def thread_seg_line(model_seg_line, frame_pil_img, q_sed_line):
argmax_feats_road, color_map_display_road = evaluateModel(model_seg_line,
frame_pil_img,
inWidth=512,
inHeight=256,
flag_road=0)
q_sed_line.put([argmax_feats_road, color_map_display_road])
success, frame_np_img = videoCapture1.read()
c=0
while success :
print('{} frame:'.format(c+1))
c+=1
st_st=time.time()
# BGR → RGB and numpy image to PIL image
frame_np_img = frame_np_img[...,[2,1,0]]
frame_pil_img = im = Image.fromarray(frame_np_img)
# object detection model
st=time.time()
annotated_image_od, bboxes = detect(model_od, frame_pil_img, min_score=0.3, max_overlap=0.5, top_k=100)
print('object detection:{}s'.format(time.time()-st))
# road segmentation model
st=time.time()
argmax_feats_road, color_map_display_road = evaluateModel(model_seg_road, frame_pil_img, inWidth=512, inHeight=256, flag_road=1)
print('road segmentation:{}s'.format(time.time()-st))
# lane segmentation model
st=time.time()
argmax_feats_lane, color_map_display_lane = evaluateModel(model_seg_lane, frame_pil_img, inWidth=512, inHeight=256, flag_road=0)
print('lane segmentation:{}s'.format(time.time()-st))
argmax_feats_road[argmax_feats_road==11]=100
argmax_feats_lane[argmax_feats_lane==11]=100
decision_boxes, img_result = fun_detection_TrafficViolation(frame_np_img, 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)
if __name__ == '__main__':
img_path = 'D:\\專案管理\\新加坡專案\\label\\OV_001-1-Segmentation\\OV_001-1 0036.jpg'
original_image = Image.open(img_path, mode='r')
original_image = original_image.convert('RGB')
# object detection model
annotated_image_od, bboxes = detect(model_od,
original_image,
min_score=0.3,
max_overlap=0.5,
top_k=100)
# road segmentation model
argmax_feats_road, color_map_display_road = evaluateModel(model_seg_road,
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)