def _create_and_add_example(self, pose, shape=None): feat_dict = {'pose': float_feature(pose.astype(np.float32))} if shape is not None: feat_dict.update( {'shape': float_feature(shape.astype(np.float32))}) self.__examples.append( tf.train.Example(features=tf.train.Features(feature=feat_dict)))
def _create_and_add_example(self, config, img, kp2d, vis, kp3d, seq): image_string = cv2.imencode('.jpg', cv2.cvtColor( img, cv2.COLOR_BGR2RGB))[1].tobytes() kp2d_vis = np.column_stack([kp2d, vis]) feat_dict = { 'image_raw': bytes_feature(tf.compat.as_bytes(image_string)), 'keypoints_2d': float_feature(kp2d_vis), 'keypoints_3d': float_feature(kp3d), 'has_3d': int64_feature(config.has_3d), } if config.name == 'test': as_bytes = tf.compat.as_bytes(seq) if as_bytes == b'': print('not value!') feat_dict.update({'sequence': bytes_feature(as_bytes)}) self.__examples.append( tf.train.Example(features=tf.train.Features(feature=feat_dict)))