`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.toydata_video¶

Module Contents¶

Functions¶

Attributes¶

`kwcoco.demo.toydata_video`¶