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出国劳务信息网,seo外链群发工具,做任务挣钱网站,网站宣传的方法程序示例精选 PythonYolov5Qt交通标志特征识别窗体界面相片视频摄像头如需安装运行环境或远程调试，见文章底部个人QQ名片，由专业技术人员远程协助！ 前言这篇博客针对《PythonYolov5Qt交通标志特征识别窗体界面相片视频摄像头》编写代码&a…

程序示例精选
Python+Yolov5+Qt交通标志特征识别窗体界面相片视频摄像头
如需安装运行环境或远程调试，见文章底部个人QQ名片，由专业技术人员远程协助！

前言

这篇博客针对《Python+Yolov5+Qt交通标志特征识别窗体界面相片视频摄像头》编写代码，代码整洁，规则，易读。学习与应用推荐首选。

运行结果

在这里插入图片描述

文章目录

一、所需工具软件
二、使用步骤
1. 主要代码
2. 运行结果
三、在线协助

一、所需工具软件

1. Python
2. Pycharm

二、使用步骤

代码如下（示例）：


def detect(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() or source.endswith('.txt') or source.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' if save_txt else save_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_sizeif half:model.half()  # to FP16# Second-stage classifierclassify = Falseif classify: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, Noneif webcam: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.names if hasattr(model, 'module') else model.namescolors = [[random.randint(0, 255) for _ in range(3)] for _ in names]# Run inferenceif device.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 Classifierif classify:pred = apply_classifier(pred, modelc, img, im0s)# Process detectionsfor i, det in enumerate(pred):  # detections per imageif webcam:  # 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) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txts += '%gx%g ' % img.shape[2:]  # print stringgn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwhif len(det):# Rescale boxes from img_size to im0 sizedet[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()# Print resultsfor c in det[:, -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, cls in reversed(det):if save_txt:  # Write to filexywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywhline = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label formatwith open(txt_path + '.txt', 'a') as f:f.write(('%g ' * len(line)).rstrip() % line + '\n')if save_img or view_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 resultsif view_img:cv2.imshow(str(p), im0)cv2.waitKey(1)  # 1 millisecond# Save results (image with detections)if save_img:if dataset.mode == 'image':cv2.imwrite(save_path, im0)else:  # 'video'if vid_path != save_path:  # new videovid_path = save_pathif isinstance(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)if save_txt or save_img:s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''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()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()

运行结果

在这里插入图片描述

三、在线协助：

如需安装运行环境或远程调试，见文章底部个人 QQ 名片，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）Visual Studio, Qt, C++, Python编程语言入门指导
3）界面美化
4）软件制作
5）云服务器申请
6）网站制作

当前文章连接：https://blog.csdn.net/alicema1111/article/details/132666851
个人博客主页：https://blog.csdn.net/alicema1111?type=blog
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