Python object_detection.protos.preprocessor_pb2.PreprocessingStep() Examples

def _get_step_config_from_proto(preprocessor_step_config, step_name):
  """Returns the value of a field named step_name from proto.

  Args:
    preprocessor_step_config: A preprocessor_pb2.PreprocessingStep object.
    step_name: Name of the field to get value from.

  Returns:
    result_dict: a sub proto message from preprocessor_step_config which will be
                 later converted to a dictionary.

  Raises:
    ValueError: If field does not exist in proto.
  """
  for field, value in preprocessor_step_config.ListFields():
    if field.name == step_name:
      return value

  raise ValueError('Could not get field %s from proto!', step_name) 

def _get_step_config_from_proto(preprocessor_step_config, step_name):
  """Returns the value of a field named step_name from proto.

  Args:
    preprocessor_step_config: A preprocessor_pb2.PreprocessingStep object.
    step_name: Name of the field to get value from.

  Returns:
    result_dict: a sub proto message from preprocessor_step_config which will be
                 later converted to a dictionary.

  Raises:
    ValueError: If field does not exist in proto.
  """
  for field, value in preprocessor_step_config.ListFields():
    if field.name == step_name:
      return value

  raise ValueError('Could not get field %s from proto!', step_name) 

def test_build_normalize_image(self):
    preprocessor_text_proto = """
    normalize_image {
      original_minval: 0.0
      original_maxval: 255.0
      target_minval: -1.0
      target_maxval: 1.0
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.normalize_image)
    self.assertEqual(args, {
        'original_minval': 0.0,
        'original_maxval': 255.0,
        'target_minval': -1.0,
        'target_maxval': 1.0,
    }) 

def test_build_random_crop_image(self):
    preprocessor_text_proto = """
    random_crop_image {
      min_object_covered: 0.75
      min_aspect_ratio: 0.75
      max_aspect_ratio: 1.5
      min_area: 0.25
      max_area: 0.875
      overlap_thresh: 0.5
      clip_boxes: False
      random_coef: 0.125
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_crop_image)
    self.assertEqual(args, {
        'min_object_covered': 0.75,
        'aspect_ratio_range': (0.75, 1.5),
        'area_range': (0.25, 0.875),
        'overlap_thresh': 0.5,
        'clip_boxes': False,
        'random_coef': 0.125,
    }) 

def test_build_random_crop_image(self):
    preprocessor_text_proto = """
    random_crop_image {
      min_object_covered: 0.75
      min_aspect_ratio: 0.75
      max_aspect_ratio: 1.5
      min_area: 0.25
      max_area: 0.875
      overlap_thresh: 0.5
      random_coef: 0.125
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_crop_image)
    self.assertEqual(args, {
        'min_object_covered': 0.75,
        'aspect_ratio_range': (0.75, 1.5),
        'area_range': (0.25, 0.875),
        'overlap_thresh': 0.5,
        'random_coef': 0.125,
    }) 

def test_build_random_vertical_flip(self):
    preprocessor_text_proto = """
    random_vertical_flip {
      keypoint_flip_permutation: 1
      keypoint_flip_permutation: 0
      keypoint_flip_permutation: 2
      keypoint_flip_permutation: 3
      keypoint_flip_permutation: 5
      keypoint_flip_permutation: 4
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_vertical_flip)
    self.assertEqual(args, {'keypoint_flip_permutation': (1, 0, 2, 3, 5, 4)}) 

def test_build_random_horizontal_flip(self):
    preprocessor_text_proto = """
    random_horizontal_flip {
      keypoint_flip_permutation: 1
      keypoint_flip_permutation: 0
      keypoint_flip_permutation: 2
      keypoint_flip_permutation: 3
      keypoint_flip_permutation: 5
      keypoint_flip_permutation: 4
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_horizontal_flip)
    self.assertEqual(args, {'keypoint_flip_permutation': (1, 0, 2, 3, 5, 4)}) 

def test_build_random_crop_image(self):
    preprocessor_text_proto = """
    random_crop_image {
      min_object_covered: 0.75
      min_aspect_ratio: 0.75
      max_aspect_ratio: 1.5
      min_area: 0.25
      max_area: 0.875
      overlap_thresh: 0.5
      random_coef: 0.125
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_crop_image)
    self.assertEqual(args, {
        'min_object_covered': 0.75,
        'aspect_ratio_range': (0.75, 1.5),
        'area_range': (0.25, 0.875),
        'overlap_thresh': 0.5,
        'random_coef': 0.125,
    }) 

def test_build_normalize_image(self):
    preprocessor_text_proto = """
    normalize_image {
      original_minval: 0.0
      original_maxval: 255.0
      target_minval: -1.0
      target_maxval: 1.0
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.normalize_image)
    self.assertEqual(args, {
        'original_minval': 0.0,
        'original_maxval': 255.0,
        'target_minval': -1.0,
        'target_maxval': 1.0,
    }) 

def test_build_normalize_image(self):
    preprocessor_text_proto = """
    normalize_image {
      original_minval: 0.0
      original_maxval: 255.0
      target_minval: -1.0
      target_maxval: 1.0
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.normalize_image)
    self.assertEqual(args, {
        'original_minval': 0.0,
        'original_maxval': 255.0,
        'target_minval': -1.0,
        'target_maxval': 1.0,
    }) 

def test_build_random_horizontal_flip(self):
    preprocessor_text_proto = """
    random_horizontal_flip {
      keypoint_flip_permutation: 1
      keypoint_flip_permutation: 0
      keypoint_flip_permutation: 2
      keypoint_flip_permutation: 3
      keypoint_flip_permutation: 5
      keypoint_flip_permutation: 4
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_horizontal_flip)
    self.assertEqual(args, {'keypoint_flip_permutation': (1, 0, 2, 3, 5, 4)}) 

def test_build_random_vertical_flip(self):
    preprocessor_text_proto = """
    random_vertical_flip {
      keypoint_flip_permutation: 1
      keypoint_flip_permutation: 0
      keypoint_flip_permutation: 2
      keypoint_flip_permutation: 3
      keypoint_flip_permutation: 5
      keypoint_flip_permutation: 4
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_vertical_flip)
    self.assertEqual(args, {'keypoint_flip_permutation': (1, 0, 2, 3, 5, 4)}) 

def _get_step_config_from_proto(preprocessor_step_config, step_name):
  """Returns the value of a field named step_name from proto.

  Args:
    preprocessor_step_config: A preprocessor_pb2.PreprocessingStep object.
    step_name: Name of the field to get value from.

  Returns:
    result_dict: a sub proto message from preprocessor_step_config which will be
                 later converted to a dictionary.

  Raises:
    ValueError: If field does not exist in proto.
  """
  for field, value in preprocessor_step_config.ListFields():
    if field.name == step_name:
      return value

  raise ValueError('Could not get field %s from proto!', step_name) 

def _get_step_config_from_proto(preprocessor_step_config, step_name):
  """Returns the value of a field named step_name from proto.

  Args:
    preprocessor_step_config: A preprocessor_pb2.PreprocessingStep object.
    step_name: Name of the field to get value from.

  Returns:
    result_dict: a sub proto message from preprocessor_step_config which will be
                 later converted to a dictionary.

  Raises:
    ValueError: If field does not exist in proto.
  """
  for field, value in preprocessor_step_config.ListFields():
    if field.name == step_name:
      return value

  raise ValueError('Could not get field %s from proto!', step_name) 

def test_build_normalize_image(self):
    preprocessor_text_proto = """
    normalize_image {
      original_minval: 0.0
      original_maxval: 255.0
      target_minval: -1.0
      target_maxval: 1.0
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.normalize_image)
    self.assertEqual(args, {
        'original_minval': 0.0,
        'original_maxval': 255.0,
        'target_minval': -1.0,
        'target_maxval': 1.0,
    }) 

def test_build_ssd_random_crop_empty_operations(self):
    preprocessor_text_proto = """
    ssd_random_crop {
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.ssd_random_crop)
    self.assertEqual(args, {}) 

def test_build_random_pixel_value_scale(self):
    preprocessor_text_proto = """
    random_pixel_value_scale {
      minval: 0.8
      maxval: 1.2
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_pixel_value_scale)
    self.assert_dictionary_close(args, {'minval': 0.8, 'maxval': 1.2}) 

def _get_dict_from_proto(config):
  """Helper function to put all proto fields into a dictionary.

  For many preprocessing steps, there's an trivial 1-1 mapping from proto fields
  to function arguments. This function automatically populates a dictionary with
  the arguments from the proto.

  Protos that CANNOT be trivially populated include:
  * nested messages.
  * steps that check if an optional field is set (ie. where None != 0).
  * protos that don't map 1-1 to arguments (ie. list should be reshaped).
  * fields requiring additional validation (ie. repeated field has n elements).

  Args:
    config: A protobuf object that does not violate the conditions above.

  Returns:
    result_dict: |config| converted into a python dictionary.
  """
  result_dict = {}
  for field, value in config.ListFields():
    result_dict[field.name] = value
  return result_dict


# A map from a PreprocessingStep proto config field name to the preprocessing
# function that should be used. The PreprocessingStep proto should be parsable
# with _get_dict_from_proto. 

def test_build_normalize_image_convert_class_logits_to_softmax(self):
    preprocessor_text_proto = """
    convert_class_logits_to_softmax {
        temperature: 2
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.convert_class_logits_to_softmax)
    self.assertEqual(args, {'temperature': 2}) 

def test_build_random_jitter_boxes(self):
    preprocessor_text_proto = """
    random_jitter_boxes {
      ratio: 0.1
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_jitter_boxes)
    self.assert_dictionary_close(args, {'ratio': 0.1}) 

def test_build_random_crop_pad_image_with_optional_parameters(self):
    preprocessor_text_proto = """
    random_crop_pad_image {
      min_object_covered: 0.75
      min_aspect_ratio: 0.75
      max_aspect_ratio: 1.5
      min_area: 0.25
      max_area: 0.875
      overlap_thresh: 0.5
      clip_boxes: False
      random_coef: 0.125
      min_padded_size_ratio: 0.5
      min_padded_size_ratio: 0.75
      max_padded_size_ratio: 0.5
      max_padded_size_ratio: 0.75
      pad_color: 0.5
      pad_color: 0.5
      pad_color: 1.0
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_crop_pad_image)
    self.assertEqual(args, {
        'min_object_covered': 0.75,
        'aspect_ratio_range': (0.75, 1.5),
        'area_range': (0.25, 0.875),
        'overlap_thresh': 0.5,
        'clip_boxes': False,
        'random_coef': 0.125,
        'min_padded_size_ratio': (0.5, 0.75),
        'max_padded_size_ratio': (0.5, 0.75),
        'pad_color': (0.5, 0.5, 1.0)
    }) 

def test_build_random_distort_color(self):
    preprocessor_text_proto = """
    random_distort_color {
      color_ordering: 1
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_distort_color)
    self.assertEqual(args, {'color_ordering': 1}) 

def test_build_random_adjust_saturation(self):
    preprocessor_text_proto = """
    random_adjust_saturation {
      min_delta: 0.75
      max_delta: 1.15
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_adjust_saturation)
    self.assert_dictionary_close(args, {'min_delta': 0.75, 'max_delta': 1.15}) 

def test_build_random_adjust_contrast(self):
    preprocessor_text_proto = """
    random_adjust_contrast {
      min_delta: 0.7
      max_delta: 1.1
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_adjust_contrast)
    self.assert_dictionary_close(args, {'min_delta': 0.7, 'max_delta': 1.1}) 

def test_build_random_adjust_brightness(self):
    preprocessor_text_proto = """
    random_adjust_brightness {
      max_delta: 0.2
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_adjust_brightness)
    self.assert_dictionary_close(args, {'max_delta': 0.2}) 

def test_build_random_rgb_to_gray(self):
    preprocessor_text_proto = """
    random_rgb_to_gray {
      probability: 0.8
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_rgb_to_gray)
    self.assert_dictionary_close(args, {'probability': 0.8}) 

def test_build_random_image_scale(self):
    preprocessor_text_proto = """
    random_image_scale {
      min_scale_ratio: 0.8
      max_scale_ratio: 2.2
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_image_scale)
    self.assert_dictionary_close(args, {'min_scale_ratio': 0.8,
                                        'max_scale_ratio': 2.2}) 

def test_build_random_pixel_value_scale(self):
    preprocessor_text_proto = """
    random_pixel_value_scale {
      minval: 0.8
      maxval: 1.2
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_pixel_value_scale)
    self.assert_dictionary_close(args, {'minval': 0.8, 'maxval': 1.2}) 

def _get_dict_from_proto(config):
  """Helper function to put all proto fields into a dictionary.

  For many preprocessing steps, there's an trivial 1-1 mapping from proto fields
  to function arguments. This function automatically populates a dictionary with
  the arguments from the proto.

  Protos that CANNOT be trivially populated include:
  * nested messages.
  * steps that check if an optional field is set (ie. where None != 0).
  * protos that don't map 1-1 to arguments (ie. list should be reshaped).
  * fields requiring additional validation (ie. repeated field has n elements).

  Args:
    config: A protobuf object that does not violate the conditions above.

  Returns:
    result_dict: |config| converted into a python dictionary.
  """
  result_dict = {}
  for field, value in config.ListFields():
    result_dict[field.name] = value
  return result_dict


# A map from a PreprocessingStep proto config field name to the preprocessing
# function that should be used. The PreprocessingStep proto should be parsable
# with _get_dict_from_proto. 

def test_build_ssd_random_crop_fixed_aspect_ratio(self):
    preprocessor_text_proto = """
    ssd_random_crop_fixed_aspect_ratio {
      operations {
        min_object_covered: 0.0
        min_area: 0.5
        max_area: 1.0
        overlap_thresh: 0.0
        random_coef: 0.375
      }
      operations {
        min_object_covered: 0.25
        min_area: 0.5
        max_area: 1.0
        overlap_thresh: 0.25
        random_coef: 0.375
      }
      aspect_ratio: 0.875
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.ssd_random_crop_fixed_aspect_ratio)
    self.assertEqual(args, {'min_object_covered': [0.0, 0.25],
                            'aspect_ratio': 0.875,
                            'area_range': [(0.5, 1.0), (0.5, 1.0)],
                            'overlap_thresh': [0.0, 0.25],
                            'random_coef': [0.375, 0.375]})