基于OpenCV+YOLOv5实现车辆跟踪与计数(附源码)

导读

本文主要介绍基于OpenCV+YOLOv5实现车辆跟踪与计数的应用，并给出源码。

资源下载

基础代码和视频下载地址：

https://github.com/freedomwebtech/win11vehiclecount

main.py代码:

import cv2import torchimport numpy as npfrom tracker import *

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
cap=cv2.VideoCapture('highway.mp4')
count=0tracker = Tracker()



def POINTS(event, x, y, flags, param):    if event == cv2.EVENT_MOUSEMOVE :          colorsBGR = [x, y]        print(colorsBGR)        
cv2.namedWindow('FRAME')cv2.setMouseCallback('FRAME', POINTS)
while True:    ret,frame=cap.read()    if not ret:        break    count += 1    if count % 3 != 0:        continue    frame=cv2.resize(frame,(1020,600))    results=model(frame)    results.pandas().xyxy[0]                       cv2.imshow("FRAME",frame)    if cv2.waitKey(0)&0xFF==27:        breakcap.release()cv2.destroyAllWindows()

tracker.py代码:

import math

class Tracker:    def __init__(self):        # Store the center positions of the objects        self.center_points = {}        # Keep the count of the IDs        # each time a new object id detected, the count will increase by one        self.id_count = 0

    def update(self, objects_rect):        # Objects boxes and ids        objects_bbs_ids = []
        # Get center point of new object        for rect in objects_rect:            x, y, w, h = rect            cx = (x + x + w) // 2            cy = (y + y + h) // 2
            # Find out if that object was detected already            same_object_detected = False            for id, pt in self.center_points.items():                dist = math.hypot(cx - pt[0], cy - pt[1])
                if dist < 35:                    self.center_points[id] = (cx, cy)#                    print(self.center_points)                    objects_bbs_ids.append([x, y, w, h, id])                    same_object_detected = True                    break
            # New object is detected we assign the ID to that object            if same_object_detected is False:                self.center_points[self.id_count] = (cx, cy)                objects_bbs_ids.append([x, y, w, h, self.id_count])                self.id_count += 1
        # Clean the dictionary by center points to remove IDS not used anymore        new_center_points = {}        for obj_bb_id in objects_bbs_ids:            _, _, _, _, object_id = obj_bb_id            center = self.center_points[object_id]            new_center_points[object_id] = center
        # Update dictionary with IDs not used removed        self.center_points = new_center_points.copy()        return objects_bbs_ids

下载测试视频highway.mp4(download.txt中有链接)：

安装ultralytics：

pip install ultralytics -i https://pypi.tuna.tsinghua.edu.cn/simple

实现车辆检测

添加代码查看YoloV5模型检测输出信息：

添加车辆检测结果绘制：

实现车辆跟踪

将车辆矩形坐标保存到list中：

添加对象跟踪：

车辆跟踪效果：

实现车辆计数

【1】划定一个多边形区域(黄色)，车辆经过此区域则计数；

cv2.polylines(frame,[np.array(areal,np.int32)],True,(0,255,255),3)

【2】绿色点标注车辆矩形框右下角坐标点，如下图：

cv2.circle(frame,(x3,y3),4,(0,255,0),-1)

【3】计数原理：判断绿色点是否在黄色四边形内，如果在内计数+1

计算点到多边形的距离使用的是OpenCV中pointPolygonTest函数：

当result > 0表示点在轮廓内，也就是车辆在多边形内。此时将车辆id编号添加到集合area_1中，然后通过len(area_1)作为车辆计数值。

注意：这里为什么不直接用count += 1来计算数量？如果用count += 1会造成很多重复计数，而代码中利用了python集合的特性，add函数添加元素时，如果元素已经存在，则不重复添加，有效的避免了重复计数问题。

最终效果如下：

基于OpenCV+YOLOv5实现车辆跟踪与计数(附源码)

导 读

导读