Python-OpenCV 处理视频（四）：运动检测

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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：

#!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()