YOLOV5实时检测屏幕目录YOLOV5实时检测屏幕思考部分先把原本的detect.py的代码贴在这里分析代码并删减不用的部分把屏幕的截图通过OpenCV进行显示写一个屏幕截图的文件用OpenCV绘制窗口并显示最终代码注：此为笔记

目的：保留模型加载和推理部分，完成实时屏幕检测

(资料图片仅供参考)

实现思路：1. 写一个实时截取屏幕的函数2. 将截取的屏幕在窗口显示出来3. 用OpenCV绘制一个窗口用来显示截取的屏幕4. 在detect找出推理的代码，推理完成后得到中心点的xy坐标，宽高组成box5. 在创建的OpenCV窗口用得到的推理结果绘制方框

实现效果：

思考部分先把原本的detect.py的代码贴在这里

import argparseimport osimport platformimport sysfrom pathlib import Pathimport torchFILE = Path(__file__).resolve()ROOT = FILE.parents[0]  # YOLOv5 root directoryif str(ROOT) not in sys.path:    sys.path.append(str(ROOT))  # add ROOT to PATHROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relativefrom models.common import DetectMultiBackendfrom utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreamsfrom utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,                           increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)from utils.plots import Annotator, colors, save_one_boxfrom utils.torch_utils import select_device, smart_inference_mode@smart_inference_mode()def run(        weights=ROOT / "yolov5s.pt",  # model path or triton URL        source=ROOT / "data/video/",        data=ROOT / "data/coco128.yaml",  # dataset.yaml path        imgsz=(640, 640),  # inference size (height, width)        conf_thres=0.25,  # confidence threshold        iou_thres=0.45,  # NMS IOU threshold        max_det=1000,  # maximum detections per image        device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu        view_img=False,  # show results        save_txt=False,  # save results to *.txt        save_conf=False,  # save confidences in --save-txt labels        save_crop=False,  # save cropped prediction boxes        nosave=False,  # do not save images/videos        classes=None,  # filter by class: --class 0, or --class 0 2 3        agnostic_nms=False,  # class-agnostic NMS        augment=False,  # augmented inference        visualize=False,  # visualize features        update=False,  # update all models        project=ROOT / "runs/detect",  # save results to project/name        name="exp",  # save results to project/name        exist_ok=False,  # existing project/name ok, do not increment        line_thickness=3,  # bounding box thickness (pixels)        hide_labels=False,  # hide labels        hide_conf=False,  # hide confidences        half=False,  # use FP16 half-precision inference        dnn=False,  # use OpenCV DNN for ONNX inference        vid_stride=1,  # video frame-rate stride):    source = str(source)    save_img = not nosave and not source.endswith(".txt")  # save inference images    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)    is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))    webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)    screenshot = source.lower().startswith("screen")    if is_url and is_file:        source = check_file(source)  # download    # Directories    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run    (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir    # Load model    device = select_device(device)    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)    stride, names, pt = model.stride, model.names, model.pt    imgsz = check_img_size(imgsz, s=stride)  # check image size    # Dataloader    bs = 1  # batch_size    if webcam:        view_img = check_imshow(warn=True)        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)        bs = len(dataset)    elif screenshot:        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)    else:        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)    vid_path, vid_writer = [None] * bs, [None] * bs    # Run inference    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())    for path, im, im0s, vid_cap, s in dataset:        with dt[0]:            im = torch.from_numpy(im).to(model.device)            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32            im /= 255  # 0 - 255 to 0.0 - 1.0            if len(im.shape) == 3:                im = im[None]  # expand for batch dim        # Inference        with dt[1]:            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False            pred = model(im, augment=augment, visualize=visualize)        # NMS        with dt[2]:            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)        # Second-stage classifier (optional)        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)        # Process predictions        for i, det in enumerate(pred):  # per image            seen += 1            if webcam:  # batch_size >= 1                p, im0, frame = path[i], im0s[i].copy(), dataset.count                s += f"{i}: "            else:                p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)            p = Path(p)  # to Path            save_path = str(save_dir / p.name)  # im.jpg            txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}")  # im.txt            s += "%gx%g " % im.shape[2:]  # print string            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh            imc = im0.copy() if save_crop else im0  # for save_crop            annotator = Annotator(im0, line_width=line_thickness, example=str(names))            if len(det):                # Rescale boxes from img_size to im0 size                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()                # Print results                for c in det[:, 5].unique():                    n = (det[:, 5] == c).sum()  # detections per class                    s += f"{n} {names[int(c)]}{"s" * (n > 1)}, "  # add to string                # Write results                for *xyxy, conf, cls in reversed(det):                    if save_txt:  # Write to file                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format                        with open(f"{txt_path}.txt", "a") as f:                            f.write(("%g " * len(line)).rstrip() % line + "\n")                    if save_img or save_crop or view_img:  # Add bbox to image                        c = int(cls)  # integer class                        label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}")                        annotator.box_label(xyxy, label, color=colors(c, True))                    if save_crop:                        save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True)            # Stream results            im0 = annotator.result()            if view_img:                if platform.system() == "Linux" and p not in windows:                    windows.append(p)                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])                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" or "stream"                    if vid_path[i] != save_path:  # new video                        vid_path[i] = save_path                        if isinstance(vid_writer[i], cv2.VideoWriter):                            vid_writer[i].release()  # release previous video writer                        if vid_cap:  # video                            fps = 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))                        else:  # stream                            fps, w, h = 30, im0.shape[1], im0.shape[0]                        save_path = str(Path(save_path).with_suffix(".mp4"))  # force *.mp4 suffix on results videos                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))                    vid_writer[i].write(im0)        # Print time (inference-only)        LOGGER.info(f"{s}{"" if len(det) else "(no detections), "}{dt[1].dt * 1E3:.1f}ms")    # Print results    t = tuple(x.t / seen * 1E3 for x in dt)  # speeds per image    LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t)    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 ""        LOGGER.info(f"Results saved to {colorstr("bold", save_dir)}{s}")    if update:        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)def parse_opt():    parser = argparse.ArgumentParser()    parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path or triton URL")    parser.add_argument("--source", type=str, default=ROOT / "0", help="file/dir/URL/glob/screen/1(webcam)")    parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path")    parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w")    parser.add_argument("--conf-thres", type=float, default=0.45, help="confidence threshold")    parser.add_argument("--iou-thres", type=float, default=0.2, help="NMS IoU threshold")    parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image")    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="show 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("--save-crop", action="store_true", help="save cropped prediction boxes")    parser.add_argument("--nosave", action="store_true", help="do not save images/videos")    parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --classes 0, or --classes 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("--visualize", action="store_true", help="visualize features")    parser.add_argument("--update", action="store_true", help="update all models")    parser.add_argument("--project", default=ROOT / "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")    parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)")    parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels")    parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences")    parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")    parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")    parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride")    opt = parser.parse_args()    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand    print_args(vars(opt))    return optdef main(opt):    check_requirements(exclude=("tensorboard", "thop"))    run(**vars(opt))if __name__ == "__main__":    opt = parse_opt()    main(opt)

分析代码并删减不用的部分

import argparseimport osimport platformimport sysfrom pathlib import Pathimport torchFILE = Path(__file__).resolve()ROOT = FILE.parents[0]  # YOLOv5 root directoryif str(ROOT) not in sys.path:    sys.path.append(str(ROOT))  # add ROOT to PATHROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relativefrom models.common import DetectMultiBackendfrom utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreamsfrom utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,                           increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)from utils.plots import Annotator, colors, save_one_boxfrom utils.torch_utils import select_device, smart_inference_mode

做了一些包的导入，定义了一些全局变量，先保留下来，没用的最后删

向下

if __name__ == "__main__":    opt = parse_opt()    main(opt)

从if __name__ == "__main__开始opt = parse_opt就是一个获取命令行参数的函数，我们并不需要,可以删

进入main函数

def main(opt):    check_requirements(exclude=("tensorboard", "thop"))    run(**vars(opt))

check_requirements函数检查requirements是否全都安装好了，无用，删了

进入run函数

source = str(source)    save_img = not nosave and not source.endswith(".txt")  # save inference images    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)    is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))    webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)    screenshot = source.lower().startswith("screen")    if is_url and is_file:        source = check_file(source)  # download    # Directories    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run    (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

判断source的类型，即要要推理的源是什么，判断源是文件还是url还是webcam或者screenshot ，定义保存文件夹，我不需要保存，只需要实时检测屏幕，删除

继续向下，是加载模型的代码

# Load modeldevice = select_device(device)model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)

得知加载模型需要几个参数，分别是weights, device=device, dnn=dnn, data=data, fp16=half通过开始的形参可知：

weights=ROOT / "yolov5s.pt"也就是模型的名称device通过select_device函数得到dnn和fp16在run函数里的参数都是FALSE

故加载模型的代码可以改写成

def LoadModule():    device = select_device("")    weights = "yolov5s.pt"    model = DetectMultiBackend(weights, device=device, dnn=False, fp16=False)    return model

继续往下读

bs = 1  # batch_size    if webcam:        view_img = check_imshow(warn=True)        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)        bs = len(dataset)    elif screenshot:        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)    else:        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)    vid_path, vid_writer = [None] * bs, [None] * bs

这里如果是使用网络摄像头作为输入，会通过LoadStreams类加载视频流，根据图像大小和步长采样，如果使用截图作为输入，则通过LoadScreenshots加载截图，都不是则通过LoadImages类加载图片文件这是YOLOV5提供的加载dataset的部分，我们可以添加自己的dataset，所以删掉

继续往下

# Run inference    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())    for path, im, im0s, vid_cap, s in dataset:        with dt[0]:            im = torch.from_numpy(im).to(model.device)            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32            im /= 255  # 0 - 255 to 0.0 - 1.0            if len(im.shape) == 3:                im = im[None]  # expand for batch dim        # Inference        with dt[1]:            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False            pred = model(im, augment=augment, visualize=visualize)        # NMS        with dt[2]:            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)        # Second-stage classifier (optional)        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)        # Process predictions

model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))用于模型预热，传入形状为(1, 3, *imgsz)的图像进行预热操作，没用删了

seen, windows, dt = 0, [], (Profile(), Profile(), Profile())未知作用，删了

for path, im, im0s, vid_cap, s in dataset:        with dt[0]:            im = torch.from_numpy(im).to(model.device)            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32            im /= 255  # 0 - 255 to 0.0 - 1.0            if len(im.shape) == 3:                im = im[None]  # expand for batch dim        # Inference        with dt[1]:            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False            pred = model(im, augment=augment, visualize=visualize)        # NMS        with dt[2]:            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)

上面这段for循环用于遍历数据集中的每个图像或视频帧进行推理，在循环的开头，将路径、图像、原始图像、视频捕获对象和步长传递给path, im, im0s, vid_cap, s。推理实时屏幕只需要传一张图片，所以不存在将遍历推理，所以要进行改写，改写成

im = torch.from_numpy(im).to(model.device)im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32im /= 255  # 0 - 255 to 0.0 - 1.0if len(im.shape) == 3:    im = im[None]  # expand for batch dimvisualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else Falsepred = model(im, augment=augment, visualize=visualize)pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)

这里是对 im 进行转换和推理，而改写的代码中没有im变量，则寻找im的来源for path, im, im0s, vid_cap, s in dataset:im来源于dataset

dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)dataset来源于LoadImages的返回值

查看LoadImages的函数返回值和返回值的来源

在dataloaders.py中可以看到

if self.transforms:    im = self.transforms(im0)  # transformselse:    im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0]  # padded resize    im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB    im = np.ascontiguousarray(im)  # contiguousreturn path, im, im0, self.cap, s

如果transforms存在，则转换，如果transforms不存在，则调用letterbox函数对图像im0进行缩放和填充，使其符合模型要求的图像大小，将图像的通道顺序由HWC转换为CHW，将图像的通道顺序由BGR转换为RGB，将图像转换为连续的内存布局

其中需要的参数是im0, self.img_size, stride=self.stride, auto=self.autoim0则是未经处理的图片，img_size填640（因为模型的图片大小训练的是640），stride填64（默认参数为64），auto填True则得到改写代码为

im = letterbox(img0, 640, stride=32, auto=True)[0]  # padded resize    im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB    im = np.ascontiguousarray(im)  # contiguous    im = torch.from_numpy(im).to(model.device)    im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32    im /= 255  # 0 - 255 to 0.0 - 1.0    if len(im.shape) == 3:        im = im[None]  # expand for batch dim    pred = model(im, augment=False, visualize=False)    pred = non_max_suppression(pred, conf_thres=conf_thres, iou_thres=iou_thres, classes=None, agnostic=False,                               max_det=1000)

继续向下

for i, det in enumerate(pred):  # per image            seen += 1            if webcam:  # batch_size >= 1                p, im0, frame = path[i], im0s[i].copy(), dataset.count                s += f"{i}: "            else:                p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)            p = Path(p)  # to Path            save_path = str(save_dir / p.name)  # im.jpg            txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}")  # im.txt            s += "%gx%g " % im.shape[2:]  # print string            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh            imc = im0.copy() if save_crop else im0  # for save_crop            annotator = Annotator(im0, line_width=line_thickness, example=str(names))            if len(det):                # Rescale boxes from img_size to im0 size                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()                # Print results                for c in det[:, 5].unique():                    n = (det[:, 5] == c).sum()  # detections per class                    s += f"{n} {names[int(c)]}{"s" * (n > 1)}, "  # add to string                # Write results                for *xyxy, conf, cls in reversed(det):                    if save_txt:  # Write to file                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format                        with open(f"{txt_path}.txt", "a") as f:                            f.write(("%g " * len(line)).rstrip() % line + "\n")                    if save_img or save_crop or view_img:  # Add bbox to image                        c = int(cls)  # integer class                        label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}")                        annotator.box_label(xyxy, label, color=colors(c, True))                    if save_crop:                        save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True)

这段代码将推理后的结果进行转换，转换为label format，成为人能看懂的格式，删去输出结果，留下写入结果中的，格式转换，删掉保存为txt文件，得到需要的box，然后自己写一个boxs=[]，将结果append进去，方便在OpenCV中绘画识别方框，改写结果为

boxs=[]    for i, det in enumerate(pred):  # per image        im0 = img0.copy()        s = " "        s += "%gx%g " % im.shape[2:]  # print string        gn = torch.tensor(img0.shape)[[1, 0, 1, 0]]  # normalization gain whwh        imc = img0  # for save_crop        if len(det):            # Rescale boxes from img_size to im0 size            det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()            # Print results            for c in det[:, 5].unique():                n = (det[:, 5] == c).sum()  # detections per class                s += f"{n} {names[int(c)]}{"s" * (n > 1)}, "  # add to string        # Write results        for *xyxy, conf, cls in reversed(det):            xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh            line = (cls, *xywh)  # label format            box = ("%g " * len(line)).rstrip() % line            box = box.split(" ")            boxs.append(box)

就此完成了推理部分的删减和重写

把屏幕的截图通过OpenCV进行显示写一个屏幕截图的文件

写成 grabscreen.py

# 文件名:grabscreen.pyimport cv2import numpy as npimport win32guiimport win32printimport win32uiimport win32conimport win32apiimport mssdef grab_screen_win32(region):    hwin = win32gui.GetDesktopWindow()    left, top, x2, y2 = region    width = x2 - left + 1    height = y2 - top + 1    hwindc = win32gui.GetWindowDC(hwin)    srcdc = win32ui.CreateDCFromHandle(hwindc)    memdc = srcdc.CreateCompatibleDC()    bmp = win32ui.CreateBitmap()    bmp.CreateCompatibleBitmap(srcdc, width, height)    memdc.SelectObject(bmp)    memdc.BitBlt((0, 0), (width, height), srcdc, (left, top), win32con.SRCCOPY)    signedIntsArray = bmp.GetBitmapBits(True)    img = np.fromstring(signedIntsArray, dtype="uint8")    img.shape = (height, width, 4)    srcdc.DeleteDC()    memdc.DeleteDC()    win32gui.ReleaseDC(hwin, hwindc)    win32gui.DeleteObject(bmp.GetHandle())    return cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)

通过img0 = grab_screen_win32(region=(0, 0, 1920, 1080))来作为im的参数传入，即可让屏幕截图作为推理图片

用OpenCV绘制窗口并显示

if len(boxs):    for i, det in enumerate(boxs):        _, x_center, y_center, width, height = det        x_center, width = re_x * float(x_center), re_x * float(width)        y_center, height = re_y * float(y_center), re_y * float(height)        top_left = (int(x_center - width / 2.), int(y_center - height / 2.))        bottom_right = (int(x_center + width / 2.), int(y_center + height / 2.))        color = (0, 0, 255)  # RGB        cv2.rectangle(img0, top_left, bottom_right, color, thickness=thickness)和cv2.namedWindow("windows", cv2.WINDOW_NORMAL)cv2.resizeWindow("windows", re_x // 2, re_y // 2)cv2.imshow("windows", img0)HWND = win32gui.FindWindow(None, "windows")win32gui.SetWindowPos(HWND, win32con.HWND_TOPMOST, 0, 0, 0, 0, win32con.SWP_NOMOVE | win32con.SWP_NOSIZE)

结合在一起

最终代码

import torch, pynputimport numpy as npimport win32gui, win32con, cv2from grabscreen import grab_screen_win32 # 本地文件from utils.augmentations import letterboxfrom models.common import DetectMultiBackendfrom utils.torch_utils import select_devicefrom utils.general import non_max_suppression, scale_boxes, xyxy2xywh# 可调参数conf_thres = 0.25iou_thres = 0.05thickness = 2x, y = (1920, 1080)re_x, re_y = (1920, 1080)def LoadModule():    device = select_device("")    weights = "yolov5s.pt"    model = DetectMultiBackend(weights, device=device, dnn=False, fp16=False)    return modelmodel = LoadModule()while True:    names = model.names    img0 = grab_screen_win32(region=(0, 0, 1920, 1080))    im = letterbox(img0, 640, stride=32, auto=True)[0]  # padded resize    im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB    im = np.ascontiguousarray(im)  # contiguous    im = torch.from_numpy(im).to(model.device)    im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32    im /= 255  # 0 - 255 to 0.0 - 1.0    if len(im.shape) == 3:        im = im[None]  # expand for batch dim    pred = model(im, augment=False, visualize=False)    pred = non_max_suppression(pred, conf_thres=conf_thres, iou_thres=iou_thres, classes=None, agnostic=False,                               max_det=1000)    boxs=[]    for i, det in enumerate(pred):  # per image        im0 = img0.copy()        s = " "        s += "%gx%g " % im.shape[2:]  # print string        gn = torch.tensor(img0.shape)[[1, 0, 1, 0]]  # normalization gain whwh        imc = img0  # for save_crop        if len(det):            # Rescale boxes from img_size to im0 size            det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()            # Print results            for c in det[:, 5].unique():                n = (det[:, 5] == c).sum()  # detections per class                s += f"{n} {names[int(c)]}{"s" * (n > 1)}, "  # add to string        # Write results        for *xyxy, conf, cls in reversed(det):            xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh            line = (cls, *xywh)  # label format            box = ("%g " * len(line)).rstrip() % line            box = box.split(" ")            boxs.append(box)        if len(boxs):            for i, det in enumerate(boxs):                _, x_center, y_center, width, height = det                x_center, width = re_x * float(x_center), re_x * float(width)                y_center, height = re_y * float(y_center), re_y * float(height)                top_left = (int(x_center - width / 2.), int(y_center - height / 2.))                bottom_right = (int(x_center + width / 2.), int(y_center + height / 2.))                color = (0, 0, 255)  # RGB                cv2.rectangle(img0, top_left, bottom_right, color, thickness=thickness)    if cv2.waitKey(1) & 0xFF == ord("q"):        cv2.destroyWindow()        break    cv2.namedWindow("windows", cv2.WINDOW_NORMAL)    cv2.resizeWindow("windows", re_x // 2, re_y // 2)    cv2.imshow("windows", img0)    HWND = win32gui.FindWindow(None, "windows")    win32gui.SetWindowPos(HWND, win32con.HWND_TOPMOST, 0, 0, 0, 0, win32con.SWP_NOMOVE | win32con.SWP_NOSIZE)

End.