include <opencv2/opencv.hpp>
include <iostream>
include <algorithm>
include <time.h>
using namespace cv;
using namespace std;
// 输出参数
struct Inputparama {
int thresh = 30; // 背景辨认阈值，该值越小，则辨认非背景区面积越大，需有适合范畴，目前为5-60int transparency = 255; // 背景替换色透明度，255为实，0为通明int size = 7; // 非背景区边缘虚化参数，该值越大，则边缘虚化水平越显著cv::Point p = cv::Point(0, 0); // 背景色采样点，可通过人机交互获取，也可用默认(0,0)点色彩作为背景色cv::Scalar color = cv::Scalar(255, 255, 255); // 背景色};
cv::Mat BackgroundSeparation(cv::Mat src, Inputparama input);
void Clear_MicroConnected_Areas(cv::Mat src, cv::Mat &dst, double min_area);
// 计算差值均方根
int geiDiff(uchar b,uchar g,uchar r,uchar tb,uchar tg,uchar tr)
{
return int(sqrt(((b - tb)*(b - tb) + (g - tg)*(g - tg) + (r - tr)*(r - tr))/3));}
int main()
{
cv::Mat src = imread("111.jpg");Inputparama input;input.thresh = 100;input.transparency = 255;input.size = 6;input.color = cv::Scalar(0, 0, 255);clock_t s, e;s = clock();cv::Mat result = BackgroundSeparation(src, input);e = clock();double dif = e - s;cout << "time:" << dif << endl;imshow("original", src);imshow("result", result);imwrite("result1.png", result);waitKey(0);return 0;}
// 背景拆散
cv::Mat BackgroundSeparation(cv::Mat src, Inputparama input)
{
cv::Mat bgra, mask;// 转化为BGRA格局，带透明度，4通道cvtColor(src, bgra, COLOR_BGR2BGRA);mask = cv::Mat::zeros(bgra.size(), CV_8UC1);int row = src.rows;int col = src.cols;// 异样数值修改input.p.x = max(0, min(col, input.p.x));input.p.y = max(0, min(row, input.p.y));input.thresh = max(5, min(200, input.thresh));input.transparency = max(0, min(255, input.transparency));input.size = max(0, min(30, input.size));// 确定背景色uchar ref_b = src.at<Vec3b>(input.p.y, input.p.x)[0];uchar ref_g = src.at<Vec3b>(input.p.y, input.p.x)[1];uchar ref_r = src.at<Vec3b>(input.p.y, input.p.x)[2];// 计算蒙版区域（掩膜）for (int i = 0; i < row; ++i){ uchar *m = mask.ptr<uchar>(i); uchar *b = src.ptr<uchar>(i); for (int j = 0; j < col; ++j) { if ((geiDiff(b[3*j],b[3*j+1],b[3*j+2],ref_b,ref_g,ref_r)) >input.thresh) { m[j] = 255; } }}cv::Mat tmask = cv::Mat::zeros(row + 50, col + 50, CV_8UC1);mask.copyTo(tmask(cv::Range(25, 25 + mask.rows), cv::Range(25, 25 + mask.cols)));// 寻找轮廓，作用是填充轮廓内黑洞vector<vector<Point>> contour;vector<Vec4i> hierarchy;// RETR_TREE以网状结构提取所有轮廓，CHAIN_APPROX_NONE获取轮廓的每个像素findContours(tmask, contour, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_NONE);drawContours(tmask, contour, -1, Scalar(255), FILLED,16);// 黑帽运算获取同背景色相似的区域，[商品期货](https://www.gendan5.com/futures/cf.html)辨认后填充cv::Mat hat;cv::Mat element = getStructuringElement(MORPH_ELLIPSE, Size(31, 31));cv::morphologyEx(tmask, hat, MORPH_BLACKHAT, element);hat.setTo(255, hat > 0);cv::Mat hatd;Clear_MicroConnected_Areas(hat, hatd, 450);tmask = tmask + hatd;mask = tmask(cv::Range(25, 25 + mask.rows), cv::Range(25, 25 + mask.cols)).clone();// 掩膜滤波，是为了边缘虚化cv::blur(mask, mask, Size(2 * input.size+1, 2 * input.size + 1));// 改色for (int i = 0; i < row; ++i){ uchar *r = bgra.ptr<uchar>(i); uchar *m = mask.ptr<uchar>(i); for (int j = 0; j < col; ++j) { // 蒙版为0的区域就是规范背景区 if (m[j] == 0) { r[4 * j] = uchar(input.color[0]); r[4 * j + 1] = uchar(input.color[1]); r[4 * j + 2] = uchar(input.color[2]); r[4 * j + 3] = uchar(input.transparency); } // 不为0且不为255的区域是轮廓区域（边缘区），须要虚化解决 else if (m[j] != 255) { // 边缘处按比例上色 int newb = (r[4 * j] * m[j] * 0.3 + input.color[0] * (255 - m[j])*0.7) / ((255 - m[j])*0.7+ m[j] * 0.3); int newg = (r[4 * j+1] * m[j] * 0.3 + input.color[1] * (255 - m[j])*0.7) / ((255 - m[j])*0.7 + m[j] * 0.3); int newr = (r[4 * j + 2] * m[j] * 0.3 + input.color[2] * (255 - m[j])*0.7) / ((255 - m[j])*0.7 + m[j] * 0.3); int newt = (r[4 * j + 3] * m[j] * 0.3 + input.transparency * (255 - m[j])*0.7) / ((255 - m[j])*0.7 + m[j] * 0.3); newb = max(0, min(255, newb)); newg = max(0, min(255, newg)); newr = max(0, min(255, newr)); newt = max(0, min(255, newt)); r[4 * j] = newb; r[4 * j + 1] = newg; r[4 * j + 2] = newr; r[4 * j + 3] = newt; } }}return bgra;}
void Clear_MicroConnected_Areas(cv::Mat src, cv::Mat &dst, double min_area)
{
// 备份复制dst = src.clone();std::vector<std::vector<cv::Point> > contours; // 创立轮廓容器std::vector<cv::Vec4i> hierarchy;// 寻找轮廓的函数// 第四个参数CV_RETR_EXTERNAL，示意寻找最外围轮廓// 第五个参数CV_CHAIN_APPROX_NONE，示意保留物体边界上所有间断的轮廓点到contours向量内cv::findContours(src, contours, hierarchy, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_NONE, cv::Point());if (!contours.empty() && !hierarchy.empty()){ std::vector<std::vector<cv::Point> >::const_iterator itc = contours.begin(); // 遍历所有轮廓 while (itc != contours.end()) { // 定位以后轮廓所在位置 cv::Rect rect = cv::boundingRect(cv::Mat(*itc)); // contourArea函数计算连通区面积 double area = contourArea(*itc); // 若面积小于设置的阈值 if (area < min_area) { // 遍历轮廓所在位置所有像素点 for (int i = rect.y; i < rect.y + rect.height; i++) { uchar *output_data = dst.ptr<uchar>(i); for (int j = rect.x; j < rect.x + rect.width; j++) { // 将连通区的值置0 if (output_data[j] == 255) { output_data[j] = 0; } } } } itc++; }}}