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从桌面工程复制而来,作为本仓库的 Xuxucheck/ 子目录。 为控制仓库体积做了两处删减: - 移除 Frameworks/opencv2.xcframework 下的两个 visionOS 切片 (xros-arm64、xros-arm64-simulator,合计约 106 MB),并同步删掉该 xcframework 内 Info.plist 里对应的两条 AvailableLibraries 记录, 只保留 ios-arm64 与 ios-arm64_x86_64-simulator。 - 移除全部 .DS_Store 与 xcuserdata/。 已用 xcodebuild 从本目录完整编译验证:BUILD SUCCEEDED, ProcessXCFramework 以 --platform ios 成功选中框架,产物含 3699 个 cv:: 符号 (OpenCV 确实链入),嵌入 framework 3 个,与移除无用 pod 之后的状态一致。 仓库约 206 MB,其中 OpenCV 203 MB、其余源码与依赖约 3 MB。
156 lines
3.4 KiB
C++
156 lines
3.4 KiB
C++
/*
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* Utils.cpp
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*
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* Created on: 2018-1-14
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* Author: neo
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*/
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#include "Utils.h"
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#define TV0 8
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#define TV1 5
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void Whitebalance(Mat src,Point wbPt)
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{
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Rect roi(wbPt.x-5,wbPt.y-5,10,10);
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Mat wbMat(src,roi);
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cv::Scalar avgRgb = mean(wbMat);
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// double d1 = avgRgb.val[0] - avgRgb.val[2];
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// double d2= avgRgb.val[1]-avgRgb.val[2];
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// double d3 = abs(avgRgb.val[0] - avgRgb.val[1]);
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//
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// if (d1 >= TV0 && d2 >= TV0 && d3 <= TV1)
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// {
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// avgRgb = mean(src);
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// }
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double scale = (avgRgb.val[0] + avgRgb.val[1] + avgRgb.val[2]) / 3;
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Mat r,g,b;
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extractChannel(src, r, 0);
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extractChannel(src, g, 1);
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extractChannel(src, b, 2);
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convertScaleAbs(r, r, scale / avgRgb.val[0], 0.0);
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convertScaleAbs(g, g, scale / avgRgb.val[1], 0.0);
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convertScaleAbs(b, b, scale / avgRgb.val[2], 0.0);
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vector<Mat> mv;
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mv.push_back(r);
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mv.push_back(g);
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mv.push_back(b);
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merge(mv, src);
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}
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//Perfect reflection color correction
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void WhitebalancePR(Mat src,Point wbPt)
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{
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// 通道分离
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vector<cv::Mat> Channel;
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cv::split(src, Channel);
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// 定义参数
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int row = src.rows;
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int col = src.cols;
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int RGBSum[766] = { 0 };
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uchar maxR, maxG, maxB;
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for (int i = 0; i < row; ++i)
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{
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uchar *b = Channel[0].ptr<uchar>(i);
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uchar *g = Channel[1].ptr<uchar>(i);
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uchar *r = Channel[2].ptr<uchar>(i);
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for (int j = 0; j < col; ++j)
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{
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int sum = b[j] + g[j] + r[j];
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RGBSum[sum]++;
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maxB = max(maxB, b[j]);
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maxG = max(maxG, g[j]);
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maxR = max(maxR, r[j]);
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}
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}
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int T = 0;
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int num = 0;
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int K = static_cast<int>(row * col * 0.1);
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for (int i = 765; i >= 0; --i)
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{
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num += RGBSum[i];
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if (num > K)
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{
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T = i;
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break;
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}
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}
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double Bm = 0.0, Gm = 0.0, Rm = 0.0;
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int count = 0;
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for (int i = 0; i < row; ++i)
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{
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uchar *b = Channel[0].ptr<uchar>(i);
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uchar *g = Channel[1].ptr<uchar>(i);
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uchar *r = Channel[2].ptr<uchar>(i);
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for (int j = 0; j < col; ++j)
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{
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int sum = b[j] + g[j] + r[j];
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if (sum > T)
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{
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Bm += b[j];
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Gm += g[j];
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Rm += r[j];
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count++;
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}
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}
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}
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Bm /= count;
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Gm /= count;
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Rm /= count;
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Channel[0] *= maxB / Bm;
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Channel[1] *= maxG / Gm;
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Channel[2] *= maxR / Rm;
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cv::merge(Channel, src);
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}
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bool pred(const std::pair<int, int>& lhs,const std::pair<int, int>& rhs)
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{
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return lhs.second < rhs.second;
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}
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int GetHModal(Mat h)
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{
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uchar *pData=h.data;
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int step=h.step;
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//key:h,value:counter
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map<int,int> mapVals;
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map<int,int>::iterator it;
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for (int y=0;y<h.rows;++y){
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for(int x=0;x<h.cols;++x){
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uchar val=pData[y*step+x];
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it=mapVals.find(val);
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if (it!=mapVals.end()){
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mapVals[val]++;
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}else{
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mapVals.insert(make_pair(val,1));
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}
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}
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}
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it=std::max_element(mapVals.begin(),mapVals.end(),pred);
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int maxHval=it->first;
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int sum=0;
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int pc=0;
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for (it=mapVals.begin();it!=mapVals.end();++it){
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int h=it->first;
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int c=it->second;
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if (h>=(maxHval-2)&&
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h<=(maxHval+2)){
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sum+=h*c;
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pc+=c;
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}
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}
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return (sum/pc);
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}
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