def test_video_predict():
model = ResNet50(weights='imagenet')
x = video_io.VideoDataset(video_path).batch(1).map(
lambda x: preprocess_input(image.resize(x, (224, 224))))
y = model.predict(x)
p = decode_predictions(y, top=1)
assert len(p) == 166
def test_video_dataset():
"""test_video_dataset"""
num_repeats = 2
video_dataset = video_io.VideoDataset([video_path]).repeat(num_repeats)
i = 0
for v in video_dataset:
assert v.shape == (320, 560, 3)
i += 1
assert i == 166 * num_repeats
def test_video_dataset():
num_repeats = 2
dataset = video_io.VideoDataset([video_path]).repeat(num_repeats)
iterator = dataset.make_initializable_iterator()
init_op = iterator.initializer
get_next = iterator.get_next()
with tensorflow.compat.v1.Session() as sess:
sess.run(init_op)
for _ in range(num_repeats):
for _ in range(166):
v = sess.run(get_next)
assert v.shape == (320, 560, 3)
with pytest.raises(errors.OutOfRangeError):
sess.run(get_next)