OPENCV图像变换-1

图像变换是指将一幅图像变换为图像数据的另一种表现形式,例如将图像进行傅立叶变换,或者对图像进行X,Y方向的求导等,经过这些变换,可以将图像数据处理中的某些问题换一个别的角度想办法,所以图像变换是图像处理的时候比较常用的一种方法.

一.sobel算子

sobel算子是一个用于边缘检测的离散微分算子,其结合了高斯平滑和微分求导,用于计算图像灰度函数的近似梯度,在图像的任何一点使用该函数,都将产生对应的梯度矢量或者是发矢量,简单地说,sobel算子适用于计算出图像像素点之间变化幅度的算子,而边缘的变化幅度是最剧烈的,所以sobel算子能用来做边缘检测.

sobel算子分方向,在x,y方向指定阶数.

API:void sobel(输入源,输出,int 输出图像深度,int X方向差分阶数,int y方向差分阶数,int sobel邻域核大小,double 可选的缩放因子,double 可选的delata值,double 边界模式)

注:源图像和目标图像尺寸类型一致,,输出图像的深度根据源图像的深度决定,默认为-1,输出图像必须比源图像的数据更宽,sobel核大小默认为3,只能为1,3,5,7其中之一,缩放因子默认为-1,不缩放,delate值默认为.0

实际功能代码如下

Mat srcImage,sobelxImage,sobelxAbsImage,sobelyImage,sobelyAbsImage,dstImage;

const int g_sobelCoreMax = 2;
int g_sobelCoreValue;

const int g_deltaxMax = 9;
int g_deltaxValue;

const int g_deltayMax = 9;
int g_deltayValue;

void onTrackBarSobelCore(int pos,void* userData);
void onTrackBarSobelDeltax(int pos,void* userData);
void onTrackBarSobelDeltay(int pos,void* userData);

int main(int argc,char* argv[])
{
   srcImage = imread("F:\\opencv\\OpenCVImage\\sobel.jpg");
   namedWindow("src image");
   namedWindow("sobelx image");
   namedWindow("sobely image");
   namedWindow("sobelxy image");

   g_sobelCoreValue = 0;
   g_deltaxValue = 0;
   g_deltayValue = 0;
   createTrackbar("core size", "src image", &g_sobelCoreValue, g_sobelCoreMax,onTrackBarSobelCore,0);
   createTrackbar("deltax value", "src image", &g_deltaxValue, g_deltaxMax,onTrackBarSobelDeltax,0);
   createTrackbar("deltay value", "src image", &g_deltayValue, g_deltayMax,onTrackBarSobelDeltay,0);
   onTrackBarSobelCore(g_sobelCoreValue, 0);

   imshow("src image", srcImage);
   moveWindow("src image", 0, 0);
   moveWindow("sobelx image", srcImage.cols, 0);
   moveWindow("sobely image", 0, srcImage.rows);
   moveWindow("sobelxy image", srcImage.cols, srcImage.rows);

   waitKey(0);
   return 0;
}

void onTrackBarSobelCore(int pos,void* userData)
{
   int coreSize = g_sobelCoreValue*2+3;
   int deltax = g_deltaxValue+1;
   int deltay = g_deltayValue+1;
   Sobel(srcImage, sobelxImage, CV_16S, deltax, 0,coreSize);
   Sobel(srcImage, sobelyImage, CV_16S, 0, deltay,coreSize);
   convertScaleAbs(sobelxImage, sobelxAbsImage);
   convertScaleAbs(sobelyImage, sobelyAbsImage);
   addWeighted(sobelxAbsImage, 0.5, sobelyAbsImage, 0.5, 0.0, dstImage);
   imshow("sobelx image", sobelxAbsImage);
   imshow("sobely image", sobelyAbsImage);
   imshow("sobelxy image", dstImage);
}

void onTrackBarSobelDeltax(int pos,void* userData)
{
   int coreSize = g_sobelCoreValue*2+3;
   int deltax = g_deltaxValue+1;
   int deltay = g_deltayValue+1;
   Sobel(srcImage, sobelxImage, CV_16S, deltax, 0,coreSize);
   Sobel(srcImage, sobelyImage, CV_16S, 0, deltay,coreSize);
   convertScaleAbs(sobelxImage, sobelxAbsImage);
   convertScaleAbs(sobelyImage, sobelyAbsImage);
   addWeighted(sobelxAbsImage, 0.5, sobelyAbsImage, 0.5, 0.0, dstImage);
   imshow("sobelx image", sobelxAbsImage);
   imshow("sobely image", sobelyAbsImage);
   imshow("sobelxy image", dstImage);
}

void onTrackBarSobelDeltay(int pos,void* userData)
{
   int coreSize = g_sobelCoreValue*2+3;
   int deltax = g_deltaxValue+1;
   int deltay = g_deltayValue+1;
   Sobel(srcImage, sobelxImage, CV_16S, deltax, 0,coreSize);
   Sobel(srcImage, sobelyImage, CV_16S, 0, deltay,coreSize);
   convertScaleAbs(sobelxImage, sobelxAbsImage);
   convertScaleAbs(sobelyImage, sobelyAbsImage);
   addWeighted(sobelxAbsImage, 0.5, sobelyAbsImage, 0.5, 0.0, dstImage);
   imshow("sobelx image", sobelxAbsImage);
   imshow("sobely image", sobelyAbsImage);
   imshow("sobelxy image", dstImage);
}

　　

当Ksize为1的时候,仅使用1*3内核或者3*1内核,而且没有平滑操作.

二.scharr算子

当sobel算子核大小为3的时候,因为计算使用的是导数的近似值,为了解决ksize为3的时候的误差问题,opencv引入了函数scharr,scharr和sobel一样快,但是结果更加精确

另外,对于sobel,因为目标图像的深度一般比源图像的深度更深,所以为了正常的显示目标图像,我们可以使用convertScalarAbs()函数,将深度缩放为八位数字图像,便于显示和保存.

API:void scharr(源图像,目标图像, int 输出图像深度,int X方向差分阶数,int y方向差分阶数 ,double 可选的缩放因子,double 可选的delata值,double 边界模式)

注:和sobel相比少了一个邻域核大小,因为默认为3

使用例程代码如下

Mat srcImage,scharrxImage,scharrxAbsImage,scharryImage,scharryAbsImage,dstImage;
const int g_deltaxMax = ;
int g_deltaxValue;
const int g_deltayMax = ;
int g_deltayValue;
void onTrackBarScharrDeltax(int pos,void* userData);
void onTrackBarScharrDeltay(int pos,void* userData);

int main(int argc,char* argv[])
{
   srcImage = imread("F:\\opencv\\OpenCVImage\\scharr.jpg");
   namedWindow("src image");
   namedWindow("scharrx image");
   namedWindow("scharry image");
   namedWindow("scharrxy image");

   g_deltaxValue = ;
   g_deltayValue = ;
   createTrackbar("deltax value", "src image", &g_deltaxValue, g_deltaxMax,onTrackBarScharrDeltax,);
   createTrackbar("deltay value", "src image", &g_deltayValue, g_deltayMax,onTrackBarScharrDeltay,);
   onTrackBarScharrDeltax(g_deltaxValue,);

   imshow("src image", srcImage);
   moveWindow("src image", , );
   moveWindow("scharrx image", srcImage.cols, );
   moveWindow("scharry image", , srcImage.rows);
   moveWindow("scharrxy image", srcImage.cols, srcImage.rows);

   waitKey();
   return ;
}

void onTrackBarScharrDeltax(int pos,void* userData)
{
   int deltax = g_deltaxValue+;
   int deltay = g_deltayValue+;
   Scharr(srcImage, scharrxImage, CV_16S, deltax, );
   Scharr(srcImage, scharryImage, CV_16S, , deltay);
   convertScaleAbs(scharrxImage, scharrxAbsImage);
   convertScaleAbs(scharryImage, scharryAbsImage);
   addWeighted(scharrxAbsImage, 0.5, scharryAbsImage, 0.5, 0.0, dstImage);
   imshow("scharrx image", scharrxAbsImage);
   imshow("scharry image", scharryAbsImage);
   imshow("scharrxy image", dstImage);
}
void onTrackBarScharrDeltay(int pos,void* userData)
{
   int deltax = g_deltaxValue+;
   int deltay = g_deltayValue+;
   Scharr(srcImage, scharrxImage, CV_16S, deltax, );
   Scharr(srcImage, scharryImage, CV_16S, , deltay);
   convertScaleAbs(scharrxImage, scharrxAbsImage);
   convertScaleAbs(scharryImage, scharryAbsImage);
   addWeighted(scharrxAbsImage, 0.5, scharryAbsImage, 0.5, 0.0, dstImage);
   imshow("scharrx image", scharrxAbsImage);
   imshow("scharry image", scharryAbsImage);
   imshow("scharrxy image", dstImage);
}

三.laplacian算子 (拉普拉斯算子)

有时候我们需要在X和y方向上同时差分,然后看整体的结果,这就需要用到laplacian算子,该算子是N维欧几里德空间中的二阶微分算子.

让一幅图的源图像减去其拉普拉斯算子的结果,图像的对比度将变得更强

API:void laplacian(源图像,目标图像,目标深度,int 邻域孔径尺寸,int 可选的缩放比例因子,int deleta可选值,int 边界模式);

注:图像深度和sobel一致,cv_8u对应cv_16s,ksize默认为1,且必须是正奇数.

实际上,laplacian是图像在x方向上的sobel算子和y方向上的sobel算子的和,使用这种算子之前,最好先进行图像的滤波操作,防止引入微笑误差.

使用代码

// lapacian拉普拉斯算子
Mat srcImage,srcImageGassianBlur,srcImageGray,laplacianImage,laplacianAbs;
const int g_coreSizeMax = ;
int g_coreSizeValue;
void onTrackBarCoreSize(int pos,void* userData);

int main(int argc,char* argv[])
{
   srcImage = imread("F:\\opencv\\OpenCVImage\\laplacian.jpg");
   GaussianBlur(srcImage, srcImageGassianBlur, Size(,), );
   cvtColor(srcImageGassianBlur, srcImageGray, CV_RGB2GRAY);

   namedWindow("src image");
   namedWindow("dst image");

   g_coreSizeValue = ;
   createTrackbar("core size", "dst image", &g_coreSizeValue, g_coreSizeMax,onTrackBarCoreSize,);
   onTrackBarCoreSize(g_coreSizeValue, );

   imshow("src image", srcImage);

   moveWindow("src image", , );
   moveWindow("dst image", srcImage.cols, );

   waitKey();
   return ;
}

void onTrackBarCoreSize(int pos,void* userData)
{
   int coreSize = g_coreSizeValue* + ;
   Laplacian(srcImageGray, laplacianImage, CV_16S,coreSize);
   convertScaleAbs(laplacianImage, laplacianAbs);
   imshow("dst image", laplacianAbs);
}

四.canny边缘检测

边缘检测在工程上,有极大的应用,依靠边缘,确定物体的形状,检测产品的良好程度,canny算法,是opencv中提供的很不错的边缘检测算法,其检测边缘的步骤如下.

首先是滤波,使用高斯平滑滤波卷积降噪.然后是计算梯度幅值与方向,类似于sobel ,laplacian,第三是非极大值一致,排除掉不是边缘的像素,最后是之后阈值化,使用两个阈值,并且绑定的时候考虑颜色之间的关联关系(高低阈值的比例在1:2或者1:3之间).

API:void canny(输入图像,输出图像,double 低与阈值,double 高阈值,int sobel算子孔径,bool 计算梯度幅值标志);

注:sobel算子孔径默认为3,计算梯度幅值的标记默认为false,低阈值用于控制图像边缘的连接,而高阈值用于控制边缘的初始点位置.

另外,使用canny检测算法之前,最好先对图像经过一次降噪处理.

使用例程如下

//低̨ª阈D值¦Ì和¨ª高?阈D值¦Ì默?认¨?1:3
//sobel算?子Á¨®孔¡Á径?只?能¨¹取¨?值¦Ì 3 5 7
//平?滑?滤?波¡§算?子Á¨®孔¡Á径? 3,5,7,9
Mat srcImage,grayImage,grayBlurImage,cannyImage,dstImage;

const int g_blurSizeMax = ;//平?滑?滤?波¡§孔¡Á径?
int g_blurValue;
const int g_sobelSizeMax = ;//sobel孔¡Á径?
int g_sobelValue;
const int g_lowThresholdMax = ;//边À?缘¦Ì检¨¬测a低̨ª阈D值¦Ì
int g_lowThresholdValue;
int g_upThresholdValue;

void onTrackBarBlurSize(int pos,void* userData);
void onTrackBarSobelSize(int pos,void* userData);
void onTrackBarLowThresholdSize(int pos,void* userData);

int main(int argc,char* argv[])
{
   srcImage = imread("F:\\opencv\\OpenCVImage\\canny2.jpg");
   if(srcImage.channels() != )
   {
       cvtColor(srcImage, grayImage, CV_BGR2GRAY);
   }
   else
   {
       grayImage = srcImage.clone();
   }
   namedWindow("src image");
   namedWindow("dst image");
   g_blurValue = ;
   g_sobelValue = ;
   g_lowThresholdValue = ;
   g_upThresholdValue = ;
   createTrackbar("blur size", "dst image", &g_blurValue, g_blurSizeMax,onTrackBarBlurSize,);
   createTrackbar("sobel size", "dst image", &g_sobelValue, g_sobelSizeMax,onTrackBarSobelSize,);
   createTrackbar("low threshold", "dst image", &g_lowThresholdValue, g_lowThresholdMax,onTrackBarLowThresholdSize,);
   onTrackBarBlurSize(g_blurValue, );

   imshow("src image", srcImage);

   moveWindow("src image", , );
   moveWindow("dst image", srcImage.cols, );

   waitKey();
   return ;
}

void onTrackBarBlurSize(int pos,void* userData)
{
   int blurValue = g_blurValue* +;
   int sobelValue = g_sobelValue* +;
   if (g_lowThresholdValue == ) {
       g_lowThresholdValue = ;
   }
   int lowThresholdValue = g_lowThresholdValue;
   int upThresholdValue = lowThresholdValue*;

   //平?滑?滤?波¡§
   blur(srcImage, grayBlurImage, Size(blurValue,blurValue));
   //计?算?canny
   Canny(grayBlurImage, cannyImage, lowThresholdValue, upThresholdValue,sobelValue);
   dstImage = Scalar::all();
   srcImage.copyTo(dstImage, cannyImage);
   imshow("dst image", dstImage);
}
void onTrackBarSobelSize(int pos,void* userData)
{
   int blurValue = g_blurValue* +;
   int sobelValue = g_sobelValue* +;
   if (g_lowThresholdValue == ) {
       g_lowThresholdValue = ;
   }
   int lowThresholdValue = g_lowThresholdValue;
   int upThresholdValue = lowThresholdValue*;

   //平?滑?滤?波¡§
   blur(srcImage, grayBlurImage, Size(blurValue,blurValue));
   //计?算?canny
   Canny(grayBlurImage, cannyImage, lowThresholdValue, upThresholdValue,sobelValue);

   //用canny为掩码?,将src拷贝到dstimage中D,应为检测到的线条才会得到拷贝,所以,目标图上检测到的线条就会变成彩色条纹?
   dstImage = Scalar::all();
   srcImage.copyTo(dstImage, cannyImage);
   imshow("dst image", dstImage);
}

void onTrackBarLowThresholdSize(int pos,void* userData)
{
   int blurValue = g_blurValue* +;
   int sobelValue = g_sobelValue* +;
   if (g_lowThresholdValue == ) {
       g_lowThresholdValue = ;
   }
   int lowThresholdValue = g_lowThresholdValue;
   int upThresholdValue = lowThresholdValue*;

   //平?滑?滤?波¡§
   blur(srcImage, grayBlurImage, Size(blurValue,blurValue));
   //计?算?canny
   Canny(grayBlurImage, cannyImage, lowThresholdValue, upThresholdValue,sobelValue);
   dstImage = Scalar::all();
   srcImage.copyTo(dstImage, cannyImage);
   imshow("dst image", dstImage);
}