def add_single_ground_truth_image_info()

in timesformer/utils/ava_evaluation/object_detection_evaluation.py [0:0]

• 47 lines of code
• 11 McCabe index (conditional complexity)

    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])