Python input_reader_pb2.PNG_MASKS屬性代碼示例

# 需要導入模塊: from object_detection.protos import input_reader_pb2 [as 別名]
# 或者: from object_detection.protos.input_reader_pb2 import PNG_MASKS [as 別名]
def testNewMaskType(self):
    """Tests that mask type can be overwritten in input readers."""
    original_mask_type = input_reader_pb2.NUMERICAL_MASKS
    new_mask_type = input_reader_pb2.PNG_MASKS
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    train_input_reader = pipeline_config.train_input_reader
    train_input_reader.mask_type = original_mask_type
    eval_input_reader = pipeline_config.eval_input_reader.add()
    eval_input_reader.mask_type = original_mask_type
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    override_dict = {"mask_type": new_mask_type}
    configs = config_util.merge_external_params_with_configs(
        configs, kwargs_dict=override_dict)
    self.assertEqual(new_mask_type, configs["train_input_config"].mask_type)
    self.assertEqual(new_mask_type, configs["eval_input_configs"][0].mask_type) 

# 需要導入模塊: from object_detection.protos import input_reader_pb2 [as 別名]
# 或者: from object_detection.protos.input_reader_pb2 import PNG_MASKS [as 別名]
def testUpdateMaskTypeForAllInputConfigs(self):
    original_mask_type = input_reader_pb2.NUMERICAL_MASKS
    new_mask_type = input_reader_pb2.PNG_MASKS

    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    train_config = pipeline_config.train_input_reader
    train_config.mask_type = original_mask_type
    eval_1 = pipeline_config.eval_input_reader.add()
    eval_1.mask_type = original_mask_type
    eval_1.name = "eval_1"
    eval_2 = pipeline_config.eval_input_reader.add()
    eval_2.mask_type = original_mask_type
    eval_2.name = "eval_2"
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    override_dict = {"mask_type": new_mask_type}
    configs = config_util.merge_external_params_with_configs(
        configs, kwargs_dict=override_dict)

    self.assertEqual(configs["train_input_config"].mask_type, new_mask_type)
    for eval_input_config in configs["eval_input_configs"]:
      self.assertEqual(eval_input_config.mask_type, new_mask_type) 

# 需要導入模塊: from object_detection.protos import input_reader_pb2 [as 別名]
# 或者: from object_detection.protos.input_reader_pb2 import PNG_MASKS [as 別名]
def testDecodeEmptyPngInstanceMasks(self):
    image_tensor = np.random.randint(256, size=(10, 10, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    encoded_masks = []
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded': self._BytesFeature(encoded_jpeg),
                'image/format': self._BytesFeature('jpeg'),
                'image/object/mask': self._BytesFeature(encoded_masks),
                'image/height': self._Int64Feature([10]),
                'image/width': self._Int64Feature([10]),
            })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder(
        load_instance_masks=True, instance_mask_type=input_reader_pb2.PNG_MASKS)
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)
      self.assertAllEqual(
          tensor_dict[fields.InputDataFields.groundtruth_instance_masks].shape,
          [0, 10, 10]) 

# 需要導入模塊: from object_detection.protos import input_reader_pb2 [as 別名]
# 或者: from object_detection.protos.input_reader_pb2 import PNG_MASKS [as 別名]
def testNewMaskType(self):
    """Tests that mask type can be overwritten in input readers."""
    original_mask_type = input_reader_pb2.NUMERICAL_MASKS
    new_mask_type = input_reader_pb2.PNG_MASKS
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    train_input_reader = pipeline_config.train_input_reader
    train_input_reader.mask_type = original_mask_type
    eval_input_reader = pipeline_config.eval_input_reader
    eval_input_reader.mask_type = original_mask_type
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs, mask_type=new_mask_type)
    self.assertEqual(new_mask_type, configs["train_input_config"].mask_type)
    self.assertEqual(new_mask_type, configs["eval_input_config"].mask_type) 

# 需要導入模塊: from object_detection.protos import input_reader_pb2 [as 別名]
# 或者: from object_detection.protos.input_reader_pb2 import PNG_MASKS [as 別名]
def testDecodePngInstanceMasks(self):
    image_tensor = np.random.randint(256, size=(10, 10, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    mask_1 = np.random.randint(0, 2, size=(10, 10, 1)).astype(np.uint8)
    mask_2 = np.random.randint(0, 2, size=(10, 10, 1)).astype(np.uint8)
    encoded_png_1 = self._EncodeImage(mask_1, encoding_type='png')
    decoded_png_1 = np.squeeze(mask_1.astype(np.float32))
    encoded_png_2 = self._EncodeImage(mask_2, encoding_type='png')
    decoded_png_2 = np.squeeze(mask_2.astype(np.float32))
    encoded_masks = [encoded_png_1, encoded_png_2]
    decoded_masks = np.stack([decoded_png_1, decoded_png_2])
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'),
                'image/object/mask':
                    dataset_util.bytes_list_feature(encoded_masks)
            })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder(
        load_instance_masks=True, instance_mask_type=input_reader_pb2.PNG_MASKS)
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(
        decoded_masks,
        tensor_dict[fields.InputDataFields.groundtruth_instance_masks]) 

# 需要導入模塊: from object_detection.protos import input_reader_pb2 [as 別名]
# 或者: from object_detection.protos.input_reader_pb2 import PNG_MASKS [as 別名]
def testDecodeEmptyPngInstanceMasks(self):
    image_tensor = np.random.randint(256, size=(10, 10, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    encoded_masks = []
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'),
                'image/object/mask':
                    dataset_util.bytes_list_feature(encoded_masks),
                'image/height':
                    dataset_util.int64_feature(10),
                'image/width':
                    dataset_util.int64_feature(10),
            })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder(
        load_instance_masks=True, instance_mask_type=input_reader_pb2.PNG_MASKS)
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)
      self.assertAllEqual(
          tensor_dict[fields.InputDataFields.groundtruth_instance_masks].shape,
          [0, 10, 10]) 

# 需要導入模塊: from object_detection.protos import input_reader_pb2 [as 別名]
# 或者: from object_detection.protos.input_reader_pb2 import PNG_MASKS [as 別名]
def testDecodePngInstanceMasks(self):
    image_tensor = np.random.randint(256, size=(10, 10, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    mask_1 = np.random.randint(0, 2, size=(10, 10, 1)).astype(np.uint8)
    mask_2 = np.random.randint(0, 2, size=(10, 10, 1)).astype(np.uint8)
    encoded_png_1 = self._EncodeImage(mask_1, encoding_type='png')
    decoded_png_1 = np.squeeze(mask_1.astype(np.float32))
    encoded_png_2 = self._EncodeImage(mask_2, encoding_type='png')
    decoded_png_2 = np.squeeze(mask_2.astype(np.float32))
    encoded_masks = [encoded_png_1, encoded_png_2]
    decoded_masks = np.stack([decoded_png_1, decoded_png_2])
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded': self._BytesFeature(encoded_jpeg),
                'image/format': self._BytesFeature('jpeg'),
                'image/object/mask': self._BytesFeature(encoded_masks)
            })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder(
        load_instance_masks=True, instance_mask_type=input_reader_pb2.PNG_MASKS)
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(
        decoded_masks,
        tensor_dict[fields.InputDataFields.groundtruth_instance_masks])