def post_procesiing(self, outputs): outputs['hm_cen'] = _sigmoid(outputs['hm_cen']) outputs['cen_offset'] = _sigmoid(outputs['cen_offset']) # detections size (batch_size, K, 10) detections = decode(outputs['hm_cen'], outputs['cen_offset'], outputs['direction'], outputs['z_coor'],outputs['dim'], K=self.configs.K) detections = detections.cpu().detach().numpy().astype(np.float32) detections = post_processing(detections, self.configs.num_classes, self.configs.down_ratio, self.configs.peak_thresh) detections = detections[0] detections = convert_det_to_real_values(detections) return detections
for sample_idx in range(len(demo_dataset)): metadatas, bev_map, img_rgb = demo_dataset.load_bevmap_front(sample_idx) detections, bev_map, fps = do_detect(configs, model, bev_map, is_front=True) # Draw prediction in the image bev_map = (bev_map.permute(1, 2, 0).numpy() * 255).astype(np.uint8) bev_map = cv2.resize(bev_map, (cnf.BEV_WIDTH, cnf.BEV_HEIGHT)) bev_map = draw_predictions(bev_map, detections, configs.num_classes) # Rotate the bev_map bev_map = cv2.rotate(bev_map, cv2.ROTATE_180) img_path = metadatas['img_path'][0] img_bgr = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR) calib = Calibration(configs.calib_path) kitti_dets = convert_det_to_real_values(detections) if len(kitti_dets) > 0: kitti_dets[:, 1:] = lidar_to_camera_box(kitti_dets[:, 1:], calib.V2C, calib.R0, calib.P2) img_bgr = show_rgb_image_with_boxes(img_bgr, kitti_dets, calib) out_img = merge_rgb_to_bev(img_bgr, bev_map, output_width=configs.output_width) write_credit(out_img, (80, 210), text_author='Cre: github.com/maudzung', org_fps=(80, 250), fps=fps) if out_cap is None: out_cap_h, out_cap_w = out_img.shape[:2] fourcc = cv2.VideoWriter_fourcc(*'MJPG') out_path = os.path.join(configs.results_dir, '{}_front.avi'.format(configs.foldername)) print('Create video writer at {}'.format(out_path)) out_cap = cv2.VideoWriter(out_path, fourcc, 30, (out_cap_w, out_cap_h)) out_cap.write(out_img)
bev_map = makeBEVMap(lidardata, cnf.boundary) bev_map = bev_map.transpose((1, 2, 0)) * 255 bev_map = cv2.resize(bev_map, (cnf.BEV_WIDTH, cnf.BEV_HEIGHT)) bev_map = draw_predictions(bev_map, detections.copy(), configs.num_classes) # Rotate the bev_map bev_map = cv2.rotate(bev_map, cv2.ROTATE_180) img_rgb = metadatas['img_rgb'][0] img_rgb = cv2.resize(img_rgb, (img_rgb.shape[1], img_rgb.shape[0])) img_bgr = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR) calib = Calibration( img_path.replace(".png", ".txt").replace("image_2", "calib")) kitti_dets = convert_det_to_real_values(detections, configs.num_classes) '''datap = img_path.split("/") filename = datap[9].split('.') lidar_path = '/' + datap[1] + '/' + datap[2]+ '/' + datap[3]+ '/' +\ datap[4] +'/' +datap[5]+ '/' +datap[6]+ '/'+datap[7]+ '/' +'velodyne' + '/' + filename[0]+'.bin' if len(kitti_dets) > 0: show3dlidar(lidar_path, kitti_dets, calib)''' if len(kitti_dets) > 0: kitti_dets[:, 1:] = lidar_to_camera_box(kitti_dets[:, 1:], calib.V2C, calib.R0, calib.P2) img_bgr = show_rgb_image_with_boxes(img_bgr, kitti_dets, calib) out_img = merge_rgb_to_bev(img_bgr,
def evaluate_mAP(val_loader, model, configs, logger): batch_time = AverageMeter('Time', ':6.3f') data_time = AverageMeter('Data', ':6.3f') progress = ProgressMeter(len(val_loader), [batch_time, data_time], prefix="Evaluation phase...") labels = [] sample_metrics = [] # List of tuples (TP, confs, pred) # switch to evaluate mode model.eval() class_id = {0:'Car', 1:'Pedestrian', 2:'Cyclist'} with torch.no_grad(): start_time = time.time() for batch_idx, batch_data in enumerate(tqdm(val_loader)): metadatas, targets= batch_data batch_size = len(metadatas['img_path']) voxelinput = metadatas['voxels'] coorinput = metadatas['coors'] numinput = metadatas['num_points'] dtype = torch.float32 voxelinputr = torch.tensor( voxelinput, dtype=torch.float32, device=configs.device).to(dtype) coorinputr = torch.tensor( coorinput, dtype=torch.int32, device=configs.device) numinputr = torch.tensor( numinput, dtype=torch.int32, device=configs.device) t1 = time_synchronized() outputs = model(voxelinputr, coorinputr, numinputr) outputs = outputs._asdict() outputs['hm_cen'] = _sigmoid(outputs['hm_cen']) outputs['cen_offset'] = _sigmoid(outputs['cen_offset']) # detections size (batch_size, K, 10) img_path = metadatas['img_path'][0] #print(img_path) calib = Calibration(img_path.replace(".png", ".txt").replace("image_2", "calib")) detections = decode(outputs['hm_cen'], outputs['cen_offset'], outputs['direction'], outputs['z_coor'], outputs['dim'], K=configs.K) detections = detections.cpu().numpy().astype(np.float32) detections = post_processing(detections, configs.num_classes, configs.down_ratio, configs.peak_thresh) for i in range(configs.batch_size): detections[i] = convert_det_to_real_values(detections[i]) img_path = metadatas['img_path'][i] #rint(img_path) datap = str.split(img_path,'/') filename = str.split(datap[7],'.') file_write_obj = open('../result/' + filename[0] + '.txt', 'w') lidar_path = '/' + datap[1] + '/' + datap[2] + '/' + datap[3] + '/' + \ datap[4] + '/' + datap[5] + '/' + 'velodyne' + '/' + filename[0] + '.bin' #print(lidar_path) #show3dlidar(lidar_path, detections[i], calib.V2C, calib.R0, calib.P2) dets = detections[i] if len(dets) >0 : dets[:, 1:] = lidar_to_camera_box(dets[:, 1:], calib.V2C, calib.R0, calib.P2) for box_idx, label in enumerate(dets): location, dim, ry = label[1:4], label[4:7], label[7] if ry < -np.pi: ry = 2*np.pi + ry if ry > np.pi: ry = -2*np.pi + ry corners_3d = compute_box_3d(dim, location, ry) corners_2d = project_to_image(corners_3d, calib.P2) minxy = np.min(corners_2d, axis=0) maxxy = np.max(corners_2d, axis=0) bbox = np.concatenate([minxy, maxxy], axis=0) if bbox[0] < 0 or bbox[2]<0: continue if bbox[1] > 1272 or bbox[3] > 375: continue oblist = ['Car',' ','0.0', ' ', '0', ' ', '-10', ' ','%.2f'%bbox[0], ' ', \ '%.2f' %bbox[1], ' ','%.2f'%bbox[2], ' ','%.2f'%bbox[3], ' ','%.2f'%dim[0], ' ','%.2f'%dim[1], ' ','%.2f'%dim[2], ' ', \ '%.2f' %location[0], ' ','%.2f'%location[1], ' ','%.2f'%location[2], ' ', '%.2f'%ry, '\n'] file_write_obj.writelines(oblist) file_write_obj.close() '''for sample_i in range(len(detections)):