if __name__ == '__main__':    """    weights:训练的权重    source:测试数据，可以是图片/视频路径，也可以是'0'(电脑自带摄像头),也可以是rtsp等视频流    output:网络预测之后的图片/视频的保存路径    img-size:网络输入图片大小    conf-thres:置信度阈值    iou-thres:做nms的iou阈值    device:设置设备    view-img:是否展示预测之后的图片/视频，默认False    save-txt:是否将预测的框坐标以txt文件形式保存，默认False    classes:设置只保留某一部分类别，形如0或者0 2 3    agnostic-nms:进行nms是否也去除不同类别之间的框，默认False    augment:推理的时候进行多尺度，翻转等操作(TTA)推理    update:如果为True，则对所有模型进行strip_optimizer操作，去除pt文件中的优化器等信息，默认为False    """    parser = argparse.ArgumentParser()    parser.add_argument('--weights', nargs='+', type=str, default='', help='model.pt path(s)')    parser.add_argument('--source', type=str, default='inference/images', help='source')  # file/folder, 0 for webcam    parser.add_argument('--output', type=str, default='inference/output', help='output folder')  # output folder    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')    parser.add_argument('--conf-thres', type=float, default=0.65, help='object confidence threshold')    parser.add_argument('--iou-thres', type=float, default=0.5, 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('--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')    opt = parser.parse_args()    print(opt)
    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()                # 去除pt文件中的优化器等信息                strip_optimizer(opt.weights)        else:            detect()

vimpo argparseimport osimport platformimport shutilimport timefrom pathlib import Path
import cv2import torchimport torch.backends.cudnn as cudnnfrom numpy import random
from models.experimental import attempt_loadfrom utils.datasets import LoadStreams, LoadImagesfrom utils.general import (    check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, plot_one_box, strip_optimizer)from utils.torch_utils import select_device, load_classifier, time_synchronized

def detect(save_img=False):    # 获取输出文件夹，输入源，权重，参数等参数    out, source, weights, view_img, save_txt, imgsz = \        opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size    webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
    # Initialize    # 获取设备    device = select_device(opt.device)    # 移除之前的输出文件夹    if os.path.exists(out):        shutil.rmtree(out)  # delete output folder    os.makedirs(out)  # make new output folder    # 如果设备为gpu，使用Float16    half = device.type != 'cpu'  # half precision only supported on CUDA
    # Load model    # 加载Float32模型，确保用户设定的输入图片分辨率能整除32(如不能则调整为能整除并返回)    model = attempt_load(weights, map_location=device)  # load FP32 model    imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size    # 设置Float16    if half:        model.half()  # to FP16
    # Second-stage classifier    # 设置第二次分类，默认不使用    classify = False    if classify:        modelc = load_classifier(name='resnet101', n=2)  # initialize        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'])  # load weights        modelc.to(device).eval()
    # Set Dataloader    # 通过不同的输入源来设置不同的数据加载方式    vid_path, vid_writer = None, None    if webcam:        view_img = True        cudnn.benchmark = True  # set True to speed up constant image size inference        dataset = LoadStreams(source, img_size=imgsz)    else:        save_img = True        # 如果检测视频的时候想显示出来，可以在这里加一行view_img = True        view_img = True        dataset = LoadImages(source, img_size=imgsz)
    # Get names and colors    # 获取类别名字    names = model.module.names if hasattr(model, 'module') else model.names    # 设置画框的颜色    colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
    # Run inference    t0 = time.time()    # 进行一次前向推理,测试程序是否正常    img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img    _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once    """    path 图片/视频路径    img 进行resize+pad之后的图片    img0 原size图片    cap 当读取图片时为None，读取视频时为视频源    """    for path, img, im0s, vid_cap in dataset:        img = torch.from_numpy(img).to(device)        # 图片也设置为Float16        img = img.half() if half else img.float()  # uint8 to fp16/32        img /= 255.0  # 0 - 255 to 0.0 - 1.0        # 没有batch_size的话则在最前面添加一个轴        if img.ndimension() == 3:            img = img.unsqueeze(0)
        # Inference        t1 = time_synchronized()        # print("preprocess_image:", t1 - t0)        # t1 = time.time()        """        前向传播 返回pred的shape是(1, num_boxes, 5+num_class)        h,w为传入网络图片的长和宽，注意dataset在检测时使用了矩形推理，所以这里h不一定等于w        num_boxes = h/32 * w/32 + h/16 * w/16 + h/8 * w/8        pred[..., 0:4]为预测框坐标        预测框坐标为xywh(中心点+宽长)格式        pred[..., 4]为objectness置信度        pred[..., 5:-1]为分类结果        """        pred = model(img, augment=opt.augment)[0]        t1_ = time_synchronized()        print('inference:', t1_ - t1)
        # Apply NMS        # 进行NMS        """        pred:前向传播的输出        conf_thres:置信度阈值        iou_thres:iou阈值        classes:是否只保留特定的类别        agnostic:进行nms是否也去除不同类别之间的框        经过nms之后，预测框格式：xywh-->xyxy(左上角右下角)        pred是一个列表list[torch.tensor]，长度为batch_size        每一个torch.tensor的shape为(num_boxes, 6),内容为box+conf+cls        """        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)        t2 = time_synchronized()        # t2 = time.time()
        # Apply Classifier        # 添加二次分类，默认不使用        if classify:            pred = apply_classifier(pred, modelc, img, im0s)
        # Process detections        # 对每一张图片作处理        for i, det in enumerate(pred):  # detections per image            # 如果输入源是webcam，则batch_size不为1，取出dataset中的一张图片            if webcam:  # batch_size >= 1                p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()            else:                p, s, im0 = path, '', im0s            # 设置保存图片/视频的路径            save_path = str(Path(out) / Path(p).name)            # 设置保存框坐标txt文件的路径            txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')            # 设置打印信息(图片长宽)            s += '%gx%g ' % img.shape[2:]  # print string            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh            if det is not None and len(det):                # Rescale boxes from img_size to im0 size                # 调整预测框的坐标：基于resize+pad的图片的坐标-->基于原size图片的坐标                # 此时坐标格式为xyxy
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
                # Print results                # 打印检测到的类别数量                for c in det[:, -1].unique():                    n = (det[:, -1] == c).sum()  # detections per class                    s += '%g %ss, ' % (n, names[int(c)])  # add to string
                # Write results                # 保存预测结果                for *xyxy, conf, cls in det:                    if save_txt:  # Write to file                        # 将xyxy(左上角+右下角)格式转为xywh(中心点+宽长)格式，并除上w，h做归一化，转化为列表再保存                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh                        with open(txt_path + '.txt', 'a') as f:                            f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format                    # 在原图上画框                    if save_img or view_img:  # Add bbox to image                        label = '%s %.2f' % (names[int(cls)], conf)                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
            # Print time (inference + NMS)            # 打印前向传播+nms时间            print('%sDone. (%.3fs)' % (s, t2 - t1))
            # Stream results            # 如果设置展示，则show图片/视频            if view_img:                cv2.imshow(p, im0)                if cv2.waitKey(1) == ord('q'):  # q to quit                    raise StopIteration
            # Save results (image with detections)            # 设置保存图片/视频            if save_img:                if dataset.mode == 'images':                    cv2.imwrite(save_path, im0)                else:                    if vid_path != save_path:  # new video                        vid_path = save_path                        if isinstance(vid_writer, cv2.VideoWriter):                            vid_writer.release()  # release previous video writer
                        fourcc = 'mp4v'  # output video codec                        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))                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))                    vid_writer.write(im0)
    if save_txt or save_img:        print('Results saved to %s' % Path(out))        # 打开保存图片和txt的路径(好像只适用于MacOS系统)        if platform == 'darwin' and not opt.update:  # MacOS            os.system('open ' + save_path)    # 打印总时间    print('Done. (%.3fs)' % (time.time() - t0))