def _get_db()

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

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

    def _get_db(self):
        # create train/val split
        file_name = os.path.join(self.root,
                                 'annot',
                                 self.image_set+'.json')
        with open(file_name) as anno_file:
            anno = json.load(anno_file)

        gt_db = []
        for a in anno:
            image_name = a['image']

            c = np.array(a['center'], dtype=np.float)
            s = np.array([a['scale'], a['scale']], dtype=np.float)

            # Adjust center/scale slightly to avoid cropping limbs
            if c[0] != -1:
                c[1] = c[1] + 15 * s[1]
                s = s * 1.25

            # MPII uses matlab format, index is based 1,
            # we should first convert to 0-based index
            c = c - 1

            joints_3d = np.zeros((self.num_joints, 3), dtype=np.float)
            joints_3d_vis = np.zeros((self.num_joints,  3), dtype=np.float)
            if self.image_set != 'test':
                joints = np.array(a['joints'])
                joints[:, 0:2] = joints[:, 0:2] - 1
                joints_vis = np.array(a['joints_vis'])
                assert len(joints) == self.num_joints, \
                    'joint num diff: {} vs {}'.format(len(joints),
                                                      self.num_joints)

                joints_3d[:, 0:2] = joints[:, 0:2]
                joints_3d_vis[:, 0] = joints_vis[:]
                joints_3d_vis[:, 1] = joints_vis[:]

            image_dir = 'images.zip@' if self.data_format == 'zip' else 'images'
            gt_db.append({
                'image': os.path.join(self.root, image_dir, image_name),
                'center': c,
                'scale': s,
                'joints_3d': joints_3d,
                'joints_3d_vis': joints_3d_vis,
                'filename': '',
                'imgnum': 0,
                })

        return gt_db