怎么用YOLOv5实现多路摄像头实时目标检测功能

这篇文章将为大家详细讲解有关怎么用YOLOv5实现多路摄像头实时目标检测功能，小编觉得挺实用的，因此分享给大家做个参考，希望大家阅读完这篇文章后可以有所收获。

前言

YOLOV5模型从发布到现在都是炙手可热的目标检测模型，被广泛运用于各大场景之中。因此，我们不光要知道如何进行yolov5模型的训练，而且还要知道怎么进行部署应用。在本篇博客中，我将利用yolov5模型简单的实现从摄像头端到web端的部署应用demo，为读者提供一些部署思路。

一、YOLOV5的强大之处

你与目标检测高手之差一个YOLOV5模型。YOLOV5可以说是现目前几乎将所有目标检测tricks运用于一身的模型了。在它身上能找到很多目前主流的数据增强、模型训练、模型后处理的方法，下面我们就简单总结一下yolov5所使用到的方法：

yolov5增加的功能：

yolov5训练和预测的tricks：

二、YOLOV5部署多路摄像头的web应用

1.多路摄像头读取

在此篇博客中，采用了yolov5源码的datasets.py代码中的LoadStreams类进行多路摄像头视频流的读取。因为，我们只会用到datasets.py中视频流读取的部分代码，所以，将其提取出来，新建一个camera.py文件，下面则是camera.py文件的代码部分：

# coding:utf-8import osimport cv2import globimport timeimport numpy as npfrom pathlib import Pathfrom utils.datasets import letterboxfrom threading import Threadfrom utils.general import clean_strimg_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp']  # acceptable image suffixesvid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv']  # acceptable video suffixesclass LoadImages:  # for inference    def __init__(self, path, img_size=640, stride=32):        p = str(Path(path).absolute())  # os-agnostic absolute path        if '*' in p:            files = sorted(glob.glob(p, recursive=True))  # glob        elif os.path.isdir(p):            files = sorted(glob.glob(os.path.join(p, '*.*')))  # dir        elif os.path.isfile(p):            files = [p]  # files        else:            raise Exception(f'ERROR: {p} does not exist')        images = [x for x in files if x.split('.')[-1].lower() in img_formats]        videos = [x for x in files if x.split('.')[-1].lower() in vid_formats]        ni, nv = len(images), len(videos)        self.img_size = img_size        self.stride = stride        self.files = images + videos        self.nf = ni + nv  # number of files        self.video_flag = [False] * ni + [True] * nv        self.mode = 'image'        if any(videos):            self.new_video(videos[0])  # new video        else:            self.cap = None        assert self.nf > 0, f'No images or videos found in {p}. ' \                            f'Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}'    def __iter__(self):        self.count = 0        return self    def __next__(self):        if self.count == self.nf:            raise StopIteration        path = self.files[self.count]        if self.video_flag[self.count]:            # Read video            self.mode = 'video'            ret_val, img0 = self.cap.read()            if not ret_val:                self.count += 1                self.cap.release()                if self.count == self.nf:  # last video                    raise StopIteration                else:                    path = self.files[self.count]                    self.new_video(path)                    ret_val, img0 = self.cap.read()            self.frame += 1            print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.nframes}) {path}: ', end='')        else:            # Read image            self.count += 1            img0 = cv2.imread(path)  # BGR            assert img0 is not None, 'Image Not Found ' + path            print(f'image {self.count}/{self.nf} {path}: ', end='')        # Padded resize        img = letterbox(img0, self.img_size, stride=self.stride)[0]        # Convert        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416        img = np.ascontiguousarray(img)        return path, img, img0, self.cap    def new_video(self, path):        self.frame = 0        self.cap = cv2.VideoCapture(path)        self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))    def __len__(self):        return self.nf  # number of filesclass LoadWebcam:  # for inference    def __init__(self, pipe='0', img_size=640, stride=32):        self.img_size = img_size        self.stride = stride        if pipe.isnumeric():            pipe = eval(pipe)  # local camera        # pipe = 'rtsp://192.168.1.64/1'  # IP camera        # pipe = 'rtsp://username:password@192.168.1.64/1'  # IP camera with login        # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg'  # IP golf camera        self.pipe = pipe        self.cap = cv2.VideoCapture(pipe)  # video capture object        self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3)  # set buffer size    def __iter__(self):        self.count = -1        return self    def __next__(self):        self.count += 1        if cv2.waitKey(1) == ord('q'):  # q to quit            self.cap.release()            cv2.destroyAllWindows()            raise StopIteration        # Read frame        if self.pipe == 0:  # local camera            ret_val, img0 = self.cap.read()            img0 = cv2.flip(img0, 1)  # flip left-right        else:  # IP camera            n = 0            while True:                n += 1                self.cap.grab()                if n % 30 == 0:  # skip frames                    ret_val, img0 = self.cap.retrieve()                    if ret_val:                        break        # Print        assert ret_val, f'Camera Error {self.pipe}'        img_path = 'webcam.jpg'        print(f'webcam {self.count}: ', end='')        # Padded resize        img = letterbox(img0, self.img_size, stride=self.stride)[0]        # Convert        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416        img = np.ascontiguousarray(img)        return img_path, img, img0, None    def __len__(self):        return 0class LoadStreams:  # multiple IP or RTSP cameras    def __init__(self, sources='streams.txt', img_size=640, stride=32):        self.mode = 'stream'        self.img_size = img_size        self.stride = stride        if os.path.isfile(sources):            with open(sources, 'r') as f:                sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())]        else:            sources = [sources]        n = len(sources)        self.imgs = [None] * n        self.sources = [clean_str(x) for x in sources]  # clean source names for later        for i, s in enumerate(sources):            # Start the thread to read frames from the video stream            print(f'{i + 1}/{n}: {s}... ', end='')            cap = cv2.VideoCapture(eval(s) if s.isnumeric() else s)            assert cap.isOpened(), f'Failed to open {s}'            w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))            h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))            fps = cap.get(cv2.CAP_PROP_FPS) % 100            _, self.imgs[i] = cap.read()  # guarantee first frame            thread = Thread(target=self.update, args=([i, cap]), daemon=True)            print(f' success ({w}x{h} at {fps:.2f} FPS).')            thread.start()        print('')  # newline        # check for common shapes        s = np.stack([letterbox(x, self.img_size, stride=self.stride)[0].shape for x in self.imgs], 0)  # shapes        self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal        if not self.rect:            print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')    def update(self, index, cap):        # Read next stream frame in a daemon thread        n = 0        while cap.isOpened():            n += 1            # _, self.imgs[index] = cap.read()            cap.grab()            if n == 4:  # read every 4th frame                success, im = cap.retrieve()                self.imgs[index] = im if success else self.imgs[index] * 0                n = 0            time.sleep(0.01)  # wait time    def __iter__(self):        self.count = -1        return self    def __next__(self):        self.count += 1        img0 = self.imgs.copy()        if cv2.waitKey(1) == ord('q'):  # q to quit            cv2.destroyAllWindows()            raise StopIteration        # Letterbox        img = [letterbox(x, self.img_size, auto=self.rect, stride=self.stride)[0] for x in img0]        # Stack        img = np.stack(img, 0)        # Convert        img = img[:, :, :, ::-1].transpose(0, 3, 1, 2)  # BGR to RGB, to bsx3x416x416        img = np.ascontiguousarray(img)        return self.sources, img, img0, None    def __len__(self):        return 0  # 1E12 frames = 32 streams at 30 FPS for 30 years

2.模型封装

接下来，我们借助detect.py文件对yolov5模型进行接口封装，使其提供模型推理能力。新建一个yolov5.py文件，构建一个名为darknet的类，使用函数detect，提供目标检测能力。其代码如下：

# coding:utf-8import cv2import jsonimport timeimport torchimport numpy as npfrom camera import LoadStreams, LoadImagesfrom utils.torch_utils import select_devicefrom models.experimental import attempt_loadfrom utils.general import non_max_suppression, scale_coords, letterbox, check_imshowclass Darknet(object):    """docstring for Darknet"""    def __init__(self, opt):        self.opt = opt        self.device = select_device(self.opt["device"])        self.half = self.device.type != 'cpu'  # half precision only supported on CUDA        self.model = attempt_load(self.opt["weights"], map_location=self.device)        self.stride = int(self.model.stride.max())         self.model.to(self.device).eval()        self.names = self.model.module.names if hasattr(self.model, 'module') else self.model.names        if self.half: self.model.half()        self.source = self.opt["source"]        self.webcam = self.source.isnumeric() or self.source.endswith('.txt') or self.source.lower().startswith(        ('rtsp://', 'rtmp://', 'http://'))        def preprocess(self, img):        img = np.ascontiguousarray(img)        img = torch.from_numpy(img).to(self.device)        img = img.half() if self.half else img.float()  # uint8 to fp16/32        img /= 255.0  # 图像归一化        if img.ndimension() == 3:            img = img.unsqueeze(0)        return img        def detect(self, dataset):        view_img = check_imshow()        t0 = time.time()        for path, img, img0s, vid_cap in dataset:            img = self.preprocess(img)            t1 = time.time()            pred = self.model(img, augment=self.opt["augment"])[0]  # 0.22s            pred = pred.float()            pred = non_max_suppression(pred, self.opt["conf_thres"], self.opt["iou_thres"])            t2 = time.time()            pred_boxes = []            for i, det in enumerate(pred):                if self.webcam:  # batch_size >= 1                    p, s, im0, frame = path[i], '%g: ' % i, img0s[i].copy(), dataset.count                else:                    p, s, im0, frame = path, '', img0s, getattr(dataset, 'frame', 0)                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):                    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} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string                    for *xyxy, conf, cls_id in det:                        lbl = self.names[int(cls_id)]                        xyxy = torch.tensor(xyxy).view(1, 4).view(-1).tolist()                        score = round(conf.tolist(), 3)                        label = "{}: {}".format(lbl, score)                        x1, y1, x2, y2 = int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3])                        pred_boxes.append((x1, y1, x2, y2, lbl, score))                        if view_img:                            self.plot_one_box(xyxy, im0, color=(255, 0, 0), label=label)                                        # Print time (inference + NMS)                # print(pred_boxes)                print(f'{s}Done. ({t2 - t1:.3f}s)')                if view_img:                    print(str(p))                    cv2.imshow(str(p), cv2.resize(im0, (800, 600)))                    if self.webcam:                        if cv2.waitKey(1) & 0xFF == ord('q'): break                    else:                            cv2.waitKey(0)        print(f'Done. ({time.time() - t0:.3f}s)')        # print('[INFO] Inference time: {:.2f}s'.format(t3-t2))        # return pred_boxes    # Plotting functions    def plot_one_box(self, x, img, color=None, label=None, line_thickness=None):        # Plots one bounding box on image img        tl = line_thickness or round(0.001 * max(img.shape[0:2])) + 1  # line thickness        color = color or [random.randint(0, 255) for _ in range(3)]        c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))        cv2.rectangle(img, c1, c2, color, thickness=tl)        if label:            tf = max(tl - 1, 1)  # font thickness            t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]            c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3            cv2.rectangle(img, c1, c2, color, -1)  # filled            cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [0, 0, 0], thickness=tf, lineType=cv2.LINE_AA)if __name__ == "__main__":    with open('yolov5_config.json', 'r', encoding='utf8') as fp:        opt = json.load(fp)        print('[INFO] YOLOv5 Config:', opt)    darknet = Darknet(opt)    if darknet.webcam:        # cudnn.benchmark = True  # set True to speed up constant image size inference        dataset = LoadStreams(darknet.source, img_size=opt["imgsz"], stride=darknet.stride)    else:        dataset = LoadImages(darknet.source, img_size=opt["imgsz"], stride=darknet.stride)    darknet.detect(dataset)    cv2.destroyAllWindows()

此外，还需要提供一个模型配置文件，我们使用json文件进行保存。新建一个yolov5_config.json文件，内容如下：

{        "source": "streams.txt",  # 为视频图像文件地址        "weights": "runs/train/exp/weights/best.pt", # 自己的模型地址        "device": "cpu", # 使用的device类别，如是GPU，可填"0"        "imgsz": 640,  # 输入图像的大小        "stride": 32,  # 步长        "conf_thres": 0.35, # 置信值阈值        "iou_thres": 0.45,  # iou阈值        "augment": false  # 是否使用图像增强}

视频图像文件可以是单独的一张图像，如:"…/images/demo.jpg"，也可以是一个视频文件，如："…/videos/demo.mp4"，也可以是一个视频流地址，如："rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mov"，还可以是一个txt文件，里面包含多个视频流地址，如：

rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.movrtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mov

- 有了如此配置信息，通过运行yolov5.py代码，我们能实现对视频文件（mp4、avi等）、视频流地址（http、rtsp、rtmp等）、图片（jpg、png）等视频图像文件进行目标检测推理的效果。

3.Flask后端处理

有了对模型封装的代码，我们就可以利用flask框架实时向前端推送算法处理之后的图像了。新建一个web_main.py文件：

# import the necessary packagesfrom yolov5 import Darknetfrom camera import LoadStreams, LoadImagesfrom utils.general import non_max_suppression, scale_coords, letterbox, check_imshowfrom flask import Responsefrom flask import Flaskfrom flask import render_templateimport timeimport torchimport jsonimport cv2import os# initialize a flask objectapp = Flask(__name__)# initialize the video stream and allow the camera sensor to warmupwith open('yolov5_config.json', 'r', encoding='utf8') as fp:    opt = json.load(fp)    print('[INFO] YOLOv5 Config:', opt)darknet = Darknet(opt)if darknet.webcam:    # cudnn.benchmark = True  # set True to speed up constant image size inference    dataset = LoadStreams(darknet.source, img_size=opt["imgsz"], stride=darknet.stride)else:    dataset = LoadImages(darknet.source, img_size=opt["imgsz"], stride=darknet.stride)time.sleep(2.0)@app.route("/")def index():    # return the rendered template    return render_template("index.html")def detect_gen(dataset, feed_type):    view_img = check_imshow()    t0 = time.time()    for path, img, img0s, vid_cap in dataset:        img = darknet.preprocess(img)        t1 = time.time()        pred = darknet.model(img, augment=darknet.opt["augment"])[0]  # 0.22s        pred = pred.float()        pred = non_max_suppression(pred, darknet.opt["conf_thres"], darknet.opt["iou_thres"])        t2 = time.time()        pred_boxes = []        for i, det in enumerate(pred):            if darknet.webcam:  # batch_size >= 1                feed_type_curr, p, s, im0, frame = "Camera_%s" % str(i), path[i], '%g: ' % i, img0s[i].copy(), dataset.count            else:                feed_type_curr, p, s, im0, frame = "Camera", path, '', img0s, getattr(dataset, 'frame', 0)            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):                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} {darknet.names[int(c)]}{'s' * (n > 1)}, "  # add to string                for *xyxy, conf, cls_id in det:                    lbl = darknet.names[int(cls_id)]                    xyxy = torch.tensor(xyxy).view(1, 4).view(-1).tolist()                    score = round(conf.tolist(), 3)                    label = "{}: {}".format(lbl, score)                    x1, y1, x2, y2 = int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3])                    pred_boxes.append((x1, y1, x2, y2, lbl, score))                    if view_img:                        darknet.plot_one_box(xyxy, im0, color=(255, 0, 0), label=label)            # Print time (inference + NMS)            # print(pred_boxes)            print(f'{s}Done. ({t2 - t1:.3f}s)')            if feed_type_curr == feed_type:                frame = cv2.imencode('.jpg', im0)[1].tobytes()                yield (b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')@app.route('/video_feed/')def video_feed(feed_type):    """Video streaming route. Put this in the src attribute of an img tag."""    if feed_type == 'Camera_0':        return Response(detect_gen(dataset=dataset, feed_type=feed_type),                        mimetype='multipart/x-mixed-replace; boundary=frame')    elif feed_type == 'Camera_1':        return Response(detect_gen(dataset=dataset, feed_type=feed_type),                        mimetype='multipart/x-mixed-replace; boundary=frame')if __name__ == '__main__':    app.run(host='0.0.0.0', port="5000", threaded=True)

通过detect_gen函数将多个视频流地址推理后的图像按照feed_type类型，通过video_feed视频流路由进行传送到前端。

4.前端展示

最后，我们写一个简单的前端代码。首先新建一个templates文件夹，再在此文件夹中新建一个index.html文件，将下面h6代码写入其中：

                    
Multi-camera with YOLOv5
      
              
                
Live stream 1
                              
              
                
Live stream 2
                              
        
  

至此，我们利用YOLOv5模型实现多路摄像头实时推理代码就写完了，下面我们开始运行：

- 在终端中进行跟目录下，直接运行：

python web_main.py

然后，会在终端中出现如下信息：

[INFO] YOLOv5 Config: {'source': 'streams.txt', 'weights': 'runs/train/exp/weights/best.pt', 'device': 'cpu', 'imgsz': 640, 'stride': 32, 'conf_thres': 0.35, 'iou_thres': 0.45, 'augment': False}Fusing layers...1/2: rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mov...  success (240x160 at 24.00 FPS).2/2: rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mov...  success (240x160 at 24.00 FPS). * Serving Flask app "web_main" (lazy loading) * Environment: production   WARNING: This is a development server. Do not use it in a production deployment.   Use a production WSGI server instead. * Debug mode: off * Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)

* 接着打开浏览器，输入localhost:5000后，终端没有报任何错误，则就会出现如下页面：

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