype(np.int16)
>>> sub_data_quant[0, 0] = -9999
>>> self = DelayedIdentity(sub_data_quant, channels='L|a|b', quantization={
>>>     'orig_min': 0.,
>>>     'orig_max': 1.,
>>>     'quant_min': 0,
>>>     'quant_max': 1000,
>>>     'nodata': -9999,
>>> })
>>> final1 = self.finalize(dequantize=True)
>>> final2 = self.finalize(dequantize=False)
>>> assert np.all(np.isnan(final1[0, 0]))
>>> scale = final2 / final1
>>> scales = scale[scale > 0]
>>> assert np.all(np.isclose(scales, 1000))
>>> # check that take channels works
>>> new_subdata = self.take_channels('a')
>>> sub_final1 = new_subdata.finalize(dequantize=True)
>>> sub_final2 = new_subdata.finalize(dequantize=False)
>>> assert sub_final1.dtype.kind == 'f'
>>> assert sub_final2.dtype.kind == 'i'

__hack_dont_optimize__ = True[source]¶

classmethod demo(cls, key='astro', chan=None, dsize=None)[source]¶

children(self)[source]¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

_optimize_paths(self, **kwargs)[source]¶

Iterate through the leaf nodes, which are virtually transformed into the root space.

This returns some sort of hueristically optimized leaf repr wrt warps.

finalize(self, **kwargs)[source]¶

take_channels(self, channels)[source]¶

kwcoco.util.util_delayed_poc.dequantize(quant_data, quantization)[source]¶: Helper for dequantization

class kwcoco.util.util_delayed_poc.DelayedNans(dsize=None, channels=None)[source]¶

Constructs nan channels as needed

Example

self = DelayedNans((10, 10), channel_spec.FusedChannelSpec.coerce(‘rgb’)) region_slices = (slice(5, 10), slice(1, 12)) delayed = self.delayed_crop(region_slices)

Example

>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> dsize = (307, 311)
>>> c1 = DelayedNans(dsize=dsize, channels=channel_spec.FusedChannelSpec.coerce('foo'))
>>> c2 = DelayedLoad.demo('astro', dsize=dsize).load_shape(True)
>>> cat = DelayedChannelConcat([c1, c2])
>>> warped_cat = cat.delayed_warp(kwimage.Affine.scale(1.07), dsize=(328, 332))
>>> warped_cat.finalize()

#>>> cropped = warped_cat.delayed_crop((slice(0, 300), slice(0, 100))) #>>> cropped.finalize().shape

property shape(self)[source]¶

property num_bands(self)[source]¶

property dsize(self)[source]¶

property channels(self)[source]¶

children(self)[source]¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

_optimize_paths(self, **kwargs)[source]¶

Iterate through the leaf nodes, which are virtually transformed into the root space.

This returns some sort of hueristically optimized leaf repr wrt warps.

finalize(self, **kwargs)[source]¶

delayed_crop(self, region_slices)[source]¶

Create a new delayed image that performs a crop in the transformed “self” space.

Parameters: region_slices (Tuple[slice, slice]) – y-slice and x-slice.

Note

Returns a heuristically “simplified” tree. In the current implementation there are only 3 operations, cat, warp, and crop. All cats go at the top, all crops go at the bottom, all warps are in the middle.

Returns: lazy executed delayed transform
Return type: DelayedWarp

Example

>>> dsize = (100, 100)
>>> tf2 = kwimage.Affine.affine(scale=3).matrix
>>> self = DelayedWarp(np.random.rand(33, 33), tf2, dsize)
>>> region_slices = (slice(5, 10), slice(1, 12))
>>> delayed_crop = self.delayed_crop(region_slices)
>>> print(ub.repr2(delayed_crop.nesting(), nl=-1, sort=0))
>>> delayed_crop.finalize()

Example

>>> chan1 = DelayedLoad.demo('astro')
>>> chan2 = DelayedLoad.demo('carl')
>>> warped1a = chan1.delayed_warp(kwimage.Affine.scale(1.2).matrix)
>>> warped2a = chan2.delayed_warp(kwimage.Affine.scale(1.5))
>>> warped1b = warped1a.delayed_warp(kwimage.Affine.scale(1.2).matrix)
>>> warped2b = warped2a.delayed_warp(kwimage.Affine.scale(1.5))
>>> #
>>> region_slices = (slice(97, 677), slice(5, 691))
>>> self = warped2b
>>> #
>>> crop1 = warped1b.delayed_crop(region_slices)
>>> crop2 = warped2b.delayed_crop(region_slices)
>>> print(ub.repr2(warped1b.nesting(), nl=-1, sort=0))
>>> print(ub.repr2(warped2b.nesting(), nl=-1, sort=0))
>>> # Notice how the crop merges the two nesting layers
>>> # (via the hueristic optimize step)
>>> print(ub.repr2(crop1.nesting(), nl=-1, sort=0))
>>> print(ub.repr2(crop2.nesting(), nl=-1, sort=0))
>>> frame1 = crop1.finalize(dsize=(500, 500))
>>> frame2 = crop2.finalize(dsize=(500, 500))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(frame1, pnum=(1, 2, 1), fnum=1)
>>> kwplot.imshow(frame2, pnum=(1, 2, 2), fnum=1)

delayed_warp(self, transform, dsize=None)[source]¶

Delayed transform the underlying data.

Note

this deviates from kwimage warp functions because instead of “output_dims” (specified in c-style shape) we specify dsize (w, h).

Returns: new delayed transform a chained transform
Return type: DelayedWarp

class kwcoco.util.util_delayed_poc.DelayedLoad(fpath, channels=None, dsize=None, num_bands=None, immediate_crop=None, immediate_chan_idxs=None, immediate_dsize=None, quantization=None)[source]¶

A load operation for a specific sub-region and sub-bands in a specified image.

Note

This class contains support for fusing certain lazy operations into this layer, namely cropping, scaling, and channel selection.

For now these are named immediates

Example

>>> fpath = kwimage.grab_test_image_fpath()
>>> self = DelayedLoad(fpath)
>>> print('self = {!r}'.format(self))
>>> self.load_shape()
>>> print('self = {!r}'.format(self))
>>> self.finalize()

>>> f1_img = DelayedLoad.demo('astro', dsize=(300, 300))
>>> f2_img = DelayedLoad.demo('carl', dsize=(256, 320))
>>> print('f1_img = {!r}'.format(f1_img))
>>> print('f2_img = {!r}'.format(f2_img))
>>> print(f2_img.finalize().shape)
>>> print(f1_img.finalize().shape)

>>> fpath = kwimage.grab_test_image_fpath()
>>> channels = channel_spec.FusedChannelSpec.coerce('rgb')
>>> self = DelayedLoad(fpath, channels=channels)

Example

>>> # Test with quantization
>>> fpath = kwimage.grab_test_image_fpath()
>>> channels = channel_spec.FusedChannelSpec.coerce('rgb')
>>> self = DelayedLoad(fpath, channels=channels, quantization={
>>>     'orig_min': 0.,
>>>     'orig_max': 1.,
>>>     'quant_min': 0,
>>>     'quant_max': 256,
>>>     'nodata': None,
>>> })
>>> final1 = self.finalize(dequantize=False)
>>> final2 = self.finalize(dequantize=True)
>>> assert final1.dtype.kind == 'u'
>>> assert final2.dtype.kind == 'f'
>>> assert final2.max() <= 1

__hack_dont_optimize__ = True[source]¶

classmethod demo(DelayedLoad, key='astro', dsize=None)[source]¶

abstract classmethod coerce(cls, data)[source]¶

children(self)[source]¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

nesting(self)[source]¶

_optimize_paths(self, **kwargs)[source]¶

Iterate through the leaf nodes, which are virtually transformed into the root space.

This returns some sort of hueristically optimized leaf repr wrt warps.

load_shape(self, use_channel_heuristic=False)[source]¶

_ensure_dsize(self)[source]¶

property shape(self)[source]¶

property num_bands(self)[source]¶

property dsize(self)[source]¶

property channels(self)[source]¶

property fpath(self)[source]¶

finalize(self, **kwargs)[source]¶

Todo

[ ] Load from overviews if a scale will be necessary

Parameters

**kwargs –

nodataif specified this data item is treated as nodata, the: data is then converted to floats and the nodata value is replaced with nan.

delayed_crop(self, region_slices)[source]¶

Parameters: region_slices (Tuple[slice, slice]) – y-slice and x-slice.
Returns: a new delayed load object with a fused crop operation
Return type: DelayedLoad

Example

>>> # Test chained crop operations
>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> self = orig = DelayedLoad.demo('astro').load_shape()
>>> region_slices = slices1 = (slice(0, 90), slice(30, 60))
>>> self = crop1 = orig.delayed_crop(slices1)
>>> region_slices = slices2 = (slice(10, 21), slice(10, 22))
>>> self = crop2 = crop1.delayed_crop(slices2)
>>> region_slices = slices3 = (slice(3, 20), slice(5, 20))
>>> crop3 = crop2.delayed_crop(slices3)
>>> # Spot check internals
>>> print('orig = {}'.format(ub.repr2(orig.__json__(), nl=2)))
>>> print('crop1 = {}'.format(ub.repr2(crop1.__json__(), nl=2)))
>>> print('crop2 = {}'.format(ub.repr2(crop2.__json__(), nl=2)))
>>> print('crop3 = {}'.format(ub.repr2(crop3.__json__(), nl=2)))
>>> # Test internals
>>> assert crop3._immediates['crop'][0].start == 13
>>> assert crop3._immediates['crop'][0].stop == 21
>>> # Test shapes work out correctly
>>> assert crop3.finalize().shape == (8, 7, 3)
>>> assert crop2.finalize().shape == (11, 12, 3)
>>> assert crop1.take_channels([1, 2]).finalize().shape == (90, 30, 2)
>>> assert orig.finalize().shape == (512, 512, 3)

Note

This chart gives an intuition on how new absolute slice coords
are computed from existing absolute coords ane relative coords.

      5 7    <- new
      3 5    <- rel
   --------
   01234567  <- relative coordinates
   --------
   2      9  <- curr
 ----------
 0123456789  <- absolute coordinates
 ----------

take_channels(self, channels)[source]¶

This method returns a subset of the vision data with only the specified bands / channels.

Parameters: channels (List[int] | slice | channel_spec.FusedChannelSpec) – List of integers indexes, a slice, or a channel spec, which is typically a pipe (|) delimited list of channel codes. See kwcoco.ChannelSpec for more detials.
Returns: a new delayed load with a fused take channel operation
Return type: DelayedLoad

Note

The channel subset must exist here or it will raise an error. A better implementation (via pymbolic) might be able to do better

Example

>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> import kwcoco
>>> self = DelayedLoad.demo('astro').load_shape()
>>> channels = [2, 0]
>>> new = self.take_channels(channels)
>>> new3 = new.take_channels([1, 0])

>>> final1 = self.finalize()
>>> final2 = new.finalize()
>>> final3 = new3.finalize()
>>> assert np.all(final1[..., 2] == final2[..., 0])
>>> assert np.all(final1[..., 0] == final2[..., 1])
>>> assert final2.shape[2] == 2

>>> assert np.all(final1[..., 2] == final3[..., 1])
>>> assert np.all(final1[..., 0] == final3[..., 0])
>>> assert final3.shape[2] == 2

class kwcoco.util.util_delayed_poc.DelayedFrameConcat(frames, dsize=None)[source]¶

Bases: DelayedVideoOperation

Represents multiple frames in a video

Note

Video[0]:
    Frame[0]:
        Chan[0]: (32) +--------------------------------+
        Chan[1]: (16) +----------------+
        Chan[2]: ( 8) +--------+
    Frame[1]:
        Chan[0]: (30) +------------------------------+
        Chan[1]: (14) +--------------+
        Chan[2]: ( 6) +------+

Todo

[ ] Support computing the transforms when none of the data is loaded

Example

>>> # Simpler case with fewer nesting levels
>>> rng = kwarray.ensure_rng(None)
>>> # Delayed warp each channel into its "image" space
>>> # Note: the images never enter the space we transform through
>>> f1_img = DelayedLoad.demo('astro', (300, 300))
>>> f2_img = DelayedLoad.demo('carl', (256, 256))
>>> # Combine frames into a video
>>> vid_dsize = np.array((100, 100))
>>> self = vid = DelayedFrameConcat([
>>>     f1_img.delayed_warp(kwimage.Affine.scale(vid_dsize / f1_img.dsize)),
>>>     f2_img.delayed_warp(kwimage.Affine.scale(vid_dsize / f2_img.dsize)),
>>> ], dsize=vid_dsize)
>>> print(ub.repr2(vid.nesting(), nl=-1, sort=0))
>>> final = vid.finalize(interpolation='nearest', dsize=(32, 32))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(final[0], pnum=(1, 2, 1), fnum=1)
>>> kwplot.imshow(final[1], pnum=(1, 2, 2), fnum=1)
>>> region_slices = (slice(0, 90), slice(30, 60))

children(self)[source]¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

property channels(self)[source]¶

property shape(self)[source]¶

finalize(self, **kwargs)[source]¶: Execute the final transform

delayed_crop(self, region_slices)[source]¶

Example

>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> # Create raw channels in some "native" resolution for frame 1
>>> f1_chan1 = DelayedIdentity.demo('astro', chan=(1, 0), dsize=(300, 300))
>>> f1_chan2 = DelayedIdentity.demo('astro', chan=2, dsize=(10, 10))
>>> # Create raw channels in some "native" resolution for frame 2
>>> f2_chan1 = DelayedIdentity.demo('carl', dsize=(64, 64), chan=(1, 0))
>>> f2_chan2 = DelayedIdentity.demo('carl', dsize=(10, 10), chan=2)
>>> #
>>> f1_dsize = np.array(f1_chan1.dsize)
>>> f2_dsize = np.array(f2_chan1.dsize)
>>> f1_img = DelayedChannelConcat([
>>>     f1_chan1.delayed_warp(kwimage.Affine.scale(f1_dsize / f1_chan1.dsize), dsize=f1_dsize),
>>>     f1_chan2.delayed_warp(kwimage.Affine.scale(f1_dsize / f1_chan2.dsize), dsize=f1_dsize),
>>> ])
>>> f2_img = DelayedChannelConcat([
>>>     f2_chan1.delayed_warp(kwimage.Affine.scale(f2_dsize / f2_chan1.dsize), dsize=f2_dsize),
>>>     f2_chan2.delayed_warp(kwimage.Affine.scale(f2_dsize / f2_chan2.dsize), dsize=f2_dsize),
>>> ])
>>> vid_dsize = np.array((280, 280))
>>> full_vid = DelayedFrameConcat([
>>>     f1_img.delayed_warp(kwimage.Affine.scale(vid_dsize / f1_img.dsize), dsize=vid_dsize),
>>>     f2_img.delayed_warp(kwimage.Affine.scale(vid_dsize / f2_img.dsize), dsize=vid_dsize),
>>> ])
>>> region_slices = (slice(80, 200), slice(80, 200))
>>> print(ub.repr2(full_vid.nesting(), nl=-1, sort=0))
>>> crop_vid = full_vid.delayed_crop(region_slices)
>>> final_full = full_vid.finalize(interpolation='nearest')
>>> final_crop = crop_vid.finalize(interpolation='nearest')
>>> import pytest
>>> with pytest.raises(ValueError):
>>>     # should not be able to crop a crop yet
>>>     crop_vid.delayed_crop(region_slices)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(final_full[0], pnum=(2, 2, 1), fnum=1)
>>> kwplot.imshow(final_full[1], pnum=(2, 2, 2), fnum=1)
>>> kwplot.imshow(final_crop[0], pnum=(2, 2, 3), fnum=1)
>>> kwplot.imshow(final_crop[1], pnum=(2, 2, 4), fnum=1)

delayed_warp(self, transform, dsize=None)[source]¶

Delayed transform the underlying data.

Note

this deviates from kwimage warp functions because instead of “output_dims” (specified in c-style shape) we specify dsize (w, h).

Returns: new delayed transform a chained transform
Return type: DelayedWarp

class kwcoco.util.util_delayed_poc.DelayedChannelConcat(components, dsize=None)[source]¶

Represents multiple channels in an image that could be concatenated

Variables: components (List[DelayedWarp]) – a list of stackable channels. Each component may be comprised of multiple channels.

Todo

[ ] can this be generalized into a delayed concat?
[ ] can all concats be delayed until the very end?

Example

>>> comp1 = DelayedWarp(np.random.rand(11, 7))
>>> comp2 = DelayedWarp(np.random.rand(11, 7, 3))
>>> comp3 = DelayedWarp(
>>>     np.random.rand(3, 5, 2),
>>>     transform=kwimage.Affine.affine(scale=(7/5, 11/3)).matrix,
>>>     dsize=(7, 11)
>>> )
>>> components = [comp1, comp2, comp3]
>>> chans = DelayedChannelConcat(components)
>>> final = chans.finalize()
>>> assert final.shape == chans.shape
>>> assert final.shape == (11, 7, 6)

>>> # We should be able to nest DelayedChannelConcat inside virutal images
>>> frame1 = DelayedWarp(
>>>     chans, transform=kwimage.Affine.affine(scale=2.2).matrix,
>>>     dsize=(20, 26))
>>> frame2 = DelayedWarp(
>>>     np.random.rand(3, 3, 6), dsize=(20, 26))
>>> frame3 = DelayedWarp(
>>>     np.random.rand(3, 3, 6), dsize=(20, 26))

>>> print(ub.repr2(frame1.nesting(), nl=-1, sort=False))
>>> frame1.finalize()
>>> vid = DelayedFrameConcat([frame1, frame2, frame3])
>>> print(ub.repr2(vid.nesting(), nl=-1, sort=False))

children(self)[source]¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

classmethod random(cls, num_parts=3, rng=None)[source]¶

Example

>>> self = DelayedChannelConcat.random()
>>> print('self = {!r}'.format(self))
>>> print(ub.repr2(self.nesting(), nl=-1, sort=0))

property channels(self)[source]¶

property shape(self)[source]¶

finalize(self, **kwargs)[source]¶: Execute the final transform

delayed_warp(self, transform, dsize=None)[source]¶

Delayed transform the underlying data.

Note

this deviates from kwimage warp functions because instead of “output_dims” (specified in c-style shape) we specify dsize (w, h).

Returns: new delayed transform a chained transform
Return type: DelayedWarp

take_channels(self, channels)[source]¶

This method returns a subset of the vision data with only the specified bands / channels.

Parameters: channels (List[int] | slice | channel_spec.FusedChannelSpec) – List of integers indexes, a slice, or a channel spec, which is typically a pipe (|) delimited list of channel codes. See kwcoco.ChannelSpec for more detials.
Returns: a delayed vision operation that only operates on the following channels.
Return type: DelayedVisionOperation

Example

>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> self = delayed = dset.delayed_load(1)
>>> channels = 'B11|B8|B1|B10'
>>> new = self.take_channels(channels)

Example

>>> # Complex case
>>> import kwcoco
>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> delayed = dset.delayed_load(1)
>>> astro = DelayedLoad.demo('astro').load_shape(use_channel_heuristic=True)
>>> aligned = astro.warp(kwimage.Affine.scale(600 / 512), dsize='auto')
>>> self = combo = DelayedChannelConcat(delayed.components + [aligned])
>>> channels = 'B1|r|B8|g'
>>> new = self.take_channels(channels)
>>> new_cropped = new.crop((slice(10, 200), slice(12, 350)))
>>> datas = new_cropped.finalize()
>>> vizable = kwimage.normalize_intensity(datas, axis=2)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> stacked = kwimage.stack_images(vizable.transpose(2, 0, 1))
>>> kwplot.imshow(stacked)

CommandLine

xdoctest -m /home/joncrall/code/kwcoco/kwcoco/util/util_delayed_poc.py DelayedChannelConcat.take_channels:2 --profile

Example

>>> # Test case where requested channel does not exist
>>> import kwcoco
>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral', use_cache=1, verbose=100)
>>> self = dset.delayed_load(1)
>>> channels = 'B1|foobar|bazbiz|B8'
>>> new = self.take_channels(channels)
>>> new_cropped = new.crop((slice(10, 200), slice(12, 350)))
>>> fused = new_cropped.finalize()
>>> assert fused.shape == (190, 338, 4)
>>> assert np.all(np.isnan(fused[..., 1:3]))
>>> assert not np.any(np.isnan(fused[..., 0]))
>>> assert not np.any(np.isnan(fused[..., 3]))

class kwcoco.util.util_delayed_poc.DelayedWarp(sub_data, transform=None, dsize=None)[source]¶

POC for chainable transforms

Note

“sub” is used to refer to the underlying data in its native coordinates and resolution.

“self” is used to refer to the data in the transformed coordinates that are exposed by this class.

Variables

sub_data (DelayedWarp | ArrayLike) – array-like image data at a naitive resolution
transform (Transform) – transforms data from native “sub”-image-space to “self”-image-space.

Example

>>> dsize = (12, 12)
>>> tf1 = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 1]])
>>> tf2 = np.array([[3, 0, 0], [0, 3, 0], [0, 0, 1]])
>>> tf3 = np.array([[4, 0, 0], [0, 4, 0], [0, 0, 1]])
>>> band1 = DelayedWarp(np.random.rand(6, 6), tf1, dsize)
>>> band2 = DelayedWarp(np.random.rand(4, 4), tf2, dsize)
>>> band3 = DelayedWarp(np.random.rand(3, 3), tf3, dsize)
>>> #
>>> # Execute a crop in a one-level transformed space
>>> region_slices = (slice(5, 10), slice(0, 12))
>>> delayed_crop = band2.delayed_crop(region_slices)
>>> final_crop = delayed_crop.finalize()
>>> #
>>> # Execute a crop in a nested transformed space
>>> tf4 = np.array([[1.5, 0, 0], [0, 1.5, 0], [0, 0, 1]])
>>> chained = DelayedWarp(band2, tf4, (18, 18))
>>> delayed_crop = chained.delayed_crop(region_slices)
>>> final_crop = delayed_crop.finalize()
>>> #
>>> tf4 = np.array([[.5, 0, 0], [0, .5, 0], [0, 0, 1]])
>>> chained = DelayedWarp(band2, tf4, (6, 6))
>>> delayed_crop = chained.delayed_crop(region_slices)
>>> final_crop = delayed_crop.finalize()
>>> #
>>> region_slices = (slice(1, 5), slice(2, 4))
>>> delayed_crop = chained.delayed_crop(region_slices)
>>> final_crop = delayed_crop.finalize()

Example

>>> dsize = (17, 12)
>>> tf = np.array([[5.2, 0, 1.1], [0, 3.1, 2.2], [0, 0, 1]])
>>> self = DelayedWarp(np.random.rand(3, 5, 13), tf, dsize=dsize)
>>> self.finalize().shape

classmethod random(cls, nesting=(2, 5), rng=None)[source]¶

Example

>>> self = DelayedWarp.random(nesting=(4, 7))
>>> print('self = {!r}'.format(self))
>>> print(ub.repr2(self.nesting(), nl=-1, sort=0))

property channels(self)[source]¶

children(self)[source]¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

property dsize(self)[source]¶

property num_bands(self)[source]¶

property shape(self)[source]¶

_optimize_paths(self, **kwargs)[source]¶

Example

>>> self = DelayedWarp.random()
>>> leafs = list(self._optimize_paths())
>>> print('leafs = {!r}'.format(leafs))

finalize(self, transform=None, dsize=None, interpolation='linear', **kwargs)[source]¶

Execute the final transform

Can pass a parent transform to augment this underlying transform.

Parameters

transform (Transform) – an additional transform to perform
dsize (Tuple[int, int]) – overrides destination canvas size

Example

>>> tf = np.array([[0.9, 0, 3.9], [0, 1.1, -.5], [0, 0, 1]])
>>> raw = kwimage.grab_test_image(dsize=(54, 65))
>>> raw = kwimage.ensure_float01(raw)
>>> # Test nested finalize
>>> layer1 = raw
>>> num = 10
>>> for _ in range(num):
...     layer1  = DelayedWarp(layer1, tf, dsize='auto')
>>> final1 = layer1.finalize()
>>> # Test non-nested finalize
>>> layer2 = list(layer1._optimize_paths())[0]
>>> final2 = layer2.finalize()
>>> #
>>> print(ub.repr2(layer1.nesting(), nl=-1, sort=0))
>>> print(ub.repr2(layer2.nesting(), nl=-1, sort=0))
>>> print('final1 = {!r}'.format(final1))
>>> print('final2 = {!r}'.format(final2))
>>> print('final1.shape = {!r}'.format(final1.shape))
>>> print('final2.shape = {!r}'.format(final2.shape))
>>> assert np.allclose(final1, final2)
>>> #
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(raw, pnum=(1, 3, 1), fnum=1)
>>> kwplot.imshow(final1, pnum=(1, 3, 2), fnum=1)
>>> kwplot.imshow(final2, pnum=(1, 3, 3), fnum=1)
>>> kwplot.show_if_requested()

Example

>>> # Test aliasing
>>> s = DelayedIdentity.demo()
>>> s = DelayedIdentity.demo('checkerboard')
>>> a = s.delayed_warp(kwimage.Affine.scale(0.05), dsize='auto')
>>> b = s.delayed_warp(kwimage.Affine.scale(3), dsize='auto')

>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> # It looks like downsampling linear and area is the same
>>> # Does warpAffine have no alias handling?
>>> pnum_ = kwplot.PlotNums(nRows=2, nCols=4)
>>> kwplot.imshow(a.finalize(interpolation='area'), pnum=pnum_(), title='warpAffine area')
>>> kwplot.imshow(a.finalize(interpolation='linear'), pnum=pnum_(), title='warpAffine linear')
>>> kwplot.imshow(a.finalize(interpolation='nearest'), pnum=pnum_(), title='warpAffine nearest')
>>> kwplot.imshow(a.finalize(interpolation='nearest', antialias=False), pnum=pnum_(), title='warpAffine nearest AA=0')
>>> kwplot.imshow(kwimage.imresize(s.finalize(), dsize=a.dsize, interpolation='area'), pnum=pnum_(), title='resize area')
>>> kwplot.imshow(kwimage.imresize(s.finalize(), dsize=a.dsize, interpolation='linear'), pnum=pnum_(), title='resize linear')
>>> kwplot.imshow(kwimage.imresize(s.finalize(), dsize=a.dsize, interpolation='nearest'), pnum=pnum_(), title='resize nearest')
>>> kwplot.imshow(kwimage.imresize(s.finalize(), dsize=a.dsize, interpolation='cubic'), pnum=pnum_(), title='resize cubic')

take_channels(self, channels)[source]¶

class kwcoco.util.util_delayed_poc.DelayedCrop(sub_data, sub_slices)[source]¶

https://pypi.org/project/python-slugify/1.2.2/

Represent a delayed crop operation

Example

>>> sub_data = DelayedLoad.demo()
>>> sub_slices = (slice(5, 10), slice(1, 12))
>>> self = DelayedCrop(sub_data, sub_slices)
>>> print(ub.repr2(self.nesting(), nl=-1, sort=0))
>>> final = self.finalize()
>>> print('final.shape = {!r}'.format(final.shape))

Example

>>> sub_data = DelayedLoad.demo()
>>> sub_slices = (slice(5, 10), slice(1, 12))
>>> crop1 = DelayedCrop(sub_data, sub_slices)
>>> import pytest
>>> # Should only error while huristics are in use.
>>> with pytest.raises(ValueError):
>>>     crop2 = DelayedCrop(crop1, sub_slices)

__hack_dont_optimize__ = True[source]¶

property channels(self)[source]¶

children(self)[source]¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

finalize(self, **kwargs)[source]¶

abstract _optimize_paths(self, **kwargs)[source]¶

Iterate through the leaf nodes, which are virtually transformed into the root space.

This returns some sort of hueristically optimized leaf repr wrt warps.

kwcoco.util.util_delayed_poc._compute_leaf_subcrop(root_region_bounds, tf_leaf_to_root)[source]¶

Given a region in a “root” image and a trasnform between that “root” and some “leaf” image, compute the appropriate quantized region in the “leaf” image and the adjusted transformation between that root and leaf.

Example

>>> region_slices = (slice(33, 100), slice(22, 62))
>>> region_shape = (100, 100, 1)
>>> root_region_box = kwimage.Boxes.from_slice(region_slices, shape=region_shape)
>>> root_region_bounds = root_region_box.to_polygons()[0]
>>> tf_leaf_to_root = kwimage.Affine.affine(scale=7).matrix
>>> slices, tf_new = _compute_leaf_subcrop(root_region_bounds, tf_leaf_to_root)
>>> print('tf_new =\n{!r}'.format(tf_new))
>>> print('slices = {!r}'.format(slices))

kwcoco.util.util_delayed_poc._largest_shape(shapes)[source]¶

Finds maximum over all shapes

Example

>>> shapes = [
>>>     (10, 20), None, (None, 30), (40, 50, 60, None), (100,)
>>> ]
>>> largest = _largest_shape(shapes)
>>> print('largest = {!r}'.format(largest))
>>> assert largest == (100, 50, 60, None)

kwcoco.util.util_delayed_poc._devcheck_corner()[source]¶

kwcoco.util.util_delayed_poc._auto_dsize(transform, sub_dsize)[source]¶

`kwcoco.util.util_futures`¶

Deprecated and functionality moved to ubelt

`kwcoco.util.util_json`¶

Module Contents¶

Functions¶

`ensure_json_serializable`(dict_, normalize_containers=False, verbose=0)	Attempt to convert common types (e.g. numpy) into something json complient
`find_json_unserializable`(data, quickcheck=False)	Recurse through json datastructure and find any component that
`indexable_allclose`(dct1, dct2, return_info=False)	Walks through two nested data structures and ensures that everything is

Attributes¶

IndexableWalker

kwcoco.util.util_json.IndexableWalker[source]¶

kwcoco.util.util_json.ensure_json_serializable(dict_, normalize_containers=False, verbose=0)[source]¶

Attempt to convert common types (e.g. numpy) into something json complient

Convert numpy and tuples into lists

Parameters: normalize_containers (bool, default=False) – if True, normalizes dict containers to be standard python structures.

Example

>>> data = ub.ddict(lambda: int)
>>> data['foo'] = ub.ddict(lambda: int)
>>> data['bar'] = np.array([1, 2, 3])
>>> data['foo']['a'] = 1
>>> data['foo']['b'] = (1, np.array([1, 2, 3]), {3: np.int32(3), 4: np.float16(1.0)})
>>> dict_ = data
>>> print(ub.repr2(data, nl=-1))
>>> assert list(find_json_unserializable(data))
>>> result = ensure_json_serializable(data, normalize_containers=True)
>>> print(ub.repr2(result, nl=-1))
>>> assert not list(find_json_unserializable(result))
>>> assert type(result) is dict

kwcoco.util.util_json.find_json_unserializable(data, quickcheck=False)[source]¶

Recurse through json datastructure and find any component that causes a serialization error. Record the location of these errors in the datastructure as we recurse through the call tree.

Parameters

data (object) – data that should be json serializable
quickcheck (bool) – if True, check the entire datastructure assuming its ok before doing the python-based recursive logic.

Returns

list of “bad part” dictionaries containing items: ’value’ - the value that caused the serialization error ‘loc’ - which contains a list of key/indexes that can be used

to lookup the location of the unserializable value. If the “loc” is a list, then it indicates a rare case where a key in a dictionary is causing the serialization error.

Return type

List[Dict]

Example

>>> from kwcoco.util.util_json import *  # NOQA
>>> part = ub.ddict(lambda: int)
>>> part['foo'] = ub.ddict(lambda: int)
>>> part['bar'] = np.array([1, 2, 3])
>>> part['foo']['a'] = 1
>>> # Create a dictionary with two unserializable parts
>>> data = [1, 2, {'nest1': [2, part]}, {frozenset({'badkey'}): 3, 2: 4}]
>>> parts = list(find_json_unserializable(data))
>>> print('parts = {}'.format(ub.repr2(parts, nl=1)))
>>> # Check expected structure of bad parts
>>> assert len(parts) == 2
>>> part = parts[1]
>>> assert list(part['loc']) == [2, 'nest1', 1, 'bar']
>>> # We can use the "loc" to find the bad value
>>> for part in parts:
>>>     # "loc" is a list of directions containing which keys/indexes
>>>     # to traverse at each descent into the data structure.
>>>     directions = part['loc']
>>>     curr = data
>>>     special_flag = False
>>>     for key in directions:
>>>         if isinstance(key, list):
>>>             # special case for bad keys
>>>             special_flag = True
>>>             break
>>>         else:
>>>             # normal case for bad values
>>>             curr = curr[key]
>>>     if special_flag:
>>>         assert part['data'] in curr.keys()
>>>         assert part['data'] is key[1]
>>>     else:
>>>         assert part['data'] is curr

kwcoco.util.util_json.indexable_allclose(dct1, dct2, return_info=False)[source]¶

Walks through two nested data structures and ensures that everything is roughly the same.

Parameters

dct1 – a nested indexable item
dct2 – a nested indexable item

Example

>>> from kwcoco.util.util_json import indexable_allclose
>>> dct1 = {
>>>     'foo': [1.222222, 1.333],
>>>     'bar': 1,
>>>     'baz': [],
>>> }
>>> dct2 = {
>>>     'foo': [1.22222, 1.333],
>>>     'bar': 1,
>>>     'baz': [],
>>> }
>>> assert indexable_allclose(dct1, dct2)

`kwcoco.util.util_monkey`¶

Module Contents¶

Classes¶

SupressPrint

Temporarily replace the print function in a module with a noop

class kwcoco.util.util_monkey.SupressPrint(*mods, **kw)[source]¶

Bases: object

Temporarily replace the print function in a module with a noop

Parameters

*mods – the modules to disable print in
**kw – only accepts “enabled” enabled (bool, default=True): enables or disables this context

__enter__(self)[source]¶

__exit__(self, a, b, c)[source]¶

`kwcoco.util.util_sklearn`¶

Extensions to sklearn constructs

Module Contents¶

Classes¶

StratifiedGroupKFold

Stratified K-Folds cross-validator with Grouping

class kwcoco.util.util_sklearn.StratifiedGroupKFold(n_splits=3, shuffle=False, random_state=None)[source]¶

Bases: sklearn.model_selection._split._BaseKFold

Stratified K-Folds cross-validator with Grouping

Provides train/test indices to split data in train/test sets.

This cross-validation object is a variation of GroupKFold that returns stratified folds. The folds are made by preserving the percentage of samples for each class.

`kwcoco.util.util_truncate`¶

Truncate utility based on python-slugify.

Module Contents¶

Functions¶

_trunc_op(string, max_length, trunc_loc)

Example

smart_truncate(string, max_length=0, separator=' ', trunc_loc=0.5)

Truncate a string.

kwcoco.util.util_truncate._trunc_op(string, max_length, trunc_loc)[source]¶

Example

>>> from kwcoco.util.util_truncate import _trunc_op
>>> string = 'DarnOvercastSculptureTipperBlazerConcaveUnsuitedDerangedHexagonRockband'
>>> max_length = 16
>>> trunc_loc = 0.5
>>> _trunc_op(string, max_length, trunc_loc)

>>> from kwcoco.util.util_truncate import _trunc_op
>>> max_length = 16
>>> string = 'a' * 16
>>> _trunc_op(string, max_length, trunc_loc)

>>> string = 'a' * 17
>>> _trunc_op(string, max_length, trunc_loc)

kwcoco.util.util_truncate.smart_truncate(string, max_length=0, separator=' ', trunc_loc=0.5)[source]¶: Truncate a string. :param string (str): string for modification :param max_length (int): output string length :param word_boundary (bool): :param save_order (bool): if True then word order of output string is like input string :param separator (str): separator between words :param trunc_loc (float): fraction of location where to remove the text :return:

Package Contents¶

Classes¶

`DictLike`	An inherited class must specify the `getitem`, `setitem`, and
`ContainerElements`	Types that contain other types
`Element`	A dictionary used to define an element of a JSON Schema.
`QuantifierElements`	Quantifier types
`ScalarElements`	Single-valued elements
`SchemaElements`	Functional interface into defining jsonschema structures.
`LazyGDalFrameFile`
`LazyRasterIOFrameFile`	fpath = '/home/joncrall/.cache/kwcoco/demo/large_hyperspectral/big_img_128.bsq'
`LazySpectralFrameFile`	Potentially faster than GDAL for HDR formats.
`Archive`	Abstraction over zipfile and tarfile
`DelayedChannelConcat`	Represents multiple channels in an image that could be concatenated
`DelayedCrop`	Represent a delayed crop operation
`DelayedFrameConcat`	Represents multiple frames in a video
`DelayedIdentity`	Noop leaf that does nothing. Can be used to hold raw data.
`DelayedImageOperation`	Operations that pertain only to images
`DelayedLoad`	A load operation for a specific sub-region and sub-bands in a specified
`DelayedNans`	Constructs nan channels as needed
`DelayedVideoOperation`	Base class for nodes in a tree of delayed computer-vision operations
`DelayedVisionOperation`	Base class for nodes in a tree of delayed computer-vision operations
`DelayedWarp`	POC for chainable transforms
`SupressPrint`	Temporarily replace the print function in a module with a noop
`StratifiedGroupKFold`	Stratified K-Folds cross-validator with Grouping

Functions¶

`unarchive_file`(archive_fpath, output_dpath='.', verbose=1, overwrite=True)
`ensure_json_serializable`(dict_, normalize_containers=False, verbose=0)	Attempt to convert common types (e.g. numpy) into something json complient
`find_json_unserializable`(data, quickcheck=False)	Recurse through json datastructure and find any component that
`indexable_allclose`(dct1, dct2, return_info=False)	Walks through two nested data structures and ensures that everything is
`smart_truncate`(string, max_length=0, separator=' ', trunc_loc=0.5)	Truncate a string.

Attributes¶

`ALLOF`
`ANY`
`ANYOF`
`ARRAY`
`BOOLEAN`
`INTEGER`
`NOT`
`NULL`
`NUMBER`
`OBJECT`
`ONEOF`
`STRING`
`elem`
`IndexableWalker`

class kwcoco.util.DictLike[source]¶

Bases: ubelt.NiceRepr

An inherited class must specify the getitem, setitem, and: keys methods.

A class is dictionary like if it has:

__iter__, __len__, __contains__, __getitem__, items, keys, values, get,

and if it should be writable it should have: __delitem__, __setitem__, update,

And perhaps: copy,

__iter__, __len__, __contains__, __getitem__, items, keys, values, get,

and if it should be writable it should have: __delitem__, __setitem__, update,

And perhaps: copy,

asdict¶

abstract getitem(self, key)¶

abstract setitem(self, key, value)¶

abstract delitem(self, key)¶

abstract keys(self)¶

__len__(self)¶

__iter__(self)¶

__contains__(self, key)¶

__delitem__(self, key)¶

__getitem__(self, key)¶

__setitem__(self, key, value)¶

items(self)¶

values(self)¶

copy(self)¶

to_dict(self)¶

update(self, other)¶

get(self, key, default=None)¶

kwcoco.util.ALLOF[source]¶

kwcoco.util.ANY[source]¶

kwcoco.util.ANYOF[source]¶

kwcoco.util.ARRAY[source]¶

kwcoco.util.BOOLEAN[source]¶

class kwcoco.util.ContainerElements[source]¶

Types that contain other types

Example

>>> from kwcoco.util.jsonschema_elements import *  # NOQA
>>> print(elem.ARRAY().validate())
>>> print(elem.OBJECT().validate())
>>> print(elem.OBJECT().validate())
{'type': 'array', 'items': {}}
{'type': 'object', 'properties': {}}
{'type': 'object', 'properties': {}}

ARRAY(self, TYPE={}, **kw)¶

https://json-schema.org/understanding-json-schema/reference/array.html

Example

>>> from kwcoco.util.jsonschema_elements import *  # NOQA
>>> ARRAY(numItems=3)
>>> schema = ARRAY(minItems=3)
>>> schema.validate()
{'type': 'array', 'items': {}, 'minItems': 3}

OBJECT(self, PROPERTIES={}, **kw)¶

https://json-schema.org/understanding-json-schema/reference/object.html

Example

>>> import jsonschema
>>> schema = elem.OBJECT()
>>> jsonschema.validate({}, schema)
>>> #
>>> import jsonschema
>>> schema = elem.OBJECT({
>>>     'key1': elem.ANY(),
>>>     'key2': elem.ANY(),
>>> }, required=['key1'])
>>> jsonschema.validate({'key1': None}, schema)
>>> #
>>> import jsonschema
>>> schema = elem.OBJECT({
>>>     'key1': elem.OBJECT({'arr': elem.ARRAY()}),
>>>     'key2': elem.ANY(),
>>> }, required=['key1'], title='a title')
>>> schema.validate()
>>> print('schema = {}'.format(ub.repr2(schema, sort=1, nl=-1)))
>>> jsonschema.validate({'key1': {'arr': []}}, schema)
schema = {
    'properties': {
        'key1': {
            'properties': {
                'arr': {'items': {}, 'type': 'array'}
            },
            'type': 'object'
        },
        'key2': {}
    },
    'required': ['key1'],
    'title': 'a title',
    'type': 'object'
}

class kwcoco.util.Element(base, options={}, _magic=None)[source]¶

Bases: dict

A dictionary used to define an element of a JSON Schema.

The exact keys/values for the element will depend on the type of element being described. The SchemaElements defines exactly what these are for the core elements. (e.g. OBJECT, INTEGER, NULL, ARRAY, ANYOF)

Example

>>> from kwcoco.coco_schema import *  # NOQA
>>> self = Element(base={'type': 'demo'}, options={'opt1', 'opt2'})
>>> new = self(opt1=3)
>>> print('self = {}'.format(ub.repr2(self, nl=1, sort=1)))
>>> print('new = {}'.format(ub.repr2(new, nl=1, sort=1)))
>>> print('new2 = {}'.format(ub.repr2(new(), nl=1, sort=1)))
>>> print('new3 = {}'.format(ub.repr2(new(title='myvar'), nl=1, sort=1)))
>>> print('new4 = {}'.format(ub.repr2(new(title='myvar')(examples=['']), nl=1, sort=1)))
>>> print('new5 = {}'.format(ub.repr2(new(badattr=True), nl=1, sort=1)))
self = {
    'type': 'demo',
}
new = {
    'opt1': 3,
    'type': 'demo',
}
new2 = {
    'opt1': 3,
    'type': 'demo',
}
new3 = {
    'opt1': 3,
    'title': 'myvar',
    'type': 'demo',
}
new4 = {
    'examples': [''],
    'opt1': 3,
    'title': 'myvar',
    'type': 'demo',
}
new5 = {
    'opt1': 3,
    'type': 'demo',
}

__generics__¶

__call__(self, *args, **kw)¶

validate(self, instance=ub.NoParam)¶

If instance is given, validates that that dictionary conforms to this schema. Otherwise validates that this is a valid schema element.

Parameters: instance (dict) – a dictionary to validate

__or__(self, other)¶

Syntax for making an anyOf relationship

Example

>>> from kwcoco.util.jsonschema_elements import *  # NOQA
>>> obj1 = OBJECT(dict(opt1=NUMBER()))
>>> obj2 = OBJECT(dict(opt2=STRING()))
>>> obj3 = OBJECT(dict(opt3=ANY()))
>>> any_v1 = obj1 | obj2
>>> any_v2 = ANYOF(obj1, obj2)
>>> assert any_v1 == any_v2
>>> any_v3 = any_v1 | obj3
>>> any_v4 = ANYOF(obj1, obj2, obj3)
>>> assert any_v3 == any_v4

kwcoco.util.INTEGER[source]¶

kwcoco.util.NOT[source]¶

kwcoco.util.NULL[source]¶

kwcoco.util.NUMBER[source]¶

kwcoco.util.OBJECT[source]¶

kwcoco.util.ONEOF[source]¶

class kwcoco.util.QuantifierElements[source]¶

Bases: object

Quantifier types

https://json-schema.org/understanding-json-schema/reference/combining.html#allof

Example

>>> from kwcoco.util.jsonschema_elements import *  # NOQA
>>> elem.ANYOF(elem.STRING, elem.NUMBER).validate()
>>> elem.ONEOF(elem.STRING, elem.NUMBER).validate()
>>> elem.NOT(elem.NULL).validate()
>>> elem.NOT(elem.ANY).validate()
>>> elem.ANY.validate()

property ANY(self)¶

ALLOF(self, *TYPES)¶

ANYOF(self, *TYPES)¶

ONEOF(self, *TYPES)¶

NOT(self, TYPE)¶

kwcoco.util.STRING[source]¶

class kwcoco.util.ScalarElements[source]¶

Bases: object

Single-valued elements

property NULL(self)¶: https://json-schema.org/understanding-json-schema/reference/null.html

property BOOLEAN(self)¶: https://json-schema.org/understanding-json-schema/reference/null.html

property STRING(self)¶: https://json-schema.org/understanding-json-schema/reference/string.html

property NUMBER(self)¶: https://json-schema.org/understanding-json-schema/reference/numeric.html#number

property INTEGER(self)¶: https://json-schema.org/understanding-json-schema/reference/numeric.html#integer

class kwcoco.util.SchemaElements[source]¶

Bases: ScalarElements, QuantifierElements, ContainerElements

Functional interface into defining jsonschema structures.

See mixin classes for details.

References

https://json-schema.org/understanding-json-schema/

Todo

[ ] Generics: title, description, default, examples

CommandLine

xdoctest -m /home/joncrall/code/kwcoco/kwcoco/util/jsonschema_elements.py SchemaElements

Example

>>> from kwcoco.util.jsonschema_elements import *  # NOQA
>>> elem = SchemaElements()
>>> elem.ARRAY(elem.ANY())
>>> schema = OBJECT({
>>>     'prop1': ARRAY(INTEGER, minItems=3),
>>>     'prop2': ARRAY(STRING, numItems=2),
>>>     'prop3': ARRAY(OBJECT({
>>>         'subprob1': NUMBER,
>>>         'subprob2': NUMBER,
>>>     }))
>>> })
>>> print('schema = {}'.format(ub.repr2(schema, nl=2, sort=1)))
schema = {
    'properties': {
        'prop1': {'items': {'type': 'integer'}, 'minItems': 3, 'type': 'array'},
        'prop2': {'items': {'type': 'string'}, 'maxItems': 2, 'minItems': 2, 'type': 'array'},
        'prop3': {'items': {'properties': {'subprob1': {'type': 'number'}, 'subprob2': {'type': 'number'}}, 'type': 'object'}, 'type': 'array'},
    },
    'type': 'object',
}

>>> TYPE = elem.OBJECT({
>>>     'p1': ANY,
>>>     'p2': ANY,
>>> }, required=['p1'])
>>> import jsonschema
>>> inst = {'p1': None}
>>> jsonschema.validate(inst, schema=TYPE)
>>> #jsonschema.validate({'p2': None}, schema=TYPE)

kwcoco.util.elem[source]¶

class kwcoco.util.LazyGDalFrameFile(fpath, nodata=None)[source]¶

Bases: ubelt.NiceRepr

Todo

[ ] Move to its own backend module
[ ] When used with COCO, allow the image metadata to populate the
height, width, and channels if possible.

Example

>>> # xdoctest: +REQUIRES(module:osgeo)
>>> self = LazyGDalFrameFile.demo()
>>> print('self = {!r}'.format(self))
>>> self[0:3, 0:3]
>>> self[:, :, 0]
>>> self[0]
>>> self[0, 3]

>>> # import kwplot
>>> # kwplot.imshow(self[:])

Parameters

nodata
masking_method

Example

>>> # See if we can reproduce the INTERLEAVE bug

data = np.random.rand(128, 128, 64) import kwimage import ubelt as ub from os.path import join dpath = ub.ensure_app_cache_dir(‘kwcoco/tests/reader’) fpath = join(dpath, ‘foo.tiff’) kwimage.imwrite(fpath, data, backend=’skimage’) recon1 = kwimage.imread(fpath) recon1.shape

self = LazyGDalFrameFile(fpath) self.shape self[:]

classmethod available(self)¶: Returns True if this backend is available

_ds(self)¶

classmethod demo(cls, key='astro', dsize=None)¶

property ndim(self)¶

property shape(self)¶

property dtype(self)¶

__nice__(self)¶

__getitem__(self, index)¶

References

https://gis.stackexchange.com/questions/162095/gdal-driver-create-typeerror

Example

>>> # Test nodata works correctly
>>> # xdoctest: +REQUIRES(module:osgeo)
>>> from kwcoco.util.lazy_frame_backends import *  # NOQA
>>> from kwcoco.util.lazy_frame_backends import _demo_geoimg_with_nodata
>>> fpath = _demo_geoimg_with_nodata()
>>> self = LazyGDalFrameFile(fpath, nodata='auto')
>>> imdata = self[:]
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> import kwarray
>>> kwplot.autompl()
>>> imdata = kwimage.normalize_intensity(imdata)
>>> imdata = np.nan_to_num(imdata)
>>> kwplot.imshow(imdata)

__array__(self)¶

Allow this object to be passed to np.asarray

References

https://numpy.org/doc/stable/user/basics.dispatch.html

class kwcoco.util.LazyRasterIOFrameFile(fpath)[source]¶

Bases: ubelt.NiceRepr

fpath = ‘/home/joncrall/.cache/kwcoco/demo/large_hyperspectral/big_img_128.bsq’ lazy_rio = LazyRasterIOFrameFile(fpath) ds = lazy_rio._ds

classmethod available(self)¶: Returns True if this backend is available

_ds(self)¶

property ndim(self)¶

property shape(self)¶

property dtype(self)¶

__nice__(self)¶

__getitem__(self, index)¶

class kwcoco.util.LazySpectralFrameFile(fpath)[source]¶

Bases: ubelt.NiceRepr

Potentially faster than GDAL for HDR formats.

_ds(self)¶

classmethod available(self)¶: Returns True if this backend is available

property ndim(self)¶

property shape(self)¶

property dtype(self)¶

__nice__(self)¶

__getitem__(self, index)¶

class kwcoco.util.Archive(fpath=None, mode='r', backend=None, file=None)[source]¶

Bases: object

Abstraction over zipfile and tarfile

Todo

see if we can use one of these other tools instead

SeeAlso:: https://github.com/RKrahl/archive-tools https://pypi.org/project/arlib/

Example

>>> from os.path import join
>>> dpath = ub.ensure_app_cache_dir('ubelt', 'tests', 'archive')
>>> ub.delete(dpath)
>>> dpath = ub.ensure_app_cache_dir(dpath)
>>> import pathlib
>>> dpath = pathlib.Path(dpath)
>>> #
>>> #
>>> mode = 'w'
>>> self1 = Archive(str(dpath / 'demo.zip'), mode=mode)
>>> self2 = Archive(str(dpath / 'demo.tar.gz'), mode=mode)
>>> #
>>> open(dpath / 'data_1only.txt', 'w').write('bazbzzz')
>>> open(dpath / 'data_2only.txt', 'w').write('buzzz')
>>> open(dpath / 'data_both.txt', 'w').write('foobar')
>>> #
>>> self1.add(dpath / 'data_both.txt')
>>> self1.add(dpath / 'data_1only.txt')
>>> #
>>> self2.add(dpath / 'data_both.txt')
>>> self2.add(dpath / 'data_2only.txt')
>>> #
>>> self1.close()
>>> self2.close()
>>> #
>>> self1 = Archive(str(dpath / 'demo.zip'), mode='r')
>>> self2 = Archive(str(dpath / 'demo.tar.gz'), mode='r')
>>> #
>>> extract_dpath = ub.ensuredir(str(dpath / 'extracted'))
>>> extracted1 = self1.extractall(extract_dpath)
>>> extracted2 = self2.extractall(extract_dpath)
>>> for fpath in extracted2:
>>>     print(open(fpath, 'r').read())
>>> for fpath in extracted1:
>>>     print(open(fpath, 'r').read())

classmethod _open(cls, fpath, mode)¶

__iter__(self)¶

classmethod coerce(cls, data)¶: Either open an archive file path or coerce an existing ZipFile or tarfile structure into this wrapper class

add(self, fpath, arcname=None)¶

close(self)¶

__enter__(self)¶

__exit__(self, *args)¶

extractall(self, output_dpath='.', verbose=1, overwrite=True)¶

kwcoco.util.unarchive_file(archive_fpath, output_dpath='.', verbose=1, overwrite=True)[source]¶

class kwcoco.util.DelayedChannelConcat(components, dsize=None)[source]¶

Represents multiple channels in an image that could be concatenated

Variables: components (List[DelayedWarp]) – a list of stackable channels. Each component may be comprised of multiple channels.

Todo

[ ] can this be generalized into a delayed concat?
[ ] can all concats be delayed until the very end?

Example

>>> comp1 = DelayedWarp(np.random.rand(11, 7))
>>> comp2 = DelayedWarp(np.random.rand(11, 7, 3))
>>> comp3 = DelayedWarp(
>>>     np.random.rand(3, 5, 2),
>>>     transform=kwimage.Affine.affine(scale=(7/5, 11/3)).matrix,
>>>     dsize=(7, 11)
>>> )
>>> components = [comp1, comp2, comp3]
>>> chans = DelayedChannelConcat(components)
>>> final = chans.finalize()
>>> assert final.shape == chans.shape
>>> assert final.shape == (11, 7, 6)

>>> # We should be able to nest DelayedChannelConcat inside virutal images
>>> frame1 = DelayedWarp(
>>>     chans, transform=kwimage.Affine.affine(scale=2.2).matrix,
>>>     dsize=(20, 26))
>>> frame2 = DelayedWarp(
>>>     np.random.rand(3, 3, 6), dsize=(20, 26))
>>> frame3 = DelayedWarp(
>>>     np.random.rand(3, 3, 6), dsize=(20, 26))

>>> print(ub.repr2(frame1.nesting(), nl=-1, sort=False))
>>> frame1.finalize()
>>> vid = DelayedFrameConcat([frame1, frame2, frame3])
>>> print(ub.repr2(vid.nesting(), nl=-1, sort=False))

children(self)¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

classmethod random(cls, num_parts=3, rng=None)¶

Example

>>> self = DelayedChannelConcat.random()
>>> print('self = {!r}'.format(self))
>>> print(ub.repr2(self.nesting(), nl=-1, sort=0))

property channels(self)¶

property shape(self)¶

finalize(self, **kwargs)¶: Execute the final transform

delayed_warp(self, transform, dsize=None)¶

Delayed transform the underlying data.

Note

this deviates from kwimage warp functions because instead of “output_dims” (specified in c-style shape) we specify dsize (w, h).

Returns: new delayed transform a chained transform
Return type: DelayedWarp

take_channels(self, channels)¶

This method returns a subset of the vision data with only the specified bands / channels.

Parameters: channels (List[int] | slice | channel_spec.FusedChannelSpec) – List of integers indexes, a slice, or a channel spec, which is typically a pipe (|) delimited list of channel codes. See kwcoco.ChannelSpec for more detials.
Returns: a delayed vision operation that only operates on the following channels.
Return type: DelayedVisionOperation

Example

>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> self = delayed = dset.delayed_load(1)
>>> channels = 'B11|B8|B1|B10'
>>> new = self.take_channels(channels)

Example

>>> # Complex case
>>> import kwcoco
>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> delayed = dset.delayed_load(1)
>>> astro = DelayedLoad.demo('astro').load_shape(use_channel_heuristic=True)
>>> aligned = astro.warp(kwimage.Affine.scale(600 / 512), dsize='auto')
>>> self = combo = DelayedChannelConcat(delayed.components + [aligned])
>>> channels = 'B1|r|B8|g'
>>> new = self.take_channels(channels)
>>> new_cropped = new.crop((slice(10, 200), slice(12, 350)))
>>> datas = new_cropped.finalize()
>>> vizable = kwimage.normalize_intensity(datas, axis=2)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> stacked = kwimage.stack_images(vizable.transpose(2, 0, 1))
>>> kwplot.imshow(stacked)

CommandLine

xdoctest -m /home/joncrall/code/kwcoco/kwcoco/util/util_delayed_poc.py DelayedChannelConcat.take_channels:2 --profile

Example

>>> # Test case where requested channel does not exist
>>> import kwcoco
>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral', use_cache=1, verbose=100)
>>> self = dset.delayed_load(1)
>>> channels = 'B1|foobar|bazbiz|B8'
>>> new = self.take_channels(channels)
>>> new_cropped = new.crop((slice(10, 200), slice(12, 350)))
>>> fused = new_cropped.finalize()
>>> assert fused.shape == (190, 338, 4)
>>> assert np.all(np.isnan(fused[..., 1:3]))
>>> assert not np.any(np.isnan(fused[..., 0]))
>>> assert not np.any(np.isnan(fused[..., 3]))

class kwcoco.util.DelayedCrop(sub_data, sub_slices)[source]¶

Represent a delayed crop operation

Example

>>> sub_data = DelayedLoad.demo()
>>> sub_slices = (slice(5, 10), slice(1, 12))
>>> self = DelayedCrop(sub_data, sub_slices)
>>> print(ub.repr2(self.nesting(), nl=-1, sort=0))
>>> final = self.finalize()
>>> print('final.shape = {!r}'.format(final.shape))

Example

>>> sub_data = DelayedLoad.demo()
>>> sub_slices = (slice(5, 10), slice(1, 12))
>>> crop1 = DelayedCrop(sub_data, sub_slices)
>>> import pytest
>>> # Should only error while huristics are in use.
>>> with pytest.raises(ValueError):
>>>     crop2 = DelayedCrop(crop1, sub_slices)

__hack_dont_optimize__ = True¶

property channels(self)¶

children(self)¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

finalize(self, **kwargs)¶

abstract _optimize_paths(self, **kwargs)¶

Iterate through the leaf nodes, which are virtually transformed into the root space.

This returns some sort of hueristically optimized leaf repr wrt warps.

class kwcoco.util.DelayedFrameConcat(frames, dsize=None)[source]¶

Bases: DelayedVideoOperation

Represents multiple frames in a video

Note

Video[0]:
    Frame[0]:
        Chan[0]: (32) +--------------------------------+
        Chan[1]: (16) +----------------+
        Chan[2]: ( 8) +--------+
    Frame[1]:
        Chan[0]: (30) +------------------------------+
        Chan[1]: (14) +--------------+
        Chan[2]: ( 6) +------+

Todo

[ ] Support computing the transforms when none of the data is loaded

Example

>>> # Simpler case with fewer nesting levels
>>> rng = kwarray.ensure_rng(None)
>>> # Delayed warp each channel into its "image" space
>>> # Note: the images never enter the space we transform through
>>> f1_img = DelayedLoad.demo('astro', (300, 300))
>>> f2_img = DelayedLoad.demo('carl', (256, 256))
>>> # Combine frames into a video
>>> vid_dsize = np.array((100, 100))
>>> self = vid = DelayedFrameConcat([
>>>     f1_img.delayed_warp(kwimage.Affine.scale(vid_dsize / f1_img.dsize)),
>>>     f2_img.delayed_warp(kwimage.Affine.scale(vid_dsize / f2_img.dsize)),
>>> ], dsize=vid_dsize)
>>> print(ub.repr2(vid.nesting(), nl=-1, sort=0))
>>> final = vid.finalize(interpolation='nearest', dsize=(32, 32))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(final[0], pnum=(1, 2, 1), fnum=1)
>>> kwplot.imshow(final[1], pnum=(1, 2, 2), fnum=1)
>>> region_slices = (slice(0, 90), slice(30, 60))

children(self)¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

property channels(self)¶

property shape(self)¶

finalize(self, **kwargs)¶: Execute the final transform

delayed_crop(self, region_slices)¶

Example

>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> # Create raw channels in some "native" resolution for frame 1
>>> f1_chan1 = DelayedIdentity.demo('astro', chan=(1, 0), dsize=(300, 300))
>>> f1_chan2 = DelayedIdentity.demo('astro', chan=2, dsize=(10, 10))
>>> # Create raw channels in some "native" resolution for frame 2
>>> f2_chan1 = DelayedIdentity.demo('carl', dsize=(64, 64), chan=(1, 0))
>>> f2_chan2 = DelayedIdentity.demo('carl', dsize=(10, 10), chan=2)
>>> #
>>> f1_dsize = np.array(f1_chan1.dsize)
>>> f2_dsize = np.array(f2_chan1.dsize)
>>> f1_img = DelayedChannelConcat([
>>>     f1_chan1.delayed_warp(kwimage.Affine.scale(f1_dsize / f1_chan1.dsize), dsize=f1_dsize),
>>>     f1_chan2.delayed_warp(kwimage.Affine.scale(f1_dsize / f1_chan2.dsize), dsize=f1_dsize),
>>> ])
>>> f2_img = DelayedChannelConcat([
>>>     f2_chan1.delayed_warp(kwimage.Affine.scale(f2_dsize / f2_chan1.dsize), dsize=f2_dsize),
>>>     f2_chan2.delayed_warp(kwimage.Affine.scale(f2_dsize / f2_chan2.dsize), dsize=f2_dsize),
>>> ])
>>> vid_dsize = np.array((280, 280))
>>> full_vid = DelayedFrameConcat([
>>>     f1_img.delayed_warp(kwimage.Affine.scale(vid_dsize / f1_img.dsize), dsize=vid_dsize),
>>>     f2_img.delayed_warp(kwimage.Affine.scale(vid_dsize / f2_img.dsize), dsize=vid_dsize),
>>> ])
>>> region_slices = (slice(80, 200), slice(80, 200))
>>> print(ub.repr2(full_vid.nesting(), nl=-1, sort=0))
>>> crop_vid = full_vid.delayed_crop(region_slices)
>>> final_full = full_vid.finalize(interpolation='nearest')
>>> final_crop = crop_vid.finalize(interpolation='nearest')
>>> import pytest
>>> with pytest.raises(ValueError):
>>>     # should not be able to crop a crop yet
>>>     crop_vid.delayed_crop(region_slices)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(final_full[0], pnum=(2, 2, 1), fnum=1)
>>> kwplot.imshow(final_full[1], pnum=(2, 2, 2), fnum=1)
>>> kwplot.imshow(final_crop[0], pnum=(2, 2, 3), fnum=1)
>>> kwplot.imshow(final_crop[1], pnum=(2, 2, 4), fnum=1)

delayed_warp(self, transform, dsize=None)¶

Delayed transform the underlying data.

Note

this deviates from kwimage warp functions because instead of “output_dims” (specified in c-style shape) we specify dsize (w, h).

Returns: new delayed transform a chained transform
Return type: DelayedWarp

class kwcoco.util.DelayedIdentity(sub_data, dsize=None, channels=None, quantization=None)[source]¶

Noop leaf that does nothing. Can be used to hold raw data.

Typically used to just hold raw data.

DelayedIdentity.demo(‘astro’, chan=0, dsize=(32, 32))

Example

>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> sub_data = np.random.rand(31, 37, 3)
>>> self = DelayedIdentity(sub_data)
>>> self = DelayedIdentity(sub_data, channels='L|a|b')

>>> # test with quantization
>>> rng = kwarray.ensure_rng(32)
>>> sub_data_quant = (rng.rand(31, 37, 3) * 1000).astype(np.int16)
>>> sub_data_quant[0, 0] = -9999
>>> self = DelayedIdentity(sub_data_quant, channels='L|a|b', quantization={
>>>     'orig_min': 0.,
>>>     'orig_max': 1.,
>>>     'quant_min': 0,
>>>     'quant_max': 1000,
>>>     'nodata': -9999,
>>> })
>>> final1 = self.finalize(dequantize=True)
>>> final2 = self.finalize(dequantize=False)
>>> assert np.all(np.isnan(final1[0, 0]))
>>> scale = final2 / final1
>>> scales = scale[scale > 0]
>>> assert np.all(np.isclose(scales, 1000))
>>> # check that take channels works
>>> new_subdata = self.take_channels('a')
>>> sub_final1 = new_subdata.finalize(dequantize=True)
>>> sub_final2 = new_subdata.finalize(dequantize=False)
>>> assert sub_final1.dtype.kind == 'f'
>>> assert sub_final2.dtype.kind == 'i'

__hack_dont_optimize__ = True¶

classmethod demo(cls, key='astro', chan=None, dsize=None)¶

children(self)¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

_optimize_paths(self, **kwargs)¶

Iterate through the leaf nodes, which are virtually transformed into the root space.

This returns some sort of hueristically optimized leaf repr wrt warps.

finalize(self, **kwargs)¶

take_channels(self, channels)¶

class kwcoco.util.DelayedImageOperation[source]¶

Operations that pertain only to images

delayed_crop(self, region_slices)¶

Create a new delayed image that performs a crop in the transformed “self” space.

Parameters: region_slices (Tuple[slice, slice]) – y-slice and x-slice.

Note

Returns a heuristically “simplified” tree. In the current implementation there are only 3 operations, cat, warp, and crop. All cats go at the top, all crops go at the bottom, all warps are in the middle.

Returns: lazy executed delayed transform
Return type: DelayedWarp

Example

>>> dsize = (100, 100)
>>> tf2 = kwimage.Affine.affine(scale=3).matrix
>>> self = DelayedWarp(np.random.rand(33, 33), tf2, dsize)
>>> region_slices = (slice(5, 10), slice(1, 12))
>>> delayed_crop = self.delayed_crop(region_slices)
>>> print(ub.repr2(delayed_crop.nesting(), nl=-1, sort=0))
>>> delayed_crop.finalize()

Example

>>> chan1 = DelayedLoad.demo('astro')
>>> chan2 = DelayedLoad.demo('carl')
>>> warped1a = chan1.delayed_warp(kwimage.Affine.scale(1.2).matrix)
>>> warped2a = chan2.delayed_warp(kwimage.Affine.scale(1.5))
>>> warped1b = warped1a.delayed_warp(kwimage.Affine.scale(1.2).matrix)
>>> warped2b = warped2a.delayed_warp(kwimage.Affine.scale(1.5))
>>> #
>>> region_slices = (slice(97, 677), slice(5, 691))
>>> self = warped2b
>>> #
>>> crop1 = warped1b.delayed_crop(region_slices)
>>> crop2 = warped2b.delayed_crop(region_slices)
>>> print(ub.repr2(warped1b.nesting(), nl=-1, sort=0))
>>> print(ub.repr2(warped2b.nesting(), nl=-1, sort=0))
>>> # Notice how the crop merges the two nesting layers
>>> # (via the hueristic optimize step)
>>> print(ub.repr2(crop1.nesting(), nl=-1, sort=0))
>>> print(ub.repr2(crop2.nesting(), nl=-1, sort=0))
>>> frame1 = crop1.finalize(dsize=(500, 500))
>>> frame2 = crop2.finalize(dsize=(500, 500))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(frame1, pnum=(1, 2, 1), fnum=1)
>>> kwplot.imshow(frame2, pnum=(1, 2, 2), fnum=1)

delayed_warp(self, transform, dsize=None)¶

Delayed transform the underlying data.

Note

this deviates from kwimage warp functions because instead of “output_dims” (specified in c-style shape) we specify dsize (w, h).

Returns: new delayed transform a chained transform
Return type: DelayedWarp

abstract take_channels(self, channels)¶

class kwcoco.util.DelayedLoad(fpath, channels=None, dsize=None, num_bands=None, immediate_crop=None, immediate_chan_idxs=None, immediate_dsize=None, quantization=None)[source]¶

A load operation for a specific sub-region and sub-bands in a specified image.

Note

This class contains support for fusing certain lazy operations into this layer, namely cropping, scaling, and channel selection.

For now these are named immediates

Example

>>> fpath = kwimage.grab_test_image_fpath()
>>> self = DelayedLoad(fpath)
>>> print('self = {!r}'.format(self))
>>> self.load_shape()
>>> print('self = {!r}'.format(self))
>>> self.finalize()

>>> f1_img = DelayedLoad.demo('astro', dsize=(300, 300))
>>> f2_img = DelayedLoad.demo('carl', dsize=(256, 320))
>>> print('f1_img = {!r}'.format(f1_img))
>>> print('f2_img = {!r}'.format(f2_img))
>>> print(f2_img.finalize().shape)
>>> print(f1_img.finalize().shape)

>>> fpath = kwimage.grab_test_image_fpath()
>>> channels = channel_spec.FusedChannelSpec.coerce('rgb')
>>> self = DelayedLoad(fpath, channels=channels)

Example

>>> # Test with quantization
>>> fpath = kwimage.grab_test_image_fpath()
>>> channels = channel_spec.FusedChannelSpec.coerce('rgb')
>>> self = DelayedLoad(fpath, channels=channels, quantization={
>>>     'orig_min': 0.,
>>>     'orig_max': 1.,
>>>     'quant_min': 0,
>>>     'quant_max': 256,
>>>     'nodata': None,
>>> })
>>> final1 = self.finalize(dequantize=False)
>>> final2 = self.finalize(dequantize=True)
>>> assert final1.dtype.kind == 'u'
>>> assert final2.dtype.kind == 'f'
>>> assert final2.max() <= 1

__hack_dont_optimize__ = True¶

classmethod demo(DelayedLoad, key='astro', dsize=None)¶

abstract classmethod coerce(cls, data)¶

children(self)¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

nesting(self)¶

_optimize_paths(self, **kwargs)¶

Iterate through the leaf nodes, which are virtually transformed into the root space.

This returns some sort of hueristically optimized leaf repr wrt warps.

load_shape(self, use_channel_heuristic=False)¶

_ensure_dsize(self)¶

property shape(self)¶

property num_bands(self)¶

property dsize(self)¶

property channels(self)¶

property fpath(self)¶

finalize(self, **kwargs)¶

Todo

[ ] Load from overviews if a scale will be necessary

Parameters

**kwargs –

nodataif specified this data item is treated as nodata, the: data is then converted to floats and the nodata value is replaced with nan.

delayed_crop(self, region_slices)¶

Parameters: region_slices (Tuple[slice, slice]) – y-slice and x-slice.
Returns: a new delayed load object with a fused crop operation
Return type: DelayedLoad

Example

>>> # Test chained crop operations
>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> self = orig = DelayedLoad.demo('astro').load_shape()
>>> region_slices = slices1 = (slice(0, 90), slice(30, 60))
>>> self = crop1 = orig.delayed_crop(slices1)
>>> region_slices = slices2 = (slice(10, 21), slice(10, 22))
>>> self = crop2 = crop1.delayed_crop(slices2)
>>> region_slices = slices3 = (slice(3, 20), slice(5, 20))
>>> crop3 = crop2.delayed_crop(slices3)
>>> # Spot check internals
>>> print('orig = {}'.format(ub.repr2(orig.__json__(), nl=2)))
>>> print('crop1 = {}'.format(ub.repr2(crop1.__json__(), nl=2)))
>>> print('crop2 = {}'.format(ub.repr2(crop2.__json__(), nl=2)))
>>> print('crop3 = {}'.format(ub.repr2(crop3.__json__(), nl=2)))
>>> # Test internals
>>> assert crop3._immediates['crop'][0].start == 13
>>> assert crop3._immediates['crop'][0].stop == 21
>>> # Test shapes work out correctly
>>> assert crop3.finalize().shape == (8, 7, 3)
>>> assert crop2.finalize().shape == (11, 12, 3)
>>> assert crop1.take_channels([1, 2]).finalize().shape == (90, 30, 2)
>>> assert orig.finalize().shape == (512, 512, 3)

Note

This chart gives an intuition on how new absolute slice coords
are computed from existing absolute coords ane relative coords.

      5 7    <- new
      3 5    <- rel
   --------
   01234567  <- relative coordinates
   --------
   2      9  <- curr
 ----------
 0123456789  <- absolute coordinates
 ----------

take_channels(self, channels)¶

This method returns a subset of the vision data with only the specified bands / channels.

Parameters: channels (List[int] | slice | channel_spec.FusedChannelSpec) – List of integers indexes, a slice, or a channel spec, which is typically a pipe (|) delimited list of channel codes. See kwcoco.ChannelSpec for more detials.
Returns: a new delayed load with a fused take channel operation
Return type: DelayedLoad

Note

The channel subset must exist here or it will raise an error. A better implementation (via pymbolic) might be able to do better

Example

>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> import kwcoco
>>> self = DelayedLoad.demo('astro').load_shape()
>>> channels = [2, 0]
>>> new = self.take_channels(channels)
>>> new3 = new.take_channels([1, 0])

>>> final1 = self.finalize()
>>> final2 = new.finalize()
>>> final3 = new3.finalize()
>>> assert np.all(final1[..., 2] == final2[..., 0])
>>> assert np.all(final1[..., 0] == final2[..., 1])
>>> assert final2.shape[2] == 2

>>> assert np.all(final1[..., 2] == final3[..., 1])
>>> assert np.all(final1[..., 0] == final3[..., 0])
>>> assert final3.shape[2] == 2

class kwcoco.util.DelayedNans(dsize=None, channels=None)[source]¶

Constructs nan channels as needed

Example

self = DelayedNans((10, 10), channel_spec.FusedChannelSpec.coerce(‘rgb’)) region_slices = (slice(5, 10), slice(1, 12)) delayed = self.delayed_crop(region_slices)

Example

>>> from kwcoco.util.util_delayed_poc import *  # NOQA
>>> dsize = (307, 311)
>>> c1 = DelayedNans(dsize=dsize, channels=channel_spec.FusedChannelSpec.coerce('foo'))
>>> c2 = DelayedLoad.demo('astro', dsize=dsize).load_shape(True)
>>> cat = DelayedChannelConcat([c1, c2])
>>> warped_cat = cat.delayed_warp(kwimage.Affine.scale(1.07), dsize=(328, 332))
>>> warped_cat.finalize()

#>>> cropped = warped_cat.delayed_crop((slice(0, 300), slice(0, 100))) #>>> cropped.finalize().shape

property shape(self)¶

property num_bands(self)¶

property dsize(self)¶

property channels(self)¶

children(self)¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

_optimize_paths(self, **kwargs)¶

Iterate through the leaf nodes, which are virtually transformed into the root space.

This returns some sort of hueristically optimized leaf repr wrt warps.

finalize(self, **kwargs)¶

delayed_crop(self, region_slices)¶

Create a new delayed image that performs a crop in the transformed “self” space.

Parameters: region_slices (Tuple[slice, slice]) – y-slice and x-slice.

Note

Returns a heuristically “simplified” tree. In the current implementation there are only 3 operations, cat, warp, and crop. All cats go at the top, all crops go at the bottom, all warps are in the middle.

Returns: lazy executed delayed transform
Return type: DelayedWarp

Example

>>> dsize = (100, 100)
>>> tf2 = kwimage.Affine.affine(scale=3).matrix
>>> self = DelayedWarp(np.random.rand(33, 33), tf2, dsize)
>>> region_slices = (slice(5, 10), slice(1, 12))
>>> delayed_crop = self.delayed_crop(region_slices)
>>> print(ub.repr2(delayed_crop.nesting(), nl=-1, sort=0))
>>> delayed_crop.finalize()

Example

>>> chan1 = DelayedLoad.demo('astro')
>>> chan2 = DelayedLoad.demo('carl')
>>> warped1a = chan1.delayed_warp(kwimage.Affine.scale(1.2).matrix)
>>> warped2a = chan2.delayed_warp(kwimage.Affine.scale(1.5))
>>> warped1b = warped1a.delayed_warp(kwimage.Affine.scale(1.2).matrix)
>>> warped2b = warped2a.delayed_warp(kwimage.Affine.scale(1.5))
>>> #
>>> region_slices = (slice(97, 677), slice(5, 691))
>>> self = warped2b
>>> #
>>> crop1 = warped1b.delayed_crop(region_slices)
>>> crop2 = warped2b.delayed_crop(region_slices)
>>> print(ub.repr2(warped1b.nesting(), nl=-1, sort=0))
>>> print(ub.repr2(warped2b.nesting(), nl=-1, sort=0))
>>> # Notice how the crop merges the two nesting layers
>>> # (via the hueristic optimize step)
>>> print(ub.repr2(crop1.nesting(), nl=-1, sort=0))
>>> print(ub.repr2(crop2.nesting(), nl=-1, sort=0))
>>> frame1 = crop1.finalize(dsize=(500, 500))
>>> frame2 = crop2.finalize(dsize=(500, 500))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(frame1, pnum=(1, 2, 1), fnum=1)
>>> kwplot.imshow(frame2, pnum=(1, 2, 2), fnum=1)

delayed_warp(self, transform, dsize=None)¶

Delayed transform the underlying data.

Note

this deviates from kwimage warp functions because instead of “output_dims” (specified in c-style shape) we specify dsize (w, h).

Returns: new delayed transform a chained transform
Return type: DelayedWarp

class kwcoco.util.DelayedVideoOperation[source]¶

Base class for nodes in a tree of delayed computer-vision operations

class kwcoco.util.DelayedVisionOperation[source]¶

Bases: ubelt.NiceRepr

Base class for nodes in a tree of delayed computer-vision operations

__nice__(self)¶

abstract finalize(self, **kwargs)¶

abstract children(self)¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

_optimize_paths(self, **kwargs)¶

Iterate through the leaf nodes, which are virtually transformed into the root space.

This returns some sort of hueristically optimized leaf repr wrt warps.

__json__(self)¶

nesting(self)¶

warp(self, *args, **kwargs)¶: alias for delayed_warp, might change to this API in the future

crop(self, *args, **kwargs)¶: alias for delayed_crop, might change to this API in the future

class kwcoco.util.DelayedWarp(sub_data, transform=None, dsize=None)[source]¶

POC for chainable transforms

Note

“sub” is used to refer to the underlying data in its native coordinates and resolution.

“self” is used to refer to the data in the transformed coordinates that are exposed by this class.

Variables

sub_data (DelayedWarp | ArrayLike) – array-like image data at a naitive resolution
transform (Transform) – transforms data from native “sub”-image-space to “self”-image-space.

Example

>>> dsize = (12, 12)
>>> tf1 = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 1]])
>>> tf2 = np.array([[3, 0, 0], [0, 3, 0], [0, 0, 1]])
>>> tf3 = np.array([[4, 0, 0], [0, 4, 0], [0, 0, 1]])
>>> band1 = DelayedWarp(np.random.rand(6, 6), tf1, dsize)
>>> band2 = DelayedWarp(np.random.rand(4, 4), tf2, dsize)
>>> band3 = DelayedWarp(np.random.rand(3, 3), tf3, dsize)
>>> #
>>> # Execute a crop in a one-level transformed space
>>> region_slices = (slice(5, 10), slice(0, 12))
>>> delayed_crop = band2.delayed_crop(region_slices)
>>> final_crop = delayed_crop.finalize()
>>> #
>>> # Execute a crop in a nested transformed space
>>> tf4 = np.array([[1.5, 0, 0], [0, 1.5, 0], [0, 0, 1]])
>>> chained = DelayedWarp(band2, tf4, (18, 18))
>>> delayed_crop = chained.delayed_crop(region_slices)
>>> final_crop = delayed_crop.finalize()
>>> #
>>> tf4 = np.array([[.5, 0, 0], [0, .5, 0], [0, 0, 1]])
>>> chained = DelayedWarp(band2, tf4, (6, 6))
>>> delayed_crop = chained.delayed_crop(region_slices)
>>> final_crop = delayed_crop.finalize()
>>> #
>>> region_slices = (slice(1, 5), slice(2, 4))
>>> delayed_crop = chained.delayed_crop(region_slices)
>>> final_crop = delayed_crop.finalize()

Example

>>> dsize = (17, 12)
>>> tf = np.array([[5.2, 0, 1.1], [0, 3.1, 2.2], [0, 0, 1]])
>>> self = DelayedWarp(np.random.rand(3, 5, 13), tf, dsize=dsize)
>>> self.finalize().shape

classmethod random(cls, nesting=(2, 5), rng=None)¶

Example

>>> self = DelayedWarp.random(nesting=(4, 7))
>>> print('self = {!r}'.format(self))
>>> print(ub.repr2(self.nesting(), nl=-1, sort=0))

property channels(self)¶

children(self)¶: Abstract method, which should generate all of the direct children of a node in the operation tree.

property dsize(self)¶

property num_bands(self)¶

property shape(self)¶

_optimize_paths(self, **kwargs)¶

Example

>>> self = DelayedWarp.random()
>>> leafs = list(self._optimize_paths())
>>> print('leafs = {!r}'.format(leafs))

finalize(self, transform=None, dsize=None, interpolation='linear', **kwargs)¶

Execute the final transform

Can pass a parent transform to augment this underlying transform.

Parameters

transform (Transform) – an additional transform to perform
dsize (Tuple[int, int]) – overrides destination canvas size

Example

>>> tf = np.array([[0.9, 0, 3.9], [0, 1.1, -.5], [0, 0, 1]])
>>> raw = kwimage.grab_test_image(dsize=(54, 65))
>>> raw = kwimage.ensure_float01(raw)
>>> # Test nested finalize
>>> layer1 = raw
>>> num = 10
>>> for _ in range(num):
...     layer1  = DelayedWarp(layer1, tf, dsize='auto')
>>> final1 = layer1.finalize()
>>> # Test non-nested finalize
>>> layer2 = list(layer1._optimize_paths())[0]
>>> final2 = layer2.finalize()
>>> #
>>> print(ub.repr2(layer1.nesting(), nl=-1, sort=0))
>>> print(ub.repr2(layer2.nesting(), nl=-1, sort=0))
>>> print('final1 = {!r}'.format(final1))
>>> print('final2 = {!r}'.format(final2))
>>> print('final1.shape = {!r}'.format(final1.shape))
>>> print('final2.shape = {!r}'.format(final2.shape))
>>> assert np.allclose(final1, final2)
>>> #
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(raw, pnum=(1, 3, 1), fnum=1)
>>> kwplot.imshow(final1, pnum=(1, 3, 2), fnum=1)
>>> kwplot.imshow(final2, pnum=(1, 3, 3), fnum=1)
>>> kwplot.show_if_requested()

Example

>>> # Test aliasing
>>> s = DelayedIdentity.demo()
>>> s = DelayedIdentity.demo('checkerboard')
>>> a = s.delayed_warp(kwimage.Affine.scale(0.05), dsize='auto')
>>> b = s.delayed_warp(kwimage.Affine.scale(3), dsize='auto')

>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> # It looks like downsampling linear and area is the same
>>> # Does warpAffine have no alias handling?
>>> pnum_ = kwplot.PlotNums(nRows=2, nCols=4)
>>> kwplot.imshow(a.finalize(interpolation='area'), pnum=pnum_(), title='warpAffine area')
>>> kwplot.imshow(a.finalize(interpolation='linear'), pnum=pnum_(), title='warpAffine linear')
>>> kwplot.imshow(a.finalize(interpolation='nearest'), pnum=pnum_(), title='warpAffine nearest')
>>> kwplot.imshow(a.finalize(interpolation='nearest', antialias=False), pnum=pnum_(), title='warpAffine nearest AA=0')
>>> kwplot.imshow(kwimage.imresize(s.finalize(), dsize=a.dsize, interpolation='area'), pnum=pnum_(), title='resize area')
>>> kwplot.imshow(kwimage.imresize(s.finalize(), dsize=a.dsize, interpolation='linear'), pnum=pnum_(), title='resize linear')
>>> kwplot.imshow(kwimage.imresize(s.finalize(), dsize=a.dsize, interpolation='nearest'), pnum=pnum_(), title='resize nearest')
>>> kwplot.imshow(kwimage.imresize(s.finalize(), dsize=a.dsize, interpolation='cubic'), pnum=pnum_(), title='resize cubic')

take_channels(self, channels)¶

kwcoco.util.IndexableWalker[source]¶

kwcoco.util.ensure_json_serializable(dict_, normalize_containers=False, verbose=0)[source]¶

Attempt to convert common types (e.g. numpy) into something json complient

Convert numpy and tuples into lists

Parameters: normalize_containers (bool, default=False) – if True, normalizes dict containers to be standard python structures.

Example

>>> data = ub.ddict(lambda: int)
>>> data['foo'] = ub.ddict(lambda: int)
>>> data['bar'] = np.array([1, 2, 3])
>>> data['foo']['a'] = 1
>>> data['foo']['b'] = (1, np.array([1, 2, 3]), {3: np.int32(3), 4: np.float16(1.0)})
>>> dict_ = data
>>> print(ub.repr2(data, nl=-1))
>>> assert list(find_json_unserializable(data))
>>> result = ensure_json_serializable(data, normalize_containers=True)
>>> print(ub.repr2(result, nl=-1))
>>> assert not list(find_json_unserializable(result))
>>> assert type(result) is dict

kwcoco.util.find_json_unserializable(data, quickcheck=False)[source]¶

Recurse through json datastructure and find any component that causes a serialization error. Record the location of these errors in the datastructure as we recurse through the call tree.

Parameters

data (object) – data that should be json serializable
quickcheck (bool) – if True, check the entire datastructure assuming its ok before doing the python-based recursive logic.

Returns

list of “bad part” dictionaries containing items: ’value’ - the value that caused the serialization error ‘loc’ - which contains a list of key/indexes that can be used

to lookup the location of the unserializable value. If the “loc” is a list, then it indicates a rare case where a key in a dictionary is causing the serialization error.

Return type

List[Dict]

Example

>>> from kwcoco.util.util_json import *  # NOQA
>>> part = ub.ddict(lambda: int)
>>> part['foo'] = ub.ddict(lambda: int)
>>> part['bar'] = np.array([1, 2, 3])
>>> part['foo']['a'] = 1
>>> # Create a dictionary with two unserializable parts
>>> data = [1, 2, {'nest1': [2, part]}, {frozenset({'badkey'}): 3, 2: 4}]
>>> parts = list(find_json_unserializable(data))
>>> print('parts = {}'.format(ub.repr2(parts, nl=1)))
>>> # Check expected structure of bad parts
>>> assert len(parts) == 2
>>> part = parts[1]
>>> assert list(part['loc']) == [2, 'nest1', 1, 'bar']
>>> # We can use the "loc" to find the bad value
>>> for part in parts:
>>>     # "loc" is a list of directions containing which keys/indexes
>>>     # to traverse at each descent into the data structure.
>>>     directions = part['loc']
>>>     curr = data
>>>     special_flag = False
>>>     for key in directions:
>>>         if isinstance(key, list):
>>>             # special case for bad keys
>>>             special_flag = True
>>>             break
>>>         else:
>>>             # normal case for bad values
>>>             curr = curr[key]
>>>     if special_flag:
>>>         assert part['data'] in curr.keys()
>>>         assert part['data'] is key[1]
>>>     else:
>>>         assert part['data'] is curr

kwcoco.util.indexable_allclose(dct1, dct2, return_info=False)[source]¶

Walks through two nested data structures and ensures that everything is roughly the same.

Parameters

dct1 – a nested indexable item
dct2 – a nested indexable item

Example

>>> from kwcoco.util.util_json import indexable_allclose
>>> dct1 = {
>>>     'foo': [1.222222, 1.333],
>>>     'bar': 1,
>>>     'baz': [],
>>> }
>>> dct2 = {
>>>     'foo': [1.22222, 1.333],
>>>     'bar': 1,
>>>     'baz': [],
>>> }
>>> assert indexable_allclose(dct1, dct2)

class kwcoco.util.SupressPrint(*mods, **kw)[source]¶

Bases: object

Temporarily replace the print function in a module with a noop

Parameters

*mods – the modules to disable print in
**kw – only accepts “enabled” enabled (bool, default=True): enables or disables this context

__enter__(self)¶

__exit__(self, a, b, c)¶

class kwcoco.util.StratifiedGroupKFold(n_splits=3, shuffle=False, random_state=None)[source]¶

Bases: sklearn.model_selection._split._BaseKFold

Stratified K-Folds cross-validator with Grouping

Provides train/test indices to split data in train/test sets.

This cross-validation object is a variation of GroupKFold that returns stratified folds. The folds are made by preserving the percentage of samples for each class.

Submodules¶

`kwcoco.main`¶

`kwcoco._helpers`¶

These items were split out of coco_dataset.py which is becoming too big

These are helper data structures used to do things like auto-increment ids, recycle ids, do renaming, extend sortedcontainers etc…

Module Contents¶

Classes¶

`_NextId`	Helper class to tracks unused ids for new items
`_ID_Remapper`	Helper to recycle ids for unions.
`UniqueNameRemapper`	helper to ensure names will be unique by appending suffixes
`SortedSetQuiet`	Sorted set is a sorted mutable set.

Functions¶

_lut_frame_index(imgs, gid)

_delitems(items, remove_idxs, thresh=750)

Parameters

items (List) -- list which will be modified

class kwcoco._helpers._NextId(parent)[source]¶

Bases: object

Helper class to tracks unused ids for new items

_update_unused(self, key)[source]¶: Scans for what the next safe id can be for key

get(self, key)[source]¶: Get the next safe item id for key

class kwcoco._helpers._ID_Remapper(reuse=False)[source]¶

Bases: object

Helper to recycle ids for unions.

For each dataset we create a mapping between each old id and a new id. If possible and reuse=True we allow the new id to match the old id. After each dataset is finished we mark all those ids as used and subsequent new-ids cannot be chosen from that pool.

Parameters: reuse (bool) – if True we are allowed to reuse ids as long as they haven’t been used before.

Example

>>> video_trackids = [[1, 1, 3, 3, 200, 4], [204, 1, 2, 3, 3, 4, 5, 9]]
>>> self = _ID_Remapper(reuse=True)
>>> for tids in video_trackids:
>>>     new_tids = [self.remap(old_tid) for old_tid in tids]
>>>     self.block_seen()
>>>     print('new_tids = {!r}'.format(new_tids))
new_tids = [1, 1, 3, 3, 200, 4]
new_tids = [204, 205, 2, 206, 206, 207, 5, 9]
>>> #
>>> self = _ID_Remapper(reuse=False)
>>> for tids in video_trackids:
>>>     new_tids = [self.remap(old_tid) for old_tid in tids]
>>>     self.block_seen()
>>>     print('new_tids = {!r}'.format(new_tids))
new_tids = [0, 0, 1, 1, 2, 3]
new_tids = [4, 5, 6, 7, 7, 8, 9, 10]

remap(self, old_id)[source]¶: Convert a old-id into a new-id. If self.reuse is True then we will return the same id if it hasn’t been blocked yet.

block_seen(self)[source]¶: Mark all seen ids as unable to be used. Any ids sent to remap will now generate new ids.

next_id(self)[source]¶: Generate a new id that hasnt been used yet

class kwcoco._helpers.UniqueNameRemapper[source]¶

Bases: object

helper to ensure names will be unique by appending suffixes

Example

>>> from kwcoco.coco_dataset import *  # NOQA
>>> self = UniqueNameRemapper()
>>> assert self.remap('foo') == 'foo'
>>> assert self.remap('foo') == 'foo_v001'
>>> assert self.remap('foo') == 'foo_v002'
>>> assert self.remap('foo_v001') == 'foo_v003'

remap(self, name)[source]¶

kwcoco._helpers._lut_frame_index(imgs, gid)[source]¶

class kwcoco._helpers.SortedSetQuiet(iterable=None, key=None)[source]¶

Bases: sortedcontainers.SortedSet

Sorted set is a sorted mutable set.

Sorted set values are maintained in sorted order. The design of sorted set is simple: sorted set uses a set for set-operations and maintains a sorted list of values.

Sorted set values must be hashable and comparable. The hash and total ordering of values must not change while they are stored in the sorted set.

Mutable set methods:

SortedSet.__contains__()
SortedSet.__iter__()
SortedSet.__len__()
SortedSet.add()
SortedSet.discard()

Sequence methods:

SortedSet.__getitem__()
SortedSet.__delitem__()
SortedSet.__reversed__()

Methods for removing values:

SortedSet.clear()
SortedSet.pop()
SortedSet.remove()

Set-operation methods:

SortedSet.difference()
SortedSet.difference_update()
SortedSet.intersection()
SortedSet.intersection_update()
SortedSet.symmetric_difference()
SortedSet.symmetric_difference_update()
SortedSet.union()
SortedSet.update()

Methods for miscellany:

SortedSet.copy()
SortedSet.count()
SortedSet.__repr__()
SortedSet._check()

Sorted list methods available:

SortedList.bisect_left()
SortedList.bisect_right()
SortedList.index()
SortedList.irange()
SortedList.islice()
SortedList._reset()

Additional sorted list methods available, if key-function used:

SortedKeyList.bisect_key_left()
SortedKeyList.bisect_key_right()
SortedKeyList.irange_key()

Sorted set comparisons use subset and superset relations. Two sorted sets are equal if and only if every element of each sorted set is contained in the other (each is a subset of the other). A sorted set is less than another sorted set if and only if the first sorted set is a proper subset of the second sorted set (is a subset, but is not equal). A sorted set is greater than another sorted set if and only if the first sorted set is a proper superset of the second sorted set (is a superset, but is not equal).

__repr__(self)[source]¶

Return string representation of sorted set.

ss.__repr__() <==> repr(ss)

Returns: string representation

kwcoco._helpers._delitems(items, remove_idxs, thresh=750)[source]¶

Parameters

items (List) – list which will be modified
remove_idxs (List[int]) – integers to remove (MUST BE UNIQUE)

`kwcoco.abstract_coco_dataset`¶

Module Contents¶

Classes¶

AbstractCocoDataset

This is a common base for all variants of the Coco Dataset

class kwcoco.abstract_coco_dataset.AbstractCocoDataset[source]¶

Bases: abc.ABC

This is a common base for all variants of the Coco Dataset

At the time of writing there is kwcoco.CocoDataset (which is the dictionary-based backend), and the kwcoco.coco_sql_dataset.CocoSqlDataset, which is experimental.

`kwcoco.category_tree`¶

The category_tree module defines the CategoryTree class, which is used for maintaining flat or hierarchical category information. The kwcoco version of this class only contains the datastructure and does not contain any torch operations. See the ndsampler version for the extension with torch operations.

Module Contents¶

Classes¶

CategoryTree

Wrapper that maintains flat or hierarchical category information.

class kwcoco.category_tree.CategoryTree(graph=None, checks=True)[source]¶

Bases: ubelt.NiceRepr

Wrapper that maintains flat or hierarchical category information.

Helps compute softmaxes and probabilities for tree-based categories where a directed edge (A, B) represents that A is a superclass of B.

Note

There are three basic properties that this object maintains:

node:
    Alphanumeric string names that should be generally descriptive.
    Using spaces and special characters in these names is
    discouraged, but can be done.  This is the COCO category "name"
    attribute.  For categories this may be denoted as (name, node,
    cname, catname).

id:
    The integer id of a category should ideally remain consistent.
    These are often given by a dataset (e.g. a COCO dataset).  This
    is the COCO category "id" attribute. For categories this is
    often denoted as (id, cid).

index:
    Contigous zero-based indices that indexes the list of
    categories.  These should be used for the fastest access in
    backend computation tasks. Typically corresponds to the
    ordering of the channels in the final linear layer in an
    associated model.  For categories this is often denoted as
    (index, cidx, idx, or cx).

Variables

idx_to_node (List[str]) – a list of class names. Implicitly maps from index to category name.
id_to_node (Dict[int, str]) – maps integer ids to category names
node_to_id (Dict[str, int]) – maps category names to ids
node_to_idx (Dict[str, int]) – maps category names to indexes
graph (networkx.Graph) – a Graph that stores any hierarchy information. For standard mutually exclusive classes, this graph is edgeless. Nodes in this graph can maintain category attributes / properties.
idx_groups (List[List[int]]) – groups of category indices that share the same parent category.

Example

>>> from kwcoco.category_tree import *
>>> graph = nx.from_dict_of_lists({
>>>     'background': [],
>>>     'foreground': ['animal'],
>>>     'animal': ['mammal', 'fish', 'insect', 'reptile'],
>>>     'mammal': ['dog', 'cat', 'human', 'zebra'],
>>>     'zebra': ['grevys', 'plains'],
>>>     'grevys': ['fred'],
>>>     'dog': ['boxer', 'beagle', 'golden'],
>>>     'cat': ['maine coon', 'persian', 'sphynx'],
>>>     'reptile': ['bearded dragon', 't-rex'],
>>> }, nx.DiGraph)
>>> self = CategoryTree(graph)
>>> print(self)
<CategoryTree(nNodes=22, maxDepth=6, maxBreadth=4...)>

Example

>>> # The coerce classmethod is the easiest way to create an instance
>>> import kwcoco
>>> kwcoco.CategoryTree.coerce(['a', 'b', 'c'])
<CategoryTree...nNodes=3, nodes=...'a', 'b', 'c'...
>>> kwcoco.CategoryTree.coerce(4)
<CategoryTree...nNodes=4, nodes=...'class_1', 'class_2', 'class_3', ...
>>> kwcoco.CategoryTree.coerce(4)

copy(self)[source]¶

classmethod from_mutex(cls, nodes, bg_hack=True)[source]¶

Parameters: nodes (List[str]) – or a list of class names (in which case they will all be assumed to be mutually exclusive)

Example

>>> print(CategoryTree.from_mutex(['a', 'b', 'c']))
<CategoryTree(nNodes=3, ...)>

classmethod from_json(cls, state)[source]¶

Parameters: state (Dict) – see __getstate__ / __json__ for details

classmethod from_coco(cls, categories)[source]¶

Create a CategoryTree object from coco categories

Parameters: List[Dict] – list of coco-style categories

classmethod coerce(cls, data, **kw)[source]¶

Attempt to coerce data as a CategoryTree object.

This is primarily useful for when the software stack depends on categories being represent

This will work if the input data is a specially formatted json dict, a list of mutually exclusive classes, or if it is already a CategoryTree. Otherwise an error will be thrown.

Parameters

data (object) – a known representation of a category tree.
**kwargs – input type specific arguments

Returns

self

Return type

CategoryTree

Raises

TypeError - if the input format is unknown –
ValueError - if kwargs are not compatible with the input format –

Example

>>> import kwcoco
>>> classes1 = kwcoco.CategoryTree.coerce(3)  # integer
>>> classes2 = kwcoco.CategoryTree.coerce(classes1.__json__())  # graph dict
>>> classes3 = kwcoco.CategoryTree.coerce(['class_1', 'class_2', 'class_3'])  # mutex list
>>> classes4 = kwcoco.CategoryTree.coerce(classes1.graph)  # nx Graph
>>> classes5 = kwcoco.CategoryTree.coerce(classes1)  # cls
>>> # xdoctest: +REQUIRES(module:ndsampler)
>>> import ndsampler
>>> classes6 = ndsampler.CategoryTree.coerce(3)
>>> classes7 = ndsampler.CategoryTree.coerce(classes1)
>>> classes8 = kwcoco.CategoryTree.coerce(classes6)

classmethod demo(cls, key='coco', **kwargs)[source]¶

Parameters: key (str) – specify which demo dataset to use. Can be ‘coco’ (which uses the default coco demo data). Can be ‘btree’ which creates a binary tree and accepts kwargs ‘r’ and ‘h’ for branching-factor and height. Can be ‘btree2’, which is the same as btree but returns strings

CommandLine

xdoctest -m ~/code/kwcoco/kwcoco/category_tree.py CategoryTree.demo

Example

>>> from kwcoco.category_tree import *
>>> self = CategoryTree.demo()
>>> print('self = {}'.format(self))
self = <CategoryTree(nNodes=10, maxDepth=2, maxBreadth=4...)>

to_coco(self)[source]¶

Converts to a coco-style data structure

Yields: Dict – coco category dictionaries

id_to_idx(self)[source]¶

Example

>>> import kwcoco
>>> self = kwcoco.CategoryTree.demo()
>>> self.id_to_idx[1]

idx_to_id(self)[source]¶

Example

>>> import kwcoco
>>> self = kwcoco.CategoryTree.demo()
>>> self.idx_to_id[0]

idx_to_ancestor_idxs(self, include_self=True)[source]¶

Mapping from a class index to its ancestors

Parameters: include_self (bool, default=True) – if True includes each node as its own ancestor.

idx_to_descendants_idxs(self, include_self=False)[source]¶

Mapping from a class index to its descendants (including itself)

Parameters: include_self (bool, default=False) – if True includes each node as its own descendant.

idx_pairwise_distance(self)[source]¶

Get a matrix encoding the distance from one class to another.

Distances

from parents to children are positive (descendants),
from children to parents are negative (ancestors),
between unreachable nodes (wrt to forward and reverse graph) are nan.

__len__(self)[source]¶

__iter__(self)[source]¶

__getitem__(self, index)[source]¶

__contains__(self, node)[source]¶

__json__(self)[source]¶

Example

>>> import pickle
>>> self = CategoryTree.demo()
>>> print('self = {!r}'.format(self.__json__()))

__getstate__(self)[source]¶

Serializes information in this class

Example

>>> from kwcoco.category_tree import *
>>> import pickle
>>> self = CategoryTree.demo()
>>> state = self.__getstate__()
>>> serialization = pickle.dumps(self)
>>> recon = pickle.loads(serialization)
>>> assert recon.__json__() == self.__json__()

__setstate__(self, state)[source]¶

__nice__(self)[source]¶

is_mutex(self)[source]¶

Returns True if all categories are mutually exclusive (i.e. flat)

If true, then the classes may be represented as a simple list of class names without any loss of information, otherwise the underlying category graph is necessary to preserve all knowledge.

Todo

[ ] what happens when we have a dummy root?

property num_classes(self)[source]¶

property class_names(self)[source]¶

property category_names(self)[source]¶

property cats(self)[source]¶

Returns a mapping from category names to category attributes.

If this category tree was constructed from a coco-dataset, then this will contain the coco category attributes.

Returns: Dict[str, Dict[str, object]]

Example

>>> from kwcoco.category_tree import *
>>> self = CategoryTree.demo()
>>> print('self.cats = {!r}'.format(self.cats))

index(self, node)[source]¶: Return the index that corresponds to the category name

_build_index(self)[source]¶: construct lookup tables

show(self)[source]¶

forest_str(self)[source]¶

normalize(self)[source]¶

Applies a normalization scheme to the categories.

Note: this may break other tasks that depend on exact category names.

Returns: CategoryTree

Example

>>> from kwcoco.category_tree import *  # NOQA
>>> import kwcoco
>>> orig = kwcoco.CategoryTree.demo('animals_v1')
>>> self = kwcoco.CategoryTree(nx.relabel_nodes(orig.graph, str.upper))
>>> norm = self.normalize()

`kwcoco.channel_spec`¶

This module defines the KWCOCO Channel Specification and API.

The KWCOCO Channel specification is a way to semantically express how a combination of image channels are grouped. This can specify how these channels (somtimes called bands or features) are arranged on disk or input to an algorithm. The core idea reduces to a Set[List[str]] — or a unordered set of ordered sequences of strings corresponding to channel “names”. The way these are specified is with a “,” to separate lists in an unordered set and with a “|” to separate the channel names. Other syntax exists for convinience, but a strict normalized channel spec only contains these core symbols.

Another way to think of a kwcoco channel spec is that splitting the spec by “,” gives groups of channels that should be processed together and “late-fused”. Within each group the “|” operator “early-fuses” the channels.

For instance, say we had a network and we wanted to process 3-channel rgb images in one stream and 1-channel infrared images in a second stream and then fuse them together. The kwcoco channel specification for channels labled as ‘red’, ‘green’, ‘blue’, and ‘infrared’ would be:

infrared,red|green|blue

Note, it is up to an algorithm to do any early-late fusion. KWCoco simply provides the specification as a tool to quickly access a particular combination of channels from disk.

The ChannelSpec has these simple rules:

* each 1D channel is a alphanumeric string.

* The pipe ('|') separates aligned early fused stremas (non-communative)

* The comma (',') separates late-fused streams, (happens after pipe operations, and is communative)

* Certain common sets of early fused channels have codenames, for example:

    rgb = r|g|b
    rgba = r|g|b|a
    dxdy = dy|dy

* Multiple channels can be specified via a "slice" notation. For example:

    mychan.0:4

    represents 4 channels:
        mychan.0, mychan.1, mychan.2, and mychan.3

    slices after the "." work like python slices

The detailed grammar for the spec is:

?start: stream

// An identifier can contain spaces IDEN: (“_”|LETTER) ("_"|” “|LETTER|DIGIT)*

chan_single : IDEN chan_getitem : IDEN “.” INT chan_getslice_0b : IDEN “:” INT chan_getslice_ab : IDEN “.” INT “:” INT

// A channel code can just be an ID, or it can have a getitem // style syntax with a scalar or slice as an argument chan_code : chan_single | chan_getslice_0b | chan_getslice_ab | chan_getitem

// Fused channels are an ordered sequence of channel codes (without sensors) fused : chan_code (“|” chan_code)*

// Channels can be specified in a sequence but must contain parens fused_seq : “(” fused (“,” fused)* “)”

channel_rhs : fused | fused_seq

stream : channel_rhs (“,” channel_rhs)*

%import common.DIGIT %import common.LETTER %import common.INT

Note that a stream refers to a the full ChannelSpec and fused refers to FusedChannelSpec.

For single arrays, the spec is always an early fused spec.

Todo

[X] : normalize representations? e.g: rgb = r|g|b? - OPTIONAL
[X] : rename to BandsSpec or SensorSpec? - REJECTED
[ ] : allow bands to be coerced, i.e. rgb -> gray, or gray->rgb

Todo

[x]: Use FusedChannelSpec as a member of ChannelSpec
[x]: Handle special slice suffix for length calculations

Todo

Let S1 = sensor 1 Let S2 = sensor 2 Let S3 = sensor 3

Using a <sensor>:<pure_fused_channel_spec> indicates that the fused channels belong to that sensor.

For example:

S1:red|green|blue S1:B.0:3 = S1:C.0|C.1|C.2

To specify that R|G|B channels exist in sensor 1 and sensor 2 you could do:

S1:R|G|B,S2:R|G|B

or the more concise syntax allows for a distributive law

(S1,S2):R|G|B

Notice, how the “R|G|B” channel code is distributed over the “,” in the parenthesis.

Note

do not specify the same channel in FusedChannelSpec twice

Example

>>> import kwcoco
>>> spec = kwcoco.ChannelSpec('b1|b2|b3,m.0:4|x1|x2,x.3|x.4|x.5')
>>> print(spec)
<ChannelSpec(b1|b2|b3,m.0:4|x1|x2,x.3|x.4|x.5)>
>>> for stream in spec.streams():
>>>     print(stream)
<FusedChannelSpec(b1|b2|b3)>
<FusedChannelSpec(m.0:4|x1|x2)>
<FusedChannelSpec(x.3|x.4|x.5)>
>>> # Normalization
>>> normalized = spec.normalize()
>>> print(normalized)
<ChannelSpec(b1|b2|b3,m.0|m.1|m.2|m.3|x1|x2,x.3|x.4|x.5)>
>>> print(normalized.fuse().spec)
b1|b2|b3|m.0|m.1|m.2|m.3|x1|x2|x.3|x.4|x.5
>>> print(normalized.fuse().concise().spec)
b1|b2|b3|m:4|x1|x2|x.3:6

Module Contents¶

Classes¶

`BaseChannelSpec`	Common code API between `FusedChannelSpec` and `ChannelSpec`
`FusedChannelSpec`	A specific type of channel spec with only one early fused stream.
`ChannelSpec`	Parse and extract information about network input channel specs for

Functions¶

`_cached_single_fused_mapping`(item_keys, parsed_items, axis=0)
`_cached_single_stream_idxs`(key, axis=0)
`subsequence_index`(oset1, oset2)	Returns a slice into the first items indicating the position of
`_parse_concise_slice_syntax`(v)	Helper for our slice syntax, which is may be a bit strange
`oset_insert`(self, index, obj)
`oset_delitem`(self, index)	for ubelt oset, todo contribute back to luminosoinsight

class kwcoco.channel_spec.BaseChannelSpec[source]¶

Bases: ubelt.NiceRepr

Common code API between FusedChannelSpec and ChannelSpec

Todo

[ ] Keep working on this base spec and ensure the inheriting classes
conform to it.

property spec(self)[source]¶

The string encodeing of this spec

Returns: str

abstract classmethod coerce(cls, data)[source]¶

Try and interpret the input data as some sort of spec

Parameters: data (str | int | list | dict | BaseChannelSpec) – any input data that is known to represent a spec
Returns: BaseChannelSpec

abstract streams(self)[source]¶

Breakup this spec into individual early-fused components

Returns: List[FusedChannelSpec]

abstract normalize(self)[source]¶

Expand all channel codes into their normalized long-form

Returns: BaseChannelSpec

abstract intersection(self, other)[source]¶

abstract union(self, other)[source]¶

abstract difference(self)[source]¶

abstract issubset(self, other)[source]¶

abstract issuperset(self, other)[source]¶

__sub__(self, other)[source]¶

__nice__(self)[source]¶

__json__(self)[source]¶

__and__(self, other)[source]¶

__or__(self, other)[source]¶

path_sanitize(self, maxlen=None)[source]¶

Clean up the channel spec so it can be used in a pathname.

Parameters: maxlen (int) – if specified, and the name is longer than this length, it is shortened. Must be 8 or greater.
Returns: path suitable for usage in a filename
Return type: str

Note

This mapping is not invertible and should not be relied on to reconstruct the path spec. This is only a convenience.

Example

>>> import kwcoco
>>> print(kwcoco.FusedChannelSpec.coerce('a chan with space|bar|baz').path_sanitize())
a chan with space_bar_baz
>>> print(kwcoco.ChannelSpec.coerce('foo|bar|baz,biz').path_sanitize())
foo_bar_baz,biz

Example

>>> import kwcoco
>>> print(kwcoco.ChannelSpec.coerce('foo.0:3').normalize().path_sanitize(24))
foo.0_foo.1_foo.2
>>> print(kwcoco.ChannelSpec.coerce('foo.0:256').normalize().path_sanitize(24))
tuuxtfnrsvdhezkdndysxo_256

class kwcoco.channel_spec.FusedChannelSpec(parsed, _is_normalized=False)[source]¶

Bases: BaseChannelSpec

A specific type of channel spec with only one early fused stream.

The channels in this stream are non-communative

Behaves like a list of atomic-channel codes (which may represent more than 1 channel), normalized codes always represent exactly 1 channel.

Note

This class name and API is in flux and subject to change.

Todo

A special code indicating a name and some number of bands that that names contains, this would primarilly be used for large numbers of channels produced by a network. Like:

resnet_d35d060_L5:512

or

resnet_d35d060_L5[:512]

might refer to a very specific (hashed) set of resnet parameters with 512 bands

maybe we can do something slicly like:

resnet_d35d060_L5[A:B] resnet_d35d060_L5:A:B

Do we want to “just store the code” and allow for parsing later?

Or do we want to ensure the serialization is parsed before we construct the data structure?

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> import pickle
>>> self = FusedChannelSpec.coerce(3)
>>> recon = pickle.loads(pickle.dumps(self))
>>> self = ChannelSpec.coerce('a|b,c|d')
>>> recon = pickle.loads(pickle.dumps(self))

_alias_lut[source]¶

_memo[source]¶

_size_lut[source]¶

__len__(self)[source]¶

__getitem__(self, index)[source]¶

classmethod concat(cls, items)[source]¶

spec(self)[source]¶

The string encodeing of this spec

Returns: str

unique(self)[source]¶

classmethod parse(cls, spec)[source]¶

classmethod coerce(cls, data)[source]¶

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> FusedChannelSpec.coerce(['a', 'b', 'c'])
>>> FusedChannelSpec.coerce('a|b|c')
>>> FusedChannelSpec.coerce(3)
>>> FusedChannelSpec.coerce(FusedChannelSpec(['a']))
>>> assert FusedChannelSpec.coerce('').numel() == 0

concise(self)[source]¶

Shorted the channel spec by de-normaliz slice syntax

Returns: concise spec
Return type: FusedChannelSpec

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> self = FusedChannelSpec.coerce(
>>>      'b|a|a.0|a.1|a.2|a.5|c|a.8|a.9|b.0:3|c.0')
>>> short = self.concise()
>>> long = short.normalize()
>>> numels = [c.numel() for c in [self, short, long]]
>>> print('self.spec  = {!r}'.format(self.spec))
>>> print('short.spec = {!r}'.format(short.spec))
>>> print('long.spec  = {!r}'.format(long.spec))
>>> print('numels = {!r}'.format(numels))
self.spec  = 'b|a|a.0|a.1|a.2|a.5|c|a.8|a.9|b.0:3|c.0'
short.spec = 'b|a|a:3|a.5|c|a.8:10|b:3|c.0'
long.spec  = 'b|a|a.0|a.1|a.2|a.5|c|a.8|a.9|b.0|b.1|b.2|c.0'
numels = [13, 13, 13]
>>> assert long.concise().spec == short.spec

normalize(self)[source]¶

Replace aliases with explicit single-band-per-code specs

Returns: normalize spec
Return type: FusedChannelSpec

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> self = FusedChannelSpec.coerce('b1|b2|b3|rgb')
>>> normed = self.normalize()
>>> print('self = {}'.format(self))
>>> print('normed = {}'.format(normed))
self = <FusedChannelSpec(b1|b2|b3|rgb)>
normed = <FusedChannelSpec(b1|b2|b3|r|g|b)>
>>> self = FusedChannelSpec.coerce('B:1:11')
>>> normed = self.normalize()
>>> print('self = {}'.format(self))
>>> print('normed = {}'.format(normed))
self = <FusedChannelSpec(B:1:11)>
normed = <FusedChannelSpec(B.1|B.2|B.3|B.4|B.5|B.6|B.7|B.8|B.9|B.10)>
>>> self = FusedChannelSpec.coerce('B.1:11')
>>> normed = self.normalize()
>>> print('self = {}'.format(self))
>>> print('normed = {}'.format(normed))
self = <FusedChannelSpec(B.1:11)>
normed = <FusedChannelSpec(B.1|B.2|B.3|B.4|B.5|B.6|B.7|B.8|B.9|B.10)>

numel(self)[source]¶: Total number of channels in this spec

sizes(self)[source]¶

Returns a list indicating the size of each atomic code

Returns: List[int]

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> self = FusedChannelSpec.coerce('b1|Z:3|b2|b3|rgb')
>>> self.sizes()
[1, 3, 1, 1, 3]
>>> assert(FusedChannelSpec.parse('a.0').numel()) == 1
>>> assert(FusedChannelSpec.parse('a:0').numel()) == 0
>>> assert(FusedChannelSpec.parse('a:1').numel()) == 1

__contains__(self, key)[source]¶

Example

>>> FCS = FusedChannelSpec.coerce
>>> 'disparity' in FCS('rgb|disparity|flowx|flowy')
True
>>> 'gray' in FCS('rgb|disparity|flowx|flowy')
False

code_list(self)[source]¶: Return the expanded code list

as_list(self)[source]¶

as_oset(self)[source]¶

as_set(self)[source]¶

as_path(self)[source]¶

Returns a string suitable for use in a path.

Note, this may no longer be a valid channel spec

__set__(self)[source]¶

difference(self, other)[source]¶

Set difference

Example

>>> FCS = FusedChannelSpec.coerce
>>> self = FCS('rgb|disparity|flowx|flowy')
>>> other = FCS('r|b')
>>> self.difference(other)
>>> other = FCS('flowx')
>>> self.difference(other)
>>> FCS = FusedChannelSpec.coerce
>>> assert len((FCS('a') - {'a'}).parsed) == 0
>>> assert len((FCS('a.0:3') - {'a.0'}).parsed) == 2

intersection(self, other)[source]¶

Example

>>> FCS = FusedChannelSpec.coerce
>>> self = FCS('rgb|disparity|flowx|flowy')
>>> other = FCS('r|b|XX')
>>> self.intersection(other)

union(self, other)[source]¶

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> FCS = FusedChannelSpec.coerce
>>> self = FCS('rgb|disparity|flowx|flowy')
>>> other = FCS('r|b|XX')
>>> self.union(other)

issubset(self, other)[source]¶

issuperset(self, other)[source]¶

component_indices(self, axis=2)[source]¶

Look up component indices within this stream

Example

>>> FCS = FusedChannelSpec.coerce
>>> self = FCS('disparity|rgb|flowx|flowy')
>>> component_indices = self.component_indices()
>>> print('component_indices = {}'.format(ub.repr2(component_indices, nl=1)))
component_indices = {
    'disparity': (slice(...), slice(...), slice(0, 1, None)),
    'flowx': (slice(...), slice(...), slice(4, 5, None)),
    'flowy': (slice(...), slice(...), slice(5, 6, None)),
    'rgb': (slice(...), slice(...), slice(1, 4, None)),
}

streams(self)[source]¶: Idempotence with ChannelSpec.streams()

fuse(self)[source]¶: Idempotence with ChannelSpec.streams()

class kwcoco.channel_spec.ChannelSpec(spec, parsed=None)[source]¶

Bases: BaseChannelSpec

Parse and extract information about network input channel specs for early or late fusion networks.

Behaves like a dictionary of FusedChannelSpec objects

Todo

[ ] Rename to something that indicates this is a collection of
FusedChannelSpec? MultiChannelSpec?

Note

This class name and API is in flux and subject to change.

Note

The pipe (‘|’) character represents an early-fused input stream, and order matters (it is non-communative).

The comma (‘,’) character separates different inputs streams/branches for a multi-stream/branch network which will be lated fused. Order does not matter

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> # Integer spec
>>> ChannelSpec.coerce(3)
<ChannelSpec(u0|u1|u2) ...>

>>> # single mode spec
>>> ChannelSpec.coerce('rgb')
<ChannelSpec(rgb) ...>

>>> # early fused input spec
>>> ChannelSpec.coerce('rgb|disprity')
<ChannelSpec(rgb|disprity) ...>

>>> # late fused input spec
>>> ChannelSpec.coerce('rgb,disprity')
<ChannelSpec(rgb,disprity) ...>

>>> # early and late fused input spec
>>> ChannelSpec.coerce('rgb|ir,disprity')
<ChannelSpec(rgb|ir,disprity) ...>

Example

>>> self = ChannelSpec('gray')
>>> print('self.info = {}'.format(ub.repr2(self.info, nl=1)))
>>> self = ChannelSpec('rgb')
>>> print('self.info = {}'.format(ub.repr2(self.info, nl=1)))
>>> self = ChannelSpec('rgb|disparity')
>>> print('self.info = {}'.format(ub.repr2(self.info, nl=1)))
>>> self = ChannelSpec('rgb|disparity,disparity')
>>> print('self.info = {}'.format(ub.repr2(self.info, nl=1)))
>>> self = ChannelSpec('rgb,disparity,flowx|flowy')
>>> print('self.info = {}'.format(ub.repr2(self.info, nl=1)))

Example

>>> specs = [
>>>     'rgb',              # and rgb input
>>>     'rgb|disprity',     # rgb early fused with disparity
>>>     'rgb,disprity',     # rgb early late with disparity
>>>     'rgb|ir,disprity',  # rgb early fused with ir and late fused with disparity
>>>     3,                  # 3 unknown channels
>>> ]
>>> for spec in specs:
>>>     print('=======================')
>>>     print('spec = {!r}'.format(spec))
>>>     #
>>>     self = ChannelSpec.coerce(spec)
>>>     print('self = {!r}'.format(self))
>>>     sizes = self.sizes()
>>>     print('sizes = {!r}'.format(sizes))
>>>     print('self.info = {}'.format(ub.repr2(self.info, nl=1)))
>>>     #
>>>     item = self._demo_item((1, 1), rng=0)
>>>     inputs = self.encode(item)
>>>     components = self.decode(inputs)
>>>     input_shapes = ub.map_vals(lambda x: x.shape, inputs)
>>>     component_shapes = ub.map_vals(lambda x: x.shape, components)
>>>     print('item = {}'.format(ub.repr2(item, precision=1)))
>>>     print('inputs = {}'.format(ub.repr2(inputs, precision=1)))
>>>     print('input_shapes = {}'.format(ub.repr2(input_shapes)))
>>>     print('components = {}'.format(ub.repr2(components, precision=1)))
>>>     print('component_shapes = {}'.format(ub.repr2(component_shapes, nl=1)))

property spec(self)[source]¶

The string encodeing of this spec

Returns: str

__contains__(self, key)[source]¶

Example

>>> 'disparity' in ChannelSpec('rgb,disparity,flowx|flowy')
True
>>> 'gray' in ChannelSpec('rgb,disparity,flowx|flowy')
False

property info(self)[source]¶

classmethod coerce(cls, data)[source]¶

Attempt to interpret the data as a channel specification

Returns: ChannelSpec

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> data = FusedChannelSpec.coerce(3)
>>> assert ChannelSpec.coerce(data).spec == 'u0|u1|u2'
>>> data = ChannelSpec.coerce(3)
>>> assert data.spec == 'u0|u1|u2'
>>> assert ChannelSpec.coerce(data).spec == 'u0|u1|u2'
>>> data = ChannelSpec.coerce('u:3')
>>> assert data.normalize().spec == 'u.0|u.1|u.2'

parse(self)[source]¶

Build internal representation

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> self = ChannelSpec('b1|b2|b3|rgb,B:3')
>>> print(self.parse())
>>> print(self.normalize().parse())
>>> ChannelSpec('').parse()

Example

>>> base = ChannelSpec('rgb|disparity,flowx|r|flowy')
>>> other = ChannelSpec('rgb')
>>> self = base.intersection(other)
>>> assert self.numel() == 4

concise(self)[source]¶

Example

>>> self = ChannelSpec('b1|b2,b3|rgb|B.0,B.1|B.2')
>>> print(self.concise().spec)
b1|b2,b3|r|g|b|B.0,B.1:3

normalize(self)[source]¶

Replace aliases with explicit single-band-per-code specs

Returns: normalized spec
Return type: ChannelSpec

Example

>>> self = ChannelSpec('b1|b2,b3|rgb,B:3')
>>> normed = self.normalize()
>>> print('self   = {}'.format(self))
>>> print('normed = {}'.format(normed))
self   = <ChannelSpec(b1|b2,b3|rgb,B:3)>
normed = <ChannelSpec(b1|b2,b3|r|g|b,B.0|B.1|B.2)>

keys(self)[source]¶

values(self)[source]¶

items(self)[source]¶

fuse(self)[source]¶

Fuse all parts into an early fused channel spec

Returns: FusedChannelSpec

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> self = ChannelSpec.coerce('b1|b2,b3|rgb,B:3')
>>> fused = self.fuse()
>>> print('self  = {}'.format(self))
>>> print('fused = {}'.format(fused))
self  = <ChannelSpec(b1|b2,b3|rgb,B:3)>
fused = <FusedChannelSpec(b1|b2|b3|rgb|B:3)>

streams(self)[source]¶

Breaks this spec up into one spec for each early-fused input stream

Example

self = ChannelSpec.coerce(‘r|g,B1|B2,fx|fy’) list(map(len, self.streams()))

code_list(self)[source]¶

as_path(self)[source]¶

Returns a string suitable for use in a path.

Note, this may no longer be a valid channel spec

difference(self, other)[source]¶

Set difference. Remove all instances of other channels from this set of channels.

Example

>>> from kwcoco.channel_spec import *
>>> self = ChannelSpec('rgb|disparity,flowx|r|flowy')
>>> other = ChannelSpec('rgb')
>>> print(self.difference(other))
>>> other = ChannelSpec('flowx')
>>> print(self.difference(other))
<ChannelSpec(disparity,flowx|flowy)>
<ChannelSpec(r|g|b|disparity,r|flowy)>

Example

>>> from kwcoco.channel_spec import *
>>> self = ChannelSpec('a|b,c|d')
>>> new = self - {'a', 'b'}
>>> len(new.sizes()) == 1
>>> empty = new - 'c|d'
>>> assert empty.numel() == 0

intersection(self, other)[source]¶

Set difference. Remove all instances of other channels from this set of channels.

Example

>>> from kwcoco.channel_spec import *
>>> self = ChannelSpec('rgb|disparity,flowx|r|flowy')
>>> other = ChannelSpec('rgb')
>>> new = self.intersection(other)
>>> print(new)
>>> print(new.numel())
>>> other = ChannelSpec('flowx')
>>> new = self.intersection(other)
>>> print(new)
>>> print(new.numel())
<ChannelSpec(r|g|b,r)>
4
<ChannelSpec(flowx)>
1

union(self, other)[source]¶

Union simply tags on a second channel spec onto this one. Duplicates are maintained.

Example

>>> from kwcoco.channel_spec import *
>>> self = ChannelSpec('rgb|disparity,flowx|r|flowy')
>>> other = ChannelSpec('rgb')
>>> new = self.union(other)
>>> print(new)
>>> print(new.numel())
>>> other = ChannelSpec('flowx')
>>> new = self.union(other)
>>> print(new)
>>> print(new.numel())
<ChannelSpec(r|g|b|disparity,flowx|r|flowy,r|g|b)>
10
<ChannelSpec(r|g|b|disparity,flowx|r|flowy,flowx)>
8

abstract issubset(self, other)[source]¶

abstract issuperset(self, other)[source]¶

numel(self)[source]¶: Total number of channels in this spec

sizes(self)[source]¶

Number of dimensions for each fused stream channel

IE: The EARLY-FUSED channel sizes

Example

>>> self = ChannelSpec('rgb|disparity,flowx|flowy,B:10')
>>> self.normalize().concise()
>>> self.sizes()

unique(self, normalize=False)[source]¶: Returns the unique channels that will need to be given or loaded

_item_shapes(self, dims)[source]¶

Expected shape for an input item

Parameters: dims (Tuple[int, int]) – the spatial dimension
Returns: Dict[int, tuple]

_demo_item(self, dims=(4, 4), rng=None)[source]¶

Create an input that satisfies this spec

Returns

an item like it might appear when its returned from the: __getitem__ method of a torch...Dataset.

Return type

Example

>>> dims = (1, 1)
>>> ChannelSpec.coerce(3)._demo_item(dims, rng=0)
>>> ChannelSpec.coerce('r|g|b|disaprity')._demo_item(dims, rng=0)
>>> ChannelSpec.coerce('rgb|disaprity')._demo_item(dims, rng=0)
>>> ChannelSpec.coerce('rgb,disaprity')._demo_item(dims, rng=0)
>>> ChannelSpec.coerce('rgb')._demo_item(dims, rng=0)
>>> ChannelSpec.coerce('gray')._demo_item(dims, rng=0)

encode(self, item, axis=0, mode=1)[source]¶

Given a dictionary containing preloaded components of the network inputs, build a concatenated (fused) network representations of each input stream.

Parameters

item (Dict[str, Tensor]) – a batch item containing unfused parts. each key should be a single-stream (optionally early fused) channel key.
axis (int, default=0) – concatenation dimension

Returns

mapping between input stream and its early fused tensor input.

Return type

Dict[str, Tensor]

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> import numpy as np
>>> dims = (4, 4)
>>> item = {
>>>     'rgb': np.random.rand(3, *dims),
>>>     'disparity': np.random.rand(1, *dims),
>>>     'flowx': np.random.rand(1, *dims),
>>>     'flowy': np.random.rand(1, *dims),
>>> }
>>> # Complex Case
>>> self = ChannelSpec('rgb,disparity,rgb|disparity|flowx|flowy,flowx|flowy')
>>> fused = self.encode(item)
>>> input_shapes = ub.map_vals(lambda x: x.shape, fused)
>>> print('input_shapes = {}'.format(ub.repr2(input_shapes, nl=1)))
>>> # Simpler case
>>> self = ChannelSpec('rgb|disparity')
>>> fused = self.encode(item)
>>> input_shapes = ub.map_vals(lambda x: x.shape, fused)
>>> print('input_shapes = {}'.format(ub.repr2(input_shapes, nl=1)))

Example

>>> # Case where we have to break up early fused data
>>> import numpy as np
>>> dims = (40, 40)
>>> item = {
>>>     'rgb|disparity': np.random.rand(4, *dims),
>>>     'flowx': np.random.rand(1, *dims),
>>>     'flowy': np.random.rand(1, *dims),
>>> }
>>> # Complex Case
>>> self = ChannelSpec('rgb,disparity,rgb|disparity,rgb|disparity|flowx|flowy,flowx|flowy,flowx,disparity')
>>> inputs = self.encode(item)
>>> input_shapes = ub.map_vals(lambda x: x.shape, inputs)
>>> print('input_shapes = {}'.format(ub.repr2(input_shapes, nl=1)))

>>> # xdoctest: +REQUIRES(--bench)
>>> #self = ChannelSpec('rgb|disparity,flowx|flowy')
>>> import timerit
>>> ti = timerit.Timerit(100, bestof=10, verbose=2)
>>> for timer in ti.reset('mode=simple'):
>>>     with timer:
>>>         inputs = self.encode(item, mode=0)
>>> for timer in ti.reset('mode=minimize-concat'):
>>>     with timer:
>>>         inputs = self.encode(item, mode=1)

decode(self, inputs, axis=1)[source]¶

break an early fused item into its components

Parameters

inputs (Dict[str, Tensor]) – dictionary of components
axis (int, default=1) – channel dimension

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> import numpy as np
>>> dims = (4, 4)
>>> item_components = {
>>>     'rgb': np.random.rand(3, *dims),
>>>     'ir': np.random.rand(1, *dims),
>>> }
>>> self = ChannelSpec('rgb|ir')
>>> item_encoded = self.encode(item_components)
>>> batch = {k: np.concatenate([v[None, :], v[None, :]], axis=0)
...          for k, v in item_encoded.items()}
>>> components = self.decode(batch)

Example

>>> # xdoctest: +REQUIRES(module:netharn, module:torch)
>>> import torch
>>> import numpy as np
>>> dims = (4, 4)
>>> components = {
>>>     'rgb': np.random.rand(3, *dims),
>>>     'ir': np.random.rand(1, *dims),
>>> }
>>> components = ub.map_vals(torch.from_numpy, components)
>>> self = ChannelSpec('rgb|ir')
>>> encoded = self.encode(components)
>>> from netharn.data import data_containers
>>> item = {k: data_containers.ItemContainer(v, stack=True)
>>>         for k, v in encoded.items()}
>>> batch = data_containers.container_collate([item, item])
>>> components = self.decode(batch)

component_indices(self, axis=2)[source]¶

Look up component indices within fused streams

Example

>>> dims = (4, 4)
>>> inputs = ['flowx', 'flowy', 'disparity']
>>> self = ChannelSpec('disparity,flowx|flowy')
>>> component_indices = self.component_indices()
>>> print('component_indices = {}'.format(ub.repr2(component_indices, nl=1)))
component_indices = {
    'disparity': ('disparity', (slice(None, None, None), slice(None, None, None), slice(0, 1, None))),
    'flowx': ('flowx|flowy', (slice(None, None, None), slice(None, None, None), slice(0, 1, None))),
    'flowy': ('flowx|flowy', (slice(None, None, None), slice(None, None, None), slice(1, 2, None))),
}

kwcoco.channel_spec._cached_single_fused_mapping(item_keys, parsed_items, axis=0)[source]¶

kwcoco.channel_spec._cached_single_stream_idxs(key, axis=0)[source]¶

kwcoco.channel_spec.subsequence_index(oset1, oset2)[source]¶

Returns a slice into the first items indicating the position of the second items if they exist.

This is a variant of the substring problem.

Returns: None | slice

Example

>>> oset1 = ub.oset([1, 2, 3, 4, 5, 6])
>>> oset2 = ub.oset([2, 3, 4])
>>> index = subsequence_index(oset1, oset2)
>>> assert index

>>> oset1 = ub.oset([1, 2, 3, 4, 5, 6])
>>> oset2 = ub.oset([2, 4, 3])
>>> index = subsequence_index(oset1, oset2)
>>> assert not index

kwcoco.channel_spec._parse_concise_slice_syntax(v)[source]¶

Helper for our slice syntax, which is may be a bit strange

Example

>>> print(_parse_concise_slice_syntax('B:10'))
>>> print(_parse_concise_slice_syntax('B.0:10:3'))
>>> print(_parse_concise_slice_syntax('B.:10:3'))
>>> print(_parse_concise_slice_syntax('B::10:3'))
>>> # Careful, this next one is quite different
>>> print(_parse_concise_slice_syntax('B:10:3'))
>>> print(_parse_concise_slice_syntax('B:3:10:3'))
>>> print(_parse_concise_slice_syntax('B.:10'))
>>> print(_parse_concise_slice_syntax('B.:3:'))
>>> print(_parse_concise_slice_syntax('B.:3:2'))
>>> print(_parse_concise_slice_syntax('B::2:3'))
>>> print(_parse_concise_slice_syntax('B.0:10:3'))
>>> print(_parse_concise_slice_syntax('B.:10:3'))
('B', 0, 10, 1)
('B', 0, 10, 3)
('B', 0, 10, 3)
('B', 0, 10, 3)
('B', 10, 3, 1)
('B', 3, 10, 3)
('B', 0, 10, 1)
('B', 0, 3, 1)
('B', 0, 3, 2)
('B', 0, 2, 3)
('B', 0, 10, 3)
('B', 0, 10, 3)
>>> import pytest
>>> with pytest.raises(ValueError):
>>>     _parse_concise_slice_syntax('B.0')
>>> with pytest.raises(ValueError):
>>>     _parse_concise_slice_syntax('B0')
>>> with pytest.raises(ValueError):
>>>     _parse_concise_slice_syntax('B:')
>>> with pytest.raises(ValueError):
>>>     _parse_concise_slice_syntax('B:0.10')
>>> with pytest.raises(ValueError):
>>>     _parse_concise_slice_syntax('B.::')

kwcoco.channel_spec.oset_insert(self, index, obj)[source]¶

kwcoco.channel_spec.oset_delitem(self, index)[source]¶

for ubelt oset, todo contribute back to luminosoinsight

>>> self = ub.oset([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> index = slice(3, 5)
>>> oset_delitem(self, index)

`kwcoco.coco_dataset`¶

An implementation and extension of the original MS-COCO API 1.

Extends the format to also include line annotations.

The following describes psuedo-code for the high level spec (some of which may not be have full support in the Python API). A formal json-schema is defined in kwcoco.coco_schema.

An informal spec is as follows:

# All object categories are defined here.
category = {
    'id': int,
    'name': str,  # unique name of the category
    'supercategory': str,   # parent category name
}

# Videos are used to manage collections or sequences of images.
# Frames do not necesarilly have to be aligned or uniform time steps
video = {
    'id': int,
    'name': str,  # a unique name for this video.

    'width': int  # the base width of this video (all associated images must have this width)
    'height': int  # the base height of this video (all associated images must have this height)

    # In the future this may be extended to allow pointing to video files
}

# Specifies how to find sensor data of a particular scene at a particular
# time. This is usually paths to rgb images, but auxiliary information
# can be used to specify multiple bands / etc...

# NOTE: in the future we will transition from calling these auxiliary items
# to calling these asset items. As such the key will change from
# "auxiliary" to "asset". The API will be updated to maintain backwards
# compatibility while this transition occurs.

image = {
    'id': int,

    'name': str,  # an encouraged but optional unique name
    'file_name': str,  # relative path to the "base" image data

    'width': int,   # pixel width of "base" image
    'height': int,  # pixel height of "base" image

    'channels': <ChannelSpec>,   # a string encoding of the channels in the main image

    'auxiliary': [  # information about any auxiliary channels / bands
        {
            'file_name': str,     # relative path to associated file
            'channels': <ChannelSpec>,   # a string encoding
            'width':     <int>    # pixel width of image asset
            'height':    <int>    # pixel height of image asset
            'warp_aux_to_img': <TransformSpec>,  # tranform from "base" image space to auxiliary/asset space. (identity if unspecified)
            'quantization': <QuantizationSpec>,  # indicates that the underlying data was quantized
        }, ...
    ]

    'video_id': str  # if this image is a frame in a video sequence, this id is shared by all frames in that sequence.
    'timestamp': str | int  # a iso-string timestamp or an integer in flicks.
    'frame_index': int  # ordinal frame index which can be used if timestamp is unknown.
    'warp_img_to_vid': <TransformSpec>  # a transform image space to video space (identity if unspecified), can be used for sensor alignment or video stabilization
}

TransformSpec:
    The spec can be anything coercable to a kwimage.Affine object.
    This can be an explicit affine transform matrix like:
        {'type': 'affine': 'matrix': <a-3x3 matrix>},

    But it can also be a concise dict containing one or more of these keys
        {
            'scale': <float|Tuple[float, float]>,
            'offset': <float|Tuple[float, float]>,
            'skew': <float>,
            'theta': <float>,  # radians counter-clock-wise
        }

ChannelSpec:
    This is a string that describes the channel composition of an image.
    For the purposes of kwcoco, separate different channel names with a
    pipe ('|'). If the spec is not specified, methods may fall back on
    grayscale or rgb processing. There are special string. For instance
    'rgb' will expand into 'r|g|b'. In other applications you can "late
    fuse" inputs by separating them with a "," and "early fuse" by
    separating with a "|". Early fusion returns a solid array/tensor, late
    fusion returns separated arrays/tensors.

QuantizationSpec:
    This is a dictionary of the form:
        {
            'orig_min': <float>, # min original intensity
            'orig_max': <float>, # min original intensity
            'quant_min': <int>, # min quantized intensity
            'quant_max': <int>, # max quantized intensity
            'nodata': <int|None>,  # integer value to interpret as nan
        }

# Ground truth is specified as annotations, each belongs to a spatial
# region in an image. This must reference a subregion of the image in pixel
# coordinates. Additional non-schma properties can be specified to track
# location in other coordinate systems. Annotations can be linked over time
# by specifying track-ids.
annotation = {
    'id': int,
    'image_id': int,
    'category_id': int,

    'track_id': <int | str | uuid>  # indicates association between annotations across images

    'bbox': [tl_x, tl_y, w, h],  # xywh format)
    'score' : float,
    'prob' : List[float],
    'weight' : float,

    'caption': str,  # a text caption for this annotation
    'keypoints' : <Keypoints | List[int] > # an accepted keypoint format
    'segmentation': <RunLengthEncoding | Polygon | MaskPath | WKT >,  # an accepted segmentation format
}

# A dataset bundles a manifest of all aformentioned data into one structure.
dataset = {
    'categories': [category, ...],
    'videos': [video, ...]
    'images': [image, ...]
    'annotations': [annotation, ...]
    'licenses': [],
    'info': [],
}

Polygon:
    A flattned list of xy coordinates.
    [x1, y1, x2, y2, ..., xn, yn]

    or a list of flattned list of xy coordinates if the CCs are disjoint
    [[x1, y1, x2, y2, ..., xn, yn], [x1, y1, ..., xm, ym],]

    Note: the original coco spec does not allow for holes in polygons.

    We also allow a non-standard dictionary encoding of polygons
        {'exterior': [(x1, y1)...],
         'interiors': [[(x1, y1), ...], ...]}

    TODO: Support WTK

RunLengthEncoding:
    The RLE can be in a special bytes encoding or in a binary array
    encoding. We reuse the original C functions are in [2]_ in
    ``kwimage.structs.Mask`` to provide a convinient way to abstract this
    rather esoteric bytes encoding.

    For pure python implementations see kwimage:
        Converting from an image to RLE can be done via kwimage.run_length_encoding
        Converting from RLE back to an image can be done via:
            kwimage.decode_run_length

        For compatibility with the COCO specs ensure the binary flags
        for these functions are set to true.

Keypoints:
    Annotation keypoints may also be specified in this non-standard (but
    ultimately more general) way:

    'annotations': [
        {
            'keypoints': [
                {
                    'xy': <x1, y1>,
                    'visible': <0 or 1 or 2>,
                    'keypoint_category_id': <kp_cid>,
                    'keypoint_category': <kp_name, optional>,  # this can be specified instead of an id
                }, ...
            ]
        }, ...
    ],
    'keypoint_categories': [{
        'name': <str>,
        'id': <int>,  # an id for this keypoint category
        'supercategory': <kp_name>  # name of coarser parent keypoint class (for hierarchical keypoints)
        'reflection_id': <kp_cid>  # specify only if the keypoint id would be swapped with another keypoint type
    },...
    ]

    In this scheme the "keypoints" property of each annotation (which used
    to be a list of floats) is now specified as a list of dictionaries that
    specify each keypoints location, id, and visibility explicitly. This
    allows for things like non-unique keypoints, partial keypoint
    annotations. This also removes the ordering requirement, which makes it
    simpler to keep track of each keypoints class type.

    We also have a new top-level dictionary to specify all the possible
    keypoint categories.

    TODO: Support WTK

Auxiliary Channels:
    For multimodal or multispectral images it is possible to specify
    auxiliary channels in an image dictionary as follows:

    {
        'id': int,
        'file_name': str,    # path to the "base" image (may be None)
        'name': str,         # a unique name for the image (must be given if file_name is None)
        'channels': <ChannelSpec>,  # a spec code that indicates the layout of the "base" image channels.
        'auxiliary': [  # information about auxiliary channels
            {
                'file_name': str,
                'channels': <ChannelSpec>
            }, ... # can have many auxiliary channels with unique specs
        ]
    }

    Note that specifing a filename / channels for the base image is not
    necessary, and mainly useful for augmenting an existing single-image
    dataset with multimodal information. Typically if an image consists of
    more than one file, all file information should be stored in the
    "auxiliary" or "assets" list.

Video Sequences:
    For video sequences, we add the following video level index:

    'videos': [
        { 'id': <int>, 'name': <video_name:str> },
    ]

    Note that the videos might be given as encoded mp4/avi/etc.. files (in
    which case the name should correspond to a path) or as a series of
    frames in which case the images should be used to index the extracted
    frames and information in them.

    Then image dictionaries are augmented as follows:

    {
        'video_id': str  # optional, if this image is a frame in a video sequence, this id is shared by all frames in that sequence.
        'timestamp': int  # optional, timestamp (ideally in flicks), used to identify the timestamp of the frame. Only applicable video inputs.
        'frame_index': int  # optional, ordinal frame index which can be used if timestamp is unknown.
    }

    And annotations are augmented as follows:

    {
        'track_id': <int | str | uuid>  # optional, indicates association between annotations across frames
    }

Note

The main object in this file is CocoDataset, which is composed of several mixin classes. See the class and method documentation for more details.

Todo

[ ] Use ijson to lazilly load pieces of the dataset in the background or on demand. This will give us faster access to categories / images, whereas we will always have to wait for annotations etc…
[X] Should img_root be changed to bundle_dpath?
[ ] Read video data, return numpy arrays (requires API for images)
[ ] Spec for video URI, and convert to frames @ framerate function.
[ ] Document channel spec
[X] remove videos

References

1: http://cocodataset.org/#format-data
2: https://github.com/nightrome/cocostuffapi/blob/master/PythonAPI/pycocotools/mask.py
3: https://www.immersivelimit.com/tutorials/create-coco-annotations-from-scratch/#coco-dataset-format

Module Contents¶

Classes¶

`MixinCocoDepricate`	These functions are marked for deprication and may be removed at any time
`MixinCocoAccessors`	TODO: better name
`MixinCocoExtras`	Misc functions for coco
`MixinCocoObjects`	Expose methods to construct object lists / groups.
`MixinCocoStats`	Methods for getting stats about the dataset
`MixinCocoDraw`	Matplotlib / display functionality
`MixinCocoAddRemove`	Mixin functions to dynamically add / remove annotations images and
`CocoIndex`	Fast lookup index for the COCO dataset with dynamic modification
`MixinCocoIndex`	Give the dataset top level access to index attributes
`CocoDataset`	The main coco dataset class with a json dataset backend.

Functions¶

demo_coco_data()

Simple data for testing.

Attributes¶

`_dict`
`SPEC_KEYS`

kwcoco.coco_dataset._dict[source]¶

kwcoco.coco_dataset.SPEC_KEYS = ['info', 'licenses', 'categories', 'keypoint_categories', 'videos', 'images', 'annotations'][source]¶

class kwcoco.coco_dataset.MixinCocoDepricate[source]¶

Bases: object

These functions are marked for deprication and may be removed at any time

class kwcoco.coco_dataset.MixinCocoAccessors[source]¶

Bases: object

TODO: better name

delayed_load(self, gid, channels=None, space='image')[source]¶

Experimental method

Parameters

gid (int) – image id to load
channels (FusedChannelSpec) – specific channels to load. if unspecified, all channels are loaded.
space (str) – can either be “image” for loading in image space, or “video” for loading in video space.

Todo

[X] Currently can only take all or none of the channels from each
base-image / auxiliary dict. For instance if the main image is r|g|b you can’t just select g|b at the moment.
[X] The order of the channels in the delayed load should
match the requested channel order.
[X] TODO: add nans to bands that don’t exist or throw an error

Example

>>> import kwcoco
>>> gid = 1
>>> #
>>> self = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> delayed = self.delayed_load(gid)
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize()))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))
>>> #
>>> self = kwcoco.CocoDataset.demo('shapes8')
>>> delayed = self.delayed_load(gid)
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize()))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))

>>> crop = delayed.delayed_crop((slice(0, 3), slice(0, 3)))
>>> crop.finalize()
>>> crop.finalize(as_xarray=True)

>>> # TODO: should only select the "red" channel
>>> self = kwcoco.CocoDataset.demo('shapes8')
>>> delayed = self.delayed_load(gid, channels='r')

>>> import kwcoco
>>> gid = 1
>>> #
>>> self = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> delayed = self.delayed_load(gid, channels='B1|B2', space='image')
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))
>>> delayed = self.delayed_load(gid, channels='B1|B2|B11', space='image')
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))
>>> delayed = self.delayed_load(gid, channels='B8|B1', space='video')
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))

>>> delayed = self.delayed_load(gid, channels='B8|foo|bar|B1', space='video')
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))

load_image(self, gid_or_img, channels=None)[source]¶

Reads an image from disk and

Parameters

gid_or_img (int or dict) – image id or image dict
channels (str | None) – if specified, load data from auxiliary channels instead

Returns

the image

Return type

np.ndarray

Todo

[ ] allow specification of multiple channels - use delayed image
for this.

get_image_fpath(self, gid_or_img, channels=None)[source]¶

Returns the full path to the image

Parameters

gid_or_img (int or dict) – image id or image dict
channels (str, default=None) – if specified, return a path to data containing auxiliary channels instead

Returns

full path to the image

Return type

PathLike

_get_img_auxiliary(self, gid_or_img, channels)[source]¶: returns the auxiliary dictionary for a specific channel

get_auxiliary_fpath(self, gid_or_img, channels)[source]¶

Returns the full path to auxiliary data for an image

Parameters

gid_or_img (int | dict) – an image or its id
channels (str) – the auxiliary channel to load (e.g. disparity)

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo('shapes8', aux=True)
>>> self.get_auxiliary_fpath(1, 'disparity')

load_annot_sample(self, aid_or_ann, image=None, pad=None)[source]¶

Reads the chip of an annotation. Note this is much less efficient than using a sampler, but it doesn’t require disk cache.

Maybe depricate?

Parameters

aid_or_int (int or dict) – annot id or dict
image (ArrayLike, default=None) – preloaded image (note: this process is inefficient unless image is specified)

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> sample = self.load_annot_sample(2, pad=100)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(sample['im'])
>>> kwplot.show_if_requested()

_resolve_to_id(self, id_or_dict)[source]¶: Ensures output is an id

_resolve_to_cid(self, id_or_name_or_dict)[source]¶: Ensures output is an category id

Note

this does not resolve aliases (yet), for that see _alias_to_cat

Todo

we could maintain an alias index to make this fast

_resolve_to_gid(self, id_or_name_or_dict)[source]¶: Ensures output is an category id

_resolve_to_vidid(self, id_or_name_or_dict)[source]¶: Ensures output is an video id

_resolve_to_ann(self, aid_or_ann)[source]¶: Ensures output is an annotation dictionary

_resolve_to_img(self, gid_or_img)[source]¶: Ensures output is an image dictionary

_resolve_to_kpcat(self, kp_identifier)[source]¶

Lookup a keypoint-category dict via its name or id

Parameters: kp_identifier (int | str | dict) – either the keypoint category name, alias, or its keypoint_category_id.
Returns: keypoint category dictionary
Return type: Dict

Example

>>> self = CocoDataset.demo('shapes')
>>> kpcat1 = self._resolve_to_kpcat(1)
>>> kpcat2 = self._resolve_to_kpcat('left_eye')
>>> assert kpcat1 is kpcat2
>>> import pytest
>>> with pytest.raises(KeyError):
>>>     self._resolve_to_cat('human')

_resolve_to_cat(self, cat_identifier)[source]¶

Lookup a coco-category dict via its name, alias, or id.

Parameters: cat_identifier (int | str | dict) – either the category name, alias, or its category_id.
Raises: KeyError – if the category doesn’t exist.

Note

If the index is not built, the method will work but may be slow.

Example

>>> self = CocoDataset.demo()
>>> cat = self._resolve_to_cat('human')
>>> import pytest
>>> assert self._resolve_to_cat(cat['id']) is cat
>>> assert self._resolve_to_cat(cat) is cat
>>> with pytest.raises(KeyError):
>>>     self._resolve_to_cat(32)
>>> self.index.clear()
>>> assert self._resolve_to_cat(cat['id']) is cat
>>> with pytest.raises(KeyError):
>>>     self._resolve_to_cat(32)

_alias_to_cat(self, alias_catname)[source]¶

Lookup a coco-category via its name or an “alias” name. In production code, use _resolve_to_cat() instead.

Parameters: alias_catname (str) – category name or alias
Returns: coco category dictionary
Return type: dict

Example

>>> self = CocoDataset.demo()
>>> cat = self._alias_to_cat('human')
>>> import pytest
>>> with pytest.raises(KeyError):
>>>     self._alias_to_cat('person')
>>> cat['alias'] = ['person']
>>> self._alias_to_cat('person')
>>> cat['alias'] = 'person'
>>> self._alias_to_cat('person')
>>> assert self._alias_to_cat(None) is None

category_graph(self)[source]¶

Construct a networkx category hierarchy

Returns: graph: a directed graph where category names are the nodes, supercategories define edges, and items in each category dict (e.g. category id) are added as node properties.
Return type: networkx.DiGraph

Example

>>> self = CocoDataset.demo()
>>> graph = self.category_graph()
>>> assert 'astronaut' in graph.nodes()
>>> assert 'keypoints' in graph.nodes['human']

object_categories(self)[source]¶

Construct a consistent CategoryTree representation of object classes

Returns: category data structure
Return type: kwcoco.CategoryTree

Example

>>> self = CocoDataset.demo()
>>> classes = self.object_categories()
>>> print('classes = {}'.format(classes))

keypoint_categories(self)[source]¶

Construct a consistent CategoryTree representation of keypoint classes

Returns: category data structure
Return type: kwcoco.CategoryTree

Example

>>> self = CocoDataset.demo()
>>> classes = self.keypoint_categories()
>>> print('classes = {}'.format(classes))

_keypoint_category_names(self)[source]¶

Construct keypoint categories names.

Uses new-style if possible, otherwise this falls back on old-style.

Returns: names: list of keypoint category names
Return type: List[str]

Example

>>> self = CocoDataset.demo()
>>> names = self._keypoint_category_names()
>>> print(names)

_lookup_kpnames(self, cid)[source]¶: Get the keypoint categories for a certain class

_coco_image(self, gid)[source]¶

coco_image(self, gid)[source]¶

Parameters: gid (int) – image id
Returns: kwcoco.coco_image.CocoImage

class kwcoco.coco_dataset.MixinCocoExtras[source]¶

Bases: object

Misc functions for coco

classmethod coerce(cls, key, **kw)[source]¶

Attempt to transform the input into the intended CocoDataset.

Parameters

key – this can either be an instance of a CocoDataset, a string URI pointing to an on-disk dataset, or a special key for creating demodata.
**kw – passed to whatever constructor is chosen (if any)

Example

>>> # test coerce for various input methods
>>> import kwcoco
>>> from kwcoco.coco_sql_dataset import assert_dsets_allclose
>>> dct_dset = kwcoco.CocoDataset.coerce('special:shapes8')
>>> copy1 = kwcoco.CocoDataset.coerce(dct_dset)
>>> copy2 = kwcoco.CocoDataset.coerce(dct_dset.fpath)
>>> assert assert_dsets_allclose(dct_dset, copy1)
>>> assert assert_dsets_allclose(dct_dset, copy2)
>>> # xdoctest: +REQUIRES(module:sqlalchemy)
>>> sql_dset = dct_dset.view_sql()
>>> copy3 = kwcoco.CocoDataset.coerce(sql_dset)
>>> copy4 = kwcoco.CocoDataset.coerce(sql_dset.fpath)
>>> assert assert_dsets_allclose(dct_dset, sql_dset)
>>> assert assert_dsets_allclose(dct_dset, copy3)
>>> assert assert_dsets_allclose(dct_dset, copy4)

classmethod demo(cls, key='photos', **kwargs)[source]¶

Create a toy coco dataset for testing and demo puposes

Parameters

key (str, default=photos) – Either ‘photos’, ‘shapes’, or ‘vidshapes’. There are also special sufixes that can control behavior.

Basic options that define which flavor of demodata to generate are: photos, shapes, and vidshapes. A numeric suffix e.g. vidshapes8 can be specified to indicate the size of the generated demo dataset. There are other special suffixes that are available. See the code in this function for explicit details on what is allowed.

TODO: better documentation for these demo datasets.

As a quick summary: the vidshapes key is the most robust and mature demodata set, and here are several useful variants of the vidshapes key.
1. vidshapes8 - the 8 suffix is the number of videos in this case.
2. vidshapes8-multispectral - generate 8 multispectral videos.
3. vidshapes8-msi - msi is an alias for multispectral.
(4) vidshapes8-frames5 - generate 8 videos with 5 frames each. (4) vidshapes2-speed0.1-frames7 - generate 2 videos with 7

frames where the objects move with with a speed of 0.1.
**kwargs – if key is shapes, these arguments are passed to toydata generation. The Kwargs section of this docstring documents a subset of the available options. For full details, see demodata_toy_dset() and random_video_dset().

Kwargs:

image_size (Tuple[int, int]): width / height size of the images

dpath (str): path to the output image directory, defaults to using: kwcoco cache dir.

aux (bool): if True generates dummy auxiliary channels

rng (int | RandomState, default=0):: random number generator or seed

verbose (int, default=3): verbosity mode

Example

>>> # Basic demodata keys
>>> print(CocoDataset.demo('photos', verbose=1))
>>> print(CocoDataset.demo('shapes', verbose=1))
>>> print(CocoDataset.demo('vidshapes', verbose=1))
>>> # Varaints of demodata keys
>>> print(CocoDataset.demo('shapes8', verbose=0))
>>> print(CocoDataset.demo('shapes8-msi', verbose=0))
>>> print(CocoDataset.demo('shapes8-frames1-speed0.2-msi', verbose=0))

Example

>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes5', num_frames=5,
>>>                                verbose=0, rng=None)
>>> dset = kwcoco.CocoDataset.demo('vidshapes5', num_frames=5,
>>>                                num_tracks=4, verbose=0, rng=44)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> pnums = kwplot.PlotNums(nSubplots=len(dset.index.imgs))
>>> fnum = 1
>>> for gx, gid in enumerate(dset.index.imgs.keys()):
>>>     canvas = dset.draw_image(gid=gid)
>>>     kwplot.imshow(canvas, pnum=pnums[gx], fnum=fnum)
>>>     #dset.show_image(gid=gid, pnum=pnums[gx])
>>> kwplot.show_if_requested()

Example

>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes5-aux', num_frames=1,
>>>                                verbose=0, rng=None)

Example

>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes1-multispectral', num_frames=5,
>>>                                verbose=0, rng=None)
>>> # This is the first use-case of image names
>>> assert len(dset.index.file_name_to_img) == 0, (
>>>     'the multispectral demo case has no "base" image')
>>> assert len(dset.index.name_to_img) == len(dset.index.imgs) == 5
>>> dset.remove_images([1])
>>> assert len(dset.index.name_to_img) == len(dset.index.imgs) == 4
>>> dset.remove_videos([1])
>>> assert len(dset.index.name_to_img) == len(dset.index.imgs) == 0

_tree(self)[source]¶: developer helper

classmethod random(cls, rng=None)[source]¶

Creates a random CocoDataset according to distribution parameters

Todo

[ ] parametarize

_build_hashid(self, hash_pixels=False, verbose=0)[source]¶

Construct a hash that uniquely identifies the state of this dataset.

Parameters

hash_pixels (bool, default=False) – If False the image data is not included in the hash, which can speed up computation, but is not 100% robust.
verbose (int) – verbosity level

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> self._build_hashid(hash_pixels=True, verbose=3)
...
>>> # Shorten hashes for readability
>>> walker = ub.IndexableWalker(self.hashid_parts)
>>> for path, val in walker:
>>>     if isinstance(val, str):
>>>         walker[path] = val[0:8]
>>> # Note: this may change in different versions of kwcoco
>>> print('self.hashid_parts = ' + ub.repr2(self.hashid_parts))
>>> print('self.hashid = {!r}'.format(self.hashid[0:8]))
self.hashid_parts = {
    'annotations': {
        'json': 'e573f49d',
        'num': 11,
    },
    'images': {
        'pixels': '67d741fe',
        'json': '2221c714',
        'num': 3,
    },
    'categories': {
        'json': '82d22e00',
        'num': 8,
    },
}
self.hashid = '77d445f0'

Doctest:

>>> self = CocoDataset.demo()
>>> self._build_hashid(hash_pixels=True, verbose=3)
>>> self.hashid_parts
>>> # Test that when we modify the dataset only the relevant
>>> # hashid parts are recomputed.
>>> orig = self.hashid_parts['categories']['json']
>>> self.add_category('foobar')
>>> assert 'categories' not in self.hashid_parts
>>> self.hashid_parts
>>> self.hashid_parts['images']['json'] = 'should not change'
>>> self._build_hashid(hash_pixels=True, verbose=3)
>>> assert self.hashid_parts['categories']['json']
>>> assert self.hashid_parts['categories']['json'] != orig
>>> assert self.hashid_parts['images']['json'] == 'should not change'

_invalidate_hashid(self, parts=None)[source]¶

Called whenever the coco dataset is modified. It is possible to specify which parts were modified so unmodified parts can be reused next time the hash is constructed.

Todo

[ ] Rename to _notify_modification — or something like that

_cached_hashid(self)[source]¶

Under Construction.

The idea is to cache the hashid when we are sure that the dataset was loaded from a file and has not been modified. We can record the modification time of the file (because we know it hasn’t changed in memory), and use that as a key to the cache. If the modification time on the file is different than the one recorded in the cache, we know the cache could be invalid, so we recompute the hashid.

_dataset_id(self)[source]¶

A human interpretable name that can be used to uniquely identify the dataset.

Note

This function is currently subject to change.

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> print(self._dataset_id())
>>> self = kwcoco.CocoDataset.demo('vidshapes8')
>>> print(self._dataset_id())
>>> self = kwcoco.CocoDataset()
>>> print(self._dataset_id())

_ensure_imgsize(self, workers=0, verbose=1, fail=False)[source]¶

Populate the imgsize field if it does not exist.

Parameters

workers (int, default=0) – number of workers for parallel processing.
verbose (int, default=1) – verbosity level
fail (bool, default=False) – if True, raises an exception if anything size fails to load.

Returns

a list of “bad” image dictionaries where the size could: not be determined. Typically these are corrupted images and should be removed.

Return type

List[dict]

Example

>>> # Normal case
>>> self = CocoDataset.demo()
>>> bad_imgs = self._ensure_imgsize()
>>> assert len(bad_imgs) == 0
>>> assert self.imgs[1]['width'] == 512
>>> assert self.imgs[2]['width'] == 300
>>> assert self.imgs[3]['width'] == 256

>>> # Fail cases
>>> self = CocoDataset()
>>> self.add_image('does-not-exist.jpg')
>>> bad_imgs = self._ensure_imgsize()
>>> assert len(bad_imgs) == 1
>>> import pytest
>>> with pytest.raises(Exception):
>>>     self._ensure_imgsize(fail=True)

_ensure_image_data(self, gids=None, verbose=1)[source]¶

Download data from “url” fields if specified.

Parameters: gids (List) – subset of images to download

missing_images(self, check_aux=False, verbose=0)[source]¶

Check for images that don’t exist

Parameters

check_aux (bool, default=False) – if specified also checks auxiliary images
verbose (int) – verbosity level

Returns

bad indexes and paths and ids

Return type

List[Tuple[int, str, int]]

corrupted_images(self, check_aux=False, verbose=0)[source]¶

Check for images that don’t exist or can’t be opened

Parameters

check_aux (bool, default=False) – if specified also checks auxiliary images
verbose (int) – verbosity level

Returns

bad indexes and paths and ids

Return type

List[Tuple[int, str, int]]

rename_categories(self, mapper, rebuild=True, merge_policy='ignore')[source]¶

Rename categories with a potentially coarser categorization.

Parameters

mapper (dict or Function) – maps old names to new names. If multiple names are mapped to the same category, those categories will be merged.
merge_policy (str) – How to handle multiple categories that map to the same name. Can be update or ignore.

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> self.rename_categories({'astronomer': 'person',
>>>                         'astronaut': 'person',
>>>                         'mouth': 'person',
>>>                         'helmet': 'hat'})
>>> assert 'hat' in self.name_to_cat
>>> assert 'helmet' not in self.name_to_cat
>>> # Test merge case
>>> self = kwcoco.CocoDataset.demo()
>>> mapper = {
>>>     'helmet': 'rocket',
>>>     'astronomer': 'rocket',
>>>     'human': 'rocket',
>>>     'mouth': 'helmet',
>>>     'star': 'gas'
>>> }
>>> self.rename_categories(mapper)

_ensure_json_serializable(self)[source]¶

_aspycoco(self)[source]¶

Converts to the official pycocotools.coco.COCO object

Todo

[ ] Maybe expose as a public API?

reroot(self, new_root=None, old_prefix=None, new_prefix=None, absolute=False, check=True, safe=True)[source]¶

Modify the prefix of the image/data paths onto a new image/data root.

Parameters

new_root (str, default=None) – New image root. If unspecified the current self.bundle_dpath is used. If old_prefix and new_prefix are unspecified, they will attempt to be determined based on the current root (which assumes the file paths exist at that root) and this new root.
old_prefix (str, default=None) – If specified, removes this prefix from file names. This also prevents any inferences that might be made via “new_root”.
new_prefix (str, default=None) – If specified, adds this prefix to the file names. This also prevents any inferences that might be made via “new_root”.
absolute (bool, default=False) – if True, file names are stored as absolute paths, otherwise they are relative to the new image root.
check (bool, default=True) – if True, checks that the images all exist.
safe (bool, default=True) – if True, does not overwrite values until all checks pass

CommandLine

xdoctest -m kwcoco.coco_dataset MixinCocoExtras.reroot

Todo

[ ] Incorporate maximum ordered subtree embedding?

Example

>>> import kwcoco
>>> def report(dset, name):
>>>     gid = 1
>>>     abs_fpath = dset.get_image_fpath(gid)
>>>     rel_fpath = dset.index.imgs[gid]['file_name']
>>>     color = 'green' if exists(abs_fpath) else 'red'
>>>     print('strategy_name = {!r}'.format(name))
>>>     print(ub.color_text('abs_fpath = {!r}'.format(abs_fpath), color))
>>>     print('rel_fpath = {!r}'.format(rel_fpath))
>>> dset = self = kwcoco.CocoDataset.demo()
>>> # Change base relative directory
>>> bundle_dpath = ub.expandpath('~')
>>> print('ORIG self.imgs = {!r}'.format(self.imgs))
>>> print('ORIG dset.bundle_dpath = {!r}'.format(dset.bundle_dpath))
>>> print('NEW bundle_dpath       = {!r}'.format(bundle_dpath))
>>> self.reroot(bundle_dpath)
>>> report(self, 'self')
>>> print('NEW self.imgs = {!r}'.format(self.imgs))
>>> assert self.imgs[1]['file_name'].startswith('.cache')

>>> # Use absolute paths
>>> self.reroot(absolute=True)
>>> assert self.imgs[1]['file_name'].startswith(bundle_dpath)

>>> # Switch back to relative paths
>>> self.reroot()
>>> assert self.imgs[1]['file_name'].startswith('.cache')

Example

>>> # demo with auxiliary data
>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo('shapes8', aux=True)
>>> bundle_dpath = ub.expandpath('~')
>>> print(self.imgs[1]['file_name'])
>>> print(self.imgs[1]['auxiliary'][0]['file_name'])
>>> self.reroot(new_root=bundle_dpath)
>>> print(self.imgs[1]['file_name'])
>>> print(self.imgs[1]['auxiliary'][0]['file_name'])
>>> assert self.imgs[1]['file_name'].startswith('.cache')
>>> assert self.imgs[1]['auxiliary'][0]['file_name'].startswith('.cache')

property data_root(self)[source]¶: In the future we will deprecate data_root for bundle_dpath

property img_root(self)[source]¶: In the future we will deprecate img_root for bundle_dpath

property data_fpath(self)[source]¶: data_fpath is an alias of fpath

class kwcoco.coco_dataset.MixinCocoObjects[source]¶

Bases: object

Expose methods to construct object lists / groups.

This is an alternative vectorized ORM-like interface to the coco dataset

annots(self, aids=None, gid=None, trackid=None)[source]¶

Return vectorized annotation objects

Parameters

aids (List[int]) – annotation ids to reference, if unspecified all annotations are returned.
gid (int) – return all annotations that belong to this image id. mutually exclusive with other arguments.
trackid (int) – return all annotations that belong to this track. mutually exclusive with other arguments.

Returns

vectorized annotation object

Return type

Annots

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> annots = self.annots()
>>> print(annots)
<Annots(num=11)>
>>> sub_annots = annots.take([1, 2, 3])
>>> print(sub_annots)
<Annots(num=3)>
>>> print(ub.repr2(sub_annots.get('bbox', None)))
[
    [350, 5, 130, 290],
    None,
    None,
]

images(self, gids=None, vidid=None, names=None)[source]¶

Return vectorized image objects

Parameters

gids (List[int]) – image ids to reference, if unspecified all images are returned.
vidid (int) – returns all images that belong to this video id. mutually exclusive with gids arg.
names (List[str]) – lookup images by their names.

Returns

vectorized image object

Return type

Images

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> images = self.images()
>>> print(images)
<Images(num=3)>

>>> self = kwcoco.CocoDataset.demo('vidshapes2')
>>> vidid = 1
>>> images = self.images(vidid=vidid)
>>> assert all(v == vidid for v in images.lookup('video_id'))
>>> print(images)
<Images(num=2)>

categories(self, cids=None)[source]¶

Return vectorized category objects

Parameters: cids (List[int]) – category ids to reference, if unspecified all categories are returned.
Returns: vectorized category object
Return type: Categories

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> categories = self.categories()
>>> print(categories)
<Categories(num=8)>

videos(self, vidids=None, names=None)[source]¶

Return vectorized video objects

Parameters

vidids (List[int]) – video ids to reference, if unspecified all videos are returned.
names (List[str]) – lookup videos by their name.

Returns

vectorized video object

Return type

Videos

Todo

[ ] This conflicts with what should be the property that
should redirect to index.videos, we should resolve this somehow. E.g. all other main members of the index (anns, imgs, cats) have a toplevel dataset property, we don’t have one for videos because the name we would pick conflicts with this.

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo('vidshapes2')
>>> videos = self.videos()
>>> print(videos)
>>> videos.lookup('name')
>>> videos.lookup('id')
>>> print('videos.objs = {}'.format(ub.repr2(videos.objs[0:2], nl=1)))

class kwcoco.coco_dataset.MixinCocoStats[source]¶

Bases: object

Methods for getting stats about the dataset

property n_annots(self)[source]¶: The number of annotations in the dataset

property n_images(self)[source]¶: The number of images in the dataset

property n_cats(self)[source]¶: The number of categories in the dataset

property n_videos(self)[source]¶: The number of videos in the dataset

keypoint_annotation_frequency(self)[source]¶

DEPRECATED

Example

>>> from kwcoco.coco_dataset import *
>>> self = CocoDataset.demo('shapes', rng=0)
>>> hist = self.keypoint_annotation_frequency()
>>> hist = ub.odict(sorted(hist.items()))
>>> # FIXME: for whatever reason demodata generation is not determenistic when seeded
>>> print(ub.repr2(hist))  # xdoc: +IGNORE_WANT
{
    'bot_tip': 6,
    'left_eye': 14,
    'mid_tip': 6,
    'right_eye': 14,
    'top_tip': 6,
}

category_annotation_frequency(self)[source]¶

Reports the number of annotations of each category

Example

>>> from kwcoco.coco_dataset import *
>>> self = CocoDataset.demo()
>>> hist = self.category_annotation_frequency()
>>> print(ub.repr2(hist))
{
    'astroturf': 0,
    'human': 0,
    'astronaut': 1,
    'astronomer': 1,
    'helmet': 1,
    'rocket': 1,
    'mouth': 2,
    'star': 5,
}

category_annotation_type_frequency(self)[source]¶

DEPRECATED

Reports the number of annotations of each type for each category

Example

>>> self = CocoDataset.demo()
>>> hist = self.category_annotation_frequency()
>>> print(ub.repr2(hist))

conform(self, **config)[source]¶

Make the COCO file conform a stricter spec, infers attibutes where possible.

Corresponds to the kwcoco conform CLI tool.

KWArgs:

**config :

pycocotools_info (default=True): returns info required by pycocotools ensure_imgsize (default=True): ensure image size is populated legacy (default=False): if true tries to convert data

structures to items compatible with the original pycocotools spec

workers (int): number of parallel jobs for IO tasks

Example

>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('shapes8')
>>> dset.index.imgs[1].pop('width')
>>> dset.conform(legacy=True)
>>> assert 'width' in dset.index.imgs[1]
>>> assert 'area' in dset.index.anns[1]

validate(self, **config)[source]¶

Performs checks on this coco dataset.

Corresponds to the kwcoco validate CLI tool.

KWArgs:

**config :: schema (default=True): validates the json-schema unique (default=True): validates unique secondary keys missing (default=True): validates registered files exist corrupted (default=False): validates data in registered files verbose (default=1): verbosity flag fastfail (default=False): if True raise errors immediately

Returns

result containing keys:: status (bool): False if any errors occurred errors (List[str]): list of all error messages missing (List): List of any missing images corrupted (List): List of any corrupted images

Return type

SeeAlso:: _check_integrity() - performs internal checks

Example

>>> from kwcoco.coco_dataset import *
>>> self = CocoDataset.demo()
>>> import pytest
>>> with pytest.warns(UserWarning):
>>>     result = self.validate()
>>> assert not result['errors']
>>> assert result['warnings']

stats(self, **kwargs)[source]¶

Compute summary statistics to describe the dataset at a high level

This function corresponds to kwcoco.cli.coco_stats.

KWargs:

basic(bool, default=True): return basic stats’ extended(bool, default=True): return extended stats’ catfreq(bool, default=True): return category frequency stats’ boxes(bool, default=False): return bounding box stats’

annot_attrs(bool, default=True): return annotation attribute information’ image_attrs(bool, default=True): return image attribute information’

Returns: info
Return type: dict

basic_stats(self)[source]¶

Reports number of images, annotations, and categories.

SeeAlso:: kwcoco.coco_dataset.MixinCocoStats.basic_stats() kwcoco.coco_dataset.MixinCocoStats.extended_stats()

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> print(ub.repr2(self.basic_stats()))
{
    'n_anns': 11,
    'n_imgs': 3,
    'n_videos': 0,
    'n_cats': 8,
}

>>> from kwcoco.demo.toydata_video import random_video_dset
>>> dset = random_video_dset(render=True, num_frames=2, num_tracks=10, rng=0)
>>> print(ub.repr2(dset.basic_stats()))
{
    'n_anns': 20,
    'n_imgs': 2,
    'n_videos': 1,
    'n_cats': 3,
}

extended_stats(self)[source]¶

Reports number of images, annotations, and categories.

SeeAlso:: kwcoco.coco_dataset.MixinCocoStats.basic_stats() kwcoco.coco_dataset.MixinCocoStats.extended_stats()

Example

>>> self = CocoDataset.demo()
>>> print(ub.repr2(self.extended_stats()))

boxsize_stats(self, anchors=None, perclass=True, gids=None, aids=None, verbose=0, clusterkw={}, statskw={})[source]¶

Compute statistics about bounding box sizes.

Also computes anchor boxes using kmeans if anchors is specified.

Parameters

anchors (int) – if specified also computes box anchors via KMeans clustering
perclass (bool) – if True also computes stats for each category
gids (List[int], default=None) – if specified only compute stats for these image ids.
aids (List[int], default=None) – if specified only compute stats for these annotation ids.
verbose (int) – verbosity level
clusterkw (dict) – kwargs for sklearn.cluster.KMeans used if computing anchors.
statskw (dict) – kwargs for kwarray.stats_dict()

Returns

Stats are returned in width-height format.

Return type

Dict[str, Dict[str, Dict | ndarray]

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo('shapes32')
>>> infos = self.boxsize_stats(anchors=4, perclass=False)
>>> print(ub.repr2(infos, nl=-1, precision=2))

>>> infos = self.boxsize_stats(gids=[1], statskw=dict(median=True))
>>> print(ub.repr2(infos, nl=-1, precision=2))

find_representative_images(self, gids=None)[source]¶

Find images that have a wide array of categories.

Attempt to find the fewest images that cover all categories using images that contain both a large and small number of annotations.

Parameters: gids (None | List) – Subset of image ids to consider when finding representative images. Uses all images if unspecified.
Returns: list of image ids determined to be representative
Return type: List

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> gids = self.find_representative_images()
>>> print('gids = {!r}'.format(gids))
>>> gids = self.find_representative_images([3])
>>> print('gids = {!r}'.format(gids))

>>> self = kwcoco.CocoDataset.demo('shapes8')
>>> gids = self.find_representative_images()
>>> print('gids = {!r}'.format(gids))
>>> valid = {7, 1}
>>> gids = self.find_representative_images(valid)
>>> assert valid.issuperset(gids)
>>> print('gids = {!r}'.format(gids))

class kwcoco.coco_dataset.MixinCocoDraw[source]¶

Bases: object

Matplotlib / display functionality

imread(self, gid)[source]¶

DEPRECATE: use load_image or delayed_image

Loads a particular image

draw_image(self, gid, channels=None)[source]¶

Use kwimage to draw all annotations on an image and return the pixels as a numpy array.

Parameters

gid (int) – image id to draw
channels (ChannelSpec) – the channel to draw on

Returns

canvas

Return type

ndarray

SeeAlso: kwcoco.coco_dataset.MixinCocoDraw.draw_image() kwcoco.coco_dataset.MixinCocoDraw.show_image()

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo('shapes8')
>>> self.draw_image(1)
>>> # Now you can dump the annotated image to disk / whatever
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(canvas)

show_image(self, gid=None, aids=None, aid=None, channels=None, **kwargs)[source]¶

Use matplotlib to show an image with annotations overlaid

Parameters

gid (int) – image to show
aids (list) – aids to highlight within the image
aid (int) – a specific aid to focus on. If gid is not give, look up gid based on this aid.
**kwargs – show_annots, show_aid, show_catname, show_kpname, show_segmentation, title, show_gid, show_filename, show_boxes,

SeeAlso: kwcoco.coco_dataset.MixinCocoDraw.draw_image() kwcoco.coco_dataset.MixinCocoDraw.show_image()

Example

>>> # xdoctest: +REQUIRES(--show)
>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-msi')
>>> dset.show_image(gid=1, channels='B8')

class kwcoco.coco_dataset.MixinCocoAddRemove[source]¶

Bases: object

Mixin functions to dynamically add / remove annotations images and categories while maintaining lookup indexes.

add_video(self, name, id=None, **kw)[source]¶

Register a new video with the dataset

Parameters

name (str) – Unique name for this video.
id (None or int) – ADVANCED. Force using this image id.
**kw – stores arbitrary key/value pairs in this new video

Returns

the video id assigned to the new video

Return type

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset()
>>> print('self.index.videos = {}'.format(ub.repr2(self.index.videos, nl=1)))
>>> print('self.index.imgs = {}'.format(ub.repr2(self.index.imgs, nl=1)))
>>> print('self.index.vidid_to_gids = {!r}'.format(self.index.vidid_to_gids))

>>> vidid1 = self.add_video('foo', id=3)
>>> vidid2 = self.add_video('bar')
>>> vidid3 = self.add_video('baz')
>>> print('self.index.videos = {}'.format(ub.repr2(self.index.videos, nl=1)))
>>> print('self.index.imgs = {}'.format(ub.repr2(self.index.imgs, nl=1)))
>>> print('self.index.vidid_to_gids = {!r}'.format(self.index.vidid_to_gids))

>>> gid1 = self.add_image('foo1.jpg', video_id=vidid1, frame_index=0)
>>> gid2 = self.add_image('foo2.jpg', video_id=vidid1, frame_index=1)
>>> gid3 = self.add_image('foo3.jpg', video_id=vidid1, frame_index=2)
>>> gid4 = self.add_image('bar1.jpg', video_id=vidid2, frame_index=0)
>>> print('self.index.videos = {}'.format(ub.repr2(self.index.videos, nl=1)))
>>> print('self.index.imgs = {}'.format(ub.repr2(self.index.imgs, nl=1)))
>>> print('self.index.vidid_to_gids = {!r}'.format(self.index.vidid_to_gids))

>>> self.remove_images([gid2])
>>> print('self.index.vidid_to_gids = {!r}'.format(self.index.vidid_to_gids))

add_image(self, file_name=None, id=None, **kw)[source]¶

Register a new image with the dataset

Parameters

file_name (str) – relative or absolute path to image
id (None or int) – ADVANCED. Force using this image id.
name (str) – a unique key to identify this image
width (int) – base width of the image
height (int) – base height of the image
channels (ChannelSpec) – specification of base channels
auxiliary (List[Dict]) – specification of auxiliary information
video_id (int) – parent video, if applicable
frame_index (int) – frame index in parent video
timestamp (number | str) – timestamp of frame index
**kw – stores arbitrary key/value pairs in this new image

Returns

the image id assigned to the new image

Return type

SeeAlso:: add_image() add_images() ensure_image()

Example

>>> self = CocoDataset.demo()
>>> import kwimage
>>> gname = kwimage.grab_test_image_fpath('paraview')
>>> gid = self.add_image(gname)
>>> assert self.imgs[gid]['file_name'] == gname

add_annotation(self, image_id, category_id=None, bbox=ub.NoParam, segmentation=ub.NoParam, keypoints=ub.NoParam, id=None, **kw)[source]¶

Register a new annotation with the dataset

Parameters

image_id (int) – image_id the annoatation is added to.
category_id (int | None) – category_id for the new annotaiton
bbox (list | kwimage.Boxes) – bounding box in xywh format
segmentation (MaskLike | MultiPolygonLike) – keypoints in some accepted format, see kwimage.Mask.to_coco() and kwimage.MultiPolygon.to_coco().
keypoints (KeypointsLike) – keypoints in some accepted format, see kwimage.Keypoints.to_coco().
id (None | int) – Force using this annotation id. Typically you should NOT specify this. A new unused id will be chosen and returned.
**kw – stores arbitrary key/value pairs in this new image, Common respected key/values include but are not limited to the following:

track_id (int | str): some value used to associate annotations
that belong to the same “track”.

score : float

prob : List[float]

weight (float): a weight, usually used to indicate if a ground
truth annotation is difficult / important. This generalizes standard “is_hard” or “ignore” attributes in other formats.

caption (str): a text caption for this annotation

Returns

the annotation id assigned to the new annotation

Return type

SeeAlso:: kwcoco.coco_dataset.MixinCocoAddRemove.add_annotation() kwcoco.coco_dataset.MixinCocoAddRemove.add_annotations()

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> image_id = 1
>>> cid = 1
>>> bbox = [10, 10, 20, 20]
>>> aid = self.add_annotation(image_id, cid, bbox)
>>> assert self.anns[aid]['bbox'] == bbox

Example

>>> import kwimage
>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> new_det = kwimage.Detections.random(1, segmentations=True, keypoints=True)
>>> # kwimage datastructures have methods to convert to coco recognized formats
>>> new_ann_data = list(new_det.to_coco(style='new'))[0]
>>> image_id = 1
>>> aid = self.add_annotation(image_id, **new_ann_data)
>>> # Lookup the annotation we just added
>>> ann = self.index.anns[aid]
>>> print('ann = {}'.format(ub.repr2(ann, nl=-2)))

Example

>>> # Attempt to add annot without a category or bbox
>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> image_id = 1
>>> aid = self.add_annotation(image_id)
>>> assert None in self.index.cid_to_aids

Example

>>> # Attempt to add annot using various styles of kwimage structures
>>> import kwcoco
>>> import kwimage
>>> self = kwcoco.CocoDataset.demo()
>>> image_id = 1
>>> #--
>>> kw = {}
>>> kw['segmentation'] = kwimage.Polygon.random()
>>> kw['keypoints'] = kwimage.Points.random()
>>> aid = self.add_annotation(image_id, **kw)
>>> ann = self.index.anns[aid]
>>> print('ann = {}'.format(ub.repr2(ann, nl=2)))
>>> #--
>>> kw = {}
>>> kw['segmentation'] = kwimage.Mask.random()
>>> aid = self.add_annotation(image_id, **kw)
>>> ann = self.index.anns[aid]
>>> assert ann.get('segmentation', None) is not None
>>> print('ann = {}'.format(ub.repr2(ann, nl=2)))
>>> #--
>>> kw = {}
>>> kw['segmentation'] = kwimage.Mask.random().to_array_rle()
>>> aid = self.add_annotation(image_id, **kw)
>>> ann = self.index.anns[aid]
>>> assert ann.get('segmentation', None) is not None
>>> print('ann = {}'.format(ub.repr2(ann, nl=2)))
>>> #--
>>> kw = {}
>>> kw['segmentation'] = kwimage.Polygon.random().to_coco()
>>> kw['keypoints'] = kwimage.Points.random().to_coco()
>>> aid = self.add_annotation(image_id, **kw)
>>> ann = self.index.anns[aid]
>>> assert ann.get('segmentation', None) is not None
>>> assert ann.get('keypoints', None) is not None
>>> print('ann = {}'.format(ub.repr2(ann, nl=2)))

add_category(self, name, supercategory=None, id=None, **kw)[source]¶

Register a new category with the dataset

Parameters

name (str) – name of the new category
supercategory (str, optional) – parent of this category
id (int, optional) – use this category id, if it was not taken
**kw – stores arbitrary key/value pairs in this new image

Returns

the category id assigned to the new category

Return type

SeeAlso:: kwcoco.coco_dataset.MixinCocoAddRemove.add_category() kwcoco.coco_dataset.MixinCocoAddRemove.ensure_category()

Example

>>> self = CocoDataset.demo()
>>> prev_n_cats = self.n_cats
>>> cid = self.add_category('dog', supercategory='object')
>>> assert self.cats[cid]['name'] == 'dog'
>>> assert self.n_cats == prev_n_cats + 1
>>> import pytest
>>> with pytest.raises(ValueError):
>>>     self.add_category('dog', supercategory='object')

ensure_image(self, file_name, id=None, **kw)[source]¶

Register an image if it is new or returns an existing id.

Like kwcoco.coco_dataset.MixinCocoAddRemove.add_image(), but returns the existing image id if it already exists instead of failing. In this case all metadata is ignored.

Parameters

file_name (str) – relative or absolute path to image
id (None or int) – ADVANCED. Force using this image id.
**kw – stores arbitrary key/value pairs in this new image

Returns

the existing or new image id

Return type

http://cocodataset.org/#format http://cocodataset.org/#download

SeeAlso:: kwcoco.coco_dataset.MixinCocoAddRemove.add_image() kwcoco.coco_dataset.MixinCocoAddRemove.add_images() kwcoco.coco_dataset.MixinCocoAddRemove.ensure_image()

ensure_category(self, name, supercategory=None, id=None, **kw)[source]¶

Register a category if it is new or returns an existing id.

Like kwcoco.coco_dataset.MixinCocoAddRemove.add_category(), but returns the existing category id if it already exists instead of failing. In this case all metadata is ignored.

Returns: the existing or new category id
Return type: int

SeeAlso:: kwcoco.coco_dataset.MixinCocoAddRemove.add_category() kwcoco.coco_dataset.MixinCocoAddRemove.ensure_category()

add_annotations(self, anns)[source]¶

Faster less-safe multi-item alternative to add_annotation.

We assume the annotations are well formatted in kwcoco compliant dictionaries, including the “id” field. No validation checks are made when calling this function.

Parameters: anns (List[Dict]) – list of annotation dictionaries

SeeAlso:: add_annotation() add_annotations()

Example

>>> self = CocoDataset.demo()
>>> anns = [self.anns[aid] for aid in [2, 3, 5, 7]]
>>> self.remove_annotations(anns)
>>> assert self.n_annots == 7 and self._check_index()
>>> self.add_annotations(anns)
>>> assert self.n_annots == 11 and self._check_index()

add_images(self, imgs)[source]¶

Faster less-safe multi-item alternative

We assume the images are well formatted in kwcoco compliant dictionaries, including the “id” field. No validation checks are made when calling this function.

Note

THIS FUNCTION WAS DESIGNED FOR SPEED, AS SUCH IT DOES NOT CHECK IF THE IMAGE-IDs or FILE_NAMES ARE DUPLICATED AND WILL BLINDLY ADD DATA EVEN IF IT IS BAD. THE SINGLE IMAGE VERSION IS SLOWER BUT SAFER.

Parameters: imgs (List[Dict]) – list of image dictionaries

SeeAlso:: kwcoco.coco_dataset.MixinCocoAddRemove.add_image() kwcoco.coco_dataset.MixinCocoAddRemove.add_images() kwcoco.coco_dataset.MixinCocoAddRemove.ensure_image()

Example

>>> imgs = CocoDataset.demo().dataset['images']
>>> self = CocoDataset()
>>> self.add_images(imgs)
>>> assert self.n_images == 3 and self._check_index()

clear_images(self)[source]¶

Removes all images and annotations (but not categories)

Example

>>> self = CocoDataset.demo()
>>> self.clear_images()
>>> print(ub.repr2(self.basic_stats(), nobr=1, nl=0, si=1))
n_anns: 0, n_imgs: 0, n_videos: 0, n_cats: 8

clear_annotations(self)[source]¶

Removes all annotations (but not images and categories)

Example

>>> self = CocoDataset.demo()
>>> self.clear_annotations()
>>> print(ub.repr2(self.basic_stats(), nobr=1, nl=0, si=1))
n_anns: 0, n_imgs: 3, n_videos: 0, n_cats: 8

remove_annotation(self, aid_or_ann)[source]¶

Remove a single annotation from the dataset

If you have multiple annotations to remove its more efficient to remove them in batch with kwcoco.coco_dataset.MixinCocoAddRemove.remove_annotations()

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> aids_or_anns = [self.anns[2], 3, 4, self.anns[1]]
>>> self.remove_annotations(aids_or_anns)
>>> assert len(self.dataset['annotations']) == 7
>>> self._check_index()

remove_annotations(self, aids_or_anns, verbose=0, safe=True)[source]¶

Remove multiple annotations from the dataset.

Parameters

anns_or_aids (List) – list of annotation dicts or ids
safe (bool, default=True) – if True, we perform checks to remove duplicates and non-existing identifiers.

Returns

num_removed: information on the number of items removed

Return type

Dict

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> prev_n_annots = self.n_annots
>>> aids_or_anns = [self.anns[2], 3, 4, self.anns[1]]
>>> self.remove_annotations(aids_or_anns)  # xdoc: +IGNORE_WANT
{'annotations': 4}
>>> assert len(self.dataset['annotations']) == prev_n_annots - 4
>>> self._check_index()

remove_categories(self, cat_identifiers, keep_annots=False, verbose=0, safe=True)[source]¶

Remove categories and all annotations in those categories.

Currently does not change any hierarchy information

Parameters

cat_identifiers (List) – list of category dicts, names, or ids
keep_annots (bool, default=False) – if True, keeps annotations, but removes category labels.
safe (bool, default=True) – if True, we perform checks to remove duplicates and non-existing identifiers.

Returns

num_removed: information on the number of items removed

Return type

Dict

Example

>>> self = CocoDataset.demo()
>>> cat_identifiers = [self.cats[1], 'rocket', 3]
>>> self.remove_categories(cat_identifiers)
>>> assert len(self.dataset['categories']) == 5
>>> self._check_index()

remove_images(self, gids_or_imgs, verbose=0, safe=True)[source]¶

Remove images and any annotations contained by them

Parameters

gids_or_imgs (List) – list of image dicts, names, or ids
safe (bool, default=True) – if True, we perform checks to remove duplicates and non-existing identifiers.

Returns

num_removed: information on the number of items removed

Return type

Dict

Example

>>> from kwcoco.coco_dataset import *
>>> self = CocoDataset.demo()
>>> assert len(self.dataset['images']) == 3
>>> gids_or_imgs = [self.imgs[2], 'astro.png']
>>> self.remove_images(gids_or_imgs)  # xdoc: +IGNORE_WANT
{'annotations': 11, 'images': 2}
>>> assert len(self.dataset['images']) == 1
>>> self._check_index()
>>> gids_or_imgs = [3]
>>> self.remove_images(gids_or_imgs)
>>> assert len(self.dataset['images']) == 0
>>> self._check_index()

remove_videos(self, vidids_or_videos, verbose=0, safe=True)[source]¶

Remove videos and any images / annotations contained by them

Parameters

vidids_or_videos (List) – list of video dicts, names, or ids
safe (bool, default=True) – if True, we perform checks to remove duplicates and non-existing identifiers.

Returns

num_removed: information on the number of items removed

Return type

Dict

Example

>>> from kwcoco.coco_dataset import *
>>> self = CocoDataset.demo('vidshapes8')
>>> assert len(self.dataset['videos']) == 8
>>> vidids_or_videos = [self.dataset['videos'][0]['id']]
>>> self.remove_videos(vidids_or_videos)  # xdoc: +IGNORE_WANT
{'annotations': 4, 'images': 2, 'videos': 1}
>>> assert len(self.dataset['videos']) == 7
>>> self._check_index()

remove_annotation_keypoints(self, kp_identifiers)[source]¶

Removes all keypoints with a particular category

Parameters: kp_identifiers (List) – list of keypoint category dicts, names, or ids
Returns: num_removed: information on the number of items removed
Return type: Dict

remove_keypoint_categories(self, kp_identifiers)[source]¶

Removes all keypoints of a particular category as well as all annotation keypoints with those ids.

Parameters: kp_identifiers (List) – list of keypoint category dicts, names, or ids
Returns: num_removed: information on the number of items removed
Return type: Dict

Example

>>> self = CocoDataset.demo('shapes', rng=0)
>>> kp_identifiers = ['left_eye', 'mid_tip']
>>> remove_info = self.remove_keypoint_categories(kp_identifiers)
>>> print('remove_info = {!r}'.format(remove_info))
>>> # FIXME: for whatever reason demodata generation is not determenistic when seeded
>>> # assert remove_info == {'keypoint_categories': 2, 'annotation_keypoints': 16, 'reflection_ids': 1}
>>> assert self._resolve_to_kpcat('right_eye')['reflection_id'] is None

set_annotation_category(self, aid_or_ann, cid_or_cat)[source]¶

Sets the category of a single annotation

Parameters

aid_or_ann (dict | int) – annotation dict or id
cid_or_cat (dict | int) – category dict or id

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> old_freq = self.category_annotation_frequency()
>>> aid_or_ann = aid = 2
>>> cid_or_cat = new_cid = self.ensure_category('kitten')
>>> self.set_annotation_category(aid, new_cid)
>>> new_freq = self.category_annotation_frequency()
>>> print('new_freq = {}'.format(ub.repr2(new_freq, nl=1)))
>>> print('old_freq = {}'.format(ub.repr2(old_freq, nl=1)))
>>> assert sum(new_freq.values()) == sum(old_freq.values())
>>> assert new_freq['kitten'] == 1

class kwcoco.coco_dataset.CocoIndex(index)[source]¶

Bases: object

Fast lookup index for the COCO dataset with dynamic modification

Variables

imgs (Dict[int, dict]) – mapping between image ids and the image dictionaries
anns (Dict[int, dict]) – mapping between annotation ids and the annotation dictionaries
cats (Dict[int, dict]) – mapping between category ids and the category dictionaries
kpcats (Dict[int, dict]) – mapping between keypoint category ids and keypoint category dictionaries
gid_to_aids (Dict[int, List[int]]) – mapping between an image-id and annotation-ids that belong to it
cid_to_aids (Dict[int, List[int]]) – mapping between an category-id and annotation-ids that belong to it
cid_to_gids (Dict[int, List[int]]) – mapping between an category-id and image-ids that contain at least one annotation with this cateogry id.
trackid_to_aids (Dict[int, List[int]]) – mapping between a track-id and annotation-ids that belong to it
vidid_to_gids (Dict[int, List[int]]) – mapping between an video-id and images-ids that belong to it
name_to_video (Dict[str, dict]) – mapping between a video name and the video dictionary.
name_to_cat (Dict[str, dict]) – mapping between a category name and the category dictionary.
name_to_img (Dict[str, dict]) – mapping between a image name and the image dictionary.
file_name_to_img (Dict[str, dict]) – mapping between a image file_name and the image dictionary.

_set[source]¶

__nonzero__[source]¶

_set_sorted_by_frame_index(index, gids=None)[source]¶: Helper for ensuring that vidid_to_gids returns image ids ordered by frame index.

__bool__(index)[source]¶

property cid_to_gids(index)[source]¶

Example

>>> import kwcoco
>>> self = dset = kwcoco.CocoDataset()
>>> self.index.cid_to_gids

_add_video(index, vidid, video)[source]¶

_add_image(index, gid, img)[source]¶

Example

>>> # Test adding image to video that doesnt exist
>>> import kwcoco
>>> self = dset = kwcoco.CocoDataset()
>>> dset.add_image(file_name='frame1', video_id=1, frame_index=0)
>>> dset.add_image(file_name='frame2', video_id=1, frame_index=0)
>>> dset._check_pointers()
>>> dset._check_index()
>>> print('dset.index.vidid_to_gids = {!r}'.format(dset.index.vidid_to_gids))
>>> assert len(dset.index.vidid_to_gids) == 1
>>> dset.add_video(name='foo-vid', id=1)
>>> assert len(dset.index.vidid_to_gids) == 1
>>> dset._check_pointers()
>>> dset._check_index()

_add_images(index, imgs)[source]¶: See ../dev/bench/bench_add_image_check.py

Note

THIS FUNCTION WAS DESIGNED FOR SPEED, AS SUCH IT DOES NOT CHECK IF THE IMAGE-IDs or FILE_NAMES ARE DUPLICATED AND WILL BLINDLY ADD DATA EVEN IF IT IS BAD. THE SINGLE IMAGE VERSION IS SLOWER BUT SAFER.

_add_annotation(index, aid, gid, cid, tid, ann)[source]¶

_add_annotations(index, anns)[source]¶

_add_category(index, cid, name, cat)[source]¶

_remove_all_annotations(index)[source]¶

_remove_all_images(index)[source]¶

_remove_annotations(index, remove_aids, verbose=0)[source]¶

_remove_categories(index, remove_cids, verbose=0)[source]¶

_remove_images(index, remove_gids, verbose=0)[source]¶

_remove_videos(index, remove_vidids, verbose=0)[source]¶

clear(index)[source]¶

build(index, parent)[source]¶

Build all id-to-obj reverse indexes from scratch.

Parameters: parent (CocoDataset) – the dataset to index

Notation:: aid - Annotation ID gid - imaGe ID cid - Category ID vidid - Video ID

Example

>>> import kwcoco
>>> parent = kwcoco.CocoDataset.demo('vidshapes1', num_frames=4, rng=1)
>>> index = parent.index
>>> index.build(parent)

class kwcoco.coco_dataset.MixinCocoIndex[source]¶

Bases: object

Give the dataset top level access to index attributes

property anns(self)[source]¶

property imgs(self)[source]¶

property cats(self)[source]¶

property gid_to_aids(self)[source]¶

property cid_to_aids(self)[source]¶

property name_to_cat(self)[source]¶

class kwcoco.coco_dataset.CocoDataset(data=None, tag=None, bundle_dpath=None, img_root=None, fname=None, autobuild=True)[source]¶

Bases: kwcoco.abstract_coco_dataset.AbstractCocoDataset, MixinCocoAddRemove, MixinCocoStats, MixinCocoObjects, MixinCocoDraw, MixinCocoAccessors, MixinCocoExtras, MixinCocoIndex, MixinCocoDepricate, ubelt.NiceRepr

The main coco dataset class with a json dataset backend.

Variables

dataset (Dict) – raw json data structure. This is the base dictionary that contains {‘annotations’: List, ‘images’: List, ‘categories’: List}
index (CocoIndex) – an efficient lookup index into the coco data structure. The index defines its own attributes like anns, cats, imgs, gid_to_aids, file_name_to_img, etc. See CocoIndex for more details on which attributes are available.
fpath (PathLike | None) – if known, this stores the filepath the dataset was loaded from
tag (str) – A tag indicating the name of the dataset.
bundle_dpath (PathLike | None) – If known, this is the root path that all image file names are relative to. This can also be manually overwritten by the user.
hashid (str | None) – If computed, this will be a hash uniquely identifing the dataset. To ensure this is computed see kwcoco.coco_dataset.MixinCocoExtras._build_hashid().

References

CommandLine

python -m kwcoco.coco_dataset CocoDataset --show

Example

>>> from kwcoco.coco_dataset import demo_coco_data
>>> import kwcoco
>>> import ubelt as ub
>>> # Returns a coco json structure
>>> dataset = demo_coco_data()
>>> # Pass the coco json structure to the API
>>> self = kwcoco.CocoDataset(dataset, tag='demo')
>>> # Now you can access the data using the index and helper methods
>>> #
>>> # Start by looking up an image by it's COCO id.
>>> image_id = 1
>>> img = self.index.imgs[image_id]
>>> print(ub.repr2(img, nl=1, sort=1))
{
    'file_name': 'astro.png',
    'id': 1,
    'url': 'https://i.imgur.com/KXhKM72.png',
}
>>> #
>>> # Use the (gid_to_aids) index to lookup annotations in the iamge
>>> annotation_id = sorted(self.index.gid_to_aids[image_id])[0]
>>> ann = self.index.anns[annotation_id]
>>> print(ub.repr2(ub.dict_diff(ann, {'segmentation'}), nl=1))
{
    'bbox': [10, 10, 360, 490],
    'category_id': 1,
    'id': 1,
    'image_id': 1,
    'keypoints': [247, 101, 2, 202, 100, 2],
}
>>> #
>>> # Use annotation category id to look up that information
>>> category_id = ann['category_id']
>>> cat = self.index.cats[category_id]
>>> print('cat = {}'.format(ub.repr2(cat, nl=1, sort=1)))
cat = {
    'id': 1,
    'name': 'astronaut',
    'supercategory': 'human',
}
>>> #
>>> # Now play with some helper functions, like extended statistics
>>> extended_stats = self.extended_stats()
>>> # xdoctest: +IGNORE_WANT
>>> print('extended_stats = {}'.format(ub.repr2(extended_stats, nl=1, precision=2, sort=1)))
extended_stats = {
    'annots_per_img': {'mean': 3.67, 'std': 3.86, 'min': 0.00, 'max': 9.00, 'nMin': 1, 'nMax': 1, 'shape': (3,)},
    'imgs_per_cat': {'mean': 0.88, 'std': 0.60, 'min': 0.00, 'max': 2.00, 'nMin': 2, 'nMax': 1, 'shape': (8,)},
    'cats_per_img': {'mean': 2.33, 'std': 2.05, 'min': 0.00, 'max': 5.00, 'nMin': 1, 'nMax': 1, 'shape': (3,)},
    'annots_per_cat': {'mean': 1.38, 'std': 1.49, 'min': 0.00, 'max': 5.00, 'nMin': 2, 'nMax': 1, 'shape': (8,)},
    'imgs_per_video': {'empty_list': True},
}
>>> # You can "draw" a raster of the annotated image with cv2
>>> canvas = self.draw_image(2)
>>> # Or if you have matplotlib you can "show" the image with mpl objects
>>> # xdoctest: +REQUIRES(--show)
>>> from matplotlib import pyplot as plt
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(1, 2, 1)
>>> self.show_image(gid=2)
>>> ax2 = fig.add_subplot(1, 2, 2)
>>> ax2.imshow(canvas)
>>> ax1.set_title('show with matplotlib')
>>> ax2.set_title('draw with cv2')
>>> plt.show()

property fpath(self)[source]¶: In the future we will deprecate img_root for bundle_dpath

_infer_dirs(self)[source]¶

classmethod from_data(CocoDataset, data, bundle_dpath=None, img_root=None)[source]¶: Constructor from a json dictionary

classmethod from_image_paths(CocoDataset, gpaths, bundle_dpath=None, img_root=None)[source]¶

Constructor from a list of images paths.

This is a convinience method.

Parameters: gpaths (List[str]) – list of image paths

Example

>>> coco_dset = CocoDataset.from_image_paths(['a.png', 'b.png'])
>>> assert coco_dset.n_images == 2

classmethod from_coco_paths(CocoDataset, fpaths, max_workers=0, verbose=1, mode='thread', union='try')[source]¶

Constructor from multiple coco file paths.

Loads multiple coco datasets and unions the result

Note

if the union operation fails, the list of individually loaded files is returned instead.

Parameters

fpaths (List[str]) – list of paths to multiple coco files to be loaded and unioned.
max_workers (int, default=0) – number of worker threads / processes
verbose (int) – verbosity level
mode (str) – thread, process, or serial
union (str | bool, default=’try’) – If True, unions the result datasets after loading. If False, just returns the result list. If ‘try’, then try to preform the union, but return the result list if it fails.

copy(self)[source]¶

Deep copies this object

Example

>>> from kwcoco.coco_dataset import *
>>> self = CocoDataset.demo()
>>> new = self.copy()
>>> assert new.imgs[1] is new.dataset['images'][0]
>>> assert new.imgs[1] == self.dataset['images'][0]
>>> assert new.imgs[1] is not self.dataset['images'][0]

__nice__(self)[source]¶

dumps(self, indent=None, newlines=False)[source]¶

Writes the dataset out to the json format

Parameters: newlines (bool) – if True, each annotation, image, category gets its own line

Note

Using newlines=True is similar to:: print(ub.repr2(dset.dataset, nl=2, trailsep=False)) However, the above may not output valid json if it contains ndarrays.

Example

>>> from kwcoco.coco_dataset import *
>>> self = CocoDataset.demo()
>>> text = self.dumps(newlines=True)
>>> print(text)
>>> self2 = CocoDataset(json.loads(text), tag='demo2')
>>> assert self2.dataset == self.dataset
>>> assert self2.dataset is not self.dataset

>>> text = self.dumps(newlines=True)
>>> print(text)
>>> self2 = CocoDataset(json.loads(text), tag='demo2')
>>> assert self2.dataset == self.dataset
>>> assert self2.dataset is not self.dataset

Example

>>> from kwcoco.coco_dataset import *
>>> self = CocoDataset.coerce('vidshapes1-msi-multisensor', verbose=3)
>>> self.remove_annotations(self.annots())
>>> text = self.dumps(newlines=True, indent='  ')
>>> print(text)

dump(self, file, indent=None, newlines=False, temp_file=True)[source]¶

Writes the dataset out to the json format

Parameters

file (PathLike | FileLike) – Where to write the data. Can either be a path to a file or an open file pointer / stream.
newlines (bool) – if True, each annotation, image, category gets its own line.
temp_file (bool | str, default=True) – Argument to safer.open(). Ignored if file is not a PathLike object.

Example

>>> import tempfile
>>> from kwcoco.coco_dataset import *
>>> self = CocoDataset.demo()
>>> file = tempfile.NamedTemporaryFile('w')
>>> self.dump(file)
>>> file.seek(0)
>>> text = open(file.name, 'r').read()
>>> print(text)
>>> file.seek(0)
>>> dataset = json.load(open(file.name, 'r'))
>>> self2 = CocoDataset(dataset, tag='demo2')
>>> assert self2.dataset == self.dataset
>>> assert self2.dataset is not self.dataset

>>> file = tempfile.NamedTemporaryFile('w')
>>> self.dump(file, newlines=True)
>>> file.seek(0)
>>> text = open(file.name, 'r').read()
>>> print(text)
>>> file.seek(0)
>>> dataset = json.load(open(file.name, 'r'))
>>> self2 = CocoDataset(dataset, tag='demo2')
>>> assert self2.dataset == self.dataset
>>> assert self2.dataset is not self.dataset

_check_json_serializable(self, verbose=1)[source]¶: Debug which part of a coco dataset might not be json serializable

_check_integrity(self)[source]¶: perform all checks

_check_index(self)[source]¶

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> self._check_index()
>>> # Force a failure
>>> self.index.anns.pop(1)
>>> self.index.anns.pop(2)
>>> import pytest
>>> with pytest.raises(AssertionError):
>>>     self._check_index()

_check_pointers(self, verbose=1)[source]¶: Check that all category and image ids referenced by annotations exist

_build_index(self)[source]¶

union(*others, disjoint_tracks=True, **kwargs)[source]¶

Merges multiple CocoDataset items into one. Names and associations are retained, but ids may be different.

Parameters

*others – a series of CocoDatasets that we will merge. Note, if called as an instance method, the “self” instance will be the first item in the “others” list. But if called like a classmethod, “others” will be empty by default.
disjoint_tracks (bool, default=True) – if True, we will assume track-ids are disjoint and if two datasets share the same track-id, we will disambiguate them. Otherwise they will be copied over as-is.
**kwargs – constructor options for the new merged CocoDataset

Returns

a new merged coco dataset

Return type

CocoDataset

CommandLine

xdoctest -m kwcoco.coco_dataset CocoDataset.union

Example

>>> # Test union works with different keypoint categories
>>> dset1 = CocoDataset.demo('shapes1')
>>> dset2 = CocoDataset.demo('shapes2')
>>> dset1.remove_keypoint_categories(['bot_tip', 'mid_tip', 'right_eye'])
>>> dset2.remove_keypoint_categories(['top_tip', 'left_eye'])
>>> dset_12a = CocoDataset.union(dset1, dset2)
>>> dset_12b = dset1.union(dset2)
>>> dset_21 = dset2.union(dset1)
>>> def add_hist(h1, h2):
>>>     return {k: h1.get(k, 0) + h2.get(k, 0) for k in set(h1) | set(h2)}
>>> kpfreq1 = dset1.keypoint_annotation_frequency()
>>> kpfreq2 = dset2.keypoint_annotation_frequency()
>>> kpfreq_want = add_hist(kpfreq1, kpfreq2)
>>> kpfreq_got1 = dset_12a.keypoint_annotation_frequency()
>>> kpfreq_got2 = dset_12b.keypoint_annotation_frequency()
>>> assert kpfreq_want == kpfreq_got1
>>> assert kpfreq_want == kpfreq_got2

>>> # Test disjoint gid datasets
>>> import kwcoco
>>> dset1 = kwcoco.CocoDataset.demo('shapes3')
>>> for new_gid, img in enumerate(dset1.dataset['images'], start=10):
>>>     for aid in dset1.gid_to_aids[img['id']]:
>>>         dset1.anns[aid]['image_id'] = new_gid
>>>     img['id'] = new_gid
>>> dset1.index.clear()
>>> dset1._build_index()
>>> # ------
>>> dset2 = kwcoco.CocoDataset.demo('shapes2')
>>> for new_gid, img in enumerate(dset2.dataset['images'], start=100):
>>>     for aid in dset2.gid_to_aids[img['id']]:
>>>         dset2.anns[aid]['image_id'] = new_gid
>>>     img['id'] = new_gid
>>> dset1.index.clear()
>>> dset2._build_index()
>>> others = [dset1, dset2]
>>> merged = kwcoco.CocoDataset.union(*others)
>>> print('merged = {!r}'.format(merged))
>>> print('merged.imgs = {}'.format(ub.repr2(merged.imgs, nl=1)))
>>> assert set(merged.imgs) & set([10, 11, 12, 100, 101]) == set(merged.imgs)

>>> # Test data is not preserved
>>> dset2 = kwcoco.CocoDataset.demo('shapes2')
>>> dset1 = kwcoco.CocoDataset.demo('shapes3')
>>> others = (dset1, dset2)
>>> cls = self = kwcoco.CocoDataset
>>> merged = cls.union(*others)
>>> print('merged = {!r}'.format(merged))
>>> print('merged.imgs = {}'.format(ub.repr2(merged.imgs, nl=1)))
>>> assert set(merged.imgs) & set([1, 2, 3, 4, 5]) == set(merged.imgs)

>>> # Test track-ids are mapped correctly
>>> dset1 = kwcoco.CocoDataset.demo('vidshapes1')
>>> dset2 = kwcoco.CocoDataset.demo('vidshapes2')
>>> dset3 = kwcoco.CocoDataset.demo('vidshapes3')
>>> others = (dset1, dset2, dset3)
>>> for dset in others:
>>>     [a.pop('segmentation', None) for a in dset.index.anns.values()]
>>>     [a.pop('keypoints', None) for a in dset.index.anns.values()]
>>> cls = self = kwcoco.CocoDataset
>>> merged = cls.union(*others, disjoint_tracks=1)
>>> print('dset1.anns = {}'.format(ub.repr2(dset1.anns, nl=1)))
>>> print('dset2.anns = {}'.format(ub.repr2(dset2.anns, nl=1)))
>>> print('dset3.anns = {}'.format(ub.repr2(dset3.anns, nl=1)))
>>> print('merged.anns = {}'.format(ub.repr2(merged.anns, nl=1)))

Example

>>> import kwcoco
>>> # Test empty union
>>> empty_union = kwcoco.CocoDataset.union()
>>> assert len(empty_union.index.imgs) == 0

Todo

[ ] are supercategories broken?
[ ] reuse image ids where possible
[ ] reuse annotation / category ids where possible
[X] handle case where no inputs are given
[x] disambiguate track-ids
[x] disambiguate video-ids

subset(self, gids, copy=False, autobuild=True)[source]¶

Return a subset of the larger coco dataset by specifying which images to port. All annotations in those images will be taken.

Parameters

gids (List[int]) – image-ids to copy into a new dataset
copy (bool, default=False) – if True, makes a deep copy of all nested attributes, otherwise makes a shallow copy.
autobuild (bool, default=True) – if True will automatically build the fast lookup index.

Example

>>> self = CocoDataset.demo()
>>> gids = [1, 3]
>>> sub_dset = self.subset(gids)
>>> assert len(self.index.gid_to_aids) == 3
>>> assert len(sub_dset.gid_to_aids) == 2

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo('vidshapes2')
>>> gids = [1, 2]
>>> sub_dset = self.subset(gids, copy=True)
>>> assert len(sub_dset.index.videos) == 1
>>> assert len(self.index.videos) == 2

Example

>>> self = CocoDataset.demo()
>>> sub1 = self.subset([1])
>>> sub2 = self.subset([2])
>>> sub3 = self.subset([3])
>>> others = [sub1, sub2, sub3]
>>> rejoined = CocoDataset.union(*others)
>>> assert len(sub1.anns) == 9
>>> assert len(sub2.anns) == 2
>>> assert len(sub3.anns) == 0
>>> assert rejoined.basic_stats() == self.basic_stats()

view_sql(self, force_rewrite=False, memory=False)[source]¶

Create a cached SQL interface to this dataset suitable for large scale multiprocessing use cases.

Parameters

force_rewrite (bool, default=False) – if True, forces an update to any existing cache file on disk
memory (bool, default=False) – if True, the database is constructed in memory.

Note

This view cache is experimental and currently depends on the timestamp of the file pointed to by self.fpath. In other words dont use this on in-memory datasets.

kwcoco.coco_dataset.demo_coco_data()[source]¶

Simple data for testing.

This contains several non-standard fields, which help ensure robustness of functions tested with this data. For more compliant demodata see the kwcoco.demodata submodule

Example

>>> # xdoctest: +REQUIRES(--show)
>>> from kwcoco.coco_dataset import demo_coco_data, CocoDataset
>>> dataset = demo_coco_data()
>>> self = CocoDataset(dataset, tag='demo')
>>> import kwplot
>>> kwplot.autompl()
>>> self.show_image(gid=1)
>>> kwplot.show_if_requested()

`kwcoco.coco_evaluator`¶

Evaluates a predicted coco dataset against a truth coco dataset.

The components in this module work programatically or as a command line script.

Todo

[ ] does evaluate return one result or multiple results
based on different configurations?
[ ] max_dets - TODO: in original pycocoutils but not here
[ ] Flag that allows for polygon instead of bounding box overlap
[ ] How do we note what iou_thresh and area-range were in
the result plots?

CommandLine

xdoctest -m kwcoco.coco_evaluator __doc__:0 --vd --slow

Example

>>> from kwcoco.coco_evaluator import *  # NOQA
>>> from kwcoco.coco_evaluator import CocoEvaluator
>>> import kwcoco
>>> # note: increase the number of images for better looking metrics
>>> true_dset = kwcoco.CocoDataset.demo('shapes8')
>>> from kwcoco.demo.perterb import perterb_coco
>>> kwargs = {
>>>     'box_noise': 0.5,
>>>     'n_fp': (0, 10),
>>>     'n_fn': (0, 10),
>>>     'with_probs': True,
>>> }
>>> pred_dset = perterb_coco(true_dset, **kwargs)
>>> print('true_dset = {!r}'.format(true_dset))
>>> print('pred_dset = {!r}'.format(pred_dset))
>>> config = {
>>>     'true_dataset': true_dset,
>>>     'pred_dataset': pred_dset,
>>>     'area_range': ['all', 'small'],
>>>     'iou_thresh': [0.3, 0.95],
>>> }
>>> coco_eval = CocoEvaluator(config)
>>> results = coco_eval.evaluate()
>>> # Now we can draw / serialize the results as we please
>>> dpath = ub.ensure_app_cache_dir('kwcoco/tests/test_out_dpath')
>>> results_fpath = join(dpath, 'metrics.json')
>>> print('results_fpath = {!r}'.format(results_fpath))
>>> results.dump(results_fpath, indent='    ')
>>> measures = results['area_range=all,iou_thresh=0.3'].nocls_measures
>>> import pandas as pd
>>> print(pd.DataFrame(ub.dict_isect(
>>>     measures, ['f1', 'g1', 'mcc', 'thresholds',
>>>                'ppv', 'tpr', 'tnr', 'npv', 'fpr',
>>>                'tp_count', 'fp_count',
>>>                'tn_count', 'fn_count'])).iloc[::100])
>>> # xdoctest: +REQUIRES(module:kwplot)
>>> # xdoctest: +REQUIRES(--slow)
>>> results.dump_figures(dpath)
>>> print('dpath = {!r}'.format(dpath))
>>> # xdoctest: +REQUIRES(--vd)
>>> if ub.argflag('--vd') or 1:
>>>     import xdev
>>>     xdev.view_directory(dpath)

Module Contents¶

Classes¶

`CocoEvalConfig`	Evaluate and score predicted versus truth detections / classifications in a COCO dataset
`CocoEvaluator`	Abstracts the evaluation process to execute on two coco datasets.
`CocoResults`	CommandLine
`CocoSingleResult`	Container class to store, draw, summarize, and serialize results from

Functions¶

`dmet_area_weights`(dmet, orig_weights, cfsn_vecs, area_ranges, coco_eval, use_area_attr=False)	Hacky function to compute confusion vector ignore weights for different
`_writefig`(fig, metrics_dpath, fname, figsize, verbose, tight)
`_load_dets`(pred_fpaths, workers=0)	Example
`_load_dets_worker`(single_pred_fpath, with_coco=True)

Attributes¶

`profile`
`COCO_SAMPLER_CLS`

kwcoco.coco_evaluator.profile[source]¶

kwcoco.coco_evaluator.COCO_SAMPLER_CLS[source]¶

class kwcoco.coco_evaluator.CocoEvalConfig(data=None, default=None, cmdline=False)[source]¶

Evaluate and score predicted versus truth detections / classifications in a COCO dataset

default[source]¶

normalize(self)[source]¶: overloadable function called after each load

class kwcoco.coco_evaluator.CocoEvaluator(coco_eval, config)[source]¶

Bases: object

Abstracts the evaluation process to execute on two coco datasets.

This can be run as a standalone script where the user specifies the paths to the true and predited dataset explicitly, or this can be used by a higher level script that produces the predictions and then sends them to this evaluator.

Example

>>> from kwcoco.coco_evaluator import CocoEvaluator
>>> from kwcoco.demo.perterb import perterb_coco
>>> import kwcoco
>>> true_dset = kwcoco.CocoDataset.demo('shapes8')
>>> kwargs = {
>>>     'box_noise': 0.5,
>>>     'n_fp': (0, 10),
>>>     'n_fn': (0, 10),
>>>     'with_probs': True,
>>> }
>>> pred_dset = perterb_coco(true_dset, **kwargs)
>>> config = {
>>>     'true_dataset': true_dset,
>>>     'pred_dataset': pred_dset,
>>>     'classes_of_interest': [],
>>> }
>>> coco_eval = CocoEvaluator(config)
>>> results = coco_eval.evaluate()

Config[source]¶

log(coco_eval, msg, level='INFO')[source]¶

_init(coco_eval)[source]¶: Performs initial coercion from given inputs into dictionaries of kwimage.Detection objects and attempts to ensure comparable category and image ids.

_ensure_init(coco_eval)[source]¶

classmethod _rectify_classes(coco_eval, true_classes, pred_classes)[source]¶

classmethod _coerce_dets(CocoEvaluator, dataset, verbose=0, workers=0)[source]¶

Coerce the input to a mapping from image-id to kwimage.Detection

Also capture a CocoDataset if possible.

Returns: gid_to_det: mapping from gid to dets extra: any extra information we gathered via coercion
Return type: Tuple[Dict[int, Detections], Dict]

Example

>>> from kwcoco.coco_evaluator import *  # NOQA
>>> import kwcoco
>>> coco_dset = kwcoco.CocoDataset.demo('shapes8')
>>> gid_to_det, extras = CocoEvaluator._coerce_dets(coco_dset)

Example

>>> # xdoctest: +REQUIRES(module:sqlalchemy)
>>> from kwcoco.coco_evaluator import *  # NOQA
>>> import kwcoco
>>> coco_dset = kwcoco.CocoDataset.demo('shapes8').view_sql()
>>> gid_to_det, extras = CocoEvaluator._coerce_dets(coco_dset)

_build_dmet(coco_eval)[source]¶

Builds the detection metrics object

Returns

DetectionMetrics - object that can perform assignment and: build confusion vectors.

evaluate(coco_eval)[source]¶

Executes the main evaluation logic. Performs assignments between detections to make DetectionMetrics object, then creates per-item and ovr confusion vectors, and performs various threshold-vs-confusion analyses.

Returns

container storing (and capable of drawing /: serializing) results

Return type

CocoResults

kwcoco.coco_evaluator.dmet_area_weights(dmet, orig_weights, cfsn_vecs, area_ranges, coco_eval, use_area_attr=False)[source]¶: Hacky function to compute confusion vector ignore weights for different area thresholds. Needs to be slightly refactored.

class kwcoco.coco_evaluator.CocoResults(results, resdata=None)[source]¶

Bases: ubelt.NiceRepr, kwcoco.metrics.util.DictProxy

CommandLine

xdoctest -m /home/joncrall/code/kwcoco/kwcoco/coco_evaluator.py CocoResults --profile

Example

>>> from kwcoco.coco_evaluator import *  # NOQA
>>> from kwcoco.coco_evaluator import CocoEvaluator
>>> import kwcoco
>>> true_dset = kwcoco.CocoDataset.demo('shapes2')
>>> from kwcoco.demo.perterb import perterb_coco
>>> kwargs = {
>>>     'box_noise': 0.5,
>>>     'n_fp': (0, 10),
>>>     'n_fn': (0, 10),
>>> }
>>> pred_dset = perterb_coco(true_dset, **kwargs)
>>> print('true_dset = {!r}'.format(true_dset))
>>> print('pred_dset = {!r}'.format(pred_dset))
>>> config = {
>>>     'true_dataset': true_dset,
>>>     'pred_dataset': pred_dset,
>>>     'area_range': ['small'],
>>>     'iou_thresh': [0.3],
>>> }
>>> coco_eval = CocoEvaluator(config)
>>> results = coco_eval.evaluate()
>>> # Now we can draw / serialize the results as we please
>>> dpath = ub.ensure_app_cache_dir('kwcoco/tests/test_out_dpath')
>>> #
>>> # test deserialization works
>>> state = results.__json__()
>>> self2 = CocoResults.from_json(state)
>>> #
>>> # xdoctest: +REQUIRES(module:kwplot)
>>> results.dump_figures(dpath, figsize=(3, 2), tight=False)  # make this go faster
>>> results.dump(join(dpath, 'metrics.json'), indent='    ')

dump_figures(results, out_dpath, expt_title=None, figsize='auto', tight=True)[source]¶

__json__(results)[source]¶

classmethod from_json(cls, state)[source]¶

dump(result, file, indent=' ')[source]¶: Serialize to json file

class kwcoco.coco_evaluator.CocoSingleResult(result, nocls_measures, ovr_measures, cfsn_vecs, meta=None)[source]¶

Bases: ubelt.NiceRepr

Container class to store, draw, summarize, and serialize results from CocoEvaluator.

Example

>>> # xdoctest: +REQUIRES(--slow)
>>> from kwcoco.coco_evaluator import *  # NOQA
>>> from kwcoco.coco_evaluator import CocoEvaluator
>>> import kwcoco
>>> true_dset = kwcoco.CocoDataset.demo('shapes8')
>>> from kwcoco.demo.perterb import perterb_coco
>>> kwargs = {
>>>     'box_noise': 0.2,
>>>     'n_fp': (0, 3),
>>>     'n_fn': (0, 3),
>>>     'with_probs': False,
>>> }
>>> pred_dset = perterb_coco(true_dset, **kwargs)
>>> print('true_dset = {!r}'.format(true_dset))
>>> print('pred_dset = {!r}'.format(pred_dset))
>>> config = {
>>>     'true_dataset': true_dset,
>>>     'pred_dataset': pred_dset,
>>>     'area_range': [(0, 32 ** 2), (32 ** 2, 96 ** 2)],
>>>     'iou_thresh': [0.3, 0.5, 0.95],
>>> }
>>> coco_eval = CocoEvaluator(config)
>>> results = coco_eval.evaluate()
>>> result = ub.peek(results.values())
>>> state = result.__json__()
>>> print('state = {}'.format(ub.repr2(state, nl=-1)))
>>> recon = CocoSingleResult.from_json(state)
>>> state = recon.__json__()
>>> print('state = {}'.format(ub.repr2(state, nl=-1)))

__nice__(result)[source]¶

classmethod from_json(cls, state)[source]¶

__json__(result)[source]¶

dump(result, file, indent=' ')[source]¶: Serialize to json file

dump_figures(result, out_dpath, expt_title=None, figsize='auto', tight=True, verbose=1)[source]¶

kwcoco.coco_evaluator._writefig(fig, metrics_dpath, fname, figsize, verbose, tight)[source]¶

kwcoco.coco_evaluator._load_dets(pred_fpaths, workers=0)[source]¶

Example

>>> from kwcoco.coco_evaluator import _load_dets, _load_dets_worker
>>> import ubelt as ub
>>> import kwcoco
>>> from os.path import join
>>> dpath = ub.ensure_app_cache_dir('kwcoco/tests/load_dets')
>>> N = 4
>>> pred_fpaths = []
>>> for i in range(1, N + 1):
>>>     dset = kwcoco.CocoDataset.demo('shapes{}'.format(i))
>>>     dset.fpath = join(dpath, 'shapes_{}.mscoco.json'.format(i))
>>>     dset.dump(dset.fpath)
>>>     pred_fpaths.append(dset.fpath)
>>> dets, coco_dset = _load_dets(pred_fpaths)
>>> print('dets = {!r}'.format(dets))
>>> print('coco_dset = {!r}'.format(coco_dset))

kwcoco.coco_evaluator._load_dets_worker(single_pred_fpath, with_coco=True)[source]¶

`kwcoco.coco_image`¶

Module Contents¶

Classes¶

`CocoImage`	An object-oriented representation of a coco image.
`CocoAsset`	A Coco Asset / Auxiliary Item

Functions¶

_delay_load_imglike(bundle_dpath, obj)

Attributes¶

Bases: kwcoco.abstract_coco_dataset.AbstractCocoDataset, kwcoco.coco_dataset.MixinCocoAccessors, kwcoco.coco_dataset.MixinCocoObjects, kwcoco.coco_dataset.MixinCocoStats, kwcoco.coco_dataset.MixinCocoDraw, ubelt.NiceRepr

kwcoco.coco_image.profile[source]¶

class kwcoco.coco_image.CocoImage(img, dset=None)[source]¶

Bases: ubelt.NiceRepr

An object-oriented representation of a coco image.

It provides helper methods that are specific to a single image.

This operates directly on a single coco image dictionary, but it can optionally be connected to a parent dataset, which allows it to use CocoDataset methods to query about relationships and resolve pointers.

This is different than the Images class in coco_object1d, which is just a vectorized interface to multiple objects.

Example

>>> import kwcoco
>>> dset1 = kwcoco.CocoDataset.demo('shapes8')
>>> dset2 = kwcoco.CocoDataset.demo('vidshapes8-multispectral')

>>> self = CocoImage(dset1.imgs[1], dset1)
>>> print('self = {!r}'.format(self))
>>> print('self.channels = {}'.format(ub.repr2(self.channels, nl=1)))

>>> self = CocoImage(dset2.imgs[1], dset2)
>>> print('self.channels = {}'.format(ub.repr2(self.channels, nl=1)))
>>> self.primary_asset()

classmethod from_gid(cls, dset, gid)[source]¶

property bundle_dpath(self)[source]¶

property video(self)[source]¶: Helper to grab the video for this image if it exists

detach(self)[source]¶: Removes references to the underlying coco dataset, but keeps special information such that it wont be needed.

__nice__(self)[source]¶

Example

>>> from kwcoco.coco_image import *  # NOQA
>>> import kwcoco
>>> with ub.CaptureStdout() as cap:
...     dset = kwcoco.CocoDataset.demo('shapes8')
>>> self = CocoImage(dset.dataset['images'][0], dset)
>>> print('self = {!r}'.format(self))

>>> dset = kwcoco.CocoDataset.demo()
>>> self = CocoImage(dset.dataset['images'][0], dset)
>>> print('self = {!r}'.format(self))

stats(self)[source]¶

__contains__(self, key)[source]¶

__getitem__(self, key)[source]¶: Proxy getter attribute for underlying self.img dictionary

keys(self)[source]¶: Proxy getter attribute for underlying self.img dictionary

get(self, key, default=ub.NoParam)[source]¶

Proxy getter attribute for underlying self.img dictionary

Example

>>> import pytest
>>> # without extra populated
>>> import kwcoco
>>> self = kwcoco.CocoImage({'foo': 1})
>>> assert self.get('foo') == 1
>>> assert self.get('foo', None) == 1
>>> # with extra populated
>>> self = kwcoco.CocoImage({'extra': {'foo': 1}})
>>> assert self.get('foo') == 1
>>> assert self.get('foo', None) == 1
>>> # without extra empty
>>> self = kwcoco.CocoImage({})
>>> with pytest.raises(KeyError):
>>>     self.get('foo')
>>> assert self.get('foo', None) is None
>>> # with extra empty
>>> self = kwcoco.CocoImage({'extra': {'bar': 1}})
>>> with pytest.raises(KeyError):
>>>     self.get('foo')
>>> assert self.get('foo', None) is None

property channels(self)[source]¶

property num_channels(self)[source]¶

property dsize(self)[source]¶

primary_image_filepath(self, requires=None)[source]¶

primary_asset(self, requires=None)[source]¶

Compute a “main” image asset.

Notes

Uses a heuristic.

First, try to find the auxiliary image that has with the smallest

distortion to the base image (if known via warp_aux_to_img)

Second, break ties by using the largest image if w / h is known
Last, if previous information not available use the first auxiliary image.

Parameters: requires (List[str]) – list of attribute that must be non-None to consider an object as the primary one.

Todo

[ ] Add in primary heuristics

Example

>>> import kwarray
>>> from kwcoco.coco_image import *  # NOQA
>>> rng = kwarray.ensure_rng(0)
>>> def random_auxiliary(name, w=None, h=None):
>>>     return {'file_name': name, 'width': w, 'height': h}
>>> self = CocoImage({
>>>     'auxiliary': [
>>>         random_auxiliary('1'),
>>>         random_auxiliary('2'),
>>>         random_auxiliary('3'),
>>>     ]
>>> })
>>> assert self.primary_asset()['file_name'] == '1'
>>> self = CocoImage({
>>>     'auxiliary': [
>>>         random_auxiliary('1'),
>>>         random_auxiliary('2', 3, 3),
>>>         random_auxiliary('3'),
>>>     ]
>>> })
>>> assert self.primary_asset()['file_name'] == '2'

iter_image_filepaths(self)[source]¶

iter_asset_objs(self)[source]¶

Iterate through base + auxiliary dicts that have file paths

Yields: dict – an image or auxiliary dictionary

find_asset_obj(self, channels)[source]¶

Find the asset dictionary with the specified channels

Example

>>> import kwcoco
>>> coco_img = kwcoco.CocoImage({'width': 128, 'height': 128})
>>> coco_img.add_auxiliary_item(
>>>     'rgb.png', channels='red|green|blue', width=32, height=32)
>>> assert coco_img.find_asset_obj('red') is not None
>>> assert coco_img.find_asset_obj('green') is not None
>>> assert coco_img.find_asset_obj('blue') is not None
>>> assert coco_img.find_asset_obj('red|blue') is not None
>>> assert coco_img.find_asset_obj('red|green|blue') is not None
>>> assert coco_img.find_asset_obj('red|green|blue') is not None
>>> assert coco_img.find_asset_obj('black') is None
>>> assert coco_img.find_asset_obj('r') is None

Example

>>> # Test with concise channel code
>>> import kwcoco
>>> coco_img = kwcoco.CocoImage({'width': 128, 'height': 128})
>>> coco_img.add_auxiliary_item(
>>>     'msi.png', channels='foo.0:128', width=32, height=32)
>>> assert coco_img.find_asset_obj('foo') is None
>>> assert coco_img.find_asset_obj('foo.3') is not None
>>> assert coco_img.find_asset_obj('foo.3:5') is not None
>>> assert coco_img.find_asset_obj('foo.3000') is None

_assets_key(self)[source]¶: Internal helper for transition from auxiliary -> assets in the image spec

add_auxiliary_item(self, file_name=None, channels=None, imdata=None, warp_aux_to_img=None, width=None, height=None, imwrite=False)[source]¶

Adds an auxiliary / asset item to the image dictionary.

This operation can be done purely in-memory (the default), or the image data can be written to a file on disk (via the imwrite=True flag).

Parameters

file_name (str | None) – The name of the file relative to the bundle directory. If unspecified, imdata must be given.
channels (str | kwcoco.FusedChannelSpec) – The channel code indicating what each of the bands represents. These channels should be disjoint wrt to the existing data in this image (this is not checked).
imdata (ndarray | None) – The underlying image data this auxiliary item represents. If unspecified, it is assumed file_name points to a path on disk that will eventually exist. If imdata, file_name, and the special imwrite=True flag are specified, this function will write the data to disk.
warp_aux_to_img (kwimage.Affine) – The transformation from this auxiliary space to image space. If unspecified, assumes this item is related to image space by only a scale factor.
width (int) – Width of the data in auxiliary space (inferred if unspecified)
height (int) – Height of the data in auxiliary space (inferred if unspecified)
imwrite (bool) – If specified, both imdata and file_name must be specified, and this will write the data to disk. Note: it it recommended that you simply call imwrite yourself before or after calling this function. This lets you better control imwrite parameters.

Todo

[ ] Allow imwrite to specify an executor that is used to

return a Future so the imwrite call does not block.

Example

>>> from kwcoco.coco_image import *  # NOQA
>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> coco_img = dset.coco_image(1)
>>> imdata = np.random.rand(32, 32, 5)
>>> channels = kwcoco.FusedChannelSpec.coerce('Aux:5')
>>> coco_img.add_auxiliary_item(imdata=imdata, channels=channels)

delay(self, channels=None, space='image', bundle_dpath=None)[source]¶

Perform a delayed load on the data in this image.

The delayed load can load a subset of channels, and perform lazy warping operations. If the underlying data is in a tiled format this can reduce the amount of disk IO needed to read the data if only a small crop or lower resolution view of the data is needed.

Note

This method is experimental and relies on the delayed load proof-of-concept.

Parameters

gid (int) – image id to load
channels (FusedChannelSpec) – specific channels to load. if unspecified, all channels are loaded.
space (str) – can either be “image” for loading in image space, or “video” for loading in video space.

Todo

[X] Currently can only take all or none of the channels from each
base-image / auxiliary dict. For instance if the main image is r|g|b you can’t just select g|b at the moment.
[X] The order of the channels in the delayed load should
match the requested channel order.
[X] TODO: add nans to bands that don’t exist or throw an error
[ ] This function could stand to have a better name. Maybe imread
with a delayed=True flag? Or maybe just delayed_load?

Example

>>> from kwcoco.coco_image import *  # NOQA
>>> import kwcoco
>>> gid = 1
>>> #
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> self = CocoImage(dset.imgs[gid], dset)
>>> delayed = self.delay()
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize()))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))
>>> #
>>> dset = kwcoco.CocoDataset.demo('shapes8')
>>> delayed = dset.delayed_load(gid)
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize()))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))

>>> crop = delayed.delayed_crop((slice(0, 3), slice(0, 3)))
>>> crop.finalize()
>>> crop.finalize(as_xarray=True)

>>> # TODO: should only select the "red" channel
>>> dset = kwcoco.CocoDataset.demo('shapes8')
>>> delayed = CocoImage(dset.imgs[gid], dset).delay(channels='r')

>>> import kwcoco
>>> gid = 1
>>> #
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> delayed = dset.delayed_load(gid, channels='B1|B2', space='image')
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))
>>> delayed = dset.delayed_load(gid, channels='B1|B2|B11', space='image')
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))
>>> delayed = dset.delayed_load(gid, channels='B8|B1', space='video')
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))

>>> delayed = dset.delayed_load(gid, channels='B8|foo|bar|B1', space='video')
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))

Example

>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo()
>>> coco_img = dset.coco_image(1)
>>> # Test case where nothing is registered in the dataset
>>> delayed = coco_img.delay()
>>> final = delayed.finalize()
>>> assert final.shape == (512, 512, 3)

Example

>>> # Test that delay works when imdata is stored in the image
>>> # dictionary itself.
>>> from kwcoco.coco_image import *  # NOQA
>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> coco_img = dset.coco_image(1)
>>> imdata = np.random.rand(6, 6, 5)
>>> imdata[:] = np.arange(5)[None, None, :]
>>> channels = kwcoco.FusedChannelSpec.coerce('Aux:5')
>>> coco_img.add_auxiliary_item(imdata=imdata, channels=channels)
>>> delayed = coco_img.delay(channels='B1|Aux:2:4')
>>> final = delayed.finalize()

Example

>>> # Test delay when loading in auxiliary space
>>> from kwcoco.coco_image import *  # NOQA
>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-msi-multisensor')
>>> coco_img = dset.coco_image(1)
>>> stream1 = coco_img.channels.streams()[0]
>>> stream2 = coco_img.channels.streams()[1]
>>> aux_delayed = coco_img.delay(stream1, space='auxiliary')
>>> img_delayed = coco_img.delay(stream1, space='image')
>>> vid_delayed = coco_img.delay(stream1, space='video')
>>> #
>>> aux_imdata = aux_delayed.finalize()
>>> img_imdata = img_delayed.finalize()
>>> assert aux_imdata.shape != img_imdata.shape
>>> # Cannot load multiple auxiliary items at the same time in
>>> # auxiliary space
>>> import pytest
>>> fused_channels = stream1 | stream2
>>> with pytest.raises(kwcoco.exceptions.CoordinateCompatibilityError):
>>>     aux_delayed2 = coco_img.delay(fused_channels, space='auxiliary')

valid_region(self, space='image')[source]¶: If this image has a valid polygon, return it in image, or video space

warp_vid_from_img(self)[source]¶

warp_img_from_vid(self)[source]¶

_annot_segmentation(self, ann, space='video')[source]¶

class kwcoco.coco_image.CocoAsset[source]¶

Bases: object

A Coco Asset / Auxiliary Item

Represents one 2D image file relative to a parent img.

Could be a single asset, or an image with sub-assets, but sub-assets are ignored here.

Initially we called these “auxiliary” items, but I think we should change their name to “assets”, which better maps with STAC terminology.

__getitem__(self, key)[source]¶: Proxy getter attribute for underlying self.obj dictionary

keys(self)[source]¶: Proxy getter attribute for underlying self.obj dictionary

get(self, key, default=ub.NoParam)[source]¶: Proxy getter attribute for underlying self.obj dictionary

kwcoco.coco_image._delay_load_imglike(bundle_dpath, obj)[source]¶

`kwcoco.coco_objects1d`¶

Vectorized ORM-like objects used in conjunction with coco_dataset

Module Contents¶

Classes¶

`ObjectList1D`	Vectorized access to lists of dictionary objects
`ObjectGroups`	An object for holding a groups of `ObjectList1D` objects
`Categories`	Vectorized access to category attributes
`Videos`	Vectorized access to video attributes
`Images`	Vectorized access to image attributes
`Annots`	Vectorized access to annotation attributes
`AnnotGroups`	An object for holding a groups of `ObjectList1D` objects
`ImageGroups`	An object for holding a groups of `ObjectList1D` objects

class kwcoco.coco_objects1d.ObjectList1D(ids, dset, key)[source]¶

Bases: ubelt.NiceRepr

Vectorized access to lists of dictionary objects

Lightweight reference to a set of object (e.g. annotations, images) that allows for convenient property access.

Parameters

ids (List[int]) – list of ids
dset (CocoDataset) – parent dataset
key (str) – main object name (e.g. ‘images’, ‘annotations’)

Types:: ObjT = Ann | Img | Cat # can be one of these types ObjectList1D gives us access to a List[ObjT]

Example

>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo()
>>> # Both annots and images are object lists
>>> self = dset.annots()
>>> self = dset.images()
>>> # can call with a list of ids or not, for everything
>>> self = dset.annots([1, 2, 11])
>>> self = dset.images([1, 2, 3])
>>> self.lookup('id')
>>> self.lookup(['id'])

__nice__(self)[source]¶

__iter__(self)[source]¶

__len__(self)[source]¶

property _id_to_obj(self)[source]¶

__getitem__(self, index)[source]¶

property objs(self)[source]¶

Returns: all object dictionaries
Return type: List[ObjT]

take(self, idxs)[source]¶

Take a subset by index

Returns: ObjectList1D

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo().annots()
>>> assert len(self.take([0, 2, 3])) == 3

compress(self, flags)[source]¶

Take a subset by flags

Returns: ObjectList1D

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo().images()
>>> assert len(self.compress([True, False, True])) == 2

peek(self)[source]¶

Return the first object dictionary

Returns: object dictionary
Return type: ObjT

Example

>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo()
>>> self = dset.images()
>>> assert self.peek()['id'] == 1
>>> # Check that subsets return correct items
>>> sub0 = self.compress([i % 2 == 0 for i in range(len(self))])
>>> sub1 = self.compress([i % 2 == 1 for i in range(len(self))])
>>> assert sub0.peek()['id'] == 1
>>> assert sub1.peek()['id'] == 2

lookup(self, key, default=ub.NoParam, keepid=False)[source]¶

Lookup a list of object attributes

Parameters

key (str | Iterable) – name of the property you want to lookup can also be a list of names, in which case we return a dict
default – if specified, uses this value if it doesn’t exist in an ObjT.
keepid – if True, return a mapping from ids to the property

Returns

a list of whatever type the object is Dict[str, ObjT]

Return type

List[ObjT]

Example

>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo()
>>> self = dset.annots()
>>> self.lookup('id')
>>> key = ['id']
>>> default = None
>>> self.lookup(key=['id', 'image_id'])
>>> self.lookup(key=['id', 'image_id'])
>>> self.lookup(key='foo', default=None, keepid=True)
>>> self.lookup(key=['foo'], default=None, keepid=True)
>>> self.lookup(key=['id', 'image_id'], keepid=True)

get(self, key, default=ub.NoParam, keepid=False)[source]¶

Lookup a list of object attributes

Parameters

key (str) – name of the property you want to lookup
default – if specified, uses this value if it doesn’t exist in an ObjT.
keepid – if True, return a mapping from ids to the property

Returns

a list of whatever type the object is Dict[str, ObjT]

Return type

List[ObjT]

Example

>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo()
>>> self = dset.annots()
>>> self.get('id')
>>> self.get(key='foo', default=None, keepid=True)

_iter_get(self, key, default=ub.NoParam)[source]¶: Iterator version of get, not in stable API yet.

set(self, key, values)[source]¶

Assign a value to each annotation

Parameters

key (str) – the annotation property to modify
values (Iterable | scalar) – an iterable of values to set for each annot in the dataset. If the item is not iterable, it is assigned to all objects.

Example

>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo()
>>> self = dset.annots()
>>> self.set('my-key1', 'my-scalar-value')
>>> self.set('my-key2', np.random.rand(len(self)))
>>> print('dset.imgs = {}'.format(ub.repr2(dset.imgs, nl=1)))
>>> self.get('my-key2')

_set(self, key, values)[source]¶: faster less safe version of set

_lookup(self, key, default=ub.NoParam)[source]¶

Example

>>> # xdoctest: +REQUIRES(--benchmark)
>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('shapes256')
>>> self = annots = dset.annots()
>>> #
>>> import timerit
>>> ti = timerit.Timerit(100, bestof=10, verbose=2)
>>> #
>>> for timer in ti.reset('lookup'):
>>>     with timer:
>>>         self.lookup('image_id')
>>> #
>>> for timer in ti.reset('_lookup'):
>>>     with timer:
>>>         self._lookup('image_id')
>>> #
>>> for timer in ti.reset('image_id'):
>>>     with timer:
>>>         self.image_id
>>> #
>>> for timer in ti.reset('raw1'):
>>>     with timer:
>>>         key = 'image_id'
>>>         [self._dset.anns[_id][key] for _id in self._ids]
>>> #
>>> for timer in ti.reset('raw2'):
>>>     with timer:
>>>         anns = self._dset.anns
>>>         key = 'image_id'
>>>         [anns[_id][key] for _id in self._ids]
>>> #
>>> for timer in ti.reset('lut-gen'):
>>>     with timer:
>>>         _lut = self._obj_lut
>>>         objs = (_lut[_id] for _id in self._ids)
>>>         [obj[key] for obj in objs]
>>> #
>>> for timer in ti.reset('lut-gen-single'):
>>>     with timer:
>>>         _lut = self._obj_lut
>>>         [_lut[_id][key] for _id in self._ids]

attribute_frequency(self)[source]¶

Compute the number of times each key is used in a dictionary

Returns: Dict[str, int]

Example

>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo()
>>> self = dset.annots()
>>> attrs = self.attribute_frequency()
>>> print('attrs = {}'.format(ub.repr2(attrs, nl=1)))

class kwcoco.coco_objects1d.ObjectGroups(groups, dset)[source]¶

Bases: ubelt.NiceRepr

An object for holding a groups of ObjectList1D objects

_lookup(self, key)[source]¶

__getitem__(self, index)[source]¶

lookup(self, key, default=ub.NoParam)[source]¶

__nice__(self)[source]¶

class kwcoco.coco_objects1d.Categories(ids, dset)[source]¶

Bases: ObjectList1D

Vectorized access to category attributes

SeeAlso:: kwcoco.coco_dataset.MixinCocoObjects.categories()

Example

>>> from kwcoco.coco_objects1d import Categories  # NOQA
>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo()
>>> ids = list(dset.cats.keys())
>>> self = Categories(ids, dset)
>>> print('self.name = {!r}'.format(self.name))
>>> print('self.supercategory = {!r}'.format(self.supercategory))

property cids(self)[source]¶

property name(self)[source]¶

property supercategory(self)[source]¶

class kwcoco.coco_objects1d.Videos(ids, dset)[source]¶

Bases: ObjectList1D

Vectorized access to video attributes

SeeAlso:: kwcoco.coco_dataset.MixinCocoObjects.videos()

Example

>>> from kwcoco.coco_objects1d import Videos  # NOQA
>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes5')
>>> ids = list(dset.index.videos.keys())
>>> self = Videos(ids, dset)
>>> print('self = {!r}'.format(self))

property images(self)[source]¶

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo('vidshapes8').videos()
>>> print(self.images)
<ImageGroups(n=8, m=2.0, s=0.0)>

class kwcoco.coco_objects1d.Images(ids, dset)[source]¶

Bases: ObjectList1D

Vectorized access to image attributes

SeeAlso:: kwcoco.coco_dataset.MixinCocoObjects.images()

property coco_images(self)[source]¶

property gids(self)[source]¶

property gname(self)[source]¶

property gpath(self)[source]¶

property width(self)[source]¶

property height(self)[source]¶

property size(self)[source]¶

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo().images()
>>> self._dset._ensure_imgsize()
>>> print(self.size)
[(512, 512), (300, 250), (256, 256)]

property area(self)[source]¶

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo().images()
>>> self._dset._ensure_imgsize()
>>> print(self.area)
[262144, 75000, 65536]

property n_annots(self)[source]¶

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo().images()
>>> print(ub.repr2(self.n_annots, nl=0))
[9, 2, 0]

property aids(self)[source]¶

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo().images()
>>> print(ub.repr2(list(map(list, self.aids)), nl=0))
[[1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11], []]

property annots(self)[source]¶

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo().images()
>>> print(self.annots)
<AnnotGroups(n=3, m=3.7, s=3.9)>

class kwcoco.coco_objects1d.Annots(ids, dset)[source]¶

Bases: ObjectList1D

Vectorized access to annotation attributes

SeeAlso:: kwcoco.coco_dataset.MixinCocoObjects.annots()

property aids(self)[source]¶: The annotation ids of this column of annotations

property images(self)[source]¶

Get the column of images

Returns: Images

property image_id(self)[source]¶

property category_id(self)[source]¶

property gids(self)[source]¶

Get the column of image-ids

Returns: list of image ids
Return type: List[int]

property cids(self)[source]¶

Get the column of category-ids

Returns: List[int]

property cnames(self)[source]¶

Get the column of category names

Returns: List[int]

property detections(self)[source]¶

Get the kwimage-style detection objects

Returns: kwimage.Detections

Example

>>> # xdoctest: +REQUIRES(module:kwimage)
>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo('shapes32').annots([1, 2, 11])
>>> dets = self.detections
>>> print('dets.data = {!r}'.format(dets.data))
>>> print('dets.meta = {!r}'.format(dets.meta))

property boxes(self)[source]¶

Get the column of kwimage-style bounding boxes

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo().annots([1, 2, 11])
>>> print(self.boxes)
<Boxes(xywh,
    array([[ 10,  10, 360, 490],
           [350,   5, 130, 290],
           [124,  96,  45,  18]]))>

property xywh(self)[source]¶

Returns raw boxes

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo().annots([1, 2, 11])
>>> print(self.xywh)

class kwcoco.coco_objects1d.AnnotGroups(groups, dset)[source]¶

Bases: ObjectGroups

An object for holding a groups of ObjectList1D objects

property cids(self)[source]¶

property cnames(self)[source]¶

class kwcoco.coco_objects1d.ImageGroups(groups, dset)[source]¶

Bases: ObjectGroups

An object for holding a groups of ObjectList1D objects

`kwcoco.coco_schema`¶

CommandLine

python -m kwcoco.coco_schema
xdoctest -m kwcoco.coco_schema __doc__

Example

>>> import kwcoco
>>> from kwcoco.coco_schema import COCO_SCHEMA
>>> import jsonschema
>>> dset = kwcoco.CocoDataset.demo('shapes1')
>>> # print('dset.dataset = {}'.format(ub.repr2(dset.dataset, nl=2)))
>>> COCO_SCHEMA.validate(dset.dataset)

>>> try:
>>>     jsonschema.validate(dset.dataset, schema=COCO_SCHEMA)
>>> except jsonschema.exceptions.ValidationError as ex:
>>>     vali_ex = ex
>>>     print('ex = {!r}'.format(ex))
>>>     raise
>>> except jsonschema.exceptions.SchemaError as ex:
>>>     print('ex = {!r}'.format(ex))
>>>     schema_ex = ex
>>>     print('schema_ex.instance = {}'.format(ub.repr2(schema_ex.instance, nl=-1)))
>>>     raise

>>> # Test the multispectral image defintino
>>> import copy
>>> dataset = dset.copy().dataset
>>> img = dataset['images'][0]
>>> img.pop('file_name')
>>> import pytest
>>> with pytest.raises(jsonschema.ValidationError):
>>>     COCO_SCHEMA.validate(dataset)
>>> import pytest
>>> img['auxiliary'] = [{'file_name': 'foobar'}]
>>> with pytest.raises(jsonschema.ValidationError):
>>>     COCO_SCHEMA.validate(dataset)
>>> img['name'] = 'aux-only images must have a name'
>>> COCO_SCHEMA.validate(dataset)

Module Contents¶

Functions¶

`deprecated`(*args)
`TUPLE`(args, *kw)

Attributes¶

`elem`
`ALLOF`
`ANY`
`ANYOF`
`ARRAY`
`BOOLEAN`
`INTEGER`
`NOT`
`NULL`
`NUMBER`
`OBJECT`
`ONEOF`
`STRING`
`UUID`
`PATH`
`KWCOCO_KEYPOINT`
`KWCOCO_POLYGON`
`ORIG_COCO_KEYPOINTS`
`KWCOCO_KEYPOINTS`
`KEYPOINTS`
`ORIG_COCO_POLYGON`
`POLYGON`
`RUN_LENGTH_ENCODING`
`BBOX`
`SEGMENTATION`
`CATEGORY`
`KEYPOINT_CATEGORY`
`VIDEO`
`CHANNELS`
`IMAGE`
`ANNOTATION`
`COCO_SCHEMA`

kwcoco.coco_schema.deprecated(*args)[source]¶

kwcoco.coco_schema.TUPLE(*args, **kw)[source]¶

kwcoco.coco_schema.elem[source]¶

kwcoco.coco_schema.ALLOF[source]¶

kwcoco.coco_schema.ANY[source]¶

kwcoco.coco_schema.ANYOF[source]¶

kwcoco.coco_schema.ARRAY[source]¶

kwcoco.coco_schema.BOOLEAN[source]¶

kwcoco.coco_schema.INTEGER[source]¶

kwcoco.coco_schema.NOT[source]¶

kwcoco.coco_schema.NULL[source]¶

kwcoco.coco_schema.NUMBER[source]¶

kwcoco.coco_schema.OBJECT[source]¶

kwcoco.coco_schema.ONEOF[source]¶

kwcoco.coco_schema.STRING[source]¶

kwcoco.coco_schema.UUID[source]¶

kwcoco.coco_schema.PATH[source]¶

kwcoco.coco_schema.KWCOCO_KEYPOINT[source]¶

kwcoco.coco_schema.KWCOCO_POLYGON[source]¶

kwcoco.coco_schema.ORIG_COCO_KEYPOINTS[source]¶

kwcoco.coco_schema.KWCOCO_KEYPOINTS[source]¶

kwcoco.coco_schema.KEYPOINTS[source]¶

kwcoco.coco_schema.ORIG_COCO_POLYGON[source]¶

kwcoco.coco_schema.POLYGON[source]¶

kwcoco.coco_schema.RUN_LENGTH_ENCODING[source]¶

kwcoco.coco_schema.BBOX[source]¶

kwcoco.coco_schema.SEGMENTATION[source]¶

kwcoco.coco_schema.CATEGORY[source]¶

kwcoco.coco_schema.KEYPOINT_CATEGORY[source]¶

kwcoco.coco_schema.VIDEO[source]¶

kwcoco.coco_schema.CHANNELS[source]¶

kwcoco.coco_schema.IMAGE[source]¶

kwcoco.coco_schema.ANNOTATION[source]¶

kwcoco.coco_schema.COCO_SCHEMA[source]¶

`kwcoco.coco_sql_dataset`¶

[ ] We get better speeds with raw SQL over alchemy. Can we mitigate the
speed difference so we can take advantage of alchemy’s expressiveness?

Finally got a baseline implementation of an SQLite backend for COCO datasets. This mostly plugs into my existing tools (as long as only read operations are used; haven’t impelmented writing yet) by duck-typing the dict API.

This solves the issue of forking and then accessing nested dictionaries in the JSON-style COCO objects. (When you access the dictionary Python will increment a reference count which triggers copy-on-write for whatever memory page that data happened to live in. Non-contiguous access had the effect of excessive memory copies).

For “medium sized” datasets its quite a bit slower. Running through a torch DataLoader with 4 workers for 10,000 images executes at a rate of 100Hz but takes 850MB of RAM. Using the duck-typed SQL backend only uses 500MB (which includes the cost of caching), but runs at 45Hz (which includes the benefit of caching).

However, once I scale up to 100,000 images I start seeing benefits. The in-memory dictionary interface chugs at 1.05HZ, and is taking more than 4GB of memory at the time I killed the process (eta was over an hour). The SQL backend ran at 45Hz and took about 3 minutes and used about 2.45GB of memory.

Without a cache, SQL runs at 30HZ and takes 400MB for 10,000 images, and for 100,000 images it gets 30Hz with 1.1GB. There is also a much larger startup time. I’m not exactly sure what it is yet, but its probably some preprocessing I’m doing.

Using a LRU cache we get 45Hz and 1.05GB of memory, so that’s a clear win. We do need to be sure to disable the cache if we ever implement write mode.

I’d like to be a bit faster on the medium sized datasets (I’d really like to avoid caching rows, which is why the speed is currently semi-reasonable), but I don’t think I can do any better than this because single-row lookup time is O(log(N)) for sqlite, whereas its O(1) for dictionaries. (I wish sqlite had an option to create a hash-table index for a table, but I dont think it does). I optimized as many of the dictionary operations as possible (for instance, iterating through keys, values, and items should be O(N) instead of O(N log(N))), but the majority of the runtime cost is in the single-row lookup time.

There are a few questions I still have if anyone has insight:

Say I want to select a subset of K rows from a table with N entries, and I have a list of all of the rowids that I want. Is there any way to do this better than O(K log(N))? I tried using a SELECT col FROM table WHERE id IN (?, ?, ?, ?, ...) filling in enough ? as there are rows in my subset. I’m not sure what the complexity of using a query like this is. I’m not sure what the IN implementation looks like. Can this be done more efficiently by with a temporary table and a JOIN?

There really is no way to do O(1) row lookup in sqlite right? Is there a way in PostgreSQL or some other backend sqlalchemy supports?

I found that PostgreSQL does support hash indexes: https://www.postgresql.org/docs/13/indexes-types.html I’m really not interested in setting up a global service though 😞. I also found a 10-year old thread with a hash-index feature request for SQLite, which I unabashedly resurrected http://sqlite.1065341.n5.nabble.com/Feature-request-hash-index-td23367.html

Module Contents¶

Classes¶

`Category`
`KeypointCategory`
`Video`
`Image`
`Annotation`
`SqlListProxy`	A view of an SQL table that behaves like a Python list
`SqlDictProxy`	Duck-types an SQL table as a dictionary of dictionaries.
`SqlIdGroupDictProxy`	Similar to `SqlDictProxy`, but maps ids to groups of other ids.
`CocoSqlIndex`	Simulates the dictionary provided by `kwcoco.coco_dataset.CocoIndex`
`CocoSqlDatabase`	Provides an API nearly identical to `kwcoco.CocoDatabase`, but uses

Functions¶

`orm_to_dict`(obj)
`_orm_yielder`(query, size=300)	TODO: figure out the best way to yield, in batches or otherwise
`_raw_yielder`(result, size=300)	TODO: figure out the best way to yield, in batches or otherwise
`_new_proxy_cache`()	By returning None, we wont use item caching
`_handle_sql_uri`(uri)	Temporary function to deal with URI. Modern tools seem to use RFC 3968
`cached_sql_coco_view`(dct_db_fpath=None, sql_db_fpath=None, dset=None, force_rewrite=False)	Attempts to load a cached SQL-View dataset, only loading and converting the
`ensure_sql_coco_view`(dset, db_fpath=None, force_rewrite=False)	Create a cached on-disk SQL view of an on-disk COCO dataset.
`demo`(num=10)
`assert_dsets_allclose`(dset1, dset2, tag1='dset1', tag2='dset2')
`_benchmark_dset_readtime`(dset, tag='?')	Helper for understanding the time differences between backends
`_benchmark_dict_proxy_ops`(proxy)	Get insight on the efficiency of operations
`devcheck`()	Scratch work for things that should eventually become unit or doc tests

Attributes¶

`CocoBase`
`ALCHEMY_MODE_DEFAULT`
`TBLNAME_TO_CLASS`
`tblname`

kwcoco.coco_sql_dataset.CocoBase[source]¶

class kwcoco.coco_sql_dataset.Category[source]¶

Bases: CocoBase

__tablename__ = categories[source]¶

id[source]¶

name[source]¶

alias[source]¶

supercategory[source]¶

extra[source]¶

class kwcoco.coco_sql_dataset.KeypointCategory[source]¶

Bases: CocoBase

__tablename__ = keypoint_categories[source]¶

id[source]¶

name[source]¶

alias[source]¶

supercategory[source]¶

reflection_id[source]¶

extra[source]¶

class kwcoco.coco_sql_dataset.Video[source]¶

Bases: CocoBase

__tablename__ = videos[source]¶

id[source]¶

name[source]¶

caption[source]¶

width[source]¶

height[source]¶

extra[source]¶

class kwcoco.coco_sql_dataset.Image[source]¶

Bases: CocoBase

__tablename__ = images[source]¶

id[source]¶

name[source]¶

file_name[source]¶

width[source]¶

height[source]¶

video_id[source]¶

timestamp[source]¶

frame_index[source]¶

channels[source]¶

auxiliary[source]¶

extra[source]¶

class kwcoco.coco_sql_dataset.Annotation[source]¶

Bases: CocoBase

__tablename__ = annotations[source]¶

id[source]¶

image_id[source]¶

category_id[source]¶

track_id[source]¶

segmentation[source]¶

keypoints[source]¶

bbox[source]¶

_bbox_x[source]¶

_bbox_y[source]¶

_bbox_w[source]¶

_bbox_h[source]¶

score[source]¶

weight[source]¶

prob[source]¶

iscrowd[source]¶

caption[source]¶

extra[source]¶

kwcoco.coco_sql_dataset.ALCHEMY_MODE_DEFAULT = 0[source]¶

kwcoco.coco_sql_dataset.TBLNAME_TO_CLASS[source]¶

kwcoco.coco_sql_dataset.tblname[source]¶

kwcoco.coco_sql_dataset.orm_to_dict(obj)[source]¶

kwcoco.coco_sql_dataset._orm_yielder(query, size=300)[source]¶: TODO: figure out the best way to yield, in batches or otherwise

kwcoco.coco_sql_dataset._raw_yielder(result, size=300)[source]¶: TODO: figure out the best way to yield, in batches or otherwise

kwcoco.coco_sql_dataset._new_proxy_cache()[source]¶: By returning None, we wont use item caching

class kwcoco.coco_sql_dataset.SqlListProxy(proxy, session, cls)[source]¶

Bases: ubelt.NiceRepr

A view of an SQL table that behaves like a Python list

__len__(proxy)[source]¶

__nice__(proxy)[source]¶

__iter__(proxy)[source]¶

__getitem__(proxy, index)[source]¶

__contains__(proxy, item)[source]¶

__setitem__(proxy, index, value)[source]¶

__delitem__(proxy, index)[source]¶

class kwcoco.coco_sql_dataset.SqlDictProxy(proxy, session, cls, keyattr=None, ignore_null=False)[source]¶

Bases: kwcoco.util.dict_like.DictLike

Duck-types an SQL table as a dictionary of dictionaries.

The key is specified by an indexed column (by default it is the id column). The values are dictionaries containing all data for that row.

Note

With SQLite indexes are B-Trees so lookup is O(log(N)) and not O(1) as will regular dictionaries. Iteration should still be O(N), but databases have much more overhead than Python dictionaries.

Parameters

session (Session) – the sqlalchemy session
cls (Type) – the declarative sqlalchemy table class
keyattr – the indexed column to use as the keys
ignore_null (bool) – if True, ignores any keys set to NULL, otherwise NULL keys are allowed.

Example

>>> # xdoctest: +REQUIRES(module:sqlalchemy)
>>> from kwcoco.coco_sql_dataset import *  # NOQA
>>> import pytest
>>> sql_dset, dct_dset = demo(num=10)
>>> proxy = sql_dset.index.anns

>>> keys = list(proxy.keys())
>>> values = list(proxy.values())
>>> items = list(proxy.items())
>>> item_keys = [t[0] for t in items]
>>> item_vals = [t[1] for t in items]
>>> lut_vals = [proxy[key] for key in keys]
>>> assert item_vals == lut_vals == values
>>> assert item_keys == keys
>>> assert len(proxy) == len(keys)

>>> goodkey1 = keys[1]
>>> badkey1 = -100000000000
>>> badkey2 = 'foobarbazbiz'
>>> badkey3 = object()
>>> assert goodkey1 in proxy
>>> assert badkey1 not in proxy
>>> assert badkey2 not in proxy
>>> assert badkey3 not in proxy
>>> with pytest.raises(KeyError):
>>>     proxy[badkey1]
>>> with pytest.raises(KeyError):
>>>     proxy[badkey2]
>>> with pytest.raises(KeyError):
>>>     proxy[badkey3]

>>> # xdoctest: +SKIP
>>> from kwcoco.coco_sql_dataset import _benchmark_dict_proxy_ops
>>> ti = _benchmark_dict_proxy_ops(proxy)
>>> print('ti.measures = {}'.format(ub.repr2(ti.measures, nl=2, align=':', precision=6)))

Example

>>> # xdoctest: +REQUIRES(module:sqlalchemy)
>>> from kwcoco.coco_sql_dataset import *  # NOQA
>>> import kwcoco
>>> # Test the variant of the SqlDictProxy where we ignore None keys
>>> # This is the case for name_to_img and file_name_to_img
>>> dct_dset = kwcoco.CocoDataset.demo('shapes1')
>>> dct_dset.add_image(name='no_file_image1')
>>> dct_dset.add_image(name='no_file_image2')
>>> dct_dset.add_image(name='no_file_image3')
>>> sql_dset = dct_dset.view_sql(memory=True)
>>> assert len(dct_dset.index.imgs) == 4
>>> assert len(dct_dset.index.file_name_to_img) == 1
>>> assert len(dct_dset.index.name_to_img) == 3
>>> assert len(sql_dset.index.imgs) == 4
>>> assert len(sql_dset.index.file_name_to_img) == 1
>>> assert len(sql_dset.index.name_to_img) == 3

>>> proxy = sql_dset.index.file_name_to_img
>>> assert len(list(proxy.keys())) == 1
>>> assert len(list(proxy.values())) == 1

>>> proxy = sql_dset.index.name_to_img
>>> assert len(list(proxy.keys())) == 3
>>> assert len(list(proxy.values())) == 3

>>> proxy = sql_dset.index.imgs
>>> assert len(list(proxy.keys())) == 4
>>> assert len(list(proxy.values())) == 4

__len__(proxy)[source]¶

__nice__(proxy)[source]¶

__contains__(proxy, key)[source]¶

__getitem__(proxy, key)[source]¶

keys(proxy)[source]¶

values(proxy)[source]¶

items(proxy)[source]¶

class kwcoco.coco_sql_dataset.SqlIdGroupDictProxy(proxy, session, valattr, keyattr, parent_keyattr, group_order_attr=None)[source]¶

Bases: kwcoco.util.dict_like.DictLike

Similar to SqlDictProxy, but maps ids to groups of other ids.

Simulates a dictionary that maps ids of a parent table to all ids of another table corresponding to rows where a specific column has that parent id.

The items in the group can be sorted by the group_order_attr if specified.

For example, imagine two tables: images with one column (id) and annotations with two columns (id, image_id). This class can help provide a mpaping from each image.id to a Set[annotation.id] where those annotation rows have annotation.image_id = image.id.

Example

>>> # xdoctest: +REQUIRES(module:sqlalchemy)
>>> from kwcoco.coco_sql_dataset import *  # NOQA
>>> sql_dset, dct_dset = demo(num=10)
>>> proxy = sql_dset.index.gid_to_aids

>>> keys = list(proxy.keys())
>>> values = list(proxy.values())
>>> items = list(proxy.items())
>>> item_keys = [t[0] for t in items]
>>> item_vals = [t[1] for t in items]
>>> lut_vals = [proxy[key] for key in keys]
>>> assert item_vals == lut_vals == values
>>> assert item_keys == keys
>>> assert len(proxy) == len(keys)

>>> # xdoctest: +SKIP
>>> from kwcoco.coco_sql_dataset import _benchmark_dict_proxy_ops
>>> ti = _benchmark_dict_proxy_ops(proxy)
>>> print('ti.measures = {}'.format(ub.repr2(ti.measures, nl=2, align=':', precision=6)))

Example

>>> # xdoctest: +REQUIRES(module:sqlalchemy)
>>> from kwcoco.coco_sql_dataset import *  # NOQA
>>> import kwcoco
>>> # Test the group sorted variant of this by using vidid_to_gids
>>> # where the "gids" must be sorted by the image frame indexes
>>> dct_dset = kwcoco.CocoDataset.demo('vidshapes1')
>>> dct_dset.add_image(name='frame-index-order-demo1', frame_index=-30, video_id=1)
>>> dct_dset.add_image(name='frame-index-order-demo2', frame_index=10, video_id=1)
>>> dct_dset.add_image(name='frame-index-order-demo3', frame_index=3, video_id=1)
>>> dct_dset.add_video(name='empty-video1')
>>> dct_dset.add_video(name='empty-video2')
>>> dct_dset.add_video(name='empty-video3')
>>> sql_dset = dct_dset.view_sql(memory=True)
>>> orig = dct_dset.index.vidid_to_gids
>>> proxy = sql_dset.index.vidid_to_gids
>>> from kwcoco.util.util_json import indexable_allclose
>>> assert indexable_allclose(orig, dict(proxy))
>>> items = list(proxy.items())
>>> vals = list(proxy.values())
>>> keys = list(proxy.keys())
>>> assert len(keys) == len(vals)
>>> assert dict(zip(keys, vals)) == dict(items)

__nice__(self)[source]¶

__len__(proxy)[source]¶

__getitem__(proxy, key)[source]¶

__contains__(proxy, key)[source]¶

keys(proxy)[source]¶

items(proxy)[source]¶

values(proxy)[source]¶

class kwcoco.coco_sql_dataset.CocoSqlIndex(index)[source]¶

Bases: object

Simulates the dictionary provided by kwcoco.coco_dataset.CocoIndex

build(index, parent)[source]¶

kwcoco.coco_sql_dataset._handle_sql_uri(uri)[source]¶: Temporary function to deal with URI. Modern tools seem to use RFC 3968 URIs, but sqlalchemy uses RFC 1738. Attempt to gracefully handle special cases. With a better understanding of the above specs, this function may be able to be written more eloquently.

class kwcoco.coco_sql_dataset.CocoSqlDatabase(uri=None, tag=None, img_root=None)[source]¶

Provides an API nearly identical to kwcoco.CocoDatabase, but uses an SQL backend data store. This makes it robust to copy-on-write memory issues that arise when forking, as discussed in 1.

Note

By default constructing an instance of the CocoSqlDatabase does not create a connection to the databse. Use the connect() method to open a connection.

References

1: https://github.com/pytorch/pytorch/issues/13246

Example

>>> # xdoctest: +REQUIRES(module:sqlalchemy)
>>> from kwcoco.coco_sql_dataset import *  # NOQA
>>> sql_dset, dct_dset = demo()
>>> dset1, dset2 = sql_dset, dct_dset
>>> tag1, tag2 = 'dset1', 'dset2'
>>> assert_dsets_allclose(sql_dset, dct_dset)

MEMORY_URI = sqlite:///:memory:[source]¶

__nice__(self)[source]¶

classmethod coerce(self, data)[source]¶

Create an SQL CocoDataset from the input pointer.

Example

import kwcoco dset = kwcoco.CocoDataset.demo(‘shapes8’) data = dset.fpath self = CocoSqlDatabase.coerce(data)

from kwcoco.coco_sql_dataset import CocoSqlDatabase import kwcoco dset = kwcoco.CocoDataset.coerce(‘spacenet7.kwcoco.json’)

self = CocoSqlDatabase.coerce(dset)

from kwcoco.coco_sql_dataset import CocoSqlDatabase sql_dset = CocoSqlDatabase.coerce(‘spacenet7.kwcoco.json’)

# from kwcoco.coco_sql_dataset import CocoSqlDatabase import kwcoco sql_dset = kwcoco.CocoDataset.coerce(‘_spacenet7.kwcoco.view.v006.sqlite’)

__getstate__(self)[source]¶

Return only the minimal info when pickling this object.

Note

This object IS pickling when the multiprocessing context is “spawn”.

This object is NOT pickled when the multiprocessing context is “fork”. In this case the user needs to be careful to create new connections in the forked subprocesses.

Example

>>> # xdoctest: +REQUIRES(module:sqlalchemy)
>>> from kwcoco.coco_sql_dataset import *  # NOQA
>>> sql_dset, dct_dset = demo()
>>> # Test pickling works correctly
>>> import pickle
>>> serialized = pickle.dumps(sql_dset)
>>> assert len(serialized) < 3e4, 'should be very small'
>>> copy = pickle.loads(serialized)
>>> dset1, dset2, tag1, tag2 = sql_dset, copy, 'orig', 'copy'
>>> assert_dsets_allclose(dset1, dset2, tag1, tag2)
>>> # --- other methods of copying ---
>>> rw_copy = CocoSqlDatabase(
>>>     sql_dset.uri, img_root=sql_dset.img_root, tag=sql_dset.tag)
>>> rw_copy.connect()
>>> ro_copy = CocoSqlDatabase(
>>>     sql_dset.uri, img_root=sql_dset.img_root, tag=sql_dset.tag)
>>> ro_copy.connect(readonly=True)
>>> assert_dsets_allclose(dset1, ro_copy, tag1, 'ro-copy')
>>> assert_dsets_allclose(dset1, rw_copy, tag1, 'rw-copy')

__setstate__(self, state)[source]¶: Reopen new readonly connnections when unpickling the object.

disconnect(self)[source]¶: Drop references to any SQL or cache objects

connect(self, readonly=False)[source]¶

Connects this instance to the underlying database.

References

# details on read only mode, some of these didnt seem to work https://github.com/sqlalchemy/sqlalchemy/blob/master/lib/sqlalchemy/dialects/sqlite/pysqlite.py#L71 https://github.com/pudo/dataset/issues/136 https://writeonly.wordpress.com/2009/07/16/simple-read-only-sqlalchemy-sessions/

property fpath(self)[source]¶

delete(self)[source]¶

populate_from(self, dset, verbose=1)[source]¶

Copy the information in a CocoDataset into this SQL database.

Example

>>> # xdoctest: +REQUIRES(module:sqlalchemy)
>>> from kwcoco.coco_sql_dataset import _benchmark_dset_readtime  # NOQA
>>> import kwcoco
>>> from kwcoco.coco_sql_dataset import *
>>> dset2 = dset = kwcoco.CocoDataset.demo()
>>> dset1 = self = CocoSqlDatabase('sqlite:///:memory:')
>>> self.connect()
>>> self.populate_from(dset)
>>> assert_dsets_allclose(dset1, dset2, tag1='sql', tag2='dct')
>>> ti_sql = _benchmark_dset_readtime(dset1, 'sql')
>>> ti_dct = _benchmark_dset_readtime(dset2, 'dct')
>>> print('ti_sql.rankings = {}'.format(ub.repr2(ti_sql.rankings, nl=2, precision=6, align=':')))
>>> print('ti_dct.rankings = {}'.format(ub.repr2(ti_dct.rankings, nl=2, precision=6, align=':')))

property dataset(self)[source]¶

property anns(self)[source]¶

property cats(self)[source]¶

property imgs(self)[source]¶

property name_to_cat(self)[source]¶

raw_table(self, table_name)[source]¶

Loads an entire SQL table as a pandas DataFrame

Parameters: table_name (str) – name of the table
Returns: DataFrame

Example

>>> # xdoctest: +REQUIRES(module:sqlalchemy)
>>> from kwcoco.coco_sql_dataset import *  # NOQA
>>> self, dset = demo()
>>> table_df = self.raw_table('annotations')
>>> print(table_df)

_column_lookup(self, tablename, key, rowids, default=ub.NoParam, keepid=False)[source]¶

Convinience method to lookup only a single column of information

Example

>>> # xdoctest: +REQUIRES(module:sqlalchemy)
>>> from kwcoco.coco_sql_dataset import *  # NOQA
>>> self, dset = demo(10)
>>> tablename = 'annotations'
>>> key = 'category_id'
>>> rowids = list(self.anns.keys())[::3]
>>> cids1 = self._column_lookup(tablename, key, rowids)
>>> cids2 = self.annots(rowids).get(key)
>>> cids3 = dset.annots(rowids).get(key)
>>> assert cids3 == cids2 == cids1

_all_rows_column_lookup(self, tablename, keys)[source]¶

Convinience method to look up all rows from a table and only a few columns.

Example

>>> # xdoctest: +REQUIRES(module:sqlalchemy)
>>> from kwcoco.coco_sql_dataset import *  # NOQA
>>> self, dset = demo(10)
>>> tablename = 'annotations'
>>> keys = ['id', 'category_id']
>>> rows = self._all_rows_column_lookup(tablename, keys)

tabular_targets(self)[source]¶

Convinience method to create an in-memory summary of basic annotation properties with minimal SQL overhead.

Example

>>> # xdoctest: +REQUIRES(module:sqlalchemy)
>>> from kwcoco.coco_sql_dataset import *  # NOQA
>>> self, dset = demo()
>>> targets = self.tabular_targets()
>>> print(targets.pandas())

property bundle_dpath(self)[source]¶

property data_fpath(self)[source]¶: data_fpath is an alias of fpath

kwcoco.coco_sql_dataset.cached_sql_coco_view(dct_db_fpath=None, sql_db_fpath=None, dset=None, force_rewrite=False)[source]¶: Attempts to load a cached SQL-View dataset, only loading and converting the json dataset if necessary.

kwcoco.coco_sql_dataset.ensure_sql_coco_view(dset, db_fpath=None, force_rewrite=False)[source]¶: Create a cached on-disk SQL view of an on-disk COCO dataset.

Note

This function is fragile. It depends on looking at file modified timestamps to determine if it needs to write the dataset.

kwcoco.coco_sql_dataset.demo(num=10)[source]¶

kwcoco.coco_sql_dataset.assert_dsets_allclose(dset1, dset2, tag1='dset1', tag2='dset2')[source]¶

kwcoco.coco_sql_dataset._benchmark_dset_readtime(dset, tag='?')[source]¶: Helper for understanding the time differences between backends

kwcoco.coco_sql_dataset._benchmark_dict_proxy_ops(proxy)[source]¶: Get insight on the efficiency of operations

kwcoco.coco_sql_dataset.devcheck()[source]¶

Scratch work for things that should eventually become unit or doc tests

from kwcoco.coco_sql_dataset import * # NOQA self, dset = demo()

`kwcoco.compat_dataset`¶

A wrapper around the basic kwcoco dataset with a pycocotools API.

We do not recommend using this API because it has some idiosyncrasies, where names can be missleading and APIs are not always clear / efficient: e.g.

catToImgs returns integer image ids but imgToAnns returns annotation dictionaries.
showAnns takes a dictionary list as an argument instead of an integer list

The cool thing is that this extends the kwcoco API so you can drop this for compatibility with the old API, but you still get access to all of the kwcoco API including dynamic addition / removal of categories / annotations / images.

Module Contents¶

Classes¶

COCO

A wrapper around the basic kwcoco dataset with a pycocotools API.

class kwcoco.compat_dataset.COCO(annotation_file=None, **kw)[source]¶

Bases: kwcoco.coco_dataset.CocoDataset

A wrapper around the basic kwcoco dataset with a pycocotools API.

Example

>>> from kwcoco.compat_dataset import *  # NOQA
>>> import kwcoco
>>> basic = kwcoco.CocoDataset.demo('shapes8')
>>> self = COCO(basic.dataset)
>>> self.info()
>>> print('self.imgToAnns = {!r}'.format(self.imgToAnns[1]))
>>> print('self.catToImgs = {!r}'.format(self.catToImgs))

createIndex(self)[source]¶

info(self)[source]¶: Print information about the annotation file. :return:

property imgToAnns(self)[source]¶

property catToImgs(self)[source]¶: unlike the name implies, this actually goes from category to image ids Name retained for backward compatibility

getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None)[source]¶

Get ann ids that satisfy given filter conditions. default skips that filter :param imgIds (int array) : get anns for given imgs

catIds (int array) : get anns for given cats areaRng (float array) : get anns for given area range (e.g. [0 inf]) iscrowd (boolean) : get anns for given crowd label (False or True)

Returns: ids (int array) : integer array of ann ids

Example

>>> from kwcoco.compat_dataset import *  # NOQA
>>> import kwcoco
>>> self = COCO(kwcoco.CocoDataset.demo('shapes8').dataset)
>>> self.getAnnIds()
>>> self.getAnnIds(imgIds=1)
>>> self.getAnnIds(imgIds=[1])
>>> self.getAnnIds(catIds=[3])

getCatIds(self, catNms=[], supNms=[], catIds=[])[source]¶

filtering parameters. default skips that filter. :param catNms (str array) : get cats for given cat names :param supNms (str array) : get cats for given supercategory names :param catIds (int array) : get cats for given cat ids :return: ids (int array) : integer array of cat ids

Example

>>> from kwcoco.compat_dataset import *  # NOQA
>>> import kwcoco
>>> self = COCO(kwcoco.CocoDataset.demo('shapes8').dataset)
>>> self.getCatIds()
>>> self.getCatIds(catNms=['superstar'])
>>> self.getCatIds(supNms=['raster'])
>>> self.getCatIds(catIds=[3])

getImgIds(self, imgIds=[], catIds=[])[source]¶

Get img ids that satisfy given filter conditions. :param imgIds (int array) : get imgs for given ids :param catIds (int array) : get imgs with all given cats :return: ids (int array) : integer array of img ids

Example

>>> from kwcoco.compat_dataset import *  # NOQA
>>> import kwcoco
>>> self = COCO(kwcoco.CocoDataset.demo('shapes8').dataset)
>>> self.getImgIds(imgIds=[1, 2])
>>> self.getImgIds(catIds=[3, 6, 7])
>>> self.getImgIds(catIds=[3, 6, 7], imgIds=[1, 2])

loadAnns(self, ids=[])[source]¶: Load anns with the specified ids. :param ids (int array) : integer ids specifying anns :return: anns (object array) : loaded ann objects

loadCats(self, ids=[])[source]¶: Load cats with the specified ids. :param ids (int array) : integer ids specifying cats :return: cats (object array) : loaded cat objects

loadImgs(self, ids=[])[source]¶: Load anns with the specified ids. :param ids (int array) : integer ids specifying img :return: imgs (object array) : loaded img objects

showAnns(self, anns, draw_bbox=False)[source]¶: Display the specified annotations. :param anns (array of object): annotations to display :return: None

loadRes(self, resFile)[source]¶: Load result file and return a result api object. :param resFile (str) : file name of result file :return: res (obj) : result api object

download(self, tarDir=None, imgIds=[])[source]¶

Download COCO images from mscoco.org server. :param tarDir (str): COCO results directory name

imgIds (list): images to be downloaded

Returns

loadNumpyAnnotations(self, data)[source]¶: Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class} :param data (numpy.ndarray) :return: annotations (python nested list)

annToRLE(self, ann)[source]¶

Convert annotation which can be polygons, uncompressed RLE to RLE. :return: binary mask (numpy 2D array)

Note

This requires the C-extensions for kwimage to be installed due to the need to interface with the bytes RLE format.

Example

>>> from kwcoco.compat_dataset import *  # NOQA
>>> import kwcoco
>>> self = COCO(kwcoco.CocoDataset.demo('shapes8').dataset)
>>> try:
>>>     rle = self.annToRLE(self.anns[1])
>>> except NotImplementedError:
>>>     import pytest
>>>     pytest.skip('missing kwimage c-extensions')
>>> else:
>>>     assert len(rle['counts']) > 2
>>> # xdoctest: +REQUIRES(module:pycocotools)
>>> self.conform(legacy=True)
>>> orig = self._aspycoco().annToRLE(self.anns[1])

annToMask(self, ann)[source]¶

Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask.

Returns: binary mask (numpy 2D array)

Note

The mask is returned as a fortran (F-style) array with the same dimensions as the parent image.

`kwcoco.exceptions`¶

Module Contents¶

exception kwcoco.exceptions.AddError[source]¶

Bases: ValueError

Generic error when trying to add a category/annotation/image

exception kwcoco.exceptions.DuplicateAddError[source]¶

Bases: ValueError

Error when trying to add a duplicate item

exception kwcoco.exceptions.InvalidAddError[source]¶

Bases: ValueError

Error when trying to invalid data

exception kwcoco.exceptions.CoordinateCompatibilityError[source]¶

Bases: ValueError

Error when trying to perform operations on data in different coordinate systems.

`kwcoco.kpf`¶

WIP:

Conversions to and from KPF format.

Module Contents¶

Functions¶

`coco_to_kpf`(coco_dset)	import kwcoco
`demo`()

kwcoco.kpf.coco_to_kpf(coco_dset)[source]¶: import kwcoco coco_dset = kwcoco.CocoDataset.demo(‘shapes8’)

kwcoco.kpf.demo()[source]¶

`kwcoco.kw18`¶

A helper for converting COCO to / from KW18 format.

KW18 File Format https://docs.google.com/spreadsheets/d/1DFCwoTKnDv8qfy3raM7QXtir2Fjfj9j8-z8px5Bu0q8/edit#gid=10

The kw18.trk files are text files, space delimited; each row is one frame of one track and all rows have the same number of columns. The fields are:

01) track_ID         : identifies the track
02) num_frames:     number of frames in the track
03) frame_id        : frame number for this track sample
04) loc_x        : X-coordinate of the track (image/ground coords)
05) loc_y        : Y-coordinate of the track (image/ground coords)
06) vel_x        : X-velocity of the object (image/ground coords)
07) vel_y        : Y-velocity of the object (image/ground coords)
08) obj_loc_x        : X-coordinate of the object (image coords)
09) obj_loc_y        : Y-coordinate of the object (image coords)
10) bbox_min_x    : minimum X-coordinate of bounding box (image coords)
11) bbox_min_y    : minimum Y-coordinate of bounding box (image coords)
12) bbox_max_x    : maximum X-coordinate of bounding box (image coords)
13) bbox_max_y    : maximum Y-coordinate of bounding box (image coords)
14) area        : area of object (pixels)
15) world_loc_x    : X-coordinate of object in world
16) world_loc_y    : Y-coordinate of object in world
17) world_loc_z    : Z-coordiante of object in world
18) timestamp        : timestamp of frame (frames)
For the location and velocity of object centroids, use fields 4-7.
Bounding box is specified using coordinates of the top-left and bottom
right corners. Fields 15-17 may be ignored.

The kw19.trk and kw20.trk files, when present, add the following field(s):
19) object class: estimated class of the object, either 1 (person), 2
(vehicle), or 3 (other).
20) Activity ID -- refer to activities.txt for index and list of activities.

Module Contents¶

Classes¶

KW18

A DataFrame like object that stores KW18 column data

Functions¶

_ensure_kw18_column_order(df)

Ensure expected kw18 columns exist and are in the correct order.

class kwcoco.kw18.KW18(data)[source]¶

Bases: kwarray.DataFrameArray

A DataFrame like object that stores KW18 column data

Example

>>> import kwcoco
>>> from kwcoco.kw18 import KW18
>>> coco_dset = kwcoco.CocoDataset.demo('shapes')
>>> kw18_dset = KW18.from_coco(coco_dset)
>>> print(kw18_dset.pandas())

DEFAULT_COLUMNS = ['track_id', 'track_length', 'frame_number', 'tracking_plane_loc_x', 'tracking_plane_loc_y',...[source]¶

classmethod demo(KW18)[source]¶

classmethod from_coco(KW18, coco_dset)[source]¶

to_coco(self, image_paths=None, video_name=None)[source]¶

Translates a kw18 files to a CocoDataset.

Note

kw18 does not contain complete information, and as such the returned coco dataset may need to be augmented.

Parameters

image_paths (Dict[int, str], default=None) – if specified, maps frame numbers to image file paths.
video_name (str, default=None) – if specified records the name of the video this kw18 belongs to

Todo

[X] allow kwargs to specify path to frames / videos

Example

>>> from kwcoco.kw18 import KW18
>>> from os.path import join
>>> import ubelt as ub
>>> import kwimage
>>> # Prep test data - autogen a demo kw18 and write it to disk
>>> dpath = ub.ensure_app_cache_dir('kwcoco/kw18')
>>> kw18_fpath = join(dpath, 'test.kw18')
>>> KW18.demo().dump(kw18_fpath)
>>> #
>>> # Load the kw18 file
>>> self = KW18.load(kw18_fpath)
>>> # Pretend that these image correspond to kw18 frame numbers
>>> frame_names = [kwimage.grab_test_image_fpath(k) for k in kwimage.grab_test_image.keys()]
>>> frame_ids = sorted(set(self['frame_number']))
>>> image_paths = dict(zip(frame_ids, frame_names))
>>> #
>>> # Convert the kw18 to kwcoco and specify paths to images
>>> coco_dset = self.to_coco(image_paths=image_paths, video_name='dummy.mp4')
>>> #
>>> # Now we can draw images
>>> canvas = coco_dset.draw_image(1)
>>> # xdoctest: +REQUIRES(--draw)
>>> kwimage.imwrite('foo.jpg', canvas)
>>> # Draw all iamges
>>> for gid in coco_dset.imgs.keys():
>>>     canvas = coco_dset.draw_image(gid)
>>>     fpath = join(dpath, 'gid_{}.jpg'.format(gid))
>>>     print('write fpath = {!r}'.format(fpath))
>>>     kwimage.imwrite(fpath, canvas)

classmethod load(KW18, file)[source]¶

Example

>>> import kwcoco
>>> from kwcoco.kw18 import KW18
>>> coco_dset = kwcoco.CocoDataset.demo('shapes')
>>> kw18_dset = KW18.from_coco(coco_dset)
>>> print(kw18_dset.pandas())

classmethod loads(KW18, text)[source]¶

Example

>>> self = KW18.demo()
>>> text = self.dumps()
>>> self2 = KW18.loads(text)
>>> empty = KW18.loads('')

dump(self, file)[source]¶

dumps(self)[source]¶

Example

>>> self = KW18.demo()
>>> text = self.dumps()
>>> print(text)

kwcoco.kw18._ensure_kw18_column_order(df)[source]¶

Ensure expected kw18 columns exist and are in the correct order.

Example

>>> import pandas as pd
>>> df = pd.DataFrame(columns=KW18.DEFAULT_COLUMNS[0:18])
>>> _ensure_kw18_column_order(df)
>>> df = pd.DataFrame(columns=KW18.DEFAULT_COLUMNS[0:19])
>>> _ensure_kw18_column_order(df)
>>> df = pd.DataFrame(columns=KW18.DEFAULT_COLUMNS[0:18] + KW18.DEFAULT_COLUMNS[20:21])
>>> assert np.all(_ensure_kw18_column_order(df).columns == df.columns)

Package Contents¶

Classes¶

`AbstractCocoDataset`	This is a common base for all variants of the Coco Dataset
`CocoDataset`	The main coco dataset class with a json dataset backend.
`CocoImage`	An object-oriented representation of a coco image.
`CategoryTree`	Wrapper that maintains flat or hierarchical category information.
`ChannelSpec`	Parse and extract information about network input channel specs for
`FusedChannelSpec`	A specific type of channel spec with only one early fused stream.

class kwcoco.AbstractCocoDataset[source]¶

Bases: abc.ABC

This is a common base for all variants of the Coco Dataset

At the time of writing there is kwcoco.CocoDataset (which is the dictionary-based backend), and the kwcoco.coco_sql_dataset.CocoSqlDataset, which is experimental.

class kwcoco.CocoDataset(data=None, tag=None, bundle_dpath=None, img_root=None, fname=None, autobuild=True)[source]¶

Bases: kwcoco.abstract_coco_dataset.AbstractCocoDataset, MixinCocoAddRemove, MixinCocoStats, MixinCocoObjects, MixinCocoDraw, MixinCocoAccessors, MixinCocoExtras, MixinCocoIndex, MixinCocoDepricate, ubelt.NiceRepr

The main coco dataset class with a json dataset backend.

Variables

dataset (Dict) – raw json data structure. This is the base dictionary that contains {‘annotations’: List, ‘images’: List, ‘categories’: List}
index (CocoIndex) – an efficient lookup index into the coco data structure. The index defines its own attributes like anns, cats, imgs, gid_to_aids, file_name_to_img, etc. See CocoIndex for more details on which attributes are available.
fpath (PathLike | None) – if known, this stores the filepath the dataset was loaded from
tag (str) – A tag indicating the name of the dataset.
bundle_dpath (PathLike | None) – If known, this is the root path that all image file names are relative to. This can also be manually overwritten by the user.
hashid (str | None) – If computed, this will be a hash uniquely identifing the dataset. To ensure this is computed see kwcoco.coco_dataset.MixinCocoExtras._build_hashid().

References

http://cocodataset.org/#format http://cocodataset.org/#download

CommandLine

python -m kwcoco.coco_dataset CocoDataset --show

Example

>>> from kwcoco.coco_dataset import demo_coco_data
>>> import kwcoco
>>> import ubelt as ub
>>> # Returns a coco json structure
>>> dataset = demo_coco_data()
>>> # Pass the coco json structure to the API
>>> self = kwcoco.CocoDataset(dataset, tag='demo')
>>> # Now you can access the data using the index and helper methods
>>> #
>>> # Start by looking up an image by it's COCO id.
>>> image_id = 1
>>> img = self.index.imgs[image_id]
>>> print(ub.repr2(img, nl=1, sort=1))
{
    'file_name': 'astro.png',
    'id': 1,
    'url': 'https://i.imgur.com/KXhKM72.png',
}
>>> #
>>> # Use the (gid_to_aids) index to lookup annotations in the iamge
>>> annotation_id = sorted(self.index.gid_to_aids[image_id])[0]
>>> ann = self.index.anns[annotation_id]
>>> print(ub.repr2(ub.dict_diff(ann, {'segmentation'}), nl=1))
{
    'bbox': [10, 10, 360, 490],
    'category_id': 1,
    'id': 1,
    'image_id': 1,
    'keypoints': [247, 101, 2, 202, 100, 2],
}
>>> #
>>> # Use annotation category id to look up that information
>>> category_id = ann['category_id']
>>> cat = self.index.cats[category_id]
>>> print('cat = {}'.format(ub.repr2(cat, nl=1, sort=1)))
cat = {
    'id': 1,
    'name': 'astronaut',
    'supercategory': 'human',
}
>>> #
>>> # Now play with some helper functions, like extended statistics
>>> extended_stats = self.extended_stats()
>>> # xdoctest: +IGNORE_WANT
>>> print('extended_stats = {}'.format(ub.repr2(extended_stats, nl=1, precision=2, sort=1)))
extended_stats = {
    'annots_per_img': {'mean': 3.67, 'std': 3.86, 'min': 0.00, 'max': 9.00, 'nMin': 1, 'nMax': 1, 'shape': (3,)},
    'imgs_per_cat': {'mean': 0.88, 'std': 0.60, 'min': 0.00, 'max': 2.00, 'nMin': 2, 'nMax': 1, 'shape': (8,)},
    'cats_per_img': {'mean': 2.33, 'std': 2.05, 'min': 0.00, 'max': 5.00, 'nMin': 1, 'nMax': 1, 'shape': (3,)},
    'annots_per_cat': {'mean': 1.38, 'std': 1.49, 'min': 0.00, 'max': 5.00, 'nMin': 2, 'nMax': 1, 'shape': (8,)},
    'imgs_per_video': {'empty_list': True},
}
>>> # You can "draw" a raster of the annotated image with cv2
>>> canvas = self.draw_image(2)
>>> # Or if you have matplotlib you can "show" the image with mpl objects
>>> # xdoctest: +REQUIRES(--show)
>>> from matplotlib import pyplot as plt
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(1, 2, 1)
>>> self.show_image(gid=2)
>>> ax2 = fig.add_subplot(1, 2, 2)
>>> ax2.imshow(canvas)
>>> ax1.set_title('show with matplotlib')
>>> ax2.set_title('draw with cv2')
>>> plt.show()

property fpath(self)¶: In the future we will deprecate img_root for bundle_dpath

_infer_dirs(self)¶

classmethod from_data(CocoDataset, data, bundle_dpath=None, img_root=None)¶: Constructor from a json dictionary

classmethod from_image_paths(CocoDataset, gpaths, bundle_dpath=None, img_root=None)¶

Constructor from a list of images paths.

This is a convinience method.

Parameters: gpaths (List[str]) – list of image paths

Example

>>> coco_dset = CocoDataset.from_image_paths(['a.png', 'b.png'])
>>> assert coco_dset.n_images == 2

classmethod from_coco_paths(CocoDataset, fpaths, max_workers=0, verbose=1, mode='thread', union='try')¶

Constructor from multiple coco file paths.

Loads multiple coco datasets and unions the result

Note

if the union operation fails, the list of individually loaded files is returned instead.

Parameters

fpaths (List[str]) – list of paths to multiple coco files to be loaded and unioned.
max_workers (int, default=0) – number of worker threads / processes
verbose (int) – verbosity level
mode (str) – thread, process, or serial
union (str | bool, default=’try’) – If True, unions the result datasets after loading. If False, just returns the result list. If ‘try’, then try to preform the union, but return the result list if it fails.

copy(self)¶

Deep copies this object

Example

>>> from kwcoco.coco_dataset import *
>>> self = CocoDataset.demo()
>>> new = self.copy()
>>> assert new.imgs[1] is new.dataset['images'][0]
>>> assert new.imgs[1] == self.dataset['images'][0]
>>> assert new.imgs[1] is not self.dataset['images'][0]

__nice__(self)¶

dumps(self, indent=None, newlines=False)¶

Writes the dataset out to the json format

Parameters: newlines (bool) – if True, each annotation, image, category gets its own line

Note

Using newlines=True is similar to:: print(ub.repr2(dset.dataset, nl=2, trailsep=False)) However, the above may not output valid json if it contains ndarrays.

Example

>>> from kwcoco.coco_dataset import *
>>> self = CocoDataset.demo()
>>> text = self.dumps(newlines=True)
>>> print(text)
>>> self2 = CocoDataset(json.loads(text), tag='demo2')
>>> assert self2.dataset == self.dataset
>>> assert self2.dataset is not self.dataset

>>> text = self.dumps(newlines=True)
>>> print(text)
>>> self2 = CocoDataset(json.loads(text), tag='demo2')
>>> assert self2.dataset == self.dataset
>>> assert self2.dataset is not self.dataset

Example

>>> from kwcoco.coco_dataset import *
>>> self = CocoDataset.coerce('vidshapes1-msi-multisensor', verbose=3)
>>> self.remove_annotations(self.annots())
>>> text = self.dumps(newlines=True, indent='  ')
>>> print(text)

dump(self, file, indent=None, newlines=False, temp_file=True)¶

Writes the dataset out to the json format

Parameters

file (PathLike | FileLike) – Where to write the data. Can either be a path to a file or an open file pointer / stream.
newlines (bool) – if True, each annotation, image, category gets its own line.
temp_file (bool | str, default=True) – Argument to safer.open(). Ignored if file is not a PathLike object.

Example

>>> import tempfile
>>> from kwcoco.coco_dataset import *
>>> self = CocoDataset.demo()
>>> file = tempfile.NamedTemporaryFile('w')
>>> self.dump(file)
>>> file.seek(0)
>>> text = open(file.name, 'r').read()
>>> print(text)
>>> file.seek(0)
>>> dataset = json.load(open(file.name, 'r'))
>>> self2 = CocoDataset(dataset, tag='demo2')
>>> assert self2.dataset == self.dataset
>>> assert self2.dataset is not self.dataset

>>> file = tempfile.NamedTemporaryFile('w')
>>> self.dump(file, newlines=True)
>>> file.seek(0)
>>> text = open(file.name, 'r').read()
>>> print(text)
>>> file.seek(0)
>>> dataset = json.load(open(file.name, 'r'))
>>> self2 = CocoDataset(dataset, tag='demo2')
>>> assert self2.dataset == self.dataset
>>> assert self2.dataset is not self.dataset

_check_json_serializable(self, verbose=1)¶: Debug which part of a coco dataset might not be json serializable

_check_integrity(self)¶: perform all checks

_check_index(self)¶

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo()
>>> self._check_index()
>>> # Force a failure
>>> self.index.anns.pop(1)
>>> self.index.anns.pop(2)
>>> import pytest
>>> with pytest.raises(AssertionError):
>>>     self._check_index()

_check_pointers(self, verbose=1)¶: Check that all category and image ids referenced by annotations exist

_build_index(self)¶

union(*others, disjoint_tracks=True, **kwargs)¶

Merges multiple CocoDataset items into one. Names and associations are retained, but ids may be different.

Parameters

*others – a series of CocoDatasets that we will merge. Note, if called as an instance method, the “self” instance will be the first item in the “others” list. But if called like a classmethod, “others” will be empty by default.
disjoint_tracks (bool, default=True) – if True, we will assume track-ids are disjoint and if two datasets share the same track-id, we will disambiguate them. Otherwise they will be copied over as-is.
**kwargs – constructor options for the new merged CocoDataset

Returns

a new merged coco dataset

Return type

CocoDataset

CommandLine

xdoctest -m kwcoco.coco_dataset CocoDataset.union

Example

>>> # Test union works with different keypoint categories
>>> dset1 = CocoDataset.demo('shapes1')
>>> dset2 = CocoDataset.demo('shapes2')
>>> dset1.remove_keypoint_categories(['bot_tip', 'mid_tip', 'right_eye'])
>>> dset2.remove_keypoint_categories(['top_tip', 'left_eye'])
>>> dset_12a = CocoDataset.union(dset1, dset2)
>>> dset_12b = dset1.union(dset2)
>>> dset_21 = dset2.union(dset1)
>>> def add_hist(h1, h2):
>>>     return {k: h1.get(k, 0) + h2.get(k, 0) for k in set(h1) | set(h2)}
>>> kpfreq1 = dset1.keypoint_annotation_frequency()
>>> kpfreq2 = dset2.keypoint_annotation_frequency()
>>> kpfreq_want = add_hist(kpfreq1, kpfreq2)
>>> kpfreq_got1 = dset_12a.keypoint_annotation_frequency()
>>> kpfreq_got2 = dset_12b.keypoint_annotation_frequency()
>>> assert kpfreq_want == kpfreq_got1
>>> assert kpfreq_want == kpfreq_got2

>>> # Test disjoint gid datasets
>>> import kwcoco
>>> dset1 = kwcoco.CocoDataset.demo('shapes3')
>>> for new_gid, img in enumerate(dset1.dataset['images'], start=10):
>>>     for aid in dset1.gid_to_aids[img['id']]:
>>>         dset1.anns[aid]['image_id'] = new_gid
>>>     img['id'] = new_gid
>>> dset1.index.clear()
>>> dset1._build_index()
>>> # ------
>>> dset2 = kwcoco.CocoDataset.demo('shapes2')
>>> for new_gid, img in enumerate(dset2.dataset['images'], start=100):
>>>     for aid in dset2.gid_to_aids[img['id']]:
>>>         dset2.anns[aid]['image_id'] = new_gid
>>>     img['id'] = new_gid
>>> dset1.index.clear()
>>> dset2._build_index()
>>> others = [dset1, dset2]
>>> merged = kwcoco.CocoDataset.union(*others)
>>> print('merged = {!r}'.format(merged))
>>> print('merged.imgs = {}'.format(ub.repr2(merged.imgs, nl=1)))
>>> assert set(merged.imgs) & set([10, 11, 12, 100, 101]) == set(merged.imgs)

>>> # Test data is not preserved
>>> dset2 = kwcoco.CocoDataset.demo('shapes2')
>>> dset1 = kwcoco.CocoDataset.demo('shapes3')
>>> others = (dset1, dset2)
>>> cls = self = kwcoco.CocoDataset
>>> merged = cls.union(*others)
>>> print('merged = {!r}'.format(merged))
>>> print('merged.imgs = {}'.format(ub.repr2(merged.imgs, nl=1)))
>>> assert set(merged.imgs) & set([1, 2, 3, 4, 5]) == set(merged.imgs)

>>> # Test track-ids are mapped correctly
>>> dset1 = kwcoco.CocoDataset.demo('vidshapes1')
>>> dset2 = kwcoco.CocoDataset.demo('vidshapes2')
>>> dset3 = kwcoco.CocoDataset.demo('vidshapes3')
>>> others = (dset1, dset2, dset3)
>>> for dset in others:
>>>     [a.pop('segmentation', None) for a in dset.index.anns.values()]
>>>     [a.pop('keypoints', None) for a in dset.index.anns.values()]
>>> cls = self = kwcoco.CocoDataset
>>> merged = cls.union(*others, disjoint_tracks=1)
>>> print('dset1.anns = {}'.format(ub.repr2(dset1.anns, nl=1)))
>>> print('dset2.anns = {}'.format(ub.repr2(dset2.anns, nl=1)))
>>> print('dset3.anns = {}'.format(ub.repr2(dset3.anns, nl=1)))
>>> print('merged.anns = {}'.format(ub.repr2(merged.anns, nl=1)))

Example

>>> import kwcoco
>>> # Test empty union
>>> empty_union = kwcoco.CocoDataset.union()
>>> assert len(empty_union.index.imgs) == 0

Todo

[ ] are supercategories broken?
[ ] reuse image ids where possible
[ ] reuse annotation / category ids where possible
[X] handle case where no inputs are given
[x] disambiguate track-ids
[x] disambiguate video-ids

subset(self, gids, copy=False, autobuild=True)¶

Return a subset of the larger coco dataset by specifying which images to port. All annotations in those images will be taken.

Parameters

gids (List[int]) – image-ids to copy into a new dataset
copy (bool, default=False) – if True, makes a deep copy of all nested attributes, otherwise makes a shallow copy.
autobuild (bool, default=True) – if True will automatically build the fast lookup index.

Example

>>> self = CocoDataset.demo()
>>> gids = [1, 3]
>>> sub_dset = self.subset(gids)
>>> assert len(self.index.gid_to_aids) == 3
>>> assert len(sub_dset.gid_to_aids) == 2

Example

>>> import kwcoco
>>> self = kwcoco.CocoDataset.demo('vidshapes2')
>>> gids = [1, 2]
>>> sub_dset = self.subset(gids, copy=True)
>>> assert len(sub_dset.index.videos) == 1
>>> assert len(self.index.videos) == 2

Example

>>> self = CocoDataset.demo()
>>> sub1 = self.subset([1])
>>> sub2 = self.subset([2])
>>> sub3 = self.subset([3])
>>> others = [sub1, sub2, sub3]
>>> rejoined = CocoDataset.union(*others)
>>> assert len(sub1.anns) == 9
>>> assert len(sub2.anns) == 2
>>> assert len(sub3.anns) == 0
>>> assert rejoined.basic_stats() == self.basic_stats()

view_sql(self, force_rewrite=False, memory=False)¶

Create a cached SQL interface to this dataset suitable for large scale multiprocessing use cases.

Parameters

force_rewrite (bool, default=False) – if True, forces an update to any existing cache file on disk
memory (bool, default=False) – if True, the database is constructed in memory.

Note

This view cache is experimental and currently depends on the timestamp of the file pointed to by self.fpath. In other words dont use this on in-memory datasets.

class kwcoco.CocoImage(img, dset=None)[source]¶

Bases: ubelt.NiceRepr

An object-oriented representation of a coco image.

It provides helper methods that are specific to a single image.

This operates directly on a single coco image dictionary, but it can optionally be connected to a parent dataset, which allows it to use CocoDataset methods to query about relationships and resolve pointers.

This is different than the Images class in coco_object1d, which is just a vectorized interface to multiple objects.

Example

>>> import kwcoco
>>> dset1 = kwcoco.CocoDataset.demo('shapes8')
>>> dset2 = kwcoco.CocoDataset.demo('vidshapes8-multispectral')

>>> self = CocoImage(dset1.imgs[1], dset1)
>>> print('self = {!r}'.format(self))
>>> print('self.channels = {}'.format(ub.repr2(self.channels, nl=1)))

>>> self = CocoImage(dset2.imgs[1], dset2)
>>> print('self.channels = {}'.format(ub.repr2(self.channels, nl=1)))
>>> self.primary_asset()

classmethod from_gid(cls, dset, gid)¶

property bundle_dpath(self)¶

property video(self)¶: Helper to grab the video for this image if it exists

detach(self)¶: Removes references to the underlying coco dataset, but keeps special information such that it wont be needed.

__nice__(self)¶

Example

>>> from kwcoco.coco_image import *  # NOQA
>>> import kwcoco
>>> with ub.CaptureStdout() as cap:
...     dset = kwcoco.CocoDataset.demo('shapes8')
>>> self = CocoImage(dset.dataset['images'][0], dset)
>>> print('self = {!r}'.format(self))

>>> dset = kwcoco.CocoDataset.demo()
>>> self = CocoImage(dset.dataset['images'][0], dset)
>>> print('self = {!r}'.format(self))

stats(self)¶

__contains__(self, key)¶

__getitem__(self, key)¶: Proxy getter attribute for underlying self.img dictionary

keys(self)¶: Proxy getter attribute for underlying self.img dictionary

get(self, key, default=ub.NoParam)¶

Proxy getter attribute for underlying self.img dictionary

Example

>>> import pytest
>>> # without extra populated
>>> import kwcoco
>>> self = kwcoco.CocoImage({'foo': 1})
>>> assert self.get('foo') == 1
>>> assert self.get('foo', None) == 1
>>> # with extra populated
>>> self = kwcoco.CocoImage({'extra': {'foo': 1}})
>>> assert self.get('foo') == 1
>>> assert self.get('foo', None) == 1
>>> # without extra empty
>>> self = kwcoco.CocoImage({})
>>> with pytest.raises(KeyError):
>>>     self.get('foo')
>>> assert self.get('foo', None) is None
>>> # with extra empty
>>> self = kwcoco.CocoImage({'extra': {'bar': 1}})
>>> with pytest.raises(KeyError):
>>>     self.get('foo')
>>> assert self.get('foo', None) is None

property channels(self)¶

property num_channels(self)¶

property dsize(self)¶

primary_image_filepath(self, requires=None)¶

primary_asset(self, requires=None)¶

Compute a “main” image asset.

Notes

Uses a heuristic.

First, try to find the auxiliary image that has with the smallest

distortion to the base image (if known via warp_aux_to_img)

Second, break ties by using the largest image if w / h is known
Last, if previous information not available use the first auxiliary image.

Parameters: requires (List[str]) – list of attribute that must be non-None to consider an object as the primary one.

Todo

[ ] Add in primary heuristics

Example

>>> import kwarray
>>> from kwcoco.coco_image import *  # NOQA
>>> rng = kwarray.ensure_rng(0)
>>> def random_auxiliary(name, w=None, h=None):
>>>     return {'file_name': name, 'width': w, 'height': h}
>>> self = CocoImage({
>>>     'auxiliary': [
>>>         random_auxiliary('1'),
>>>         random_auxiliary('2'),
>>>         random_auxiliary('3'),
>>>     ]
>>> })
>>> assert self.primary_asset()['file_name'] == '1'
>>> self = CocoImage({
>>>     'auxiliary': [
>>>         random_auxiliary('1'),
>>>         random_auxiliary('2', 3, 3),
>>>         random_auxiliary('3'),
>>>     ]
>>> })
>>> assert self.primary_asset()['file_name'] == '2'

iter_image_filepaths(self)¶

iter_asset_objs(self)¶

Iterate through base + auxiliary dicts that have file paths

Yields: dict – an image or auxiliary dictionary

find_asset_obj(self, channels)¶

Find the asset dictionary with the specified channels

Example

>>> import kwcoco
>>> coco_img = kwcoco.CocoImage({'width': 128, 'height': 128})
>>> coco_img.add_auxiliary_item(
>>>     'rgb.png', channels='red|green|blue', width=32, height=32)
>>> assert coco_img.find_asset_obj('red') is not None
>>> assert coco_img.find_asset_obj('green') is not None
>>> assert coco_img.find_asset_obj('blue') is not None
>>> assert coco_img.find_asset_obj('red|blue') is not None
>>> assert coco_img.find_asset_obj('red|green|blue') is not None
>>> assert coco_img.find_asset_obj('red|green|blue') is not None
>>> assert coco_img.find_asset_obj('black') is None
>>> assert coco_img.find_asset_obj('r') is None

Example

>>> # Test with concise channel code
>>> import kwcoco
>>> coco_img = kwcoco.CocoImage({'width': 128, 'height': 128})
>>> coco_img.add_auxiliary_item(
>>>     'msi.png', channels='foo.0:128', width=32, height=32)
>>> assert coco_img.find_asset_obj('foo') is None
>>> assert coco_img.find_asset_obj('foo.3') is not None
>>> assert coco_img.find_asset_obj('foo.3:5') is not None
>>> assert coco_img.find_asset_obj('foo.3000') is None

_assets_key(self)¶: Internal helper for transition from auxiliary -> assets in the image spec

add_auxiliary_item(self, file_name=None, channels=None, imdata=None, warp_aux_to_img=None, width=None, height=None, imwrite=False)¶

Adds an auxiliary / asset item to the image dictionary.

This operation can be done purely in-memory (the default), or the image data can be written to a file on disk (via the imwrite=True flag).

Parameters

file_name (str | None) – The name of the file relative to the bundle directory. If unspecified, imdata must be given.
channels (str | kwcoco.FusedChannelSpec) – The channel code indicating what each of the bands represents. These channels should be disjoint wrt to the existing data in this image (this is not checked).
imdata (ndarray | None) – The underlying image data this auxiliary item represents. If unspecified, it is assumed file_name points to a path on disk that will eventually exist. If imdata, file_name, and the special imwrite=True flag are specified, this function will write the data to disk.
warp_aux_to_img (kwimage.Affine) – The transformation from this auxiliary space to image space. If unspecified, assumes this item is related to image space by only a scale factor.
width (int) – Width of the data in auxiliary space (inferred if unspecified)
height (int) – Height of the data in auxiliary space (inferred if unspecified)
imwrite (bool) – If specified, both imdata and file_name must be specified, and this will write the data to disk. Note: it it recommended that you simply call imwrite yourself before or after calling this function. This lets you better control imwrite parameters.

Todo

[ ] Allow imwrite to specify an executor that is used to

return a Future so the imwrite call does not block.

Example

>>> from kwcoco.coco_image import *  # NOQA
>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> coco_img = dset.coco_image(1)
>>> imdata = np.random.rand(32, 32, 5)
>>> channels = kwcoco.FusedChannelSpec.coerce('Aux:5')
>>> coco_img.add_auxiliary_item(imdata=imdata, channels=channels)

delay(self, channels=None, space='image', bundle_dpath=None)¶

Perform a delayed load on the data in this image.

The delayed load can load a subset of channels, and perform lazy warping operations. If the underlying data is in a tiled format this can reduce the amount of disk IO needed to read the data if only a small crop or lower resolution view of the data is needed.

Note

This method is experimental and relies on the delayed load proof-of-concept.

Parameters

gid (int) – image id to load
channels (FusedChannelSpec) – specific channels to load. if unspecified, all channels are loaded.
space (str) – can either be “image” for loading in image space, or “video” for loading in video space.

Todo

[X] Currently can only take all or none of the channels from each
base-image / auxiliary dict. For instance if the main image is r|g|b you can’t just select g|b at the moment.
[X] The order of the channels in the delayed load should
match the requested channel order.
[X] TODO: add nans to bands that don’t exist or throw an error
[ ] This function could stand to have a better name. Maybe imread
with a delayed=True flag? Or maybe just delayed_load?

Example

>>> from kwcoco.coco_image import *  # NOQA
>>> import kwcoco
>>> gid = 1
>>> #
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> self = CocoImage(dset.imgs[gid], dset)
>>> delayed = self.delay()
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize()))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))
>>> #
>>> dset = kwcoco.CocoDataset.demo('shapes8')
>>> delayed = dset.delayed_load(gid)
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize()))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))

>>> crop = delayed.delayed_crop((slice(0, 3), slice(0, 3)))
>>> crop.finalize()
>>> crop.finalize(as_xarray=True)

>>> # TODO: should only select the "red" channel
>>> dset = kwcoco.CocoDataset.demo('shapes8')
>>> delayed = CocoImage(dset.imgs[gid], dset).delay(channels='r')

>>> import kwcoco
>>> gid = 1
>>> #
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> delayed = dset.delayed_load(gid, channels='B1|B2', space='image')
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))
>>> delayed = dset.delayed_load(gid, channels='B1|B2|B11', space='image')
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))
>>> delayed = dset.delayed_load(gid, channels='B8|B1', space='video')
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))

>>> delayed = dset.delayed_load(gid, channels='B8|foo|bar|B1', space='video')
>>> print('delayed = {!r}'.format(delayed))
>>> print('delayed.finalize() = {!r}'.format(delayed.finalize(as_xarray=True)))

Example

>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo()
>>> coco_img = dset.coco_image(1)
>>> # Test case where nothing is registered in the dataset
>>> delayed = coco_img.delay()
>>> final = delayed.finalize()
>>> assert final.shape == (512, 512, 3)

Example

>>> # Test that delay works when imdata is stored in the image
>>> # dictionary itself.
>>> from kwcoco.coco_image import *  # NOQA
>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> coco_img = dset.coco_image(1)
>>> imdata = np.random.rand(6, 6, 5)
>>> imdata[:] = np.arange(5)[None, None, :]
>>> channels = kwcoco.FusedChannelSpec.coerce('Aux:5')
>>> coco_img.add_auxiliary_item(imdata=imdata, channels=channels)
>>> delayed = coco_img.delay(channels='B1|Aux:2:4')
>>> final = delayed.finalize()

Example

>>> # Test delay when loading in auxiliary space
>>> from kwcoco.coco_image import *  # NOQA
>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-msi-multisensor')
>>> coco_img = dset.coco_image(1)
>>> stream1 = coco_img.channels.streams()[0]
>>> stream2 = coco_img.channels.streams()[1]
>>> aux_delayed = coco_img.delay(stream1, space='auxiliary')
>>> img_delayed = coco_img.delay(stream1, space='image')
>>> vid_delayed = coco_img.delay(stream1, space='video')
>>> #
>>> aux_imdata = aux_delayed.finalize()
>>> img_imdata = img_delayed.finalize()
>>> assert aux_imdata.shape != img_imdata.shape
>>> # Cannot load multiple auxiliary items at the same time in
>>> # auxiliary space
>>> import pytest
>>> fused_channels = stream1 | stream2
>>> with pytest.raises(kwcoco.exceptions.CoordinateCompatibilityError):
>>>     aux_delayed2 = coco_img.delay(fused_channels, space='auxiliary')

valid_region(self, space='image')¶: If this image has a valid polygon, return it in image, or video space

warp_vid_from_img(self)¶

warp_img_from_vid(self)¶

_annot_segmentation(self, ann, space='video')¶

class kwcoco.CategoryTree(graph=None, checks=True)[source]¶

Bases: ubelt.NiceRepr

Wrapper that maintains flat or hierarchical category information.

Helps compute softmaxes and probabilities for tree-based categories where a directed edge (A, B) represents that A is a superclass of B.

Note

There are three basic properties that this object maintains:

node:
    Alphanumeric string names that should be generally descriptive.
    Using spaces and special characters in these names is
    discouraged, but can be done.  This is the COCO category "name"
    attribute.  For categories this may be denoted as (name, node,
    cname, catname).

id:
    The integer id of a category should ideally remain consistent.
    These are often given by a dataset (e.g. a COCO dataset).  This
    is the COCO category "id" attribute. For categories this is
    often denoted as (id, cid).

index:
    Contigous zero-based indices that indexes the list of
    categories.  These should be used for the fastest access in
    backend computation tasks. Typically corresponds to the
    ordering of the channels in the final linear layer in an
    associated model.  For categories this is often denoted as
    (index, cidx, idx, or cx).

Variables

idx_to_node (List[str]) – a list of class names. Implicitly maps from index to category name.
id_to_node (Dict[int, str]) – maps integer ids to category names
node_to_id (Dict[str, int]) – maps category names to ids
node_to_idx (Dict[str, int]) – maps category names to indexes
graph (networkx.Graph) – a Graph that stores any hierarchy information. For standard mutually exclusive classes, this graph is edgeless. Nodes in this graph can maintain category attributes / properties.
idx_groups (List[List[int]]) – groups of category indices that share the same parent category.

Example

>>> from kwcoco.category_tree import *
>>> graph = nx.from_dict_of_lists({
>>>     'background': [],
>>>     'foreground': ['animal'],
>>>     'animal': ['mammal', 'fish', 'insect', 'reptile'],
>>>     'mammal': ['dog', 'cat', 'human', 'zebra'],
>>>     'zebra': ['grevys', 'plains'],
>>>     'grevys': ['fred'],
>>>     'dog': ['boxer', 'beagle', 'golden'],
>>>     'cat': ['maine coon', 'persian', 'sphynx'],
>>>     'reptile': ['bearded dragon', 't-rex'],
>>> }, nx.DiGraph)
>>> self = CategoryTree(graph)
>>> print(self)
<CategoryTree(nNodes=22, maxDepth=6, maxBreadth=4...)>

Example

>>> # The coerce classmethod is the easiest way to create an instance
>>> import kwcoco
>>> kwcoco.CategoryTree.coerce(['a', 'b', 'c'])
<CategoryTree...nNodes=3, nodes=...'a', 'b', 'c'...
>>> kwcoco.CategoryTree.coerce(4)
<CategoryTree...nNodes=4, nodes=...'class_1', 'class_2', 'class_3', ...
>>> kwcoco.CategoryTree.coerce(4)

copy(self)¶

classmethod from_mutex(cls, nodes, bg_hack=True)¶

Parameters: nodes (List[str]) – or a list of class names (in which case they will all be assumed to be mutually exclusive)

Example

>>> print(CategoryTree.from_mutex(['a', 'b', 'c']))
<CategoryTree(nNodes=3, ...)>

classmethod from_json(cls, state)¶

Parameters: state (Dict) – see __getstate__ / __json__ for details

classmethod from_coco(cls, categories)¶

Create a CategoryTree object from coco categories

Parameters: List[Dict] – list of coco-style categories

classmethod coerce(cls, data, **kw)¶

Attempt to coerce data as a CategoryTree object.

This is primarily useful for when the software stack depends on categories being represent

This will work if the input data is a specially formatted json dict, a list of mutually exclusive classes, or if it is already a CategoryTree. Otherwise an error will be thrown.

Parameters

data (object) – a known representation of a category tree.
**kwargs – input type specific arguments

Returns

self

Return type

CategoryTree

Raises

TypeError - if the input format is unknown –
ValueError - if kwargs are not compatible with the input format –

Example

>>> import kwcoco
>>> classes1 = kwcoco.CategoryTree.coerce(3)  # integer
>>> classes2 = kwcoco.CategoryTree.coerce(classes1.__json__())  # graph dict
>>> classes3 = kwcoco.CategoryTree.coerce(['class_1', 'class_2', 'class_3'])  # mutex list
>>> classes4 = kwcoco.CategoryTree.coerce(classes1.graph)  # nx Graph
>>> classes5 = kwcoco.CategoryTree.coerce(classes1)  # cls
>>> # xdoctest: +REQUIRES(module:ndsampler)
>>> import ndsampler
>>> classes6 = ndsampler.CategoryTree.coerce(3)
>>> classes7 = ndsampler.CategoryTree.coerce(classes1)
>>> classes8 = kwcoco.CategoryTree.coerce(classes6)

classmethod demo(cls, key='coco', **kwargs)¶

Parameters: key (str) – specify which demo dataset to use. Can be ‘coco’ (which uses the default coco demo data). Can be ‘btree’ which creates a binary tree and accepts kwargs ‘r’ and ‘h’ for branching-factor and height. Can be ‘btree2’, which is the same as btree but returns strings

CommandLine

xdoctest -m ~/code/kwcoco/kwcoco/category_tree.py CategoryTree.demo

Example

>>> from kwcoco.category_tree import *
>>> self = CategoryTree.demo()
>>> print('self = {}'.format(self))
self = <CategoryTree(nNodes=10, maxDepth=2, maxBreadth=4...)>

to_coco(self)¶

Converts to a coco-style data structure

Yields: Dict – coco category dictionaries

id_to_idx(self)¶

Example

>>> import kwcoco
>>> self = kwcoco.CategoryTree.demo()
>>> self.id_to_idx[1]

idx_to_id(self)¶

Example

>>> import kwcoco
>>> self = kwcoco.CategoryTree.demo()
>>> self.idx_to_id[0]

idx_to_ancestor_idxs(self, include_self=True)¶

Mapping from a class index to its ancestors

Parameters: include_self (bool, default=True) – if True includes each node as its own ancestor.

idx_to_descendants_idxs(self, include_self=False)¶

Mapping from a class index to its descendants (including itself)

Parameters: include_self (bool, default=False) – if True includes each node as its own descendant.

idx_pairwise_distance(self)¶

Get a matrix encoding the distance from one class to another.

Distances

from parents to children are positive (descendants),
from children to parents are negative (ancestors),
between unreachable nodes (wrt to forward and reverse graph) are nan.

__len__(self)¶

__iter__(self)¶

__getitem__(self, index)¶

__contains__(self, node)¶

__json__(self)¶

Example

>>> import pickle
>>> self = CategoryTree.demo()
>>> print('self = {!r}'.format(self.__json__()))

__getstate__(self)¶

Serializes information in this class

Example

>>> from kwcoco.category_tree import *
>>> import pickle
>>> self = CategoryTree.demo()
>>> state = self.__getstate__()
>>> serialization = pickle.dumps(self)
>>> recon = pickle.loads(serialization)
>>> assert recon.__json__() == self.__json__()

__setstate__(self, state)¶

__nice__(self)¶

is_mutex(self)¶

Returns True if all categories are mutually exclusive (i.e. flat)

If true, then the classes may be represented as a simple list of class names without any loss of information, otherwise the underlying category graph is necessary to preserve all knowledge.

Todo

[ ] what happens when we have a dummy root?

property num_classes(self)¶

property class_names(self)¶

property category_names(self)¶

property cats(self)¶

Returns a mapping from category names to category attributes.

If this category tree was constructed from a coco-dataset, then this will contain the coco category attributes.

Returns: Dict[str, Dict[str, object]]

Example

>>> from kwcoco.category_tree import *
>>> self = CategoryTree.demo()
>>> print('self.cats = {!r}'.format(self.cats))

index(self, node)¶: Return the index that corresponds to the category name

_build_index(self)¶: construct lookup tables

show(self)¶

forest_str(self)¶

normalize(self)¶

Applies a normalization scheme to the categories.

Note: this may break other tasks that depend on exact category names.

Returns: CategoryTree

Example

>>> from kwcoco.category_tree import *  # NOQA
>>> import kwcoco
>>> orig = kwcoco.CategoryTree.demo('animals_v1')
>>> self = kwcoco.CategoryTree(nx.relabel_nodes(orig.graph, str.upper))
>>> norm = self.normalize()

class kwcoco.ChannelSpec(spec, parsed=None)[source]¶

Bases: BaseChannelSpec

Parse and extract information about network input channel specs for early or late fusion networks.

Behaves like a dictionary of FusedChannelSpec objects

Todo

[ ] Rename to something that indicates this is a collection of
FusedChannelSpec? MultiChannelSpec?

Note

This class name and API is in flux and subject to change.

Note

The pipe (‘|’) character represents an early-fused input stream, and order matters (it is non-communative).

The comma (‘,’) character separates different inputs streams/branches for a multi-stream/branch network which will be lated fused. Order does not matter

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> # Integer spec
>>> ChannelSpec.coerce(3)
<ChannelSpec(u0|u1|u2) ...>

>>> # single mode spec
>>> ChannelSpec.coerce('rgb')
<ChannelSpec(rgb) ...>

>>> # early fused input spec
>>> ChannelSpec.coerce('rgb|disprity')
<ChannelSpec(rgb|disprity) ...>

>>> # late fused input spec
>>> ChannelSpec.coerce('rgb,disprity')
<ChannelSpec(rgb,disprity) ...>

>>> # early and late fused input spec
>>> ChannelSpec.coerce('rgb|ir,disprity')
<ChannelSpec(rgb|ir,disprity) ...>

Example

>>> self = ChannelSpec('gray')
>>> print('self.info = {}'.format(ub.repr2(self.info, nl=1)))
>>> self = ChannelSpec('rgb')
>>> print('self.info = {}'.format(ub.repr2(self.info, nl=1)))
>>> self = ChannelSpec('rgb|disparity')
>>> print('self.info = {}'.format(ub.repr2(self.info, nl=1)))
>>> self = ChannelSpec('rgb|disparity,disparity')
>>> print('self.info = {}'.format(ub.repr2(self.info, nl=1)))
>>> self = ChannelSpec('rgb,disparity,flowx|flowy')
>>> print('self.info = {}'.format(ub.repr2(self.info, nl=1)))

Example

>>> specs = [
>>>     'rgb',              # and rgb input
>>>     'rgb|disprity',     # rgb early fused with disparity
>>>     'rgb,disprity',     # rgb early late with disparity
>>>     'rgb|ir,disprity',  # rgb early fused with ir and late fused with disparity
>>>     3,                  # 3 unknown channels
>>> ]
>>> for spec in specs:
>>>     print('=======================')
>>>     print('spec = {!r}'.format(spec))
>>>     #
>>>     self = ChannelSpec.coerce(spec)
>>>     print('self = {!r}'.format(self))
>>>     sizes = self.sizes()
>>>     print('sizes = {!r}'.format(sizes))
>>>     print('self.info = {}'.format(ub.repr2(self.info, nl=1)))
>>>     #
>>>     item = self._demo_item((1, 1), rng=0)
>>>     inputs = self.encode(item)
>>>     components = self.decode(inputs)
>>>     input_shapes = ub.map_vals(lambda x: x.shape, inputs)
>>>     component_shapes = ub.map_vals(lambda x: x.shape, components)
>>>     print('item = {}'.format(ub.repr2(item, precision=1)))
>>>     print('inputs = {}'.format(ub.repr2(inputs, precision=1)))
>>>     print('input_shapes = {}'.format(ub.repr2(input_shapes)))
>>>     print('components = {}'.format(ub.repr2(components, precision=1)))
>>>     print('component_shapes = {}'.format(ub.repr2(component_shapes, nl=1)))

property spec(self)¶

The string encodeing of this spec

Returns: str

__contains__(self, key)¶

Example

>>> 'disparity' in ChannelSpec('rgb,disparity,flowx|flowy')
True
>>> 'gray' in ChannelSpec('rgb,disparity,flowx|flowy')
False

property info(self)¶

classmethod coerce(cls, data)¶

Attempt to interpret the data as a channel specification

Returns: ChannelSpec

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> data = FusedChannelSpec.coerce(3)
>>> assert ChannelSpec.coerce(data).spec == 'u0|u1|u2'
>>> data = ChannelSpec.coerce(3)
>>> assert data.spec == 'u0|u1|u2'
>>> assert ChannelSpec.coerce(data).spec == 'u0|u1|u2'
>>> data = ChannelSpec.coerce('u:3')
>>> assert data.normalize().spec == 'u.0|u.1|u.2'

parse(self)¶

Build internal representation

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> self = ChannelSpec('b1|b2|b3|rgb,B:3')
>>> print(self.parse())
>>> print(self.normalize().parse())
>>> ChannelSpec('').parse()

Example

>>> base = ChannelSpec('rgb|disparity,flowx|r|flowy')
>>> other = ChannelSpec('rgb')
>>> self = base.intersection(other)
>>> assert self.numel() == 4

concise(self)¶

Example

>>> self = ChannelSpec('b1|b2,b3|rgb|B.0,B.1|B.2')
>>> print(self.concise().spec)
b1|b2,b3|r|g|b|B.0,B.1:3

normalize(self)¶

Replace aliases with explicit single-band-per-code specs

Returns: normalized spec
Return type: ChannelSpec

Example

>>> self = ChannelSpec('b1|b2,b3|rgb,B:3')
>>> normed = self.normalize()
>>> print('self   = {}'.format(self))
>>> print('normed = {}'.format(normed))
self   = <ChannelSpec(b1|b2,b3|rgb,B:3)>
normed = <ChannelSpec(b1|b2,b3|r|g|b,B.0|B.1|B.2)>

keys(self)¶

values(self)¶

items(self)¶

fuse(self)¶

Fuse all parts into an early fused channel spec

Returns: FusedChannelSpec

Example

>>> from kwcoco.channel_spec import *  # NOQA
>>> self = ChannelSpec.coerce('b1|b2,b3|rgb,B:3')
>>> fused = self.fuse()
>>> print('self  = {}'.format(self))
>>> print('fused = {}'.format(fused))
self  = <ChannelSpec(b1|b2,b3|rgb,B:3)>
fused = <FusedChannelSpec(b1|b2|b3|rgb|B:3)>

streams(self)¶

Breaks this spec up into one spec for each early-fused input stream

Example

self = ChannelSpec.coerce(‘r|g,B1|B2,fx|fy’) list(map(len, self.streams()))

code_list(self)¶

as_path(self)¶

Returns a string suitable for use in a path.

Note, this may no longer be a valid channel spec

difference(self, other)¶

Set difference. Remove all instances of other channels from this set of channels.

Example

>>> from kwcoco.channel_spec import *
>>> self = ChannelSpec('rgb|disparity,flowx|r|flowy')
>>> other = ChannelSpec('rgb')
>>> print(self.difference(other))
>>> other = ChannelSpec('flowx')
>>> print(self.difference(other))
<ChannelSpec(disparity,flowx|flowy)>
<ChannelSpec(r|g|b|disparity,r|flowy)>

Example

>>> from kwcoco.channel_spec import *
>>> self = ChannelSpec('a|b,c|d')
>>> new = self - {'a', 'b'}
>>> len(new.sizes()) == 1
>>> empty = new - 'c|d'
>>> assert empty.numel() == 0

intersection(self, other)¶

Set difference. Remove all instances of other channels from this set of channels.

Example

>>> from kwcoco.channel_spec import *
>>> self = ChannelSpec('rgb|disparity,flowx|r|flowy')
>>> other = ChannelSpec('rgb')
>>> new = self.intersection(other)
>>> print(new)
>>> print(new.numel())
>>> other = ChannelSpec('flowx')
>>> new = self.intersection(other)
>>> print(new)
>>> print(new.numel())
<ChannelSpec(r|g|b,r)>
4
<ChannelSpec(flowx)>
1

union(self, other)¶

Union simply tags on a second channel spec onto this one. Duplicates are maintained.

Example

>>> from kwcoco.channel_spec import *
>>> self = ChannelSpec('rgb|disparity,flowx|r|flowy')
>>> other = ChannelSpec('rgb')
>>> new = self.union(other)
>>> print(new)
>>> print(new.numel())
>>> other = ChannelSpec('flowx')
>>> new = self.union(other)
>>> print(new)
>>> print(new.numel())
<ChannelSpec(r|g|b|disparity,flowx|r|flowy,r|g|b)>
10
<ChannelSpec(r|g|b|disparity,flowx|r|flowy,flowx)>
8

abstract issubset(self, other)¶

abstract issuperset(self, other)¶

numel(self)¶: Total number of channels in this spec

sizes(self)¶

Number of dimensions for each fused stream channel

IE: The EARLY-FUSED channel sizes

Example

>>> self = ChannelSpec('rgb|disparity,flowx|flowy,B:10')
>>> self.normalize().concise()
>>> self.sizes()

unique(self, normalize=False)¶: Returns the unique channels that will need to be given or loaded

_item_shapes(self, dims)¶

Expected shape for an input item

Parameters: dims (Tuple[int, int]) – the spatial dimension
Returns: Dict[int, tuple]

_demo_item(self, dims=(4, 4), rng=None)¶

Create an input that satisfies this spec

Returns

an item like it might appear when its returned from the: __getitem__ method of a torch...Dataset.

Return type