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Update ImageDataIncrease
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‎ImageDataIncrease.cpp

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//1. 图像旋转
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Mat RotateImage(const Mat &img, int degree) {
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degree = -degree; //warpAffine默认的旋转方向是逆时针,所以加负号表示转化为顺时针
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double angle = degree * CV_PI / 180.; //弧度
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double a = sin(angle), b = cos(angle);
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int width = img.cols;
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int height = img.rows;
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int width_rotate = int(height * fabs(a) + width * fabs(b));
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int height_rotate = int(width * fabs(a) + height * fabs(b));
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float map[6];
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Mat map_matrix = Mat(2, 3, CV_32F, map);
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//旋转中心
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CvPoint2D32f center = cvPoint2D32f(width / 2, height / 2);
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CvMat map_matrix2 = map_matrix;
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cv2DRotationMatrix(center, degree, 1.0, &map_matrix2);
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//Adjust rotation center to dst's center,
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// otherwise you will get only part of the result
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map[2] += (width_rotate - width) / 2;
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map[5] += (height_rotate - height) / 2;
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Mat img_rotate;
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//对图像做仿射变换
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warpAffine(img, img_rotate, map_matrix, Size(width_rotate, height_rotate), 1, 0, 0);
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return img_rotate;
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}
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//生成高斯分布随机数数列(Marsgglia和Bray在1964年提出)
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double generateGaussianNoise(double mu, double sigma)
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{
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static double V1, V2, S;
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static int phase = 0;
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double X;
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double U1, U2;
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if (phase == 0) {
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do {
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U1 = (double)rand() / RAND_MAX;
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U2 = (double)rand() / RAND_MAX;
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V1 = 2 * U1 - 1;
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V2 = 2 * U2 - 1;
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S = V1 * V1 + V2 * V2;
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} while (S >= 1 || S == 0);
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X = V1 * sqrt(-2 * log(S) / S);
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}else{
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X = V2 * sqrt(-2 * log(S) / S);
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}
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phase = 1 - phase;
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return mu + sigma * X;
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}
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//2. 添加高斯噪声（加性噪声）
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//k表示高斯噪声系数，k越大，高斯噪声系数越强
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Mat AddGaussianNoise(const Mat &img, double mu, double sigma, int k) {
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Mat dst;
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dst.create(img.rows, img.cols, img.type());
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for (int x = 0; x < img.rows; x++) {
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for (int y = 0; y < img.cols; y++) {
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double temp = img.at<uchar>(x, y) + k * generateGaussianNoise(mu, sigma);
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if (temp > 255) {
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temp = 255;
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}
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else if (temp < 0) {
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temp = 0;
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}
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dst.at<uchar>(x, y) = temp;
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}
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}
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return dst;
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}
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//3.添加椒盐噪声，椒盐噪声是根据图像信噪比，随机生成一些图像内的像素位置并随机对这些像素点赋值为0或255
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// SNR等于0-1的浮点数，用来控制选取位置的多少
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Mat AddSaltNoise(const Mat &img, double SNR) {
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Mat dst;
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dst.create(img.rows, img.cols, img.type());
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int SP = img.rows * img.cols;
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int NP = SP*(1 - SNR); //获得需要添加椒盐噪声的像素个数
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dst = img.clone();
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for (int i = 0; i < NP; i++) {
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int x = (int)(rand()*1.0 / RAND_MAX * (double)img.rows);
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int y = (int)(rand()*1.0 / RAND_MAX * (double)img.cols);
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int r = rand() % 2;
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if (r) {
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dst.at<uchar>(x, y) = 0;
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}
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else {
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dst.at<uchar>(x, y) = 255;
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}
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}
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return dst;
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}
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//4. 调整图像饱和度(PhotoShop中的饱和度调节)
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//(1) 计算每个像素点三基色最小值和最大值
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//(2) delta为2值之差/255，如果二值之差为0不操作
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//(3)value为两值之和
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//(4)RGB图像空间转化为HSL(H色调,S饱和度,L亮度)
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//L = value/2
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//如果L<0.5，则S=delta/value
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//否则S=delta/(2-value)
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//Increment为饱和度,正值为增加饱和度,负值为降低饱和度,取值为(-1,1)
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//(5)根据不同的公式得到新的rgb值
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Mat ChangeColor(const Mat &img, const float Increment) {
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Mat dst;
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Mat Img_out(img.size(), CV_32FC3);
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img.convertTo(Img_out, CV_32FC3);
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Mat Img_in(img.size(), CV_32FC3);
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img.convertTo(Img_in, CV_32FC3);
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//定义输入图像的迭代器
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MatIterator_<Vec3f>inp_begin, inp_end;
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inp_begin = Img_in.begin<Vec3f>();
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inp_end = Img_in.end<Vec3f>();
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//定义输出图像的迭代器
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MatIterator_<Vec3f>out_begin, out_end;
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out_begin = Img_out.begin<Vec3f>();
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out_end = Img_out.end<Vec3f>();
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float delta = 0;
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float minVal, maxVal, t1, t2, t3, L, S, alpha;
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for (; inp_begin != inp_end; inp_begin++, out_begin++) {
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t1 = (*inp_begin)[0];
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t2 = (*inp_begin)[1];
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t3 = (*inp_begin)[2];
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minVal = std::min(std::min(t1, t2), t3);
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maxVal = std::max(std::max(t1, t2), t3);
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delta = (maxVal - minVal) / 255.0;
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L = 0.5 * (maxVal + minVal) / 255.0;
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S = std::max(0.5 * delta / L, 0.5 * delta / (1 - L));
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if (Increment > 0) {
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alpha = max(S, 1 - Increment);
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alpha = 1.0 / alpha - 1;
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(*out_begin)[0] = (*inp_begin)[0] + ((*inp_begin)[0] - L*255.0) * alpha;
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(*out_begin)[1] = (*inp_begin)[1] + ((*inp_begin)[1] - L*255.0) * alpha;
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(*out_begin)[2] = (*inp_begin)[2] + ((*inp_begin)[2] - L*255.0) * alpha;
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}
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else {
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alpha = Increment;
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(*out_begin)[0] = L*255.0 + ((*inp_begin)[0] - L*255.0) * (1 + alpha);
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(*out_begin)[1] = L*255.0 + ((*inp_begin)[1] - L*255.0) * (1 + alpha);
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(*out_begin)[2] = L*255.0 + ((*inp_begin)[2] - L*255.0) * (1 + alpha);
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}
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}
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Img_out.convertTo(dst, CV_8UC3);
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return dst;
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}
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//5. 给图片增加老照片效果
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Mat OldPicture(const Mat &src) {
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Mat Image_out(src.size(), CV_32FC3);
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src.convertTo(Image_out, CV_32FC3);
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Mat Image_2(src.size(), CV_32FC3);
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src.convertTo(Image_2, CV_32FC3);
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Mat r(src.rows, src.cols, CV_32FC1);
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Mat g(src.rows, src.cols, CV_32FC1);
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Mat b(src.rows, src.cols, CV_32FC1);
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Mat out[] = {b, g, r};
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split(Image_2, out);
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Mat r_new(src.rows, src.cols, CV_32FC1);
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Mat g_new(src.rows, src.cols, CV_32FC1);
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Mat b_new(src.rows, src.cols, CV_32FC1);
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r_new = 0.393*r + 0.769*g + 0.189*b;
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g_new = 0.349*r + 0.686*g + 0.168*b;
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b_new = 0.272*r + 0.534*g + 0.131*b;
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Mat rgb[] = { b_new, g_new, r_new };
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merge(rgb, 3, Image_out);
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Mat dstImg;
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Image_out.convertTo(dstImg, CV_8UC3);
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return dstImg;
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}
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‎README.md

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# 记录一些图像处理中的论文及代码复现
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- msrcr.cpp 带色彩恢复的多尺度视网膜增强算法 学习链接：http://www.cnblogs.com/Imageshop/archive/2013/04/17/3026881.html
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- ImageDataIncrease.cpp 常见的图片数据扩充
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-
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