opencv直方图，lomo，cartoon

直方图绘制和直方图均衡化、局部直方图自适应均衡化
图像LOMO效果（暗处更暗，亮处更亮，产生光晕，虚焦）
图像cartoon效果

#include<opencv2/opencv.hpp>
#include<iostream>using namespace std;
using namespace cv;void lomo(string filename);
void cartoon(string filename);
void drawhist(string filename);
int main()
{drawhist("dsn.tif");lomo("dsn.tif");cartoon("dsn.tif");waitKey(0);return 0;
}void drawhist(string filename) {Mat src = imread(filename);if (src.empty()) {return ;}vector<Mat> bgr;//0:blue,1:green,2:redsplit(src, bgr);Mat b, g, r;b = bgr[0];g = bgr[1];r = bgr.at(2);Mat hist_b, hist_g, hist_r;//存放直方图计算结果float range[] = { 0,256 };int histsize = 256;const float*ranges = { range };//直方图数值计算calcHist(&b, 1, 0, Mat(), hist_b, 1, &histsize, &ranges);calcHist(&g, 1, 0, Mat(), hist_g, 1, &histsize, &ranges);calcHist(&r, 1, 0, Mat(), hist_r, 1, &histsize, &ranges);//建画布int height = 300, width = 500;Mat histImg(height, width, CV_8UC3, Scalar(0, 0, 0));Mat histImg_b(height, width, CV_8UC3, Scalar(0, 0, 0));Mat histImg_g(height, width, CV_8UC3, Scalar(0, 0, 0));Mat histImg_r(height, width, CV_8UC3, Scalar(0, 0, 0));//归一化防溢出normalize(hist_b, hist_b, 0, histImg.rows, NORM_MINMAX, -1, Mat());normalize(hist_g, hist_g, 0, histImg.rows, NORM_MINMAX, -1, Mat());normalize(hist_r, hist_r, 0, histImg.rows, NORM_MINMAX, -1, Mat());int bin_w = cvRound((double)width / height);for (int i = 1; i < hist_b.rows; i++) {//Point q((i - 1)*bin_w, height - cvRound(hist_b.at<float>(i - 1, 0)));//Point h((i)*bin_w, height - cvRound(hist_b.at<float>(i,0)));//line(histImg, q, h, Scalar(255, 0, 0));//画线line(histImg_b, Point((i - 1)*bin_w, height - cvRound(hist_b.at<float>(i - 1, 0))), Point((i)*bin_w, height - cvRound(hist_b.at<float>(i, 0))),Scalar(255, 0, 0), bin_w);line(histImg_g, Point((i - 1)*bin_w, height - cvRound(hist_g.at<float>(i - 1, 0))), Point((i)*bin_w, height - cvRound(hist_g.at<float>(i, 0))),Scalar(0, 255, 0), bin_w);line(histImg_r, Point((i - 1)*bin_w, height - cvRound(hist_r.at<float>(i - 1, 0))), Point((i)*bin_w, height - cvRound(hist_r.at<float>(i, 0))),Scalar(0, 0, 255), bin_w);}histImg = histImg_b + histImg_g + histImg_r;//直方图均衡化和局部直方图自适应均衡化都需要的是当通道的图像Mat grayImg, dst;cvtColor(src, grayImg, COLOR_BGR2GRAY);//直方图均衡化equalizeHist(grayImg, dst);//局部直方图自适应均衡化//createCLAHE(clipLimit:颜色对比度的阈值,titleGridSize:进行像素均衡化的网格大小，即在多少网格下进行直方图的均衡操作//1、实例化自适应直方图均衡化函数cv::Ptr<cv::CLAHE> clahe = createCLAHE(100, Size(10, 10));//2、进行自适应直方图均衡化Mat jubudst;clahe->apply(grayImg, jubudst);waitKey(0);
}void lomo(string filename){Mat src = imread(filename);//Mat src = imread("lena.png");if (src.empty()) {return ;}//Mat src = Mat::zeros(Size(200, 200), CV_8UC3);//circle(src, Point(src.cols / 2, src.rows / 2), src.cols / 4, Scalar(255, 255, 255), -1);const double exponential_e = std::exp(1.0);Mat lut(1, 256, CV_8UC1);Mat result;//uchar* plut = lut.data;for (int i = 0; i < 256; i++) {double x = (double)(i / 256.0);//plut[i] = cvRound(256 * (  1.0  /  (1.0 + pow(exponential_e, -((x - 0.5) / 0.1)))  ) );lut.at<uchar>(i) = cvRound(256 * (1 / (1 + pow(exponential_e, -((x - 0.5) / 0.1)))));//pow(InputArray src, double p, OutputArray dst)}//类似于根据新的计算公式,把原像素值 "映射" 到新像素值上去//2.分离通道std::vector<Mat> bgr;split(src, bgr);//3.调用LUT函数处理红色通道//也可以是蓝色，绿色cv::LUT(bgr[2], lut, bgr[2]);//cv::LUT(bgr[0], lut, bgr[0]);//cv::LUT(bgr[1], lut, bgr[1]);//4.合并通道cv::merge(bgr, result);//5.创建黑暗光环Mat halo(src.size(), CV_32FC3, Scalar(0.3, 0.3, 0.3));//这里画光环宽度应该是-1circle(halo, Point(src.cols / 2, src.rows / 2), src.cols / 3, Scalar(1, 1, 1), -1);//大模糊blur(halo, halo, Size(src.cols / 3, src.rows / 3));//6.将光环放到原图像上去,方法是将两个图像相乘（相乘必须是浮点型，所以需要类型转换）Mat resultf;result.convertTo(resultf, CV_32FC3);cv::multiply(resultf, halo, resultf);resultf.convertTo(result, CV_8UC3);waitKey(0);
}void cartoon(string filename) {Mat src = imread(filename);//Mat src = imread("lena.png");if (src.empty()) {return;}Mat imgMedian;//1、中值滤波器去噪声，根据噪声特性medianBlur(src, imgMedian, 7);// Detect edges with canny//2、消除噪声后，用canny算子检测强边缘Mat imgCanny;Canny(imgMedian, imgCanny, 50, 150);// Dilate(膨胀) the edges使断续的边缘连接Mat kernel = getStructuringElement(MORPH_RECT, Size(2, 2));dilate(imgCanny, imgCanny, kernel);// Scale edges values to 1 and invert values//为防止乘法溢出，先将边缘图像整合到0-1区间imgCanny = imgCanny / 255;imgCanny = 1 - imgCanny;// Use float values to allow multiply between 0 and 1//3、乘法（将边缘图像加到原图像上（如lomo一样））Mat imgCannyf;imgCanny.convertTo(imgCannyf, CV_32FC3);// Blur the edgest to do smooth effect平滑边缘（小模糊）blur(imgCannyf, imgCannyf, Size(5, 5));/** COLOR **///颜色过滤// Apply bilateral filter to homogenizes color//为了得到卡通外观使用bilateralFilter双边滤波滤镜//一种滤波器，在保持边缘的同时降低图像的噪声，通过适当的参数（输入图像，输出图像，像素领域直径d，sigma色值，sigma坐标空间）Mat imgBF;bilateralFilter(src, imgBF, 9, 150.0, 150.0);//当直径（第三个参数）大于5的时候,bilateral开始变慢//当sigma的值大于150的时候,会出现卡通效果// truncate colors//为了更强大的卡通效果，通过乘除像素值将可能的颜色值截断为10Mat result = imgBF / 25;result = result * 25;/** MERGES COLOR + EDGES **/// Create a 3 channles for edges//必须合并颜色和边缘结果，然后创建一个三通道图像Mat imgCanny3c;Mat cannyChannels[] = { imgCannyf, imgCannyf, imgCannyf };merge(cannyChannels, 3, imgCanny3c);//合并// Convert color result to floatMat resultf;result.convertTo(resultf, CV_32FC3);// Multiply color and edges matricesmultiply(resultf, imgCanny3c, resultf);// convert to 8 bits colorresultf.convertTo(result, CV_8UC3);waitKey(0);
}

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