def _load_coco_keypoint_annotation_kernal()

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

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

    def _load_coco_keypoint_annotation_kernal(self, index):
        """
        coco ann: [u'segmentation', u'area', u'iscrowd', u'image_id', u'bbox', u'category_id', u'id']
        iscrowd:
            crowd instances are handled by marking their overlaps with all categories to -1
            and later excluded in training
        bbox:
            [x1, y1, w, h]
        :param index: coco image id
        :return: db entry
        """
        im_ann = self.coco.loadImgs(index)[0]
        width = im_ann['width']
        height = im_ann['height']

        annIds = self.coco.getAnnIds(imgIds=index, iscrowd=False)
        objs = self.coco.loadAnns(annIds)

        # sanitize bboxes
        valid_objs = []
        for obj in objs:
            x, y, w, h = obj['bbox']
            x1 = np.max((0, x))
            y1 = np.max((0, y))
            x2 = np.min((width - 1, x1 + np.max((0, w - 1))))
            y2 = np.min((height - 1, y1 + np.max((0, h - 1))))
            if obj['area'] > 0 and x2 >= x1 and y2 >= y1:
                # obj['clean_bbox'] = [x1, y1, x2, y2]
                obj['clean_bbox'] = [x1, y1, x2-x1, y2-y1]
                valid_objs.append(obj)
        objs = valid_objs

        rec = []
        for obj in objs:
            cls = self._coco_ind_to_class_ind[obj['category_id']]
            if cls != 1:
                continue

            # ignore objs without keypoints annotation
            if max(obj['keypoints']) == 0:
                continue

            joints_3d = np.zeros((self.num_joints, 3), dtype=np.float)
            joints_3d_vis = np.zeros((self.num_joints, 3), dtype=np.float)
            for ipt in range(self.num_joints):
                joints_3d[ipt, 0] = obj['keypoints'][ipt * 3 + 0]
                joints_3d[ipt, 1] = obj['keypoints'][ipt * 3 + 1]
                joints_3d[ipt, 2] = 0
                t_vis = obj['keypoints'][ipt * 3 + 2]
                if t_vis > 1:
                    t_vis = 1
                joints_3d_vis[ipt, 0] = t_vis
                joints_3d_vis[ipt, 1] = t_vis
                joints_3d_vis[ipt, 2] = 0

            center, scale = self._box2cs(obj['clean_bbox'][:4])
            rec.append({
                'image': self.image_path_from_index(index),
                'center': center,
                'scale': scale,
                'joints_3d': joints_3d,
                'joints_3d_vis': joints_3d_vis,
                'filename': '',
                'imgnum': 0,
            })

        return rec