程序示例精选
Python+Yolov8入口人流量统计
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前言
Yolov5比较Yolov4,Yolov3等其他识别框架，速度快，代码结构简单，识别效率高，对硬件要求比较低。这篇博客针对<<Python Yolov5火焰烟雾识别>>编写代码，代码整洁，规则，易读。 学习与应用推荐首选。
文章目录
一、所需工具软件
二、使用步骤
1. 引入库
2. 识别图像特征
3. 识别参数定义
4. 运行结果
三、在线协助
一、所需工具软件
1. Python3.6以上
2. Pycharm代码编辑器
3. Torch, OpenCV库
二、使用步骤
1.引入库
代码如下（示例）：
importcv2importtorchfromnumpyimportrandomfrommodels.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
2.识别图像特征
代码如下（示例）：
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() forpath, img, im0s, vid_capindataset: img=torch.from_numpy(img).to(device) img=img.half() ifhalfelseimg.float() # uint8 to fp16/32img/=255.0# 0 - 255 to 0.0 - 1.0ifimg.ndimension() ==3: img=img.unsqueeze(0) # Inferencet1=time_synchronized() pred=model(img, augment=opt.augment)[0] # 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() # 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)') # 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)') 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()
该处使用的url网络请求的数据。
3.识别参数定义：
代码如下（示例）：
if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default='yolov5_best_road_crack_recog.pt', help='model.pt path(s)') parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') 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('--classes', nargs='+', type=int, default='0', 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() with torch.no_grad(): if opt.update: # update all models (to fix SourceChangeWarning) for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']: detect() strip_optimizer(opt.weights) else: detect()
4.运行结果如下：
编辑
编辑
三、在线协助：
如需安装运行环境或远程调试， 可点击右边 博主头像 或 昵称 ， 进入个人主页查看博主联系方式 ，由专业技术人员远程协助！ 1）远程安装运行环境，代码调试 2）Qt, C++, Python入门指导 3）界面美化 4）软件制作
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