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长春作网站建设的公司,怎么做建设网站,网站开发建设合同范本,网络营销导向型企业网站建设的原则PythonYolov5道路障碍物识别如需安装运行环境或远程调试#xff0c;见文章底部个人QQ名片#xff0c;由专业技术人员远程协助#xff01;前言这篇博客针对PythonYolov5道路障碍物识别编写代码#xff0c;代码整洁#xff0c;规则#xff0c;易读。 学习与…PythonYolov5道路障碍物识别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PythonYolov5道路障碍物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. 参数设置4. 运行结果三、在线协助一、所需工具软件1. Pycharm, Python2. Qt, OpenCV二、使用步骤1.引入库代码如下示例import cv2 import torch from numpy import randomfrom models.experimental import attempt_load from utils.datasets import LoadStreams, LoadImages from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path from utils.plots import plot_one_box from utils.torch_utils import select_device, load_classifier, time_synchronized2.识别图像特征代码如下示例defdetect(save_imgFalse):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_okopt.exist_ok)) # increment run(save_dir / labelsif save_txt else save_dir).mkdir(parentsTrue, exist_okTrue) # 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_locationdevice) # load FP32 modelstride int(model.stride.max()) # model strideimgsz check_img_size(imgsz, sstride) # check img_sizeif half:model.half() # to FP16# Second-stage classifierclassify Falseif classify:modelc load_classifier(nameresnet101, n2) # initializemodelc.load_state_dict(torch.load(weights/resnet101.pt, map_locationdevice)[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_sizeimgsz, stridestride)else:save_img Truedataset LoadImages(source, img_sizeimgsz, stridestride)# Get names and colorsnames model.module.names ifhasattr(model, module) else model.namescolors [[random.randint(0, 255) for _ inrange(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()for path, img, im0s, vid_cap in dataset:img torch.from_numpy(img).to(device)img img.half() if half else img.float() # uint8 to fp16/32img / 255.0# 0 - 255 to 0.0 - 1.0if img.ndimension() 3:img img.unsqueeze(0)# Inferencet1 time_synchronized()pred model(img, augmentopt.augment)[0]# Apply NMSpred non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classesopt.classes, agnosticopt.agnostic_nms)t2 time_synchronized()# Apply Classifierif classify:pred apply_classifier(pred, modelc, img, im0s)# Process detectionsfor i, det inenumerate(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(savedir / labels / p.stem) (if dataset.mode imageelsef{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, cls inreversed(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 formatwithopen(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, labellabel, colorcolors[int(cls)], line_thickness3)# Print time (inference NMS)print(f{s}Done. ({t2 - t1:.3f}s))# Save results (image with detections)if save_img:if dataset.mode image:cv2.imwrite(save_path, im0)else: # videoif vid_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)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 elseprint(fResults saved to {save_dir}{s})print(fDone. ({time.time() - t0:.3f}s))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()3.参数定义代码如下示例if name main:parser argparse.ArgumentParser()parser.add_argument(–weights, nargs, typestr, defaultyolov5_best_road_crack_recog.pt, helpmodel.pt path(s))parser.add_argument(–img-size, typeint, default640, helpinference size (pixels))parser.add_argument(–conf-thres, typefloat, default0.25, helpobject confidence threshold)parser.add_argument(–iou-thres, typefloat, default0.45, helpIOU threshold for NMS)parser.add_argument(–view-img, actionstore_true, helpdisplay results)parser.add_argument(–save-txt, actionstore_true, helpsave results to *.txt)parser.add_argument(–classes, nargs, typeint, default0, helpfilter by class: –class 0, or –class 0 2 3)parser.add_argument(–agnostic-nms, actionstore_true, helpclass-agnostic NMS)parser.add_argument(–augment, actionstore_true, helpaugmented inference)parser.add_argument(–update, actionstore_true, helpupdate all models)parser.add_argument(–project, defaultruns/detect, helpsave results to project/name)parser.add_argument(–name, defaultexp, helpsave results to project/name)parser.add_argument(–exist-ok, actionstore_true, helpexisting 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()运行结果如下 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2Qt, C, Python入门指导3界面美化4软件制作博主推荐文章https://blog.csdn.net/alicema1111/article/details/123851014博主推荐文章https://blog.csdn.net/alicema1111/article/details/128420453个人博客主页https://blog.csdn.net/alicema1111?typeblog博主所有文章点这里https://blog.csdn.net/alicema1111?typeblog