图像分割与边缘检测

OTSU

#include <stdio.h>#include <string>#include "opencv2/highgui/highgui.hpp"#include "opencv2/opencv.hpp"using namespace std;using namespace cv;// OTSU函数实现int OTSU(cv::Mat srcImage){    int nCols = srcImage.cols;    int nRows = srcImage.rows;    int threshold = 0;    // 初始化统计参数    int nSumPix[256];    float nProDis[256];    for (int i = 0; i < 256; i++)    {        nSumPix[i] = 0;        nProDis[i] = 0;    }    // 统计灰度级中每个像素在整幅图像中的个数    for (int i = 0; i < nCols; i++)    {        for (int j = 0; j < nRows; j++)        {            nSumPix[(int)srcImage.at<uchar>(i, j)]++;        }    }    // 计算每个灰度级占图像中的概率分布    for (int i = 0; i < 256; i++)    {        nProDis[i] = (float)nSumPix[i] / (nCols * nRows);    }    // 遍历灰度级[0,255],计算出最大类间方差下的阈值    float w0, w1, u0_temp, u1_temp, u0, u1, delta_temp;    double delta_max = 0.0;    for (int i = 0; i < 256; i++)    {        // 初始化相关参数        w0 = w1 = u0_temp = u1_temp = u0 = u1 = delta_temp = 0;        for (int j = 0; j < 256; j++)        {            //背景部分            if (j <= i)            {                // 当前i为分割阈值，第一类总的概率                w0 += nProDis[j];                u0_temp += j * nProDis[j];            }            //前景部分            else            {                // 当前i为分割阈值，第一类总的概率                w1 += nProDis[j];                u1_temp += j * nProDis[j];            }        }        // 分别计算各类的平均灰度        u0 = u0_temp / w0;        u1 = u1_temp / w1;        delta_temp = (float)(w0 *w1* pow((u0 - u1), 2));        // 依次找到最大类间方差下的阈值        if (delta_temp > delta_max)        {            delta_max = delta_temp;            threshold = i;        }    }    return threshold;}int main(){    // 图像读取及判断    cv::Mat srcImage = cv::imread("images/hand1.jpg");    if (!srcImage.data)        return 1;    // 灰度转换    cv::Mat srcGray;    cv::cvtColor(srcImage, srcGray, CV_RGB2GRAY);    cv::imshow("srcGray", srcGray);    // 调用OTSU二值化算法得到阈值    int  ostuThreshold = OTSU(srcGray);    std::cout << ostuThreshold << std::endl;    // 定义输出结果图像    cv::Mat otsuResultImage =        cv::Mat::zeros(srcGray.rows, srcGray.cols, CV_8UC1);    // 利用得到的阈值实现二值化操作    for (int i = 0; i < srcGray.rows; i++)    {        for (int j = 0; j < srcGray.cols; j++)        {            // 满足大于阈值ostuThreshold置255            if (srcGray.at<uchar>(i, j) > ostuThreshold)                otsuResultImage.at<uchar>(i, j) = 255;            else                otsuResultImage.at<uchar>(i, j) = 0;        }    }    cv::imshow("otsuResultImage", otsuResultImage);    cv::waitKey(0);    return 0;}

霍夫变换

#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp"#include <iostream>using namespace cv;using namespace std;int main( ){  cv::Mat srcImage =      cv::imread("images/Pic3_3.png", 0);  if (!srcImage.data)       return -1;  cv::Mat edgeMat, houghMat;  // Canny边缘检测 二值图像  Canny(srcImage, edgeMat, 50, 200, 3);  cvtColor(edgeMat, houghMat, CV_GRAY2BGR);  #if 0  // 标准的霍夫变换  vector<Vec2f> lines;  HoughLines(edgeMat, lines, 1, CV_PI/180, 100, 0, 0 );  for( size_t i = 0; i < lines.size(); i++ )  {     // 根据直线参数表达式绘制相应检测结果     float rho = lines[i][0], theta = lines[i][1];     Point pt1, pt2;     double a = cos(theta), b = sin(theta);     double x0 = a*rho, y0 = b*rho;     pt1.x = cvRound(x0 + 1000*(-b));     pt1.y = cvRound(y0 + 1000*(a));     pt2.x = cvRound(x0 - 1000*(-b));     pt2.y = cvRound(y0 - 1000*(a));     line( houghMat, pt1, pt2, Scalar(0,0,255), 3, CV_AA);  }  #else  // 统计概率的霍夫变换  vector<Vec4i> lines;  HoughLinesP(edgeMat, lines, 1, CV_PI/180, 50, 50, 10 );  for( size_t i = 0; i < lines.size(); i++ )  {    Vec4i l = lines[i];    // 绘制线检测结果    line( houghMat, Point(l[0], l[1]),       Point(l[2], l[3]), Scalar(255,255,255), 3, CV_AA);  }  #endif  cv::imshow("srcImage", srcImage);  cv::imshow("houghMat", houghMat);  cv::waitKey();  return 0;}