python+torch+yolov卷积神经网络训练识别人脸口罩

python+torch+yolov卷积神经网络训练识别人脸口罩

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运行结果如下：

主代码如下：

import argparse
import time
from pathlib import Pathimport cv2
import torch
import torch.backends.cudnn as cudnn
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, 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.names
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.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("----")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：

C++学习参考实例：

C++实现图形界面五子棋游戏源码：

https://blog.csdn.net/alicema1111/article/details/90035420

C++实现图形界面五子棋游戏源码2：

https://blog.csdn.net/alicema1111/article/details/106479579

C++ OpenCV相片视频人脸识别统计人数：

https://blog.csdn.net/alicema1111/article/details/105833928

VS2017+PCL开发环境配置：

https://blog.csdn.net/alicema1111/article/details/106877145

VS2017+Qt+PCL点云开发环境配置：

https://blog.csdn.net/alicema1111/article/details/105433636

C++ OpenCV汽车检测障碍物与测距：

https://blog.csdn.net/alicema1111/article/details/105833449

Windows VS2017安装配置PCL点云库：

https://blog.csdn.net/alicema1111/article/details/105111110

VS+VTK+Dicom(dcm)+CT影像切片窗体界面显示源码

https://blog.csdn.net/alicema1111/article/details/106994839

Python学习参考实例：

Python相片更换背景颜色qt窗体程序：

https://blog.csdn.net/alicema1111/article/details/106919140

OpenCV汽车识别检测数量统计：

https://blog.csdn.net/alicema1111/article/details/106597260

OpenCV视频识别检测人数跟踪统计：

https://blog.csdn.net/alicema1111/article/details/106113042

OpenCV米粒检测数量统计：

https://blog.csdn.net/alicema1111/article/details/106089697

opencv人脸识别与变形哈哈镜：

https://blog.csdn.net/alicema1111/article/details/105833123

OpenCV人脸检测打卡系统：

https://blog.csdn.net/alicema1111/article/details/105315066

Python+OpenCV摄像头人脸识别：

https://blog.csdn.net/alicema1111/article/details/105107286

Python+Opencv识别视频统计人数：

https://blog.csdn.net/alicema1111/article/details/103804032

Python+OpenCV图像人脸识别人数统计：

https://blog.csdn.net/alicema1111/article/details/105378639

python人脸头发身体部位识别人数统计：

https://blog.csdn.net/alicema1111/article/details/116424942

PHP网页框架：

PHP Laravel框架安装与配置后台管理前台页面显示：

https://blog.csdn.net/alicema1111/article/details/106686523