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一个基于行人跟踪的例子
目标跟踪是对摄像头视频移动目标进行定位的过程，可用于监控（surveillance）、基于感知的（perceptual）用户界面、增强现实、基于对象的视频压缩以及辅助驾驶等。 应用程序的工作流程 检查第一帧检查后面输入的帧，从场景的开始通过背景分割器来识别场景中的行人为每个行人建立ROI（Region of interest），并利用Kalman/CAMShift来跟踪行人ID检查下一帧是否有进入场景的新行人 代码下面介绍一个基于行人跟踪的例子。demo.avi # -*- coding: utf-8 -*- """ Created on Sat Jan 9 14:48:10 2021 @author: gkm0120 """ import cv2 import numpy as np import os.path as path import argparse parser = argparse.ArgumentParser() parser.add_argument("-a", "--algorithm", help = "m (or nothing) for meanShift and c for camshift") args = vars(parser.parse_args()) def center(points): """计算给定矩阵的质心""" x = (points[0][0] + points[1][0] + points[2][0] + points[3][0]) / 4 y = (points[0][1] + points[1][1] + points[2][1] + points[3][1]) / 4 return np.array([np.float32(x), np.float32(y)], np.float32) font = cv2.FONT_HERSHEY_SIMPLEX class Pedestrian(): """Pedestrian（行人） 每个行人都由ROI，ID和卡尔曼过滤器组成，因此我们创建了一个步行者类来保存对象状态 """ def __init__(self, id, frame, track_window): """使用跟踪窗口坐标初始化行人对象""" # 设置ROI区域 self.id = int(id) x,y,w,h = track_window self.track_window = track_window self.roi = cv2.cvtColor(frame[y:y+h, x:x+w], cv2.COLOR_BGR2HSV) roi_hist = cv2.calcHist([self.roi], [0], None, [16], [0, 180]) self.roi_hist = cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) # 设置卡尔曼滤波器 self.kalman = cv2.KalmanFilter(4,2) self.kalman.measurementMatrix = np.array([[1,0,0,0],[0,1,0,0]],np.float32) self.kalman.transitionMatrix = np.array([[1,0,1,0],[0,1,0,1],[0,0,1,0],[0,0,0,1]],np.float32) self.kalman.processNoiseCov = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]],np.float32) * 0.03 self.measurement = np.array((2,1), np.float32) self.prediction = np.zeros((2,1), np.float32) self.term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 ) self.center = None self.update(frame) def __del__(self): print ("Pedestrian %d destroyed" % self.id) def update(self, frame): # print ("updating %d " % self.id) hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) back_project = cv2.calcBackProject([hsv],[0], self.roi_hist,[0,180],1) if args.get("algorithm") == "c": ret, self.track_window = cv2.CamShift(back_project, self.track_window, self.term_crit) pts = cv2.boxPoints(ret) pts = np.int0(pts) self.center = center(pts) cv2.polylines(frame,[pts],True, 255,1) if not args.get("algorithm") or args.get("algorithm") == "m": ret, self.track_window = cv2.meanShift(back_project, self.track_window, self.term_crit) x,y,w,h = self.track_window self.center = center([[x,y],[x+w, y],[x,y+h],[x+w, y+h]]) cv2.rectangle(frame, (x,y), (x+w, y+h), (255, 255, 0), 2) self.kalman.correct(self.center) prediction = self.kalman.predict() cv2.circle(frame, (int(prediction[0]), int(prediction[1])), 4, (255, 0, 0), -1) # 反向投影 cv2.putText(frame, "ID: %d -> %s" % (self.id, self.center), (11, (self.id + 1) * 25 + 1), font, 0.6, (0, 0, 0), 1, cv2.LINE_AA) # 实际位置信息 cv2.putText(frame, "ID: %d -> %s" % (self.id, self.center), (10, (self.id + 1) * 25), font, 0.6, (0, 255, 0), 1, cv2.LINE_AA) def main(): camera = cv2.VideoCapture(path.join(path.dirname(__file__), "demo.avi")) #加载视频 # camera = cv2.VideoCapture(0) #网络摄像头 history = 20 #设置20帧作为背景模型的帧 # KNN背景分割器 bs = cv2.createBackgroundSubtractorKNN() # MOG背景分割器 # bs = cv2.bgsegm.createBackgroundSubtractorMOG(history = history) # bs.setHistory(history) # GMG背景分割器 # bs = cv2.bgsegm.createBackgroundSubtractorGMG(initializationFrames = history) # 创建主窗口显示，设置行人字典和firstFrame标志，该标志能使得背景分割器利用这些帧构造历史 cv2.namedWindow("surveillance") pedestrians = {} firstFrame = True frames = 0 fourcc = cv2.VideoWriter_fourcc(*'XVID') out = cv2.VideoWriter('output.avi',fourcc, 20.0, (640,480)) while True: print (" -------------------- FRAME %d --------------------" % frames) grabbed, frame = camera.read() if (grabbed is False): print ("failed to grab frame.") break fgmask = bs.apply(frame) # 这只是为了让背景分割器建立一些历史 if frames 500: (x,y,w,h) = cv2.boundingRect(c) cv2.rectangle(frame, (x,y), (x+w, y+h), (0, 255, 0), 1) # 只在第一帧中的行人的每个轮廓进行实例化 if firstFrame is True: pedestrians[counter] = Pedestrian(counter, frame, (x,y,w,h)) counter += 1 # 对每个检测到的行人，都执行update()函数来传递当前帧 for i, p in pedestrians.items(): p.update(frame) firstFrame = False # 表示不会跟踪更多的行人，而是跟踪已有的行人 frames += 1 cv2.imshow("surveillance", frame) #窗口显示结果 out.write(frame) if cv2.waitKey(110) & 0xff == 27: break out.release() camera.release() if __name__ == "__main__": main() 图例
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这张截图中，蓝色矩形框是CAMShift检测的结果，绿色矩形框是卡尔曼滤波器预测的结果，其中心为蓝色圆圈。【本文地址】
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