def test_video_ffmpeg(self):
video_path = os.path.join(self._test_data_path, 'video.mkv')
video_json_path = os.path.join(self._test_data_path, 'video.json')
with tf.io.gfile.GFile(video_json_path) as fp:
video_array = np.asarray(json.load(fp))
self.assertFeature(
feature=features.Video(shape=(5, 4, 2, 3)),
shape=(5, 4, 2, 3),
dtype=tf.uint8,
tests=[
testing.FeatureExpectationItem(
value=video_path,
expected=video_array,
),
],
)
self.assertFeature(
feature=features.Video(shape=(5, 4, 2, 3)),
shape=(5, 4, 2, 3),
dtype=tf.uint8,
tests=[
testing.FeatureExpectationItem(
value=video_path,
expected=video_array,
),
],
)
class GFileWithSeekOnRead(tf.io.gfile.GFile):
def read(self, *args, **kwargs):
data_read = super(GFileWithSeekOnRead,
self).read(*args, **kwargs)
self.seek(0)
return data_read
with GFileWithSeekOnRead(video_path, 'rb') as video_fp:
self.assertFeature(
feature=features.Video(shape=(5, 4, 2, 3)),
shape=(5, 4, 2, 3),
dtype=tf.uint8,
tests=[
testing.FeatureExpectationItem(
value=video_fp,
expected=video_array,
),
],
)
def test_video_concatenated_frames(self):
video_shape = (None, 400, 640, 3)
lsun_examples_path = os.path.join(self._test_data_path,
'lsun_examples')
frames_paths = [
os.path.join(lsun_examples_path, '{}.jpg'.format(i))
for i in (1, 2, 3, 4)
]
frames = []
for frame_path in frames_paths:
with tf.io.gfile.GFile(frame_path, 'rb') as frame_fp:
frames.append(tf.image.decode_jpeg(frame_fp.read(),
channels=3))
video = tf.stack(frames)
self.assertFeature(
feature=features.Video(shape=video_shape),
shape=video_shape,
dtype=tf.uint8,
tests=[
# Numpy array
testing.FeatureExpectationItem(
value=frames_paths,
expected=video,
),
],
)
def test_video_numpy(self):
np_video = np.random.randint(256, size=(128, 64, 64, 3), dtype=np.uint8)
self.assertFeature(
feature=features.Video(shape=(None, 64, 64, 3)),
shape=(None, 64, 64, 3),
dtype=tf.uint8,
tests=[
testing.FeatureExpectationItem(
value=np_video,
expected=np_video,
),
],
)
def test_video_numpy(self):
np_video = np.random.randint(256, size=(128, 64, 64, 3), dtype=np.uint8)
self.assertFeature(
feature=features.Video(shape=(None, 64, 64, 3)),
shape=(None, 64, 64, 3),
dtype=tf.uint8,
tests=[
testing.FeatureExpectationItem(
value=np_video,
expected=np_video,
),
],
test_attributes=dict(_encoding_format='png', _extra_ffmpeg_args=[]))
def test_video(self):
np_video = np.random.randint(256, size=(128, 64, 64, 3), dtype=np.uint8)
self.assertFeature(
feature=features.Video(shape=(None, 64, 64, 3)),
shape=(None, 64, 64, 3),
dtype=tf.uint8,
tests=[
# Numpy array
test_utils.FeatureExpectationItem(
value=np_video,
expected=np_video,
),
# File path (Gif)
# File path (.mp4)
],
)
def test_video_custom_decode(self):
image_path = os.fspath(
utils.tfds_path('testing/test_data/test_image.jpg'))
with tf.io.gfile.GFile(image_path, 'rb') as f:
serialized_img = f.read()
self.assertFeature(
# Image with statically defined shape
feature=features_lib.Video(shape=(None, 30, 60, 3)),
shape=(None, 30, 60, 3),
dtype=tf.uint8,
tests=[
testing.FeatureExpectationItem(
value=[image_path] * 15, # 15 frames of video
expected=[serialized_img] * 15, # Non-decoded image
shape=(15, ),
dtype=tf.string, # Only string are decoded
decoders=decode_lib.SkipDecoding(),
),
],
)
# Test with FeatureDict
self.assertFeature(
feature=features_lib.FeaturesDict({
'image':
features_lib.Image(shape=(30, 60, 3), encoding_format='jpeg'),
'label':
tf.int64,
}),
shape={
'image': (30, 60, 3),
'label': (),
},
dtype={
'image': tf.uint8,
'label': tf.int64,
},
tests=[
testing.FeatureExpectationItem(
decoders={
'image': decode_lib.SkipDecoding(),
},
value={
'image': image_path,
'label': 123,
},
expected={
'image': serialized_img,
'label': 123,
},
shape={
'image': (),
'label': (),
},
dtype={
'image': tf.string,
'label': tf.int64,
},
),
],
)
