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)