/******************************HOG detector************************************************/dst = src.clone();vector<Rect> findrects, findrect;HOGDescriptor HOG;//SVM分类器
    HOG.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());//多尺度检测HOG.detectMultiScale(src, findrects, 0, Size(4, 4), Size(0, 0), 1.05, 2);//若rects有嵌套,则取最外面的矩形存入rectfor (int i = 0; i < findrects.size(); i++){Rect rect = findrects[i];int j = 0;for (; j < findrects.size(); j++)if (j != i && (rect & findrects[j]) == rect)break;if (j == findrects.size())findrect.push_back(rect);}Rect r;//用来选中所测图像中的测量对象。r.height = -1;//框选出检测结果并选中图片测量对象for (int i = 0; i < findrect.size(); i++)r = r.height > findrect[i].height ? r : findrect[i];
  //HOG detector返回的矩形框比真正图像轮廓大，所以我们减少矩形框的大小来使得更符合外界边框r.x += cvRound(r.width*0.1);r.width = cvRound(r.width*0.8);r.y += cvRound(r.height*0.07);r.height = cvRound(r.height*0.8);Scalar color = Scalar(0,0,255);rectangle(dst, r.tl(), r.br(), color, 2);
//    imshow("src", src);
//    imshow("dst", dst);

/********************************grabCut**********************************************/cv::Mat mask = Mat::zeros(src.size(), CV_8UC1);//分割后的结果//两个临时矩阵变量，作为算法的中间变量使用
    cv::Mat bgModel, fgModel;cv::Rect rectangle(r.tl(),r.br());//图像的前景对象也就是矩形选中图像// GrabCut 分段cv::grabCut(src,    //输入图像mask,   //分段结果rectangle,// 包含前景的矩形 bgModel, fgModel, // 前景、背景1,        // 迭代次数cv::GC_INIT_WITH_RECT); // 用矩形// 得到可能是前景的像素//比较函数保留值为GC_PR_FGD的像素
    cv::compare(mask, cv::GC_PR_FGD, mask, cv::CMP_EQ);// 产生输出图像cv::Mat foreground(src.size(), CV_8UC3, cv::Scalar(0, 0, 0));//背景值为 GC_BGD=0，作为掩码
    src.copyTo(foreground, mask);imshow("foreground", foreground);

/********************************BorderMatting**********************************************/BorderMatting bm;Mat rst = Mat(src.size(), src.type());Mat rstBm;src.copyTo(rst);for (int i = 0; i<rst.rows; i++)for (int j = 0; j < rst.cols; j++){if (mask.at<uchar>(i, j) == 0)rst.at<Vec3b>(i, j) = Vec3b(255, 255, 255);}bm.Initialize(src, mask);rstBm=bm.Run();imshow("bordingmatting", rstBm);

/******************************灰度化、二值化************************************************/Mat  rstGray, rstBin;cvtColor(rstBm, rstGray, CV_BGR2GRAY);//灰度化
//    imshow("灰度图", rstGray);threshold(rstGray, rstBin, 100, 255, CV_THRESH_BINARY);//二值化imshow("二值图", rstBin);

/**********************************计算最长像素距离********************************************/double MAXPX = 0;int x1 = 0, y1 = 0;int x2 = 0, y2 = 0;int maxX1, maxX2, maxY1, maxY2;int i, j;int flag = 0;for (i = 0; i < rstBin.rows; i++){for (j = 0; j < rstBin.cols; j++)if (rstBin.at<uchar>(i, j) == 255){x1 = i;y1 = j;flag = 1;break;}if (flag)break;}flag = 0;for (i = rstBin.rows; i >= 0; i--){for (j = 0; j < rstBin.cols; j++)if (rstBin.at<uchar>(i, j) == 255){x2 = i;y2 = j;flag = 1;break;}if (flag)break;}cout << x1 << " " << y1 << endl;cout << x2 << " " << y2 << endl;line(src, Point(y1, x1), Point(y2, x2), Scalar(0, 0, 255), 3);
/*    for ( i = 1; i <= rstBin.rows*rstBin.cols; i++)//第一个像素点开始遍历{x1 = i / rstBin.rows;y1 = !x1 ? (i - 1) : (i % x1 - 1);if (rstBin.at<uchar>(x1, y1) == 0)continue;for ( j = i + 1; j <= rstBin.rows*rstBin.cols; j++){x2 = j / rstBin.rows;y2 = !x2 ? (j - 1) : (j % x2 - 1);if (rstBin.at<uchar>(x2, y2) == 255)//同为白色像素点时计算距离{double distance=abs(x2 - x1) +abs(y2 - y1);//避免欧氏距离计算浪费时间if (MAXPX < distance){    maxX1 = x1;maxX2 = x2;maxY1 = y1;maxY2 = y2;MAXPX = distance;}}}}*//*MAXPX = sqrt((maxX1 - maxX2)*(maxX1 - maxX2) + (maxY1 - maxY2)*(maxY1 - maxY2));cout << MAXPX << endl;cout << maxX1 << " " << maxY1 << endl;cout << maxX2 << " " << maxY2 << endl;line(src, Point(maxY1, maxX1), Point(maxY2, maxX2), Scalar(0, 0, 255), 3);*/imshow("final", src);