def test_animate(self):
with patch('matplotlib.image.AxesImage') as mock_axes:
with patch('matplotlib.pyplot.imshow',
return_value=mock_axes) as mock_imshow:
with patch('matplotlib.pyplot.figure') as mock_figure:
with patch('matplotlib.pyplot.show',
return_value=mock_axes) as mock_show:
num_frames = 10
images = [
np.ndarray(shape=(10, 10, 3))
for i in range(num_frames)
]
viewer.animate(images, auto_close=True)
assert mock_imshow.call_count == 1
assert mock_show.call_count == 1
def test_animate_unsupoprted_dimension(self):
with patch('matplotlib.image.AxesImage') as mock_axes:
with patch('matplotlib.pyplot.imshow',
return_value=mock_axes) as mock_imshow:
with patch('matplotlib.pyplot.figure') as mock_figure:
with patch('matplotlib.pyplot.show') as mock_show:
with patch(
'matplotlib.pyplot.subplot') as mock_subplot:
num_frames = 10
images = [
np.ndarray(shape=(10, 10, 3))
for i in range(num_frames)
]
with pytest.raises(ValueError):
viewer.animate(images=images,
comparisons=[[images, images],
[images, images]],
auto_close=True)
def animate_predictions(model, dataset, mean_image, args):
initial_frame = args.initial_frame
last_frame = args.last_frame
frames = dataset['frames'][initial_frame:last_frame]
actions = dataset['actions'][initial_frame + 1:last_frame + 1]
if args.lstm:
ground_truths, predicted_frames = animate_lstm(model, frames, actions,
mean_image, args)
else:
ground_truths, predicted_frames = animate_ff(model, frames, actions,
mean_image, args)
viewer.animate(ground_truths,
predicted_frames,
titles=['ground truth', 'predicted'],
fps=10,
repeat=True,
save_mp4=True,
auto_close=True)
def show_sequence(images):
viewer.animate(images=images, is_gray=True, save_mp4=True, save_gif=True)
def animate_dataset(dataset, args):
initial_frame = args.initial_frame
last_frame = args.last_frame
frames = [converter.chw2hwc(frame) for frame in dataset['frames']]
viewer.animate(frames[initial_frame:last_frame], titles=['dataset'])