OpenCV和C++实现图像的翻转（镜像）、平移、旋转、仿射与透视变换 OpenCV和C++实现图像的翻转（镜

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

官网教程

一、翻转（镜像）头文件 quick_opencv.h：声明类与公共函数

#pragma once#include 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 #include #include using namespace cv; int main(int argc, char** argv) { Mat src = https://www.it610.com/article/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}

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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

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(原图大小，不执行缩放)

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获取变换矩阵
变换矩阵计算：

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其中：

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【OpenCV和C++实现图像的翻转（镜像）、平移、旋转、仿射与透视变换】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 = https://www.it610.com/article/Scalar()边缘填充值，默认=0
);

保留所有原图像素的旋转，原理：

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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(0, 0)); double sin = abs(M.at(0, 1)); int new_w = cos * w + sin * h; int new_h = cos * h + sin * w; M.at(0, 2) += (new_w / 2.0 - w / 2); M.at(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

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平移

void QuickDemo::move_Demo(Mat& image) { Mat dst_move; Mat move_mat = (Mat_(2, 3) << 1, 0, 10, 0, 1, 30); //沿x轴移动10沿y轴移动30 warpAffine(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_(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); }

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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); }

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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 = https://www.it610.com/article/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, y0 toushi_after[1] = Point2f(width+2, 2); // x1, y0 toushi_after[2] = Point2f(2, height+2); // x0, y1 toushi_after[3] = Point2f(width + 2, height + 2); // x1, y1 for (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); }

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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

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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((B1 * C2 - B2 * C1) / (A1 * B2 - A2 * B1)); interPoint.y = static_cast((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 contours; vector 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 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 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 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