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)