def _create_tfrecord_dataset(tmpdir):
if not gfile.Exists(tmpdir):
gfile.MakeDirs(tmpdir)
data_sources = test_utils.create_tfrecord_files(tmpdir, num_files=1)
keys_to_features = {
'image/encoded':
parsing_ops.FixedLenFeature(
shape=(), dtype=dtypes.string, default_value=''),
'image/format':
parsing_ops.FixedLenFeature(
shape=(), dtype=dtypes.string, default_value='jpeg'),
'image/class/label':
parsing_ops.FixedLenFeature(
shape=[1],
dtype=dtypes.int64,
default_value=array_ops.zeros(
[1], dtype=dtypes.int64))
}
items_to_handlers = {
'image': tfexample_decoder.Image(),
'label': tfexample_decoder.Tensor('image/class/label'),
}
decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
return dataset.Dataset(
data_sources=data_sources,
reader=io_ops.TFRecordReader,
decoder=decoder,
num_samples=100,
items_to_descriptions=None)
def testDecodeExampleWithJpegEncoding(self):
image_shape = (2, 3, 3)
image, serialized_example = self.GenerateImage(
image_format='jpeg', image_shape=image_shape)
decoded_image = self.RunDecodeExample(
serialized_example, tfexample_decoder.Image(), image_format='jpeg')
# Need to use a tolerance of 1 because of noise in the jpeg encode/decode
self.assertAllClose(image, decoded_image, atol=1.001)
def testDecodeExampleWithJpegEncodingAt16BitDoesNotCauseError(self):
image_shape = (2, 3, 3)
# Image has type uint8 but decoding at uint16 should not cause problems.
image, serialized_example = self.GenerateImage(
image_format='jpeg', image_shape=image_shape)
decoded_image = self.RunDecodeExample(
serialized_example,
tfexample_decoder.Image(dtype=tf.uint16),
image_format='jpeg')
self.assertAllClose(image, decoded_image, atol=1.001)
def testDecodeExampleWithRawEncodingFloatDtype(self):
image_shape = (2, 3, 3)
image, serialized_example = self.GenerateImage(
image_format='raw', image_shape=image_shape, image_dtype=np.float32)
decoded_image = self.RunDecodeExample(
serialized_example,
tfexample_decoder.Image(shape=image_shape, dtype=tf.float32),
image_format='raw')
self.assertAllClose(image, decoded_image, atol=0)
def testDecodeExampleWithRAWEncoding(self):
image_shape = (2, 3, 3)
image, serialized_example = self.GenerateImage(
image_format='RAW', image_shape=image_shape)
decoded_image = self.RunDecodeExample(
serialized_example,
tfexample_decoder.Image(shape=image_shape),
image_format='RAW')
self.assertAllClose(image, decoded_image, atol=0)
def testDecodeExampleWithNoShapeInfo(self):
test_image_channels = [1, 3]
for channels in test_image_channels:
image_shape = (2, 3, channels)
_, serialized_example = self.GenerateImage(
image_format='jpeg', image_shape=image_shape)
tf_decoded_image = self.DecodeExample(
serialized_example,
tfexample_decoder.Image(shape=None, channels=channels),
image_format='jpeg')
self.assertEqual(tf_decoded_image.get_shape().ndims, 3)
def testDecodeExampleWithPNGEncoding(self):
test_image_channels = [1, 3, 4]
for channels in test_image_channels:
image_shape = (2, 3, channels)
image, serialized_example = self.GenerateImage(
image_format='PNG', image_shape=image_shape)
decoded_image = self.RunDecodeExample(
serialized_example,
tfexample_decoder.Image(channels=channels),
image_format='PNG')
self.assertAllClose(image, decoded_image, atol=0)
def testDecodeExampleWithJPEGEncoding(self):
test_image_channels = [1, 3]
for channels in test_image_channels:
image_shape = (2, 3, channels)
image, serialized_example = self.GenerateImage(
image_format='JPEG', image_shape=image_shape)
decoded_image = self.RunDecodeExample(
serialized_example,
tfexample_decoder.Image(channels=channels),
image_format='JPEG')
# Need to use a tolerance of 1 because of noise in the jpeg encode/decode
self.assertAllClose(image, decoded_image, atol=1.001)
def testDecodeExampleWithRepeatedImages(self):
image_shape = (2, 3, 3)
image_format = 'png'
image, _ = self.GenerateImage(
image_format=image_format, image_shape=image_shape)
tf_encoded = self._Encoder(image, image_format)
with self.cached_session():
tf_string = tf_encoded.eval()
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
tf.train.Feature(
bytes_list=tf.train.BytesList(
value=[tf_string, tf_string])),
'image/format':
self._StringFeature(image_format),
}))
serialized_example = example.SerializeToString()
with self.cached_session():
serialized_example = array_ops.reshape(serialized_example, shape=[])
decoder = tfexample_decoder.TFExampleDecoder(
keys_to_features={
'image/encoded':
parsing_ops.FixedLenFeature((2,), tf.string),
'image/format':
parsing_ops.FixedLenFeature((),
tf.string,
default_value=image_format),
},
items_to_handlers={'image': tfexample_decoder.Image(repeated=True)})
[tf_image] = decoder.decode(serialized_example, ['image'])
output_image = tf_image.eval()
self.assertEqual(output_image.shape, (2, 2, 3, 3))
self.assertAllEqual(np.squeeze(output_image[0, :, :, :]), image)
self.assertAllEqual(np.squeeze(output_image[1, :, :, :]), image)