Python coco_tools.ExportSingleImageGroundtruthToCoco方法代码示例

# 需要导入模块: from object_detection.metrics import coco_tools [as 别名]
# 或者: from object_detection.metrics.coco_tools import ExportSingleImageGroundtruthToCoco [as 别名]
def testSingleImageGroundtruthExport(self):
    masks = np.array(
        [[[1, 1,], [1, 1]],
         [[0, 0], [0, 1]],
         [[0, 0], [0, 0]]], dtype=np.uint8)
    boxes = np.array([[0, 0, 1, 1],
                      [0, 0, .5, .5],
                      [.5, .5, 1, 1]], dtype=np.float32)
    coco_boxes = np.array([[0, 0, 1, 1],
                           [0, 0, .5, .5],
                           [.5, .5, .5, .5]], dtype=np.float32)
    classes = np.array([1, 2, 3], dtype=np.int32)
    next_annotation_id = 1
    coco_annotations = coco_tools.ExportSingleImageGroundtruthToCoco(
        image_id='first_image',
        category_id_set=set([1, 2, 3]),
        next_annotation_id=next_annotation_id,
        groundtruth_boxes=boxes,
        groundtruth_classes=classes,
        groundtruth_masks=masks)
    expected_counts = ['04', '31', '4']
    for i, annotation in enumerate(coco_annotations):
      self.assertEqual(annotation['segmentation']['counts'],
                       expected_counts[i])
      self.assertTrue(np.all(np.equal(mask.decode(
          annotation['segmentation']), masks[i])))
      self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i])))
      self.assertEqual(annotation['image_id'], 'first_image')
      self.assertEqual(annotation['category_id'], classes[i])
      self.assertEqual(annotation['id'], i + next_annotation_id) 

# 需要导入模块: from object_detection.metrics import coco_tools [as 别名]
# 或者: from object_detection.metrics.coco_tools import ExportSingleImageGroundtruthToCoco [as 别名]
def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
    """Add groundtruth results of all frames to the eval pipeline.

    This method overrides the function defined in the base class.

    Args:
      image_id: A unique string/integer identifier for the image.
      groundtruth_dict: A list of dictionary containing -
        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.
        InputDataFields.groundtruth_classes: integer numpy array of shape
          [num_boxes] containing 1-indexed groundtruth classes for the boxes.
        InputDataFields.groundtruth_is_crowd (optional): integer numpy array of
          shape [num_boxes] containing iscrowd flag for groundtruth boxes.
    """
    for idx, gt in enumerate(groundtruth_dict):
      if not gt:
        continue

      image_frame_id = '{}_{}'.format(image_id, idx)
      if image_frame_id in self._image_ids:
        tf.logging.warning(
            'Ignoring ground truth with image id %s since it was '
            'previously added', image_frame_id)
        continue

      self._groundtruth_list.extend(
          coco_tools.ExportSingleImageGroundtruthToCoco(
              image_id=image_frame_id,
              next_annotation_id=self._annotation_id,
              category_id_set=self._category_id_set,
              groundtruth_boxes=gt[
                  standard_fields.InputDataFields.groundtruth_boxes],
              groundtruth_classes=gt[
                  standard_fields.InputDataFields.groundtruth_classes]))
      self._annotation_id += (
          gt[standard_fields.InputDataFields.groundtruth_boxes].shape[0])

      # Boolean to indicate whether a detection has been added for this image.
      self._image_ids[image_frame_id] = False 

# 需要导入模块: from object_detection.metrics import coco_tools [as 别名]
# 或者: from object_detection.metrics.coco_tools import ExportSingleImageGroundtruthToCoco [as 别名]
def testSingleImageGroundtruthExport(self):
    masks = np.array(
        [[[1, 1,], [1, 1]],
         [[0, 0], [0, 1]],
         [[0, 0], [0, 0]]], dtype=np.uint8)
    boxes = np.array([[0, 0, 1, 1],
                      [0, 0, .5, .5],
                      [.5, .5, 1, 1]], dtype=np.float32)
    coco_boxes = np.array([[0, 0, 1, 1],
                           [0, 0, .5, .5],
                           [.5, .5, .5, .5]], dtype=np.float32)
    classes = np.array([1, 2, 3], dtype=np.int32)
    is_crowd = np.array([0, 1, 0], dtype=np.int32)
    next_annotation_id = 1
    expected_counts = ['04', '31', '4']

    # Tests exporting without passing in is_crowd (for backward compatibility).
    coco_annotations = coco_tools.ExportSingleImageGroundtruthToCoco(
        image_id='first_image',
        category_id_set=set([1, 2, 3]),
        next_annotation_id=next_annotation_id,
        groundtruth_boxes=boxes,
        groundtruth_classes=classes,
        groundtruth_masks=masks)
    for i, annotation in enumerate(coco_annotations):
      self.assertEqual(annotation['segmentation']['counts'],
                       expected_counts[i])
      self.assertTrue(np.all(np.equal(mask.decode(
          annotation['segmentation']), masks[i])))
      self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i])))
      self.assertEqual(annotation['image_id'], 'first_image')
      self.assertEqual(annotation['category_id'], classes[i])
      self.assertEqual(annotation['id'], i + next_annotation_id)

    # Tests exporting with is_crowd.
    coco_annotations = coco_tools.ExportSingleImageGroundtruthToCoco(
        image_id='first_image',
        category_id_set=set([1, 2, 3]),
        next_annotation_id=next_annotation_id,
        groundtruth_boxes=boxes,
        groundtruth_classes=classes,
        groundtruth_masks=masks,
        groundtruth_is_crowd=is_crowd)
    for i, annotation in enumerate(coco_annotations):
      self.assertEqual(annotation['segmentation']['counts'],
                       expected_counts[i])
      self.assertTrue(np.all(np.equal(mask.decode(
          annotation['segmentation']), masks[i])))
      self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i])))
      self.assertEqual(annotation['image_id'], 'first_image')
      self.assertEqual(annotation['category_id'], classes[i])
      self.assertEqual(annotation['iscrowd'], is_crowd[i])
      self.assertEqual(annotation['id'], i + next_annotation_id) 

# 需要导入模块: from object_detection.metrics import coco_tools [as 别名]
# 或者: from object_detection.metrics.coco_tools import ExportSingleImageGroundtruthToCoco [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.

    If the image has already been added, a warning is logged, and groundtruth is
    ignored.

    Args:
      image_id: A unique string/integer identifier for the image.
      groundtruth_dict: A dictionary containing -
        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.
        InputDataFields.groundtruth_classes: integer numpy array of shape
          [num_boxes] containing 1-indexed groundtruth classes for the boxes.
        InputDataFields.groundtruth_is_crowd (optional): integer numpy array of
          shape [num_boxes] containing iscrowd flag for groundtruth boxes.
    """
    if image_id in self._image_ids:
      tf.logging.warning('Ignoring ground truth with image id %s since it was '
                         'previously added', image_id)
      return

    groundtruth_is_crowd = groundtruth_dict.get(
        standard_fields.InputDataFields.groundtruth_is_crowd)
    # Drop groundtruth_is_crowd if empty tensor.
    if groundtruth_is_crowd is not None and not groundtruth_is_crowd.shape[0]:
      groundtruth_is_crowd = None

    self._groundtruth_list.extend(
        coco_tools.ExportSingleImageGroundtruthToCoco(
            image_id=image_id,
            next_annotation_id=self._annotation_id,
            category_id_set=self._category_id_set,
            groundtruth_boxes=groundtruth_dict[
                standard_fields.InputDataFields.groundtruth_boxes],
            groundtruth_classes=groundtruth_dict[
                standard_fields.InputDataFields.groundtruth_classes],
            groundtruth_is_crowd=groundtruth_is_crowd))
    self._annotation_id += groundtruth_dict[standard_fields.InputDataFields.
                                            groundtruth_boxes].shape[0]
    # Boolean to indicate whether a detection has been added for this image.
    self._image_ids[image_id] = False