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我用的是python27版本，
在opencv上 https://opencv.org/releases/page/6/
下载这个版本的：opencv-2.4.10.exe
解压到一个文件夹，如 D:opencvpython27
再添加到环境变量里新建了个 OpenCv
再安装 numpy这是opencv默认依赖的。
版本：numpy-1.12.1.zip
其他高一些版本安装后也有问题。比如像这个
ImportError: numpy.core.multiarray failed to import
只好从1.13,1.15,换到1.12版本的。
//var filename = @"E: estamap-binary.bmp"; //读取灰度图 using (Mat src = new Mat(filename, ImreadModes.Grayscale)) { //Cv2.ImShow("Demo", src); //1.Sobel算子，x方向求梯度 Mat sobel = new Mat(); Cv2.Sobel(src, sobel, MatType.CV_8U, 1, 0, 3); //2.二值化 Mat binary = new Mat(); Cv2.Threshold(sobel, binary, 0, 255, ThresholdTypes.Otsu | ThresholdTypes.Binary); //3. 膨胀和腐蚀操作的核函
数 Mat element1 = new Mat(); Mat element2 = new Mat(); OpenCvSharp.Size size1 = new OpenCvSharp.Size(30, 9); OpenCvSharp.Size size2 = new OpenCvSharp.Size(24, 6); element1 = Cv2.GetStructuringElement(MorphShapes.Rect, size1); element2 = Cv2.GetStructuringElement(MorphShapes.Rect, size2); //4. 膨胀一次，让轮廓突出 Mat dilation = new Mat(); Cv2.Dilate(binary, dilation, element2); //5. 腐蚀一次，去掉细节，如表格线等。注意这里去掉的是竖直的线 Mat erosion = new Mat(); Cv2.Erode(dilation, erosion, element1); //6. 再次膨胀，让轮廓明显一些 Mat dilation2 = new Mat(); Cv2.Dilate(erosion, dilation2, element2, null, 2); OpenCvSharp.Point[][] contours; HierarchyIndex[] hierarchly; Cv2.FindContours(dilation2, out contours, out hierarchly, RetrievalModes.Tree, ContourApproximationModes.ApproxSimple, new OpenCvSharp.Point(0, 0)); //Mat dst_Image = Mat.Zeros(src.Size(), src.Type()); Mat dst_Image = src.Clone(); string pts = ""; for (int i = 0; i < contours.Length; i++) { Scalar color = new Scalar(0, 255, 255);// //Cv2.DrawContours(dst_Image, contours, i, color, 1, LineTypes.Link8, hierarchly); double area = Cv2.ContourArea(contours[i]); if (area < 1000) continue; var epsilon = 0.001 * Cv2.ArcLength(contours[i], true); var approx = Cv2.ApproxPolyDP(contours[i], epsilon, true); //找到最小矩形，可能有方向 var rect2 = Cv2.MinAreaRect(contours[i]); var box = rect2.Points(); //筛选那些太细的矩形，留下扁的 var height = Math.Abs(box[0].Y - box[2].Y); var width = Math.Abs(box[0].X - box[2].X); if (height > width * 1.2) continue; pts += rect2.Center.X.ToString() + "," + rect2.Center.Y.ToString() + ";"; Cv2.DrawContours(dst_Image, contours, i, color, 1, LineTypes.Link8, hierarchly); } Cv2.ImShow(pts, dst_Image); Cv2.WaitKey(0);
#coding=utf-8 import sys import cv2 import numpy as np # img = cv2.imread(r'E: estamap-binary.bmp') # cv2.namedWindow("Image") # cv2.imshow("Image", img) # cv2.waitKey (0) # cv2.destroyAllWindows() def preprocess(gray): # 1. Sobel算子，x方向求梯度 sobel = cv2.Sobel(gray, cv2.CV_8U, 1, 0, ksize = 3) # 2. 二值化 ret, binary = cv2.threshold(sobel, 0, 255, cv2.THRESH_OTSU+cv2.THRESH_BINARY) # 3. 膨胀和腐蚀操作的核函数 element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 9)) element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (24, 6)) # 4. 膨胀一次，让轮廓突出 dilation = cv2.dilate(binary, element2, iterations = 1) # 5. 腐蚀一次，去掉细节，如表格线等。注意这里去掉的是竖直的线 erosion = cv2.erode(dilation, element1, iterations = 1) # 6. 再次膨胀，让轮廓明显一些 dilation2 = cv2.dilate(erosion, element2, iterations = 2) # 7. 存储中间图片 cv2.imwrite("binary.png", binary) cv2.imwrite("dilation.png", dilation) cv2.imwrite("erosion.png", erosion) cv2.imwrite("dilation2.png", dilation2) return dilation2 def findTextRegion(img): region = [] # 1. 查找轮廓 contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # 2. 筛选那些面积小的 for i in range(len(contours)): cnt = contours[i] # 计算该轮廓的面积 area = cv2.contourArea(cnt) # 面积小的都筛选掉 if(area < 1000): continue # 轮廓近似，作用很小 epsilon = 0.001 * cv2.arcLength(cnt, True) approx = cv2.approxPolyDP(cnt, epsilon, True) # 找到最小的矩形，该矩形可能有方向 rect = cv2.minAreaRect(cnt) print "rect is: " print rect # box是四个点的坐标 box = cv2.cv.BoxPoints(rect) box = np.int0(box) # 计算高和宽 height = abs(box[0][1] - box[2][1]) width = abs(box[0][0] - box[2][0]) # 筛选那些太细的矩形，留下扁的 if(height > width * 1.2): continue region.append(box) return region def detect(img): # 1. 转化成灰度图 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 2. 形态学变换的预处理，得到可以查找矩形的图片 dilation = preprocess(gray) # 3. 查找和筛选文字区域 region = findTextRegion(dilation) # 4. 用绿线画出这些找到的轮廓 for box in region: cv2.drawContours(img, [box], 0, (0, 255, 0), 2) cv2.namedWindow("img", cv2.WINDOW_NORMAL) cv2.imshow("img", img) # 带轮廓的图片 cv2.imwrite("contours.png", img) cv2.waitKey(0) cv2.destroyAllWindows() if __name__ == '__main__': # 读取文件 imagePath = r'E: estamap-binary.bmp' img = cv2.imread(imagePath) detect(img)