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Merge pull request #13837 from amithjkamath:test

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New computeECC function, and updated findTransformECC function to make gaussian filtering optional (#13837)

* fix for https://github.com/opencv/opencv/issues/12432 with doc and tests

* Added doc string for new parameter.

* Fixes suggested by Alalek for getting around ABI incompatibility.

* Update to docstring, to remove parameter that isn't relevant.

* More updates based on Alalek's usggestions.
This commit is contained in:
AKAMath
2019-02-22 21:06:40 +05:30
committed by Alexander Alekhin
parent 682e03bdb2
commit 4c94804bb0
3 changed files with 97 additions and 11 deletions
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modules/video/src/ecc.cpp
+45 -5
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@@ -309,16 +309,48 @@ static void update_warping_matrix_ECC (Mat& map_matrix, const Mat& update, const
}
/** Function that computes enhanced corelation coefficient from Georgios et.al. 2008
* See https://github.com/opencv/opencv/issues/12432
*/
double cv::computeECC(InputArray templateImage, InputArray inputImage, InputArray inputMask)
{
CV_Assert(!templateImage.empty());
CV_Assert(!inputImage.empty());
if( ! (templateImage.type()==inputImage.type()))
CV_Error( Error::StsUnmatchedFormats, "Both input images must have the same data type" );
Scalar meanTemplate, sdTemplate;
int active_pixels = inputMask.empty() ? templateImage.size().area() : countNonZero(inputMask);
meanStdDev(templateImage, meanTemplate, sdTemplate, inputMask);
Mat templateImage_zeromean = Mat::zeros(templateImage.size(), templateImage.type());
subtract(templateImage, meanTemplate, templateImage_zeromean, inputMask);
double templateImagenorm = std::sqrt(active_pixels*sdTemplate.val[0]*sdTemplate.val[0]);
Scalar meanInput, sdInput;
Mat inputImage_zeromean = Mat::zeros(inputImage.size(), inputImage.type());
meanStdDev(inputImage, meanInput, sdInput, inputMask);
subtract(inputImage, meanInput, inputImage_zeromean, inputMask);
double inputImagenorm = std::sqrt(active_pixels*sdInput.val[0]*sdInput.val[0]);
return templateImage_zeromean.dot(inputImage_zeromean)/(templateImagenorm*inputImagenorm);
}
double cv::findTransformECC(InputArray templateImage,
InputArray inputImage,
InputOutputArray warpMatrix,
int motionType,
TermCriteria criteria,
InputArray inputMask)
InputArray inputMask,
int gaussFiltSize)
{
Mat src = templateImage.getMat();//template iamge
Mat src = templateImage.getMat();//template image
Mat dst = inputImage.getMat(); //input image (to be warped)
Mat map = warpMatrix.getMat(); //warp (transformation)
@@ -416,11 +448,11 @@ double cv::findTransformECC(InputArray templateImage,
//gaussian filtering is optional
src.convertTo(templateFloat, templateFloat.type());
GaussianBlur(templateFloat, templateFloat, Size(5, 5), 0, 0);
GaussianBlur(templateFloat, templateFloat, Size(gaussFiltSize, gaussFiltSize), 0, 0);
Mat preMaskFloat;
preMask.convertTo(preMaskFloat, CV_32F);
GaussianBlur(preMaskFloat, preMaskFloat, Size(5, 5), 0, 0);
GaussianBlur(preMaskFloat, preMaskFloat, Size(gaussFiltSize, gaussFiltSize), 0, 0);
// Change threshold.
preMaskFloat *= (0.5/0.95);
// Rounding conversion.
@@ -428,7 +460,7 @@ double cv::findTransformECC(InputArray templateImage,
preMask.convertTo(preMaskFloat, preMaskFloat.type());
dst.convertTo(imageFloat, imageFloat.type());
GaussianBlur(imageFloat, imageFloat, Size(5, 5), 0, 0);
GaussianBlur(imageFloat, imageFloat, Size(gaussFiltSize, gaussFiltSize), 0, 0);
// needed matrices for gradients and warped gradients
Mat gradientX = Mat::zeros(hd, wd, CV_32FC1);
@@ -557,5 +589,13 @@ double cv::findTransformECC(InputArray templateImage,
return rho;
}
double cv::findTransformECC(InputArray templateImage, InputArray inputImage,
InputOutputArray warpMatrix, int motionType,
TermCriteria criteria,
InputArray inputMask)
{
// Use default value of 5 for gaussFiltSize to maintain backward compatibility.
return findTransformECC(templateImage, inputImage, warpMatrix, motionType, criteria, inputMask, 5);
}
/* End of file. */