openCV提取图像中的矩形区域_Python

改编自详解利用OpenCV提取图像中的矩形区域（PPT屏幕等）原文是c++版，我改成了python版，供大家参考学习。
主要思想：边缘检测—》轮廓检测—》找出最大的面积的轮廓—》找出顶点—》投影变换

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									import numpy as np

									import cv2

									# 这个成功的扣下了ppt白板

									srcPic = cv2.imread('2345.jpg')

									length=srcPic.shape[0]

									depth=srcPic.shape[1]

									polyPic = srcPic

									shrinkedPic = srcPic

									greyPic = cv2.cvtColor(shrinkedPic, cv2.COLOR_BGR2GRAY)

									ret, binPic = cv2.threshold(greyPic, 130, 255, cv2.THRESH_BINARY)

									print(binPic.shape)

									median = cv2.medianBlur(binPic, 5)

									# 进行边缘检测

									cannyPic = cv2.Canny(median, 10, 200)

									cv2.namedWindow("binary", 0)

									cv2.namedWindow("binary2", 0)

									cv2.imshow("binary", cannyPic)

									# 找出轮廓

									contours, hierarchy = cv2.findContours(cannyPic, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)

									cv2.imwrite('binary2.png', cannyPic)

									cv2.imshow("binary2", cannyPic)

									i = 0

									maxArea = 0

									# 挨个检查看那个轮廓面积最大

									for i in range(len(contours)):

									 if cv2.contourArea(contours[i]) > cv2.contourArea(contours[maxArea]):

									 maxArea = i

									#检查轮廓得到分布在四个角上的点

									hull = cv2.convexHull(contours[maxArea])

									s = [[1,2]]

									z = [[2,3]]

									for i in hull:

									 s.append([i[0][0],i[0][1]])

									 z.append([i[0][0],i[0][1]])

									del s[0]

									del z[0]

									#现在的目标是从一堆点中挑出分布在四个角落的点，决定把图片分为四等份，每个区域的角度来划分点，

									#默认四个角分别分布在图像的四等分的区间上，也就是矩形在图像中央

									# 我们把所有点的坐标，都减去图片中央的那个点（当成原点），然后按照x y坐标值的正负 判断属于哪一个区间

									center=[length/2,depth/2]

									# 可以得到小数

									for i in range(len(s)):

									 s[i][0] = s[i][0] - center[0]

									 s[i][1] = s[i][1] - center[1]

									one = []

									two = []

									three = []

									four = []

									# 判断是那个区间的

									for i in range(len(z)):

									 if s[i][0] <= 0 and s[i][1] <0 :

									 one.append(i)

									 elif s[i][0] > 0 and s[i][1] <0 :

									 two.append(i)

									 elif s[i][0] >= 0 and s[i][1] > 0:

									 four.append(i)

									 else:three.append(i)

									p=[]

									distance=0

									temp = 0

									# 下面开始判断每个区间的极值,要选择距离中心点最远的点，就是角点

									for i in one :

									 x=z[i][0]-center[0]

									 y=z[i][1]-center[1]

									 d=x*x+y*y

									 if d > distance :

									 temp = i

									 distance = d

									p.append([z[temp][0],z[temp][1]])

									distance=0

									temp=0

									for i in two :

									 x=z[i][0]-center[0]

									 y=z[i][1]-center[1]

									 d=x*x+y*y

									 if d > distance :

									 temp = i

									 distance = d

									p.append([z[temp][0],z[temp][1]])

									distance=0

									temp=0

									for i in three :

									 x=z[i][0]-center[0]

									 y=z[i][1]-center[1]

									 d=x*x+y*y

									 if d > distance :

									 temp = i

									 distance = d

									p.append([z[temp][0],z[temp][1]])

									distance=0

									temp=0

									for i in four :

									 x=z[i][0]-center[0]

									 y=z[i][1]-center[1]

									 d=x*x+y*y

									 if d > distance :

									 temp = i

									 distance = d

									p.append([z[temp][0],z[temp][1]])

									for i in p:

									 cv2.circle(polyPic, (i[0],i[1]),2,(0,255,0),2)

									# 给四个点排一下顺序

									a=[]

									b=[]

									st=[]

									for i in p:

									 a.append(i[0])

									 b.append(i[1])

									index=np.lexsort((b, a))

									for i in index:

									 st.append(p[i])

									p = st

									print(p)

									pts1 = np.float32([[p[0][0],p[0][1]],[p[1][0],p[1][1]],[p[2][0],p[2][1]],[p[3][0],p[3][1]]])

									# dst=np.float32([[0,0],[0,srcPic.shape[1]],[srcPic.shape[0],0],[srcPic.shape[0],srcPic.shape[1]]])

									dst=np.float32([[0,0],[0,600],[400,0],[400,600]])

									# 投影变换

									M = cv2.getPerspectiveTransform(pts1,dst)

									cv2.namedWindow("srcPic2", 0)

									cv2.imshow("srcPic2", srcPic)

									#dstImage = cv2.warpPerspective(srcPic,M,(srcPic.shape[0],srcPic.shape[1]))

									dstImage = cv2.warpPerspective(srcPic,M,(400,600))

									# 在原图上画出红色的检测痕迹，先生成一个黑色图

									black = np.zeros((shrinkedPic.shape[0], shrinkedPic.shape[1]), dtype=np.uint8)

									# 二值图转为三通道图

									black3 = cv2.merge([black, black, black])

									# black=black2

									cv2.drawContours(black, contours, maxArea, 255, 11)

									cv2.drawContours(black3, contours, maxArea, (255, 0, 0), 11)

									cv2.imwrite('cv.png', black)

									cv2.namedWindow("cannyPic", 0)

									cv2.imshow("cannyPic", black)

									cv2.namedWindow("shrinkedPic", 0)

									cv2.imshow("shrinkedPic", polyPic)

									cv2.namedWindow("dstImage", 0)

									cv2.imshow("dstImage", dstImage)

									# 等待一个按下键盘事件

									cv2.waitKey(0)

									# 销毁所有创建出的窗口