openCV学习（三）提取图像中的矩形区域 |
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openCV学习（三）提取图像中的矩形区域
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改编自 https://segmentfault.com/a/1190000013925648 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 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: 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运行效果 |
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