def _coco_keypoint_results_one_category_kernel()

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

• 24 lines of code
• 6 McCabe index (conditional complexity)

    def _coco_keypoint_results_one_category_kernel(self, data_pack):
        cat_id = data_pack['cat_id']
        keypoints = data_pack['keypoints']
        cat_results = []

        for img_kpts in keypoints:
            if len(img_kpts) == 0:
                continue

            _key_points = np.array([img_kpts[k]['keypoints']
                                    for k in range(len(img_kpts))])
            key_points = np.zeros(
                (_key_points.shape[0], self.num_joints * 3), dtype=np.float)

            for ipt in range(self.num_joints):
                key_points[:, ipt * 3 + 0] = _key_points[:, ipt, 0]
                key_points[:, ipt * 3 + 1] = _key_points[:, ipt, 1]
                key_points[:, ipt * 3 + 2] = _key_points[:, ipt, 2]  # keypoints score.

            result = [{'image_id': img_kpts[k]['image'],
                       'category_id': cat_id,
                       'keypoints': list(key_points[k]),
                       'score': img_kpts[k]['score'],
                       'center': list(img_kpts[k]['center']),
                       'scale': list(img_kpts[k]['scale'])
                       } for k in range(len(img_kpts))]
            cat_results.extend(result)

        return cat_results