Merge branch 'main' of http://snsopush.com:10100/jerryzeng/camAlgo
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commit
25151a4c61
@ -326,6 +326,10 @@ int main()
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std::vector<RgnSubPix> subpixPnt;
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std::vector<RgnSubPixCalib> calibSubpixPnt;
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#endif
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//cv::Mat img;
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//inputImg.convertTo(img, CV_64FC1);
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//cv::GaussianBlur(gray, gray, cv::Size(5, 5), 1.2, 1.2);
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cv::GaussianBlur(gray, gray, cv::Size(5, 5), 1.2, 1.2);
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camAlgoSW(
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gray.rows,
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gray.cols,
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@ -8,7 +8,7 @@
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#include "sourceCode/MonoLaserCalibrate.h"
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#include "sourceCode/FitMapParam.h"
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#define _DO_CAMERA_CALIB 1
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#define _DO_CAMERA_CALIB 0
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void sg_outputCalibK(const char* fileName, cv::Mat& fitMap)
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{
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@ -338,7 +338,7 @@ int main()
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#else
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char calibKDName[256];
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sprintf_s(calibKDName, "%scalib_param_K_D.txt", cbImagePath);
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sprintf_s(calibKDName, "%scalib_param_K_D.txt", calibDataPath[grp]);
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cv::Mat K, D;
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sg_readCalibKD(calibKDName, K, D);
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#endif
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@ -475,7 +475,7 @@ int main()
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#if 1
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sprintf_s(filename, "%slaser_rotate_mask_%03d.png", calibDataPath[grp], index);
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cv::imwrite(filename, laserImg);
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/*
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cv::Mat calibImg;
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remap(laserImg,
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calibImg,
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@ -486,6 +486,7 @@ int main()
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cv::Scalar(0, 0, 0));
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sprintf_s(filename, "%slaser_%03d_calib.bmp", calibDataPath[grp], index);
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cv::imwrite(filename, calibImg);
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*/
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#endif
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std::vector<cv::Point2f> pts2d = detectLaserLine(laserImg);
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@ -527,8 +528,8 @@ int main()
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std::cout << "pe: " << pe << std::endl;
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//output K and D
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char calibKDName[256];
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sprintf_s(calibKDName, "%scalib_param_K_D.txt", calibDataPath[grp]);
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char calibKDPName[256];
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sprintf_s(calibKDPName, "%scalib_param_K_D.txt", calibDataPath[grp]);
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sg_outputCalibKD(calibKDName, K, D, pe);
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}
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@ -5,6 +5,7 @@
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#include <corecrt_math_defines.h>
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#include "lineDetection_steger.h"
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#define USING_1D_MASK 1
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#if 1
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using namespace std;
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using namespace cv;
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@ -265,6 +266,45 @@ void generate_2nd_2Dmask_directSample(double sigma, int radius, cv::Mat& mask_xx
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return;
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}
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double gaussKernel_1D(double x, double sigma)
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{
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double sigma2 = sigma * sigma;
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double norm = 1.0 / (sqrt(2 * M_PI) * sigma);
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return (norm * exp(-(x * x ) / (2 * sigma2)));
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}
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// 2D高斯一阶导数掩模生成
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void generate_1st_1Dmask_directSample(double sigma, int radius, cv::Mat& mask_x)
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{
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int size = 2 * radius + 1;
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mask_x = cv::Mat::zeros(1, size, CV_64FC1);
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double norm = -1.0 / (sigma * sigma);
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for (int col = 0; col < size; col++)
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{
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double x = (double)(col - radius);
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double dx = x * norm * gaussKernel_1D(x, sigma);
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mask_x.at<double>(0, col) = dx;
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}
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return;
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}
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// 2D高斯二阶导数掩模生成
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void generate_2nd_1Dmask_directSample(double sigma, int radius, cv::Mat& mask_xx)
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{
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int size = 2 * radius + 1;
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mask_xx = cv::Mat::zeros(1, size, CV_64FC1);
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double sigma2 = sigma * sigma;
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double norm = 1.0 / (sigma2 * sigma2);
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double offset = 1.0 / sigma2;
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for (int col = 0; col < size; col++)
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{
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double x = (double)(col - radius);
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double dxx = (x * x * norm - offset) * gaussKernel_1D(x, sigma);
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mask_xx.at<double>(0, col) = dxx;
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}
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return;
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}
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// 单点卷积函数
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sPtDerivate pointConvolve(
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cv::Point ptPos, //点位置
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@ -302,6 +342,35 @@ sPtDerivate pointConvolve(
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return result;
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}
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// 单点卷积函数
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sPtDerivate pointConvolve_1D(
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cv::Point ptPos, //点位置
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int radius, //卷积窗半径
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cv::Mat& img, //double型单通道图像
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cv::Mat& mask_x, //一阶掩膜x
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cv::Mat& mask_xx //二阶掩膜xx
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)
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{
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sPtDerivate result = { 0.0, 0.0, 0.0, 0.0, 0.0 };
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cv::Size imgSize = img.size(); //
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if ((ptPos.x < radius) || (ptPos.x > (imgSize.width - radius - 1)) || (ptPos.y < radius) || (ptPos.y > (imgSize.height - radius - 1)))
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return result;
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int size = 2 * radius + 1;
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for (int j = 0; j < size; j++)
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{
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int col = j - radius;
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int x = ptPos.x + col;
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double value = img.at<double>(ptPos.y, x);
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result.dx += mask_x.at<double>(0, j) * value;
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result.dy += 0;
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result.dxx += mask_xx.at<double>(0, j) * value;
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result.dyy += 0;
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result.dxy += 0;
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}
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return result;
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}
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bool isMax(int i, int j, cv::Mat& img, int dx, int dy)//在法线方向上是否为极值
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{
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double val = img.at<double>(j, i);
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@ -380,6 +449,30 @@ cv::Point2f computeHessianSubpix(
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return subPix;
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}
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//计算1D得到亚像素
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cv::Point2f compute1DSubpix(
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cv::Point ptPos, //点位置
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cv::Mat& img, //double型单通道图像
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sPtDerivate _ptDerivate
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)
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{
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double subTh = 0.7;
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cv::Point2f subPix = { -1.0f, -1.0f }; //初始化成无效亚像素值
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//t 公式 泰勒展开到二阶导数
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double a = _ptDerivate.dxx;
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double b = _ptDerivate.dx;
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if (a != 0.0)
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{
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double t = b / a; // -b / a;
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if (fabs(t) <= subTh )//(x + t * Nx, y + t * Ny)为亚像素坐标
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{
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subPix.x = (float)(ptPos.x + t);
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subPix.y = (float)(ptPos.y);
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}
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}
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return subPix;
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}
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// 从点的整数坐标计算亚像素
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void computePointSubpix(
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cv::Mat& inputImg, //uchar型单通道输入图像（灰度图像）
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@ -391,8 +484,10 @@ void computePointSubpix(
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cv::Mat mask_x, mask_y, mask_xx, mask_yy, mask_xy;
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double sigma = (double)gaussWin / sqrt(3);
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//double sigma = 1.0;
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#if 0
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SetFilterMat(mask_x, mask_y, mask_xx, mask_yy, mask_xy, gaussWin*2+1);
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#if USING_1D_MASK
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//SetFilterMat(mask_x, mask_y, mask_xx, mask_yy, mask_xy, gaussWin*2+1);
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generate_1st_1Dmask_directSample(sigma, gaussWin, mask_x);
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generate_2nd_1Dmask_directSample(sigma, gaussWin, mask_xx);
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#else
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generate_1st_2Dmask_directSample(sigma, gaussWin, mask_x, mask_y);
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generate_2nd_2Dmask_directSample(sigma, gaussWin, mask_xx, mask_yy, mask_xy);
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@ -409,7 +504,7 @@ void computePointSubpix(
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//高斯滤波
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cv::Mat img;
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inputImg.convertTo(img, CV_64FC1);
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cv::GaussianBlur(img, img, cv::Size(3, 3), 0.9, 0.9);
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cv::GaussianBlur(img, img, cv::Size(5, 5), 1.2, 1.2);
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//cv::imwrite("gauss_blur_src.bmp", img);
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for (int i = 0, i_max = (int)pos.size(); i < i_max; i++)
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@ -418,6 +513,20 @@ void computePointSubpix(
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int doSubPix = 0;
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while (doSubPix >= 0)
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{
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#if USING_1D_MASK
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sPtDerivate a_ptDerivate = pointConvolve_1D(
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ptPos, //点位置
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gaussWin, //卷积窗半径
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img, //double型单通道图像
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mask_x, //一阶掩膜x
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mask_xx //二阶掩膜xx
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);
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cv::Point2f subpix = compute1DSubpix(
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ptPos, //点位置
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img, //double型单通道图像
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a_ptDerivate
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);
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#else
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sPtDerivate a_ptDerivate = pointConvolve(
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ptPos, //点位置
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gaussWin, //卷积窗半径
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@ -433,6 +542,7 @@ void computePointSubpix(
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img, //double型单通道图像
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a_ptDerivate
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);
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#endif
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if ((subpix.x >= 0) && (subpix.y >= 0))
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{
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subpix.x += 0.5; //图像中的像素位置表示的是像素是左下角，而此处像素坐标是指向像素中间位置，所以有0.5像素差
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@ -497,7 +497,7 @@ std::vector<cv::Point2f> detectLaserLine(
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//cv::imwrite("gauss_blur_src.bmp", img);
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// 提取最大值点
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int scaleWin = 5;
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double minPkValue = 100;
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double minPkValue = 20;
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std::vector< cv::Point> pkPoints;
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for (int i = 0; i < img.rows; i++)
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{
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