kwcoco.util

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

Submodules

• kwcoco.util.dict_like
• kwcoco.util.jsonschema_elements
• kwcoco.util.lazy_frame_backends
• kwcoco.util.util_archive
• kwcoco.util.util_delayed_poc
• kwcoco.util.util_futures
• kwcoco.util.util_json
• kwcoco.util.util_monkey
• kwcoco.util.util_sklearn
• kwcoco.util.util_truncate

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

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

kwcoco.util.ANY

kwcoco.util.ANYOF

kwcoco.util.ARRAY

kwcoco.util.BOOLEAN

class kwcoco.util.ContainerElements

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)

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

kwcoco.util.NOT

kwcoco.util.NULL

kwcoco.util.NUMBER

kwcoco.util.OBJECT

kwcoco.util.ONEOF

class kwcoco.util.QuantifierElements

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

class kwcoco.util.ScalarElements

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

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

class kwcoco.util.LazyGDalFrameFile(fpath, nodata=None)

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)

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)

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)

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)

class kwcoco.util.DelayedChannelConcat(components, dsize=None)

Bases: DelayedImageOperation

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)

Bases: DelayedImageOperation

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)

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)

Bases: DelayedImageOperation

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

Bases: DelayedVisionOperation

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)

Bases: DelayedImageOperation

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)

Bases: DelayedImageOperation

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

Bases: DelayedVisionOperation

Base class for nodes in a tree of delayed computer-vision operations

class kwcoco.util.DelayedVisionOperation

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)

Bases: DelayedImageOperation

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

kwcoco.util.ensure_json_serializable(dict_, normalize_containers=False, verbose=0)

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)

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)

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)

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)

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.

Read more in the User Guide.

Parameters

n_splits (int, default=3) – Number of folds. Must be at least 2.

_make_test_folds(self, X, y=None, groups=None)

Parameters

• X (ndarray) – data

• y (ndarray) – labels

• groups (ndarray) – groupids for items. Items with the same groupid must be placed in the same group.

Returns

test_folds

Return type

list

Example

>>> import kwarray
>>> rng = kwarray.ensure_rng(0)
>>> groups = [1, 1, 3, 4, 2, 2, 7, 8, 8]
>>> y      = [1, 1, 1, 1, 2, 2, 2, 3, 3]
>>> X = np.empty((len(y), 0))
>>> self = StratifiedGroupKFold(random_state=rng, shuffle=True)
>>> skf_list = list(self.split(X=X, y=y, groups=groups))
...
>>> import ubelt as ub
>>> print(ub.repr2(skf_list, nl=1, with_dtype=False))
[
    (np.array([2, 3, 4, 5, 6]), np.array([0, 1, 7, 8])),
    (np.array([0, 1, 2, 7, 8]), np.array([3, 4, 5, 6])),
    (np.array([0, 1, 3, 4, 5, 6, 7, 8]), np.array([2])),
]

_iter_test_masks(self, X, y=None, groups=None)

Generates boolean masks corresponding to test sets.

By default, delegates to _iter_test_indices(X, y, groups)

split(self, X, y, groups=None)

Generate indices to split data into training and test set.

kwcoco.util.smart_truncate(string, max_length=0, separator=' ', trunc_loc=0.5)

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: