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Merge pull request #1567 from ilya-lavrenov:warn_fix

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pull/1580/head
Roman Donchenko 12 years ago committed by OpenCV Buildbot
parent b08233a3ca 35d9ce0c0c
commit 685eceb68a
23 changed files with 98 additions and 98 deletions
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  1. 6
      modules/bioinspired/src/retina.cpp
  2. 2
      modules/calib3d/test/test_chesscorners.cpp
  3. 6
      modules/features2d/test/test_descriptors_regression.cpp
  4. 4
      modules/highgui/src/grfmt_tiff.cpp
  5. 12
      modules/highgui/src/grfmt_webp.cpp
  6. 2
      modules/imgproc/src/intersection.cpp
  7. 3
      modules/imgproc/src/lsd.cpp
  8. 6
      modules/imgproc/test/test_intersection.cpp
  9. 4
      modules/nonfree/test/test_features2d.cpp
  10. 10
      modules/objdetect/src/erfilter.cpp
  11. 16
      modules/optim/src/lpsolver.cpp
  12. 4
      modules/photo/src/align.cpp
  13. 8
      modules/photo/src/calibrate.cpp
  14. 6
      modules/photo/src/merge.cpp
  15. 2
      modules/photo/src/tonemap.cpp
  16. 2
      modules/shape/src/aff_trans.cpp
  17. 10
      modules/shape/src/sc_dis.cpp
  18. 26
      modules/shape/src/tps_trans.cpp
  19. 19
      modules/shape/test/test_emdl1.cpp
  20. 20
      modules/shape/test/test_hausdorff.cpp
  21. 22
      modules/shape/test/test_shape.cpp
  22. 2
      samples/cpp/shape_example.cpp
  23. 4
      samples/cpp/textdetection.cpp

6
modules/bioinspired/src/retina.cpp
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@ -599,7 +599,7 @@ void RetinaImpl::getParvoRAW(OutputArray parvoOutputBufferCopy){
// original API level data accessors : get buffers addresses...
const Mat RetinaImpl::getMagnoRAW() const {
// create a cv::Mat header for the valarray
return Mat(_retinaFilter->getMovingContours().size(),1, CV_32F, (void*)get_data(_retinaFilter->getMovingContours()));
return Mat((int)_retinaFilter->getMovingContours().size(),1, CV_32F, (void*)get_data(_retinaFilter->getMovingContours()));
}
@ -607,11 +607,11 @@ const Mat RetinaImpl::getParvoRAW() const {
if (_retinaFilter->getColorMode()) // check if color mode is enabled
{
// create a cv::Mat table (for RGB planes as a single vector)
return Mat(_retinaFilter->getColorOutput().size(), 1, CV_32F, (void*)get_data(_retinaFilter->getColorOutput()));
return Mat((int)_retinaFilter->getColorOutput().size(), 1, CV_32F, (void*)get_data(_retinaFilter->getColorOutput()));
}
// otherwise, output is gray level
// create a cv::Mat header for the valarray
return Mat( _retinaFilter->getContours().size(), 1, CV_32F, (void*)get_data(_retinaFilter->getContours()));
return Mat((int)_retinaFilter->getContours().size(), 1, CV_32F, (void*)get_data(_retinaFilter->getContours()));
}
// private method called by constructirs

2
modules/calib3d/test/test_chesscorners.cpp
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@ -208,7 +208,7 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename )
}
int progress = 0;
int max_idx = board_list.size()/2;
int max_idx = (int)board_list.size()/2;
double sum_error = 0.0;
int count = 0;

6
modules/features2d/test/test_descriptors_regression.cpp
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@ -80,10 +80,10 @@ static Mat readMatFromBin( const string& filename )
size_t elements_read4 = fread( (void*)&dataSize, sizeof(int), 1, f );
CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1);
size_t step = dataSize / rows / CV_ELEM_SIZE(type);
CV_Assert(step >= (size_t)cols);
int step = dataSize / rows / CV_ELEM_SIZE(type);
CV_Assert(step >= cols);
Mat m = Mat( rows, step, type).colRange(0, cols);
Mat m = Mat(rows, step, type).colRange(0, cols);
size_t elements_read = fread( m.ptr(), 1, dataSize, f );
CV_Assert(elements_read == (size_t)(dataSize));

4
modules/highgui/src/grfmt_tiff.cpp
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@ -406,9 +406,9 @@ bool TiffDecoder::readHdrData(Mat& img)
TIFFGetField( tif, TIFFTAG_PHOTOMETRIC, &photometric );
TIFFSetField(tif, TIFFTAG_SGILOGDATAFMT, SGILOGDATAFMT_FLOAT);
int size = 3 * m_width * m_height * sizeof (float);
int strip_size = 3 * m_width * rows_per_strip;
tstrip_t strip_size = 3 * m_width * rows_per_strip;
float *ptr = img.ptr<float>();
for (size_t i = 0; i < TIFFNumberOfStrips(tif); i++, ptr += strip_size)
for (tstrip_t i = 0; i < TIFFNumberOfStrips(tif); i++, ptr += strip_size)
{
TIFFReadEncodedStrip(tif, i, ptr, size);
size -= strip_size * sizeof(float);

12
modules/highgui/src/grfmt_webp.cpp
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@ -174,12 +174,12 @@ bool WebPDecoder::readData(Mat &img)
if (channels == 3)
{
res_ptr = WebPDecodeBGRInto(data.data, data.total(), out_data,
out_data_size, img.step);
(int)out_data_size, (int)img.step);
}
else if (channels == 4)
{
res_ptr = WebPDecodeBGRAInto(data.data, data.total(), out_data,
out_data_size, img.step);
(int)out_data_size, (int)img.step);
}
if(res_ptr == out_data)
@ -255,22 +255,22 @@ bool WebPEncoder::write(const Mat& img, const std::vector<int>& params)
{
if(channels == 3)
{
size = WebPEncodeLosslessBGR(image->data, width, height, image->step, &out);
size = WebPEncodeLosslessBGR(image->data, width, height, (int)image->step, &out);
}
else if(channels == 4)
{
size = WebPEncodeLosslessBGRA(image->data, width, height, image->step, &out);
size = WebPEncodeLosslessBGRA(image->data, width, height, (int)image->step, &out);
}
}
else
{
if(channels == 3)
{
size = WebPEncodeBGR(image->data, width, height, image->step, quality, &out);
size = WebPEncodeBGR(image->data, width, height, (int)image->step, quality, &out);
}
else if(channels == 4)
{
size = WebPEncodeBGRA(image->data, width, height, image->step, quality, &out);
size = WebPEncodeBGRA(image->data, width, height, (int)image->step, quality, &out);
}
}

2
modules/imgproc/src/intersection.cpp
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@ -49,7 +49,7 @@ namespace cv
int rotatedRectangleIntersection( const RotatedRect& rect1, const RotatedRect& rect2, OutputArray intersectingRegion )
{
const float samePointEps = 0.00001; // used to test if two points are the same
const float samePointEps = 0.00001f; // used to test if two points are the same
Point2f vec1[4], vec2[4];
Point2f pts1[4], pts2[4];

3
modules/imgproc/src/lsd.cpp
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@ -473,8 +473,7 @@ void LineSegmentDetectorImpl::flsd(std::vector<Vec4i>& lines,
// Search for line segments
unsigned int ls_count = 0;
unsigned int list_size = list.size();
for(unsigned int i = 0; i < list_size; ++i)
for(size_t i = 0, list_size = list.size(); i < list_size; ++i)
{
unsigned int adx = list[i].p.x + list[i].p.y * img_width;
if((used.data[adx] == NOTUSED) && (angles_data[adx] != NOTDEF))

6
modules/imgproc/test/test_intersection.cpp
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@ -376,8 +376,8 @@ void CV_RotatedRectangleIntersectionTest::test7()
rect1.center.x = 0;
rect1.center.y = 0;
rect1.size.width = 12.34;
rect1.size.height = 56.78;
rect1.size.width = 12.34f;
rect1.size.height = 56.78f;
rect1.angle = 0;
rect2.center.x = 0;
@ -464,7 +464,7 @@ void CV_RotatedRectangleIntersectionTest::test9()
rect2.center.x = 2;
rect2.center.y = 0;
rect2.size.width = 2;
rect2.size.height = 123.45;
rect2.size.height = 123.45f;
rect2.angle = 0;
vector<Point2f> vertices;

4
modules/nonfree/test/test_features2d.cpp
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@ -275,8 +275,8 @@ static Mat readMatFromBin( const string& filename )
size_t elements_read4 = fread( (void*)&dataSize, sizeof(int), 1, f );
CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1);
size_t step = dataSize / rows / CV_ELEM_SIZE(type);
CV_Assert(step >= (size_t)cols);
int step = dataSize / rows / CV_ELEM_SIZE(type);
CV_Assert(step >= cols);
Mat m = Mat( rows, step, type).colRange(0, cols);

10
modules/objdetect/src/erfilter.cpp
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@ -795,7 +795,7 @@ ERStat* ERFilterNM::er_tree_filter ( InputArray image, ERStat * stat, ERStat *pa
int p_prev = p-1;
int p_next = p+1;
if (p_prev == -1)
p_prev = contour_poly.size()-1;
p_prev = (int)contour_poly.size()-1;
if (p_next == (int)contour_poly.size())
p_next = 0;
@ -2455,9 +2455,9 @@ void MaxMeaningfulClustering::build_merge_info(double *Z, int N, vector<HCluster
cluster.dist = dist;
if (cluster.dist >= 1)
cluster.dist = 0.999999;
cluster.dist = 0.999999f;
if (cluster.dist == 0)
cluster.dist = 1.e-25;
cluster.dist = 1.e-25f;
cluster.dist_ext = 1;
@ -2811,7 +2811,7 @@ void erGrouping(InputArrayOfArrays _src, vector<vector<ERStat> > &regions, const
for (int f=0; f<num_features; f++)
{
unsigned int N = regions.at(c).size();
unsigned int N = (unsigned int)regions.at(c).size();
if (N<3) break;
int dim = dims[f];
double *data = (double*)malloc(dim*N * sizeof(double));
@ -2892,7 +2892,7 @@ void erGrouping(InputArrayOfArrays _src, vector<vector<ERStat> > &regions, const
}
// Find the Max. Meaningful Clusters in the co-occurrence matrix
mm_clustering(D, regions.at(c).size(), METHOD_METR_AVERAGE, &meaningful_clusters);
mm_clustering(D, (unsigned int)regions.at(c).size(), METHOD_METR_AVERAGE, &meaningful_clusters);
free(D);

16
modules/optim/src/lpsolver.cpp
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@ -84,11 +84,12 @@ int solveLP(const Mat& Func, const Mat& Constr, Mat& z){
//return the optimal solution
z.create(c.cols,1,CV_64FC1);
MatIterator_<double> it=z.begin<double>();
unsigned int nsize = (unsigned int)N.size();
for(int i=1;i<=c.cols;i++,it++){
if(indexToRow[i]<N.size()){
if(indexToRow[i]<nsize){
*it=0;
}else{
*it=b.at<double>(indexToRow[i]-N.size(),b.cols-1);
*it=b.at<double>(indexToRow[i]-nsize,b.cols-1);
}
}
@ -102,7 +103,7 @@ static int initialize_simplex(Mat_<double>& c, Mat_<double>& b,double& v,vector<
*it=it[-1]+1;
}
B.resize(b.rows);
B[0]=N.size();
B[0]=(int)N.size();
for (std::vector<int>::iterator it = B.begin()+1 ; it != B.end(); ++it){
*it=it[-1]+1;
}
@ -151,8 +152,9 @@ static int initialize_simplex(Mat_<double>& c, Mat_<double>& b,double& v,vector<
dprintf(("\tAFTER INNER_SIMPLEX\n"));
print_simplex_state(c,b,v,N,B);
if(indexToRow[0]>=N.size()){
int iterator_offset=indexToRow[0]-N.size();
unsigned int nsize = (unsigned int)N.size();
if(indexToRow[0]>=nsize){
int iterator_offset=indexToRow[0]-nsize;
if(b(iterator_offset,b.cols-1)>0){
return SOLVELP_UNFEASIBLE;
}
@ -176,14 +178,14 @@ static int initialize_simplex(Mat_<double>& c, Mat_<double>& b,double& v,vector<
c=0;
v=0;
for(int I=1;I<old_c.cols;I++){
if(indexToRow[I]<N.size()){
if(indexToRow[I]<nsize){
dprintf(("I=%d from nonbasic\n",I));
int iterator_offset=indexToRow[I];
c(0,iterator_offset)+=old_c(0,I);
print_matrix(c);
}else{
dprintf(("I=%d from basic\n",I));
int iterator_offset=indexToRow[I]-N.size();
int iterator_offset=indexToRow[I]-nsize;
c-=old_c(0,I)*b.row(iterator_offset).colRange(0,b.cols-1);
v+=old_c(0,I)*b(iterator_offset,b.cols-1);
print_matrix(c);

4
modules/photo/src/align.cpp
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@ -135,7 +135,7 @@ public:
computeBitmaps(pyr0[level], tb1, eb1);
computeBitmaps(pyr1[level], tb2, eb2);
int min_err = pyr0[level].total();
int min_err = (int)pyr0[level].total();
Point new_shift(shift);
for(int i = -1; i <= 1; i++) {
for(int j = -1; j <= 1; j++) {
@ -253,7 +253,7 @@ protected:
calcHist(&img, 1, &channels, Mat(), hist, 1, &hist_size, ranges);
float *ptr = hist.ptr<float>();
int median = 0, sum = 0;
int thresh = img.total() / 2;
int thresh = (int)img.total() / 2;
while(sum < thresh && median < LDR_SIZE) {
sum += static_cast<int>(ptr[median]);
median++;

8
modules/photo/src/calibrate.cpp
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@ -97,7 +97,7 @@ public:
std::vector<Mat> result_split(channels);
for(int channel = 0; channel < channels; channel++) {
Mat A = Mat::zeros(sample_points.size() * images.size() + LDR_SIZE + 1, LDR_SIZE + sample_points.size(), CV_32F);
Mat A = Mat::zeros((int)sample_points.size() * (int)images.size() + LDR_SIZE + 1, LDR_SIZE + (int)sample_points.size(), CV_32F);
Mat B = Mat::zeros(A.rows, 1, CV_32F);
int eq = 0;
@ -106,8 +106,8 @@ public:
int val = images[j].ptr()[3*(sample_points[i].y * images[j].cols + sample_points[j].x) + channel];
A.at<float>(eq, val) = w.at<float>(val);
A.at<float>(eq, LDR_SIZE + i) = -w.at<float>(val);
B.at<float>(eq, 0) = w.at<float>(val) * log(times.at<float>(j));
A.at<float>(eq, LDR_SIZE + (int)i) = -w.at<float>(val);
B.at<float>(eq, 0) = w.at<float>(val) * log(times.at<float>((int)j));
eq++;
}
}
@ -219,7 +219,7 @@ public:
float* rad_ptr = radiance.ptr<float>();
for(size_t pos = 0; pos < images[i].total(); pos++) {
for(int c = 0; c < channels; c++, ptr++, rad_ptr++) {
new_response.at<Vec3f>(*ptr)[c] += times.at<float>(i) * *rad_ptr;
new_response.at<Vec3f>(*ptr)[c] += times.at<float>((int)i) * *rad_ptr;
}
}
}

6
modules/photo/src/merge.cpp
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@ -106,7 +106,7 @@ public:
LUT(images[i], response, response_img);
split(response_img, splitted);
for(int c = 0; c < channels; c++) {
result_split[c] += w.mul(splitted[c] - exp_values.at<float>(i));
result_split[c] += w.mul(splitted[c] - exp_values.at<float>((int)i));
}
weight_sum += w;
}
@ -327,8 +327,8 @@ public:
LUT(images[i], weight, w);
LUT(images[i], response, im);
result += times.at<float>(i) * w.mul(im);
wsum += times.at<float>(i) * times.at<float>(i) * w;
result += times.at<float>((int)i) * w.mul(im);
wsum += times.at<float>((int)i) * times.at<float>((int)i) * w;
}
result = result.mul(1 / wsum);
}

2
modules/photo/src/tonemap.cpp
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@ -505,7 +505,7 @@ protected:
void calculateSum(std::vector<Mat>& x_contrast, std::vector<Mat>& y_contrast, Mat& sum)
{
sum = Mat::zeros(x_contrast[x_contrast.size() - 1].size(), CV_32F);
for(int i = x_contrast.size() - 1; i >= 0; i--)
for(int i = (int)x_contrast.size() - 1; i >= 0; i--)
{
Mat grad_x, grad_y;
getGradient(x_contrast[i], grad_x, 1);

2
modules/shape/src/aff_trans.cpp
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@ -112,7 +112,7 @@ static Mat _localAffineEstimate(const std::vector<Point2f>& shape1, const std::v
bool fullAfine)
{
Mat out(2,3,CV_32F);
int siz=2*shape1.size();
int siz=2*(int)shape1.size();
if (fullAfine)
{

10
modules/shape/src/sc_dis.cpp
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@ -67,10 +67,10 @@ public:
comparer=_comparer;
iterations=_iterations;
transformer=_transformer;
bendingEnergyWeight=0.3;
imageAppearanceWeight=0.0;
shapeContextWeight=1.0;
sigma=10;
bendingEnergyWeight=0.3f;
imageAppearanceWeight=0.0f;
shapeContextWeight=1.0f;
sigma=10.0f;
name_ = "ShapeDistanceExtractor.SCD";
}
@ -505,7 +505,7 @@ void SCDMatcher::hungarian(cv::Mat &costMatrix, std::vector<cv::DMatch> &outMatc
std::vector<int> matches(costMatrix.rows, 0), colsol(costMatrix.rows), rowsol(costMatrix.rows);
std::vector<float> d(costMatrix.rows), pred(costMatrix.rows), v(costMatrix.rows);
const float LOWV=1e-10;
const float LOWV = 1e-10f;
bool unassignedfound;
int i=0, imin=0, numfree=0, prvnumfree=0, f=0, i0=0, k=0, freerow=0;
int j=0, j1=0, j2=0, endofpath=0, last=0, low=0, up=0;

26
modules/shape/src/tps_trans.cpp
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@ -212,9 +212,9 @@ void ThinPlateSplineShapeTransformerImpl::estimateTransformation(InputArray _pts
}
// Organizing the correspondent points in matrix style //
Mat shape1(matches.size(),2,CV_32F); // transforming shape
Mat shape2(matches.size(),2,CV_32F); // target shape
for (size_t i=0; i<matches.size(); i++)
Mat shape1((int)matches.size(),2,CV_32F); // transforming shape
Mat shape2((int)matches.size(),2,CV_32F); // target shape
for (int i=0, end = (int)matches.size(); i<end; i++)
{
Point2f pt1=pts1.at<Point2f>(0,matches[i].queryIdx);
shape1.at<float>(i,0) = pt1.x;
@ -229,11 +229,11 @@ void ThinPlateSplineShapeTransformerImpl::estimateTransformation(InputArray _pts
// Building the matrices for solving the L*(w|a)=(v|0) problem with L={[K|P];[P'|0]}
//Building K and P (Neede to buil L)
Mat matK(matches.size(),matches.size(),CV_32F);
Mat matP(matches.size(),3,CV_32F);
for (size_t i=0; i<matches.size(); i++)
Mat matK((int)matches.size(),(int)matches.size(),CV_32F);
Mat matP((int)matches.size(),3,CV_32F);
for (int i=0, end=(int)matches.size(); i<end; i++)
{
for (size_t j=0; j<matches.size(); j++)
for (int j=0; j<end; j++)
{
if (i==j)
{
@ -251,19 +251,19 @@ void ThinPlateSplineShapeTransformerImpl::estimateTransformation(InputArray _pts
}
//Building L
Mat matL=Mat::zeros(matches.size()+3,matches.size()+3,CV_32F);
Mat matLroi(matL, Rect(0,0,matches.size(),matches.size())); //roi for K
Mat matL=Mat::zeros((int)matches.size()+3,(int)matches.size()+3,CV_32F);
Mat matLroi(matL, Rect(0,0,(int)matches.size(),(int)matches.size())); //roi for K
matK.copyTo(matLroi);
matLroi = Mat(matL,Rect(matches.size(),0,3,matches.size())); //roi for P
matLroi = Mat(matL,Rect((int)matches.size(),0,3,(int)matches.size())); //roi for P
matP.copyTo(matLroi);
Mat matPt;
transpose(matP,matPt);
matLroi = Mat(matL,Rect(0,matches.size(),matches.size(),3)); //roi for P'
matLroi = Mat(matL,Rect(0,(int)matches.size(),(int)matches.size(),3)); //roi for P'
matPt.copyTo(matLroi);
//Building B (v|0)
Mat matB = Mat::zeros(matches.size()+3,2,CV_32F);
for (size_t i=0; i<matches.size(); i++)
Mat matB = Mat::zeros((int)matches.size()+3,2,CV_32F);
for (int i=0, end = (int)matches.size(); i<end; i++)
{
matB.at<float>(i,0) = shape2.at<float>(i,0); //x's
matB.at<float>(i,1) = shape2.at<float>(i,1); //y's

19
modules/shape/test/test_emdl1.cpp
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@ -46,11 +46,11 @@ using namespace std;
const int angularBins=12;
const int radialBins=4;
const float minRad=0.2;
const float minRad=0.2f;
const float maxRad=2;
const int NSN=5;//10;//20; //number of shapes per class
const int NP=100; //number of points sympliying the contour
const float outlierWeight=0.1;
const float outlierWeight=0.1f;
const int numOutliers=20;
const float CURRENT_MAX_ACCUR=95; //98% and 99% reached in several tests, 95 is fixed as minimum boundary
@ -96,7 +96,7 @@ vector <Point2f> CV_ShapeEMDTest::convertContourType(const Mat& currentQuery, in
// In case actual number of points is less than n
int dum=0;
for (int add=contoursQuery.size()-1; add<n; add++)
for (int add=(int)contoursQuery.size()-1; add<n; add++)
{
contoursQuery.push_back(contoursQuery[dum++]); //adding dummy values
}
@ -148,14 +148,14 @@ void CV_ShapeEMDTest::mpegTest()
listShapeNames(namesHeaders);
// distance matrix //
Mat distanceMat=Mat::zeros(NSN*namesHeaders.size(), NSN*namesHeaders.size(), CV_32F);
Mat distanceMat=Mat::zeros(NSN*(int)namesHeaders.size(), NSN*(int)namesHeaders.size(), CV_32F);
// query contours (normal v flipped, h flipped) and testing contour //
vector<Point2f> contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting;
// reading query and computing its properties //
int counter=0;
const int loops=NSN*namesHeaders.size()*NSN*namesHeaders.size();
const int loops=NSN*(int)namesHeaders.size()*NSN*(int)namesHeaders.size();
for (size_t n=0; n<namesHeaders.size(); n++)
{
for (int i=1; i<=NSN; i++)
@ -165,7 +165,6 @@ void CV_ShapeEMDTest::mpegTest()
thepathandname<<path+namesHeaders[n]<<"-"<<i<<".png";
Mat currentQuery, flippedHQuery, flippedVQuery;
currentQuery=imread(thepathandname.str(), IMREAD_GRAYSCALE);
Mat currentQueryBuf=currentQuery.clone();
flip(currentQuery, flippedHQuery, 0);
flip(currentQuery, flippedVQuery, 1);
// compute border of the query and its flipped versions //
@ -184,8 +183,8 @@ void CV_ShapeEMDTest::mpegTest()
counter++;
if (nt==n && it==i)
{
distanceMat.at<float>(NSN*n+i-1,
NSN*nt+it-1)=0;
distanceMat.at<float>(NSN*(int)n+i-1,
NSN*(int)nt+it-1)=0;
continue;
}
// read testing image //
@ -200,9 +199,9 @@ void CV_ShapeEMDTest::mpegTest()
std::cout<<std::endl<<"Progress: "<<counter<<"/"<<loops<<": "<<100*double(counter)/loops<<"% *******"<<std::endl;
std::cout<<"Computing shape distance between "<<namesHeaders[n]<<i<<
" and "<<namesHeaders[nt]<<it<<": ";
distanceMat.at<float>(NSN*n+i-1, NSN*nt+it-1)=
distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)=
computeShapeDistance(contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting);
std::cout<<distanceMat.at<float>(NSN*n+i-1, NSN*nt+it-1)<<std::endl;
std::cout<<distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)<<std::endl;
}
}
}

20
modules/shape/test/test_hausdorff.cpp
Unescape View File

@ -75,13 +75,13 @@ CV_HaussTest::~CV_HaussTest()
vector<Point2f> CV_HaussTest::normalizeContour(const vector<Point> &contour)
{
vector<Point2f> output(contour.size());
Mat disMat(contour.size(),contour.size(),CV_32F);
Mat disMat((int)contour.size(),(int)contour.size(),CV_32F);
Point2f meanpt(0,0);
float meanVal=1;
for (size_t ii=0; ii<contour.size(); ii++)
for (int ii=0, end1 = (int)contour.size(); ii<end1; ii++)
{
for (size_t jj=0; jj<contour.size(); jj++)
for (int jj=0, end2 = (int)contour.size(); end2; jj++)
{
if (ii==jj) disMat.at<float>(ii,jj)=0;
else
@ -128,7 +128,7 @@ vector <Point> CV_HaussTest::convertContourType(const Mat& currentQuery, int n)
}
// In case actual number of points is less than n
for (int add=contoursQuery.size()-1; add<n; add++)
for (int add=(int)contoursQuery.size()-1; add<n; add++)
{
contoursQuery.push_back(contoursQuery[contoursQuery.size()-add+1]); //adding dummy values
}
@ -160,14 +160,14 @@ void CV_HaussTest::mpegTest()
listShapeNames(namesHeaders);
// distance matrix //
Mat distanceMat=Mat::zeros(NSN*namesHeaders.size(), NSN*namesHeaders.size(), CV_32F);
Mat distanceMat=Mat::zeros(NSN*(int)namesHeaders.size(), NSN*(int)namesHeaders.size(), CV_32F);
// query contours (normal v flipped, h flipped) and testing contour //
vector<Point> contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting;
// reading query and computing its properties //
int counter=0;
const int loops=NSN*namesHeaders.size()*NSN*namesHeaders.size();
const int loops=NSN*(int)namesHeaders.size()*NSN*(int)namesHeaders.size();
for (size_t n=0; n<namesHeaders.size(); n++)
{
for (int i=1; i<=NSN; i++)
@ -195,8 +195,8 @@ void CV_HaussTest::mpegTest()
counter++;
if (nt==n && it==i)
{
distanceMat.at<float>(NSN*n+i-1,
NSN*nt+it-1)=0;
distanceMat.at<float>(NSN*(int)n+i-1,
NSN*(int)nt+it-1)=0;
continue;
}
// read testing image //
@ -212,9 +212,9 @@ void CV_HaussTest::mpegTest()
std::cout<<std::endl<<"Progress: "<<counter<<"/"<<loops<<": "<<100*double(counter)/loops<<"% *******"<<std::endl;
std::cout<<"Computing shape distance between "<<namesHeaders[n]<<i<<
" and "<<namesHeaders[nt]<<it<<": ";
distanceMat.at<float>(NSN*n+i-1, NSN*nt+it-1)=
distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)=
computeShapeDistance(contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting);
std::cout<<distanceMat.at<float>(NSN*n+i-1, NSN*nt+it-1)<<std::endl;
std::cout<<distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)<<std::endl;
}
}
}

22
modules/shape/test/test_shape.cpp
Unescape View File

@ -46,13 +46,13 @@ using namespace std;
const int angularBins=12;
const int radialBins=4;
const float minRad=0.2;
const float minRad=0.2f;
const float maxRad=2;
const int NSN=5;//10;//20; //number of shapes per class
const int NP=120; //number of points sympliying the contour
const float outlierWeight=0.1;
const float outlierWeight=0.1f;
const int numOutliers=20;
const float CURRENT_MAX_ACCUR=95.0; //99% and 100% reached in several tests, 95 is fixed as minimum boundary
const float CURRENT_MAX_ACCUR=95; //99% and 100% reached in several tests, 95 is fixed as minimum boundary
class CV_ShapeTest : public cvtest::BaseTest
{
@ -95,7 +95,7 @@ vector <Point2f> CV_ShapeTest::convertContourType(const Mat& currentQuery, int n
}
// In case actual number of points is less than n
for (int add=contoursQuery.size()-1; add<n; add++)
for (int add=(int)contoursQuery.size()-1; add<n; add++)
{
contoursQuery.push_back(contoursQuery[contoursQuery.size()-add+1]); //adding dummy values
}
@ -126,7 +126,7 @@ float CV_ShapeTest::computeShapeDistance(vector <Point2f>& query1, vector <Point
//waitKey(0);
Ptr <ShapeContextDistanceExtractor> mysc = createShapeContextDistanceExtractor(angularBins, radialBins, minRad, maxRad);
//Ptr <HistogramCostExtractor> cost = createNormHistogramCostExtractor(cv::DIST_L1);
Ptr <HistogramCostExtractor> cost = createChiHistogramCostExtractor(30,0.15);
Ptr <HistogramCostExtractor> cost = createChiHistogramCostExtractor(30,0.15f);
//Ptr <HistogramCostExtractor> cost = createEMDHistogramCostExtractor();
//Ptr <HistogramCostExtractor> cost = createEMDL1HistogramCostExtractor();
mysc->setIterations(1);
@ -148,14 +148,14 @@ void CV_ShapeTest::mpegTest()
listShapeNames(namesHeaders);
// distance matrix //
Mat distanceMat=Mat::zeros(NSN*namesHeaders.size(), NSN*namesHeaders.size(), CV_32F);
Mat distanceMat=Mat::zeros(NSN*(int)namesHeaders.size(), NSN*(int)namesHeaders.size(), CV_32F);
// query contours (normal v flipped, h flipped) and testing contour //
vector<Point2f> contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting;
// reading query and computing its properties //
int counter=0;
const int loops=NSN*namesHeaders.size()*NSN*namesHeaders.size();
const int loops=NSN*(int)namesHeaders.size()*NSN*(int)namesHeaders.size();
for (size_t n=0; n<namesHeaders.size(); n++)
{
for (int i=1; i<=NSN; i++)
@ -184,8 +184,8 @@ void CV_ShapeTest::mpegTest()
counter++;
if (nt==n && it==i)
{
distanceMat.at<float>(NSN*n+i-1,
NSN*nt+it-1)=0;
distanceMat.at<float>(NSN*(int)n+i-1,
NSN*(int)nt+it-1)=0;
continue;
}
// read testing image //
@ -200,9 +200,9 @@ void CV_ShapeTest::mpegTest()
std::cout<<std::endl<<"Progress: "<<counter<<"/"<<loops<<": "<<100*double(counter)/loops<<"% *******"<<std::endl;
std::cout<<"Computing shape distance between "<<namesHeaders[n]<<i<<
" and "<<namesHeaders[nt]<<it<<": ";
distanceMat.at<float>(NSN*n+i-1, NSN*nt+it-1)=
distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)=
computeShapeDistance(contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting);
std::cout<<distanceMat.at<float>(NSN*n+i-1, NSN*nt+it-1)<<std::endl;
std::cout<<distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)<<std::endl;
}
}
}

2
samples/cpp/shape_example.cpp
Unescape View File

@ -36,7 +36,7 @@ static vector<Point> simpleContour( const Mat& currentQuery, int n=300 )
// In case actual number of points is less than n
int dummy=0;
for (int add=contoursQuery.size()-1; add<n; add++)
for (int add=(int)contoursQuery.size()-1; add<n; add++)
{
contoursQuery.push_back(contoursQuery[dummy++]); //adding dummy values
}

4
samples/cpp/textdetection.cpp
Unescape View File

@ -44,7 +44,7 @@ int main(int argc, const char * argv[])
channels.push_back(255-channels[c]);
// Create ERFilter objects with the 1st and 2nd stage default classifiers
Ptr<ERFilter> er_filter1 = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"),16,0.00015,0.13,0.2,true,0.1);
Ptr<ERFilter> er_filter1 = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"),16,0.00015f,0.13f,0.2f,true,0.1f);
Ptr<ERFilter> er_filter2 = createERFilterNM2(loadClassifierNM2("trained_classifierNM2.xml"),0.5);
vector<vector<ERStat> > regions(channels.size());
@ -94,7 +94,7 @@ void show_help_and_exit(const char *cmd)
void groups_draw(Mat &src, vector<Rect> &groups)
{
for (int i=groups.size()-1; i>=0; i--)
for (int i=(int)groups.size()-1; i>=0; i--)
{
if (src.type() == CV_8UC3)
rectangle(src,groups.at(i).tl(),groups.at(i).br(),Scalar( 0, 255, 255 ), 3, 8 );

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