def testDecodeExampleWithBoundingBoxDense(self):
num_bboxes = 10
np_ymin = np.random.rand(num_bboxes, 1)
np_xmin = np.random.rand(num_bboxes, 1)
np_ymax = np.random.rand(num_bboxes, 1)
np_xmax = np.random.rand(num_bboxes, 1)
np_bboxes = np.hstack([np_ymin, np_xmin, np_ymax, np_xmax])
example = example_pb2.Example(features=feature_pb2.Features(
feature={
'image/object/bbox/ymin': self._EncodedFloatFeature(np_ymin),
'image/object/bbox/xmin': self._EncodedFloatFeature(np_xmin),
'image/object/bbox/ymax': self._EncodedFloatFeature(np_ymax),
'image/object/bbox/xmax': self._EncodedFloatFeature(np_xmax),
}))
serialized_example = example.SerializeToString()
with self.test_session():
serialized_example = array_ops.reshape(serialized_example,
shape=[])
keys_to_features = {
'image/object/bbox/ymin':
parsing_ops.FixedLenSequenceFeature([],
dtypes.float32,
allow_missing=True),
'image/object/bbox/xmin':
parsing_ops.FixedLenSequenceFeature([],
dtypes.float32,
allow_missing=True),
'image/object/bbox/ymax':
parsing_ops.FixedLenSequenceFeature([],
dtypes.float32,
allow_missing=True),
'image/object/bbox/xmax':
parsing_ops.FixedLenSequenceFeature([],
dtypes.float32,
allow_missing=True),
}
items_to_handlers = {
'object/bbox':
tfexample_decoder.BoundingBox(['ymin', 'xmin', 'ymax', 'xmax'],
'image/object/bbox/'),
}
decoder = tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handlers)
[tf_bboxes] = decoder.decode(serialized_example, ['object/bbox'])
bboxes = tf_bboxes.eval()
self.assertAllClose(np_bboxes, bboxes)
def test_decode_example_with_bounding_box(self):
num_bboxes = 10
np_ymin = np.random.rand(num_bboxes, 1)
np_xmin = np.random.rand(num_bboxes, 1)
np_ymax = np.random.rand(num_bboxes, 1)
np_xmax = np.random.rand(num_bboxes, 1)
np_bboxes = np.hstack([np_ymin, np_xmin, np_ymax, np_xmax])
example = example_pb2.Example(features=feature_pb2.Features(
feature={
'image/object/bbox/ymin': self._encode_float_feature(np_ymin),
'image/object/bbox/xmin': self._encode_float_feature(np_xmin),
'image/object/bbox/ymax': self._encode_float_feature(np_ymax),
'image/object/bbox/xmax': self._encode_float_feature(np_xmax),
}))
serialized_example = example.SerializeToString()
with self.test_session():
serialized_example = array_ops.reshape(serialized_example,
shape=[])
keys_to_features = {
'image/object/bbox/ymin':
parsing_ops.VarLenFeature(dtypes.float32),
'image/object/bbox/xmin':
parsing_ops.VarLenFeature(dtypes.float32),
'image/object/bbox/ymax':
parsing_ops.VarLenFeature(dtypes.float32),
'image/object/bbox/xmax':
parsing_ops.VarLenFeature(dtypes.float32),
}
items_to_handlers = {
'object/bbox':
tfexample_decoder.BoundingBox(['ymin', 'xmin', 'ymax', 'xmax'],
'image/object/bbox/'),
}
decoder = TFExampleDecoder(keys_to_features, items_to_handlers)
[tf_bboxes] = decoder.decode(serialized_example, ['object/bbox'])
bboxes = tf_bboxes.eval()
self.assertAllClose(np_bboxes, bboxes)