opencv实现目标跟踪及视频转存

创建跟踪器

def createTypeTracker(trackerType):
读取视频第一帧，选择跟踪的目标

读第一帧。

ok, frame = video.read()

选择边界框

bbox = cv2.selectROI(frame, False)
初始化跟踪器
tracker_type = ‘MIL’
tracker = createTypeTracker(tracker_type)

用第一帧和包围框初始化跟踪器

ok = tracker.init(frame, bbox)
随着视频进行更新跟踪

更新跟踪器

ok, bbox = tracker.update(frame)

完整代码如下：

import cv2
import sys
import numpy as np# 创建一个跟踪器，algorithm: KCF、CSRT、DaSiamRPN、GOTURM、MIL
trackerTypes = ['BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE', 'CSRT']def createTypeTracker(trackerType):if trackerType == trackerTypes[0]:tracker = cv2.legacy.TrackerBoosting_create()elif trackerType == trackerTypes[1]:tracker = cv2.TrackerMIL_create()elif trackerType == trackerTypes[2]:tracker = cv2.legacy.TrackerKCF_create()elif trackerType == trackerTypes[3]:tracker = cv2.legacy.TrackerTLD_create()elif trackerType == trackerTypes[4]:tracker = cv2.legacy.TrackerMedianFlow_create()elif trackerType == trackerTypes[5]:  # 暂时存在问题tracker = cv2.TrackerGOTURN_create()elif trackerType == trackerTypes[6]:tracker = cv2.legacy.TrackerMOSSE_create()elif trackerType == trackerTypes[7]:tracker = cv2.legacy.TrackerCSRT_create()else:tracker = Nonereturn trackervideoPth = 'D:/workspace_all/pyCharm/BicycleTrackerByOpenCV/video2.mp4'if __name__ == '__main__':# 经测试CSRT效果最好tracker_type = 'CSRT'tracker = createTypeTracker(tracker_type)# 读取视频video = cv2.VideoCapture(videoPth)#fps = video.get(cv2.CAP_PROP_FPS)# 用于展示cv2.namedWindow("test", 0)cv2.resizeWindow("test", 1200, 900)  # 设置窗口的长和宽# 获取视频的帧率和尺寸fps = video.get(cv2.CAP_PROP_FPS)width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))# 保存跟踪视频output = cv2.VideoWriter('D:/desktop/output.mp4', cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))# 如果视频没有打开，退出。if not video.isOpened():print"Could not open video"sys.exit()# 读第一帧。ok, frame = video.read()#cv2.imshow('test', frame)if not ok:print('Cannot read video file')sys.exit()# 定义一个初始边界框bbox = (287, 23, 86, 320)# 选择一个不同的边界框bbox = cv2.selectROI('test', frame, False)# 用第一帧和包围框初始化跟踪器ok = tracker.init(frame, bbox)while True:# 读取一个新的帧ok, frame = video.read()if not ok:break# 启动计时器timer = cv2.getTickCount()# 更新跟踪器ok, bbox = tracker.update(frame)# 计算帧率(FPS)fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer);# 绘制包围框if ok:# 跟踪成功p1 = (int(bbox[0]), int(bbox[1]))p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))cv2.rectangle(frame, p1, p2, (0, 0, 255), 2, 3)else:# 跟踪失败cv2.putText(frame, "Tracking failure detected", (100, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)# 在帧上显示跟踪器类型名字cv2.putText(frame, tracker_type + " Tracker", (100, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2);# 在帧上显示帧率FPScv2.putText(frame, "FPS : " + str(int(fps)), (100, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2);# 显示结果# cv2.imshow("Tracking", frame)cv2.imshow('test', frame)# 保存视频output.write(frame)# 按ESC键退出k = cv2.waitKey(1) & 0xffif k == 27: breakoutput.release()

跟踪效果

常见问题

https://blog.csdn.net/taotao_guiwang/article/details/133301116?spm=1001.2014.3001.5501

参考

https://blog.csdn.net/weixin_45823221/article/details/128470499