kwcoco.demo.toydata_image module

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.

kwcoco.demo.toydata_image.demodata_toy_dset(image_size=(600, 600), n_imgs=5, verbose=3, rng=0, newstyle=True, dpath=None, fpath=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 | None) – random number generator or seed. Defaults to 0.

• newstyle (bool) – create newstyle kwcoco data. default=True

• dpath (str | PathLike | None) – path to the directory that will contain the bundle, (defaults to a kwcoco cache dir). Ignored if bundle_dpath is given.

• fpath (str | PathLike | None) – path to the kwcoco file. The parent will be the bundle if it is not specified. Should be a descendant of the dpath if specified.

• bundle_dpath (str | PathLike | None) – path to the directory that will store images. If specified, dpath is ignored. If unspecified, a bundle will be written inside dpath.

• aux (bool | None) – if True generates dummy auxiliary channels

• verbose (int) – verbosity mode. default=3

• use_cache (bool) – if True caches the generated json in the dpath. Default=True

• **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.urepr(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)

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 | None) – Nx2 base width / height of boxes

• gsize (Tuple[int, int]) – width / height of the image

• categories (List[str] | None) – 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 | None) – 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 | int | None) – the random state used to seed the process

• aux (bool | None) – 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.urepr(img)))
>>> print('anns = {}'.format(ub.urepr(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.urepr(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.urepr(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()