Python metrics.classes方法代码示例

# 需要导入模块: from object_detection.utils import metrics [as 别名]
# 或者: from object_detection.utils.metrics import classes [as 别名]
def add_single_detected_image_info(self, image_id, detections_dict):
    """Adds detections for a single image to be used for evaluation.

    Args:
      image_id: A unique string/integer identifier for the image.
      detections_dict: A dictionary containing -
        standard_fields.DetectionResultFields.detection_boxes: float32 numpy
          array of shape [num_boxes, 4] containing `num_boxes` detection boxes
          of the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
        standard_fields.DetectionResultFields.detection_scores: float32 numpy
          array of shape [num_boxes] containing detection scores for the boxes.
        standard_fields.DetectionResultFields.detection_classes: integer numpy
          array of shape [num_boxes] containing 1-indexed detection classes for
          the boxes.
    """
    detection_classes = detections_dict[
        standard_fields.DetectionResultFields.detection_classes]
    detection_classes -= self._label_id_offset
    self._evaluation.add_single_detected_image_info(
        image_id,
        detections_dict[standard_fields.DetectionResultFields.detection_boxes],
        detections_dict[standard_fields.DetectionResultFields.detection_scores],
        detection_classes) 

# 需要导入模块: from object_detection.utils import metrics [as 别名]
# 或者: from object_detection.utils.metrics import classes [as 别名]
def add_single_detected_image_info(self, image_id, detections_dict):
    """Adds detections for a single image to be used for evaluation.

    Args:
      image_id: A unique string/integer identifier for the image.
      detections_dict: A dictionary containing -
        standard_fields.DetectionResultFields.detection_boxes: float32 numpy
          array of shape [num_boxes, 4] containing `num_boxes` detection boxes
          of the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
        standard_fields.DetectionResultFields.detection_scores: float32 numpy
          array of shape [num_boxes] containing detection scores for the boxes.
        standard_fields.DetectionResultFields.detection_classes: integer numpy
          array of shape [num_boxes] containing 1-indexed detection classes for
          the boxes.
        standard_fields.DetectionResultFields.detection_masks: uint8 numpy
          array of shape [num_boxes, height, width] containing `num_boxes` masks
          of values ranging between 0 and 1.

    Raises:
      ValueError: If detection masks are not in detections dictionary.
    """
    detection_classes = (
        detections_dict[standard_fields.DetectionResultFields.detection_classes]
        - self._label_id_offset)
    detection_masks = None
    if self._evaluate_masks:
      if (standard_fields.DetectionResultFields.detection_masks not in
          detections_dict):
        raise ValueError('Detection masks not in detections dictionary.')
      detection_masks = detections_dict[
          standard_fields.DetectionResultFields.detection_masks]
    self._evaluation.add_single_detected_image_info(
        image_key=image_id,
        detected_boxes=detections_dict[
            standard_fields.DetectionResultFields.detection_boxes],
        detected_scores=detections_dict[
            standard_fields.DetectionResultFields.detection_scores],
        detected_class_labels=detection_classes,
        detected_masks=detection_masks) 

# 需要导入模块: from object_detection.utils import metrics [as 别名]
# 或者: from object_detection.utils.metrics import classes [as 别名]
def evaluate(self):
    """Compute evaluation result.

    Returns:
      A dictionary of metrics with the following fields -

      1. summary_metrics:
        'Precision/mAP@<matching_iou_threshold>IOU': mean average precision at
        the specified IOU threshold.

      2. per_category_ap: category specific results with keys of the form
        'PerformanceByCategory/mAP@<matching_iou_threshold>IOU/category'.
    """
    (per_class_ap, mean_ap, _, _, per_class_corloc, mean_corloc) = (
        self._evaluation.evaluate())
    pascal_metrics = {
        self._metric_prefix +
        'Precision/mAP@{}IOU'.format(self._matching_iou_threshold):
            mean_ap
    }
    if self._evaluate_corlocs:
      pascal_metrics[self._metric_prefix + 'Precision/meanCorLoc@{}IOU'.format(
          self._matching_iou_threshold)] = mean_corloc
    category_index = label_map_util.create_category_index(self._categories)
    for idx in range(per_class_ap.size):
      if idx + self._label_id_offset in category_index:
        display_name = (
            self._metric_prefix + 'PerformanceByCategory/AP@{}IOU/{}'.format(
                self._matching_iou_threshold,
                category_index[idx + self._label_id_offset]['name']))
        pascal_metrics[display_name] = per_class_ap[idx]

        # Optionally add CorLoc metrics.classes
        if self._evaluate_corlocs:
          display_name = (
              self._metric_prefix + 'PerformanceByCategory/CorLoc@{}IOU/{}'
              .format(self._matching_iou_threshold,
                      category_index[idx + self._label_id_offset]['name']))
          pascal_metrics[display_name] = per_class_corloc[idx]

    return pascal_metrics 

# 需要导入模块: from object_detection.utils import metrics [as 别名]
# 或者: from object_detection.utils.metrics import classes [as 别名]
def __init__(self,
               categories,
               matching_iou_threshold=0.5,
               evaluate_corlocs=False,
               metric_prefix=None,
               use_weighted_mean_ap=False):
    """Constructor.

    Args:
      categories: A list of dicts, each of which has the following keys -
        'id': (required) an integer id uniquely identifying this category.
        'name': (required) string representing category name e.g., 'cat', 'dog'.
      matching_iou_threshold: IOU threshold to use for matching groundtruth
        boxes to detection boxes.
      evaluate_corlocs: (optional) boolean which determines if corloc scores
        are to be returned or not.
      metric_prefix: (optional) string prefix for metric name; if None, no
        prefix is used.
      use_weighted_mean_ap: (optional) boolean which determines if the mean
        average precision is computed directly from the scores and tp_fp_labels
        of all classes.
    """
    super(ObjectDetectionEvaluator, self).__init__(categories)
    self._num_classes = max([cat['id'] for cat in categories])
    self._matching_iou_threshold = matching_iou_threshold
    self._use_weighted_mean_ap = use_weighted_mean_ap
    self._label_id_offset = 1
    self._evaluation = ObjectDetectionEvaluation(
        self._num_classes,
        matching_iou_threshold=self._matching_iou_threshold,
        use_weighted_mean_ap=self._use_weighted_mean_ap,
        label_id_offset=self._label_id_offset)
    self._image_ids = set([])
    self._evaluate_corlocs = evaluate_corlocs
    self._metric_prefix = (metric_prefix + '/') if metric_prefix else '' 

# 需要导入模块: from object_detection.utils import metrics [as 别名]
# 或者: from object_detection.utils.metrics import classes [as 别名]
def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
    """Adds groundtruth for a single image to be used for evaluation.

    Args:
      image_id: A unique string/integer identifier for the image.
      groundtruth_dict: A dictionary containing -
        standard_fields.InputDataFields.groundtruth_boxes: float32 numpy array
          of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of
          the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
        standard_fields.InputDataFields.groundtruth_classes: integer numpy array
          of shape [num_boxes] containing 1-indexed groundtruth classes for the
          boxes.
        standard_fields.InputDataFields.groundtruth_image_classes: integer 1D
          numpy array containing all classes for which labels are verified.
        standard_fields.InputDataFields.groundtruth_group_of: Optional length
          M numpy boolean array denoting whether a groundtruth box contains a
          group of instances.

    Raises:
      ValueError: On adding groundtruth for an image more than once.
    """
    super(OpenImagesDetectionChallengeEvaluator,
          self).add_single_ground_truth_image_info(image_id, groundtruth_dict)
    groundtruth_classes = (
        groundtruth_dict[standard_fields.InputDataFields.groundtruth_classes] -
        self._label_id_offset)
    self._evaluatable_labels[image_id] = np.unique(
        np.concatenate(((groundtruth_dict.get(
            standard_fields.InputDataFields.groundtruth_image_classes,
            np.array([], dtype=int)) - self._label_id_offset),
                        groundtruth_classes))) 

# 需要导入模块: from object_detection.utils import metrics [as 别名]
# 或者: from object_detection.utils.metrics import classes [as 别名]
def add_single_detected_image_info(self, image_id, detections_dict):
    """Adds detections for a single image to be used for evaluation.

    Args:
      image_id: A unique string/integer identifier for the image.
      detections_dict: A dictionary containing -
        standard_fields.DetectionResultFields.detection_boxes: float32 numpy
          array of shape [num_boxes, 4] containing `num_boxes` detection boxes
          of the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
        standard_fields.DetectionResultFields.detection_scores: float32 numpy
          array of shape [num_boxes] containing detection scores for the boxes.
        standard_fields.DetectionResultFields.detection_classes: integer numpy
          array of shape [num_boxes] containing 1-indexed detection classes for
          the boxes.
        standard_fields.DetectionResultFields.detection_masks: uint8 numpy array
          of shape [num_boxes, height, width] containing `num_boxes` masks of
          values ranging between 0 and 1.

    Raises:
      ValueError: If detection masks are not in detections dictionary.
    """
    detection_classes = (
        detections_dict[standard_fields.DetectionResultFields.detection_classes]
        - self._label_id_offset)
    detection_masks = None
    if self._evaluate_masks:
      if (standard_fields.DetectionResultFields.detection_masks not in
          detections_dict):
        raise ValueError('Detection masks not in detections dictionary.')
      detection_masks = detections_dict[
          standard_fields.DetectionResultFields.detection_masks]
    self._evaluation.add_single_detected_image_info(
        image_key=image_id,
        detected_boxes=detections_dict[
            standard_fields.DetectionResultFields.detection_boxes],
        detected_scores=detections_dict[
            standard_fields.DetectionResultFields.detection_scores],
        detected_class_labels=detection_classes,
        detected_masks=detection_masks) 

# 需要导入模块: from object_detection.utils import metrics [as 别名]
# 或者: from object_detection.utils.metrics import classes [as 别名]
def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
    """Adds groundtruth for a single image to be used for evaluation.

    Args:
      image_id: A unique string/integer identifier for the image.
      groundtruth_dict: A dictionary containing -
        standard_fields.InputDataFields.groundtruth_boxes: float32 numpy array
          of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of
          the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
        standard_fields.InputDataFields.groundtruth_classes: integer numpy array
          of shape [num_boxes] containing 1-indexed groundtruth classes for the
          boxes.
        standard_fields.InputDataFields.groundtruth_image_classes: integer 1D
          numpy array containing all classes for which labels are verified.
        standard_fields.InputDataFields.groundtruth_group_of: Optional length M
          numpy boolean array denoting whether a groundtruth box contains a
          group of instances.

    Raises:
      ValueError: On adding groundtruth for an image more than once.
    """
    super(OpenImagesChallengeEvaluator,
          self).add_single_ground_truth_image_info(image_id, groundtruth_dict)
    groundtruth_classes = (
        groundtruth_dict[standard_fields.InputDataFields.groundtruth_classes] -
        self._label_id_offset)
    self._evaluatable_labels[image_id] = np.unique(
        np.concatenate(((groundtruth_dict.get(
            standard_fields.InputDataFields.groundtruth_image_classes,
            np.array([], dtype=int)) - self._label_id_offset),
                        groundtruth_classes))) 

# 需要导入模块: from object_detection.utils import metrics [as 别名]
# 或者: from object_detection.utils.metrics import classes [as 别名]
def __init__(self,
               categories,
               matching_iou_threshold=0.5,
               evaluate_corlocs=False,
               metric_prefix=None,
               use_weighted_mean_ap=False,
               evaluate_masks=False,
               group_of_weight=0.0):
    """Constructor.

    Args:
      categories: A list of dicts, each of which has the following keys -
        'id': (required) an integer id uniquely identifying this category.
        'name': (required) string representing category name e.g., 'cat', 'dog'.
      matching_iou_threshold: IOU threshold to use for matching groundtruth
        boxes to detection boxes.
      evaluate_corlocs: (optional) boolean which determines if corloc scores
        are to be returned or not.
      metric_prefix: (optional) string prefix for metric name; if None, no
        prefix is used.
      use_weighted_mean_ap: (optional) boolean which determines if the mean
        average precision is computed directly from the scores and tp_fp_labels
        of all classes.
      evaluate_masks: If False, evaluation will be performed based on boxes.
        If True, mask evaluation will be performed instead.
      group_of_weight: Weight of group-of boxes.If set to 0, detections of the
        correct class within a group-of box are ignored. If weight is > 0, then
        if at least one detection falls within a group-of box with
        matching_iou_threshold, weight group_of_weight is added to true
        positives. Consequently, if no detection falls within a group-of box,
        weight group_of_weight is added to false negatives.

    Raises:
      ValueError: If the category ids are not 1-indexed.
    """
    super(ObjectDetectionEvaluator, self).__init__(categories)
    self._num_classes = max([cat['id'] for cat in categories])
    if min(cat['id'] for cat in categories) < 1:
      raise ValueError('Classes should be 1-indexed.')
    self._matching_iou_threshold = matching_iou_threshold
    self._use_weighted_mean_ap = use_weighted_mean_ap
    self._label_id_offset = 1
    self._evaluate_masks = evaluate_masks
    self._group_of_weight = group_of_weight
    self._evaluation = ObjectDetectionEvaluation(
        num_groundtruth_classes=self._num_classes,
        matching_iou_threshold=self._matching_iou_threshold,
        use_weighted_mean_ap=self._use_weighted_mean_ap,
        label_id_offset=self._label_id_offset,
        group_of_weight=self._group_of_weight)
    self._image_ids = set([])
    self._evaluate_corlocs = evaluate_corlocs
    self._metric_prefix = (metric_prefix + '_') if metric_prefix else '' 

# 需要导入模块: from object_detection.utils import metrics [as 别名]
# 或者: from object_detection.utils.metrics import classes [as 别名]
def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
    """Adds groundtruth for a single image to be used for evaluation.

    Args:
      image_id: A unique string/integer identifier for the image.
      groundtruth_dict: A dictionary containing -
        standard_fields.InputDataFields.groundtruth_boxes: float32 numpy array
          of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of
          the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
        standard_fields.InputDataFields.groundtruth_classes: integer numpy array
          of shape [num_boxes] containing 1-indexed groundtruth classes for the
          boxes.
        standard_fields.InputDataFields.groundtruth_difficult: Optional length
          M numpy boolean array denoting whether a ground truth box is a
          difficult instance or not. This field is optional to support the case
          that no boxes are difficult.
        standard_fields.InputDataFields.groundtruth_instance_masks: Optional
          numpy array of shape [num_boxes, height, width] with values in {0, 1}.

    Raises:
      ValueError: On adding groundtruth for an image more than once. Will also
        raise error if instance masks are not in groundtruth dictionary.
    """
    if image_id in self._image_ids:
      raise ValueError('Image with id {} already added.'.format(image_id))

    groundtruth_classes = (
        groundtruth_dict[standard_fields.InputDataFields.groundtruth_classes] -
        self._label_id_offset)
    # If the key is not present in the groundtruth_dict or the array is empty
    # (unless there are no annotations for the groundtruth on this image)
    # use values from the dictionary or insert None otherwise.
    if (standard_fields.InputDataFields.groundtruth_difficult in
        groundtruth_dict.keys() and
        (groundtruth_dict[standard_fields.InputDataFields.groundtruth_difficult]
         .size or not groundtruth_classes.size)):
      groundtruth_difficult = groundtruth_dict[
          standard_fields.InputDataFields.groundtruth_difficult]
    else:
      groundtruth_difficult = None
      if not len(self._image_ids) % 1000:
        logging.warn(
            'image %s does not have groundtruth difficult flag specified',
            image_id)
    groundtruth_masks = None
    if self._evaluate_masks:
      if (standard_fields.InputDataFields.groundtruth_instance_masks not in
          groundtruth_dict):
        raise ValueError('Instance masks not in groundtruth dictionary.')
      groundtruth_masks = groundtruth_dict[
          standard_fields.InputDataFields.groundtruth_instance_masks]
    self._evaluation.add_single_ground_truth_image_info(
        image_key=image_id,
        groundtruth_boxes=groundtruth_dict[
            standard_fields.InputDataFields.groundtruth_boxes],
        groundtruth_class_labels=groundtruth_classes,
        groundtruth_is_difficult_list=groundtruth_difficult,
        groundtruth_masks=groundtruth_masks)
    self._image_ids.update([image_id]) 

# 需要导入模块: from object_detection.utils import metrics [as 别名]
# 或者: from object_detection.utils.metrics import classes [as 别名]
def evaluate(self):
    """Compute evaluation result.

    Returns:
      A dictionary of metrics with the following fields -

      1. summary_metrics:
        'Precision/mAP@<matching_iou_threshold>IOU': mean average precision at
        the specified IOU threshold.

      2. per_category_ap: category specific results with keys of the form
        'PerformanceByCategory/mAP@<matching_iou_threshold>IOU/category'.
    """
    (per_class_ap, mean_ap, _, _, per_class_corloc, mean_corloc) = (
        self._evaluation.evaluate())
    pascal_metrics = {
        self._metric_prefix +
        'Precision/mAP@{}IOU'.format(self._matching_iou_threshold):
            mean_ap
    }
    if self._evaluate_corlocs:
      pascal_metrics[self._metric_prefix + 'Precision/meanCorLoc@{}IOU'.format(
          self._matching_iou_threshold)] = mean_corloc
    category_index = label_map_util.create_category_index(self._categories)
    for idx in range(per_class_ap.size):
      if idx + self._label_id_offset in category_index:
        category_name = category_index[idx + self._label_id_offset]['name']
        try:
          category_name = unicode(category_name, 'utf-8')
        except TypeError:
          pass
        category_name = unicodedata.normalize(
            'NFKD', category_name).encode('ascii', 'ignore')
        display_name = (
            self._metric_prefix + 'PerformanceByCategory/AP@{}IOU/{}'.format(
                self._matching_iou_threshold, category_name))
        pascal_metrics[display_name] = per_class_ap[idx]

        # Optionally add CorLoc metrics.classes
        if self._evaluate_corlocs:
          display_name = (
              self._metric_prefix + 'PerformanceByCategory/CorLoc@{}IOU/{}'
              .format(self._matching_iou_threshold, category_name))
          pascal_metrics[display_name] = per_class_corloc[idx]

    return pascal_metrics