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基本的运动检测

一种最直观的方法就是计算帧之间的差异，或考虑“背景”帧与其他帧之间的差异

basic_motion_detection.py

import cv2import numpy as npcamera = cv2.VideoCapture(0)es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10,10))#返回指定形状和尺寸的结构元素kernel = np.ones((5,5),np.uint8)background = Nonewhile (True):    ret, frame = camera.read()    if background is None:        background = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)        background = cv2.GaussianBlur(background, (21, 21), 0)        continue        gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)    gray_frame = cv2.GaussianBlur(gray_frame, (21, 21), 0)        diff = cv2.absdiff(background, gray_frame)#得到一个差分图    diff = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1]#固定阈值处理黑白图    diff = cv2.dilate(diff, es, iterations = 2)#膨胀处理图像        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), (255, 255, 0), 2)        cv2.imshow("contours", frame)    cv2.imshow("dif", diff)    if cv2.waitKey(int(1000 / 12)) & 0xff == ord("q"):        break    cv2.waitKey(0)cv2.destroyAllWindows()camera.release()

       存在的问题：这个方法需要提前设置“默认”帧作为背景，在一些光照变化频繁的情况下，这种方法就不灵活。所以后面引入背景分割器

背景分割器：KNN、MOG2和GMG

    BackgroundSubtractor类是专门用于视频分析的，即BackgroundSubtractor类会对每帧的环境进行‘学习’，他的另一个特征是它可以计算阴影。通过检测阴影，可排除检测图像的阴影区域（采用阈值方式），从而关注实际特征。

mog

import cv2import numpy as npbs = cv2.createBackgroundSubtractorKNN(detectShadows = True)camera = cv2.VideoCapture("movie.mpg")while True:    ret, frame = camera.read()    fgmask = bs.apply(frame)    th = cv2.threshold(fgmask.copy(), 244, 255, cv2.THRESH_BINARY)[1]    th = cv2.erode(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3)), iterations = 2)    dilated = cv2.dilate(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8,3)), iterations = 2)        image, contours, hier = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)    for c in contours:        if cv2.contourArea(c) > 1000:            (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("diff", frame & cv2.cvtColor(fgmask, cv2.COLOR_GRAY2BGR))    cv2.imshow("detection", frame)    k = cv2.waitKey(30) & 0xff    if k == 27:        breakcamera.release()cv2.destroyAllWindows()

用BackgroundSubtractorKNN来实现运动检测的例子

import cv2import numpy as npknn = cv2.createBackgroundSubtractorKNN(detectShadows = True)es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (20,12))camera = cv2.VideoCapture("traffic.flv")def drawCnt(fn, cnt):    if cv2.contourArea(cnt) > 1400:        (x, y, w, h) = cv2.boundingRect(cnt)        cv2.rectangle(fn, (x, y), (x + w, y + h), (255, 255, 0), 2)while True:    ret, frame = camera.read()    if not ret:        break    fg = knn.apply(frame.copy()) #计算了前景掩码    fg_bgr = cv2.cvtColor(fg, cv2.COLOR_GRAY2BGR)    bw_and = cv2.bitwise_and(fg_bgr, frame)    draw = cv2.cvtColor(bw_and, cv2.COLOR_BGR2GRAY)    draw = cv2.GaussianBlur(draw, (21, 21), 0)    draw = cv2.threshold(draw, 20, 255, cv2.THRESH_BINARY)[1]    draw = cv2.dilate(draw, es, iterations = 2)    image, contours, hierarchy = cv2.findContours(draw.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)    for c in contours:        drawCnt(frame, c)    cv2.imshow("motion detection", frame)    if cv2.waitKey(int(1000 / 12)) & 0xff == ord("q"):        breakcamera.release()cv2.destroyAllWindows()

均值漂移：

均值漂移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（例如感兴趣的图像区域），并且重新计算在下一帧种的最大密度，该算法给出了目标的移动。

meanshit.py

import numpy as npimport cv2cap = cv2.VideoCapture(0)# capture the first frameret,frame = cap.read()# mark the ROIr,h,c,w = 10, 200, 10, 200# wrap in a tupletrack_window = (c,r,w,h)# extract the ROI for trackingroi = frame[r:r+h, c:c+w]# switch to HSVhsv_roi =  cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)# create a mask with upper and lower boundaries of colors you want to trackmask = cv2.inRange(hsv_roi, np.array((100., 30.,32.)), np.array((180.,120.,255.)))# calculate histograms of roiroi_hist = cv2.calcHist([hsv_roi],[0],mask,[180],[0,180])cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX)# Setup the termination criteria, either 10 iteration or move by atleast 1 ptterm_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )while(1):    ret ,frame = cap.read()    if ret == True:        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)        dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1)        #print (dst)        # apply meanshift to get the new location        ret, track_window = cv2.meanShift(dst, track_window, term_crit)        # Draw it on image        x,y,w,h = track_window        img2 = cv2.rectangle(frame, (x,y), (x+w,y+h), 255,2)        cv2.imshow('img2',img2)        k = cv2.waitKey(60) & 0xff        if k == 27:            break    else:        breakcv2.destroyAllWindows()cap.release()

如果在计算机上运行这些代码，会观察到均值漂移窗口是怎么搜索指定颜色范围的；如果没有找到，就只能看到串口抖动。如果有指定颜色范围的目标进入窗口，该窗口就会开始跟踪这个目标

CAMShit

在调用CAMShit以后，会根据具体的旋转来绘制矩阵，这种旋转会与被跟踪对象一起旋转；其中boxPoint函数会找到被旋转的顶点，而折现函数会在帧上绘制矩形线段

import numpy as npimport cv2cap = cv2.VideoCapture(0)# take first frame of the videoret,frame = cap.read()# setup initial location of windowr,h,c,w = 300,200,400,300  # simply hardcoded the valuestrack_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(1):    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.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)        k = cv2.waitKey(60) & 0xff        if k == 27:            break    else:        breakcv2.destroyAllWindows()cap.release()

卡尔曼滤波器

主要包含预测和更新

预测：卡尔曼滤波器使用当前点计算的协方差来估计目标的新位置

更新：卡尔曼滤波器记录目标的位置，并为下一次循环计算修正协方差

Kalman.py

import cv2, numpy as npmeasurements = []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) * 0.03while 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)        breakcv2.destroyAllWindows()

输出：

一个基于行人跟踪的例子

#! /usr/bin/python"""Surveillance Demo: Tracking Pedestrians in Camera FeedThe application opens a video (could be a camera or a video file)and tracks pedestrians in the video."""# __author__ = "joe minichino"# __copyright__ = "property of mankind."# __license__ = "MIT"# __version__ = "0.0.1"# __maintainer__ = "Joe Minichino"# __email__ = "joe.minichino@gmail.com"# __status__ = "Development"import cv2import numpy as npimport os.path as pathimport argparseparser = 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):    """calculates centroid of a given matrix"""    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_SIMPLEXclass Pedestrian():  """Pedestrian class  each pedestrian is composed of a ROI, an ID and a Kalman filter  so we create a Pedestrian class to hold the object state  """  def __init__(self, id, frame, track_window):    """init the pedestrian object with track window coordinates"""    # set up the roi    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)    # set up the kalman    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)    # fake shadow    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)    # actual info    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__), "traffic.flv"))  camera = cv2.VideoCapture("768x576.avi")  # camera = cv2.VideoCapture(path.join(path.dirname(__file__), "..", "movie.mpg"))  # camera = cv2.VideoCapture(0)  history = 20  # KNN background subtractor  bs = cv2.createBackgroundSubtractorKNN()  # MOG subtractor  # bs = cv2.bgsegm.createBackgroundSubtractorMOG(history = history)  # bs.setHistory(history)  # GMG  # bs = cv2.bgsegm.createBackgroundSubtractorGMG(initializationFrames = history)    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)    # this is just to let the background subtractor build a bit of history    if frames < history:      frames += 1      continue    th = cv2.threshold(fgmask.copy(), 127, 255, cv2.THRESH_BINARY)[1]    th = cv2.erode(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3)), iterations = 2)    dilated = cv2.dilate(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8,3)), iterations = 2)    image, contours, hier = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)    counter = 0    for c in contours:      if cv2.contourArea(c) > 500:        (x,y,w,h) = cv2.boundingRect(c)        cv2.rectangle(frame, (x,y), (x+w, y+h), (0, 255, 0), 1)        # only create pedestrians in the first frame, then just follow the ones you have        if firstFrame is True:          pedestrians[counter] = Pedestrian(counter, frame, (x,y,w,h))        counter += 1        for i, p in pedestrians.items():      p.update(frame)        firstFrame = False    frames += 1    cv2.imshow("surveillance", frame)    out.write(frame)    if cv2.waitKey(110) & 0xff == 27:        break  out.release()  camera.release()if __name__ == "__main__":  main()

输出：蓝色矩形框为CAMShit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