Python-OpenCV 处理视频（四）： 运动检测，python-opencv视频, 这里主要用到 Absd

Python-OpenCV 处理视频（四）： 运动检测，python-opencv视频, 这里主要用到 Absd

0x00. 平均值法

通过计算两帧图像之间变化了的像素点占的百分比，来确定图像中是否有动作产生。

这里主要用到 Absdiff 函数，比较两帧图像之间有差异的点，当然需要将图像进行一些处理，例如平滑处理，灰度化处理，二值化处理，经过处理之后的二值图像上的点将更有效。

代码示例：

import cv2.cv as cv

capture=cv.CaptureFromCAM(0)

frame1 = cv.QueryFrame(capture)
frame1gray = cv.CreateMat(frame1.height, frame1.width, cv.CV_8U)
cv.CvtColor(frame1, frame1gray, cv.CV_RGB2GRAY)

res = cv.CreateMat(frame1.height, frame1.width, cv.CV_8U)

frame2gray = cv.CreateMat(frame1.height, frame1.width, cv.CV_8U)

w= frame2gray.width
h= frame2gray.height
nb_pixels = frame2gray.width * frame2gray.height

while True:
    frame2 = cv.QueryFrame(capture)
    cv.CvtColor(frame2, frame2gray, cv.CV_RGB2GRAY)

    cv.AbsDiff(frame1gray, frame2gray, res)
    cv.ShowImage("After AbsDiff", res)

    cv.Smooth(res, res, cv.CV_BLUR, 5,5)
    element = cv.CreateStructuringElementEx(5*2+1, 5*2+1, 5, 5,  cv.CV_SHAPE_RECT)
    cv.MorphologyEx(res, res, None, None, cv.CV_MOP_OPEN)
    cv.MorphologyEx(res, res, None, None, cv.CV_MOP_CLOSE)
    cv.Threshold(res, res, 10, 255, cv.CV_THRESH_BINARY_INV)

    cv.ShowImage("Image", frame2)
    cv.ShowImage("Res", res)

    #-----------
    nb=0
    for y in range(h):
        for x in range(w):
            if res[y,x] == 0.0:
                nb += 1
    avg = (nb*100.0)/nb_pixels
    #print "Average: ",avg, "%r",
    if avg >= 5:
        print "Something is moving !"
    #-----------


    cv.Copy(frame2gray, frame1gray)
    c=cv.WaitKey(1)
    if c==27: #Break if user enters 'Esc'.
        break

0x01. 背景建模与前景检测

背景建模也是检测运动物体的一种办法，下面是代码示例：

import cv2.cv as cv

capture = cv.CaptureFromCAM(0)
width = int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_WIDTH))
height = int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_HEIGHT))

gray = cv.CreateImage((width,height), cv.IPL_DEPTH_8U, 1)

background = cv.CreateMat(height, width, cv.CV_32F)
backImage = cv.CreateImage((width,height), cv.IPL_DEPTH_8U, 1)
foreground = cv.CreateImage((width,height), cv.IPL_DEPTH_8U, 1)
output = cv.CreateImage((width,height), 8, 1)

begin = True
threshold = 10

while True:
  frame = cv.QueryFrame( capture )

  cv.CvtColor(frame, gray, cv.CV_BGR2GRAY)

  if begin:
      cv.Convert(gray, background) #Convert gray into background format
      begin = False

  cv.Convert(background, backImage) #convert existing background to backImage

  cv.AbsDiff(backImage, gray, foreground) #Absdiff to get differences

  cv.Threshold(foreground, output, threshold, 255, cv.CV_THRESH_BINARY_INV)

  cv.Acc(foreground, background,output) #Accumulate to background

  cv.ShowImage("Output", output)
  cv.ShowImage("Gray", gray)
  c=cv.WaitKey(1)
  if c==27: #Break if user enters 'Esc'.
    break

0x02. 我的方法

上面的几种办法我都试了下，基本上能识别出运动的物体，但是发现总是有点瑕疵，所以又比对了几种别人的方案，然后合成了一个自己的方案：

具体处理思路：

• 对两帧图像做一个absdiff得到新图像。
• 对新图像做灰度和二值化处理。
• 使用findContours函数获取二值化处理之后的图片中的轮廓。
• 使用contourArea()过滤掉自己不想要的面积范围的轮廓。

这个办法基本上能够检测出物体的图像中物体的移动，而且我觉得通过设定contourArea()函数的过滤范围，可以检测距离摄像头不同距离范围的运动物体。

以下是代码示例：

Python

#!usr/bin/env python
#coding=utf-8

import cv2
import numpy as np

camera = cv2.VideoCapture(0)
width = int(camera.get(3))
height = int(camera.get(4))

firstFrame = None

while True:
  (grabbed, frame) = camera.read()
  gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
  gray = cv2.GaussianBlur(gray, (21, 21), 0)

  if firstFrame is None:
    firstFrame = gray
    continue

  frameDelta = cv2.absdiff(firstFrame, gray)
  thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
  # thresh = cv2.adaptiveThreshold(frameDelta,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
            # cv2.THRESH_BINARY,11,2)
  # thresh = cv2.adaptiveThreshold(frameDelta,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
  #           cv2.THRESH_BINARY,11,2)
  thresh = cv2.dilate(thresh, None, iterations=2)
  (_, cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
  for c in cnts:
     if cv2.contourArea(c) < 10000:
       continue
     (x, y, w, h) = cv2.boundingRect(c)

     cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

   cv2.imshow("Security Feed", frame)
  
  firstFrame = gray.copy()
camera.release()
cv2.destroyAllWindows()