Python eval_util.result_dict_for_single_example方法代码示例

# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import result_dict_for_single_example [as 别名]
def _make_evaluation_dict(self):
    input_data_fields = fields.InputDataFields
    detection_fields = fields.DetectionResultFields

    image = tf.zeros(shape=[1, 20, 20, 3], dtype=tf.uint8)
    key = tf.constant('image1')
    detection_boxes = tf.constant([[[0., 0., 1., 1.]]])
    detection_scores = tf.constant([[0.8]])
    detection_classes = tf.constant([[0]])
    detection_masks = tf.ones(shape=[1, 1, 20, 20], dtype=tf.float32)
    num_detections = tf.constant([1])
    groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
    groundtruth_classes = tf.constant([1])
    groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
    detections = {
        detection_fields.detection_boxes: detection_boxes,
        detection_fields.detection_scores: detection_scores,
        detection_fields.detection_classes: detection_classes,
        detection_fields.detection_masks: detection_masks,
        detection_fields.num_detections: num_detections
    }
    groundtruth = {
        input_data_fields.groundtruth_boxes: groundtruth_boxes,
        input_data_fields.groundtruth_classes: groundtruth_classes,
        input_data_fields.groundtruth_instance_masks: groundtruth_instance_masks
    }
    return eval_util.result_dict_for_single_example(image, key, detections,
                                                    groundtruth) 

# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import result_dict_for_single_example [as 别名]
def _make_evaluation_dict(self, resized_groundtruth_masks=False):
    input_data_fields = fields.InputDataFields
    detection_fields = fields.DetectionResultFields

    image = tf.zeros(shape=[1, 20, 20, 3], dtype=tf.uint8)
    key = tf.constant('image1')
    detection_boxes = tf.constant([[[0., 0., 1., 1.]]])
    detection_scores = tf.constant([[0.8]])
    detection_classes = tf.constant([[0]])
    detection_masks = tf.ones(shape=[1, 1, 20, 20], dtype=tf.float32)
    num_detections = tf.constant([1])
    groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
    groundtruth_classes = tf.constant([1])
    groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
    if resized_groundtruth_masks:
      groundtruth_instance_masks = tf.ones(shape=[1, 10, 10], dtype=tf.uint8)
    detections = {
        detection_fields.detection_boxes: detection_boxes,
        detection_fields.detection_scores: detection_scores,
        detection_fields.detection_classes: detection_classes,
        detection_fields.detection_masks: detection_masks,
        detection_fields.num_detections: num_detections
    }
    groundtruth = {
        input_data_fields.groundtruth_boxes: groundtruth_boxes,
        input_data_fields.groundtruth_classes: groundtruth_classes,
        input_data_fields.groundtruth_instance_masks: groundtruth_instance_masks
    }
    return eval_util.result_dict_for_single_example(image, key, detections,
                                                    groundtruth) 

# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import result_dict_for_single_example [as 别名]
def _make_evaluation_dict(self,
                            resized_groundtruth_masks=False,
                            batch_size=1,
                            max_gt_boxes=None,
                            scale_to_absolute=False):
    input_data_fields = fields.InputDataFields
    detection_fields = fields.DetectionResultFields

    image = tf.zeros(shape=[batch_size, 20, 20, 3], dtype=tf.uint8)
    if batch_size == 1:
      key = tf.constant('image1')
    else:
      key = tf.constant([str(range(batch_size))])
    detection_boxes = tf.tile(tf.constant([[[0., 0., 1., 1.]]]),
                              multiples=[batch_size, 1, 1])
    detection_scores = tf.tile(tf.constant([[0.8]]), multiples=[batch_size, 1])
    detection_classes = tf.tile(tf.constant([[0]]), multiples=[batch_size, 1])
    detection_masks = tf.tile(tf.ones(shape=[1, 1, 20, 20], dtype=tf.float32),
                              multiples=[batch_size, 1, 1, 1])
    num_detections = tf.ones([batch_size])
    groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
    groundtruth_classes = tf.constant([1])
    groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
    if resized_groundtruth_masks:
      groundtruth_instance_masks = tf.ones(shape=[1, 10, 10], dtype=tf.uint8)

    if batch_size > 1:
      groundtruth_boxes = tf.tile(tf.expand_dims(groundtruth_boxes, 0),
                                  multiples=[batch_size, 1, 1])
      groundtruth_classes = tf.tile(tf.expand_dims(groundtruth_classes, 0),
                                    multiples=[batch_size, 1])
      groundtruth_instance_masks = tf.tile(
          tf.expand_dims(groundtruth_instance_masks, 0),
          multiples=[batch_size, 1, 1, 1])

    detections = {
        detection_fields.detection_boxes: detection_boxes,
        detection_fields.detection_scores: detection_scores,
        detection_fields.detection_classes: detection_classes,
        detection_fields.detection_masks: detection_masks,
        detection_fields.num_detections: num_detections
    }
    groundtruth = {
        input_data_fields.groundtruth_boxes: groundtruth_boxes,
        input_data_fields.groundtruth_classes: groundtruth_classes,
        input_data_fields.groundtruth_instance_masks: groundtruth_instance_masks
    }
    if batch_size > 1:
      return eval_util.result_dict_for_batched_example(
          image, key, detections, groundtruth,
          scale_to_absolute=scale_to_absolute,
          max_gt_boxes=max_gt_boxes)
    else:
      return eval_util.result_dict_for_single_example(
          image, key, detections, groundtruth,
          scale_to_absolute=scale_to_absolute) 

# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import result_dict_for_single_example [as 别名]
def _extract_prediction_tensors(model,
                                create_input_dict_fn,
                                ignore_groundtruth=False):
  """Restores the model in a tensorflow session.

  Args:
    model: model to perform predictions with.
    create_input_dict_fn: function to create input tensor dictionaries.
    ignore_groundtruth: whether groundtruth should be ignored.

  Returns:
    tensor_dict: A tensor dictionary with evaluations.
  """
  input_dict = create_input_dict_fn()
  prefetch_queue = prefetcher.prefetch(input_dict, capacity=500)
  input_dict = prefetch_queue.dequeue()
  original_image = tf.expand_dims(input_dict[fields.InputDataFields.image], 0)
  preprocessed_image = model.preprocess(tf.to_float(original_image))
  prediction_dict = model.predict(preprocessed_image)
  detections = model.postprocess(prediction_dict)

  groundtruth = None
  if not ignore_groundtruth:
    groundtruth = {
        fields.InputDataFields.groundtruth_boxes:
            input_dict[fields.InputDataFields.groundtruth_boxes],
        fields.InputDataFields.groundtruth_classes:
            input_dict[fields.InputDataFields.groundtruth_classes],
        fields.InputDataFields.groundtruth_area:
            input_dict[fields.InputDataFields.groundtruth_area],
        fields.InputDataFields.groundtruth_is_crowd:
            input_dict[fields.InputDataFields.groundtruth_is_crowd],
        fields.InputDataFields.groundtruth_difficult:
            input_dict[fields.InputDataFields.groundtruth_difficult]
    }
    if fields.InputDataFields.groundtruth_group_of in input_dict:
      groundtruth[fields.InputDataFields.groundtruth_group_of] = (
          input_dict[fields.InputDataFields.groundtruth_group_of])
    if fields.DetectionResultFields.detection_masks in detections:
      groundtruth[fields.InputDataFields.groundtruth_instance_masks] = (
          input_dict[fields.InputDataFields.groundtruth_instance_masks])

  return eval_util.result_dict_for_single_example(
      original_image,
      input_dict[fields.InputDataFields.source_id],
      detections,
      groundtruth,
      class_agnostic=(
          fields.DetectionResultFields.detection_classes not in detections),
      scale_to_absolute=True) 

# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import result_dict_for_single_example [as 别名]
def _extract_prediction_tensors(model,
                                create_input_dict_fn,
                                ignore_groundtruth=False):
  """Restores the model in a tensorflow session.

  Args:
    model: model to perform predictions with.
    create_input_dict_fn: function to create input tensor dictionaries.
    ignore_groundtruth: whether groundtruth should be ignored.

  Returns:
    tensor_dict: A tensor dictionary with evaluations.
  """
  input_dict = create_input_dict_fn()
  prefetch_queue = prefetcher.prefetch(input_dict, capacity=500)
  input_dict = prefetch_queue.dequeue()
  original_image = tf.expand_dims(input_dict[fields.InputDataFields.image], 0)
  preprocessed_image, true_image_shapes = model.preprocess(
      tf.to_float(original_image))
  prediction_dict = model.predict(preprocessed_image, true_image_shapes)
  detections = model.postprocess(prediction_dict, true_image_shapes)

  groundtruth = None
  if not ignore_groundtruth:
    groundtruth = {
        fields.InputDataFields.groundtruth_boxes:
            input_dict[fields.InputDataFields.groundtruth_boxes],
        fields.InputDataFields.groundtruth_classes:
            input_dict[fields.InputDataFields.groundtruth_classes],
        fields.InputDataFields.groundtruth_area:
            input_dict[fields.InputDataFields.groundtruth_area],
        fields.InputDataFields.groundtruth_is_crowd:
            input_dict[fields.InputDataFields.groundtruth_is_crowd],
        fields.InputDataFields.groundtruth_difficult:
            input_dict[fields.InputDataFields.groundtruth_difficult]
    }
    if fields.InputDataFields.groundtruth_group_of in input_dict:
      groundtruth[fields.InputDataFields.groundtruth_group_of] = (
          input_dict[fields.InputDataFields.groundtruth_group_of])
    if fields.DetectionResultFields.detection_masks in detections:
      groundtruth[fields.InputDataFields.groundtruth_instance_masks] = (
          input_dict[fields.InputDataFields.groundtruth_instance_masks])

  return eval_util.result_dict_for_single_example(
      original_image,
      input_dict[fields.InputDataFields.source_id],
      detections,
      groundtruth,
      class_agnostic=(
          fields.DetectionResultFields.detection_classes not in detections),
      scale_to_absolute=True) 

# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import result_dict_for_single_example [as 别名]
def _extract_prediction_tensors(model,
                                create_input_dict_fn,
                                ignore_groundtruth=False):
    """Restores the model in a tensorflow session.

    Args:
      model: model to perform predictions with.
      create_input_dict_fn: function to create input tensor dictionaries.
      ignore_groundtruth: whether groundtruth should be ignored.

    Returns:
      tensor_dict: A tensor dictionary with evaluations.
    """
    input_dict = create_input_dict_fn()
    prefetch_queue = prefetcher.prefetch(input_dict, capacity=500)
    input_dict = prefetch_queue.dequeue()
    original_image = tf.expand_dims(
        input_dict[fields.InputDataFields.image], 0)
    preprocessed_image, true_image_shapes = model.preprocess(
        tf.to_float(original_image))
    prediction_dict = model.predict(preprocessed_image, true_image_shapes)
    detections = model.postprocess(prediction_dict, true_image_shapes)

    groundtruth = None
    if not ignore_groundtruth:
        groundtruth = {
            fields.InputDataFields.groundtruth_boxes:
                input_dict[fields.InputDataFields.groundtruth_boxes],
            fields.InputDataFields.groundtruth_classes:
                input_dict[fields.InputDataFields.groundtruth_classes],
            fields.InputDataFields.groundtruth_area:
                input_dict[fields.InputDataFields.groundtruth_area],
            fields.InputDataFields.groundtruth_is_crowd:
                input_dict[fields.InputDataFields.groundtruth_is_crowd],
            fields.InputDataFields.groundtruth_difficult:
                input_dict[fields.InputDataFields.groundtruth_difficult]
        }
        if fields.InputDataFields.groundtruth_group_of in input_dict:
            groundtruth[fields.InputDataFields.groundtruth_group_of] = (
                input_dict[fields.InputDataFields.groundtruth_group_of])
        if fields.DetectionResultFields.detection_masks in detections:
            groundtruth[fields.InputDataFields.groundtruth_instance_masks] = (
                input_dict[fields.InputDataFields.groundtruth_instance_masks])

    return eval_util.result_dict_for_single_example(
        original_image,
        input_dict[fields.InputDataFields.source_id],
        detections,
        groundtruth,
        class_agnostic=(
            fields.DetectionResultFields.detection_classes not in detections),
        scale_to_absolute=True) 

# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import result_dict_for_single_example [as 别名]
def _make_evaluation_dict(self,
                            resized_groundtruth_masks=False,
                            batch_size=1,
                            max_gt_boxes=None,
                            scale_to_absolute=False):
    input_data_fields = fields.InputDataFields
    detection_fields = fields.DetectionResultFields

    image = tf.zeros(shape=[batch_size, 20, 20, 3], dtype=tf.uint8)
    if batch_size == 1:
      key = tf.constant('image1')
    else:
      key = tf.constant([str(i) for i in range(batch_size)])
    detection_boxes = tf.tile(tf.constant([[[0., 0., 1., 1.]]]),
                              multiples=[batch_size, 1, 1])
    detection_scores = tf.tile(tf.constant([[0.8]]), multiples=[batch_size, 1])
    detection_classes = tf.tile(tf.constant([[0]]), multiples=[batch_size, 1])
    detection_masks = tf.tile(tf.ones(shape=[1, 1, 20, 20], dtype=tf.float32),
                              multiples=[batch_size, 1, 1, 1])
    num_detections = tf.ones([batch_size])
    groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
    groundtruth_classes = tf.constant([1])
    groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
    if resized_groundtruth_masks:
      groundtruth_instance_masks = tf.ones(shape=[1, 10, 10], dtype=tf.uint8)

    if batch_size > 1:
      groundtruth_boxes = tf.tile(tf.expand_dims(groundtruth_boxes, 0),
                                  multiples=[batch_size, 1, 1])
      groundtruth_classes = tf.tile(tf.expand_dims(groundtruth_classes, 0),
                                    multiples=[batch_size, 1])
      groundtruth_instance_masks = tf.tile(
          tf.expand_dims(groundtruth_instance_masks, 0),
          multiples=[batch_size, 1, 1, 1])

    detections = {
        detection_fields.detection_boxes: detection_boxes,
        detection_fields.detection_scores: detection_scores,
        detection_fields.detection_classes: detection_classes,
        detection_fields.detection_masks: detection_masks,
        detection_fields.num_detections: num_detections
    }
    groundtruth = {
        input_data_fields.groundtruth_boxes: groundtruth_boxes,
        input_data_fields.groundtruth_classes: groundtruth_classes,
        input_data_fields.groundtruth_instance_masks: groundtruth_instance_masks
    }
    if batch_size > 1:
      return eval_util.result_dict_for_batched_example(
          image, key, detections, groundtruth,
          scale_to_absolute=scale_to_absolute,
          max_gt_boxes=max_gt_boxes)
    else:
      return eval_util.result_dict_for_single_example(
          image, key, detections, groundtruth,
          scale_to_absolute=scale_to_absolute) 

# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import result_dict_for_single_example [as 别名]
def _make_evaluation_dict(self,
                            resized_groundtruth_masks=False,
                            batch_size=1,
                            max_gt_boxes=None,
                            scale_to_absolute=False):
    input_data_fields = fields.InputDataFields
    detection_fields = fields.DetectionResultFields

    image = tf.zeros(shape=[batch_size, 20, 20, 3], dtype=tf.uint8)
    if batch_size == 1:
      key = tf.constant('image1')
    else:
      key = tf.constant([str(i) for i in range(batch_size)])
    detection_boxes = tf.tile(tf.constant([[[0., 0., 1., 1.]]]),
                              multiples=[batch_size, 1, 1])
    detection_scores = tf.tile(tf.constant([[0.8]]), multiples=[batch_size, 1])
    detection_classes = tf.tile(tf.constant([[0]]), multiples=[batch_size, 1])
    detection_masks = tf.tile(tf.ones(shape=[1, 1, 20, 20], dtype=tf.float32),
                              multiples=[batch_size, 1, 1, 1])
    num_detections = tf.ones([batch_size])
    groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
    groundtruth_classes = tf.constant([1])
    groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
    groundtruth_keypoints = tf.constant([[0.0, 0.0], [0.5, 0.5], [1.0, 1.0]])
    if resized_groundtruth_masks:
      groundtruth_instance_masks = tf.ones(shape=[1, 10, 10], dtype=tf.uint8)

    if batch_size > 1:
      groundtruth_boxes = tf.tile(tf.expand_dims(groundtruth_boxes, 0),
                                  multiples=[batch_size, 1, 1])
      groundtruth_classes = tf.tile(tf.expand_dims(groundtruth_classes, 0),
                                    multiples=[batch_size, 1])
      groundtruth_instance_masks = tf.tile(
          tf.expand_dims(groundtruth_instance_masks, 0),
          multiples=[batch_size, 1, 1, 1])
      groundtruth_keypoints = tf.tile(
          tf.expand_dims(groundtruth_keypoints, 0),
          multiples=[batch_size, 1, 1])

    detections = {
        detection_fields.detection_boxes: detection_boxes,
        detection_fields.detection_scores: detection_scores,
        detection_fields.detection_classes: detection_classes,
        detection_fields.detection_masks: detection_masks,
        detection_fields.num_detections: num_detections
    }
    groundtruth = {
        input_data_fields.groundtruth_boxes: groundtruth_boxes,
        input_data_fields.groundtruth_classes: groundtruth_classes,
        input_data_fields.groundtruth_keypoints: groundtruth_keypoints,
        input_data_fields.groundtruth_instance_masks: groundtruth_instance_masks
    }
    if batch_size > 1:
      return eval_util.result_dict_for_batched_example(
          image, key, detections, groundtruth,
          scale_to_absolute=scale_to_absolute,
          max_gt_boxes=max_gt_boxes)
    else:
      return eval_util.result_dict_for_single_example(
          image, key, detections, groundtruth,
          scale_to_absolute=scale_to_absolute)