def get_calib(self, idx): calib_file = os.path.join(self.calib_dir, '{:06d}.txt'.format(idx)) # assert os.path.isfile(calib_file) return kitti_data_utils.Calibration(calib_file)
configs.dataset_dir = os.path.join('../../', 'dataset', 'kitti') if configs.show_train_data: dataloader, _ = create_train_dataloader(configs) print('len train dataloader: {}'.format(len(dataloader))) else: dataloader = create_val_dataloader(configs) print('len val dataloader: {}'.format(len(dataloader))) print('\n\nPress n to see the next sample >>> Press Esc to quit...') for batch_i, (img_files, imgs, targets) in enumerate(dataloader): if not (configs.mosaic and configs.show_train_data): img_file = img_files[0] img_rgb = cv2.imread(img_file) calib = kitti_data_utils.Calibration( img_file.replace(".png", ".txt").replace("image_2", "calib")) objects_pred = invert_target(targets[:, 1:], calib, img_rgb.shape, RGB_Map=None) img_rgb = show_image_with_boxes(img_rgb, objects_pred, calib, False) # Rescale target targets[:, 2:6] *= configs.img_size # Get yaw angle targets[:, 6] = torch.atan2(targets[:, 6], targets[:, 7]) img_bev = imgs.squeeze() * 255 img_bev = img_bev.permute(1, 2, 0).numpy().astype(np.uint8) img_bev = cv2.resize(img_bev, (configs.img_size, configs.img_size))
def client_recv(self, client, address): while True: # read message from socket # client_msg_0\x00\x00\x00\x00\x00... msg = client.recv(1024).decode("utf-8") msg = msg.rstrip("\x00") if msg == '': return if msg == "EOF": return elif msg == "quit_client": client.close() # self.sock.close() print("> client exit...") return elif msg == "quit_server": client.close() self.sock.close() print("> server exit...") sys.exit(0) else: print("> -------", time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())), "-------") print("> receive the msg from client : {0}".format(msg)) print('> inference for {0}'.format(msg)) if(self.need_create_window): # NOTE ObjSLAM cv2.namedWindow("YOLO", flags=cv2.WINDOW_GUI_NORMAL) self.need_create_window = False # Inference with torch.no_grad(): # img_paths, imgs_bev = self.test_dataloader_iter.next() img_paths, imgs_bev = self.test_dataset[int(msg)] img_paths = [img_paths] imgs_bev = torch.from_numpy( np.expand_dims(imgs_bev, axis=0)) input_imgs = imgs_bev.to( device=self.configs.device).float() outputs = self.model(input_imgs) detections = post_processing_v2( outputs, conf_thresh=self.configs.conf_thresh, nms_thresh=self.configs.nms_thresh) img_detections = [] # Stores detections for each image index img_detections.extend(detections) img_bev = imgs_bev.squeeze() * 255 img_bev = img_bev.permute(1, 2, 0).numpy().astype(np.uint8) img_bev = cv2.resize( img_bev, (self.configs.img_size, self.configs.img_size)) for detections in img_detections: if detections is None: continue # Rescale boxes to original image detections = rescale_boxes( detections, self.configs.img_size, img_bev.shape[:2]) for x, y, w, l, im, re, *_, cls_pred in detections: yaw = np.arctan2(im, re) # Draw rotated box kitti_bev_utils.drawRotatedBox( img_bev, x, y, w, l, yaw, cnf.colors[int(cls_pred)]) img_rgb = cv2.imread(img_paths[0]) calib = kitti_data_utils.Calibration(img_paths[0].replace( ".png", ".txt").replace("image_2", "calib")) objects_pred = predictions_to_kitti_format( img_detections, calib, img_rgb.shape, self.configs.img_size) # NOTE: 输出json的代码 frame_object_list = [] for i in objects_pred: frame_object = dict() frame_object['type'] = i.type frame_object['center'] = i.t frame_object['length'] = i.l frame_object['width'] = i.w frame_object['height'] = i.h frame_object['theta'] = i.ry box3d_pts_2d, _ = kitti_data_utils.compute_box_3d( i, calib.P) if box3d_pts_2d is None: frame_object['box3d_pts_2d'] = box3d_pts_2d elif box3d_pts_2d.size == 16: frame_object['box3d_pts_2d'] = box3d_pts_2d else: frame_object['box3d_pts_2d'] = box3d_pts_2d[:8, :] frame_object_list.append(frame_object) result = json.dumps(frame_object_list, cls=NumpyEncoder) img_bev = cv2.flip(cv2.flip(img_bev, 0), 1) scale = 1.5 cv2.resizeWindow("YOLO", width=int(img_bev.shape[1] * scale), height=int(img_bev.shape[0] * scale)) cv2.imshow('YOLO', img_bev) cv2.waitKey(10) self.batch_idx += 1 if len(result) > self.configs.max_length: print("> WARNING: STRING IS TOO LONG! (MAX_LENGTH {0})".format( self.configs.max_length)) client.send(result.encode(encoding='utf-8')) print("> send the responce back to client, string length: {0}".format( len(result))) return