def test_image_custom_decode(self):
# Do not uses random here because Jpeg compression has loss, so decoded
# value isn't the same
img_shaped = np.ones(shape=(30, 60, 3), dtype=np.uint8)
x, y, w, h = 4, 7, 10, 13
img_cropped = img_shaped[y:y + h, x:x + w, :]
class DecodeCrop(decode_lib.Decoder):
"""Simple class on how to customize the decoding."""
def decode_example(self, serialized_image):
return tf.image.decode_and_crop_jpeg(
serialized_image,
[y, x, h, w],
channels=self.feature.shape[-1],
)
@decode_lib.make_decoder()
def decode_crop(serialized_image, feature):
return tf.image.decode_and_crop_jpeg(
serialized_image,
[y, x, h, w],
channels=feature.shape[-1],
)
image_path = utils.get_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.Image(shape=(30, 60, 3),
encoding_format='jpeg'),
shape=(30, 60, 3),
dtype=tf.uint8,
tests=[
testing.FeatureExpectationItem(
value=img_shaped,
expected=img_cropped,
shape=(13, 10, 3), # Shape is cropped
decoders=DecodeCrop(),
),
testing.FeatureExpectationItem(
value=img_shaped,
expected=img_cropped,
shape=(13, 10, 3), # Shape is cropped
decoders=decode_crop(), # pylint: disable=no-value-for-parameter
),
testing.FeatureExpectationItem(
value=image_path,
expected=serialized_img,
shape=(),
dtype=tf.string,
decoders=decode_lib.SkipDecoding(),
),
],
)
def test_video_custom_decode(self):
image_path = utils.get_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,
},
),
],
)
