YOLOv5如何进行区域目标检测（手把手教学）

YOLOv5如何进行区域目标检测（手把手教学）

提示：本项目的源码是基于yolov5 6.0版本修改

文章目录

• YOLOv5如何进行区域目标检测（手把手教学）
• 效果展示
• 一、确定检测范围
• 二、detect.py代码修改
• • 1.确定区域检测范围
  • 2.画检测区域线（若不想像效果图一样显示出检测区域可不添加）
• 总结
• 整体detect.py修改代码

效果展示

在使用YOLOv5的有些时候，我们会遇到一些具体的目标检测要求，比如要求不检测全图，只在规定的区域内才检测。所以为了满足这个需求，可以用一个mask覆盖掉不想检测的区域，使得YOLOv5在检测的时候，该覆盖区域就不纳入检测范围。

话不多说，直接上检测效果，可以很直观的看到目标在进入规定的检测区域才得到检测。

一、确定检测范围

快捷查询方法：

1. 用windows自带画图打开图片
2. 鼠标移到想要框选检测区域的四个顶点，查询点的像素坐标
3. 分别计算点的像素坐标与图片总像素坐标的比例（后面要用）
   查询方法如下图所示：
   
   提示：以下是计算的举例说明，仅供参考
   例如：图中所标注的点（1010，174）代表（x，y）坐标
   hl1 = 174 / 768 （可约分）监测区域纵坐标距离图片***顶部*** 比例
   wl1 = 1010 / 1614 （可约分）监测区域横坐标距离图片***左部*** 比例
   这里只举例了一个点，如检测范围是四边形，需要计算左上，右上，右下，左下四个顶点。

二、detect.py代码修改

1.确定区域检测范围

在下面代码位置填上计算好的比例：

 # mask for certain region        #1,2,3,4 分别对应左上，右上，右下，左下四个点        hl1 = 1.4 / 10 #监测区域高度距离图片顶部比例        wl1 = 6.4 / 10 #监测区域高度距离图片左部比例        hl2 = 1.4 / 10  # 监测区域高度距离图片顶部比例        wl2 = 6.8 / 10  # 监测区域高度距离图片左部比例        hl3 = 4.5 / 10  # 监测区域高度距离图片顶部比例        wl3 = 7.6 / 10  # 监测区域高度距离图片左部比例        hl4 = 4.5 / 10  # 监测区域高度距离图片顶部比例        wl4 = 5.5 / 10  # 监测区域高度距离图片左部比例

在135行：for path, img, im0s, vid_cap in dataset: 下插入代码：

        # mask for certain region        #1,2,3,4 分别对应左上，右上，右下，左下四个点        hl1 = 1.6 / 10 #监测区域高度距离图片顶部比例        wl1 = 6.4 / 10 #监测区域高度距离图片左部比例        hl2 = 1.6 / 10  # 监测区域高度距离图片顶部比例        wl2 = 6.8 / 10  # 监测区域高度距离图片左部比例        hl3 = 4.5 / 10  # 监测区域高度距离图片顶部比例        wl3 = 7.6 / 10  # 监测区域高度距离图片左部比例        hl4 = 4.5 / 10  # 监测区域高度距离图片顶部比例        wl4 = 5.5 / 10  # 监测区域高度距离图片左部比例        if WEBcam:            for b in range(0,img.shape[0]):                mask = np.zeros([img[b].shape[1], img[b].shape[2]], dtype=np.uint8)                #mask[round(img[b].shape[1] * hl1):img[b].shape[1], round(img[b].shape[2] * wl1):img[b].shape[2]] = 255                pts = np.array([[int(img[b].shape[2] * wl1), int(img[b].shape[1] * hl1)],  # pts1    [int(img[b].shape[2] * wl2), int(img[b].shape[1] * hl2)],  # pts2    [int(img[b].shape[2] * wl3), int(img[b].shape[1] * hl3)],  # pts3    [int(img[b].shape[2] * wl4), int(img[b].shape[1] * hl4)]], np.int32)                mask = cv2.fillPoly(mask,[pts],(255,255,255))                imGC = img[b].transpose((1, 2, 0))                imgc = cv2.add(imgc, np.zeros(np.shape(imgc), dtype=np.uint8), mask=mask)                #cv2.imshow('1',imgc)                img[b] = imgc.transpose((2, 0, 1))        else:            mask = np.zeros([img.shape[1], img.shape[2]], dtype=np.uint8)            #mask[round(img.shape[1] * hl1):img.shape[1], round(img.shape[2] * wl1):img.shape[2]] = 255            pts = np.array([[int(img.shape[2] * wl1), int(img.shape[1] * hl1)],  # pts1[int(img.shape[2] * wl2), int(img.shape[1] * hl2)],  # pts2[int(img.shape[2] * wl3), int(img.shape[1] * hl3)],  # pts3[int(img.shape[2] * wl4), int(img.shape[1] * hl4)]], np.int32)            mask = cv2.fillPoly(mask, [pts], (255,255,255))            img = img.transpose((1, 2, 0))            img = cv2.add(img, np.zeros(np.shape(img), dtype=np.uint8), mask=mask)            img = img.transpose((2, 0, 1))

2.画检测区域线（若不想像效果图一样显示出检测区域可不添加）

在196行： p, s, im0, frame = path, ‘’, im0s.copy(), getattr(dataset, ‘frame’, 0) 后添加

            if webcam:  # batch_size >= 1                p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count                cv2.putText(im0, "Detection_Region", (int(im0.shape[1] * wl1 - 5), int(im0.shape[0] * hl1 - 5)),cv2.FONT_HERSHEY_SIMPLEX,1.0, (255, 255, 0), 2, cv2.LINE_AA)                pts = np.array([[int(im0.shape[1] * wl1), int(im0.shape[0] * hl1)],  # pts1    [int(im0.shape[1] * wl2), int(im0.shape[0] * hl2)],  # pts2    [int(im0.shape[1] * wl3), int(im0.shape[0] * hl3)],  # pts3    [int(im0.shape[1] * wl4), int(im0.shape[0] * hl4)]], np.int32)  # pts4                # pts = pts.reshape((-1, 1, 2))                zeros = np.zeros((im0.shape), dtype=np.uint8)                mask = cv2.fillPoly(zeros, [pts], color=(0, 165, 255))                im0 = cv2.addWeighted(im0, 1, mask, 0.2, 0)                cv2.polylines(im0, [pts], True, (255, 255, 0), 3)                # plot_one_box(dr, im0, label='Detection_Region', color=(0, 255, 0), line_thickness=2)            else:                p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)                cv2.putText(im0, "Detection_Region", (int(im0.shape[1] * wl1 - 5), int(im0.shape[0] * hl1 - 5)),cv2.FONT_HERSHEY_SIMPLEX,1.0, (255, 255, 0), 2, cv2.LINE_AA)                pts = np.array([[int(im0.shape[1] * wl1), int(im0.shape[0] * hl1)],  # pts1    [int(im0.shape[1] * wl2), int(im0.shape[0] * hl2)],  # pts2    [int(im0.shape[1] * wl3), int(im0.shape[0] * hl3)],  # pts3    [int(im0.shape[1] * wl4), int(im0.shape[0] * hl4)]], np.int32)  # pts4                # pts = pts.reshape((-1, 1, 2))                zeros = np.zeros((im0.shape), dtype=np.uint8)                mask = cv2.fillPoly(zeros, [pts], color=(0, 165, 255))                im0 = cv2.addWeighted(im0, 1, mask, 0.2, 0)                cv2.polylines(im0, [pts], True, (255, 255, 0), 3)

总结

基于yolov5的区域目标检测实质上就是在图片选定检测区域做一个遮掩mask，检测区域不一定为四边形，也可是其他形状。该方法可检测图片/视频/摄像头。
提示：使用该方法要先确定数据集图像是否像监控图像一样，画面主体保持不变
原理展现如图所示：（图片参考http://t.csdn.cn/lgyO1）

整体detect.py修改代码

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license"""Run inference on images, videos, directories, streams, etc.Usage:    $ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640"""import argparseimport osimport sysfrom pathlib import Pathimport cv2import numpy as npimport torchimport torch.backends.cudnn as cudnnFILE = 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.experimental import attempt_loadfrom utils.datasets import LoadImages, LoadStreamsfrom utils.general import apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, \    increment_path, non_max_suppression, print_args, save_one_box, scale_coords, set_logging, \    strip_optimizer, xyxy2xywhfrom utils.plots import Annotator, colorsfrom utils.torch_utils import load_classifier, select_device, time_sync@torch.no_grad()def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)        source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam        imgsz=640,  # inference size (pixels)        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        ):    source = str(source)    save_img = not nosave and not source.endswith('.txt')  # save inference images    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(        ('rtsp://', 'rtmp://', 'Http://', 'https://'))    # 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    # Initialize    set_logging()    device = select_device(device)    half &= device.type != 'cpu'  # half precision only supported on CUDA    # Load model    w = str(weights[0] if isinstance(weights, list) else weights)    classify, suffix, suffixes = False, Path(w).suffix.lower(), ['.pt', '.onnx', '.tflite', '.pb', '']    check_suffix(w, suffixes)  # check weights have acceptable suffix    pt, onnx, tflite, pb, saved_model = (suffix == x for x in suffixes)  # backend booleans    stride, names = 64, [f'class{i}' for i in range(1000)]  # assign defaults    if pt:        model = torch.jit.load(w) if 'torchscript' in w else attempt_load(weights, map_location=device)        stride = int(model.stride.max())  # model stride        names = model.module.names if hasattr(model, 'module') else model.names  # get class names        if half:            model.half()  # to FP16        if classify:  # second-stage classifier            modelc = load_classifier(name='resnet50', n=2)  # initialize            modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()    elif onnx:        if dnn:            # check_requirements(('opencv-python>=4.5.4',))            net = cv2.dnn.readNetFromONNX(w)        else:            check_requirements(('onnx', 'onnxruntime'))            import onnxruntime            session = onnxruntime.InferenceSession(w, None)    else:  # Tensorflow models        check_requirements(('tensorflow>=2.4.1',))        import tensorflow as tf        if pb:  # https://www.tensorflow.org/guide/migrate#a_graPHPb_or_graphpbtxt            def wrap_frozen_graph(gd, inputs, outputs):                x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), [])  # wrapped import                return x.prune(tf.nest.map_structure(x.graph.as_graph_element, inputs),   tf.nest.map_structure(x.graph.as_graph_element, outputs))            graph_def = tf.Graph().as_graph_def()            graph_def.ParseFromString(open(w, 'rb').read())            frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0")        elif saved_model:            model = tf.keras.models.load_model(w)        elif tflite:            interpreter = tf.lite.Interpreter(model_path=w)  # load TFLite model            interpreter.allocate_tensors()  # allocate            input_details = interpreter.get_input_details()  # inputs            output_details = interpreter.get_output_details()  # outputs            int8 = input_details[0]['dtype'] == np.uint8  # is TFLite quantized uint8 model    imgsz = check_img_size(imgsz, s=stride)  # check image size    # Dataloader    if webcam:        view_img = check_imshow()        cudnn.benchmark = True  # set True to speed up constant image size inference        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)        bs = len(dataset)  # batch_size    else:        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)        bs = 1  # batch_size    vid_path, vid_writer = [None] * bs, [None] * bs    # Run inference    if pt and device.type != 'cpu':        model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.parameters())))  # run once    dt, seen = [0.0, 0.0, 0.0], 0    for path, img, im0s, vid_cap in dataset:        # mask for certain region        #1,2,3,4 分别对应左上，右上，右下，左下四个点        hl1 = 1.6 / 10 #监测区域高度距离图片顶部比例        wl1 = 6.4 / 10 #监测区域高度距离图片左部比例        hl2 = 1.6 / 10  # 监测区域高度距离图片顶部比例        wl2 = 6.8 / 10  # 监测区域高度距离图片左部比例        hl3 = 4.5 / 10  # 监测区域高度距离图片顶部比例        wl3 = 7.6 / 10  # 监测区域高度距离图片左部比例        hl4 = 4.5 / 10  # 监测区域高度距离图片顶部比例        wl4 = 5.5 / 10  # 监测区域高度距离图片左部比例        if webcam:            for b in range(0,img.shape[0]):                mask = np.zeros([img[b].shape[1], img[b].shape[2]], dtype=np.uint8)                #mask[round(img[b].shape[1] * hl1):img[b].shape[1], round(img[b].shape[2] * wl1):img[b].shape[2]] = 255                pts = np.array([[int(img[b].shape[2] * wl1), int(img[b].shape[1] * hl1)],  # pts1    [int(img[b].shape[2] * wl2), int(img[b].shape[1] * hl2)],  # pts2    [int(img[b].shape[2] * wl3), int(img[b].shape[1] * hl3)],  # pts3    [int(img[b].shape[2] * wl4), int(img[b].shape[1] * hl4)]], np.int32)                mask = cv2.fillPoly(mask,[pts],(255,255,255))                imgc = img[b].transpose((1, 2, 0))                imgc = cv2.add(imgc, np.zeros(np.shape(imgc), dtype=np.uint8), mask=mask)                #cv2.imshow('1',imgc)                img[b] = imgc.transpose((2, 0, 1))        else:            mask = np.zeros([img.shape[1], img.shape[2]], dtype=np.uint8)            #mask[round(img.shape[1] * hl1):img.shape[1], round(img.shape[2] * wl1):img.shape[2]] = 255            pts = np.array([[int(img.shape[2] * wl1), int(img.shape[1] * hl1)],  # pts1[int(img.shape[2] * wl2), int(img.shape[1] * hl2)],  # pts2[int(img.shape[2] * wl3), int(img.shape[1] * hl3)],  # pts3[int(img.shape[2] * wl4), int(img.shape[1] * hl4)]], np.int32)            mask = cv2.fillPoly(mask, [pts], (255,255,255))            img = img.transpose((1, 2, 0))            img = cv2.add(img, np.zeros(np.shape(img), dtype=np.uint8), mask=mask)            img = img.transpose((2, 0, 1))        t1 = time_sync()        if onnx:            img = img.astype('float32')        else:            img = torch.from_numpy(img).to(device)            img = img.half() if half else img.float()  # uint8 to fp16/32        img = img / 255.0  # 0 - 255 to 0.0 - 1.0        if len(img.shape) == 3:            img = img[None]  # expand for batch dim        t2 = time_sync()        dt[0] += t2 - t1        # Inference        if pt:            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False            pred = model(img, augment=augment, visualize=visualize)[0]        elif onnx:            if dnn:                net.setInput(img)                pred = torch.tensor(net.forward())            else:                pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))        else:  # tensorflow model (tflite, pb, saved_model)            imn = img.permute(0, 2, 3, 1).cpu().numpy()  # image in numpy            if pb:                pred = frozen_func(x=tf.constant(imn)).numpy()            elif saved_model:                pred = model(imn, training=False).numpy()            elif tflite:                if int8:                    scale, zero_point = input_details[0]['quantization']                    imn = (imn / scale + zero_point).astype(np.uint8)  # de-scale                interpreter.set_tensor(input_details[0]['index'], imn)                interpreter.invoke()                pred = interpreter.get_tensor(output_details[0]['index'])                if int8:                    scale, zero_point = output_details[0]['quantization']                    pred = (pred.astype(np.float32) - zero_point) * scale  # re-scale            pred[..., 0] *= imgsz[1]  # x            pred[..., 1] *= imgsz[0]  # y            pred[..., 2] *= imgsz[1]  # w            pred[..., 3] *= imgsz[0]  # h            pred = torch.tensor(pred)        t3 = time_sync()        dt[1] += t3 - t2        # NMS        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)        dt[2] += time_sync() - t3        # Second-stage classifier (optional)        if classify:            pred = apply_classifier(pred, modelc, img, im0s)        # Process predictions        for i, det in enumerate(pred):  # per image            seen += 1            # if webcam:  # batch_size >= 1            #     p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count            # else:            #     p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)            if webcam:  # batch_size >= 1                p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count                cv2.putText(im0, "Detection_Region", (int(im0.shape[1] * wl1 - 5), int(im0.shape[0] * hl1 - 5)),cv2.FONT_HERSHEY_SIMPLEX,1.0, (255, 255, 0), 2, cv2.LINE_AA)                pts = np.array([[int(im0.shape[1] * wl1), int(im0.shape[0] * hl1)],  # pts1    [int(im0.shape[1] * wl2), int(im0.shape[0] * hl2)],  # pts2    [int(im0.shape[1] * wl3), int(im0.shape[0] * hl3)],  # pts3    [int(im0.shape[1] * wl4), int(im0.shape[0] * hl4)]], np.int32)  # pts4                # pts = pts.reshape((-1, 1, 2))                zeros = np.zeros((im0.shape), dtype=np.uint8)                mask = cv2.fillPoly(zeros, [pts], color=(0, 165, 255))                im0 = cv2.addWeighted(im0, 1, mask, 0.2, 0)                cv2.polylines(im0, [pts], True, (255, 255, 0), 3)                # plot_one_box(dr, im0, label='Detection_Region', color=(0, 255, 0), line_thickness=2)            else:                p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)                cv2.putText(im0, "Detection_Region", (int(im0.shape[1] * wl1 - 5), int(im0.shape[0] * hl1 - 5)),cv2.FONT_HERSHEY_SIMPLEX,1.0, (255, 255, 0), 2, cv2.LINE_AA)                pts = np.array([[int(im0.shape[1] * wl1), int(im0.shape[0] * hl1)],  # pts1    [int(im0.shape[1] * wl2), int(im0.shape[0] * hl2)],  # pts2    [int(im0.shape[1] * wl3), int(im0.shape[0] * hl3)],  # pts3    [int(im0.shape[1] * wl4), int(im0.shape[0] * hl4)]], np.int32)  # pts4                # pts = pts.reshape((-1, 1, 2))                zeros = np.zeros((im0.shape), dtype=np.uint8)                mask = cv2.fillPoly(zeros, [pts], color=(0, 165, 255))                im0 = cv2.addWeighted(im0, 1, mask, 0.2, 0)                cv2.polylines(im0, [pts], True, (255, 255, 0), 3)            p = Path(p)  # to Path            save_path = str(save_dir / p.name)  # img.jpg            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt            s += '%gx%g ' % img.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_coords(img.shape[2:], det[:, :4], im0.shape).round()                # Print results                for c in det[:, -1].unique():                    n = (det[:, -1] == 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(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)            # Print time (inference-only)            print(f'{s}Done. ({t3 - t2:.3f}s)')            # Stream results            im0 = annotator.result()            if 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' 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:  # videofps = 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:  # streamfps, w, h = 30, im0.shape[1], im0.shape[0]save_path += '.mp4'                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))                    vid_writer[i].write(im0)    # Print results    t = tuple(x / seen * 1E3 for x in dt)  # speeds per image    print(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 ''        print(f"Results saved to {colorstr('bold', save_dir)}{s}")    if update:        strip_optimizer(weights)  # update model (to fix SourceChangeWarning)def parse_opt():    parser = argparse.ArgumentParser()    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / '权重文件', help='model path(s)')    parser.add_argument('--source', type=str, default=ROOT / '检测图片', help='file/dir/URL/glob, 0 for webcam')    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=0.25, help='confidence threshold')    parser.add_argument('--iou-thres', type=float, default=0.45, 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=1, type=int, help='bounding box thickness (pixels)')    parser.add_argument('--hide-labels', default=True, 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')    opt = parser.parse_args()    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand    print_args(FILE.stem, opt)    return optdef main(opt):    check_requirements(exclude=('tensorboard', 'thop'))    run(**vars(opt))if __name__ == "__main__":    opt = parse_opt()    main(opt)

来源地址：https://blog.csdn.net/qq_39740357/article/details/125149010