yolov5 deepsort 基于yolov5和deepsort的行人跟踪计数系统【yolov5】【deepsort】【行人检测】【跟踪计数】

行人跟踪计数系统

*
– 介绍
– 环境
– 项目源码（github)
– 部分代码
* 虚拟环境

介绍

本项目使用yolov5作为检测器，使用deepsort作为跟踪器，跟踪并计数镜头前走过的行人数量。
代码中设置的是只计数行人的数量，如果要计数其他东西比如车辆之类的也非常简单，只需要稍微修改一下代码即可，在 parser.add_argument('--classes', default=0, type=int, help='filter by class: --class 0, or --class 0 1 2 3')中，通过classes来过滤yolov5要检测类别，0表示检测人，1表示自行车，还有其他的类别。

环境

项目测试环境：win10，pycharm，python3.6+
主要需要的包：pytorch 1.7.1 + cuda110，opencv

项目源码（github)

https://github.com/MichistaLin/yolov5-deepsort-pedestraintracking

部分代码

导包

import torch, sys, argparse, cv2, os, time
from datetime import datetime
from self_utils.multi_tasks import Counting_Processing
from self_utils.overall_method import Object_Counter, Image_Capture
from deep_sort.configs.parser import get_config
from deep_sort.deep_sort import DeepSort
import imutils

if __name__ == "__main__":
    torch.multiprocessing.set_start_method('spawn')
    parser = argparse.ArgumentParser()

    parser.add_argument('--input', type=str, default="./test.mp4",
                        help='test imgs folder or video or camera')

    parser.add_argument('--output', type=str, default="./output",
                        help='folder to save result imgs, can not use input folder')
    parser.add_argument('--weights', type=str, default='weights/yolov5l.pt', help='model.pt path(s)')
    parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf_thres', type=float, default=0.4, help='object confidence threshold')
    parser.add_argument('--iou_thres', type=float, default=0.4, help='IOU threshold for NMS')

    parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')

    parser.add_argument('--classes', default=0, type=int, help='filter by class: --class 0, or --class 0 1 2 3')

    yolo5_config = parser.parse_args()
    print(yolo5_config)
    main(yolo5_config)
    print("结果保存在：", yolo5_config.output)

检测和计数:该函数中使用yolov5进行检测，使用deepsort进行跟踪

def Counting_Processing(input_img,yolo5_config,model,class_names,Tracker,Obj_Counter):
    try:

        tensor_img=img_preprocessing(input_img,yolo5_config.device,yolo5_config.img_size)

        pred=yolov5_prediction(model,tensor_img,yolo5_config.conf_thres, yolo5_config.iou_thres,yolo5_config.classes)

        result_img=count_post_processing(input_img,pred,class_names,tensor_img.shape,Tracker,Obj_Counter)

        return result_img

    except Exception as e:
        print("Wrong:",e, 'multi_tasks.py--->Counting_Processing')
        return e

main函数：初始化模型，加载视频或图片，处理并检测跟踪视频、图片中的人

def main(yolo5_config):
    print("=> main task started: {}".format(datetime.now().strftime('%H:%M:%S')))
    a = time.time()
    class_names = []

    Model = torch.load(yolo5_config.weights, map_location=lambda storage, loc: storage.cuda(int(yolo5_config.device)))[
        'model'].float().fuse().eval()

    classnames = Model.module.names if hasattr(Model, 'module') else Model.names

    class_names.append(classnames[0])
    b = time.time()
    print("==> class names: ", class_names)
    print("=> load model, cost:{:.2f}s".format(b - a))

    os.makedirs(yolo5_config.output, exist_ok=True)
    c = time.time()

    cfg = get_config()
    cfg.merge_from_file("deep_sort/configs/deep_sort.yaml")
    deepsort_tracker = DeepSort(cfg.DEEPSORT.REID_CKPT, max_dist=cfg.DEEPSORT.MAX_DIST,
                                min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
                                nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP,
                                max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE, max_age=cfg.DEEPSORT.MAX_AGE,
                                n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
                                use_cuda=True, use_appearence=True)

    mycap = Image_Capture(yolo5_config.input)

    Obj_Counter = Object_Counter(class_names)

    total_num = mycap.get_length()
    videowriter = None
    fps = int(mycap.get(5))
    t = int(1000 / fps)
    while mycap.ifcontinue():
        ret, img = mycap.read()
        if ret:

            result_img = Counting_Processing(img, yolo5_config, Model, class_names, deepsort_tracker, Obj_Counter)

            if videowriter is None:
                fourcc = cv2.VideoWriter_fourcc(
                    'm', 'p', '4', 'v')
                videowriter = cv2.VideoWriter(
                    './output/result.mp4', fourcc, fps, (result_img.shape[1], result_img.shape[0]))
            videowriter.write(result_img)
            result_img = imutils.resize(result_img, height=500)
            cv2.imshow('video', result_img)
            cv2.waitKey(t)

            if cv2.getWindowProperty('video', cv2.WND_PROP_AUTOSIZE) < 1:

                break

        sys.stdout.write("\r=> processing at %d; total: %d" % (mycap.get_index(), total_num))
        sys.stdout.flush()

    videowriter.release()
    cv2.destroyAllWindows()
    mycap.release()
    print("\n=> process done {}/{} images, total cost: {:.2f}s [{:.2f} fps]".format(len(os.listdir(yolo5_config.output)),total_num, time.time() - c,len(os.listdir(yolo5_config.output)) / (time.time() - c)))

    print("=> main task finished: {}".format(datetime.now().strftime('%H:%M:%S')))

项目入口：主要的外部输入的参数，defalt为默认参数

if __name__ == "__main__":
    torch.multiprocessing.set_start_method('spawn')
    parser = argparse.ArgumentParser()
    # 视频的路径，默认是本项目中的一个测试视频test.mp4，可自行更改
    parser.add_argument('--input', type=str, default="./test.mp4",
                        help='test imgs folder or video or camera')  # 输入'0'表示调用电脑默认摄像头
    # 处理后视频的输出路径
    parser.add_argument('--output', type=str, default="./output",
                        help='folder to save result imgs, can not use input folder')
    parser.add_argument('--weights', type=str, default='weights/yolov5l.pt', help='model.pt path(s)')
    parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf_thres', type=float, default=0.4, help='object confidence threshold')
    parser.add_argument('--iou_thres', type=float, default=0.4, help='IOU threshold for NMS')
    # GPU（0表示设备的默认的显卡）或CPU
    parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    # 通过classes来过滤yolo要检测类别，0表示检测人，1表示自行车，更多具体类别数字可以在19行附近打印出来
    parser.add_argument('--classes', default=0, type=int, help='filter by class: --class 0, or --class 0 1 2 3')

    yolo5_config = parser.parse_args()
    print(yolo5_config)
    main(yolo5_config)
    print("结果保存在：", yolo5_config.output)

效果：有的时候街道上服装店里的假人也会识别为一个人，这yolo就没办法了，毕竟实在是太相似了

虚拟环境

我为项目添加了一个虚拟环境，但是这个虚拟环境太大了所以没有把它上传在github，而是放在了123网盘中，链接在github的 README.md文件里，如果不想自己下载pytorch的同学可以使用我提供的虚拟环境直接运行该项目，但是移植虚拟环境需要做一点改动，具体如下图：

Original: https://blog.csdn.net/m0_57110410/article/details/126168009
Author: 再游于北方知寒
Title: yolov5 deepsort 基于yolov5和deepsort的行人跟踪计数系统【yolov5】【deepsort】【行人检测】【跟踪计数】