def load_img_with_targets(self, index): """Load images and targets for the training and validation phase""" sample_id = int(self.sample_id_list[index]) img_path = os.path.join(self.image_dir, '{:06d}.png'.format(sample_id)) lidarData = self.get_lidar(sample_id) calib = self.get_calib(sample_id) labels, has_labels = self.get_label(sample_id) if has_labels: labels[:, 1:] = transformation.camera_to_lidar_box( labels[:, 1:], calib.V2C, calib.R0, calib.P2) if self.lidar_aug: lidarData, labels[:, 1:] = self.lidar_aug(lidarData, labels[:, 1:]) lidarData, labels = get_filtered_lidar(lidarData, cnf.boundary, labels) bev_map = makeBEVMap(lidarData, cnf.boundary) bev_map = torch.from_numpy(bev_map) hflipped = False if np.random.random() < self.hflip_prob: hflipped = True # C, H, W bev_map = torch.flip(bev_map, [-1]) targets = self.build_targets(labels, hflipped) metadatas = {'img_path': img_path, 'hflipped': hflipped} return metadatas, bev_map, targets
def load_bevmap_front_vs_back(self, index): """Load only image for the testing phase""" sample_id = int(self.sample_id_list[index]) img_path, img_rgb = self.get_image(sample_id) lidarData = self.get_lidar(sample_id) front_lidar = get_filtered_lidar(lidarData, cnf.boundary) front_bevmap = makeBEVMap(front_lidar, cnf.boundary) front_bevmap = torch.from_numpy(front_bevmap) back_lidar = get_filtered_lidar(lidarData, cnf.boundary_back) back_bevmap = makeBEVMap(back_lidar, cnf.boundary_back) back_bevmap = torch.from_numpy(back_bevmap) metadatas = { 'img_path': img_path, } return metadatas, front_bevmap, back_bevmap, img_rgb
def load_img_only(self, index): """Load only image for the testing phase""" sample_id = int(self.sample_id_list[index]) img_path, img_rgb = self.get_image(sample_id) lidarData = self.get_lidar(sample_id) lidarData = get_filtered_lidar(lidarData, cnf.boundary) bev_map = makeBEVMap(lidarData, cnf.boundary) bev_map = torch.from_numpy(bev_map) metadatas = { 'img_path': img_path, } return metadatas, bev_map, img_rgb
def draw_img_with_label(self, index): sample_id = int(self.sample_id_list[index]) img_path, img_rgb = self.get_image(sample_id) lidarData = self.get_lidar(sample_id) calib = self.get_calib(sample_id) labels, has_labels = self.get_label(sample_id) if has_labels: labels[:, 1:] = transformation.camera_to_lidar_box( labels[:, 1:], calib.V2C, calib.R0, calib.P2) if self.lidar_aug: lidarData, labels[:, 1:] = self.lidar_aug(lidarData, labels[:, 1:]) lidarData, labels = get_filtered_lidar(lidarData, cnf.boundary, labels) bev_map = makeBEVMap(lidarData, cnf.boundary) return bev_map, labels, img_rgb, img_path
def on_scan(scan): start = timeit.default_timer() rospy.loginfo("Got scan") gen = [] for p in pc2.read_points(scan, field_names=("x", "y", "z", "intensity"), skip_nans=True): gen.append(np.array([p[0], p[1], p[2], p[3] / 100.0])) gen_numpy = np.array(gen, dtype=np.float32) front_lidar = get_filtered_lidar(gen_numpy, cnf.boundary) bev_map = makeBEVMap(front_lidar, cnf.boundary) bev_map = torch.from_numpy(bev_map) with torch.no_grad(): detections, bev_map, fps = do_detect(configs, model, bev_map, is_front=True) print(fps) objects_msg = DetectedObjectArray() objects_msg.header.stamp = rospy.Time.now() objects_msg.header.frame_id = scan.header.frame_id flag = False for j in range(configs.num_classes): class_name = ID_TO_CLASS_NAME[j] if len(detections[j]) > 0: flag = True for det in detections[j]: _score, _x, _y, _z, _h, _w, _l, _yaw = det yaw = -_yaw x = _y / cnf.BEV_HEIGHT * cnf.bound_size_x + cnf.boundary[ 'minX'] y = _x / cnf.BEV_WIDTH * cnf.bound_size_y + cnf.boundary['minY'] z = _z + cnf.boundary['minZ'] w = _w / cnf.BEV_WIDTH * cnf.bound_size_y l = _l / cnf.BEV_HEIGHT * cnf.bound_size_x obj = DetectedObject() obj.header.stamp = rospy.Time.now() obj.header.frame_id = scan.header.frame_id obj.score = 0.9 obj.pose_reliable = True obj.space_frame = scan.header.frame_id obj.label = class_name obj.score = _score obj.pose.position.x = x obj.pose.position.y = y obj.pose.position.z = z [qx, qy, qz, qw] = euler_to_quaternion(yaw, 0, 0) obj.pose.orientation.x = qx obj.pose.orientation.y = qy obj.pose.orientation.z = qz obj.pose.orientation.w = qw obj.dimensions.x = l obj.dimensions.y = w obj.dimensions.z = _h objects_msg.objects.append(obj) if flag is True: pub.publish(objects_msg) stop = timeit.default_timer() print('Time: ', stop - start)