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Move legacy part of opencv_contrib to separate header

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This commit is contained in:
Andrey Kamaev
2013-04-12 16:19:48 +04:00
parent 3b364330ad
commit 909d6fcf51
20 changed files with 1025 additions and 987 deletions
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1
modules/contrib/src/adaptiveskindetector.cpp
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@@ -36,6 +36,7 @@
#include "precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/contrib/compat.hpp"
#define ASD_INTENSITY_SET_PIXEL(pointer, qq) {(*pointer) = (unsigned char)qq;}

1
modules/contrib/src/ba.cpp
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@@ -41,6 +41,7 @@
#include "precomp.hpp"
#include "opencv2/calib3d.hpp"
#include "opencv2/contrib/compat.hpp"
#include "opencv2/calib3d/calib3d_c.h"
#include <iostream>

18
modules/contrib/src/chamfermatching.cpp
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@@ -142,7 +142,7 @@ private:
LocationScaleImageRange(const std::vector<Point>& locations, const std::vector<float>& _scales) :
locations_(locations), scales_(_scales)
{
assert(locations.size()==_scales.size());
CV_Assert(locations.size()==_scales.size());
}
ImageIterator* iterator() const
@@ -393,7 +393,7 @@ private:
LocationScaleImageIterator(const std::vector<Point>& locations, const std::vector<float>& _scales) :
locations_(locations), scales_(_scales)
{
assert(locations.size()==_scales.size());
CV_Assert(locations.size()==_scales.size());
reset();
}
@@ -622,7 +622,7 @@ void ChamferMatcher::Matching::followContour(Mat& templ_img, template_coords_t&
coordinate_t next;
unsigned char ptr;
assert (direction==-1 || !coords.empty());
CV_Assert (direction==-1 || !coords.empty());
coordinate_t crt = coords.back();
@@ -903,18 +903,18 @@ void ChamferMatcher::Template::show() const
p2.x = x + pad*(int)(sin(orientations[i])*100)/100;
p2.y = y + pad*(int)(cos(orientations[i])*100)/100;
line(templ_color, p1,p2, CV_RGB(255,0,0));
line(templ_color, p1,p2, Scalar(255,0,0));
}
}
circle(templ_color,Point(center.x + pad, center.y + pad),1,CV_RGB(0,255,0));
circle(templ_color,Point(center.x + pad, center.y + pad),1,Scalar(0,255,0));
#ifdef HAVE_OPENCV_HIGHGUI
namedWindow("templ",1);
imshow("templ",templ_color);
waitKey();
#else
CV_Error(CV_StsNotImplemented, "OpenCV has been compiled without GUI support");
CV_Error(Error::StsNotImplemented, "OpenCV has been compiled without GUI support");
#endif
templ_color.release();
@@ -1059,7 +1059,7 @@ void ChamferMatcher::Matching::fillNonContourOrientations(Mat& annotated_img, Ma
int cols = annotated_img.cols;
int rows = annotated_img.rows;
assert(orientation_img.cols==cols && orientation_img.rows==rows);
CV_Assert(orientation_img.cols==cols && orientation_img.rows==rows);
for (int y=0;y<rows;++y) {
for (int x=0;x<cols;++x) {
@@ -1279,7 +1279,7 @@ void ChamferMatcher::showMatch(Mat& img, int index)
std::cout << "Index too big.\n" << std::endl;
}
assert(img.channels()==3);
CV_Assert(img.channels()==3);
Match match = matches[index];
@@ -1298,7 +1298,7 @@ void ChamferMatcher::showMatch(Mat& img, int index)
void ChamferMatcher::showMatch(Mat& img, Match match)
{
assert(img.channels()==3);
CV_Assert(img.channels()==3);
const template_coords_t& templ_coords = match.tpl->coords;
for (size_t i=0;i<templ_coords.size();++i) {

16
modules/contrib/src/colormap.cpp
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@@ -40,7 +40,7 @@ static Mat linspace(float x0, float x1, int n)
static void sortMatrixRowsByIndices(InputArray _src, InputArray _indices, OutputArray _dst)
{
if(_indices.getMat().type() != CV_32SC1)
CV_Error(CV_StsUnsupportedFormat, "cv::sortRowsByIndices only works on integer indices!");
CV_Error(Error::StsUnsupportedFormat, "cv::sortRowsByIndices only works on integer indices!");
Mat src = _src.getMat();
std::vector<int> indices = _indices.getMat();
_dst.create(src.rows, src.cols, src.type());
@@ -64,8 +64,8 @@ static Mat argsort(InputArray _src, bool ascending=true)
{
Mat src = _src.getMat();
if (src.rows != 1 && src.cols != 1)
CV_Error(CV_StsBadArg, "cv::argsort only sorts 1D matrices.");
int flags = CV_SORT_EVERY_ROW+(ascending ? CV_SORT_ASCENDING : CV_SORT_DESCENDING);
CV_Error(Error::StsBadArg, "cv::argsort only sorts 1D matrices.");
int flags = SORT_EVERY_ROW | (ascending ? SORT_ASCENDING : SORT_DESCENDING);
Mat sorted_indices;
sortIdx(src.reshape(1,1),sorted_indices,flags);
return sorted_indices;
@@ -116,8 +116,8 @@ static Mat interp1(InputArray _x, InputArray _Y, InputArray _xi)
Mat Y = _Y.getMat();
Mat xi = _xi.getMat();
// check types & alignment
assert((x.type() == Y.type()) && (Y.type() == xi.type()));
assert((x.cols == 1) && (x.rows == Y.rows) && (x.cols == Y.cols));
CV_Assert((x.type() == Y.type()) && (Y.type() == xi.type()));
CV_Assert((x.cols == 1) && (x.rows == Y.rows) && (x.cols == Y.cols));
// call templated interp1
switch(x.type()) {
case CV_8SC1: return interp1_<char>(x,Y,xi); break;
@@ -127,7 +127,7 @@ static Mat interp1(InputArray _x, InputArray _Y, InputArray _xi)
case CV_32SC1: return interp1_<int>(x,Y,xi); break;
case CV_32FC1: return interp1_<float>(x,Y,xi); break;
case CV_64FC1: return interp1_<double>(x,Y,xi); break;
default: CV_Error(CV_StsUnsupportedFormat, ""); break;
default: CV_Error(Error::StsUnsupportedFormat, ""); break;
}
return Mat();
}
@@ -473,7 +473,7 @@ namespace colormap
void ColorMap::operator()(InputArray _src, OutputArray _dst) const
{
if(_lut.total() != 256)
CV_Error(CV_StsAssert, "cv::LUT only supports tables of size 256.");
CV_Error(Error::StsAssert, "cv::LUT only supports tables of size 256.");
Mat src = _src.getMat();
// Return original matrix if wrong type is given (is fail loud better here?)
if(src.type() != CV_8UC1 && src.type() != CV_8UC3)
@@ -521,7 +521,7 @@ namespace colormap
colormap == COLORMAP_WINTER ? (colormap::ColorMap*)(new colormap::Winter) : 0;
if( !cm )
CV_Error( CV_StsBadArg, "Unknown colormap id; use one of COLORMAP_*");
CV_Error( Error::StsBadArg, "Unknown colormap id; use one of COLORMAP_*");
(*cm)(src, dst);

54
modules/contrib/src/facerec.cpp
View File

@@ -51,7 +51,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
// make sure the input data is a vector of matrices or vector of vector
if(src.kind() != _InputArray::STD_VECTOR_MAT && src.kind() != _InputArray::STD_VECTOR_VECTOR) {
String error_message = "The data is expected as InputArray::STD_VECTOR_MAT (a std::vector<Mat>) or _InputArray::STD_VECTOR_VECTOR (a std::vector< std::vector<...> >).";
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// number of samples
size_t n = src.total();
@@ -67,7 +67,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
// make sure data can be reshaped, throw exception if not!
if(src.getMat(i).total() != d) {
String error_message = format("Wrong number of elements in matrix #%d! Expected %d was %d.", i, d, src.getMat(i).total());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// get a hold of the current row
Mat xi = data.row(i);
@@ -306,13 +306,13 @@ void FaceRecognizer::update(InputArrayOfArrays src, InputArray labels ) {
}
String error_msg = format("This FaceRecognizer (%s) does not support updating, you have to use FaceRecognizer::train to update it.", this->name().c_str());
CV_Error(CV_StsNotImplemented, error_msg);
CV_Error(Error::StsNotImplemented, error_msg);
}
void FaceRecognizer::save(const String& filename) const {
FileStorage fs(filename, FileStorage::WRITE);
if (!fs.isOpened())
CV_Error(CV_StsError, "File can't be opened for writing!");
CV_Error(Error::StsError, "File can't be opened for writing!");
this->save(fs);
fs.release();
}
@@ -320,7 +320,7 @@ void FaceRecognizer::save(const String& filename) const {
void FaceRecognizer::load(const String& filename) {
FileStorage fs(filename, FileStorage::READ);
if (!fs.isOpened())
CV_Error(CV_StsError, "File can't be opened for writing!");
CV_Error(Error::StsError, "File can't be opened for writing!");
this->load(fs);
fs.release();
}
@@ -331,17 +331,17 @@ void FaceRecognizer::load(const String& filename) {
void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) {
if(_src.total() == 0) {
String error_message = format("Empty training data was given. You'll need more than one sample to learn a model.");
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
} else if(_local_labels.getMat().type() != CV_32SC1) {
String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _local_labels.type());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// make sure data has correct size
if(_src.total() > 1) {
for(int i = 1; i < static_cast<int>(_src.total()); i++) {
if(_src.getMat(i-1).total() != _src.getMat(i).total()) {
String error_message = format("In the Eigenfaces method all input samples (training images) must be of equal size! Expected %d pixels, but was %d pixels.", _src.getMat(i-1).total(), _src.getMat(i).total());
CV_Error(CV_StsUnsupportedFormat, error_message);
CV_Error(Error::StsUnsupportedFormat, error_message);
}
}
}
@@ -355,7 +355,7 @@ void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) {
// assert there are as much samples as labels
if(static_cast<int>(labels.total()) != n) {
String error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.", n, labels.total());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// clear existing model data
_labels.release();
@@ -365,7 +365,7 @@ void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) {
_num_components = n;
// perform the PCA
PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW, _num_components);
PCA pca(data, Mat(), PCA::DATA_AS_ROW, _num_components);
// copy the PCA results
_mean = pca.mean.reshape(1,1); // store the mean vector
_eigenvalues = pca.eigenvalues.clone(); // eigenvalues by row
@@ -386,11 +386,11 @@ void Eigenfaces::predict(InputArray _src, int &minClass, double &minDist) const
if(_projections.empty()) {
// throw error if no data (or simply return -1?)
String error_message = "This Eigenfaces model is not computed yet. Did you call Eigenfaces::train?";
CV_Error(CV_StsError, error_message);
CV_Error(Error::StsError, error_message);
} else if(_eigenvectors.rows != static_cast<int>(src.total())) {
// check data alignment just for clearer exception messages
String error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %d.", _eigenvectors.rows, src.total());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// project into PCA subspace
Mat q = subspaceProject(_eigenvectors, _mean, src.reshape(1,1));
@@ -440,17 +440,17 @@ void Eigenfaces::save(FileStorage& fs) const {
void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) {
if(src.total() == 0) {
String error_message = format("Empty training data was given. You'll need more than one sample to learn a model.");
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
} else if(_lbls.getMat().type() != CV_32SC1) {
String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _lbls.type());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// make sure data has correct size
if(src.total() > 1) {
for(int i = 1; i < static_cast<int>(src.total()); i++) {
if(src.getMat(i-1).total() != src.getMat(i).total()) {
String error_message = format("In the Fisherfaces method all input samples (training images) must be of equal size! Expected %d pixels, but was %d pixels.", src.getMat(i-1).total(), src.getMat(i).total());
CV_Error(CV_StsUnsupportedFormat, error_message);
CV_Error(Error::StsUnsupportedFormat, error_message);
}
}
}
@@ -462,10 +462,10 @@ void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) {
// make sure labels are passed in correct shape
if(labels.total() != (size_t) N) {
String error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.", N, labels.total());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
} else if(labels.rows != 1 && labels.cols != 1) {
String error_message = format("Expected the labels in a matrix with one row or column! Given dimensions are rows=%s, cols=%d.", labels.rows, labels.cols);
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// clear existing model data
_labels.release();
@@ -481,7 +481,7 @@ void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) {
if((_num_components <= 0) || (_num_components > (C-1)))
_num_components = (C-1);
// perform a PCA and keep (N-C) components
PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW, (N-C));
PCA pca(data, Mat(), PCA::DATA_AS_ROW, (N-C));
// project the data and perform a LDA on it
LDA lda(pca.project(data),labels, _num_components);
// store the total mean vector
@@ -506,10 +506,10 @@ void Fisherfaces::predict(InputArray _src, int &minClass, double &minDist) const
if(_projections.empty()) {
// throw error if no data (or simply return -1?)
String error_message = "This Fisherfaces model is not computed yet. Did you call Fisherfaces::train?";
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
} else if(src.total() != (size_t) _eigenvectors.rows) {
String error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %d.", _eigenvectors.rows, src.total());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// project into LDA subspace
Mat q = subspaceProject(_eigenvectors, _mean, src.reshape(1,1));
@@ -641,7 +641,7 @@ static void elbp(InputArray src, OutputArray dst, int radius, int neighbors)
case CV_64FC1: elbp_<double>(src,dst, radius, neighbors); break;
default:
String error_msg = format("Using Original Local Binary Patterns for feature extraction only works on single-channel images (given %d). Please pass the image data as a grayscale image!", type);
CV_Error(CV_StsNotImplemented, error_msg);
CV_Error(Error::StsNotImplemented, error_msg);
break;
}
}
@@ -687,7 +687,7 @@ static Mat histc(InputArray _src, int minVal, int maxVal, bool normed)
return histc_(src, minVal, maxVal, normed);
break;
default:
CV_Error(CV_StsUnmatchedFormats, "This type is not implemented yet."); break;
CV_Error(Error::StsUnmatchedFormats, "This type is not implemented yet."); break;
}
return Mat();
}
@@ -769,14 +769,14 @@ void LBPH::update(InputArrayOfArrays _in_src, InputArray _in_labels) {
void LBPH::train(InputArrayOfArrays _in_src, InputArray _in_labels, bool preserveData) {
if(_in_src.kind() != _InputArray::STD_VECTOR_MAT && _in_src.kind() != _InputArray::STD_VECTOR_VECTOR) {
String error_message = "The images are expected as InputArray::STD_VECTOR_MAT (a std::vector<Mat>) or _InputArray::STD_VECTOR_VECTOR (a std::vector< std::vector<...> >).";
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
if(_in_src.total() == 0) {
String error_message = format("Empty training data was given. You'll need more than one sample to learn a model.");
CV_Error(CV_StsUnsupportedFormat, error_message);
CV_Error(Error::StsUnsupportedFormat, error_message);
} else if(_in_labels.getMat().type() != CV_32SC1) {
String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _in_labels.type());
CV_Error(CV_StsUnsupportedFormat, error_message);
CV_Error(Error::StsUnsupportedFormat, error_message);
}
// get the vector of matrices
std::vector<Mat> src;
@@ -786,7 +786,7 @@ void LBPH::train(InputArrayOfArrays _in_src, InputArray _in_labels, bool preserv
// check if data is well- aligned
if(labels.total() != src.size()) {
String error_message = format("The number of samples (src) must equal the number of labels (labels). Was len(samples)=%d, len(labels)=%d.", src.size(), _labels.total());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// if this model should be trained without preserving old data, delete old model data
if(!preserveData) {
@@ -817,7 +817,7 @@ void LBPH::predict(InputArray _src, int &minClass, double &minDist) const {
if(_histograms.empty()) {
// throw error if no data (or simply return -1?)
String error_message = "This LBPH model is not computed yet. Did you call the train method?";
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
Mat src = _src.getMat();
// get the spatial histogram from input image

1
modules/contrib/src/fuzzymeanshifttracker.cpp
View File

@@ -35,6 +35,7 @@
//M*/
#include "precomp.hpp"
#include "opencv2/contrib/compat.hpp"
CvFuzzyPoint::CvFuzzyPoint(double _x, double _y)
{

2
modules/contrib/src/gencolors.cpp
View File

@@ -85,7 +85,7 @@ static void downsamplePoints( const Mat& src, Mat& dst, size_t count )
candidatePointsMask.at<uchar>(0, maxLoc.x) = 0;
Mat minDists;
reduce( activedDists, minDists, 0, CV_REDUCE_MIN );
reduce( activedDists, minDists, 0, REDUCE_MIN );
minMaxLoc( minDists, 0, &maxVal, 0, &maxLoc, candidatePointsMask );
dst.at<Point3_<uchar> >((int)i) = src.at<Point3_<uchar> >(maxLoc.x);
}

28
modules/contrib/src/lda.cpp
View File

@@ -43,9 +43,9 @@ static Mat argsort(InputArray _src, bool ascending=true)
Mat src = _src.getMat();
if (src.rows != 1 && src.cols != 1) {
String error_message = "Wrong shape of input matrix! Expected a matrix with one row or column.";
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
int flags = CV_SORT_EVERY_ROW+(ascending ? CV_SORT_ASCENDING : CV_SORT_DESCENDING);
int flags = SORT_EVERY_ROW | (ascending ? SORT_ASCENDING : SORT_DESCENDING);
Mat sorted_indices;
sortIdx(src.reshape(1,1),sorted_indices,flags);
return sorted_indices;
@@ -55,7 +55,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
// make sure the input data is a vector of matrices or vector of vector
if(src.kind() != _InputArray::STD_VECTOR_MAT && src.kind() != _InputArray::STD_VECTOR_VECTOR) {
String error_message = "The data is expected as InputArray::STD_VECTOR_MAT (a std::vector<Mat>) or _InputArray::STD_VECTOR_VECTOR (a std::vector< std::vector<...> >).";
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// number of samples
size_t n = src.total();
@@ -71,7 +71,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
// make sure data can be reshaped, throw exception if not!
if(src.getMat(i).total() != d) {
String error_message = format("Wrong number of elements in matrix #%d! Expected %d was %d.", i, (int)d, (int)src.getMat(i).total());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// get a hold of the current row
Mat xi = data.row(i);
@@ -87,7 +87,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
static void sortMatrixColumnsByIndices(InputArray _src, InputArray _indices, OutputArray _dst) {
if(_indices.getMat().type() != CV_32SC1) {
CV_Error(CV_StsUnsupportedFormat, "cv::sortColumnsByIndices only works on integer indices!");
CV_Error(Error::StsUnsupportedFormat, "cv::sortColumnsByIndices only works on integer indices!");
}
Mat src = _src.getMat();
std::vector<int> indices = _indices.getMat();
@@ -179,12 +179,12 @@ Mat subspaceProject(InputArray _W, InputArray _mean, InputArray _src) {
// make sure the data has the correct shape
if(W.rows != d) {
String error_message = format("Wrong shapes for given matrices. Was size(src) = (%d,%d), size(W) = (%d,%d).", src.rows, src.cols, W.rows, W.cols);
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// make sure mean is correct if not empty
if(!mean.empty() && (mean.total() != (size_t) d)) {
String error_message = format("Wrong mean shape for the given data matrix. Expected %d, but was %d.", d, mean.total());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// create temporary matrices
Mat X, Y;
@@ -217,12 +217,12 @@ Mat subspaceReconstruct(InputArray _W, InputArray _mean, InputArray _src)
// make sure the data has the correct shape
if(W.cols != d) {
String error_message = format("Wrong shapes for given matrices. Was size(src) = (%d,%d), size(W) = (%d,%d).", src.rows, src.cols, W.rows, W.cols);
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// make sure mean is correct if not empty
if(!mean.empty() && (mean.total() != (size_t) W.rows)) {
String error_message = format("Wrong mean shape for the given eigenvector matrix. Expected %d, but was %d.", W.cols, mean.total());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// initalize temporary matrices
Mat X, Y;
@@ -939,7 +939,7 @@ public:
void LDA::save(const String& filename) const {
FileStorage fs(filename, FileStorage::WRITE);
if (!fs.isOpened()) {
CV_Error(CV_StsError, "File can't be opened for writing!");
CV_Error(Error::StsError, "File can't be opened for writing!");
}
this->save(fs);
fs.release();
@@ -949,7 +949,7 @@ void LDA::save(const String& filename) const {
void LDA::load(const String& filename) {
FileStorage fs(filename, FileStorage::READ);
if (!fs.isOpened())
CV_Error(CV_StsError, "File can't be opened for writing!");
CV_Error(Error::StsError, "File can't be opened for writing!");
this->load(fs);
fs.release();
}
@@ -1002,12 +1002,12 @@ void LDA::lda(InputArrayOfArrays _src, InputArray _lbls) {
// want to separate from each other then?
if(C == 1) {
String error_message = "At least two classes are needed to perform a LDA. Reason: Only one class was given!";
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// throw error if less labels, than samples
if (labels.size() != static_cast<size_t>(N)) {
String error_message = format("The number of samples must equal the number of labels. Given %d labels, %d samples. ", labels.size(), N);
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
}
// warn if within-classes scatter matrix becomes singular
if (N < D) {
@@ -1090,7 +1090,7 @@ void LDA::compute(InputArrayOfArrays _src, InputArray _lbls) {
break;
default:
String error_message= format("InputArray Datatype %d is not supported.", _src.kind());
CV_Error(CV_StsBadArg, error_message);
CV_Error(Error::StsBadArg, error_message);
break;
}
}

2
modules/contrib/src/octree.cpp
View File

@@ -258,7 +258,7 @@ namespace cv
void Octree::buildTree(const std::vector<Point3f>& points3d, int maxLevels, int _minPoints)
{
assert((size_t)maxLevels * 8 < MAX_STACK_SIZE);
CV_Assert((size_t)maxLevels * 8 < MAX_STACK_SIZE);
points.resize(points3d.size());
std::copy(points3d.begin(), points3d.end(), points.begin());
minPoints = _minPoints;

2
modules/contrib/src/retina.cpp
View File

@@ -450,7 +450,7 @@ bool Retina::_convertCvMat2ValarrayBuffer(const cv::Mat inputMatToConvert, std::
inputMatToConvert.convertTo(dst, dsttype);
}
else
CV_Error(CV_StsUnsupportedFormat, "input image must be single channel (gray levels), bgr format (color) or bgra (color with transparency which won't be considered");
CV_Error(Error::StsUnsupportedFormat, "input image must be single channel (gray levels), bgr format (color) or bgra (color with transparency which won't be considered");
return imageNumberOfChannels>1; // return bool : false for gray level image processing, true for color mode
}

2
modules/contrib/src/rgbdodometry.cpp
View File

@@ -422,7 +422,7 @@ bool computeKsi( int transformType,
computeCFuncPtr = computeC_Translation;
}
else
CV_Error( CV_StsBadFlag, "Unsupported value of transformation type flag.");
CV_Error(Error::StsBadFlag, "Unsupported value of transformation type flag.");
Mat C( correspsCount, Cwidth, CV_64FC1 );
Mat dI_dt( correspsCount, 1, CV_64FC1 );

75
modules/contrib/src/spinimages.cpp
View File

@@ -56,24 +56,24 @@ namespace
{
const static Scalar colors[] =
{
CV_RGB(255, 0, 0),
CV_RGB( 0, 255, 0),
CV_RGB( 0, 0, 255),
CV_RGB(255, 255, 0),
CV_RGB(255, 0, 255),
CV_RGB( 0, 255, 255),
CV_RGB(255, 127, 127),
CV_RGB(127, 127, 255),
CV_RGB(127, 255, 127),
CV_RGB(255, 255, 127),
CV_RGB(127, 255, 255),
CV_RGB(255, 127, 255),
CV_RGB(127, 0, 0),
CV_RGB( 0, 127, 0),
CV_RGB( 0, 0, 127),
CV_RGB(127, 127, 0),
CV_RGB(127, 0, 127),
CV_RGB( 0, 127, 127)
Scalar(255, 0, 0),
Scalar( 0, 255, 0),
Scalar( 0, 0, 255),
Scalar(255, 255, 0),
Scalar(255, 0, 255),
Scalar( 0, 255, 255),
Scalar(255, 127, 127),
Scalar(127, 127, 255),
Scalar(127, 255, 127),
Scalar(255, 255, 127),
Scalar(127, 255, 255),
Scalar(255, 127, 255),
Scalar(127, 0, 0),
Scalar( 0, 127, 0),
Scalar( 0, 0, 127),
Scalar(127, 127, 0),
Scalar(127, 0, 127),
Scalar( 0, 127, 127)
};
size_t colors_mum = sizeof(colors)/sizeof(colors[0]);
@@ -199,7 +199,7 @@ void convertTransformMatrix(const float* matrix, float* sseMatrix)
inline __m128 transformSSE(const __m128* matrix, const __m128& in)
{
assert(((size_t)matrix & 15) == 0);
CV_DbgAssert(((size_t)matrix & 15) == 0);
__m128 a0 = _mm_mul_ps(_mm_load_ps((float*)(matrix+0)), _mm_shuffle_ps(in,in,_MM_SHUFFLE(0,0,0,0)));
__m128 a1 = _mm_mul_ps(_mm_load_ps((float*)(matrix+1)), _mm_shuffle_ps(in,in,_MM_SHUFFLE(1,1,1,1)));
__m128 a2 = _mm_mul_ps(_mm_load_ps((float*)(matrix+2)), _mm_shuffle_ps(in,in,_MM_SHUFFLE(2,2,2,2)));
@@ -221,8 +221,8 @@ void computeSpinImages( const Octree& Octree, const std::vector<Point3f>& points
float pixelsPerMeter = 1.f / binSize;
float support = imageWidth * binSize;
assert(normals.size() == points.size());
assert(mask.size() == points.size());
CV_Assert(normals.size() == points.size());
CV_Assert(mask.size() == points.size());
size_t points_size = points.size();
mask.resize(points_size);
@@ -250,7 +250,7 @@ void computeSpinImages( const Octree& Octree, const std::vector<Point3f>& points
if (mask[i] == 0)
continue;
int t = cvGetThreadNum();
int t = getThreadNum();
std::vector<Point3f>& pointsInSphere = pointsInSpherePool[t];
const Point3f& center = points[i];
@@ -289,7 +289,7 @@ void computeSpinImages( const Octree& Octree, const std::vector<Point3f>& points
__m128 ppm4 = _mm_set1_ps(pixelsPerMeter);
__m128i height4m1 = _mm_set1_epi32(spinImage.rows-1);
__m128i width4m1 = _mm_set1_epi32(spinImage.cols-1);
assert( spinImage.step <= 0xffff );
CV_Assert( spinImage.step <= 0xffff );
__m128i step4 = _mm_set1_epi16((short)step);
__m128i zero4 = _mm_setzero_si128();
__m128i one4i = _mm_set1_epi32(1);
@@ -472,7 +472,7 @@ float cv::Mesh3D::estimateResolution(float /*tryRatio*/)
return resolution = (float)dist[ dist.size() / 2 ];
#else
CV_Error(CV_StsNotImplemented, "");
CV_Error(Error::StsNotImplemented, "");
return 1.f;
#endif
}
@@ -686,16 +686,15 @@ inline float cv::SpinImageModel::groupingCreteria(const Point3f& pointScene1, co
}
cv::SpinImageModel::SpinImageModel(const Mesh3D& _mesh) : mesh(_mesh) , out(0)
cv::SpinImageModel::SpinImageModel(const Mesh3D& _mesh) : mesh(_mesh)
{
if (mesh.vtx.empty())
throw Mesh3D::EmptyMeshException();
defaultParams();
}
cv::SpinImageModel::SpinImageModel() : out(0) { defaultParams(); }
cv::SpinImageModel::~SpinImageModel() {}
void cv::SpinImageModel::setLogger(std::ostream* log) { out = log; }
cv::SpinImageModel::SpinImageModel() { defaultParams(); }
cv::SpinImageModel::~SpinImageModel() {}
void cv::SpinImageModel::defaultParams()
{
@@ -756,7 +755,7 @@ Mat cv::SpinImageModel::packRandomScaledSpins(bool separateScale, size_t xCount,
int sz = spins.front().cols;
Mat result((int)(yCount * sz + (yCount - 1)), (int)(xCount * sz + (xCount - 1)), CV_8UC3);
result = colors[(static_cast<int64>(cvGetTickCount()/cvGetTickFrequency())/1000) % colors_mum];
result = colors[(static_cast<int64>(getTickCount()/getTickFrequency())/1000) % colors_mum];
int pos = 0;
for(int y = 0; y < (int)yCount; ++y)
@@ -1030,12 +1029,8 @@ private:
matchSpinToModel(scene.spinImages.row(i), indeces, coeffs);
for(size_t t = 0; t < indeces.size(); ++t)
allMatches.push_back(Match(i, indeces[t], coeffs[t]));
if (out) if (i % 100 == 0) *out << "Comparing scene spinimage " << i << " of " << scene.spinImages.rows << std::endl;
}
corr_timer.stop();
if (out) *out << "Spin correlation time = " << corr_timer << std::endl;
if (out) *out << "Matches number = " << allMatches.size() << std::endl;
if(allMatches.empty())
return;
@@ -1046,7 +1041,6 @@ private:
allMatches.erase(
remove_if(allMatches.begin(), allMatches.end(), bind2nd(std::less<float>(), maxMeasure * fraction)),
allMatches.end());
if (out) *out << "Matches number [filtered by similarity measure] = " << allMatches.size() << std::endl;
int matchesSize = (int)allMatches.size();
if(matchesSize == 0)
@@ -1095,15 +1089,12 @@ private:
allMatches.erase(
std::remove_if(allMatches.begin(), allMatches.end(), std::bind2nd(std::equal_to<float>(), infinity)),
allMatches.end());
if (out) *out << "Matches number [filtered by geometric consistency] = " << allMatches.size() << std::endl;
matchesSize = (int)allMatches.size();
if(matchesSize == 0)
return;
if (out) *out << "grouping ..." << std::endl;
Mat groupingMat((int)matchesSize, (int)matchesSize, CV_32F);
groupingMat = Scalar(0);
@@ -1151,8 +1142,6 @@ private:
for(int g = 0; g < matchesSize; ++g)
{
if (out) if (g % 100 == 0) *out << "G = " << g << std::endl;
group_t left = allMatchesInds;
group_t group;
@@ -1201,16 +1190,16 @@ private:
cv::TickMeter::TickMeter() { reset(); }
int64 cv::TickMeter::getTimeTicks() const { return sumTime; }
double cv::TickMeter::getTimeMicro() const { return (double)getTimeTicks()/cvGetTickFrequency(); }
double cv::TickMeter::getTimeMicro() const { return (double)getTimeTicks()/getTickFrequency(); }
double cv::TickMeter::getTimeMilli() const { return getTimeMicro()*1e-3; }
double cv::TickMeter::getTimeSec() const { return getTimeMilli()*1e-3; }
int64 cv::TickMeter::getCounter() const { return counter; }
void cv::TickMeter::reset() {startTime = 0; sumTime = 0; counter = 0; }
void cv::TickMeter::start(){ startTime = cvGetTickCount(); }
void cv::TickMeter::start(){ startTime = getTickCount(); }
void cv::TickMeter::stop()
{
int64 time = cvGetTickCount();
int64 time = getTickCount();
if ( startTime == 0 )
return;
@@ -1220,4 +1209,4 @@ void cv::TickMeter::stop()
startTime = 0;
}
std::ostream& cv::operator<<(std::ostream& out, const TickMeter& tm){ return out << tm.getTimeSec() << "sec"; }
//std::ostream& cv::operator<<(std::ostream& out, const TickMeter& tm){ return out << tm.getTimeSec() << "sec"; }

12
modules/contrib/src/stereovar.cpp
View File

@@ -239,8 +239,8 @@ void StereoVar::VariationalSolver(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level)
void StereoVar::VCycle_MyFAS(Mat &I1, Mat &I2, Mat &I2x, Mat &_u, int level)
{
CvSize imgSize = _u.size();
CvSize frmSize = cvSize((int) (imgSize.width * pyrScale + 0.5), (int) (imgSize.height * pyrScale + 0.5));
Size imgSize = _u.size();
Size frmSize = Size((int) (imgSize.width * pyrScale + 0.5), (int) (imgSize.height * pyrScale + 0.5));
Mat I1_h, I2_h, I2x_h, u_h, U, U_h;
//PRE relaxation
@@ -285,7 +285,7 @@ void StereoVar::VCycle_MyFAS(Mat &I1, Mat &I2, Mat &I2x, Mat &_u, int level)
void StereoVar::FMG(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level)
{
double scale = std::pow(pyrScale, (double) level);
CvSize frmSize = cvSize((int) (u.cols * scale + 0.5), (int) (u.rows * scale + 0.5));
Size frmSize = Size((int) (u.cols * scale + 0.5), (int) (u.rows * scale + 0.5));
Mat I1_h, I2_h, I2x_h, u_h;
//scaling DOWN
@@ -350,7 +350,7 @@ void StereoVar::autoParams()
void StereoVar::operator ()( const Mat& left, const Mat& right, Mat& disp )
{
CV_Assert(left.size() == right.size() && left.type() == right.type());
CvSize imgSize = left.size();
Size imgSize = left.size();
int MaxD = MAX(labs(minDisp), labs(maxDisp));
int SignD = 1; if (MIN(minDisp, maxDisp) < 0) SignD = -1;
if (minDisp >= maxDisp) {MaxD = 256; SignD = 1;}
@@ -378,8 +378,8 @@ void StereoVar::operator ()( const Mat& left, const Mat& right, Mat& disp )
equalizeHist(rightgray, rightgray);
}
if (poly_sigma > 0.0001) {
GaussianBlur(leftgray, leftgray, cvSize(poly_n, poly_n), poly_sigma);
GaussianBlur(rightgray, rightgray, cvSize(poly_n, poly_n), poly_sigma);
GaussianBlur(leftgray, leftgray, Size(poly_n, poly_n), poly_sigma);
GaussianBlur(rightgray, rightgray, Size(poly_n, poly_n), poly_sigma);
}
if (flags & USE_AUTO_PARAMS) {