def _load_coco_person_detection_results()

in lib/dataset/coco.py [0:0]

• 35 lines of code
• 5 McCabe index (conditional complexity)

    def _load_coco_person_detection_results(self):
        all_boxes = None
        with open(self.bbox_file, 'r') as f:
            all_boxes = json.load(f)

        if not all_boxes:
            logger.error('=> Load %s fail!' % self.bbox_file)
            return None

        logger.info('=> Total boxes: {}'.format(len(all_boxes)))

        kpt_db = []
        num_boxes = 0
        for n_img in range(0, len(all_boxes)):
            det_res = all_boxes[n_img]
            if det_res['category_id'] != 1:
                continue
            img_name = self.image_path_from_index(det_res['image_id'])
            box = det_res['bbox']
            score = det_res['score']

            if score < self.image_thre:
                continue

            num_boxes = num_boxes + 1

            center, scale = self._box2cs(box)
            joints_3d = np.zeros((self.num_joints, 3), dtype=np.float)
            joints_3d_vis = np.ones(
                (self.num_joints, 3), dtype=np.float)
            kpt_db.append({
                'image': img_name,
                'center': center,
                'scale': scale,
                'score': score,
                'joints_3d': joints_3d,
                'joints_3d_vis': joints_3d_vis,
            })

        logger.info('=> Total boxes after fliter low score@{}: {}'.format(
            self.image_thre, num_boxes))
        return kpt_db