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：
