目录一、翻转（镜像）二、仿射扭曲获取变换矩阵仿射扭曲函数 warpAffine旋转平移三、仿射变换四、透视变换综合示例总结
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一、翻转（镜像）
头文件 quick_opencv.h：声明类与公共函数
#pragma once#include <opencv2\opencv.hpp>using namespace cv;class QuickDemo {public:...void flip_Demo(Mat& image);void rotate_Demo(Mat& image);void move_Demo(Mat& image);void Affine_Demo(Mat& image);void toushi_Demo(Mat& image);void perspective_detect(Mat& image);};
主函数调用该类的公共成员函数
#include <opencv2\opencv.hpp>#include <quick_opencv.h>#include <iostream>using namespace cv;int main(int argc, char** argv) {Mat src = imread("D:\\Desktop\\pandas.jpg");if (src.empty()) {printf("Could not load images...\n");return -1;}namedWindow("input", WINDOW_NORMAL);imshow("input", src);QuickDemo qk;...qk.Affine_Demo(src);qk.move_Demo(src);qk.flip_Demo(src);qk.toushi_Demo(src);qk.perspective_detect(src);waitKey(0);destroyAllWindows();return 0;}
源文件 quick_demo.cpp：实现类与公共函数
void QuickDemo::flip_Demo(Mat& image) {Mat dst0, dst1, dst2;flip(image, dst0, 0);flip(image, dst1, 1);flip(image, dst2, -1);imshow("dst0_上下翻转", dst0);imshow("dst1_左右翻转", dst1);imshow("dst2_对角线翻转", dst2); //旋转180度}
二、仿射扭曲
二维图像一般情况下的变换矩阵（旋转+平移），当我们只需要平移的时候，取 θ 的值为0，a和b的值就代表了图像沿x轴和y轴移动的距离；其中原图 (原图大小，不执行缩放)
获取变换矩阵
变换矩阵计算：
其中：
Mat getRotationMatrix2D( Point2f center, 源图像中旋转的中心 double angle, 角度以度为单位的旋转角度。正值表示逆时针旋转（坐标原点假定为左上角）。 double scale 各向同性比例因子。 )
仿射扭曲函数 warpAffine
函数签名
void warpAffine( InputArray src, 输入矩阵 OutputArray dst, 输出矩阵 InputArray M, 2×3 变换矩阵 Size dsize, 输出图像大小 int flags = INTER_LINEAR, 插值方式：默认线性插值 int borderMode = BORDER_CONSTANT, 边缘处理方式 const Scalar& borderValue = Scalar() 边缘填充值，默认=0 );
保留所有原图像素的旋转，原理：
旋转
void QuickDemo::rotate_Demo(Mat& image) {Mat dst_0, dst_1, M;int h = image.rows;int w = image.cols;M = getRotationMatrix2D(Point(w / 2, h / 2), 45, 1.0);warpAffine(image, dst_0, M, image.size());double cos = abs(M.at<double>(0, 0));double sin = abs(M.at<double>(0, 1));int new_w = cos * w + sin * h;int new_h = cos * h + sin * w;M.at<double>(0, 2) += (new_w / 2.0 - w / 2);M.at<double>(1, 2) += (new_h / 2.0 - h / 2);warpAffine(image, dst_1, M, Size(new_w, new_h), INTER_LINEAR, 0, Scalar(255, 255, 0));imshow("旋转演示0", dst_0);imshow("旋转演示1", dst_1);}
依次为：原图，旋转45度，保留所有原图像素的旋转45度
平移
void QuickDemo::move_Demo(Mat& image) {Mat dst_move;Mat move_mat = (Mat_<double>(2, 3) << 1, 0, 10, 0, 1, 30);//沿x轴移动10沿y轴移动30warpAffine(image, dst_move, move_mat, image.size());imshow("dst_move", dst_move);double angle_ = 3.14159265354 / 16.0;cout << "pi=" << cos(angle_) << endl;Mat rota_mat = (Mat_<double>(2, 3) << cos(angle_), -sin(angle_), 1, sin(angle_), cos(angle_), 1);warpAffine(image, rotate_dst, rota_mat, image.size());imshow("rotate_dst", rotate_dst);}
三、仿射变换
Mat getAffineTransform( 返回变换矩阵const Point2f src[], 变换前三个点的数组const Point2f dst[] 变换后三个点的数组);void
void QuickDemo::Affine_Demo(Mat& image) {Mat warp_dst;Mat warp_mat(2, 3, CV_32FC1);Point2f srcTri[3];Point2f dstTri[3];/// 设置源图像和目标图像上的三组点以计算仿射变换srcTri[0] = Point2f(0, 0);srcTri[1] = Point2f(image.cols - 1, 0);srcTri[2] = Point2f(0, image.rows - 1);for (size_t i = 0; i < 3; i++){circle(image, srcTri[i], 2, Scalar(0, 0, 255), 5, 8);}dstTri[0] = Point2f(image.cols * 0.0, image.rows * 0.13);dstTri[1] = Point2f(image.cols * 0.95, image.rows * 0.15);dstTri[2] = Point2f(image.cols * 0.15, image.rows * 0.9);warp_mat = getAffineTransform(srcTri, dstTri);warpAffine(image, warp_dst, warp_mat, warp_dst.size());imshow("warp_dst", warp_dst);}
四、透视变换
获取透射变换的矩阵：
Mat getPerspectiveTransform( 返回变换矩阵const Point2f src[], 透视变换前四个点的 数组const Point2f dst[], 透视变换后四个点的 数组int solveMethod = DECOMP_LU)
透射变换
void warpPerspective( InputArray src, 原图像 OutputArray dst, 返回图像 InputArray M, 透视变换矩阵 Size dsize, 返回图像的大小（宽，高） int flags = INTER_LINEAR, 插值方法 int borderMode = BORDER_CONSTANT, 边界处理 const Scalar& borderValue = Scalar() 缩放处理 )
void QuickDemo::toushi_Demo(Mat& image) {Mat toushi_dst, toushi_mat;Point2f toushi_before[4];toushi_before[0] = Point2f(122, 220);toushi_before[1] = Point2f(397, 121);toushi_before[2] = Point2f(133, 339);toushi_before[3] = Point2f(397, 218);int width_0 = toushi_before[1].x - toushi_before[0].x;int height_0 = toushi_before[1].y - toushi_before[0].y;int width_1 = toushi_before[2].x - toushi_before[0].x;int height_1 = toushi_before[2].y - toushi_before[0].y;int width = (int)sqrt(width_0 * width_0 + height_0 * height_0);int height = (int)sqrt(width_1 * width_1 + height_1 * height_1);Point2f toushi_after[4];toushi_after[0] = Point2f(2, 2); // x0, y0toushi_after[1] = Point2f(width+2, 2); // x1, y0toushi_after[2] = Point2f(2, height+2); // x0, y1toushi_after[3] = Point2f(width + 2, height + 2); // x1, y1for (size_t i = 0; i < 4; i++){cout << toushi_after[i] << endl;}toushi_mat = getPerspectiveTransform(toushi_before, toushi_after);warpPerspective(image, toushi_dst, toushi_mat, Size(width, height));imshow("toushi_dst", toushi_dst);}
综合示例
自动化透视矫正图像:
流程：
- 灰度化二值化 形态学去除噪点 获取轮廓 检测直线 计算直线交点 获取四个透视顶点 透视变换
inline void Intersection(Point2i& interPoint, Vec4i& line1, Vec4i& line2) {// x1, y1, x2, y2 = line1[0], line1[1], line1[2], line1[3]int A1 = line1[3] - line1[1];int B1 = line1[0] - line1[2];int C1 = line1[1] * line1[2] - line1[0] * line1[3];int A2 = line2[3] - line2[1];int B2 = line2[0] - line2[2];int C2 = line2[1] * line2[2] - line2[0] * line2[3];interPoint.x = static_cast<int>((B1 * C2 - B2 * C1) / (A1 * B2 - A2 * B1));interPoint.y = static_cast<int>((C1 * A2 - A1 * C2) / (A1 * B2 - A2 * B1));}void QuickDemo::perspective_detect(Mat& image) {Mat gray_dst, binary_dst, morph_dst;// 二值化cvtColor(image, gray_dst, COLOR_BGR2GRAY);threshold(gray_dst, binary_dst, 0, 255, THRESH_BINARY_INV | THRESH_OTSU);//形态学操作Mat kernel = getStructuringElement(MORPH_RECT, Size(5, 5));morphologyEx(binary_dst, morph_dst, MORPH_CLOSE, kernel, Point(-1, -1), 3);bitwise_not(morph_dst, morph_dst);imshow("morph_dst2", morph_dst);//轮廓查找与可视化vector<vector<Point>> contours;vector<Vec4i> hierarches;int height = image.rows;int width = image.cols;Mat contours_Img = Mat::zeros(image.size(), CV_8UC3);findContours(morph_dst, contours, hierarches, RETR_TREE, CHAIN_APPROX_SIMPLE);for (size_t i = 0; i < contours.size(); i++){Rect rect = boundingRect(contours[i]);if (rect.width > width / 2 && rect.width < width - 5) {drawContours(contours_Img, contours, i, Scalar(0, 0, 255), 2, 8, hierarches, 0, Point());}}imshow("contours_Img", contours_Img);vector<Vec4i> lines;Mat houghImg;int accu = min(width * 0.5, height * 0.5);cvtColor(contours_Img, houghImg, COLOR_BGR2GRAY);HoughLinesP(houghImg, lines, 1, CV_PI / 180, accu, accu*0.6, 0);Mat lineImg = Mat::zeros(image.size(), CV_8UC3);for (size_t i = 0; i < lines.size(); i++){Vec4i ln = lines[i];line(lineImg, Point(ln[0], ln[1]), Point(ln[2], ln[3]), Scalar(0, 0, 255), 2, 8, 0);}// 寻找与定位上下左右四条直线int delta = 0;Vec4i topline = { 0, 0, 0, 0 };Vec4i bottomline;Vec4i leftline, rightline;for (size_t i = 0; i < lines.size(); i++) {Vec4i ln = lines[i];delta = abs(ln[3] - ln[1]); // y2-y1//toplineif (ln[3] < height / 2.0 && ln[1] < height / 2.0 && delta < 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 && delta < 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];}}cout << "topline: " << topline << endl;cout << "bottomline: " << bottomline << endl;cout << "leftline: " << leftline << endl;cout << "rightline: " << rightline << endl;// 计算上述四条直线交点（两条线的交点:依次为左上，右上，左下,右下）Point2i p0, p1, p2, p3;Intersection(p0, topline, leftline);Intersection(p1, topline, rightline);Intersection(p2, bottomline, leftline);Intersection(p3, bottomline, rightline);circle(lineImg, p0, 2, Scalar(255, 0, 0), 2, 8, 0);circle(lineImg, p1, 2, Scalar(255, 0, 0), 2, 8, 0);circle(lineImg, p2, 2, Scalar(255, 0, 0), 2, 8, 0);circle(lineImg, p3, 2, Scalar(255, 0, 0), 2, 8, 0);imshow("Intersection", lineImg);//透视变换vector<Point2f> src_point(4);src_point[0] = p0;src_point[1] = p1;src_point[2] = p2;src_point[3] = p3;int new_height = max(abs(p2.y - p0.y), abs(p3.y - p1.y));int new_width = max(abs(p1.x - p0.x), abs(p3.x - p2.x));cout << "new_height = " << new_height << endl;cout << "new_width = " << new_width << endl;vector<Point2f> dst_point(4);dst_point[0] = Point(0,0);dst_point[1] = Point(new_width, 0);dst_point[2] = Point(0, new_height);dst_point[3] = Point(new_width, new_height);Mat resultImg;Mat wrap_mat = getPerspectiveTransform(src_point, dst_point);warpPerspective(image, resultImg, wrap_mat, Size(new_width, new_height));imshow("resultImg", resultImg);}
关键步骤可视化
总结
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