1、概述
案例：使用OpenCV将一张折射的图片给矫正过来
实现步骤：
1.载入图像
2.图像灰度化
3.二值分割
4.形态学操作去除噪点
5.轮廓发现
6.使用霍夫直线检测，检测上下左右四条直线（有可能是多条，但是无所谓）
7.绘制出直线
8.寻找与定位上下左右是条直线
9.拟合四条直线方程
10.计算四条直线的交点，ps：这四个交点其实就是我们最终要寻找的，用于透视变换使用的
11.进行透视变换
12.输出透视变换的结果
说明：
解释一下为啥是上面那些步骤。
1.其实我们的最终目的是通过透视矩阵getPerspectiveTransform+透视变换warpPerspective来完成图像的矫正
2.但是getPerspectiveTransform需要两个参数，输入矩阵参数和目标矩阵参数。
3.由于输入矩阵参数就是原图像是个角的顶点，由于我们没有所以要求出来
4.所以我们以上的所有步骤都是为11、12步打基础的
ps：核心就是利用透视矩阵做透视变换
重点：
1.直线方程y=kx+c
2.如果两条直线有交点，则必有k1x1+c1=k2x2+c2
2、代码演示
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//【1】载入图像 Mat src = imread(filePath); if(src.empty()){ qDebug()<<"图片为空"; return; } imshow("src",src); //【2】图像灰度化 Mat gray; cvtColor(src,gray,COLOR_BGR2GRAY); //【3】执行二值分割 threshold(gray,gray,0,255,THRESH_BINARY_INV|THRESH_OTSU); imshow("threshold",gray); //【4】执行形态学开操作去除图像中的造点 Mat kernel = getStructuringElement(MORPH_RECT,Size(5,5),Point(-1,-1)); morphologyEx(gray,gray,MORPH_CLOSE,kernel,Point(-1,-1),3); imshow("morphologyEx",gray); //【5】轮廓发现 bitwise_not(gray,gray); imshow("bitwise_not",gray); vector<vector<Point>> contours; vector<Vec4i> hier; RNG rng(12345); findContours(gray,contours,hier,RETR_TREE,CHAIN_APPROX_SIMPLE); Mat colorImage = Mat::zeros(gray.size(),CV_8UC3); for(size_t i = 0;i<contours.size();i++){ Rect rect = boundingRect(contours[i]); //过滤目标轮廓 if(rect.width<src.cols-5&&rect.height<src.rows-5&&rect.width>src.cols/2){ drawContours(colorImage,contours,i,Scalar(rng.uniform(0,255),rng.uniform(0,255),rng.uniform(0,255)),1); } } imshow("findContours",colorImage); //【6】使用霍夫直线检测 vector<Vec4i> lines; cvtColor(colorImage,colorImage,COLOR_BGR2GRAY); kernel = getStructuringElement(MORPH_RECT,Size(3,3),Point(-1,-1)); dilate(colorImage,colorImage,kernel,Point(-1,-1),1); imshow("colorImage_gray",colorImage); int accu = min(src.cols*0.5, src.rows*0.5); HoughLinesP(colorImage,lines,1,CV_PI/180,accu,accu,0); //【7】绘制出直线 Mat lineColorImage = Mat::zeros(gray.size(),CV_8UC3); qDebug()<<"line count:"<<lines.size(); for(size_t i = 0;i<lines.size();i++){ Vec4i ll = lines[i]; line(lineColorImage,Point(ll[0],ll[1]),Point(ll[2],ll[3]),Scalar(rng.uniform(0,255),rng.uniform(0,255),rng.uniform(0,255)),2,LINE_8); } imshow("lines",lineColorImage); //【8】寻找与定位上下左右四条直线 int deltah = 0; int width = src.cols; int height = src.rows; Vec4i topLine, bottomLine; Vec4i leftLine, rightLine; for(size_t i=0;i<lines.size();i++){ Vec4i ln = lines[i]; deltah = abs(ln[3]-ln[1]);//直线高度 if (ln[3] < height / 2.0 && ln[1] < height / 2.0 && deltah < accu - 1) { if (topLine[3] > ln[3] && topLine[3]>0) { topLine = lines[i]; } else { topLine = lines[i]; } } if (ln[3] > height / 2.0 && ln[1] > height / 2.0 && deltah < accu - 1) { bottomLine = lines[i]; } if (ln[0] < width / 2.0 && ln[2] < width/2.0) { leftLine = lines[i]; } if (ln[0] > width / 2.0 && ln[2] > width / 2.0) { rightLine = lines[i]; } } //直线方程y=kx+c // 【9】拟合四条直线方程 float k1, c1; k1 = float(topLine[3] - topLine[1]) / float(topLine[2] - topLine[0]); c1 = topLine[1] - k1*topLine[0]; float k2, c2; k2 = float(bottomLine[3] - bottomLine[1]) / float(bottomLine[2] - bottomLine[0]); c2 = bottomLine[1] - k2*bottomLine[0]; float k3, c3; k3 = float(leftLine[3] - leftLine[1]) / float(leftLine[2] - leftLine[0]); c3 = leftLine[1] - k3*leftLine[0]; float k4, c4; k4 = float(rightLine[3] - rightLine[1]) / float(rightLine[2] - rightLine[0]); c4 = rightLine[1] - k4*rightLine[0]; // 【10】四条直线交点，其实最终的目的就是找这是条直线的交点 Point p1; // 左上角 p1.x = static_cast<int>((c1 - c3) / (k3 - k1)); p1.y = static_cast<int>(k1*p1.x + c1); Point p2; // 右上角 p2.x = static_cast<int>((c1 - c4) / (k4 - k1)); p2.y = static_cast<int>(k1*p2.x + c1); Point p3; // 左下角 p3.x = static_cast<int>((c2 - c3) / (k3 - k2)); p3.y = static_cast<int>(k2*p3.x + c2); Point p4; // 右下角 p4.x = static_cast<int>((c2 - c4) / (k4 - k2)); p4.y = static_cast<int>(k2*p4.x + c2); // 显示四个点坐标 circle(lineColorImage, p1, 2, Scalar(255, 0, 0), 2, 8, 0); circle(lineColorImage, p2, 2, Scalar(255, 0, 0), 2, 8, 0); circle(lineColorImage, p3, 2, Scalar(255, 0, 0), 2, 8, 0); circle(lineColorImage, p4, 2, Scalar(255, 0, 0), 2, 8, 0); line(lineColorImage, Point(topLine[0], topLine[1]), Point(topLine[2], topLine[3]), Scalar(0, 255, 0), 2, 8, 0); imshow("four corners", lineColorImage); // 【11】透视变换 vector<Point2f> src_corners(4); src_corners[0] = p1; src_corners[1] = p2; src_corners[2] = p3; src_corners[3] = p4; vector<Point2f> dst_corners(4); dst_corners[0] = Point(0, 0); dst_corners[1] = Point(width, 0); dst_corners[2] = Point(0, height); dst_corners[3] = Point(width, height); // 【12】获取透视变换矩阵，并最终显示变换后的结果 Mat resultImage; Mat warpmatrix = getPerspectiveTransform(src_corners, dst_corners); warpPerspective(src, resultImage, warpmatrix, resultImage.size(), INTER_LINEAR); imshow("Final Result", resultImage); |
3、示例图片
以上就是OpenCV通过透视变换实现矫正图像详解的详细内容，更多关于OpenCV矫正图像的资料请关注服务器之家其它相关文章！
原文链接：https://blog.csdn.net/m0_60259116/article/details/129047841
