importargparseimporttimefrompathlibimportPathimportcv2importtorchimporttorch.backends.cudnnascudnnfromnumpyimportrandomfrommodels.experimentalimportattempt_loadfromutils.datasetsimportLoadStreams, LoadImagesfromutils.generalimportcheck_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_pathfromutils.plotsimportplot_one_boxfromutils.torch_utilsimportselect_device, load_classifier, time_synchronized

defdetect(save_img=False):
source, weights, view_img, save_txt, imgsz=opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_sizewebcam=source.isnumeric() orsource.endswith('.txt') orsource.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://'))
# Directoriessave_dir=Path(increment_path(Path(opt.project) /opt.name, exist_ok=opt.exist_ok))  # increment run    (save_dir/'labels'ifsave_txtelsesave_dir).mkdir(parents=True, exist_ok=True)  # make dir# Initializeset_logging()
device=select_device(opt.device)
half=device.type!='cpu'# half precision only supported on CUDA# Load modelmodel=attempt_load(weights, map_location=device)  # load FP32 modelstride=int(model.stride.max())  # model strideimgsz=check_img_size(imgsz, s=stride)  # check img_sizeifhalf:
model.half()  # to FP16# Second-stage classifierclassify=Falseifclassify:
modelc=load_classifier(name='resnet101', n=2)  # initializemodelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
# Set Dataloadervid_path, vid_writer=None, Noneifwebcam:
view_img=check_imshow()
cudnn.benchmark=True# set True to speed up constant image size inferencedataset=LoadStreams(source, img_size=imgsz, stride=stride)
else:
save_img=Truedataset=LoadImages(source, img_size=imgsz, stride=stride)
# Get names and colorsnames=model.module.namesifhasattr(model, 'module') elsemodel.namescolors= [[random.randint(0, 255) for_inrange(3)] for_innames]
# Run inferenceifdevice.type!='cpu':
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run oncet0=time.time()
# Apply NMSpred=non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
t2=time_synchronized()
# Apply Classifierifclassify:
pred=apply_classifier(pred, modelc, img, im0s)
# Process detectionsfori, detinenumerate(pred):  # detections per imageifwebcam:  # batch_size >= 1p, s, im0, frame=path[i], '%g: '%i, im0s[i].copy(), dataset.countelse:
p, s, im0, frame=path, '', im0s, getattr(dataset, 'frame', 0)
p=Path(p)  # to Pathsave_path=str(save_dir/p.name)  # img.jpgtxt_path=str(save_dir/'labels'/p.stem) + (''ifdataset.mode=='image'elsef'_{frame}')  # img.txts+='%gx%g '%img.shape[2:]  # print stringgn=torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwhiflen(det):
# Rescale boxes from img_size to im0 sizedet[:, :4] =scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print resultsforcindet[:, -1].unique():
n= (det[:, -1] ==c).sum()  # detections per classs+=f"{n}{names[int(c)]}{'s'* (n>1)}, "# add to string# Write resultsfor*xyxy, conf, clsinreversed(det):
ifsave_txt:  # Write to filexywh= (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) /gn).view(-1).tolist()  # normalized xywhline= (cls, *xywh, conf) ifopt.save_confelse (cls, *xywh)  # label formatwithopen(txt_path+'.txt', 'a') asf:
f.write(('%g '*len(line)).rstrip() %line+'\n')
ifsave_imgorview_img:  # Add bbox to imagelabel=f'{names[int(cls)]}{conf:.2f}'plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
# Print time (inference + NMS)print(f'{s}Done. ({t2-t1:.3f}s)')
# Stream resultsifview_img:
cv2.imshow(str(p), im0)
cv2.waitKey(1)  # 1 millisecond# Save results (image with detections)ifsave_img:
ifdataset.mode=='image':
cv2.imwrite(save_path, im0)
else:  # 'video'ifvid_path!=save_path:  # new videovid_path=save_pathifisinstance(vid_writer, cv2.VideoWriter):
vid_writer.release()  # release previous video writerfourcc='mp4v'# output video codecfps=vid_cap.get(cv2.CAP_PROP_FPS)
w=int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h=int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
vid_writer=cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
vid_writer.write(im0)
ifsave_txtorsave_img:
s=f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir/'labels'}"ifsave_txtelse''print(f"Results saved to {save_dir}{s}")
print(f'Done. ({time.time() -t0:.3f}s)')
if__name__=='__main__':
parser=argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='yolov5_crack_wall_epoach150_batchsize5.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='data/images', help='source')  # file/folder, 0 for webcamparser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.4, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='display results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--update', action='store_true', help='update all models')
parser.add_argument('--project', default='runs/detect', help='save results to project/name')
parser.add_argument('--name', default='exp', help='save results to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
opt=parser.parse_args()
print(opt)
check_requirements()
withtorch.no_grad():
ifopt.update:  # update all models (to fix SourceChangeWarning)foropt.weightsin ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
detect()
strip_optimizer(opt.weights)
else:
detect()