Python object_detection.protos.input_reader_pb2.PNG_MASKS Examples

The following are 30 code examples of object_detection.protos.input_reader_pb2.PNG_MASKS(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module object_detection.protos.input_reader_pb2 , or try the search function .
Example #1
Source File: config_util_test.py    From MAX-Object-Detector with Apache License 2.0 6 votes vote down vote up 
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)
Example #2
Source File: tf_example_decoder_test.py    From ros_tensorflow with Apache License 2.0 6 votes vote down vote up 
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])
Example #3
Source File: config_util_test.py    From ros_tensorflow with Apache License 2.0 6 votes vote down vote up 
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)
Example #4
Source File: tf_example_decoder_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
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])
Example #5
Source File: config_util_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
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)
Example #6
Source File: config_util_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 6 votes vote down vote up 
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)
Example #7
Source File: tf_example_decoder_test.py    From Elphas with Apache License 2.0 6 votes vote down vote up 
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])
Example #8
Source File: config_util_test.py    From Elphas with Apache License 2.0 6 votes vote down vote up 
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)
Example #9
Source File: config_util_test.py    From AniSeg with Apache License 2.0 6 votes vote down vote up 
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)
Example #10
Source File: config_util_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 6 votes vote down vote up 
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)
Example #11
Source File: config_util_test.py    From MAX-Object-Detector with Apache License 2.0 6 votes vote down vote up 
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)
Example #12
Source File: config_util_test.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
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)
Example #13
Source File: config_util_test.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
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)
Example #14
Source File: config_util_test.py    From models with Apache License 2.0 6 votes vote down vote up 
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)
Example #15
Source File: config_util_test.py    From models with Apache License 2.0 6 votes vote down vote up 
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)
Example #16
Source File: config_util_test.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
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)
Example #17
Source File: config_util_test.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
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)
Example #18
Source File: config_util_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
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)
Example #19
Source File: config_util_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
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)
Example #20
Source File: config_util_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
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)
Example #21
Source File: tf_example_decoder_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
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])
Example #22
Source File: config_util_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
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)
Example #23
Source File: tf_example_decoder_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
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])
Example #24
Source File: config_util_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
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)
Example #25
Source File: tf_example_decoder_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
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])
Example #26
Source File: config_util_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
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)
Example #27
Source File: tf_example_decoder_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
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])
Example #28
Source File: tf_example_decoder_test.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
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])
Example #29
Source File: tf_example_decoder_test.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
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])
Example #30
Source File: tf_example_decoder_test.py    From ros_tensorflow with Apache License 2.0 5 votes vote down vote up 
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])