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)

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)

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)

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)

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()