导入 XuXuCheck 工程(iOS 干化学尿液试纸分析 App)

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。
2026-09-23 14:17:31 +08:00
commit d6aeede2bc
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+/*
+ * Utils.cpp
+ *
+ *  Created on: 2018-1-14
+ *      Author: neo
+ */
+#include "Utils.h"
+#define TV0 8
+#define TV1 5
+void Whitebalance(Mat src,Point wbPt)
+{
+	Rect roi(wbPt.x-5,wbPt.y-5,10,10);
+    Mat wbMat(src,roi);
+	cv::Scalar avgRgb = mean(wbMat);
+//    double d1 = avgRgb.val[0] - avgRgb.val[2];
+//    double d2= avgRgb.val[1]-avgRgb.val[2];
+//    double d3 = abs(avgRgb.val[0] - avgRgb.val[1]);
+//
+//    if (d1 >= TV0 && d2 >= TV0 && d3 <= TV1)
+//    {
+//    	avgRgb = mean(src);
+//    }
+
+	double scale = (avgRgb.val[0] + avgRgb.val[1] + avgRgb.val[2]) / 3;
+    Mat r,g,b;
+	extractChannel(src, r, 0);
+	extractChannel(src, g, 1);
+	extractChannel(src, b, 2);
+
+	convertScaleAbs(r, r, scale / avgRgb.val[0], 0.0);
+	convertScaleAbs(g, g, scale / avgRgb.val[1], 0.0);
+	convertScaleAbs(b, b, scale / avgRgb.val[2], 0.0);
+
+	vector<Mat> mv;
+	mv.push_back(r);
+	mv.push_back(g);
+	mv.push_back(b);
+	merge(mv, src);
+
+}
+
+
+//Perfect reflection color correction
+void WhitebalancePR(Mat src,Point wbPt)
+{
+
+    // 通道分离
+    vector<cv::Mat> Channel;
+    cv::split(src, Channel);
+
+    // 定义参数
+    int row = src.rows;
+    int col = src.cols;
+    int RGBSum[766] = { 0 };
+    uchar maxR, maxG, maxB;
+
+    for (int i = 0; i < row; ++i)
+    {
+        uchar *b = Channel[0].ptr<uchar>(i);
+        uchar *g = Channel[1].ptr<uchar>(i);
+        uchar *r = Channel[2].ptr<uchar>(i);
+        for (int j = 0; j < col; ++j)
+        {
+            int sum = b[j] + g[j] + r[j];
+            RGBSum[sum]++;
+            maxB = max(maxB, b[j]);
+            maxG = max(maxG, g[j]);
+            maxR = max(maxR, r[j]);
+        }
+    }
+
+    int T = 0;
+    int num = 0;
+    int K = static_cast<int>(row * col * 0.1);
+    for (int i = 765; i >= 0; --i)
+    {
+        num += RGBSum[i];
+        if (num > K)
+        {
+            T = i;
+            break;
+        }
+    }
+
+    double Bm = 0.0, Gm = 0.0, Rm = 0.0;
+    int count = 0;
+    for (int i = 0; i < row; ++i)
+    {
+        uchar *b = Channel[0].ptr<uchar>(i);
+        uchar *g = Channel[1].ptr<uchar>(i);
+        uchar *r = Channel[2].ptr<uchar>(i);
+        for (int j = 0; j < col; ++j)
+        {
+            int sum = b[j] + g[j] + r[j];
+            if (sum > T)
+            {
+                Bm += b[j];
+                Gm += g[j];
+                Rm += r[j];
+                count++;
+            }
+
+        }
+    }
+    Bm /= count;
+    Gm /= count;
+    Rm /= count;
+
+    Channel[0] *= maxB / Bm;
+    Channel[1] *= maxG / Gm;
+    Channel[2] *= maxR / Rm;
+
+    cv::merge(Channel, src);
+}
+
+bool pred(const std::pair<int, int>& lhs,const std::pair<int, int>& rhs)
+{
+	return lhs.second < rhs.second;
+}
+
+int GetHModal(Mat h)
+{
+	uchar *pData=h.data;
+	int step=h.step;
+	//key:h,value:counter
+	map<int,int> mapVals;
+	map<int,int>::iterator it;
+	for (int y=0;y<h.rows;++y){
+		for(int x=0;x<h.cols;++x){
+			uchar val=pData[y*step+x];
+			it=mapVals.find(val);
+			if (it!=mapVals.end()){
+				mapVals[val]++;
+			}else{
+				mapVals.insert(make_pair(val,1));
+			}
+		}
+	}
+	it=std::max_element(mapVals.begin(),mapVals.end(),pred);
+	int maxHval=it->first;
+	int sum=0;
+	int pc=0;
+	for (it=mapVals.begin();it!=mapVals.end();++it){
+		int h=it->first;
+		int c=it->second;
+		if (h>=(maxHval-2)&&
+			h<=(maxHval+2)){
+			sum+=h*c;
+			pc+=c;
+		}
+	}
+	return (sum/pc);
+}
+
+