第八章 目标跟踪
8.1 检测移动的目标
基本的运动检测
计算帧之间的差异，或考虑“背景”帧与其他帧之间的差异。
例：
import cv2 import numpy as np camera = cv2.VideoCapture(0) es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(9,4)) kernel = np.ones((5,5),np.uint8) #通过摄像头读入帧，将第一帧设置为背景 background = None while(True): rat,frame = camera.read() if background is None: background = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) background = cv2.GaussianBlur(background,(21,21),0) continue #对帧进行简单处理：转化灰阶、模糊处理（减少噪声影响） gray_frame = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) gray_frame = cv2.GaussianBlur(gray_frame,(21,21),0) #差分图difference map， diff = cv2.absdiff(background,gray_frame) diff = cv2.threshold(diff,25,255,cv2.THRESH_BINARY)[1] diff = cv2.dilate(diff,es,iterations=2) #显示矩形 image,cnts,hierarchy = cv2.findContours(diff.copy(),cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for c in cnts: if cv2.contourArea(c)<1500: continue (x,y,w,h) = cv2.boundingRect(c) cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2) cv2.imshow("contours",frame) cv2.imshow("dif",diff) if cv2.waitKey(82) & 0xff == ord("q"): break cv2.destroyAllWindows()
8.2 背景分割器：KNN、MOG2和GMG
OpenCV提供了一个BackgroundSubtractor的类，在分割前景和背景时很方便。BackgroundSubtractor类 可以计算阴影。
例子BackgroundSubtractor
import numpy as np import cv2 cap = cv2.VideoCapture(0) mog = cv2.createBackgroundSubtractorMOG2() while(1): rat,frame = cap.read() fgmask = mog.apply(frame) cv2.imshow('frame',fgmask) if cv2.waitKey(30) & 0xff == ord("q"): break cap.release() cv2.destroyAllWindows()
例：使用BackgroundSubtractorKNN来实现运动检测
import cv2 import numpy as np bs = cv2.createBackgroundSubtractorKNN(detectShadows=True) camera = cv2.VideoCapture('traffic.flv') # traffic.flv一段公路上的交通情况 while True: rat,frame = camera.read() fgmask = bs.apply(frame) th = cv2.threshold(fgmask.copy(),244,255,cv2.THRESH_BINARY)[1] dilated = cv2.dilate(th,cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)), iterations = 2) image,contours,hier = cv2.findContours(dilated,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for c in contours: if cv2.contourArea(c)>1600: (x,y,w,h) = cv2.boundingRect(c) cv2.rectangle(frame,(x,y),(x+w,y+h),(255,255,0),2) cv2.imshow("mog",fgmask) cv2.imshow("thresh",th) cv2.imshow("detection",frame) if cv2.waitKey(30) & 0xff ==27: break camera.release() cv2.destroyAllWindows()
8.2.1 均值漂移和CAMShift
均值漂移Meanshift是一种目标跟踪算法，该算法寻找概率函数离散样本的最大密度，并且重新计算下一帧的最大密度，该算法给出了目标的移动方向。
均值漂移在追踪视频中感兴趣的区域时非常有用。
例：标记并追踪感兴趣的区域：
import numpy as np import cv2 cap = cv2.VideoCapture(0) ret,frame = cap.read() r,h,c,w = 10,200,10,200 track_window = (c,r,w,h) roi = frame[r:r+h,c:c+w] hsv_roi = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV) mask = cv2.inRange(hsv_roi,np.array((100.,30.,32.)), np.array((180.,120.,255.))) roi_hist = cv2.calcHist([hsv_roi],[0],mask,[180],[0,180]) cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX) term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT,10,1) while True: ret,frame = cap.read() if ret == True: hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV) dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1) ret,track_window = cv2.meanShift(dst,track_window,term_crit) x,y,w,h = track_window img2 = cv2.rectangle(frame,(x,y),(x+w,y+h),255,2) cv2.imshow('img2',img2) if cv2.waitKey(60) & 0xff == ord("q"): break else: break cv2.destroyAllWindows() cap.release()
如果有颜色范围内的物体进入窗口，就会追踪物体。
8.2.2 彩色直方图
calcHist 函数用来计算图像的彩色直方图。彩色直方图是图像的颜色分布。x轴是色彩值，y轴是像素数量。
calcBackProject 函数 在均值漂移算法中非常重要。称为直方图方向投影，它得到直方图并将其投影到一副图像上，其结果是每个像素属于生成原始直方图的原图像的概率。
import numpy as np import cv2 cap = cv2.VideoCapture(0) ret,frame = cap.read() #标记感兴趣的区域 r,h,c,w = 10,200,10,200 track_window = (c,r,w,h) #提取roi并将其转换为HSV空间 roi = frame[r:r+h,c:c+w] hsv_roi = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV) mask = cv2.inRange(hsv_roi,np.array((100.,30.,32.)), np.array((180.,120.,255.))) #roi的直方图 roi_hist = cv2.calcHist([hsv_roi],[0],mask,[180],[0,180]) cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX) #均值漂移停止条件 ：迭代10次或 中心移动1个像素 term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT,10,1) while True: ret,frame = cap.read() if ret == True: hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV) #反向投影 dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1) ret,track_window = cv2.meanShift(dst,track_window,term_crit) x,y,w,h = track_window img2 = cv2.rectangle(frame,(x,y),(x+w,y+h),255,2) cv2.imshow('img2',img2) if cv2.waitKey(60) & 0xff == ord("q"): break else: break cv2.destroyAllWindows() cap.release() 8.3CAMShift import numpy as np import cv2 cap = cv2.VideoCapture(0) ret,frame = cap.read() #标记感兴趣的区域 r,h,c,w = 10,200,10,200 track_window = (c,r,w,h) #提取roi并将其转换为HSV空间 roi = frame[r:r+h,c:c+w] hsv_roi = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV) mask = cv2.inRange(hsv_roi,np.array((100.,30.,32.)), np.array((180.,120.,255.))) #roi的直方图 roi_hist = cv2.calcHist([hsv_roi],[0],mask,[180],[0,180]) cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX) #均值漂移停止条件 ：迭代10次或 中心移动1个像素 term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT,10,1) while True: ret,frame = cap.read() if ret == True: hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV) #反向投影 dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1) ret,track_window = cv2.CamShift(dst,track_window,term_crit) pts = cv2.boxPoints(ret) pts = np.int0(pts) img2 = cv2.polylines(frame,[pts],True,255,2) cv2.imshow('img2',img2) if cv2.waitKey(60) & 0xff == ord("q"): break else: break cv2.destroyAllWindows() cap.release()
8.4 卡尔曼滤波器
卡尔曼滤波器对含有噪声的输入数据流进行递归操作，产生底层系统状态在统计意义上的最优估计。
8.4.1 预测和更新
可将卡尔曼滤波算法分为两个阶段：
预测：使用当前点计算的协方差来估计目标的新位置
更新：记录目标位置，为下一次循环计算修正协方差
例：卡尔曼滤波器预测鼠标轨迹
import cv2, numpy as np measurements = [] predictions = [] frame = np.zeros((800, 800, 3), np.uint8) last_measurement = current_measurement = np.array((2,1), np.float32) last_prediction = current_prediction = np.zeros((2,1), np.float32) def mousemove(event, x, y, s, p): global frame, current_measurement, measurements, last_measurement, current_prediction, last_prediction last_prediction = current_prediction last_measurement = current_measurement current_measurement = np.array([[np.float32(x)],[np.float32(y)]]) kalman.correct(current_measurement) current_prediction = kalman.predict() lmx, lmy = last_measurement[0], last_measurement[1] cmx, cmy = current_measurement[0], current_measurement[1] lpx, lpy = last_prediction[0], last_prediction[1] cpx, cpy = current_prediction[0], current_prediction[1] cv2.line(frame, (lmx, lmy), (cmx, cmy), (0,100,0)) cv2.line(frame, (lpx, lpy), (cpx, cpy), (0,0,200)) cv2.namedWindow("kalman_tracker") cv2.setMouseCallback("kalman_tracker", mousemove); kalman = cv2.KalmanFilter(4,2,1) kalman.measurementMatrix = np.array([[1,0,0,0],[0,1,0,0]],np.float32) kalman.transitionMatrix = np.array([[1,0,1,0],[0,1,0,1],[0,0,1,0],[0,0,0,1]],np.float32) kalman.processNoiseCov = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]],np.float32) * 0.03 while True: cv2.imshow("kalman_tracker", frame) if (cv2.waitKey(30) & 0xFF) == 27: break if (cv2.waitKey(30) & 0xFF) == ord('q'): cv2.imwrite('kalman.jpg', frame) break cv2.destroyAllWindows()
8.3 CAMShift
