det_performance = eval.measure_detection_performance( detections, frame.laser_labels, valid_label_flags) else: print('loading detection performance measures from file') det_performance = load_object_from_file( results_fullpath, data_filename, 'det_performance_' + configs_det.arch + '_' + str(configs_det.conf_thresh), cnt_frame) det_performance_all.append( det_performance ) # store all evaluation results in a list for performance assessment at the end ## Visualization for object detection if 'show_range_image' in exec_list: img_range = pcl.show_range_image(frame) #lider_name S3 img_range = img_range.astype(np.uint8) cv2.imshow('range_image', img_range) cv2.waitKey(vis_pause_time) if 'show_pcl' in exec_list: pcl.show_pcl(lidar_pcl) if 'show_bev' in exec_list: tools.show_bev(lidar_bev, configs_det) cv2.waitKey(vis_pause_time) if 'show_labels_in_image' in exec_list: img_labels = tools.project_labels_into_camera( camera_calibration, image, frame.laser_labels, valid_label_flags, 0.5)
detections, frame.laser_labels, valid_label_flags, configs_det.min_iou) else: print('loading detection performance measures from file') # det_performance = load_object_from_file(results_fullpath, data_filename, 'det_performance_' + configs_det.arch + '_' + str(configs_det.conf_thresh), cnt_frame) det_performance = load_object_from_file(results_fullpath, data_filename, 'det_performance', cnt_frame) det_performance_all.append( det_performance ) # store all evaluation results in a list for performance assessment at the end ## Visualization for object detection if 'show_range_image' in exec_list: img_range = pcl.show_range_image(frame, lidar_name) img_range = img_range.astype(np.uint8) cv2.imshow('range_image', img_range) cv2.waitKey(vis_pause_time) if 'show_pcl' in exec_list: pcl.show_pcl(lidar_pcl) if 'show_bev' in exec_list: tools.show_bev(lidar_bev, configs_det) cv2.waitKey(vis_pause_time) if 'show_labels_in_image' in exec_list: img_labels = tools.project_labels_into_camera( camera_calibration, image, frame.laser_labels, valid_label_flags, 0.5)