def test_pngs():
images_luminance = io.video2luminance(video_file, nimages=100)
images_luminance_resized = io.video2luminance(video_file,
size=small_size,
nimages=100)
# Convert the first 100 video frames to PNGs
video_buffer = io.video_buffer(video_file, nimages=100)
# store frames in temporary directory
tmpdir = tempfile.mkdtemp()
for frameidx, video_frame in enumerate(video_buffer):
image_object = PIL.Image.fromarray(video_frame)
image_object.save(os.path.join(tmpdir, 'frame%08i.png' % frameidx))
image_files = sorted(glob(os.path.join(tmpdir, '*.png')))
files_luminance = io.load_image_luminance(image_files)
files_luminance_resized = io.load_image_luminance(image_files,
vdim=96,
hdim=int(96 *
aspect_ratio))
assert np.allclose(files_luminance, images_luminance)
assert np.allclose(files_luminance_resized, images_luminance_resized)
def test_video2luminance():
# test video2luminance generator
nimages = 256
video_buffer = io.video_buffer(video_file, nimages=nimages)
# load and downsample 1000 images
aspect_ratio = 16 / 9.0
small_size = (96, int(96 * aspect_ratio))
luminance_images = np.asarray([io.imagearray2luminance(image, size=small_size).squeeze() \
for image in video_buffer])
lum = io.video2luminance(video_file, size=small_size, nimages=nimages)
assert np.allclose(luminance_images, lum)
def test_smoke_test():
# high accuracy for test
DTYPE = np.float64
temp_freq = 4.0
video_file = 'http://anwarnunez.github.io/downloads/avsnr150s24fps_tiny.mp4'
nimages = 200
small_size = (72, 128) # downsampled size (vdim, hdim) 16:9 aspect ratio
luminance_images = io.video2luminance(video_file,
size=small_size,
nimages=nimages)
#luminance_images = io.video2grey(video_file, size=small_size, nimages=nimages)
nimages, vdim, hdim = luminance_images.shape
stimulus_fps = 24
aspect_ratio = hdim / vdim
pyramid = pyramids.MotionEnergyPyramid(
stimulus_vhsize=(vdim, hdim),
stimulus_fps=stimulus_fps,
spatial_frequencies=[8], # only small filters
temporal_frequencies=[temp_freq], # only one temporal freq
filter_temporal_width=16)
print(pyramid)
# centered projection
# vhsize=(0,0): top left
location_filters = {
'BL': [pyramid.filters_at_vhposition(0.9, 0.1)[0]],
'BR': [pyramid.filters_at_vhposition(0.9, 0.9 * aspect_ratio)[0]],
'TL': [pyramid.filters_at_vhposition(0.1, 0.1)[0]],
'TR': [pyramid.filters_at_vhposition(0.1, 0.9 * aspect_ratio)[0]]
}
# normal output
output = {}
for location, filters in sorted(location_filters.items()):
output[location] = pyramid.raw_project_stimulus(luminance_images,
filters,
dtype=DTYPE)
vdim, hdim = small_size
vhalf, hhalf = int(vdim / 2), int(hdim / 2)
# vdim (vstart, vend)
# hdim (hstart, hend)
blank_squares = {
'BL': ((-vhalf, None), (0, hhalf)),
'BR': ((-vhalf, None), (hhalf, None)),
'TL': ((0, vhalf), (0, hhalf)),
'TR': ((0, vhalf), (hhalf, None))
}
tcenter = int(nimages / 2)
controls = {}
for location, filters in sorted(location_filters.items()):
fig, ax = plt.subplots()
movie = luminance_images.copy()
movie /= movie.max()
(vstart, vend), (hstart, hend) = blank_squares[location]
movie[:, vstart:vend, hstart:hend] = 0.0
# insert spike in the middle
movie[tcenter, vstart:vend, hstart:hend] = 100
ax.matshow(movie[0])
ax.set_title(location)
filters = location_filters[location]
controls[location] = pyramid.raw_project_stimulus(
movie,
filters,
dtype=DTYPE,
)
pyramid.show_filter(filters[0])
fig, ax = plt.subplots(nrows=len(location_filters),
sharey=True,
sharex=True)
time = np.arange(nimages) * (1. / stimulus_fps)
for idx, (location,
filters) in enumerate(sorted(location_filters.items())):
ax[idx].plot(time, output[location][0], ':')
ax[idx].plot(time, controls[location][0], '-')
ax[idx].set_title(location)
ax[idx].vlines(tcenter / stimulus_fps, -1000, 1000)
ax[idx].set_ylim(-200, 200)
ax[idx].grid(True)
info = (tcenter / stimulus_fps, tcenter, temp_freq)
fig.suptitle('spike at %0.02f [sec] (frame #%i) filter=%0.02f[Hz]' % info)
pyramids,
utils,
core,
io,
)
DTYPE = np.float64
##############################
# preliminaries
##############################
video_file = 'http://anwarnunez.github.io/downloads/avsnr150s24fps_tiny.mp4'
nimages = 200
small_size = (72, 128) # downsampled size (vdim, hdim) 16:9 aspect ratio
luminance_images = io.video2luminance(video_file,
size=small_size,
nimages=nimages)
nimages, vdim, hdim = luminance_images.shape
stimulus_fps = 24
aspect_ratio = hdim / vdim
pyramid = pyramids.MotionEnergyPyramid(
stimulus_vhsize=(vdim, hdim),
stimulus_fps=stimulus_fps,
spatial_frequencies=[8], # only small filters
temporal_frequencies=[12], # only one temporal freq
filter_temporal_width=16)
print(pyramid)
# centered projection
# vhsize=(0,0): top left