基于OpenCV的目标跟踪实例(源码)

import cv2 # 获取视频 video = cv2.VideoCapture('007.mp4') # 生成椭圆结构元素 es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 4)) # 设置背景帧 background = None while True: # 读取视频每一帧 ret, frame = video.read() # 获取背景帧 if background is None: # 将视频的第一帧图像转为灰度图 background = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 对灰度图进行高斯模糊,平滑图像 background = cv2.GaussianBlur(background, (21, 21), 0) continue # 将视频的每一帧图像转为灰度图 gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 对灰度图进行高斯模糊,平滑图像 gray_frame = cv2.GaussianBlur(gray_frame, (21, 21), 0) # 获取当前帧与背景帧之间的图像差异,得到差分图 diff = cv2.absdiff(background, gray_frame) # 利用像素点值进行阈值分割,得到一副黑白图像 diff = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1] # 膨胀图像,减少错误 diff = cv2.dilate(diff, es, iterations=2) # 得到图像中的目标轮廓 image, cnts, hierarchy = cv2.findContours(diff.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for c in cnts: if cv2.contourArea(c) < 1500: continue # 绘制目标矩形框 (x, y, w, h) = cv2.boundingRect(c) cv2.rectangle(frame, (x+2, y+2), (x+w, y+h), (0, 255, 0), 2) # 显示检测视频 cv2.namedWindow('contours', 0) cv2.resizeWindow('contours', 600, 400) cv2.imshow('contours', frame) # 显示差异视频 cv2.namedWindow('diff', 0) cv2.resizeWindow('diff', 600, 400) cv2.imshow('diff', diff) if cv2.waitKey(1) & 0xff == ord('q'): break # 结束 cv2.destroyAllWindows() video.release()