tensorflow/sagemakercv/data/coco/coco_metric.py [96:125]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    assert isinstance(predictions, list), 'results in not an array of objects'
    if predictions:
      image_ids = [pred['image_id'] for pred in predictions]
      assert set(image_ids) == (set(image_ids) & set(self.getImgIds())), \
             'Results do not correspond to current coco set'

      if (predictions and 'bbox' in predictions[0] and predictions[0]['bbox']):
        res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
        for idx, pred in enumerate(predictions):
          bb = pred['bbox']
          x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]
          if 'segmentation' not in pred:
            pred['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
          pred['area'] = bb[2] * bb[3]
          pred['id'] = idx + 1
          pred['iscrowd'] = 0
      elif 'segmentation' in predictions[0]:
        res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
        for idx, pred in enumerate(predictions):
          # now only support compressed RLE format as segmentation results
          pred['area'] = maskUtils.area(pred['segmentation'])
          if 'bbox' not in pred:
            pred['bbox'] = maskUtils.toBbox(pred['segmentation'])
          pred['id'] = idx + 1
          pred['iscrowd'] = 0

      res.dataset['annotations'] = predictions

    res.createIndex() # use_ext=True)
    return res
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



tensorflow/sagemakercv/data/coco/coco_metric.py [140:169]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    assert isinstance(predictions, list), 'results in not an array of objects'
    if predictions:
      image_ids = [pred['image_id'] for pred in predictions]
      assert set(image_ids) == (set(image_ids) & set(self.getImgIds())), \
             'Results do not correspond to current coco set'

      if (predictions and 'bbox' in predictions[0] and predictions[0]['bbox']):
        res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
        for idx, pred in enumerate(predictions):
          bb = pred['bbox']
          x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]
          if 'segmentation' not in pred:
            pred['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
          pred['area'] = bb[2] * bb[3]
          pred['id'] = idx + 1
          pred['iscrowd'] = 0
      elif 'segmentation' in predictions[0]:
        res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
        for idx, pred in enumerate(predictions):
          # now only support compressed RLE format as segmentation results
          pred['area'] = maskUtils.area(pred['segmentation'])
          if 'bbox' not in pred:
            pred['bbox'] = maskUtils.toBbox(pred['segmentation'])
          pred['id'] = idx + 1
          pred['iscrowd'] = 0

      res.dataset['annotations'] = predictions

    res.createIndex() # use_ext=True)
    return res
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -