大津算法二值化连通区域标记

#include <iostream> #include <string> #include <list> #include <vector> #include <map> #include <cv.h> #include <highgui.h> #include <stdio.h> void icvprCcaByTwoPass(const cv::Mat& _binImg, cv::Mat& _lableImg) { // connected component analysis (4-component) // use two-pass algorithm // 1. first pass: label each foreground pixel with a label // 2. second pass: visit each labeled pixel and merge neighbor labels // // foreground pixel: _binImg(x,y) = 1 // background pixel: _binImg(x,y) = 0 if (_binImg.empty() || _binImg.type() != CV_8UC1) { return ; } // 1. first pass _lableImg.release() ; _binImg.convertTo(_lableImg, CV_32SC1) ; int label = 1 ; // start by 2 std::vector<int> labelSet ; labelSet.push_back(0) ; // background: 0 labelSet.push_back(1) ; // foreground: 1 int rows = _binImg.rows - 1 ; int cols = _binImg.cols - 1 ; for (int i = 1; i < rows; i++) { int* data_preRow = _lableImg.ptr<int>(i-1) ; //用Mat::ptr()来获得指向某行元素的指针，在通过行数与通道数计算相应点的指针。 int* data_curRow = _lableImg.ptr<int>(i) ; for (int j = 1; j < cols; j++) { if (data_curRow[j] == 1) { std::vector<int> neighborLabels ; neighborLabels.reserve(2) ; // vector 的reserve增加了vector的capacity，但是它的size没有改变,加入新的元素时，要调用push_back()/insert()函数。 int leftPixel = data_curRow[j-1] ; int upPixel = data_preRow[j] ; if ( leftPixel > 1) { neighborLabels.push_back(leftPixel) ; //可以把vector简单理解为动态一维数组push_back作用是在这个一维数组尾部插入一个元素 } if (upPixel > 1) { neighborLabels.push_back(upPixel) ; } if (neighborLabels.empty()) { labelSet.push_back(++label) ; // assign to a new label data_curRow[j] = label ; labelSet[label] = label ; } else { std::sort(neighborLabels.begin(), neighborLabels.end()) ; int smallestLabel = neighborLabels[0] ; data_curRow[j] = smallestLabel ; // save equivalence for (size_t k = 1; k < neighborLabels.size(); k++) { int tempLabel = neighborLabels[k] ; int& oldSmallestLabel = labelSet[tempLabel] ; if (oldSmallestLabel > smallestLabel) { labelSet[oldSmallestLabel] = smallestLabel ; oldSmallestLabel = smallestLabel ; } else if (oldSmallestLabel < smallestLabel) { labelSet[smallestLabel] = oldSmallestLabel ; } } } } } } // update equivalent labels // assigned with the smallest label in each equivalent label set for (size_t i = 2; i < labelSet.size(); i++) { int curLabel = labelSet[i] ; int preLabel = labelSet[curLabel] ; while (preLabel != curLabel) { curLabel = preLabel ; preLabel = labelSet[preLabel] ; } labelSet[i] = curLabel ; } // 2. second pass for (int i = 0; i < rows; i++) { int* data = _lableImg.ptr<int>(i) ; for (int j = 0; j < cols; j++) { int& pixelLabel = data[j] ; pixelLabel = labelSet[pixelLabel] ; } } } cv::Scalar icvprGetRandomColor() //一个包含四个元素的结构体变量 { uchar r = 255 * (rand()/(1.0 + RAND_MAX)); uchar g = 255 * (rand()/(1.0 + RAND_MAX)); uchar b = 255 * (rand()/(1.0 + RAND_MAX)); return cv::Scalar(b,g,r) ; } void icvprLabelColor(const cv::Mat& _labelImg, cv::Mat& _colorLabelImg) { if (_labelImg.empty() || _labelImg.type() != CV_32SC1) { return ; } std::map<int, cv::Scalar> colors ; int rows = _labelImg.rows ; int cols = _labelImg.cols ; _colorLabelImg.release() ; //释放 _colorLabelImg.create(rows, cols, CV_8UC3) ; _colorLabelImg = cv::Scalar::all(0) ; for (int i = 0; i < rows; i++) { const int* data_src = (int*)_labelImg.ptr<int>(i) ; uchar* data_dst = _colorLabelImg.ptr<uchar>(i) ; for (int j = 0; j < cols; j++) { int pixelValue = data_src[j] ; if (pixelValue > 1) { if (colors.count(pixelValue) <= 0) { colors[pixelValue] = icvprGetRandomColor() ; } cv::Scalar color = colors[pixelValue] ; *data_dst++ = color[0] ; *data_dst++ = color[1] ; *data_dst++ = color[2] ; } else { data_dst++ ; data_dst++ ; data_dst++ ; } } } /* int counter[1000]={0}; for (int j = 0; j < cols; j++) { int oldpixel = 0; for (int i = 100; i < rows; i++) { const int* data_src = (int*)_labelImg.ptr<int>(i) ; int pixelValue = data_src[j] ; if(pixelValue != oldpixel && pixelValue != 0) { counter[j]=counter[j]+1; } oldpixel = pixelValue; } // printf("%d ",counter[j]); } int max=0; for(int i=0;i<=1000;i++) { if(counter[i]>max) max=counter[i]; } printf("连通区域数目： %d\n",max); int max; if(n==2) { ret = counter[0]>counter[1]?counter[0]:counter[1]; } else { ret = counter[0]>max(counter+1,n-1)?counter[0]:max(counter+1,n-1); } return ret; } printf("max is %d\n", max(counter,1000));//zuidahua*/ } void icvprGetLabelNumber(const cv::Mat& _labelImg) { int rows = _labelImg.rows ; int cols = _labelImg.cols ; int counter[10000]={0}; for (int j = 0; j < cols; j++) { int oldpixel = 0; for (int i = 100; i < rows; i++) { const int* data_src = (int*)_labelImg.ptr<int>(i) ; int pixelValue = data_src[j] ; if(pixelValue != oldpixel && pixelValue != 0) { counter[j]=counter[j]+1; } oldpixel = pixelValue; } printf("%d ",counter[j]); } int max=0; for(int j=0;j<=1000;j++) { if(counter[j]>max) max=counter[j]; } printf("\n连通区域数目： %d\n",max); int value=0; value=1000-(max-1)*10; printf("量筒液面刻度为：%d\n",value); } int Otsu(IplImage* image) { //求出最佳阈值 int width; width=image->width; int height; height=image->height; int i,j,pixelSum,threshold; pixelSum= width * height; threshold = 0; uchar* data ; data=(uchar*)image->imageData; float w0, w1, u0tmp, u1tmp, u0, u1, deltaTmp, deltaMax; //灰度级 float pixelPro[256]; int pixelCount[256]; deltaMax = 0; //统计每个灰度级中像素的个数 for(i = 0; i < height; i++) { for(j = 0;j < width;j++) { pixelCount[(int)data[i * width + j]]++; }