# object detection model
t1 = threading.Thread(target = thread_detect, args=(model_od, frame_pil_img, q_detect))
#t2 = threading.Thread(target = thread_seg_models, args=(model_seg_road, model_seg_lane ,frame_pil_img, BGRimg, q_sed))
t2 = threading.Thread(target = thread_seg_models, args=(model_seg_road, model_seg_lane ,frame_pil_img, q_sed))
t1.start()
t2.start()
t1.join()
t2.join()
bboxes = q_detect.get()
argmax_feats_road, argmax_feats_lane, color_map_display_road, color_map_display_lane = q_sed.get()
argmax_feats_road[argmax_feats_road == 11] = 100
argmax_feats_lane[argmax_feats_lane == 11] = 100
print('inference time:{}s'.format(time.time() - st_st))
argmax_feats_lane, argmax_feats_road = fun_intergate_seg_LaneandRoad(argmax_feats_lane, argmax_feats_road)
decision_boxes, img_result = fun_detection_TrafficViolation(frame_np_img, bboxes, argmax_feats_lane, argmax_feats_road)
t_start_decision = time.time()
img_fusion = frame_np_img.copy()
for bbox in bboxes:
img_fusion = plot_bbox(img_fusion, bbox)
img_fusion = cv2.addWeighted(img_fusion, 1, color_map_display_road, 0.5, 0)
imgs = np.hstack([color_map_display_lane, img_fusion, img_result])
# RGB → BGR
imgs = imgs[...,[2,1,0]]
#videoWriter.write(img_result)
videoWriter.write(cv2.resize(imgs, (int(1920), int(1080/3))))
#videoWriter.write(imgs)
print('decision time : {}s'.format(time.time() - t_start_decision))
original_image,
inWidth=512,
inHeight=256,
flag_road=1)
# lane segmentation model
argmax_feats_lane, color_map_display_lane = evaluateModel(model_seg_lane,
original_image,
inWidth=512,
inHeight=256,
flag_road=0)
argmax_feats_road[argmax_feats_road == 11] = 100
argmax_feats_lane[argmax_feats_lane == 11] = 100
original_image = np.array(original_image)
decision_boxes, img_result = fun_detection_TrafficViolation(
original_image, bboxes, argmax_feats_lane, argmax_feats_road)
map_seg_label_line = argmax_feats_lane
map_seg_label_road = argmax_feats_road
# annotated_image_od_ = cv2.cvtColor(np.asarray(annotated_image_od),cv2.COLOR_RGB2BGR)
# imfusion = cv2.addWeighted(color_map_display_road, 0.1, annotated_image_od_, 1, 0)
# imfusion = cv2.addWeighted(color_map_display_lane, 0.5, annotated_image_od_, 1, 0)
img_fusion = original_image.copy()
for bbox in bboxes:
img_fusion = plot_bbox(img_fusion, bbox)
img_fusion = cv2.addWeighted(img_fusion, 1, color_map_display_road, 0.5, 0)
img_fusion = cv2.addWeighted(img_fusion, 1, color_map_display_lane, 1, 0)
imgs = np.hstack([img_fusion, img_result])