Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
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#include "precomp.hpp"
#include "opencl_kernels.hpp"
////////////////////////////////////////////////// matchTemplate //////////////////////////////////////////////////////////
namespace cv
{
#ifdef HAVE_OPENCL
static bool useNaive(int method, int depth, const Size & size)
{
#ifdef HAVE_CLAMDFFT
if (method == TM_SQDIFF && depth == CV_32F)
return true;
else if(method == TM_CCORR || (method == TM_SQDIFF && depth == CV_8U))
return size.height < 18 && size.width < 18;
else
return false;
#else
(void)(method);
(void)(depth);
(void)(size);
return true;
#endif
}
/////////////////////////////////////////////////// CCORR //////////////////////////////////////////////////////////////
static bool matchTemplateNaive_CCORR(InputArray _image, InputArray _templ, OutputArray _result)
{
int type = _image.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
ocl::Kernel k("matchTemplate_Naive_CCORR", ocl::imgproc::match_template_oclsrc,
format("-D type=%s -D elem_type=%s -D cn=%d", ocl::typeToStr(type), ocl::typeToStr(depth), cn));
if (k.empty())
return false;
UMat image = _image.getUMat(), templ = _templ.getUMat();
_result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
UMat result = _result.getUMat();
size_t globalsize[2] = { result.cols, result.rows };
return k.args(ocl::KernelArg::ReadOnlyNoSize(image), ocl::KernelArg::ReadOnly(templ),
ocl::KernelArg::WriteOnly(result)).run(2, globalsize, NULL, false);
}
static bool matchTemplate_CCORR_NORMED(InputArray _image, InputArray _templ, OutputArray _result)
{
matchTemplate(_image, _templ, _result, CV_TM_CCORR);
int type = _image.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
ocl::Kernel k("matchTemplate_CCORR_NORMED", ocl::imgproc::match_template_oclsrc,
format("-D type=%s -D elem_type=%s -D cn=%d", ocl::typeToStr(type),
ocl::typeToStr(depth), cn));
if (k.empty())
return false;
UMat image = _image.getUMat(), templ = _templ.getUMat();
_result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
UMat result = _result.getUMat();
UMat image_sums, image_sqsums;
integral(image.reshape(1), image_sums, image_sqsums, CV_32F, CV_32F);
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UMat temp;
multiply(templ, templ, temp, 1, CV_32F);
Scalar s = sum(temp);
float templ_sqsum = 0;
for (int i = 0; i < cn; ++i)
templ_sqsum += static_cast<float>(s[i]);
size_t globalsize[2] = { result.cols, result.rows };
return k.args(ocl::KernelArg::ReadOnlyNoSize(image_sqsums), ocl::KernelArg::ReadWrite(result),
templ.rows, templ.cols, templ_sqsum).run(2, globalsize, NULL, false);
}
static bool matchTemplate_CCORR(InputArray _image, InputArray _templ, OutputArray _result)
{
if (useNaive(TM_CCORR, _image.depth(), _templ.size()) )
return matchTemplateNaive_CCORR(_image, _templ, _result);
else
return false;
}
////////////////////////////////////// SQDIFF //////////////////////////////////////////////////////////////
static bool matchTemplateNaive_SQDIFF(InputArray _image, InputArray _templ, OutputArray _result)
{
int type = _image.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
ocl::Kernel k("matchTemplate_Naive_SQDIFF", ocl::imgproc::match_template_oclsrc,
format("-D type=%s -D elem_type=%s -D cn=%d", ocl::typeToStr(type),
ocl::typeToStr(depth), cn));
if (k.empty())
return false;
UMat image = _image.getUMat(), templ = _templ.getUMat();
_result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
UMat result = _result.getUMat();
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size_t globalsize[2] = { result.cols, result.rows };
return k.args(ocl::KernelArg::ReadOnlyNoSize(image), ocl::KernelArg::ReadOnly(templ),
ocl::KernelArg::WriteOnly(result)).run(2, globalsize, NULL, false);
}
static bool matchTemplate_SQDIFF_NORMED(InputArray _image, InputArray _templ, OutputArray _result)
{
matchTemplate(_image, _templ, _result, CV_TM_CCORR);
int type = _image.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
ocl::Kernel k("matchTemplate_SQDIFF_NORMED", ocl::imgproc::match_template_oclsrc,
format("-D type=%s -D elem_type=%s -D cn=%d",
ocl::typeToStr(type), ocl::typeToStr(depth), cn));
if (k.empty())
return false;
UMat image = _image.getUMat(), templ = _templ.getUMat();
_result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
UMat result = _result.getUMat();
UMat image_sums, image_sqsums;
integral(image.reshape(1), image_sums, image_sqsums, CV_32F, CV_32F);
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UMat temp;
multiply(templ, templ, temp, 1, CV_32F);
Scalar s = sum(temp);
float templ_sqsum = 0;
for (int i = 0; i < cn; ++i)
templ_sqsum += (float)s[i];
size_t globalsize[2] = { result.cols, result.rows };
return k.args(ocl::KernelArg::ReadOnlyNoSize(image_sqsums), ocl::KernelArg::ReadWrite(result),
templ.rows, templ.cols, templ_sqsum).run(2, globalsize, NULL, false);
}
static bool matchTemplate_SQDIFF(InputArray _image, InputArray _templ, OutputArray _result)
{
if (useNaive(TM_SQDIFF, _image.depth(), _templ.size()))
return matchTemplateNaive_SQDIFF(_image, _templ, _result);
else
return false;
}
///////////////////////////////////// CCOEFF /////////////////////////////////////////////////////////////////
static bool matchTemplate_CCOEFF(InputArray _image, InputArray _templ, OutputArray _result)
{
matchTemplate(_image, _templ, _result, CV_TM_CCORR);
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UMat image_sums, temp;
integral(_image, temp);
if(temp.depth() == CV_64F)
temp.convertTo(image_sums, CV_32F);
else
image_sums = temp;
int type = image_sums.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
ocl::Kernel k(cv::format("matchTemplate_Prepared_CCOEFF_C%d", cn).c_str(), ocl::imgproc::match_template_oclsrc,
format("-D type=%s -D elem_type=%s -D cn=%d", ocl::typeToStr(type), ocl::typeToStr(depth), cn));
if (k.empty())
return false;
UMat templ = _templ.getUMat();
Size size = _image.size(), tsize = templ.size();
_result.create(size.height - templ.rows + 1, size.width - templ.cols + 1, CV_32F);
UMat result = _result.getUMat();
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size_t globalsize[2] = { result.cols, result.rows };
if (cn == 1)
{
float templ_sum = static_cast<float>(sum(_templ)[0]) / tsize.area();
return k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadWrite(result),
templ.rows, templ.cols, templ_sum).run(2, globalsize, NULL, false);
}
else
{
Vec4f templ_sum = Vec4f::all(0);
templ_sum = sum(templ) / tsize.area();
if (cn == 2)
return k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols,
templ_sum[0], templ_sum[1]).run(2, globalsize, NULL, false);
return k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols,
templ_sum[0], templ_sum[1], templ_sum[2], templ_sum[3]).run(2, globalsize, NULL, false);
}
}
static bool matchTemplate_CCOEFF_NORMED(InputArray _image, InputArray _templ, OutputArray _result)
{
matchTemplate(_image, _templ, _result, CV_TM_CCORR);
UMat temp, image_sums, image_sqsums;
integral(_image, image_sums, image_sqsums, CV_32F, CV_32F);
int type = image_sums.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
ocl::Kernel k(format("matchTemplate_CCOEFF_NORMED_C%d", cn).c_str(), ocl::imgproc::match_template_oclsrc,
format("-D type=%s -D elem_type=%s -D cn=%d", ocl::typeToStr(type), ocl::typeToStr(depth), cn));
if (k.empty())
return false;
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UMat templ = _templ.getUMat();
Size size = _image.size(), tsize = templ.size();
_result.create(size.height - templ.rows + 1, size.width - templ.cols + 1, CV_32F);
UMat result = _result.getUMat();
size_t globalsize[2] = { result.cols, result.rows };
float scale = 1.f / tsize.area();
if (cn == 1)
{
float templ_sum = (float)sum(templ)[0];
multiply(templ, templ, temp, 1, CV_32F);
float templ_sqsum = (float)sum(temp)[0];
templ_sqsum -= scale * templ_sum * templ_sum;
templ_sum *= scale;
if (templ_sqsum < DBL_EPSILON)
{
result = Scalar::all(1);
return true;
}
return k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadOnlyNoSize(image_sqsums),
ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, scale, templ_sum, templ_sqsum)
.run(2,globalsize,NULL,false);
}
else
{
Vec4f templ_sum = Vec4f::all(0), templ_sqsum = Vec4f::all(0);
templ_sum = sum(templ);
multiply(templ, templ, temp, 1, CV_32F);
templ_sqsum = sum(temp);
float templ_sqsum_sum = 0;
for (int i = 0; i < cn; i ++)
templ_sqsum_sum += templ_sqsum[i] - scale * templ_sum[i] * templ_sum[i];
templ_sum *= scale;
if (templ_sqsum_sum < DBL_EPSILON)
{
result = Scalar::all(1);
return true;
}
if (cn == 2)
return k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadOnlyNoSize(image_sqsums),
ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, scale,
templ_sum[0], templ_sum[1], templ_sqsum_sum).run(2, globalsize, NULL, false);
return k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadOnlyNoSize(image_sqsums),
ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, scale,
templ_sum[0], templ_sum[1], templ_sum[2], templ_sum[3],
templ_sqsum_sum).run(2, globalsize, NULL, false);
}
}
///////////////////////////////////////////////////////////////////////////////////////////////////////////
static bool ocl_matchTemplate( InputArray _img, InputArray _templ, OutputArray _result, int method)
{
int cn = _img.channels();
if (cn == 3 || cn > 4)
return false;
typedef bool (*Caller)(InputArray _img, InputArray _templ, OutputArray _result);
static const Caller callers[] =
{
matchTemplate_SQDIFF, matchTemplate_SQDIFF_NORMED, matchTemplate_CCORR,
matchTemplate_CCORR_NORMED, matchTemplate_CCOEFF, matchTemplate_CCOEFF_NORMED
};
const Caller caller = callers[method];
return caller(_img, _templ, _result);
}
#endif
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void crossCorr( const Mat& img, const Mat& _templ, Mat& corr,
Size corrsize, int ctype,
Point anchor, double delta, int borderType )
{
const double blockScale = 4.5;
const int minBlockSize = 256;
std::vector<uchar> buf;
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Mat templ = _templ;
int depth = img.depth(), cn = img.channels();
int tdepth = templ.depth(), tcn = templ.channels();
int cdepth = CV_MAT_DEPTH(ctype), ccn = CV_MAT_CN(ctype);
CV_Assert( img.dims <= 2 && templ.dims <= 2 && corr.dims <= 2 );
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if( depth != tdepth && tdepth != std::max(CV_32F, depth) )
{
_templ.convertTo(templ, std::max(CV_32F, depth));
tdepth = templ.depth();
}
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CV_Assert( depth == tdepth || tdepth == CV_32F);
CV_Assert( corrsize.height <= img.rows + templ.rows - 1 &&
corrsize.width <= img.cols + templ.cols - 1 );
CV_Assert( ccn == 1 || delta == 0 );
corr.create(corrsize, ctype);
int maxDepth = depth > CV_8S ? CV_64F : std::max(std::max(CV_32F, tdepth), cdepth);
Size blocksize, dftsize;
blocksize.width = cvRound(templ.cols*blockScale);
blocksize.width = std::max( blocksize.width, minBlockSize - templ.cols + 1 );
blocksize.width = std::min( blocksize.width, corr.cols );
blocksize.height = cvRound(templ.rows*blockScale);
blocksize.height = std::max( blocksize.height, minBlockSize - templ.rows + 1 );
blocksize.height = std::min( blocksize.height, corr.rows );
dftsize.width = std::max(getOptimalDFTSize(blocksize.width + templ.cols - 1), 2);
dftsize.height = getOptimalDFTSize(blocksize.height + templ.rows - 1);
if( dftsize.width <= 0 || dftsize.height <= 0 )
CV_Error( CV_StsOutOfRange, "the input arrays are too big" );
// recompute block size
blocksize.width = dftsize.width - templ.cols + 1;
blocksize.width = MIN( blocksize.width, corr.cols );
blocksize.height = dftsize.height - templ.rows + 1;
blocksize.height = MIN( blocksize.height, corr.rows );
Mat dftTempl( dftsize.height*tcn, dftsize.width, maxDepth );
Mat dftImg( dftsize, maxDepth );
int i, k, bufSize = 0;
if( tcn > 1 && tdepth != maxDepth )
bufSize = templ.cols*templ.rows*CV_ELEM_SIZE(tdepth);
if( cn > 1 && depth != maxDepth )
bufSize = std::max( bufSize, (blocksize.width + templ.cols - 1)*
(blocksize.height + templ.rows - 1)*CV_ELEM_SIZE(depth));
if( (ccn > 1 || cn > 1) && cdepth != maxDepth )
bufSize = std::max( bufSize, blocksize.width*blocksize.height*CV_ELEM_SIZE(cdepth));
buf.resize(bufSize);
// compute DFT of each template plane
for( k = 0; k < tcn; k++ )
{
int yofs = k*dftsize.height;
Mat src = templ;
Mat dst(dftTempl, Rect(0, yofs, dftsize.width, dftsize.height));
Mat dst1(dftTempl, Rect(0, yofs, templ.cols, templ.rows));
if( tcn > 1 )
{
src = tdepth == maxDepth ? dst1 : Mat(templ.size(), tdepth, &buf[0]);
int pairs[] = {k, 0};
mixChannels(&templ, 1, &src, 1, pairs, 1);
}
if( dst1.data != src.data )
src.convertTo(dst1, dst1.depth());
if( dst.cols > templ.cols )
{
Mat part(dst, Range(0, templ.rows), Range(templ.cols, dst.cols));
part = Scalar::all(0);
}
dft(dst, dst, 0, templ.rows);
}
int tileCountX = (corr.cols + blocksize.width - 1)/blocksize.width;
int tileCountY = (corr.rows + blocksize.height - 1)/blocksize.height;
int tileCount = tileCountX * tileCountY;
Size wholeSize = img.size();
Point roiofs(0,0);
Mat img0 = img;
if( !(borderType & BORDER_ISOLATED) )
{
img.locateROI(wholeSize, roiofs);
img0.adjustROI(roiofs.y, wholeSize.height-img.rows-roiofs.y,
roiofs.x, wholeSize.width-img.cols-roiofs.x);
}
borderType |= BORDER_ISOLATED;
// calculate correlation by blocks
for( i = 0; i < tileCount; i++ )
{
int x = (i%tileCountX)*blocksize.width;
int y = (i/tileCountX)*blocksize.height;
Size bsz(std::min(blocksize.width, corr.cols - x),
std::min(blocksize.height, corr.rows - y));
Size dsz(bsz.width + templ.cols - 1, bsz.height + templ.rows - 1);
int x0 = x - anchor.x + roiofs.x, y0 = y - anchor.y + roiofs.y;
int x1 = std::max(0, x0), y1 = std::max(0, y0);
int x2 = std::min(img0.cols, x0 + dsz.width);
int y2 = std::min(img0.rows, y0 + dsz.height);
Mat src0(img0, Range(y1, y2), Range(x1, x2));
Mat dst(dftImg, Rect(0, 0, dsz.width, dsz.height));
Mat dst1(dftImg, Rect(x1-x0, y1-y0, x2-x1, y2-y1));
Mat cdst(corr, Rect(x, y, bsz.width, bsz.height));
for( k = 0; k < cn; k++ )
{
Mat src = src0;
dftImg = Scalar::all(0);
if( cn > 1 )
{
src = depth == maxDepth ? dst1 : Mat(y2-y1, x2-x1, depth, &buf[0]);
int pairs[] = {k, 0};
mixChannels(&src0, 1, &src, 1, pairs, 1);
}
if( dst1.data != src.data )
src.convertTo(dst1, dst1.depth());
if( x2 - x1 < dsz.width || y2 - y1 < dsz.height )
copyMakeBorder(dst1, dst, y1-y0, dst.rows-dst1.rows-(y1-y0),
x1-x0, dst.cols-dst1.cols-(x1-x0), borderType);
dft( dftImg, dftImg, 0, dsz.height );
Mat dftTempl1(dftTempl, Rect(0, tcn > 1 ? k*dftsize.height : 0,
dftsize.width, dftsize.height));
mulSpectrums(dftImg, dftTempl1, dftImg, 0, true);
dft( dftImg, dftImg, DFT_INVERSE + DFT_SCALE, bsz.height );
src = dftImg(Rect(0, 0, bsz.width, bsz.height));
if( ccn > 1 )
{
if( cdepth != maxDepth )
{
Mat plane(bsz, cdepth, &buf[0]);
src.convertTo(plane, cdepth, 1, delta);
src = plane;
}
int pairs[] = {0, k};
mixChannels(&src, 1, &cdst, 1, pairs, 1);
}
else
{
if( k == 0 )
src.convertTo(cdst, cdepth, 1, delta);
else
{
if( maxDepth != cdepth )
{
Mat plane(bsz, cdepth, &buf[0]);
src.convertTo(plane, cdepth);
src = plane;
}
add(src, cdst, cdst);
}
}
}
}
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////
void cv::matchTemplate( InputArray _img, InputArray _templ, OutputArray _result, int method )
{
CV_Assert( CV_TM_SQDIFF <= method && method <= CV_TM_CCOEFF_NORMED );
CV_Assert( (_img.depth() == CV_8U || _img.depth() == CV_32F) && _img.type() == _templ.type() && _img.dims() <= 2 );
bool needswap = _img.size().height < _templ.size().height || _img.size().width < _templ.size().width;
if (needswap)
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{
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CV_Assert(_img.size().height <= _templ.size().height && _img.size().width <= _templ.size().width);
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}
CV_OCL_RUN(_img.dims() <= 2 && _result.isUMat(),
(!needswap ? ocl_matchTemplate(_img, _templ, _result, method) : ocl_matchTemplate(_templ, _img, _result, method)))
int numType = method == CV_TM_CCORR || method == CV_TM_CCORR_NORMED ? 0 :
method == CV_TM_CCOEFF || method == CV_TM_CCOEFF_NORMED ? 1 : 2;
bool isNormed = method == CV_TM_CCORR_NORMED ||
method == CV_TM_SQDIFF_NORMED ||
method == CV_TM_CCOEFF_NORMED;
Mat img = _img.getMat(), templ = _templ.getMat();
if (needswap)
std::swap(img, templ);
Size corrSize(img.cols - templ.cols + 1, img.rows - templ.rows + 1);
_result.create(corrSize, CV_32F);
Mat result = _result.getMat();
#ifdef HAVE_TEGRA_OPTIMIZATION
if (tegra::matchTemplate(img, templ, result, method))
return;
#endif
int cn = img.channels();
crossCorr( img, templ, result, result.size(), result.type(), Point(0,0), 0, 0);
if( method == CV_TM_CCORR )
return;
double invArea = 1./((double)templ.rows * templ.cols);
Mat sum, sqsum;
Scalar templMean, templSdv;
double *q0 = 0, *q1 = 0, *q2 = 0, *q3 = 0;
double templNorm = 0, templSum2 = 0;
if( method == CV_TM_CCOEFF )
{
integral(img, sum, CV_64F);
templMean = mean(templ);
}
else
{
integral(img, sum, sqsum, CV_64F);
meanStdDev( templ, templMean, templSdv );
templNorm = templSdv[0]*templSdv[0] + templSdv[1]*templSdv[1] + templSdv[2]*templSdv[2] + templSdv[3]*templSdv[3];
if( templNorm < DBL_EPSILON && method == CV_TM_CCOEFF_NORMED )
{
result = Scalar::all(1);
return;
}
templSum2 = templNorm + templMean[0]*templMean[0] + templMean[1]*templMean[1] + templMean[2]*templMean[2] + templMean[3]*templMean[3];
if( numType != 1 )
{
templMean = Scalar::all(0);
templNorm = templSum2;
}
templSum2 /= invArea;
templNorm = std::sqrt(templNorm);
templNorm /= std::sqrt(invArea); // care of accuracy here
q0 = (double*)sqsum.data;
q1 = q0 + templ.cols*cn;
q2 = (double*)(sqsum.data + templ.rows*sqsum.step);
q3 = q2 + templ.cols*cn;
}
double* p0 = (double*)sum.data;
double* p1 = p0 + templ.cols*cn;
double* p2 = (double*)(sum.data + templ.rows*sum.step);
double* p3 = p2 + templ.cols*cn;
int sumstep = sum.data ? (int)(sum.step / sizeof(double)) : 0;
int sqstep = sqsum.data ? (int)(sqsum.step / sizeof(double)) : 0;
int i, j, k;
for( i = 0; i < result.rows; i++ )
{
float* rrow = (float*)(result.data + i*result.step);
int idx = i * sumstep;
int idx2 = i * sqstep;
for( j = 0; j < result.cols; j++, idx += cn, idx2 += cn )
{
double num = rrow[j], t;
double wndMean2 = 0, wndSum2 = 0;
if( numType == 1 )
{
for( k = 0; k < cn; k++ )
{
t = p0[idx+k] - p1[idx+k] - p2[idx+k] + p3[idx+k];
wndMean2 += t*t;
num -= t*templMean[k];
}
wndMean2 *= invArea;
}
if( isNormed || numType == 2 )
{
for( k = 0; k < cn; k++ )
{
t = q0[idx2+k] - q1[idx2+k] - q2[idx2+k] + q3[idx2+k];
wndSum2 += t;
}
if( numType == 2 )
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{
num = wndSum2 - 2*num + templSum2;
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num = MAX(num, 0.);
}
}
if( isNormed )
{
t = std::sqrt(MAX(wndSum2 - wndMean2,0))*templNorm;
if( fabs(num) < t )
num /= t;
else if( fabs(num) < t*1.125 )
num = num > 0 ? 1 : -1;
else
num = method != CV_TM_SQDIFF_NORMED ? 0 : 1;
}
rrow[j] = (float)num;
}
}
}
CV_IMPL void
cvMatchTemplate( const CvArr* _img, const CvArr* _templ, CvArr* _result, int method )
{
cv::Mat img = cv::cvarrToMat(_img), templ = cv::cvarrToMat(_templ),
result = cv::cvarrToMat(_result);
CV_Assert( result.size() == cv::Size(std::abs(img.cols - templ.cols) + 1,
std::abs(img.rows - templ.rows) + 1) &&
result.type() == CV_32F );
matchTemplate(img, templ, result, method);
}
/* End of file. */