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Fixed number of warnings. Fixed mingw64 build.

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This commit is contained in:
Andrey Kamaev
2012-06-12 14:46:12 +00:00
parent 02e3afae3a
commit c5aba337e9
110 changed files with 2232 additions and 2367 deletions
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3rdparty/libjasper/CMakeLists.txt
Vendored
+2 -1
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@@ -23,7 +23,8 @@ if(MSVC)
add_definitions(-DJAS_WIN_MSVC_BUILD)
endif()
ocv_warnings_disable(CMAKE_C_FLAGS -Wno-implicit-function-declaration -Wno-uninitialized -Wmissing-prototypes -Wmissing-declarations -Wunused)
ocv_warnings_disable(CMAKE_C_FLAGS -Wno-implicit-function-declaration -Wno-uninitialized -Wmissing-prototypes -Wmissing-declarations -Wunused -Wshadow
/wd4013 /wd4018 /wd4715 /wd4244 /wd4101 /wd4267)
if(UNIX)
if(CMAKE_COMPILER_IS_GNUCXX OR CV_ICC)
3rdparty/libtiff/CMakeLists.txt
Vendored
+3 -2
View File
@@ -90,8 +90,9 @@ if(WIN32)
list(APPEND lib_srcs tif_win32.c)
endif(WIN32)
ocv_warnings_disable(CMAKE_C_FLAGS -Wno-unused-but-set-variable -Wmissing-prototypes -Wmissing-declarations -Wundef -Wcast-align)
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wmissing-declarations)
ocv_warnings_disable(CMAKE_C_FLAGS -Wno-unused-but-set-variable -Wmissing-prototypes -Wmissing-declarations -Wundef
-Wcast-align -Wshadow -Wno-maybe-uninitialized -Wno-pointer-to-int-cast -Wno-int-to-pointer-cast)
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wmissing-declarations /wd4100 /wd4244 /wd4706 /wd4127 /wd4701 /wd4018 /wd4267 /wd4306 /wd4305 /wd4312 /wd4311)
if(UNIX AND (CMAKE_COMPILER_IS_GNUCXX OR CV_ICC))
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fPIC")
3rdparty/zlib/zconf.h.cmakein
Vendored
+1 -1
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@@ -419,7 +419,7 @@ typedef uLong FAR uLongf;
#endif
#ifndef _FILE_OFFSET_BITS
# define _FILE_OFFSET_BITS
# define _FILE_OFFSET_BITS 0
#endif
#if defined(_LARGEFILE64_SOURCE) && _LFS64_LARGEFILE-0
apps/haartraining/cvhaartraining.cpp
+16 -16
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@@ -2191,15 +2191,15 @@ void cvCreateCascadeClassifier( const char* dirname,
{
char xml_path[1024];
int len = (int)strlen(dirname);
CvHaarClassifierCascade* cascade = 0;
CvHaarClassifierCascade* cascade1 = 0;
strcpy( xml_path, dirname );
if( xml_path[len-1] == '\\' || xml_path[len-1] == '/' )
len--;
strcpy( xml_path + len, ".xml" );
cascade = cvLoadHaarClassifierCascade( dirname, cvSize(winwidth,winheight) );
if( cascade )
cvSave( xml_path, cascade );
cvReleaseHaarClassifierCascade( &cascade );
cascade1 = cvLoadHaarClassifierCascade( dirname, cvSize(winwidth,winheight) );
if( cascade1 )
cvSave( xml_path, cascade1 );
cvReleaseHaarClassifierCascade( &cascade1 );
}
}
else
@@ -2502,7 +2502,6 @@ void cvCreateTreeCascadeClassifier( const char* dirname,
{
CvTreeCascadeNode* single_cluster;
CvTreeCascadeNode* multiple_clusters;
CvSplit* cur_split;
int single_num;
icvSetNumSamples( training_data, poscount + negcount );
@@ -2675,18 +2674,19 @@ void cvCreateTreeCascadeClassifier( const char* dirname,
} /* try different number of clusters */
cvReleaseMat( &vals );
CV_CALL( cur_split = (CvSplit*) cvAlloc( sizeof( *cur_split ) ) );
CV_ZERO_OBJ( cur_split );
CvSplit* curSplit;
CV_CALL( curSplit = (CvSplit*) cvAlloc( sizeof( *curSplit ) ) );
CV_ZERO_OBJ( curSplit );
if( last_split ) last_split->next = cur_split;
else first_split = cur_split;
last_split = cur_split;
if( last_split ) last_split->next = curSplit;
else first_split = curSplit;
last_split = curSplit;
cur_split->single_cluster = single_cluster;
cur_split->multiple_clusters = multiple_clusters;
cur_split->num_clusters = best_clusters;
cur_split->parent = parent;
cur_split->single_multiple_ratio = (float) single_num / best_num;
curSplit->single_cluster = single_cluster;
curSplit->multiple_clusters = multiple_clusters;
curSplit->num_clusters = best_clusters;
curSplit->parent = parent;
curSplit->single_multiple_ratio = (float) single_num / best_num;
}
if( parent ) parent = parent->next_same_level;
apps/haartraining/cvsamples.cpp
+1 -4
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@@ -323,8 +323,6 @@ static void cvWarpPerspective( CvArr* src, CvArr* dst, double quad[4][2] )
int i00, i10, i01, i11;
i00 = i10 = i01 = i11 = (int) fill_value;
double i = fill_value;
/* linear interpolation using 2x2 neighborhood */
if( isrc_x >= 0 && isrc_x <= src_size.width &&
isrc_y >= 0 && isrc_y <= src_size.height )
@@ -349,9 +347,8 @@ static void cvWarpPerspective( CvArr* src, CvArr* dst, double quad[4][2] )
double i0 = i00 + (i10 - i00)*delta_x;
double i1 = i01 + (i11 - i01)*delta_x;
i = i0 + (i1 - i0)*delta_y;
((uchar*)(dst_data + y * dst_step))[x] = (uchar) i;
((uchar*)(dst_data + y * dst_step))[x] = (uchar) (i0 + (i1 - i0)*delta_y);
}
x_min += k_left;
x_max += k_right;
apps/haartraining/performance.cpp
+6 -5
View File
@@ -214,7 +214,7 @@ int main( int argc, char* argv[] )
totaltime = 0.0;
if( info != NULL )
{
int x, y, width, height;
int x, y;
IplImage* img;
int hits, missed, falseAlarms;
int totalHits, totalMissed, totalFalseAlarms;
@@ -249,11 +249,12 @@ int main( int argc, char* argv[] )
ref = (ObjectPos*) cvAlloc( refcount * sizeof( *ref ) );
for( i = 0; i < refcount; i++ )
{
error = (fscanf( info, "%d %d %d %d", &x, &y, &width, &height ) != 4);
int w, h;
error = (fscanf( info, "%d %d %d %d", &x, &y, &w, &h ) != 4);
if( error ) break;
ref[i].x = 0.5F * width + x;
ref[i].y = 0.5F * height + y;
ref[i].width = sqrtf( 0.5F * (width * width + height * height) );
ref[i].x = 0.5F * w + x;
ref[i].y = 0.5F * h + y;
ref[i].width = sqrtf( 0.5F * (w * w + h * h) );
ref[i].found = 0;
ref[i].neghbors = 0;
}
cmake/OpenCVCompilerOptions.cmake
+16 -4
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@@ -14,6 +14,8 @@ if(MINGW)
endif()
if(MSVC)
string(REGEX REPLACE "^ *| * $" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
string(REGEX REPLACE "^ *| * $" "" CMAKE_CXX_FLAGS_INIT "${CMAKE_CXX_FLAGS_INIT}")
if(CMAKE_CXX_FLAGS STREQUAL CMAKE_CXX_FLAGS_INIT)
# override cmake default exception handling option
string(REPLACE "/EHsc" "/EHa" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
@@ -72,10 +74,16 @@ if(CMAKE_COMPILER_IS_GNUCXX)
add_extra_compiler_option(-Wundef)
add_extra_compiler_option(-Winit-self)
add_extra_compiler_option(-Wpointer-arith)
#add_extra_compiler_option(-Wcast-align)
#add_extra_compiler_option(-Wstrict-aliasing=2)
#add_extra_compiler_option(-Wshadow)
#add_extra_compiler_option(-Wno-unnamed-type-template-args)
add_extra_compiler_option(-Wshadow)
if(ENABLE_NOISY_WARNINGS)
add_extra_compiler_option(-Wcast-align)
add_extra_compiler_option(-Wstrict-aliasing=2)
else()
add_extra_compiler_option(-Wno-narrowing)
add_extra_compiler_option(-Wno-delete-non-virtual-dtor)
#add_extra_compiler_option(-Wno-unnamed-type-template-args)
endif()
# The -Wno-long-long is required in 64bit systems when including sytem headers.
if(X86_64)
@@ -259,6 +267,10 @@ if(MSVC)
string(REPLACE "/W3" "/W4" CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE}")
string(REPLACE "/W3" "/W4" CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG}")
if(NOT ENABLE_NOISY_WARNINGS AND MSVC_VERSION EQUAL 1400)
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4510 /wd4610 /wd4312 /wd4201 /wd4244 /wd4328 /wd4267)
endif()
# allow extern "C" functions throw exceptions
foreach(flags CMAKE_C_FLAGS CMAKE_C_FLAGS_RELEASE CMAKE_C_FLAGS_RELEASE CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG)
string(REPLACE "/EHsc-" "/EHs" ${flags} "${${flags}}")
cmake/OpenCVFindXimea.cmake
+1 -1
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@@ -18,7 +18,7 @@ if(WIN32)
# Try to find the XIMEA API path in registry.
GET_FILENAME_COMPONENT(XIMEA_PATH "[HKEY_CURRENT_USER\\Software\\XIMEA\\CamSupport\\API;Path]" ABSOLUTE)
if(XIMEA_PATH)
if(EXISTS XIMEA_PATH)
set(XIMEA_FOUND 1)
# set LIB folders
modules/calib3d/src/precomp.hpp
-4
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@@ -42,10 +42,6 @@
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4251 4710 4711 4514 4996 )
#endif
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif
modules/contrib/src/basicretinafilter.cpp
+1 -1
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@@ -335,7 +335,7 @@ void BasicRetinaFilter::_localLuminanceAdaptation(const float *inputFrame, const
{
float X0=*(localLuminancePTR++)*_localLuminanceFactor+_localLuminanceAddon;
// TODO : the following line can lead to a divide by zero ! A small offset is added, take care if the offset is too large in case of High Dynamic Range images which can use very small values...
*(outputFramePTR++) = (_maxInputValue+X0)**inputFramePTR/(*inputFramePTR +X0+0.00000000001);
*(outputFramePTR++) = (_maxInputValue+X0)**inputFramePTR/(*inputFramePTR +X0+0.00000000001f);
//std::cout<<"BasicRetinaFilter::inputFrame[IDpixel]=%f, X0=%f, outputFrame[IDpixel]=%f\n", inputFrame[IDpixel], X0, outputFrame[IDpixel]);
}
}
modules/contrib/src/colormap.cpp
+1 -1
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@@ -18,7 +18,7 @@
#include "precomp.hpp"
#include <iostream>
#if defined _MSC_VER && _MSC_VER >= 1400
#ifdef _MSC_VER
#pragma warning( disable: 4305 )
#endif
modules/contrib/src/facerec.cpp
+32 -32
View File
@@ -22,7 +22,7 @@ namespace cv
{
using std::set;
// Reads a sequence from a FileNode::SEQ with type _Tp into a result vector.
template<typename _Tp>
inline void readFileNodeList(const FileNode& fn, vector<_Tp>& result) {
@@ -48,7 +48,7 @@ inline void writeFileNodeList(FileStorage& fs, const string& name,
}
fs << "]";
}
static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double beta=0) {
// 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) {
@@ -63,7 +63,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
// dimensionality of (reshaped) samples
size_t d = src.getMat(0).total();
// create data matrix
Mat data(n, d, rtype);
Mat data((int)n, (int)d, rtype);
// now copy data
for(unsigned int i = 0; i < n; i++) {
// make sure data can be reshaped, throw exception if not!
@@ -83,7 +83,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
return data;
}
// Removes duplicate elements in a given vector.
template<typename _Tp>
inline vector<_Tp> remove_dups(const vector<_Tp>& src) {
@@ -98,7 +98,7 @@ inline vector<_Tp> remove_dups(const vector<_Tp>& src) {
return elems;
}
// Turk, M., and Pentland, A. "Eigenfaces for recognition.". Journal of
// Cognitive Neuroscience 3 (1991), 71–86.
class Eigenfaces : public FaceRecognizer
@@ -146,10 +146,10 @@ public:
// See FaceRecognizer::save.
void save(FileStorage& fs) const;
AlgorithmInfo* info() const;
};
// Belhumeur, P. N., Hespanha, J., and Kriegman, D. "Eigenfaces vs. Fisher-
// faces: Recognition using class specific linear projection.". IEEE
// Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997),
@@ -232,13 +232,13 @@ public:
//
// radius, neighbors are used in the local binary patterns creation.
// grid_x, grid_y control the grid size of the spatial histograms.
LBPH(int radius=1, int neighbors=8,
int grid_x=8, int grid_y=8,
LBPH(int radius_=1, int neighbors_=8,
int gridx=8, int gridy=8,
double threshold = DBL_MAX) :
_grid_x(grid_x),
_grid_y(grid_y),
_radius(radius),
_neighbors(neighbors),
_grid_x(gridx),
_grid_y(gridy),
_radius(radius_),
_neighbors(neighbors_),
_threshold(threshold) {}
// Initializes and computes this LBPH Model. The current implementation is
@@ -248,13 +248,13 @@ public:
// (grid_x=8), (grid_y=8) controls the grid size of the spatial histograms.
LBPH(InputArray src,
InputArray labels,
int radius=1, int neighbors=8,
int grid_x=8, int grid_y=8,
int radius_=1, int neighbors_=8,
int gridx=8, int gridy=8,
double threshold = DBL_MAX) :
_grid_x(grid_x),
_grid_y(grid_y),
_radius(radius),
_neighbors(neighbors),
_grid_x(gridx),
_grid_y(gridy),
_radius(radius_),
_neighbors(neighbors_),
_threshold(threshold) {
train(src, labels);
}
@@ -367,7 +367,7 @@ void Eigenfaces::predict(InputArray _src, int &minClass, double &minDist) const
double dist = norm(_projections[sampleIdx], q, NORM_L2);
if((dist < minDist) && (dist < _threshold)) {
minDist = dist;
minClass = _labels.at<int>(sampleIdx);
minClass = _labels.at<int>((int)sampleIdx);
}
}
}
@@ -473,7 +473,7 @@ void Fisherfaces::predict(InputArray _src, int &minClass, double &minDist) const
double dist = norm(_projections[sampleIdx], q, NORM_L2);
if((dist < minDist) && (dist < _threshold)) {
minDist = dist;
minClass = _labels.at<int>(sampleIdx);
minClass = _labels.at<int>((int)sampleIdx);
}
}
}
@@ -641,9 +641,9 @@ static Mat histc(InputArray _src, int minVal, int maxVal, bool normed)
return Mat();
}
static Mat spatial_histogram(InputArray _src, int numPatterns,
int grid_x, int grid_y, bool normed)
int grid_x, int grid_y, bool /*normed*/)
{
Mat src = _src.getMat();
// calculate LBP patch size
@@ -681,7 +681,7 @@ static Mat elbp(InputArray src, int radius, int neighbors) {
elbp(src, dst, radius, neighbors);
return dst;
}
void LBPH::load(const FileStorage& fs) {
fs["radius"] >> _radius;
fs["neighbors"] >> _neighbors;
@@ -758,7 +758,7 @@ void LBPH::predict(InputArray _src, int &minClass, double &minDist) const {
double dist = compareHist(_histograms[sampleIdx], query, CV_COMP_CHISQR);
if((dist < minDist) && (dist < _threshold)) {
minDist = dist;
minClass = _labels.at<int>(sampleIdx);
minClass = _labels.at<int>((int)sampleIdx);
}
}
}
@@ -770,23 +770,23 @@ int LBPH::predict(InputArray _src) const {
return label;
}
Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components, double threshold)
{
return new Eigenfaces(num_components, threshold);
}
Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components, double threshold)
{
return new Fisherfaces(num_components, threshold);
}
Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius, int neighbors,
int grid_x, int grid_y, double threshold)
{
return new LBPH(radius, neighbors, grid_x, grid_y, threshold);
}
CV_INIT_ALGORITHM(Eigenfaces, "FaceRecognizer.Eigenfaces",
obj.info()->addParam(obj, "ncomponents", obj._num_components);
obj.info()->addParam(obj, "threshold", obj._threshold);
@@ -803,8 +803,8 @@ CV_INIT_ALGORITHM(Fisherfaces, "FaceRecognizer.Fisherfaces",
obj.info()->addParam(obj, "labels", obj._labels, true);
obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true);
obj.info()->addParam(obj, "eigenvalues", obj._eigenvalues, true);
obj.info()->addParam(obj, "mean", obj._mean, true));
obj.info()->addParam(obj, "mean", obj._mean, true));
CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
obj.info()->addParam(obj, "radius", obj._radius);
obj.info()->addParam(obj, "neighbors", obj._neighbors);
@@ -813,7 +813,7 @@ CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
obj.info()->addParam(obj, "threshold", obj._threshold);
obj.info()->addParam(obj, "histograms", obj._histograms, true);
obj.info()->addParam(obj, "labels", obj._labels, true));
bool initModule_contrib()
{
Ptr<Algorithm> efaces = createEigenfaces(), ffaces = createFisherfaces(), lbph = createLBPH();
modules/contrib/src/lda.cpp
+5 -5
View File
@@ -70,12 +70,12 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
// dimensionality of (reshaped) samples
size_t d = src.getMat(0).total();
// create data matrix
Mat data(n, d, rtype);
Mat data((int)n, (int)d, rtype);
// now copy data
for(size_t i = 0; i < n; i++) {
for(int i = 0; i < (int)n; i++) {
// 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());
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);
}
// get a hold of the current row
@@ -987,7 +987,7 @@ void LDA::lda(InputArray _src, InputArray _lbls) {
vector<int> mapped_labels(labels.size());
vector<int> num2label = remove_dups(labels);
map<int, int> label2num;
for (size_t i = 0; i < num2label.size(); i++)
for (int i = 0; i < (int)num2label.size(); i++)
label2num[num2label[i]] = i;
for (size_t i = 0; i < labels.size(); i++)
mapped_labels[i] = label2num[labels[i]];
@@ -995,7 +995,7 @@ void LDA::lda(InputArray _src, InputArray _lbls) {
int N = data.rows;
int D = data.cols;
// number of unique labels
int C = num2label.size();
int C = (int)num2label.size();
// we can't do a LDA on one class, what do you
// want to separate from each other then?
if(C == 1) {
modules/contrib/src/precomp.hpp
-4
View File
@@ -43,10 +43,6 @@
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4251 4710 4711 4514 4996 )
#endif
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif
modules/contrib/src/spinimages.cpp
+1 -1
View File
@@ -808,7 +808,7 @@ void cv::SpinImageModel::selectRandomSubset(float ratio)
subset.resize(setSize);
for(size_t i = 0; i < setSize; ++i)
{
int pos = rnd.next() % left.size();
int pos = rnd.next() % (int)left.size();
subset[i] = (int)left[pos];
left[pos] = left.back();
modules/core/include/opencv2/core/eigen.hpp
+5
View File
@@ -48,6 +48,11 @@
#include "opencv2/core/core_c.h"
#include "opencv2/core/core.hpp"
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4714 ) //__forceinline is not inlined
#pragma warning( disable: 4127 ) //conditional expression is constant
#endif
namespace cv
{
modules/core/include/opencv2/core/operations.hpp
+4 -4
View File
@@ -984,13 +984,13 @@ _AccTp normL2Sqr(const _Tp* a, const _Tp* b, int n)
#if CV_ENABLE_UNROLLED
for(; i <= n - 4; i += 4 )
{
_AccTp v0 = a[i] - b[i], v1 = a[i+1] - b[i+1], v2 = a[i+2] - b[i+2], v3 = a[i+3] - b[i+3];
_AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]);
s += v0*v0 + v1*v1 + v2*v2 + v3*v3;
}
#endif
for( ; i < n; i++ )
{
_AccTp v = (_AccTp)(a[i] - b[i]);
_AccTp v = _AccTp(a[i] - b[i]);
s += v*v;
}
return s;
@@ -1024,13 +1024,13 @@ _AccTp normL1(const _Tp* a, const _Tp* b, int n)
#if CV_ENABLE_UNROLLED
for(; i <= n - 4; i += 4 )
{
_AccTp v0 = a[i] - b[i], v1 = a[i+1] - b[i+1], v2 = a[i+2] - b[i+2], v3 = a[i+3] - b[i+3];
_AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]);
s += std::abs(v0) + std::abs(v1) + std::abs(v2) + std::abs(v3);
}
#endif
for( ; i < n; i++ )
{
_AccTp v = (_AccTp)(a[i] - b[i]);
_AccTp v = _AccTp(a[i] - b[i]);
s += std::abs(v);
}
return s;
modules/core/include/opencv2/core/types_c.h
+1 -1
View File
@@ -79,7 +79,7 @@
# define CV_ENABLE_UNROLLED 1
#endif
#if (defined _M_X64 && _MSC_VER >= 1400) || (__GNUC__ >= 4 && defined __x86_64__)
#if (defined _M_X64 && defined _MSC_VER && _MSC_VER >= 1400) || (__GNUC__ >= 4 && defined __x86_64__)
# if defined WIN32
# include <intrin.h>
# endif
modules/core/src/dxt.cpp
+2 -2
View File
@@ -46,8 +46,8 @@ namespace cv
// On Win64 optimized versions of DFT and DCT fail the tests (fixed in VS2010)
#if defined _MSC_VER && !defined CV_ICC && defined _M_X64 && _MSC_VER < 1600
#pragma optimize("", off)
#pragma warning( disable : 4748 )
# pragma optimize("", off)
# pragma warning(disable: 4748)
#endif
/****************************************************************************************\
modules/core/src/precomp.hpp
-5
View File
@@ -43,11 +43,6 @@
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#if defined _MSC_VER && _MSC_VER >= 1200
// disable warnings related to inline functions
#pragma warning( disable: 4251 4711 4710 4514 )
#endif
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif
modules/core/test/test_arithm.cpp
+1 -1
View File
@@ -1264,7 +1264,7 @@ struct NormOp : public BaseElemWiseOp
dst.at<double>(0,0) = cvtest::norm(src[0], normType, mask);
dst.at<double>(0,1) = cvtest::norm(src[0], src[1], normType, mask);
}
void generateScalars(int, RNG& rng)
void generateScalars(int, RNG& /*rng*/)
{
}
double getMaxErr(int)
modules/core/test/test_ds.cpp
-7
View File
@@ -2023,14 +2023,7 @@ void Core_GraphScanTest::run( int )
event = "End of procedure";
break;
default:
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( push )
#pragma warning( disable : 4127 )
#endif
CV_TS_SEQ_CHECK_CONDITION( 0, "Invalid code appeared during graph scan" );
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( pop )
#endif
}
ts->printf( cvtest::TS::LOG, "%s", event );
modules/features2d/src/keypoint.cpp
+1
View File
@@ -274,6 +274,7 @@ public:
private:
const Mat mask;
MaskPredicate& operator=(const MaskPredicate&);
};
void KeyPointsFilter::runByPixelsMask( vector<KeyPoint>& keypoints, const Mat& mask )
modules/features2d/src/precomp.hpp
-4
View File
@@ -43,10 +43,6 @@
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4251 4512 4710 4711 4514 4996 )
#endif
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif
modules/flann/src/flann.cpp
-4
View File
@@ -27,10 +27,6 @@
*************************************************************************/
#include "precomp.hpp"
#ifdef _MSC_VER
#pragma warning(disable: 4996)
#endif
#include "opencv2/flann/flann.hpp"
namespace cvflann
modules/flann/src/precomp.hpp
-4
View File
@@ -5,10 +5,6 @@
#include <cstdarg>
#include <sstream>
#ifdef _MSC_VER
#pragma warning(disable: 4996)
#endif
#ifdef HAVE_CVCONFIG_H
# include "cvconfig.h"
#endif
modules/gpu/include/opencv2/gpu/gpu.hpp
+13 -9
View File
@@ -1702,15 +1702,7 @@ class CV_EXPORTS GoodFeaturesToTrackDetector_GPU
{
public:
explicit GoodFeaturesToTrackDetector_GPU(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0,
int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04)
{
this->maxCorners = maxCorners;
this->qualityLevel = qualityLevel;
this->minDistance = minDistance;
this->blockSize = blockSize;
this->useHarrisDetector = useHarrisDetector;
this->harrisK = harrisK;
}
int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04);
//! return 1 rows matrix with CV_32FC2 type
void operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat());
@@ -1742,6 +1734,18 @@ private:
GpuMat tmpCorners_;
};
inline GoodFeaturesToTrackDetector_GPU::GoodFeaturesToTrackDetector_GPU(int maxCorners_, double qualityLevel_, double minDistance_,
int blockSize_, bool useHarrisDetector_, double harrisK_)
{
maxCorners = maxCorners_;
qualityLevel = qualityLevel_;
minDistance = minDistance_;
blockSize = blockSize_;
useHarrisDetector = useHarrisDetector_;
harrisK = harrisK_;
}
class CV_EXPORTS PyrLKOpticalFlow
{
public:
modules/gpu/src/video_reader.cpp
+1 -1
View File
@@ -57,7 +57,7 @@ void cv::gpu::VideoReader_GPU::open(const cv::Ptr<VideoSource>&) { throw_nogpu()
bool cv::gpu::VideoReader_GPU::isOpened() const { return false; }
void cv::gpu::VideoReader_GPU::close() { }
bool cv::gpu::VideoReader_GPU::read(GpuMat&) { throw_nogpu(); return false; }
cv::gpu::VideoReader_GPU::FormatInfo cv::gpu::VideoReader_GPU::format() const { throw_nogpu(); FormatInfo format = {MPEG1,Monochrome,0,0}; return format; }
cv::gpu::VideoReader_GPU::FormatInfo cv::gpu::VideoReader_GPU::format() const { throw_nogpu(); FormatInfo format_ = {MPEG1,Monochrome,0,0}; return format_; }
bool cv::gpu::VideoReader_GPU::VideoSource::parseVideoData(const unsigned char*, size_t, bool) { throw_nogpu(); return false; }
void cv::gpu::VideoReader_GPU::dumpFormat(std::ostream&) { throw_nogpu(); }
modules/gpu/test/test_filters.cpp
-1
View File
@@ -513,7 +513,6 @@ PARAM_TEST_CASE(Filter2D, cv::gpu::DeviceInfo, cv::Size, MatType, KSize, Anchor,
bool useRoi;
cv::Mat img;
cv::Mat kernel;
virtual void SetUp()
{
modules/gpu/test/test_imgproc.cpp
+5 -19
View File
@@ -150,8 +150,6 @@ PARAM_TEST_CASE(CalcHist, cv::gpu::DeviceInfo, cv::Size)
cv::gpu::DeviceInfo devInfo;
cv::Size size;
cv::Mat src;
cv::Mat hist_gold;
virtual void SetUp()
{
@@ -202,7 +200,7 @@ TEST_P(EqualizeHist, Accuracy)
cv::gpu::GpuMat dst;
cv::gpu::equalizeHist(loadMat(src), dst);
cv::Mat dst_gold;
cv::equalizeHist(src, dst_gold);
@@ -221,8 +219,6 @@ PARAM_TEST_CASE(ColumnSum, cv::gpu::DeviceInfo, cv::Size)
cv::gpu::DeviceInfo devInfo;
cv::Size size;
cv::Mat src;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
@@ -276,8 +272,6 @@ PARAM_TEST_CASE(Canny, cv::gpu::DeviceInfo, AppertureSize, L2gradient, UseRoi)
bool useL2gradient;
bool useRoi;
cv::Mat edges_gold;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
@@ -361,7 +355,7 @@ TEST_P(MeanShift, Filtering)
else
img_template = readImage("meanshift/con_result_CC1X.png");
ASSERT_FALSE(img_template.empty());
cv::gpu::GpuMat d_dst;
cv::gpu::meanShiftFiltering(loadMat(img), d_dst, spatialRad, colorRad);
@@ -396,7 +390,7 @@ TEST_P(MeanShift, Proc)
cv::gpu::meanShiftProc(loadMat(img), rmap, spmap, spatialRad, colorRad);
ASSERT_EQ(CV_8UC4, rmap.type());
EXPECT_MAT_NEAR(rmap_filtered, rmap, 0.0);
EXPECT_MAT_NEAR(spmap_template, spmap, 0.0);
}
@@ -573,11 +567,6 @@ PARAM_TEST_CASE(Convolve, cv::gpu::DeviceInfo, cv::Size, KSize, Ccorr)
int ksize;
bool ccorr;
cv::Mat src;
cv::Mat kernel;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
@@ -596,7 +585,7 @@ TEST_P(Convolve, Accuracy)
cv::gpu::GpuMat dst;
cv::gpu::convolve(loadMat(src), loadMat(kernel), dst, ccorr);
cv::Mat dst_gold;
convolveDFT(src, kernel, dst_gold, ccorr);
@@ -670,9 +659,6 @@ PARAM_TEST_CASE(MatchTemplate32F, cv::gpu::DeviceInfo, cv::Size, TemplateSize, C
int method;
int n, m, h, w;
cv::Mat image, templ;
cv::Mat dst_gold;
virtual void SetUp()
{
@@ -1080,7 +1066,7 @@ TEST_P(CornerHarris, Accuracy)
cv::gpu::GpuMat dst;
cv::gpu::cornerHarris(loadMat(src), dst, blockSize, apertureSize, k, borderType);
cv::Mat dst_gold;
cv::cornerHarris(src, dst_gold, blockSize, apertureSize, k, borderType);
modules/gpu/test/test_objdetect.cpp
+12 -12
View File
@@ -69,16 +69,16 @@ struct HOG : testing::TestWithParam<cv::gpu::DeviceInfo>, cv::gpu::HOGDescriptor
}
#ifdef DUMP
void dump(const cv::Mat& block_hists, const std::vector<cv::Point>& locations)
void dump(const cv::Mat& blockHists, const std::vector<cv::Point>& locations)
{
f.write((char*)&block_hists.rows, sizeof(block_hists.rows));
f.write((char*)&block_hists.cols, sizeof(block_hists.cols));
f.write((char*)&blockHists.rows, sizeof(blockHists.rows));
f.write((char*)&blockHists.cols, sizeof(blockHists.cols));
for (int i = 0; i < block_hists.rows; ++i)
for (int i = 0; i < blockHists.rows; ++i)
{
for (int j = 0; j < block_hists.cols; ++j)
for (int j = 0; j < blockHists.cols; ++j)
{
float val = block_hists.at<float>(i, j);
float val = blockHists.at<float>(i, j);
f.write((char*)&val, sizeof(val));
}
}
@@ -90,21 +90,21 @@ struct HOG : testing::TestWithParam<cv::gpu::DeviceInfo>, cv::gpu::HOGDescriptor
f.write((char*)&locations[i], sizeof(locations[i]));
}
#else
void compare(const cv::Mat& block_hists, const std::vector<cv::Point>& locations)
void compare(const cv::Mat& blockHists, const std::vector<cv::Point>& locations)
{
int rows, cols;
f.read((char*)&rows, sizeof(rows));
f.read((char*)&cols, sizeof(cols));
ASSERT_EQ(rows, block_hists.rows);
ASSERT_EQ(cols, block_hists.cols);
ASSERT_EQ(rows, blockHists.rows);
ASSERT_EQ(cols, blockHists.cols);
for (int i = 0; i < block_hists.rows; ++i)
for (int i = 0; i < blockHists.rows; ++i)
{
for (int j = 0; j < block_hists.cols; ++j)
for (int j = 0; j < blockHists.cols; ++j)
{
float val;
f.read((char*)&val, sizeof(val));
ASSERT_NEAR(val, block_hists.at<float>(i, j), 1e-3);
ASSERT_NEAR(val, blockHists.at<float>(i, j), 1e-3);
}
}
modules/highgui/CMakeLists.txt
+4
View File
@@ -76,6 +76,10 @@ if(HAVE_QT)
endif()
include(${QT_USE_FILE})
if(QT_INCLUDE_DIR)
ocv_include_directories(${QT_INCLUDE_DIR})
endif()
QT4_ADD_RESOURCES(_RCC_OUTFILES src/window_QT.qrc)
QT4_WRAP_CPP(_MOC_OUTFILES src/window_QT.h)
modules/highgui/src/cap.cpp
+1 -5
View File
@@ -41,13 +41,9 @@
#include "precomp.hpp"
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4711 )
#endif
#if defined _M_X64 && defined _MSC_VER && !defined CV_ICC
#pragma optimize("",off)
#pragma warning( disable: 4748 )
#pragma warning(disable: 4748)
#endif
namespace cv
modules/highgui/src/cap_dshow.cpp
+8 -7
View File
@@ -90,6 +90,7 @@ Thanks to:
#include "precomp.hpp"
#if defined _MSC_VER && _MSC_VER >= 100
//'sprintf': name was marked as #pragma deprecated
#pragma warning(disable: 4995)
#endif
@@ -1170,10 +1171,10 @@ bool videoInput::setupDevice(int deviceNumber){
//
// ----------------------------------------------------------------------
bool videoInput::setupDevice(int deviceNumber, int connection){
bool videoInput::setupDevice(int deviceNumber, int _connection){
if(deviceNumber >= VI_MAX_CAMERAS || VDList[deviceNumber]->readyToCapture) return false;
setPhyCon(deviceNumber, connection);
setPhyCon(deviceNumber, _connection);
if(setup(deviceNumber))return true;
return false;
}
@@ -1220,11 +1221,11 @@ bool videoInput::setupDeviceFourcc(int deviceNumber, int w, int h,int fourcc){
//
// ----------------------------------------------------------------------
bool videoInput::setupDevice(int deviceNumber, int w, int h, int connection){
bool videoInput::setupDevice(int deviceNumber, int w, int h, int _connection){
if(deviceNumber >= VI_MAX_CAMERAS || VDList[deviceNumber]->readyToCapture) return false;
setAttemptCaptureSize(deviceNumber,w,h);
setPhyCon(deviceNumber, connection);
setPhyCon(deviceNumber, _connection);
if(setup(deviceNumber))return true;
return false;
}
@@ -2945,7 +2946,7 @@ HRESULT videoInput::ShowFilterPropertyPages(IBaseFilter *pFilter){
return hr;
}
HRESULT videoInput::ShowStreamPropertyPages(IAMStreamConfig *pStream){
HRESULT videoInput::ShowStreamPropertyPages(IAMStreamConfig * /*pStream*/){
HRESULT hr = NOERROR;
return hr;
@@ -3035,11 +3036,11 @@ HRESULT videoInput::routeCrossbar(ICaptureGraphBuilder2 **ppBuild, IBaseFilter *
LONG lInpin, lOutpin;
hr = Crossbar->get_PinCounts(&lOutpin , &lInpin);
BOOL IPin=TRUE; LONG pIndex=0 , pRIndex=0 , pType=0;
BOOL iPin=TRUE; LONG pIndex=0 , pRIndex=0 , pType=0;
while( pIndex < lInpin)
{
hr = Crossbar->get_CrossbarPinInfo( IPin , pIndex , &pRIndex , &pType);
hr = Crossbar->get_CrossbarPinInfo( iPin , pIndex , &pRIndex , &pType);
if( pType == conType){
if(verbose)printf("SETUP: Found Physical Interface");
modules/highgui/src/cap_vfw.cpp
+3 -7
View File
@@ -43,16 +43,12 @@
#include <vfw.h>
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4711 )
#endif
#ifdef __GNUC__
#define WM_CAP_FIRSTA (WM_USER)
#define capSendMessage(hwnd,m,w,l) (IsWindow(hwnd)?SendMessage(hwnd,m,w,l):0)
#endif
#if defined _M_X64
#if defined _M_X64 && defined _MSC_VER
#pragma optimize("",off)
#pragma warning(disable: 4748)
#endif
@@ -177,13 +173,13 @@ bool CvCaptureAVI_VFW::open( const char* filename )
{
size.width = aviinfo.rcFrame.right - aviinfo.rcFrame.left;
size.height = aviinfo.rcFrame.bottom - aviinfo.rcFrame.top;
BITMAPINFOHEADER bmih = icvBitmapHeader( size.width, size.height, 24 );
BITMAPINFOHEADER bmihdr = icvBitmapHeader( size.width, size.height, 24 );
film_range.start_index = (int)aviinfo.dwStart;
film_range.end_index = film_range.start_index + (int)aviinfo.dwLength;
fps = (double)aviinfo.dwRate/aviinfo.dwScale;
pos = film_range.start_index;
getframe = AVIStreamGetFrameOpen( avistream, &bmih );
getframe = AVIStreamGetFrameOpen( avistream, &bmihdr );
if( getframe != 0 )
return true;
}
modules/highgui/src/grfmt_jpeg.cpp
+9 -1
View File
@@ -45,7 +45,8 @@
#ifdef HAVE_JPEG
#ifdef _MSC_VER
#pragma warning(disable: 4324 4611)
//interaction between '_setjmp' and C++ object destruction is non-portable
#pragma warning(disable: 4611)
#endif
#include <stdio.h>
@@ -69,11 +70,18 @@ extern "C" {
namespace cv
{
#ifdef _MSC_VER
# pragma warning(push)
# pragma warning(disable:4324) //structure was padded due to __declspec(align())
#endif
struct JpegErrorMgr
{
struct jpeg_error_mgr pub;
jmp_buf setjmp_buffer;
};
#ifdef _MSC_VER
# pragma warning(pop)
#endif
struct JpegSource
{
modules/highgui/src/grfmt_png.cpp
+1 -1
View File
@@ -60,7 +60,7 @@
#include "grfmt_png.hpp"
#if defined _MSC_VER && _MSC_VER >= 1200
// disable warnings related to _setjmp
// interaction between '_setjmp' and C++ object destruction is non-portable
#pragma warning( disable: 4611 )
#endif
modules/highgui/src/precomp.hpp
-4
View File
@@ -42,10 +42,6 @@
#ifndef __HIGHGUI_H_
#define __HIGHGUI_H_
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4251 )
#endif
#include "cvconfig.h"
#include "opencv2/highgui/highgui.hpp"
modules/highgui/src/window_QT.cpp
+106 -106
View File
@@ -1216,27 +1216,27 @@ double GuiReceiver::isOpenGl(QString name)
// CvTrackbar
CvTrackbar::CvTrackbar(CvWindow* arg, QString name, int* value, int count, CvTrackbarCallback2 on_change, void* data)
CvTrackbar::CvTrackbar(CvWindow* arg, QString name, int* value, int _count, CvTrackbarCallback2 on_change, void* data)
{
callback = NULL;
callback2 = on_change;
userdata = data;
create(arg, name, value, count);
create(arg, name, value, _count);
}
CvTrackbar::CvTrackbar(CvWindow* arg, QString name, int* value, int count, CvTrackbarCallback on_change)
CvTrackbar::CvTrackbar(CvWindow* arg, QString name, int* value, int _count, CvTrackbarCallback on_change)
{
callback = on_change;
callback2 = NULL;
userdata = NULL;
create(arg, name, value, count);
create(arg, name, value, _count);
}
void CvTrackbar::create(CvWindow* arg, QString name, int* value, int count)
void CvTrackbar::create(CvWindow* arg, QString name, int* value, int _count)
{
type = type_CvTrackbar;
myparent = arg;
@@ -1247,7 +1247,7 @@ void CvTrackbar::create(CvWindow* arg, QString name, int* value, int count)
slider = new QSlider(Qt::Horizontal);
slider->setFocusPolicy(Qt::StrongFocus);
slider->setMinimum(0);
slider->setMaximum(count);
slider->setMaximum(_count);
slider->setPageStep(5);
slider->setValue(*value);
slider->setTickPosition(QSlider::TicksBelow);
@@ -1473,7 +1473,7 @@ void CvRadioButton::callCallBack(bool checked)
//here CvWinProperties class
CvWinProperties::CvWinProperties(QString name_paraWindow, QObject* parent)
CvWinProperties::CvWinProperties(QString name_paraWindow, QObject* /*parent*/)
{
//setParent(parent);
type = type_CvWinProperties;
@@ -1502,7 +1502,7 @@ void CvWinProperties::closeEvent(QCloseEvent* e)
}
void CvWinProperties::showEvent(QShowEvent* event)
void CvWinProperties::showEvent(QShowEvent* evnt)
{
//why -1,-1 ?: do this trick because the first time the code is run,
//no value pos was saved so we let Qt move the window in the middle of its parent (event ignored).
@@ -1514,20 +1514,20 @@ void CvWinProperties::showEvent(QShowEvent* event)
if (mypos.x() >= 0)
{
move(mypos);
event->accept();
evnt->accept();
}
else
{
event->ignore();
evnt->ignore();
}
}
void CvWinProperties::hideEvent(QHideEvent* event)
void CvWinProperties::hideEvent(QHideEvent* evnt)
{
QSettings settings("OpenCV2", windowTitle());
settings.setValue("pos", pos()); //there is an offset of 6 pixels (so the window's position is wrong -- why ?)
event->accept();
evnt->accept();
}
@@ -1651,8 +1651,8 @@ void CvWindow::readSettings()
//organisation and application's name
QSettings settings("OpenCV2", QFileInfo(QApplication::applicationFilePath()).fileName());
QPoint pos = settings.value("pos", QPoint(200, 200)).toPoint();
QSize size = settings.value("size", QSize(400, 400)).toSize();
QPoint _pos = settings.value("pos", QPoint(200, 200)).toPoint();
QSize _size = settings.value("size", QSize(400, 400)).toSize();
param_flags = settings.value("mode_resize", param_flags).toInt();
param_gui_mode = settings.value("mode_gui", param_gui_mode).toInt();
@@ -1664,8 +1664,8 @@ void CvWindow::readSettings()
//trackbar here
icvLoadTrackbars(&settings);
resize(size);
move(pos);
resize(_size);
move(_pos);
if (global_control_panel)
{
@@ -1852,10 +1852,10 @@ bool CvWindow::isOpenGl()
}
void CvWindow::setViewportSize(QSize size)
void CvWindow::setViewportSize(QSize _size)
{
myView->getWidget()->resize(size);
myView->setSize(size);
myView->getWidget()->resize(_size);
myView->setSize(_size);
}
@@ -2059,29 +2059,29 @@ void CvWindow::displayPropertiesWin()
//Need more test here !
void CvWindow::keyPressEvent(QKeyEvent *event)
void CvWindow::keyPressEvent(QKeyEvent *evnt)
{
//see http://doc.trolltech.com/4.6/qt.html#Key-enum
int key = event->key();
int key = evnt->key();
Qt::Key qtkey = static_cast<Qt::Key>(key);
char asciiCode = QTest::keyToAscii(qtkey);
if (asciiCode != 0)
key = static_cast<int>(asciiCode);
else
key = event->nativeVirtualKey(); //same codes as returned by GTK-based backend
key = evnt->nativeVirtualKey(); //same codes as returned by GTK-based backend
//control plus (Z, +, -, up, down, left, right) are used for zoom/panning functions
if (event->modifiers() != Qt::ControlModifier)
if (evnt->modifiers() != Qt::ControlModifier)
{
mutexKey.lock();
last_key = key;
mutexKey.unlock();
key_pressed.wakeAll();
//event->accept();
//evnt->accept();
}
QWidget::keyPressEvent(event);
QWidget::keyPressEvent(evnt);
}
@@ -2089,11 +2089,11 @@ void CvWindow::icvLoadControlPanel()
{
QSettings settings("OpenCV2", QFileInfo(QApplication::applicationFilePath()).fileName() + " control panel");
int size = settings.beginReadArray("bars");
int bsize = settings.beginReadArray("bars");
if (size == global_control_panel->myLayout->layout()->count())
if (bsize == global_control_panel->myLayout->layout()->count())
{
for (int i = 0; i < size; ++i)
for (int i = 0; i < bsize; ++i)
{
CvBar* t = (CvBar*) global_control_panel->myLayout->layout()->itemAt(i);
settings.setArrayIndex(i);
@@ -2215,13 +2215,13 @@ void CvWindow::icvLoadButtonbar(CvButtonbar* b, QSettings* settings)
void CvWindow::icvLoadTrackbars(QSettings* settings)
{
int size = settings->beginReadArray("trackbars");
int bsize = settings->beginReadArray("trackbars");
//trackbar are saved in the same order, so no need to use icvFindTrackbarByName
if (myBarLayout->layout()->count() == size) //if not the same number, the window saved and loaded is not the same (nb trackbar not equal)
if (myBarLayout->layout()->count() == bsize) //if not the same number, the window saved and loaded is not the same (nb trackbar not equal)
{
for (int i = 0; i < size; ++i)
for (int i = 0; i < bsize; ++i)
{
settings->setArrayIndex(i);
@@ -2544,7 +2544,7 @@ void DefaultViewPort::saveView()
}
void DefaultViewPort::contextMenuEvent(QContextMenuEvent* event)
void DefaultViewPort::contextMenuEvent(QContextMenuEvent* evnt)
{
if (centralWidget->vect_QActions.size() > 0)
{
@@ -2553,12 +2553,12 @@ void DefaultViewPort::contextMenuEvent(QContextMenuEvent* event)
foreach (QAction *a, centralWidget->vect_QActions)
menu.addAction(a);
menu.exec(event->globalPos());
menu.exec(evnt->globalPos());
}
}
void DefaultViewPort::resizeEvent(QResizeEvent* event)
void DefaultViewPort::resizeEvent(QResizeEvent* evnt)
{
controlImagePosition();
@@ -2569,7 +2569,7 @@ void DefaultViewPort::resizeEvent(QResizeEvent* event)
if (param_keepRatio == CV_WINDOW_KEEPRATIO)//to keep the same aspect ratio
{
QSize newSize = QSize(image2Draw_mat->cols, image2Draw_mat->rows);
newSize.scale(event->size(), Qt::KeepAspectRatio);
newSize.scale(evnt->size(), Qt::KeepAspectRatio);
//imageWidth/imageHeight = newWidth/newHeight +/- epsilon
//ratioX = ratioY +/- epsilon
@@ -2580,7 +2580,7 @@ void DefaultViewPort::resizeEvent(QResizeEvent* event)
//move to the middle
//newSize get the delta offset to place the picture in the middle of its parent
newSize = (event->size() - newSize) / 2;
newSize = (evnt->size() - newSize) / 2;
//if the toolbar is displayed, avoid drawing myview on top of it
if (centralWidget->myToolBar)
@@ -2591,78 +2591,78 @@ void DefaultViewPort::resizeEvent(QResizeEvent* event)
}
}
return QGraphicsView::resizeEvent(event);
return QGraphicsView::resizeEvent(evnt);
}
void DefaultViewPort::wheelEvent(QWheelEvent* event)
void DefaultViewPort::wheelEvent(QWheelEvent* evnt)
{
scaleView(event->delta() / 240.0, event->pos());
scaleView(evnt->delta() / 240.0, evnt->pos());
viewport()->update();
}
void DefaultViewPort::mousePressEvent(QMouseEvent* event)
void DefaultViewPort::mousePressEvent(QMouseEvent* evnt)
{
int cv_event = -1, flags = 0;
QPoint pt = event->pos();
QPoint pt = evnt->pos();
//icvmouseHandler: pass parameters for cv_event, flags
icvmouseHandler(event, mouse_down, cv_event, flags);
icvmouseHandler(evnt, mouse_down, cv_event, flags);
icvmouseProcessing(QPointF(pt), cv_event, flags);
if (param_matrixWorld.m11()>1)
{
setCursor(Qt::ClosedHandCursor);
positionGrabbing = event->pos();
positionGrabbing = evnt->pos();
}
QWidget::mousePressEvent(event);
QWidget::mousePressEvent(evnt);
}
void DefaultViewPort::mouseReleaseEvent(QMouseEvent* event)
void DefaultViewPort::mouseReleaseEvent(QMouseEvent* evnt)
{
int cv_event = -1, flags = 0;
QPoint pt = event->pos();
QPoint pt = evnt->pos();
//icvmouseHandler: pass parameters for cv_event, flags
icvmouseHandler(event, mouse_up, cv_event, flags);
icvmouseHandler(evnt, mouse_up, cv_event, flags);
icvmouseProcessing(QPointF(pt), cv_event, flags);
if (param_matrixWorld.m11()>1)
setCursor(Qt::OpenHandCursor);
QWidget::mouseReleaseEvent(event);
QWidget::mouseReleaseEvent(evnt);
}
void DefaultViewPort::mouseDoubleClickEvent(QMouseEvent* event)
void DefaultViewPort::mouseDoubleClickEvent(QMouseEvent* evnt)
{
int cv_event = -1, flags = 0;
QPoint pt = event->pos();
QPoint pt = evnt->pos();
//icvmouseHandler: pass parameters for cv_event, flags
icvmouseHandler(event, mouse_dbclick, cv_event, flags);
icvmouseHandler(evnt, mouse_dbclick, cv_event, flags);
icvmouseProcessing(QPointF(pt), cv_event, flags);
QWidget::mouseDoubleClickEvent(event);
QWidget::mouseDoubleClickEvent(evnt);
}
void DefaultViewPort::mouseMoveEvent(QMouseEvent* event)
void DefaultViewPort::mouseMoveEvent(QMouseEvent* evnt)
{
int cv_event = CV_EVENT_MOUSEMOVE, flags = 0;
QPoint pt = event->pos();
QPoint pt = evnt->pos();
//icvmouseHandler: pass parameters for cv_event, flags
icvmouseHandler(event, mouse_move, cv_event, flags);
icvmouseHandler(evnt, mouse_move, cv_event, flags);
icvmouseProcessing(QPointF(pt), cv_event, flags);
if (param_matrixWorld.m11() > 1 && event->buttons() == Qt::LeftButton)
if (param_matrixWorld.m11() > 1 && evnt->buttons() == Qt::LeftButton)
{
QPointF dxy = (pt - positionGrabbing)/param_matrixWorld.m11();
positionGrabbing = event->pos();
positionGrabbing = evnt->pos();
moveView(dxy);
}
@@ -2671,11 +2671,11 @@ void DefaultViewPort::mouseMoveEvent(QMouseEvent* event)
if (centralWidget->myStatusBar)
viewport()->update();
QWidget::mouseMoveEvent(event);
QWidget::mouseMoveEvent(evnt);
}
void DefaultViewPort::paintEvent(QPaintEvent* event)
void DefaultViewPort::paintEvent(QPaintEvent* evnt)
{
QPainter myPainter(viewport());
myPainter.setWorldTransform(param_matrixWorld);
@@ -2707,7 +2707,7 @@ void DefaultViewPort::paintEvent(QPaintEvent* event)
if (centralWidget->myStatusBar)
drawStatusBar();
QGraphicsView::paintEvent(event);
QGraphicsView::paintEvent(evnt);
}
@@ -2811,10 +2811,10 @@ void DefaultViewPort::scaleView(qreal factor,QPointF center)
//up, down, dclick, move
void DefaultViewPort::icvmouseHandler(QMouseEvent *event, type_mouse_event category, int &cv_event, int &flags)
void DefaultViewPort::icvmouseHandler(QMouseEvent *evnt, type_mouse_event category, int &cv_event, int &flags)
{
Qt::KeyboardModifiers modifiers = event->modifiers();
Qt::MouseButtons buttons = event->buttons();
Qt::KeyboardModifiers modifiers = evnt->modifiers();
Qt::MouseButtons buttons = evnt->buttons();
flags = 0;
if(modifiers & Qt::ShiftModifier)
@@ -2832,7 +2832,7 @@ void DefaultViewPort::icvmouseHandler(QMouseEvent *event, type_mouse_event categ
flags |= CV_EVENT_FLAG_MBUTTON;
cv_event = CV_EVENT_MOUSEMOVE;
switch(event->button())
switch(evnt->button())
{
case Qt::LeftButton:
cv_event = tableMouseButtons[category][0];
@@ -2933,12 +2933,12 @@ void DefaultViewPort::drawImgRegion(QPainter *painter)
QSize view = size();
QVarLengthArray<QLineF, 30> linesX;
for (qreal x = offsetX*param_matrixWorld.m11(); x < view.width(); x += param_matrixWorld.m11() )
linesX.append(QLineF(x, 0, x, view.height()));
for (qreal _x = offsetX*param_matrixWorld.m11(); _x < view.width(); _x += param_matrixWorld.m11() )
linesX.append(QLineF(_x, 0, _x, view.height()));
QVarLengthArray<QLineF, 30> linesY;
for (qreal y = offsetY*param_matrixWorld.m11(); y < view.height(); y += param_matrixWorld.m11() )
linesY.append(QLineF(0, y, view.width(), y));
for (qreal _y = offsetY*param_matrixWorld.m11(); _y < view.height(); _y += param_matrixWorld.m11() )
linesY.append(QLineF(0, _y, view.width(), _y));
QFont f = painter->font();
@@ -3043,17 +3043,17 @@ void DefaultViewPort::drawInstructions(QPainter *painter)
QFontMetrics metrics = QFontMetrics(font());
int border = qMax(4, metrics.leading());
QRect rect = metrics.boundingRect(0, 0, width() - 2*border, int(height()*0.125),
QRect qrect = metrics.boundingRect(0, 0, width() - 2*border, int(height()*0.125),
Qt::AlignCenter | Qt::TextWordWrap, infoText);
painter->setRenderHint(QPainter::TextAntialiasing);
painter->fillRect(QRect(0, 0, width(), rect.height() + 2*border),
painter->fillRect(QRect(0, 0, width(), qrect.height() + 2*border),
QColor(0, 0, 0, 127));
painter->setPen(Qt::white);
painter->fillRect(QRect(0, 0, width(), rect.height() + 2*border),
painter->fillRect(QRect(0, 0, width(), qrect.height() + 2*border),
QColor(0, 0, 0, 127));
painter->drawText((width() - rect.width())/2, border,
rect.width(), rect.height(),
painter->drawText((width() - qrect.width())/2, border,
qrect.width(), qrect.height(),
Qt::AlignCenter | Qt::TextWordWrap, infoText);
}
@@ -3068,7 +3068,7 @@ void DefaultViewPort::setSize(QSize size_)
#ifdef HAVE_QT_OPENGL
OpenGlViewPort::OpenGlViewPort(QWidget* parent) : QGLWidget(parent), size(-1, -1)
OpenGlViewPort::OpenGlViewPort(QWidget* _parent) : QGLWidget(_parent), size(-1, -1)
{
mouseCallback = 0;
mouseData = 0;
@@ -3381,26 +3381,26 @@ void OpenGlViewPort::initializeGL()
glHint(GL_PERSPECTIVE_CORRECTION_HINT, GL_NICEST);
#ifdef Q_WS_WIN
std::auto_ptr<GlFuncTab_QT> glFuncTab(new GlFuncTab_QT(getDC()));
std::auto_ptr<GlFuncTab_QT> qglFuncTab(new GlFuncTab_QT(getDC()));
#else
std::auto_ptr<GlFuncTab_QT> glFuncTab(new GlFuncTab_QT);
std::auto_ptr<GlFuncTab_QT> qglFuncTab(new GlFuncTab_QT);
#endif
// Load extensions
glFuncTab->glGenBuffersExt = (PFNGLGENBUFFERSPROC)context()->getProcAddress("glGenBuffers");
glFuncTab->glDeleteBuffersExt = (PFNGLDELETEBUFFERSPROC)context()->getProcAddress("glDeleteBuffers");
glFuncTab->glBufferDataExt = (PFNGLBUFFERDATAPROC)context()->getProcAddress("glBufferData");
glFuncTab->glBufferSubDataExt = (PFNGLBUFFERSUBDATAPROC)context()->getProcAddress("glBufferSubData");
glFuncTab->glBindBufferExt = (PFNGLBINDBUFFERPROC)context()->getProcAddress("glBindBuffer");
glFuncTab->glMapBufferExt = (PFNGLMAPBUFFERPROC)context()->getProcAddress("glMapBuffer");
glFuncTab->glUnmapBufferExt = (PFNGLUNMAPBUFFERPROC)context()->getProcAddress("glUnmapBuffer");
qglFuncTab->glGenBuffersExt = (PFNGLGENBUFFERSPROC)context()->getProcAddress("glGenBuffers");
qglFuncTab->glDeleteBuffersExt = (PFNGLDELETEBUFFERSPROC)context()->getProcAddress("glDeleteBuffers");
qglFuncTab->glBufferDataExt = (PFNGLBUFFERDATAPROC)context()->getProcAddress("glBufferData");
qglFuncTab->glBufferSubDataExt = (PFNGLBUFFERSUBDATAPROC)context()->getProcAddress("glBufferSubData");
qglFuncTab->glBindBufferExt = (PFNGLBINDBUFFERPROC)context()->getProcAddress("glBindBuffer");
qglFuncTab->glMapBufferExt = (PFNGLMAPBUFFERPROC)context()->getProcAddress("glMapBuffer");
qglFuncTab->glUnmapBufferExt = (PFNGLUNMAPBUFFERPROC)context()->getProcAddress("glUnmapBuffer");
glFuncTab->initialized = true;
qglFuncTab->initialized = true;
this->glFuncTab = glFuncTab.release();
glFuncTab = qglFuncTab.release();
icvSetOpenGlFuncTab(this->glFuncTab);
icvSetOpenGlFuncTab(glFuncTab);
}
void OpenGlViewPort::resizeGL(int w, int h)
@@ -3420,58 +3420,58 @@ void OpenGlViewPort::paintGL()
CV_CheckGlError();
}
void OpenGlViewPort::mousePressEvent(QMouseEvent* event)
void OpenGlViewPort::mousePressEvent(QMouseEvent* evnt)
{
int cv_event = -1, flags = 0;
QPoint pt = event->pos();
QPoint pt = evnt->pos();
icvmouseHandler(event, mouse_down, cv_event, flags);
icvmouseHandler(evnt, mouse_down, cv_event, flags);
icvmouseProcessing(QPointF(pt), cv_event, flags);
QGLWidget::mousePressEvent(event);
QGLWidget::mousePressEvent(evnt);
}
void OpenGlViewPort::mouseReleaseEvent(QMouseEvent* event)
void OpenGlViewPort::mouseReleaseEvent(QMouseEvent* evnt)
{
int cv_event = -1, flags = 0;
QPoint pt = event->pos();
QPoint pt = evnt->pos();
icvmouseHandler(event, mouse_up, cv_event, flags);
icvmouseHandler(evnt, mouse_up, cv_event, flags);
icvmouseProcessing(QPointF(pt), cv_event, flags);
QGLWidget::mouseReleaseEvent(event);
QGLWidget::mouseReleaseEvent(evnt);
}
void OpenGlViewPort::mouseDoubleClickEvent(QMouseEvent* event)
void OpenGlViewPort::mouseDoubleClickEvent(QMouseEvent* evnt)
{
int cv_event = -1, flags = 0;
QPoint pt = event->pos();
QPoint pt = evnt->pos();
icvmouseHandler(event, mouse_dbclick, cv_event, flags);
icvmouseHandler(evnt, mouse_dbclick, cv_event, flags);
icvmouseProcessing(QPointF(pt), cv_event, flags);
QGLWidget::mouseDoubleClickEvent(event);
QGLWidget::mouseDoubleClickEvent(evnt);
}
void OpenGlViewPort::mouseMoveEvent(QMouseEvent* event)
void OpenGlViewPort::mouseMoveEvent(QMouseEvent* evnt)
{
int cv_event = CV_EVENT_MOUSEMOVE, flags = 0;
QPoint pt = event->pos();
QPoint pt = evnt->pos();
//icvmouseHandler: pass parameters for cv_event, flags
icvmouseHandler(event, mouse_move, cv_event, flags);
icvmouseHandler(evnt, mouse_move, cv_event, flags);
icvmouseProcessing(QPointF(pt), cv_event, flags);
QGLWidget::mouseMoveEvent(event);
QGLWidget::mouseMoveEvent(evnt);
}
void OpenGlViewPort::icvmouseHandler(QMouseEvent* event, type_mouse_event category, int& cv_event, int& flags)
void OpenGlViewPort::icvmouseHandler(QMouseEvent* evnt, type_mouse_event category, int& cv_event, int& flags)
{
Qt::KeyboardModifiers modifiers = event->modifiers();
Qt::MouseButtons buttons = event->buttons();
Qt::KeyboardModifiers modifiers = evnt->modifiers();
Qt::MouseButtons buttons = evnt->buttons();
flags = 0;
if (modifiers & Qt::ShiftModifier)
@@ -3489,7 +3489,7 @@ void OpenGlViewPort::icvmouseHandler(QMouseEvent* event, type_mouse_event catego
flags |= CV_EVENT_FLAG_MBUTTON;
cv_event = CV_EVENT_MOUSEMOVE;
switch (event->button())
switch (evnt->button())
{
case Qt::LeftButton:
cv_event = tableMouseButtons[category][0];
modules/highgui/src/window_gtk.cpp
-10
View File
@@ -55,16 +55,6 @@
#include <GL/glu.h>
#endif
/*#if _MSC_VER >= 1200
#pragma warning( disable: 4505 )
#pragma comment(lib,"gtk-win32-2.0.lib")
#pragma comment(lib,"glib-2.0.lib")
#pragma comment(lib,"gobject-2.0.lib")
#pragma comment(lib,"gdk-win32-2.0.lib")
#pragma comment(lib,"gdk_pixbuf-2.0.lib")
#endif*/
// TODO Fix the initial window size when flags=0. Right now the initial window is by default
// 320x240 size. A better default would be actual size of the image. Problem
// is determining desired window size with trackbars while still allowing resizing.
modules/highgui/src/window_w32.cpp
+5 -9
View File
@@ -43,10 +43,6 @@
#if defined WIN32 || defined _WIN32
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4710 )
#endif
#define COMPILE_MULTIMON_STUBS // Required for multi-monitor support
#ifndef _MULTIMON_USE_SECURE_CRT
# define _MULTIMON_USE_SECURE_CRT 0 // some MinGW platforms have no strncpy_s
@@ -907,7 +903,7 @@ namespace
void releaseGlContext(CvWindow* window)
{
CV_FUNCNAME( "releaseGlContext" );
//CV_FUNCNAME( "releaseGlContext" );
__BEGIN__;
@@ -1907,18 +1903,18 @@ static LRESULT CALLBACK WindowProc( HWND hwnd, UINT uMsg, WPARAM wParam, LPARAM
if( hg_on_preprocess )
{
int was_processed = 0;
int ret = hg_on_preprocess(hwnd, uMsg, wParam, lParam, &was_processed);
int rethg = hg_on_preprocess(hwnd, uMsg, wParam, lParam, &was_processed);
if( was_processed )
return ret;
return rethg;
}
ret = HighGUIProc(hwnd, uMsg, wParam, lParam);
if(hg_on_postprocess)
{
int was_processed = 0;
int ret = hg_on_postprocess(hwnd, uMsg, wParam, lParam, &was_processed);
int rethg = hg_on_postprocess(hwnd, uMsg, wParam, lParam, &was_processed);
if( was_processed )
return ret;
return rethg;
}
return ret;
modules/highgui/test/test_video_pos.cpp
+2 -2
View File
@@ -112,9 +112,9 @@ public:
return;
}
int N0 = cap.get(CV_CAP_PROP_FRAME_COUNT);
int N0 = (int)cap.get(CV_CAP_PROP_FRAME_COUNT);
cap.set(CV_CAP_PROP_POS_FRAMES, 0);
int N = cap.get(CV_CAP_PROP_FRAME_COUNT);
int N = (int)cap.get(CV_CAP_PROP_FRAME_COUNT);
if (N != n_frames || N != N0)
{
modules/imgproc/src/hough.cpp
-4
View File
@@ -169,10 +169,6 @@ icvHoughLinesStandard( const CvMat* img, float rho, float theta,
* Multi-Scale variant of Classical Hough Transform *
\****************************************************************************************/
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4714 )
#endif
//DECLARE_AND_IMPLEMENT_LIST( _index, h_ );
IMPLEMENT_LIST( _index, h_ )
modules/imgproc/src/imgwarp.cpp
+1 -1
View File
@@ -1521,7 +1521,7 @@ void cv::resize( InputArray _src, OutputArray _dst, Size dsize,
assert( k < ssize.width*2 );
xofs[k].di = dx*cn;
xofs[k].si = sx*cn;
xofs[k++].alpha = 1.f / min(scale_x, src.cols - fsx1);
xofs[k++].alpha = float(1.0 / min(scale_x, src.cols - fsx1));
}
if( fsx2 - sx2 > 1e-3 )
modules/imgproc/src/precomp.hpp
-5
View File
@@ -43,11 +43,6 @@
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#if defined _MSC_VER && _MSC_VER >= 1200
// disable warnings related to inline functions
#pragma warning( disable: 4251 4711 4710 4514 )
#endif
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif
modules/imgproc/src/smooth.cpp
+3 -13
View File
@@ -453,11 +453,6 @@ void cv::GaussianBlur( InputArray _src, OutputArray _dst, Size ksize,
namespace cv
{
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4244 )
#endif
typedef ushort HT;
/**
@@ -569,7 +564,7 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
for( c = 0; c < cn; c++ )
{
for( j = 0; j < n; j++ )
COP( c, j, src[cn*j+c], += r+2 );
COP( c, j, src[cn*j+c], += (cv::HT)(r+2) );
for( i = 1; i < r; i++ )
{
@@ -628,7 +623,7 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
if ( luc[c][k] <= j-r )
{
memset( &H[c].fine[k], 0, 16 * sizeof(HT) );
for ( luc[c][k] = j-r; luc[c][k] < MIN(j+r+1,n); ++luc[c][k] )
for ( luc[c][k] = cv::HT(j-r); luc[c][k] < MIN(j+r+1,n); ++luc[c][k] )
histogram_add_simd( &h_fine[16*(n*(16*c+k)+luc[c][k])], H[c].fine[k] );
if ( luc[c][k] < j+r+1 )
@@ -691,7 +686,7 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
if ( luc[c][k] <= j-r )
{
memset( &H[c].fine[k], 0, 16 * sizeof(HT) );
for ( luc[c][k] = j-r; luc[c][k] < MIN(j+r+1,n); ++luc[c][k] )
for ( luc[c][k] = cv::HT(j-r); luc[c][k] < MIN(j+r+1,n); ++luc[c][k] )
histogram_add( &h_fine[16*(n*(16*c+k)+luc[c][k])], H[c].fine[k] );
if ( luc[c][k] < j+r+1 )
@@ -733,11 +728,6 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
#undef COP
}
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( default: 4244 )
#endif
static void
medianBlur_8u_Om( const Mat& _src, Mat& _dst, int m )
{
modules/legacy/include/opencv2/legacy/legacy.hpp
+1 -1
View File
@@ -2826,7 +2826,7 @@ template<class Distance>
class CV_EXPORTS BruteForceMatcher : public BFMatcher
{
public:
BruteForceMatcher( Distance d = Distance() ) : BFMatcher(Distance::normType, false) {}
BruteForceMatcher( Distance d = Distance() ) : BFMatcher(Distance::normType, false) {(void)d;}
virtual ~BruteForceMatcher() {}
};
modules/legacy/src/3dtracker.cpp
-9
View File
@@ -41,18 +41,9 @@
#include "precomp.hpp"
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning(disable:4786) // Disable MSVC warnings in the standard library.
#pragma warning(disable:4100)
#pragma warning(disable:4512)
#endif
#include <stdio.h>
#include <map>
#include <algorithm>
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning(default:4100)
#pragma warning(default:4512)
#endif
#define ARRAY_SIZEOF(a) (sizeof(a)/sizeof((a)[0]))
modules/legacy/src/_kdtree.hpp
+2 -4
View File
@@ -53,10 +53,6 @@
#include "assert.h"
#include "math.h"
#if defined _MSC_VER && _MSC_VER >= 1400
#pragma warning(disable: 4512) // suppress "assignment operator could not be generated"
#endif
// J.S. Beis and D.G. Lowe. Shape indexing using approximate nearest-neighbor search
// in highdimensional spaces. In Proc. IEEE Conf. Comp. Vision Patt. Recog.,
// pages 1000--1006, 1997. http://citeseer.ist.psu.edu/beis97shape.html
@@ -140,6 +136,8 @@ private:
bool operator() (const __instype & lhs) const {
return deref(ctor(lhs), dim) <= deref(ctor(pivot), dim);
}
private:
median_pr& operator=(const median_pr&);
};
template < class __instype, class __valuector >
modules/legacy/src/blobtrackanalysishist.cpp
+1219 -1227
View File
@@ -46,195 +46,193 @@
typedef struct DefBlobFVN
{
CvBlob blob;
CvBlob BlobSeq[BLOB_NUM];
int state;
int LastFrame;
int FrameNum;
CvBlob blob;
CvBlob BlobSeq[BLOB_NUM];
int state;
int LastFrame;
int FrameNum;
} DefBlobFVN;
class CvBlobTrackFVGenN: public CvBlobTrackFVGen
{
private:
CvBlobSeq m_BlobList;
CvMemStorage* m_pMem;
CvSeq* m_pFVSeq;
float m_FVMax[MAX_FV_SIZE];
float m_FVMin[MAX_FV_SIZE];
float m_FVVar[MAX_FV_SIZE];
int m_Dim;
CvBlob m_BlobSeq[BLOB_NUM];
int m_Frame;
int m_State;
int m_LastFrame;
int m_ClearFlag;
void Clear()
{
if(m_pMem)
{
cvClearMemStorage(m_pMem);
m_pFVSeq = cvCreateSeq(0,sizeof(CvSeq),sizeof(float)*(m_Dim+1), m_pMem);
m_ClearFlag = 1;
}
}
CvBlobSeq m_BlobList;
CvMemStorage* m_pMem;
CvSeq* m_pFVSeq;
float m_FVMax[MAX_FV_SIZE];
float m_FVMin[MAX_FV_SIZE];
float m_FVVar[MAX_FV_SIZE];
int m_Dim;
CvBlob m_BlobSeq[BLOB_NUM];
int m_Frame;
int m_State;
int m_LastFrame;
int m_ClearFlag;
void Clear()
{
if(m_pMem)
{
cvClearMemStorage(m_pMem);
m_pFVSeq = cvCreateSeq(0,sizeof(CvSeq),sizeof(float)*(m_Dim+1), m_pMem);
m_ClearFlag = 1;
}
}
public:
CvBlobTrackFVGenN(int dim = 2 ):m_BlobList(sizeof(DefBlobFVN))
{
int i;
assert(dim <= MAX_FV_SIZE);
m_Dim = dim;
for(i=0; i<m_Dim; ++i)
{
m_FVVar[i] = 0.01f;
m_FVMax[i] = 1;
m_FVMin[i] = 0;
}
m_Frame = 0;
m_State = 0;
m_pMem = cvCreateMemStorage();
m_pFVSeq = NULL;
Clear();
CvBlobTrackFVGenN(int dim = 2 ):m_BlobList(sizeof(DefBlobFVN))
{
int i;
assert(dim <= MAX_FV_SIZE);
m_Dim = dim;
for(i=0; i<m_Dim; ++i)
{
m_FVVar[i] = 0.01f;
m_FVMax[i] = 1;
m_FVMin[i] = 0;
}
m_Frame = 0;
m_State = 0;
m_pMem = cvCreateMemStorage();
m_pFVSeq = NULL;
Clear();
switch(dim) {
case 2: SetModuleName("P"); break;
case 4: SetModuleName("PV"); break;
case 5: SetModuleName("PVS"); break;
}
};
switch(dim) {
case 2: SetModuleName("P"); break;
case 4: SetModuleName("PV"); break;
case 5: SetModuleName("PVS"); break;
}
};
~CvBlobTrackFVGenN()
{
if(m_pMem)cvReleaseMemStorage(&m_pMem);
};
~CvBlobTrackFVGenN()
{
if(m_pMem)cvReleaseMemStorage(&m_pMem);
};
void AddBlob(CvBlob* pBlob)
{
float FV[MAX_FV_SIZE+1];
int i;
DefBlobFVN* pFVBlob = (DefBlobFVN*)m_BlobList.GetBlobByID(CV_BLOB_ID(pBlob));
void AddBlob(CvBlob* pBlob)
{
float FV[MAX_FV_SIZE+1];
DefBlobFVN* pFVBlob = (DefBlobFVN*)m_BlobList.GetBlobByID(CV_BLOB_ID(pBlob));
if(!m_ClearFlag) Clear();
if(!m_ClearFlag) Clear();
if(pFVBlob==NULL)
{
DefBlobFVN BlobNew;
BlobNew.blob = pBlob[0];
BlobNew.LastFrame = m_Frame;
BlobNew.state = 0;;
BlobNew.FrameNum = 0;
m_BlobList.AddBlob((CvBlob*)&BlobNew);
pFVBlob = (DefBlobFVN*)m_BlobList.GetBlobByID(CV_BLOB_ID(pBlob));
} /* Add new record if necessary. */
if(pFVBlob==NULL)
{
DefBlobFVN BlobNew;
BlobNew.blob = pBlob[0];
BlobNew.LastFrame = m_Frame;
BlobNew.state = 0;;
BlobNew.FrameNum = 0;
m_BlobList.AddBlob((CvBlob*)&BlobNew);
pFVBlob = (DefBlobFVN*)m_BlobList.GetBlobByID(CV_BLOB_ID(pBlob));
} /* Add new record if necessary. */
pFVBlob->blob = pBlob[0];
pFVBlob->blob = pBlob[0];
/* Shift: */
for(i=(BLOB_NUM-1); i>0; --i)
{
pFVBlob->BlobSeq[i] = pFVBlob->BlobSeq[i-1];
}
/* Shift: */
for(int i=(BLOB_NUM-1); i>0; --i)
{
pFVBlob->BlobSeq[i] = pFVBlob->BlobSeq[i-1];
}
pFVBlob->BlobSeq[0] = pBlob[0];
pFVBlob->BlobSeq[0] = pBlob[0];
if(m_Dim>0)
{ /* Calculate FV position: */
FV[0] = CV_BLOB_X(pBlob);
FV[1] = CV_BLOB_Y(pBlob);
}
if(m_Dim>0)
{ /* Calculate FV position: */
FV[0] = CV_BLOB_X(pBlob);
FV[1] = CV_BLOB_Y(pBlob);
}
if(m_Dim<=2)
{ /* Add new FV if position is enough: */
*(int*)(FV+m_Dim) = CV_BLOB_ID(pBlob);
cvSeqPush( m_pFVSeq, FV );
}
else if(pFVBlob->FrameNum > BLOB_NUM)
{ /* Calculate velocity for more complex FV: */
float AverVx = 0;
float AverVy = 0;
{ /* Average velocity: */
CvBlob* pBlobSeq = pFVBlob->BlobSeq;
int i;
for(i=1;i<BLOB_NUM;++i)
{
AverVx += CV_BLOB_X(pBlobSeq+i-1)-CV_BLOB_X(pBlobSeq+i);
AverVy += CV_BLOB_Y(pBlobSeq+i-1)-CV_BLOB_Y(pBlobSeq+i);
}
AverVx /= BLOB_NUM-1;
AverVy /= BLOB_NUM-1;
if(m_Dim<=2)
{ /* Add new FV if position is enough: */
*(int*)(FV+m_Dim) = CV_BLOB_ID(pBlob);
cvSeqPush( m_pFVSeq, FV );
}
else if(pFVBlob->FrameNum > BLOB_NUM)
{ /* Calculate velocity for more complex FV: */
float AverVx = 0;
float AverVy = 0;
{ /* Average velocity: */
CvBlob* pBlobSeq = pFVBlob->BlobSeq;
for(int i=1;i<BLOB_NUM;++i)
{
AverVx += CV_BLOB_X(pBlobSeq+i-1)-CV_BLOB_X(pBlobSeq+i);
AverVy += CV_BLOB_Y(pBlobSeq+i-1)-CV_BLOB_Y(pBlobSeq+i);
}
AverVx /= BLOB_NUM-1;
AverVy /= BLOB_NUM-1;
FV[2] = AverVx;
FV[3] = AverVy;
}
FV[2] = AverVx;
FV[3] = AverVy;
}
if(m_Dim>4)
{ /* State duration: */
float T = (CV_BLOB_WX(pBlob)+CV_BLOB_WY(pBlob))*0.01f;
if(m_Dim>4)
{ /* State duration: */
float T = (CV_BLOB_WX(pBlob)+CV_BLOB_WY(pBlob))*0.01f;
if( fabs(AverVx) < T && fabs(AverVy) < T)
pFVBlob->state++;
else
pFVBlob->state=0;
FV[4] = (float)pFVBlob->state;
} /* State duration. */
if( fabs(AverVx) < T && fabs(AverVy) < T)
pFVBlob->state++;
else
pFVBlob->state=0;
FV[4] = (float)pFVBlob->state;
} /* State duration. */
/* Add new FV: */
*(int*)(FV+m_Dim) = CV_BLOB_ID(pBlob);
cvSeqPush( m_pFVSeq, FV );
/* Add new FV: */
*(int*)(FV+m_Dim) = CV_BLOB_ID(pBlob);
cvSeqPush( m_pFVSeq, FV );
} /* If velocity is calculated. */
} /* If velocity is calculated. */
pFVBlob->FrameNum++;
pFVBlob->LastFrame = m_Frame;
}; /* AddBlob */
pFVBlob->FrameNum++;
pFVBlob->LastFrame = m_Frame;
}; /* AddBlob */
void Process(IplImage* pImg, IplImage* /*pFG*/)
{
int i;
if(!m_ClearFlag) Clear();
for(i=m_BlobList.GetBlobNum(); i>0; --i)
{ /* Delete unused blob: */
DefBlobFVN* pFVBlob = (DefBlobFVN*)m_BlobList.GetBlob(i-1);
if(pFVBlob->LastFrame < m_Frame)
{
m_BlobList.DelBlob(i-1);
}
} /* Check next blob in list. */
void Process(IplImage* pImg, IplImage* /*pFG*/)
{
int i;
if(!m_ClearFlag) Clear();
for(i=m_BlobList.GetBlobNum(); i>0; --i)
{ /* Delete unused blob: */
DefBlobFVN* pFVBlob = (DefBlobFVN*)m_BlobList.GetBlob(i-1);
if(pFVBlob->LastFrame < m_Frame)
{
m_BlobList.DelBlob(i-1);
}
} /* Check next blob in list. */
m_FVMin[0] = 0;
m_FVMin[1] = 0;
m_FVMax[0] = (float)(pImg->width-1);
m_FVMax[1] = (float)(pImg->height-1);
m_FVVar[0] = m_FVMax[0]*0.01f;
m_FVVar[1] = m_FVMax[1]*0.01f;
m_FVVar[2] = (float)(pImg->width-1)/1440.0f;
m_FVMax[2] = (float)(pImg->width-1)*0.02f;
m_FVMin[2] = -m_FVMax[2];
m_FVVar[3] = (float)(pImg->width-1)/1440.0f;
m_FVMax[3] = (float)(pImg->height-1)*0.02f;
m_FVMin[3] = -m_FVMax[3];
m_FVMax[4] = 25*32.0f; /* max state is 32 sec */
m_FVMin[4] = 0;
m_FVVar[4] = 10;
m_FVMin[0] = 0;
m_FVMin[1] = 0;
m_FVMax[0] = (float)(pImg->width-1);
m_FVMax[1] = (float)(pImg->height-1);
m_FVVar[0] = m_FVMax[0]*0.01f;
m_FVVar[1] = m_FVMax[1]*0.01f;
m_FVVar[2] = (float)(pImg->width-1)/1440.0f;
m_FVMax[2] = (float)(pImg->width-1)*0.02f;
m_FVMin[2] = -m_FVMax[2];
m_FVVar[3] = (float)(pImg->width-1)/1440.0f;
m_FVMax[3] = (float)(pImg->height-1)*0.02f;
m_FVMin[3] = -m_FVMax[3];
m_FVMax[4] = 25*32.0f; /* max state is 32 sec */
m_FVMin[4] = 0;
m_FVVar[4] = 10;
m_Frame++;
m_ClearFlag = 0;
};
virtual void Release(){delete this;};
virtual int GetFVSize(){return m_Dim;};
virtual int GetFVNum()
{
return m_pFVSeq->total;
};
m_Frame++;
m_ClearFlag = 0;
};
virtual void Release(){delete this;};
virtual int GetFVSize(){return m_Dim;};
virtual int GetFVNum()
{
return m_pFVSeq->total;
};
virtual float* GetFV(int index, int* pFVID)
{
float* pFV = (float*)cvGetSeqElem( m_pFVSeq, index );
if(pFVID)pFVID[0] = *(int*)(pFV+m_Dim);
return pFV;
};
virtual float* GetFVMin(){return m_FVMin;}; /* returned pointer to array of minimal values of FV, if return 0 then FVrange is not exist */
virtual float* GetFVMax(){return m_FVMax;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */
virtual float* GetFVVar(){return m_FVVar;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */
virtual float* GetFV(int index, int* pFVID)
{
float* pFV = (float*)cvGetSeqElem( m_pFVSeq, index );
if(pFVID)pFVID[0] = *(int*)(pFV+m_Dim);
return pFV;
};
virtual float* GetFVMin(){return m_FVMin;}; /* returned pointer to array of minimal values of FV, if return 0 then FVrange is not exist */
virtual float* GetFVMax(){return m_FVMax;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */
virtual float* GetFVVar(){return m_FVVar;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */
};/* CvBlobTrackFVGenN */
inline CvBlobTrackFVGen* cvCreateFVGenP(){return (CvBlobTrackFVGen*)new CvBlobTrackFVGenN(2);}
@@ -246,166 +244,164 @@ inline CvBlobTrackFVGen* cvCreateFVGenPVS(){return (CvBlobTrackFVGen*)new CvBlob
class CvBlobTrackFVGenSS: public CvBlobTrackFVGen
{
private:
CvBlobSeq m_BlobList;
CvMemStorage* m_pMem;
CvSeq* m_pFVSeq;
float m_FVMax[MAX_FV_SIZE];
float m_FVMin[MAX_FV_SIZE];
float m_FVVar[MAX_FV_SIZE];
int m_Dim;
CvBlob m_BlobSeq[BLOB_NUM];
int m_Frame;
int m_State;
int m_LastFrame;
int m_ClearFlag;
void Clear()
{
cvClearMemStorage(m_pMem);
m_pFVSeq = cvCreateSeq(0,sizeof(CvSeq),sizeof(float)*(m_Dim+1), m_pMem);
m_ClearFlag = 1;
}
CvBlobSeq m_BlobList;
CvMemStorage* m_pMem;
CvSeq* m_pFVSeq;
float m_FVMax[MAX_FV_SIZE];
float m_FVMin[MAX_FV_SIZE];
float m_FVVar[MAX_FV_SIZE];
int m_Dim;
CvBlob m_BlobSeq[BLOB_NUM];
int m_Frame;
int m_State;
int m_LastFrame;
int m_ClearFlag;
void Clear()
{
cvClearMemStorage(m_pMem);
m_pFVSeq = cvCreateSeq(0,sizeof(CvSeq),sizeof(float)*(m_Dim+1), m_pMem);
m_ClearFlag = 1;
}
public:
CvBlobTrackFVGenSS(int dim = 2 ):m_BlobList(sizeof(DefBlobFVN))
{
int i;
assert(dim <= MAX_FV_SIZE);
m_Dim = dim;
for(i=0;i<m_Dim;++i)
{
m_FVVar[i] = 0.01f;
m_FVMax[i] = 1;
m_FVMin[i] = 0;
}
m_Frame = 0;
m_State = 0;
m_pMem = cvCreateMemStorage();
m_pFVSeq = NULL;
CvBlobTrackFVGenSS(int dim = 2 ):m_BlobList(sizeof(DefBlobFVN))
{
int i;
assert(dim <= MAX_FV_SIZE);
m_Dim = dim;
for(i=0;i<m_Dim;++i)
{
m_FVVar[i] = 0.01f;
m_FVMax[i] = 1;
m_FVMin[i] = 0;
}
m_Frame = 0;
m_State = 0;
m_pMem = cvCreateMemStorage();
m_pFVSeq = NULL;
SetModuleName("SS");
};
~CvBlobTrackFVGenSS()
{
if(m_pMem)cvReleaseMemStorage(&m_pMem);
};
SetModuleName("SS");
};
~CvBlobTrackFVGenSS()
{
if(m_pMem)cvReleaseMemStorage(&m_pMem);
};
void AddBlob(CvBlob* pBlob)
{
//float FV[MAX_FV_SIZE+1];
int i;
DefBlobFVN* pFVBlob = (DefBlobFVN*)m_BlobList.GetBlobByID(CV_BLOB_ID(pBlob));
void AddBlob(CvBlob* pBlob)
{
//float FV[MAX_FV_SIZE+1];
DefBlobFVN* pFVBlob = (DefBlobFVN*)m_BlobList.GetBlobByID(CV_BLOB_ID(pBlob));
if(!m_ClearFlag) Clear();
if(!m_ClearFlag) Clear();
if(pFVBlob==NULL)
{
DefBlobFVN BlobNew;
BlobNew.blob = pBlob[0];
BlobNew.LastFrame = m_Frame;
BlobNew.state = 0;;
BlobNew.FrameNum = 0;
m_BlobList.AddBlob((CvBlob*)&BlobNew);
pFVBlob = (DefBlobFVN*)m_BlobList.GetBlobByID(CV_BLOB_ID(pBlob));
} /* Add new record if necessary. */
if(pFVBlob==NULL)
{
DefBlobFVN BlobNew;
BlobNew.blob = pBlob[0];
BlobNew.LastFrame = m_Frame;
BlobNew.state = 0;;
BlobNew.FrameNum = 0;
m_BlobList.AddBlob((CvBlob*)&BlobNew);
pFVBlob = (DefBlobFVN*)m_BlobList.GetBlobByID(CV_BLOB_ID(pBlob));
} /* Add new record if necessary. */
/* Shift: */
for(i=(BLOB_NUM-1); i>0; --i)
{
pFVBlob->BlobSeq[i] = pFVBlob->BlobSeq[i-1];
}
/* Shift: */
for(int i=(BLOB_NUM-1); i>0; --i)
{
pFVBlob->BlobSeq[i] = pFVBlob->BlobSeq[i-1];
}
pFVBlob->BlobSeq[0] = pBlob[0];
pFVBlob->BlobSeq[0] = pBlob[0];
if(pFVBlob->FrameNum > BLOB_NUM)
{ /* Average velocity: */
CvBlob* pBlobSeq = pFVBlob->BlobSeq;
float T = (CV_BLOB_WX(pBlob)+CV_BLOB_WY(pBlob))*0.01f;
float AverVx = 0;
float AverVy = 0;
int i;
for(i=1; i<BLOB_NUM; ++i)
{
AverVx += CV_BLOB_X(pBlobSeq+i-1)-CV_BLOB_X(pBlobSeq+i);
AverVy += CV_BLOB_Y(pBlobSeq+i-1)-CV_BLOB_Y(pBlobSeq+i);
}
AverVx /= BLOB_NUM-1;
AverVy /= BLOB_NUM-1;
if(pFVBlob->FrameNum > BLOB_NUM)
{ /* Average velocity: */
CvBlob* pBlobSeq = pFVBlob->BlobSeq;
float T = (CV_BLOB_WX(pBlob)+CV_BLOB_WY(pBlob))*0.01f;
float AverVx = 0;
float AverVy = 0;
for(int i=1; i<BLOB_NUM; ++i)
{
AverVx += CV_BLOB_X(pBlobSeq+i-1)-CV_BLOB_X(pBlobSeq+i);
AverVy += CV_BLOB_Y(pBlobSeq+i-1)-CV_BLOB_Y(pBlobSeq+i);
}
AverVx /= BLOB_NUM-1;
AverVy /= BLOB_NUM-1;
if( fabs(AverVx) < T && fabs(AverVy) < T)
pFVBlob->state++;
else
pFVBlob->state=0;
}
if( fabs(AverVx) < T && fabs(AverVy) < T)
pFVBlob->state++;
else
pFVBlob->state=0;
}
if(pFVBlob->state == 5)
{ /* Object is stopped: */
float FV[MAX_FV_SIZE];
FV[0] = pFVBlob->blob.x;
FV[1] = pFVBlob->blob.y;
FV[2] = pFVBlob->BlobSeq[0].x;
FV[3] = pFVBlob->BlobSeq[0].y;
*(int*)(FV+m_Dim) = CV_BLOB_ID(pBlob);
cvSeqPush( m_pFVSeq, FV );
} /* Object is stopped. */
if(pFVBlob->state == 5)
{ /* Object is stopped: */
float FV[MAX_FV_SIZE];
FV[0] = pFVBlob->blob.x;
FV[1] = pFVBlob->blob.y;
FV[2] = pFVBlob->BlobSeq[0].x;
FV[3] = pFVBlob->BlobSeq[0].y;
*(int*)(FV+m_Dim) = CV_BLOB_ID(pBlob);
cvSeqPush( m_pFVSeq, FV );
} /* Object is stopped. */
pFVBlob->FrameNum++;
pFVBlob->LastFrame = m_Frame;
}; /* AddBlob */
void Process(IplImage* pImg, IplImage* /*pFG*/)
{
int i;
pFVBlob->FrameNum++;
pFVBlob->LastFrame = m_Frame;
}; /* AddBlob */
void Process(IplImage* pImg, IplImage* /*pFG*/)
{
int i;
if(!m_ClearFlag) Clear();
if(!m_ClearFlag) Clear();
for(i=m_BlobList.GetBlobNum();i>0;--i)
{ /* Delete unused blob: */
DefBlobFVN* pFVBlob = (DefBlobFVN*)m_BlobList.GetBlob(i-1);
if(pFVBlob->LastFrame < m_Frame)
{
float FV[MAX_FV_SIZE+1];
FV[0] = pFVBlob->blob.x;
FV[1] = pFVBlob->blob.y;
FV[2] = pFVBlob->BlobSeq[0].x;
FV[3] = pFVBlob->BlobSeq[0].y;
*(int*)(FV+m_Dim) = CV_BLOB_ID(pFVBlob);
cvSeqPush( m_pFVSeq, FV );
m_BlobList.DelBlob(i-1);
}
} /* Check next blob in list. */
for(i=m_BlobList.GetBlobNum();i>0;--i)
{ /* Delete unused blob: */
DefBlobFVN* pFVBlob = (DefBlobFVN*)m_BlobList.GetBlob(i-1);
if(pFVBlob->LastFrame < m_Frame)
{
float FV[MAX_FV_SIZE+1];
FV[0] = pFVBlob->blob.x;
FV[1] = pFVBlob->blob.y;
FV[2] = pFVBlob->BlobSeq[0].x;
FV[3] = pFVBlob->BlobSeq[0].y;
*(int*)(FV+m_Dim) = CV_BLOB_ID(pFVBlob);
cvSeqPush( m_pFVSeq, FV );
m_BlobList.DelBlob(i-1);
}
} /* Check next blob in list. */
/* Set max min range: */
m_FVMin[0] = 0;
m_FVMin[1] = 0;
m_FVMin[2] = 0;
m_FVMin[3] = 0;
m_FVMax[0] = (float)(pImg->width-1);
m_FVMax[1] = (float)(pImg->height-1);
m_FVMax[2] = (float)(pImg->width-1);
m_FVMax[3] = (float)(pImg->height-1);
m_FVVar[0] = m_FVMax[0]*0.01f;
m_FVVar[1] = m_FVMax[1]*0.01f;
m_FVVar[2] = m_FVMax[2]*0.01f;
m_FVVar[3] = m_FVMax[3]*0.01f;
/* Set max min range: */
m_FVMin[0] = 0;
m_FVMin[1] = 0;
m_FVMin[2] = 0;
m_FVMin[3] = 0;
m_FVMax[0] = (float)(pImg->width-1);
m_FVMax[1] = (float)(pImg->height-1);
m_FVMax[2] = (float)(pImg->width-1);
m_FVMax[3] = (float)(pImg->height-1);
m_FVVar[0] = m_FVMax[0]*0.01f;
m_FVVar[1] = m_FVMax[1]*0.01f;
m_FVVar[2] = m_FVMax[2]*0.01f;
m_FVVar[3] = m_FVMax[3]*0.01f;
m_Frame++;
m_ClearFlag = 0;
};
virtual void Release(){delete this;};
virtual int GetFVSize(){return m_Dim;};
virtual int GetFVNum()
{
return m_pFVSeq->total;
};
m_Frame++;
m_ClearFlag = 0;
};
virtual void Release(){delete this;};
virtual int GetFVSize(){return m_Dim;};
virtual int GetFVNum()
{
return m_pFVSeq->total;
};
virtual float* GetFV(int index, int* pFVID)
{
float* pFV = (float*)cvGetSeqElem( m_pFVSeq, index );
if(pFVID)pFVID[0] = *(int*)(pFV+m_Dim);
return pFV;
};
virtual float* GetFV(int index, int* pFVID)
{
float* pFV = (float*)cvGetSeqElem( m_pFVSeq, index );
if(pFVID)pFVID[0] = *(int*)(pFV+m_Dim);
return pFV;
};
virtual float* GetFVMin(){return m_FVMin;}; /* returned pointer to array of minimal values of FV, if return 0 then FVrange is not exist */
virtual float* GetFVMax(){return m_FVMax;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */
virtual float* GetFVVar(){return m_FVVar;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */
virtual float* GetFVMin(){return m_FVMin;}; /* returned pointer to array of minimal values of FV, if return 0 then FVrange is not exist */
virtual float* GetFVMax(){return m_FVMax;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */
virtual float* GetFVVar(){return m_FVVar;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */
};/* CvBlobTrackFVGenSS */
inline CvBlobTrackFVGen* cvCreateFVGenSS(){return (CvBlobTrackFVGen*)new CvBlobTrackFVGenSS;}
@@ -418,696 +414,692 @@ inline CvBlobTrackFVGen* cvCreateFVGenSS(){return (CvBlobTrackFVGen*)new CvBlobT
class DefMat
{
private:
CvSparseMatIterator m_SparseIterator;
CvSparseNode* m_pSparseNode;
int* m_IDXs;
int m_Dim;
CvSparseMatIterator m_SparseIterator;
CvSparseNode* m_pSparseNode;
int* m_IDXs;
int m_Dim;
public:
CvSparseMat* m_pSparse;
CvMatND* m_pND;
int m_Volume;
int m_Max;
DefMat(int dim = 0, int* sizes = NULL, int type = SPARSE)
{
/* Create sparse or ND matrix but not both: */
m_pSparseNode = NULL;
m_pSparse = NULL;
m_pND = NULL;
m_Volume = 0;
m_Max = 0;
m_IDXs = NULL;
m_Dim = 0;
if(dim>0 && sizes != 0)
Realloc(dim, sizes, type);
}
~DefMat()
{
if(m_pSparse)cvReleaseSparseMat(&m_pSparse);
if(m_pND)cvReleaseMatND(&m_pND);
if(m_IDXs) cvFree(&m_IDXs);
}
CvSparseMat* m_pSparse;
CvMatND* m_pND;
int m_Volume;
int m_Max;
DefMat(int dim = 0, int* sizes = NULL, int type = SPARSE)
{
/* Create sparse or ND matrix but not both: */
m_pSparseNode = NULL;
m_pSparse = NULL;
m_pND = NULL;
m_Volume = 0;
m_Max = 0;
m_IDXs = NULL;
m_Dim = 0;
if(dim>0 && sizes != 0)
Realloc(dim, sizes, type);
}
~DefMat()
{
if(m_pSparse)cvReleaseSparseMat(&m_pSparse);
if(m_pND)cvReleaseMatND(&m_pND);
if(m_IDXs) cvFree(&m_IDXs);
}
void Realloc(int dim, int* sizes, int type = SPARSE)
{
if(m_pSparse)cvReleaseSparseMat(&m_pSparse);
if(m_pND)cvReleaseMatND(&m_pND);
void Realloc(int dim, int* sizes, int type = SPARSE)
{
if(m_pSparse)cvReleaseSparseMat(&m_pSparse);
if(m_pND)cvReleaseMatND(&m_pND);
if(type == BYSIZE )
{
int size = 0;
int i;
for(size=1,i=0;i<dim;++i)
{
size *= sizes[i];
}
size *= sizeof(int);
if(size > (2<<20))
{ /* if size > 1M */
type = SPARSE;
}
else
{
type = ND;
}
} /* Define matrix type. */
if(type == BYSIZE )
{
int size = 0;
int i;
for(size=1,i=0;i<dim;++i)
{
size *= sizes[i];
}
size *= sizeof(int);
if(size > (2<<20))
{ /* if size > 1M */
type = SPARSE;
}
else
{
type = ND;
}
} /* Define matrix type. */
if(type == SPARSE)
{
m_pSparse = cvCreateSparseMat( dim, sizes, CV_32SC1 );
m_Dim = dim;
}
if(type == ND )
{
m_pND = cvCreateMatND( dim, sizes, CV_32SC1 );
cvZero(m_pND);
m_IDXs = (int*)cvAlloc(sizeof(int)*dim);
m_Dim = dim;
}
m_Volume = 0;
m_Max = 0;
}
void Save(const char* File)
{
if(m_pSparse)cvSave(File, m_pSparse );
if(m_pND)cvSave(File, m_pND );
}
void Save(CvFileStorage* fs, const char* name)
{
if(m_pSparse)
{
cvWrite(fs, name, m_pSparse );
}
else if(m_pND)
{
cvWrite(fs, name, m_pND );
}
}
void Load(const char* File)
{
CvFileStorage* fs = cvOpenFileStorage( File, NULL, CV_STORAGE_READ );
if(fs)
{
void* ptr;
if(m_pSparse) cvReleaseSparseMat(&m_pSparse);
if(m_pND) cvReleaseMatND(&m_pND);
m_Volume = 0;
m_Max = 0;
ptr = cvLoad(File);
if(ptr && CV_IS_MATND_HDR(ptr)) m_pND = (CvMatND*)ptr;
if(ptr && CV_IS_SPARSE_MAT_HDR(ptr)) m_pSparse = (CvSparseMat*)ptr;
cvReleaseFileStorage(&fs);
}
AfterLoad();
} /* Load. */
if(type == SPARSE)
{
m_pSparse = cvCreateSparseMat( dim, sizes, CV_32SC1 );
m_Dim = dim;
}
if(type == ND )
{
m_pND = cvCreateMatND( dim, sizes, CV_32SC1 );
cvZero(m_pND);
m_IDXs = (int*)cvAlloc(sizeof(int)*dim);
m_Dim = dim;
}
m_Volume = 0;
m_Max = 0;
}
void Save(const char* File)
{
if(m_pSparse)cvSave(File, m_pSparse );
if(m_pND)cvSave(File, m_pND );
}
void Save(CvFileStorage* fs, const char* name)
{
if(m_pSparse)
{
cvWrite(fs, name, m_pSparse );
}
else if(m_pND)
{
cvWrite(fs, name, m_pND );
}
}
void Load(const char* File)
{
CvFileStorage* fs = cvOpenFileStorage( File, NULL, CV_STORAGE_READ );
if(fs)
{
void* ptr;
if(m_pSparse) cvReleaseSparseMat(&m_pSparse);
if(m_pND) cvReleaseMatND(&m_pND);
m_Volume = 0;
m_Max = 0;
ptr = cvLoad(File);
if(ptr && CV_IS_MATND_HDR(ptr)) m_pND = (CvMatND*)ptr;
if(ptr && CV_IS_SPARSE_MAT_HDR(ptr)) m_pSparse = (CvSparseMat*)ptr;
cvReleaseFileStorage(&fs);
}
AfterLoad();
} /* Load. */
void Load(CvFileStorage* fs, CvFileNode* node, const char* name)
{
CvFileNode* n = cvGetFileNodeByName(fs,node,name);
void* ptr = n?cvRead(fs,n):NULL;
if(ptr)
{
if(m_pSparse) cvReleaseSparseMat(&m_pSparse);
if(m_pND) cvReleaseMatND(&m_pND);
m_Volume = 0;
m_Max = 0;
if(CV_IS_MATND_HDR(ptr)) m_pND = (CvMatND*)ptr;
if(CV_IS_SPARSE_MAT_HDR(ptr)) m_pSparse = (CvSparseMat*)ptr;
}
else
{
printf("WARNING!!! Can't load %s matrix\n",name);
}
AfterLoad();
} /* Load. */
void Load(CvFileStorage* fs, CvFileNode* node, const char* name)
{
CvFileNode* n = cvGetFileNodeByName(fs,node,name);
void* ptr = n?cvRead(fs,n):NULL;
if(ptr)
{
if(m_pSparse) cvReleaseSparseMat(&m_pSparse);
if(m_pND) cvReleaseMatND(&m_pND);
m_Volume = 0;
m_Max = 0;
if(CV_IS_MATND_HDR(ptr)) m_pND = (CvMatND*)ptr;
if(CV_IS_SPARSE_MAT_HDR(ptr)) m_pSparse = (CvSparseMat*)ptr;
}
else
{
printf("WARNING!!! Can't load %s matrix\n",name);
}
AfterLoad();
} /* Load. */
void AfterLoad()
{
m_Volume = 0;
m_Max = 0;
if(m_pSparse)
{ /* Calculate Volume of loaded hist: */
CvSparseMatIterator mat_iterator;
CvSparseNode* node = cvInitSparseMatIterator( m_pSparse, &mat_iterator );
void AfterLoad()
{
m_Volume = 0;
m_Max = 0;
if(m_pSparse)
{ /* Calculate Volume of loaded hist: */
CvSparseMatIterator mat_iterator;
CvSparseNode* node = cvInitSparseMatIterator( m_pSparse, &mat_iterator );
for( ; node != 0; node = cvGetNextSparseNode( &mat_iterator ))
{
int val = *(int*)CV_NODE_VAL( m_pSparse, node ); /* get value of the element
for( ; node != 0; node = cvGetNextSparseNode( &mat_iterator ))
{
int val = *(int*)CV_NODE_VAL( m_pSparse, node ); /* get value of the element
(assume that the type is CV_32SC1) */
m_Volume += val;
if(m_Max < val)m_Max = val;
}
} /* Calculate Volume of loaded hist. */
m_Volume += val;
if(m_Max < val)m_Max = val;
}
} /* Calculate Volume of loaded hist. */
if(m_pND)
{ /* Calculate Volume of loaded hist: */
CvMat mat;
double max_val;
double vol;
cvGetMat( m_pND, &mat, NULL, 1 );
if(m_pND)
{ /* Calculate Volume of loaded hist: */
CvMat mat;
double max_val;
double vol;
cvGetMat( m_pND, &mat, NULL, 1 );
vol = cvSum(&mat).val[0];
m_Volume = cvRound(vol);
cvMinMaxLoc( &mat, NULL, &max_val);
m_Max = cvRound(max_val);
/* MUST BE WRITTEN LATER */
} /* Calculate Volume of loaded hist. */
} /* AfterLoad. */
vol = cvSum(&mat).val[0];
m_Volume = cvRound(vol);
cvMinMaxLoc( &mat, NULL, &max_val);
m_Max = cvRound(max_val);
/* MUST BE WRITTEN LATER */
} /* Calculate Volume of loaded hist. */
} /* AfterLoad. */
int* GetPtr(int* indx)
{
if(m_pSparse) return (int*)cvPtrND( m_pSparse, indx, NULL, 1, NULL);
if(m_pND) return (int*)cvPtrND( m_pND, indx, NULL, 1, NULL);
return NULL;
} /* GetPtr. */
int* GetPtr(int* indx)
{
if(m_pSparse) return (int*)cvPtrND( m_pSparse, indx, NULL, 1, NULL);
if(m_pND) return (int*)cvPtrND( m_pND, indx, NULL, 1, NULL);
return NULL;
} /* GetPtr. */
int GetVal(int* indx)
{
int* p = GetPtr(indx);
if(p)return p[0];
return -1;
} /* GetVal. */
int GetVal(int* indx)
{
int* p = GetPtr(indx);
if(p)return p[0];
return -1;
} /* GetVal. */
int Add(int* indx, int val)
{
int NewVal;
int* pVal = GetPtr(indx);
if(pVal == NULL) return -1;
pVal[0] += val;
NewVal = pVal[0];
m_Volume += val;
if(m_Max < NewVal)m_Max = NewVal;
return NewVal;
} /* Add. */
int Add(int* indx, int val)
{
int NewVal;
int* pVal = GetPtr(indx);
if(pVal == NULL) return -1;
pVal[0] += val;
NewVal = pVal[0];
m_Volume += val;
if(m_Max < NewVal)m_Max = NewVal;
return NewVal;
} /* Add. */
void Add(DefMat* pMatAdd)
{
int* pIDXS = NULL;
int Val = 0;
for(Val = pMatAdd->GetNext(&pIDXS, 1 );pIDXS;Val=pMatAdd->GetNext(&pIDXS, 0 ))
{
Add(pIDXS,Val);
}
} /* Add. */
void Add(DefMat* pMatAdd)
{
int* pIDXS = NULL;
int Val = 0;
for(Val = pMatAdd->GetNext(&pIDXS, 1 );pIDXS;Val=pMatAdd->GetNext(&pIDXS, 0 ))
{
Add(pIDXS,Val);
}
} /* Add. */
int SetMax(int* indx, int val)
{
int NewVal;
int* pVal = GetPtr(indx);
if(pVal == NULL) return -1;
if(val > pVal[0])
{
m_Volume += val-pVal[0];
pVal[0] = val;
}
NewVal = pVal[0];
if(m_Max < NewVal)m_Max = NewVal;
return NewVal;
} /* Add. */
int SetMax(int* indx, int val)
{
int NewVal;
int* pVal = GetPtr(indx);
if(pVal == NULL) return -1;
if(val > pVal[0])
{
m_Volume += val-pVal[0];
pVal[0] = val;
}
NewVal = pVal[0];
if(m_Max < NewVal)m_Max = NewVal;
return NewVal;
} /* Add. */
int GetNext(int** pIDXS, int init = 0)
{
int Val = 0;
pIDXS[0] = NULL;
if(m_pSparse)
{
m_pSparseNode = (init || m_pSparseNode==NULL)?
cvInitSparseMatIterator( m_pSparse, &m_SparseIterator ):
cvGetNextSparseNode( &m_SparseIterator );
int GetNext(int** pIDXS, int init = 0)
{
int Val = 0;
pIDXS[0] = NULL;
if(m_pSparse)
{
m_pSparseNode = (init || m_pSparseNode==NULL)?
cvInitSparseMatIterator( m_pSparse, &m_SparseIterator ):
cvGetNextSparseNode( &m_SparseIterator );
if(m_pSparseNode)
{
int* pVal = (int*)CV_NODE_VAL( m_pSparse, m_pSparseNode );
if(pVal)Val = pVal[0];
pIDXS[0] = CV_NODE_IDX( m_pSparse, m_pSparseNode );
}
}/* Sparse matrix. */
if(m_pSparseNode)
{
int* pVal = (int*)CV_NODE_VAL( m_pSparse, m_pSparseNode );
if(pVal)Val = pVal[0];
pIDXS[0] = CV_NODE_IDX( m_pSparse, m_pSparseNode );
}
}/* Sparse matrix. */
if(m_pND)
{
int i;
if(init)
{
for(i=0;i<m_Dim;++i)
{
m_IDXs[i] = cvGetDimSize( m_pND, i )-1;
}
pIDXS[0] = m_IDXs;
Val = GetVal(m_IDXs);
}
else
{
for(i=0;i<m_Dim;++i)
{
if((m_IDXs[i]--)>0)
break;
m_IDXs[i] = cvGetDimSize( m_pND, i )-1;
}
if(i==m_Dim)
{
pIDXS[0] = NULL;
}
else
{
pIDXS[0] = m_IDXs;
Val = GetVal(m_IDXs);
}
if(m_pND)
{
int i;
if(init)
{
for(i=0;i<m_Dim;++i)
{
m_IDXs[i] = cvGetDimSize( m_pND, i )-1;
}
pIDXS[0] = m_IDXs;
Val = GetVal(m_IDXs);
}
else
{
for(i=0;i<m_Dim;++i)
{
if((m_IDXs[i]--)>0)
break;
m_IDXs[i] = cvGetDimSize( m_pND, i )-1;
}
if(i==m_Dim)
{
pIDXS[0] = NULL;
}
else
{
pIDXS[0] = m_IDXs;
Val = GetVal(m_IDXs);
}
} /* Get next ND. */
} /* Get next ND. */
} /* Sparse matrix. */
} /* Sparse matrix. */
return Val;
return Val;
}; /* GetNext. */
}; /* GetNext. */
};
#define FV_NUM 10
#define FV_SIZE 10
typedef struct DefTrackFG
{
CvBlob blob;
// CvBlobTrackFVGen* pFVGen;
int LastFrame;
float state;
DefMat* pHist;
CvBlob blob;
// CvBlobTrackFVGen* pFVGen;
int LastFrame;
float state;
DefMat* pHist;
} DefTrackFG;
class CvBlobTrackAnalysisHist : public CvBlobTrackAnalysis
{
/*---------------- Internal functions: --------------------*/
/*---------------- Internal functions: --------------------*/
private:
int m_BinNumParam;
int m_SmoothRadius;
const char* m_SmoothKernel;
float m_AbnormalThreshold;
int m_TrackNum;
int m_Frame;
int m_BinNum;
char m_DataFileName[1024];
int m_Dim;
int* m_Sizes;
DefMat m_HistMat;
int m_HistVolumeSaved;
int* m_pFVi;
int* m_pFViVar;
int* m_pFViVarRes;
CvBlobSeq m_TrackFGList;
//CvBlobTrackFVGen* (*m_CreateFVGen)();
CvBlobTrackFVGen* m_pFVGen;
void SaveHist()
{
if(m_DataFileName[0])
{
m_HistMat.Save(m_DataFileName);
m_HistVolumeSaved = m_HistMat.m_Volume;
}
};
void LoadHist()
{
if(m_DataFileName[0])m_HistMat.Load(m_DataFileName);
m_HistVolumeSaved = m_HistMat.m_Volume;
}
void AllocData()
{ /* AllocData: */
m_pFVi = (int*)cvAlloc(sizeof(int)*m_Dim);
m_pFViVar = (int*)cvAlloc(sizeof(int)*m_Dim);
m_pFViVarRes = (int*)cvAlloc(sizeof(int)*m_Dim);
m_Sizes = (int*)cvAlloc(sizeof(int)*m_Dim);
int m_BinNumParam;
int m_SmoothRadius;
const char* m_SmoothKernel;
float m_AbnormalThreshold;
int m_TrackNum;
int m_Frame;
int m_BinNum;
char m_DataFileName[1024];
int m_Dim;
int* m_Sizes;
DefMat m_HistMat;
int m_HistVolumeSaved;
int* m_pFVi;
int* m_pFViVar;
int* m_pFViVarRes;
CvBlobSeq m_TrackFGList;
//CvBlobTrackFVGen* (*m_CreateFVGen)();
CvBlobTrackFVGen* m_pFVGen;
void SaveHist()
{
if(m_DataFileName[0])
{
m_HistMat.Save(m_DataFileName);
m_HistVolumeSaved = m_HistMat.m_Volume;
}
};
void LoadHist()
{
if(m_DataFileName[0])m_HistMat.Load(m_DataFileName);
m_HistVolumeSaved = m_HistMat.m_Volume;
}
void AllocData()
{ /* AllocData: */
m_pFVi = (int*)cvAlloc(sizeof(int)*m_Dim);
m_pFViVar = (int*)cvAlloc(sizeof(int)*m_Dim);
m_pFViVarRes = (int*)cvAlloc(sizeof(int)*m_Dim);
m_Sizes = (int*)cvAlloc(sizeof(int)*m_Dim);
{ /* Create init sparce matrix: */
int i;
for(i=0;i<m_Dim;++i)m_Sizes[i] = m_BinNum;
m_HistMat.Realloc(m_Dim,m_Sizes,SPARSE);
m_HistVolumeSaved = 0;
} /* Create init sparce matrix. */
} /* AllocData. */
{ /* Create init sparce matrix: */
int i;
for(i=0;i<m_Dim;++i)m_Sizes[i] = m_BinNum;
m_HistMat.Realloc(m_Dim,m_Sizes,SPARSE);
m_HistVolumeSaved = 0;
} /* Create init sparce matrix. */
} /* AllocData. */
void FreeData()
{ /* FreeData. */
int i;
for(i=m_TrackFGList.GetBlobNum();i>0;--i)
{
//DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlob(i-1);
// pF->pFVGen->Release();
m_TrackFGList.DelBlob(i-1);
}
cvFree(&m_pFVi);
cvFree(&m_pFViVar);
cvFree(&m_pFViVarRes);
cvFree(&m_Sizes);
} /* FreeData. */
void FreeData()
{ /* FreeData. */
int i;
for(i=m_TrackFGList.GetBlobNum();i>0;--i)
{
//DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlob(i-1);
// pF->pFVGen->Release();
m_TrackFGList.DelBlob(i-1);
}
cvFree(&m_pFVi);
cvFree(&m_pFViVar);
cvFree(&m_pFViVarRes);
cvFree(&m_Sizes);
} /* FreeData. */
virtual void ParamUpdate()
{
if(m_BinNum != m_BinNumParam)
{
FreeData();
m_BinNum = m_BinNumParam;
AllocData();
}
}
virtual void ParamUpdate()
{
if(m_BinNum != m_BinNumParam)
{
FreeData();
m_BinNum = m_BinNumParam;
AllocData();
}
}
public:
CvBlobTrackAnalysisHist(CvBlobTrackFVGen* (*createFVGen)()):m_TrackFGList(sizeof(DefTrackFG))
{
m_pFVGen = createFVGen();
m_Dim = m_pFVGen->GetFVSize();
m_Frame = 0;
m_pFVi = 0;
m_TrackNum = 0;
m_BinNum = 32;
m_DataFileName[0] = 0;
CvBlobTrackAnalysisHist(CvBlobTrackFVGen* (*createFVGen)()):m_TrackFGList(sizeof(DefTrackFG))
{
m_pFVGen = createFVGen();
m_Dim = m_pFVGen->GetFVSize();
m_Frame = 0;
m_pFVi = 0;
m_TrackNum = 0;
m_BinNum = 32;
m_DataFileName[0] = 0;
m_AbnormalThreshold = 0.02f;
AddParam("AbnormalThreshold",&m_AbnormalThreshold);
CommentParam("AbnormalThreshold","If trajectory histogram value is lesst then <AbnormalThreshold*DataBaseTrackNum> then trajectory is abnormal");
m_AbnormalThreshold = 0.02f;
AddParam("AbnormalThreshold",&m_AbnormalThreshold);
CommentParam("AbnormalThreshold","If trajectory histogram value is lesst then <AbnormalThreshold*DataBaseTrackNum> then trajectory is abnormal");
m_SmoothRadius = 1;
AddParam("SmoothRadius",&m_SmoothRadius);
CommentParam("AbnormalThreshold","Radius (in bins) for histogram smoothing");
m_SmoothRadius = 1;
AddParam("SmoothRadius",&m_SmoothRadius);
CommentParam("AbnormalThreshold","Radius (in bins) for histogram smoothing");
m_SmoothKernel = "L";
AddParam("SmoothKernel",&m_SmoothKernel);
CommentParam("SmoothKernel","L - Linear, G - Gaussian");
m_SmoothKernel = "L";
AddParam("SmoothKernel",&m_SmoothKernel);
CommentParam("SmoothKernel","L - Linear, G - Gaussian");
m_BinNumParam = m_BinNum;
AddParam("BinNum",&m_BinNumParam);
CommentParam("BinNum","Number of bin for each dimention of feature vector");
m_BinNumParam = m_BinNum;
AddParam("BinNum",&m_BinNumParam);
CommentParam("BinNum","Number of bin for each dimention of feature vector");
AllocData();
SetModuleName("Hist");
AllocData();
SetModuleName("Hist");
} /* Constructor. */
} /* Constructor. */
~CvBlobTrackAnalysisHist()
{
SaveHist();
FreeData();
m_pFVGen->Release();
} /* Destructor. */
~CvBlobTrackAnalysisHist()
{
SaveHist();
FreeData();
m_pFVGen->Release();
} /* Destructor. */
/*----------------- Interface: --------------------*/
virtual void AddBlob(CvBlob* pBlob)
{
DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(CV_BLOB_ID(pBlob));
if(pF == NULL)
{ /* create new filter */
DefTrackFG F;
F.state = 0;
F.blob = pBlob[0];
F.LastFrame = m_Frame;
// F.pFVGen = m_CreateFVGen();
F.pHist = new DefMat(m_Dim,m_Sizes,SPARSE);
m_TrackFGList.AddBlob((CvBlob*)&F);
pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(CV_BLOB_ID(pBlob));
}
/*----------------- Interface: --------------------*/
virtual void AddBlob(CvBlob* pBlob)
{
DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(CV_BLOB_ID(pBlob));
if(pF == NULL)
{ /* create new filter */
DefTrackFG F;
F.state = 0;
F.blob = pBlob[0];
F.LastFrame = m_Frame;
// F.pFVGen = m_CreateFVGen();
F.pHist = new DefMat(m_Dim,m_Sizes,SPARSE);
m_TrackFGList.AddBlob((CvBlob*)&F);
pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(CV_BLOB_ID(pBlob));
}
assert(pF);
pF->blob = pBlob[0];
pF->LastFrame = m_Frame;
m_pFVGen->AddBlob(pBlob);
};
virtual void Process(IplImage* pImg, IplImage* pFG)
{
int i;
m_pFVGen->Process(pImg, pFG);
int SK = m_SmoothKernel[0];
assert(pF);
pF->blob = pBlob[0];
pF->LastFrame = m_Frame;
m_pFVGen->AddBlob(pBlob);
};
virtual void Process(IplImage* pImg, IplImage* pFG)
{
m_pFVGen->Process(pImg, pFG);
int SK = m_SmoothKernel[0];
for(i=0; i<m_pFVGen->GetFVNum(); ++i)
{
int BlobID = 0;
float* pFV = m_pFVGen->GetFV(i,&BlobID);
float* pFVMax = m_pFVGen->GetFVMax();
float* pFVMin = m_pFVGen->GetFVMin();
DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(BlobID);
int HistVal = 1;
for(int i=0; i<m_pFVGen->GetFVNum(); ++i)
{
int BlobID = 0;
float* pFV = m_pFVGen->GetFV(i,&BlobID);
float* pFVMax = m_pFVGen->GetFVMax();
float* pFVMin = m_pFVGen->GetFVMin();
DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(BlobID);
int HistVal = 1;
if(pFV==NULL) break;
if(pFV==NULL) break;
pF->LastFrame = m_Frame;
pF->LastFrame = m_Frame;
{ /* Binarize FV: */
int j;
for(j=0; j<m_Dim; ++j)
{
int index;
float f0 = pFVMin?pFVMin[j]:0;
float f1 = pFVMax?pFVMax[j]:1;
assert(f1>f0);
index = cvRound((m_BinNum-1)*(pFV[j]-f0)/(f1-f0));
if(index<0)index=0;
if(index>=m_BinNum)index=m_BinNum-1;
m_pFVi[j] = index;
}
}
{ /* Binarize FV: */
int j;
for(j=0; j<m_Dim; ++j)
{
int index;
float f0 = pFVMin?pFVMin[j]:0;
float f1 = pFVMax?pFVMax[j]:1;
assert(f1>f0);
index = cvRound((m_BinNum-1)*(pFV[j]-f0)/(f1-f0));
if(index<0)index=0;
if(index>=m_BinNum)index=m_BinNum-1;
m_pFVi[j] = index;
}
}
HistVal = m_HistMat.GetVal(m_pFVi);/* get bin value*/
pF->state = 0;
{ /* Calculate state: */
float T = m_HistMat.m_Max*m_AbnormalThreshold; /* calc threshold */
HistVal = m_HistMat.GetVal(m_pFVi);/* get bin value*/
pF->state = 0;
{ /* Calculate state: */
float T = m_HistMat.m_Max*m_AbnormalThreshold; /* calc threshold */
if(m_TrackNum>0) T = 256.0f * m_TrackNum*m_AbnormalThreshold;
if(T>0)
{
pF->state = (T - HistVal)/(T*0.2f) + 0.5f;
}
if(pF->state<0)pF->state=0;
if(pF->state>1)pF->state=1;
}
if(m_TrackNum>0) T = 256.0f * m_TrackNum*m_AbnormalThreshold;
if(T>0)
{
pF->state = (T - HistVal)/(T*0.2f) + 0.5f;
}
if(pF->state<0)pF->state=0;
if(pF->state>1)pF->state=1;
}
{ /* If it is a new FV then add it to trajectory histogram: */
int i,flag = 1;
int r = m_SmoothRadius;
{ /* If it is a new FV then add it to trajectory histogram: */
int flag = 1;
int r = m_SmoothRadius;
// printf("BLob %3d NEW FV [", CV_BLOB_ID(pF));
// for(i=0;i<m_Dim;++i) printf("%d,", m_pFVi[i]);
// printf("]");
// printf("BLob %3d NEW FV [", CV_BLOB_ID(pF));
// for(i=0;i<m_Dim;++i) printf("%d,", m_pFVi[i]);
// printf("]");
for(i=0; i<m_Dim; ++i)
{
m_pFViVar[i]=-r;
}
for(int k=0; k<m_Dim; ++k)
{
m_pFViVar[k]=-r;
}
while(flag)
{
float dist = 0;
int HistAdd = 0;
int i;
int good = 1;
for(i=0; i<m_Dim; ++i)
{
m_pFViVarRes[i] = m_pFVi[i]+m_pFViVar[i];
if(m_pFViVarRes[i]<0) good= 0;
if(m_pFViVarRes[i]>=m_BinNum) good= 0;
dist += m_pFViVar[i]*m_pFViVar[i];
}/* Calculate next dimension. */
while(flag)
{
float dist = 0;
int HistAdd = 0;
int good = 1;
for(int k=0; k<m_Dim; ++k)
{
m_pFViVarRes[k] = m_pFVi[k]+m_pFViVar[k];
if(m_pFViVarRes[k]<0) good= 0;
if(m_pFViVarRes[k]>=m_BinNum) good= 0;
dist += m_pFViVar[k]*m_pFViVar[k];
}/* Calculate next dimension. */
if(SK=='G' || SK=='g')
{
double dist2 = dist/(r*r);
HistAdd = cvRound(256*exp(-dist2)); /* Hist Add for (dist=1) = 25.6*/
}
else if(SK=='L' || SK=='l')
{
dist = (float)(sqrt(dist)/(r+1));
HistAdd = cvRound(256*(1-dist));
}
else
{
HistAdd = 255; /* Flat smoothing. */
}
if(SK=='G' || SK=='g')
{
double dist2 = dist/(r*r);
HistAdd = cvRound(256*exp(-dist2)); /* Hist Add for (dist=1) = 25.6*/
}
else if(SK=='L' || SK=='l')
{
dist = (float)(sqrt(dist)/(r+1));
HistAdd = cvRound(256*(1-dist));
}
else
{
HistAdd = 255; /* Flat smoothing. */
}
if(good && HistAdd>0)
{ /* Update histogram: */
assert(pF->pHist);
pF->pHist->SetMax(m_pFViVarRes, HistAdd);
} /* Update histogram. */
if(good && HistAdd>0)
{ /* Update histogram: */
assert(pF->pHist);
pF->pHist->SetMax(m_pFViVarRes, HistAdd);
} /* Update histogram. */
for(i=0; i<m_Dim; ++i)
{ /* Next config: */
if((m_pFViVar[i]++) < r)
break;
m_pFViVar[i] = -r;
} /* Increase next dimension variable. */
if(i==m_Dim)break;
} /* Next variation. */
} /* If new FV. */
} /* Next FV. */
int idx = 0;
for( ; idx<m_Dim; ++idx)
{ /* Next config: */
if((m_pFViVar[idx]++) < r)
break;
m_pFViVar[idx] = -r;
} /* Increase next dimension variable. */
if(idx==m_Dim)break;
} /* Next variation. */
} /* If new FV. */
} /* Next FV. */
{ /* Check all blobs on list: */
int i;
for(i=m_TrackFGList.GetBlobNum(); i>0; --i)
{ /* Add histogram and delete blob from list: */
DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlob(i-1);
if(pF->LastFrame+3 < m_Frame && pF->pHist)
{
m_HistMat.Add(pF->pHist);
delete pF->pHist;
m_TrackNum++;
m_TrackFGList.DelBlob(i-1);
}
}/* next blob */
}
{ /* Check all blobs on list: */
int i;
for(i=m_TrackFGList.GetBlobNum(); i>0; --i)
{ /* Add histogram and delete blob from list: */
DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlob(i-1);
if(pF->LastFrame+3 < m_Frame && pF->pHist)
{
m_HistMat.Add(pF->pHist);
delete pF->pHist;
m_TrackNum++;
m_TrackFGList.DelBlob(i-1);
}
}/* next blob */
}
m_Frame++;
m_Frame++;
if(m_Wnd)
{ /* Debug output: */
int* idxs = NULL;
int Val = 0;
IplImage* pI = cvCloneImage(pImg);
if(m_Wnd)
{ /* Debug output: */
int* idxs = NULL;
int Val = 0;
IplImage* pI = cvCloneImage(pImg);
cvZero(pI);
cvZero(pI);
for(Val = m_HistMat.GetNext(&idxs,1); idxs; Val=m_HistMat.GetNext(&idxs,0))
{ /* Draw all elements: */
float vf;
int x,y;
for(Val = m_HistMat.GetNext(&idxs,1); idxs; Val=m_HistMat.GetNext(&idxs,0))
{ /* Draw all elements: */
if(!idxs) break;
if(Val == 0) continue;
if(!idxs) break;
if(Val == 0) continue;
float vf = (float)Val/(m_HistMat.m_Max?m_HistMat.m_Max:1);
int x = cvRound((float)(pI->width-1)*(float)idxs[0] / (float)m_BinNum);
int y = cvRound((float)(pI->height-1)*(float)idxs[1] / (float)m_BinNum);
vf = (float)Val/(m_HistMat.m_Max?m_HistMat.m_Max:1);
x = cvRound((float)(pI->width-1)*(float)idxs[0] / (float)m_BinNum);
y = cvRound((float)(pI->height-1)*(float)idxs[1] / (float)m_BinNum);
cvCircle(pI, cvPoint(x,y), cvRound(vf*pI->height/(m_BinNum*2)),CV_RGB(255,0,0),CV_FILLED);
if(m_Dim > 3)
{
int dx = -2*(idxs[2]-m_BinNum/2);
int dy = -2*(idxs[3]-m_BinNum/2);
cvLine(pI,cvPoint(x,y),cvPoint(x+dx,y+dy),CV_RGB(0,cvRound(vf*255),1));
}
if( m_Dim==4 &&
m_pFVGen->GetFVMax()[0]==m_pFVGen->GetFVMax()[2] &&
m_pFVGen->GetFVMax()[1]==m_pFVGen->GetFVMax()[3])
{
int x1 = cvRound((float)(pI->width-1)*(float)idxs[2] / (float)m_BinNum);
int y1 = cvRound((float)(pI->height-1)*(float)idxs[3] / (float)m_BinNum);
cvCircle(pI, cvPoint(x1,y1), cvRound(vf*pI->height/(m_BinNum*2)),CV_RGB(0,0,255),CV_FILLED);
}
} /* Draw all elements. */
cvCircle(pI, cvPoint(x,y), cvRound(vf*pI->height/(m_BinNum*2)),CV_RGB(255,0,0),CV_FILLED);
if(m_Dim > 3)
{
int dx = -2*(idxs[2]-m_BinNum/2);
int dy = -2*(idxs[3]-m_BinNum/2);
cvLine(pI,cvPoint(x,y),cvPoint(x+dx,y+dy),CV_RGB(0,cvRound(vf*255),1));
}
if( m_Dim==4 &&
m_pFVGen->GetFVMax()[0]==m_pFVGen->GetFVMax()[2] &&
m_pFVGen->GetFVMax()[1]==m_pFVGen->GetFVMax()[3])
{
int x = cvRound((float)(pI->width-1)*(float)idxs[2] / (float)m_BinNum);
int y = cvRound((float)(pI->height-1)*(float)idxs[3] / (float)m_BinNum);
cvCircle(pI, cvPoint(x,y), cvRound(vf*pI->height/(m_BinNum*2)),CV_RGB(0,0,255),CV_FILLED);
}
} /* Draw all elements. */
for(int i=m_TrackFGList.GetBlobNum();i>0;--i)
{
DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlob(i-1);
DefMat* pHist = pF?pF->pHist:NULL;
for(i=m_TrackFGList.GetBlobNum();i>0;--i)
{
DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlob(i-1);
DefMat* pHist = pF?pF->pHist:NULL;
if(pHist==NULL) continue;
if(pHist==NULL) continue;
for(Val = pHist->GetNext(&idxs,1);idxs;Val=pHist->GetNext(&idxs,0))
{ /* Draw all elements: */
float vf;
int x,y;
for(Val = pHist->GetNext(&idxs,1);idxs;Val=pHist->GetNext(&idxs,0))
{ /* Draw all elements: */
float vf;
int x,y;
if(!idxs) break;
if(Val == 0) continue;
if(!idxs) break;
if(Val == 0) continue;
vf = (float)Val/(pHist->m_Max?pHist->m_Max:1);
x = cvRound((float)(pI->width-1)*(float)idxs[0] / (float)m_BinNum);
y = cvRound((float)(pI->height-1)*(float)idxs[1] / (float)m_BinNum);
vf = (float)Val/(pHist->m_Max?pHist->m_Max:1);
x = cvRound((float)(pI->width-1)*(float)idxs[0] / (float)m_BinNum);
y = cvRound((float)(pI->height-1)*(float)idxs[1] / (float)m_BinNum);
cvCircle(pI, cvPoint(x,y), cvRound(2*vf),CV_RGB(0,0,cvRound(255*vf)),CV_FILLED);
if(m_Dim > 3)
{
int dx = -2*(idxs[2]-m_BinNum/2);
int dy = -2*(idxs[3]-m_BinNum/2);
cvLine(pI,cvPoint(x,y),cvPoint(x+dx,y+dy),CV_RGB(0,0,255));
}
if( m_Dim==4 &&
m_pFVGen->GetFVMax()[0]==m_pFVGen->GetFVMax()[2] &&
m_pFVGen->GetFVMax()[1]==m_pFVGen->GetFVMax()[3])
{ /* if SS feature vector */
int x1 = cvRound((float)(pI->width-1)*(float)idxs[2] / (float)m_BinNum);
int y1 = cvRound((float)(pI->height-1)*(float)idxs[3] / (float)m_BinNum);
cvCircle(pI, cvPoint(x1,y1), cvRound(vf*pI->height/(m_BinNum*2)),CV_RGB(0,0,255),CV_FILLED);
}
} /* Draw all elements. */
} /* Next track. */
cvCircle(pI, cvPoint(x,y), cvRound(2*vf),CV_RGB(0,0,cvRound(255*vf)),CV_FILLED);
if(m_Dim > 3)
{
int dx = -2*(idxs[2]-m_BinNum/2);
int dy = -2*(idxs[3]-m_BinNum/2);
cvLine(pI,cvPoint(x,y),cvPoint(x+dx,y+dy),CV_RGB(0,0,255));
}
if( m_Dim==4 &&
m_pFVGen->GetFVMax()[0]==m_pFVGen->GetFVMax()[2] &&
m_pFVGen->GetFVMax()[1]==m_pFVGen->GetFVMax()[3])
{ /* if SS feature vector */
int x = cvRound((float)(pI->width-1)*(float)idxs[2] / (float)m_BinNum);
int y = cvRound((float)(pI->height-1)*(float)idxs[3] / (float)m_BinNum);
cvCircle(pI, cvPoint(x,y), cvRound(vf*pI->height/(m_BinNum*2)),CV_RGB(0,0,255),CV_FILLED);
}
} /* Draw all elements. */
} /* Next track. */
//cvNamedWindow("Hist",0);
//cvShowImage("Hist", pI);
cvReleaseImage(&pI);
}
};
//cvNamedWindow("Hist",0);
//cvShowImage("Hist", pI);
cvReleaseImage(&pI);
}
};
float GetState(int BlobID)
{
DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(BlobID);
return pF?pF->state:0.0f;
};
float GetState(int BlobID)
{
DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(BlobID);
return pF?pF->state:0.0f;
};
/* Return 0 if trajectory is normal;
/* Return 0 if trajectory is normal;
rreturn >0 if trajectory abnormal. */
virtual const char* GetStateDesc(int BlobID)
{
if(GetState(BlobID)>0.5) return "abnormal";
return NULL;
}
virtual const char* GetStateDesc(int BlobID)
{
if(GetState(BlobID)>0.5) return "abnormal";
return NULL;
}
virtual void SetFileName(char* DataBaseName)
{
if(m_HistMat.m_Volume!=m_HistVolumeSaved)SaveHist();
m_DataFileName[0] = m_DataFileName[1000] = 0;
virtual void SetFileName(char* DataBaseName)
{
if(m_HistMat.m_Volume!=m_HistVolumeSaved)SaveHist();
m_DataFileName[0] = m_DataFileName[1000] = 0;
if(DataBaseName)
{
strncpy(m_DataFileName,DataBaseName,1000);
strcat(m_DataFileName, ".yml");
}
LoadHist();
};
if(DataBaseName)
{
strncpy(m_DataFileName,DataBaseName,1000);
strcat(m_DataFileName, ".yml");
}
LoadHist();
};
virtual void SaveState(CvFileStorage* fs)
{
int b, bN = m_TrackFGList.GetBlobNum();
cvWriteInt(fs,"BlobNum",bN);
cvStartWriteStruct(fs,"BlobList",CV_NODE_SEQ);
virtual void SaveState(CvFileStorage* fs)
{
int b, bN = m_TrackFGList.GetBlobNum();
cvWriteInt(fs,"BlobNum",bN);
cvStartWriteStruct(fs,"BlobList",CV_NODE_SEQ);
for(b=0; b<bN; ++b)
{
DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlob(b);
cvStartWriteStruct(fs,NULL,CV_NODE_MAP);
cvWriteStruct(fs,"Blob", &(pF->blob), "ffffi");
cvWriteInt(fs,"LastFrame",pF->LastFrame);
cvWriteReal(fs,"State",pF->state);
pF->pHist->Save(fs, "Hist");
cvEndWriteStruct(fs);
}
cvEndWriteStruct(fs);
m_HistMat.Save(fs, "Hist");
};
for(b=0; b<bN; ++b)
{
DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlob(b);
cvStartWriteStruct(fs,NULL,CV_NODE_MAP);
cvWriteStruct(fs,"Blob", &(pF->blob), "ffffi");
cvWriteInt(fs,"LastFrame",pF->LastFrame);
cvWriteReal(fs,"State",pF->state);
pF->pHist->Save(fs, "Hist");
cvEndWriteStruct(fs);
}
cvEndWriteStruct(fs);
m_HistMat.Save(fs, "Hist");
};
virtual void LoadState(CvFileStorage* fs, CvFileNode* node)
{
CvFileNode* pBLN = cvGetFileNodeByName(fs,node,"BlobList");
virtual void LoadState(CvFileStorage* fs, CvFileNode* node)
{
CvFileNode* pBLN = cvGetFileNodeByName(fs,node,"BlobList");
if(pBLN && CV_NODE_IS_SEQ(pBLN->tag))
{
int b, bN = pBLN->data.seq->total;
for(b=0; b<bN; ++b)
{
DefTrackFG* pF = NULL;
CvBlob Blob;
CvFileNode* pBN = (CvFileNode*)cvGetSeqElem(pBLN->data.seq,b);
if(pBLN && CV_NODE_IS_SEQ(pBLN->tag))
{
int b, bN = pBLN->data.seq->total;
for(b=0; b<bN; ++b)
{
DefTrackFG* pF = NULL;
CvBlob Blob;
CvFileNode* pBN = (CvFileNode*)cvGetSeqElem(pBLN->data.seq,b);
assert(pBN);
cvReadStructByName(fs, pBN, "Blob", &Blob, "ffffi");
AddBlob(&Blob);
pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(Blob.ID);
if(pF==NULL) continue;
assert(pF);
pF->state = (float)cvReadIntByName(fs,pBN,"State",cvRound(pF->state));
assert(pF->pHist);
pF->pHist->Load(fs,pBN,"Hist");
}
}
assert(pBN);
cvReadStructByName(fs, pBN, "Blob", &Blob, "ffffi");
AddBlob(&Blob);
pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(Blob.ID);
if(pF==NULL) continue;
assert(pF);
pF->state = (float)cvReadIntByName(fs,pBN,"State",cvRound(pF->state));
assert(pF->pHist);
pF->pHist->Load(fs,pBN,"Hist");
}
}
m_HistMat.Load(fs, node, "Hist");
}; /* LoadState */
m_HistMat.Load(fs, node, "Hist");
}; /* LoadState */
virtual void Release(){ delete this; };
virtual void Release(){ delete this; };
};
@@ -1127,386 +1119,386 @@ CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysisHistSS()
typedef struct DefTrackSVM
{
CvBlob blob;
// CvBlobTrackFVGen* pFVGen;
int LastFrame;
float state;
CvBlob BlobLast;
CvSeq* pFVSeq;
CvMemStorage* pMem;
CvBlob blob;
// CvBlobTrackFVGen* pFVGen;
int LastFrame;
float state;
CvBlob BlobLast;
CvSeq* pFVSeq;
CvMemStorage* pMem;
} DefTrackSVM;
class CvBlobTrackAnalysisSVM : public CvBlobTrackAnalysis
{
/*---------------- Internal functions: --------------------*/
/*---------------- Internal functions: --------------------*/
private:
CvMemStorage* m_pMem;
int m_TrackNum;
int m_Frame;
char m_DataFileName[1024];
int m_Dim;
float* m_pFV;
//CvStatModel* m_pStatModel;
void* m_pStatModel;
CvBlobSeq m_Tracks;
CvMat* m_pTrainData;
int m_LastTrainDataSize;
// CvBlobTrackFVGen* (*m_CreateFVGen)();
CvBlobTrackFVGen* m_pFVGen;
float m_NU;
float m_RBFWidth;
IplImage* m_pStatImg; /* for debug purpose */
CvSize m_ImgSize;
void RetrainStatModel()
{
///////// !!!!! TODO !!!!! Repair /////////////
CvMemStorage* m_pMem;
int m_TrackNum;
int m_Frame;
char m_DataFileName[1024];
int m_Dim;
float* m_pFV;
//CvStatModel* m_pStatModel;
void* m_pStatModel;
CvBlobSeq m_Tracks;
CvMat* m_pTrainData;
int m_LastTrainDataSize;
// CvBlobTrackFVGen* (*m_CreateFVGen)();
CvBlobTrackFVGen* m_pFVGen;
float m_NU;
float m_RBFWidth;
IplImage* m_pStatImg; /* for debug purpose */
CvSize m_ImgSize;
void RetrainStatModel()
{
///////// !!!!! TODO !!!!! Repair /////////////
#if 0
float nu = 0;
CvSVMModelParams SVMParams = {0};
CvStatModel* pM = NULL;
float nu = 0;
CvSVMModelParams SVMParams = {0};
CvStatModel* pM = NULL;
memset(&SVMParams,0,sizeof(SVMParams));
SVMParams.svm_type = CV_SVM_ONE_CLASS;
SVMParams.kernel_type = CV_SVM_RBF;
SVMParams.gamma = 2.0/(m_RBFWidth*m_RBFWidth);
SVMParams.nu = m_NU;
SVMParams.degree = 3;
SVMParams.criteria = cvTermCriteria(CV_TERMCRIT_EPS, 100, 1e-3 );
SVMParams.C = 1;
SVMParams.p = 0.1;
memset(&SVMParams,0,sizeof(SVMParams));
SVMParams.svm_type = CV_SVM_ONE_CLASS;
SVMParams.kernel_type = CV_SVM_RBF;
SVMParams.gamma = 2.0/(m_RBFWidth*m_RBFWidth);
SVMParams.nu = m_NU;
SVMParams.degree = 3;
SVMParams.criteria = cvTermCriteria(CV_TERMCRIT_EPS, 100, 1e-3 );
SVMParams.C = 1;
SVMParams.p = 0.1;
if(m_pTrainData == NULL) return;
{
int64 TickCount = cvGetTickCount();
printf("Frame: %d\n Retrain SVM\nData Size = %d\n",m_Frame, m_pTrainData->rows);
pM = cvTrainSVM( m_pTrainData,CV_ROW_SAMPLE, NULL, (CvStatModelParams*)&SVMParams, NULL, NULL);
TickCount = cvGetTickCount() - TickCount ;
printf("SV Count = %d\n",((CvSVMModel*)pM)->sv_total);
printf("Processing Time = %.1f(ms)\n",TickCount/(1000*cvGetTickFrequency()));
if(m_pTrainData == NULL) return;
{
int64 TickCount = cvGetTickCount();
printf("Frame: %d\n Retrain SVM\nData Size = %d\n",m_Frame, m_pTrainData->rows);
pM = cvTrainSVM( m_pTrainData,CV_ROW_SAMPLE, NULL, (CvStatModelParams*)&SVMParams, NULL, NULL);
TickCount = cvGetTickCount() - TickCount ;
printf("SV Count = %d\n",((CvSVMModel*)pM)->sv_total);
printf("Processing Time = %.1f(ms)\n",TickCount/(1000*cvGetTickFrequency()));
}
if(pM==NULL) return;
if(m_pStatModel) cvReleaseStatModel(&m_pStatModel);
m_pStatModel = pM;
}
if(pM==NULL) return;
if(m_pStatModel) cvReleaseStatModel(&m_pStatModel);
m_pStatModel = pM;
if(m_pTrainData && m_Wnd)
{
float MaxVal = 0;
IplImage* pW = cvCreateImage(m_ImgSize,IPL_DEPTH_32F,1);
IplImage* pI = cvCreateImage(m_ImgSize,IPL_DEPTH_8U,1);
float* pFVVar = m_pFVGen->GetFVVar();
int i;
cvZero(pW);
if(m_pTrainData && m_Wnd)
{
float MaxVal = 0;
IplImage* pW = cvCreateImage(m_ImgSize,IPL_DEPTH_32F,1);
IplImage* pI = cvCreateImage(m_ImgSize,IPL_DEPTH_8U,1);
float* pFVVar = m_pFVGen->GetFVVar();
int i;
cvZero(pW);
for(i=0; i<m_pTrainData->rows; ++i)
{ /* Draw all elements: */
float* pFV = (float*)(m_pTrainData->data.ptr + m_pTrainData->step*i);
int x = cvRound(pFV[0]*pFVVar[0]);
int y = cvRound(pFV[1]*pFVVar[1]);
float r;
for(i=0; i<m_pTrainData->rows; ++i)
{ /* Draw all elements: */
float* pFV = (float*)(m_pTrainData->data.ptr + m_pTrainData->step*i);
int x = cvRound(pFV[0]*pFVVar[0]);
int y = cvRound(pFV[1]*pFVVar[1]);
float r;
if(x<0)x=0;
if(x>=pW->width)x=pW->width-1;
if(y<0)y=0;
if(y>=pW->height)y=pW->height-1;
if(x<0)x=0;
if(x>=pW->width)x=pW->width-1;
if(y<0)y=0;
if(y>=pW->height)y=pW->height-1;
r = ((float*)(pW->imageData + y*pW->widthStep))[x];
r++;
((float*)(pW->imageData + y*pW->widthStep))[x] = r;
r = ((float*)(pW->imageData + y*pW->widthStep))[x];
r++;
((float*)(pW->imageData + y*pW->widthStep))[x] = r;
if(r>MaxVal)MaxVal=r;
} /* Next point. */
if(r>MaxVal)MaxVal=r;
} /* Next point. */
if(MaxVal>0)cvConvertScale(pW,pI,255/MaxVal,0);
cvNamedWindow("SVMData",0);
cvShowImage("SVMData",pI);
cvSaveImage("SVMData.bmp",pI);
cvReleaseImage(&pW);
cvReleaseImage(&pI);
} /* Prepare for debug. */
if(MaxVal>0)cvConvertScale(pW,pI,255/MaxVal,0);
cvNamedWindow("SVMData",0);
cvShowImage("SVMData",pI);
cvSaveImage("SVMData.bmp",pI);
cvReleaseImage(&pW);
cvReleaseImage(&pI);
} /* Prepare for debug. */
if(m_pStatModel && m_Wnd && m_Dim == 2)
{
float* pFVVar = m_pFVGen->GetFVVar();
int x,y;
if(m_pStatImg==NULL)
{
m_pStatImg = cvCreateImage(m_ImgSize,IPL_DEPTH_8U,1);
}
cvZero(m_pStatImg);
if(m_pStatModel && m_Wnd && m_Dim == 2)
{
float* pFVVar = m_pFVGen->GetFVVar();
int x,y;
if(m_pStatImg==NULL)
{
m_pStatImg = cvCreateImage(m_ImgSize,IPL_DEPTH_8U,1);
}
cvZero(m_pStatImg);
for(y=0; y<m_pStatImg->height; y+=1) for(x=0; x<m_pStatImg->width; x+=1)
{ /* Draw all elements: */
float res;
uchar* pData = (uchar*)m_pStatImg->imageData + x + y*m_pStatImg->widthStep;
CvMat FVmat;
float xy[2] = {x/pFVVar[0],y/pFVVar[1]};
cvInitMatHeader( &FVmat, 1, 2, CV_32F, xy );
res = cvStatModelPredict( m_pStatModel, &FVmat, NULL );
pData[0]=((res>0.5)?255:0);
} /* Next point. */
for(y=0; y<m_pStatImg->height; y+=1) for(x=0; x<m_pStatImg->width; x+=1)
{ /* Draw all elements: */
float res;
uchar* pData = (uchar*)m_pStatImg->imageData + x + y*m_pStatImg->widthStep;
CvMat FVmat;
float xy[2] = {x/pFVVar[0],y/pFVVar[1]};
cvInitMatHeader( &FVmat, 1, 2, CV_32F, xy );
res = cvStatModelPredict( m_pStatModel, &FVmat, NULL );
pData[0]=((res>0.5)?255:0);
} /* Next point. */
cvNamedWindow("SVMMask",0);
cvShowImage("SVMMask",m_pStatImg);
cvSaveImage("SVMMask.bmp",m_pStatImg);
} /* Prepare for debug. */
cvNamedWindow("SVMMask",0);
cvShowImage("SVMMask",m_pStatImg);
cvSaveImage("SVMMask.bmp",m_pStatImg);
} /* Prepare for debug. */
#endif
};
void SaveStatModel()
{
if(m_DataFileName[0])
{
if(m_pTrainData)cvSave(m_DataFileName, m_pTrainData);
}
};
void LoadStatModel()
{
if(m_DataFileName[0])
{
CvMat* pTrainData = (CvMat*)cvLoad(m_DataFileName);
if(CV_IS_MAT(pTrainData) && pTrainData->width == m_Dim)
{
if(m_pTrainData) cvReleaseMat(&m_pTrainData);
m_pTrainData = pTrainData;
RetrainStatModel();
}
}
}
};
void SaveStatModel()
{
if(m_DataFileName[0])
{
if(m_pTrainData)cvSave(m_DataFileName, m_pTrainData);
}
};
void LoadStatModel()
{
if(m_DataFileName[0])
{
CvMat* pTrainData = (CvMat*)cvLoad(m_DataFileName);
if(CV_IS_MAT(pTrainData) && pTrainData->width == m_Dim)
{
if(m_pTrainData) cvReleaseMat(&m_pTrainData);
m_pTrainData = pTrainData;
RetrainStatModel();
}
}
}
public:
CvBlobTrackAnalysisSVM(CvBlobTrackFVGen* (*createFVGen)()):m_Tracks(sizeof(DefTrackSVM))
{
m_pFVGen = createFVGen();
m_Dim = m_pFVGen->GetFVSize();
m_pFV = (float*)cvAlloc(sizeof(float)*m_Dim);
m_Frame = 0;
m_TrackNum = 0;
m_pTrainData = NULL;
m_pStatModel = NULL;
m_DataFileName[0] = 0;
m_pStatImg = NULL;
m_LastTrainDataSize = 0;
CvBlobTrackAnalysisSVM(CvBlobTrackFVGen* (*createFVGen)()):m_Tracks(sizeof(DefTrackSVM))
{
m_pFVGen = createFVGen();
m_Dim = m_pFVGen->GetFVSize();
m_pFV = (float*)cvAlloc(sizeof(float)*m_Dim);
m_Frame = 0;
m_TrackNum = 0;
m_pTrainData = NULL;
m_pStatModel = NULL;
m_DataFileName[0] = 0;
m_pStatImg = NULL;
m_LastTrainDataSize = 0;
m_NU = 0.2f;
AddParam("Nu",&m_NU);
CommentParam("Nu","Parameters that tunes SVM border elastic");
m_NU = 0.2f;
AddParam("Nu",&m_NU);
CommentParam("Nu","Parameters that tunes SVM border elastic");
m_RBFWidth = 1;
AddParam("RBFWidth",&m_RBFWidth);
CommentParam("RBFWidth","Parameters that tunes RBF kernel function width.");
m_RBFWidth = 1;
AddParam("RBFWidth",&m_RBFWidth);
CommentParam("RBFWidth","Parameters that tunes RBF kernel function width.");
SetModuleName("SVM");
SetModuleName("SVM");
} /* Constructor. */
} /* Constructor. */
~CvBlobTrackAnalysisSVM()
{
int i;
SaveStatModel();
for(i=m_Tracks.GetBlobNum();i>0;--i)
{
DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlob(i-1);
if(pF->pMem) cvReleaseMemStorage(&pF->pMem);
//pF->pFVGen->Release();
}
if(m_pStatImg)cvReleaseImage(&m_pStatImg);
cvFree(&m_pFV);
} /* Destructor. */
~CvBlobTrackAnalysisSVM()
{
int i;
SaveStatModel();
for(i=m_Tracks.GetBlobNum();i>0;--i)
{
DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlob(i-1);
if(pF->pMem) cvReleaseMemStorage(&pF->pMem);
//pF->pFVGen->Release();
}
if(m_pStatImg)cvReleaseImage(&m_pStatImg);
cvFree(&m_pFV);
} /* Destructor. */
/*----------------- Interface: --------------------*/
virtual void AddBlob(CvBlob* pBlob)
{
DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlobByID(CV_BLOB_ID(pBlob));
/*----------------- Interface: --------------------*/
virtual void AddBlob(CvBlob* pBlob)
{
DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlobByID(CV_BLOB_ID(pBlob));
m_pFVGen->AddBlob(pBlob);
m_pFVGen->AddBlob(pBlob);
if(pF == NULL)
{ /* Create new record: */
DefTrackSVM F;
F.state = 0;
F.blob = pBlob[0];
F.LastFrame = m_Frame;
//F.pFVGen = m_CreateFVGen();
F.pMem = cvCreateMemStorage();
F.pFVSeq = cvCreateSeq(0,sizeof(CvSeq),sizeof(float)*m_Dim,F.pMem);
if(pF == NULL)
{ /* Create new record: */
DefTrackSVM F;
F.state = 0;
F.blob = pBlob[0];
F.LastFrame = m_Frame;
//F.pFVGen = m_CreateFVGen();
F.pMem = cvCreateMemStorage();
F.pFVSeq = cvCreateSeq(0,sizeof(CvSeq),sizeof(float)*m_Dim,F.pMem);
F.BlobLast.x = -1;
F.BlobLast.y = -1;
F.BlobLast.w = -1;
F.BlobLast.h = -1;
m_Tracks.AddBlob((CvBlob*)&F);
pF = (DefTrackSVM*)m_Tracks.GetBlobByID(CV_BLOB_ID(pBlob));
}
F.BlobLast.x = -1;
F.BlobLast.y = -1;
F.BlobLast.w = -1;
F.BlobLast.h = -1;
m_Tracks.AddBlob((CvBlob*)&F);
pF = (DefTrackSVM*)m_Tracks.GetBlobByID(CV_BLOB_ID(pBlob));
}
assert(pF);
pF->blob = pBlob[0];
pF->LastFrame = m_Frame;
};
assert(pF);
pF->blob = pBlob[0];
pF->LastFrame = m_Frame;
};
virtual void Process(IplImage* pImg, IplImage* pFG)
{
int i;
float* pFVVar = m_pFVGen->GetFVVar();
virtual void Process(IplImage* pImg, IplImage* pFG)
{
int i;
float* pFVVar = m_pFVGen->GetFVVar();
m_pFVGen->Process(pImg, pFG);
m_ImgSize = cvSize(pImg->width,pImg->height);
m_pFVGen->Process(pImg, pFG);
m_ImgSize = cvSize(pImg->width,pImg->height);
for(i=m_pFVGen->GetFVNum(); i>0; --i)
{
int BlobID = 0;
float* pFV = m_pFVGen->GetFV(i,&BlobID);
DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlobByID(BlobID);
for(i=m_pFVGen->GetFVNum(); i>0; --i)
{
int BlobID = 0;
float* pFV = m_pFVGen->GetFV(i,&BlobID);
DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlobByID(BlobID);
if(pF && pFV)
{ /* Process: */
float dx,dy;
CvMat FVmat;
if(pF && pFV)
{ /* Process: */
float dx,dy;
CvMat FVmat;
pF->state = 0;
pF->state = 0;
if(m_pStatModel)
{
int j;
for(j=0; j<m_Dim; ++j)
{
m_pFV[j] = pFV[j]/pFVVar[j];
}
if(m_pStatModel)
{
int j;
for(j=0; j<m_Dim; ++j)
{
m_pFV[j] = pFV[j]/pFVVar[j];
}
cvInitMatHeader( &FVmat, 1, m_Dim, CV_32F, m_pFV );
//pF->state = cvStatModelPredict( m_pStatModel, &FVmat, NULL )<0.5;
pF->state = 1.f;
}
cvInitMatHeader( &FVmat, 1, m_Dim, CV_32F, m_pFV );
//pF->state = cvStatModelPredict( m_pStatModel, &FVmat, NULL )<0.5;
pF->state = 1.f;
}
dx = (pF->blob.x - pF->BlobLast.x);
dy = (pF->blob.y - pF->BlobLast.y);
dx = (pF->blob.x - pF->BlobLast.x);
dy = (pF->blob.y - pF->BlobLast.y);
if(pF->BlobLast.x<0 || (dx*dx+dy*dy) >= 2*2)
{ /* Add feature vector to train data base: */
pF->BlobLast = pF->blob;
cvSeqPush(pF->pFVSeq,pFV);
}
} /* Process one blob. */
} /* Next FV. */
if(pF->BlobLast.x<0 || (dx*dx+dy*dy) >= 2*2)
{ /* Add feature vector to train data base: */
pF->BlobLast = pF->blob;
cvSeqPush(pF->pFVSeq,pFV);
}
} /* Process one blob. */
} /* Next FV. */
for(i=m_Tracks.GetBlobNum(); i>0; --i)
{ /* Check each blob record: */
DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlob(i-1);
for(i=m_Tracks.GetBlobNum(); i>0; --i)
{ /* Check each blob record: */
DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlob(i-1);
if(pF->LastFrame+3 < m_Frame )
{ /* Retrain stat model and delete blob filter: */
int mult = 1+m_Dim;
int old_height = m_pTrainData?m_pTrainData->height:0;
int height = old_height + pF->pFVSeq->total*mult;
CvMat* pTrainData = cvCreateMat(height, m_Dim, CV_32F);
int j;
if(m_pTrainData && pTrainData)
{ /* Create new train data matrix: */
int h = pTrainData->height;
pTrainData->height = MIN(pTrainData->height, m_pTrainData->height);
cvCopy(m_pTrainData,pTrainData);
pTrainData->height = h;
}
if(pF->LastFrame+3 < m_Frame )
{ /* Retrain stat model and delete blob filter: */
int mult = 1+m_Dim;
int old_height = m_pTrainData?m_pTrainData->height:0;
int height = old_height + pF->pFVSeq->total*mult;
CvMat* pTrainData = cvCreateMat(height, m_Dim, CV_32F);
int j;
if(m_pTrainData && pTrainData)
{ /* Create new train data matrix: */
int h = pTrainData->height;
pTrainData->height = MIN(pTrainData->height, m_pTrainData->height);
cvCopy(m_pTrainData,pTrainData);
pTrainData->height = h;
}
for(j=0; j<pF->pFVSeq->total; ++j)
{ /* Copy new data to train data: */
float* pFVVar = m_pFVGen->GetFVVar();
float* pFV = (float*)cvGetSeqElem(pF->pFVSeq,j);
int k;
for(j=0; j<pF->pFVSeq->total; ++j)
{ /* Copy new data to train data: */
float* pFVvar = m_pFVGen->GetFVVar();
float* pFV = (float*)cvGetSeqElem(pF->pFVSeq,j);
int k;
for(k=0; k<mult; ++k)
{
int t;
float* pTD = (float*)CV_MAT_ELEM_PTR( pTrainData[0], old_height+j*mult+k, 0);
memcpy(pTD,pFV,sizeof(float)*m_Dim);
for(k=0; k<mult; ++k)
{
int t;
float* pTD = (float*)CV_MAT_ELEM_PTR( pTrainData[0], old_height+j*mult+k, 0);
memcpy(pTD,pFV,sizeof(float)*m_Dim);
if(pFVVar)for(t=0;t<m_Dim;++t)
{ /* Scale FV: */
pTD[t] /= pFVVar[t];
}
if(pFVvar)for(t=0;t<m_Dim;++t)
{ /* Scale FV: */
pTD[t] /= pFVvar[t];
}
if(k>0)
{ /* Variate: */
for(t=0; t<m_Dim; ++t)
{
pTD[t] += m_RBFWidth*0.5f*(1-2.0f*rand()/(float)RAND_MAX);
}
}
}
} /* Next new datum. */
if(k>0)
{ /* Variate: */
for(t=0; t<m_Dim; ++t)
{
pTD[t] += m_RBFWidth*0.5f*(1-2.0f*rand()/(float)RAND_MAX);
}
}
}
} /* Next new datum. */
if(m_pTrainData) cvReleaseMat(&m_pTrainData);
m_pTrainData = pTrainData;
if(m_pTrainData) cvReleaseMat(&m_pTrainData);
m_pTrainData = pTrainData;
/* delete track record */
cvReleaseMemStorage(&pF->pMem);
m_TrackNum++;
m_Tracks.DelBlob(i-1);
/* delete track record */
cvReleaseMemStorage(&pF->pMem);
m_TrackNum++;
m_Tracks.DelBlob(i-1);
} /* End delete. */
} /* Next track. */
} /* End delete. */
} /* Next track. */
/* Retrain data each 1 minute if new data exist: */
if(m_Frame%(25*60) == 0 && m_pTrainData && m_pTrainData->rows > m_LastTrainDataSize)
{
RetrainStatModel();
}
/* Retrain data each 1 minute if new data exist: */
if(m_Frame%(25*60) == 0 && m_pTrainData && m_pTrainData->rows > m_LastTrainDataSize)
{
RetrainStatModel();
}
m_Frame++;
m_Frame++;
if(m_Wnd && m_Dim==2)
{ /* Debug output: */
int x,y;
IplImage* pI = cvCloneImage(pImg);
if(m_Wnd && m_Dim==2)
{ /* Debug output: */
int x,y;
IplImage* pI = cvCloneImage(pImg);
if(m_pStatModel && m_pStatImg)
if(m_pStatModel && m_pStatImg)
for(y=0; y<pI->height; y+=2)
{
uchar* pStatData = (uchar*)m_pStatImg->imageData + y*m_pStatImg->widthStep;
uchar* pData = (uchar*)pI->imageData + y*pI->widthStep;
for(y=0; y<pI->height; y+=2)
{
uchar* pStatData = (uchar*)m_pStatImg->imageData + y*m_pStatImg->widthStep;
uchar* pData = (uchar*)pI->imageData + y*pI->widthStep;
for(x=0;x<pI->width;x+=2)
{ /* Draw all elements: */
int d = pStatData[x];
d = (d<<8) | (d^0xff);
*(ushort*)(pData + x*3) = (ushort)d;
}
} /* Next line. */
for(x=0;x<pI->width;x+=2)
{ /* Draw all elements: */
int d = pStatData[x];
d = (d<<8) | (d^0xff);
*(ushort*)(pData + x*3) = (ushort)d;
}
} /* Next line. */
//cvNamedWindow("SVMMap",0);
//cvShowImage("SVMMap", pI);
cvReleaseImage(&pI);
} /* Debug output. */
};
float GetState(int BlobID)
{
DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlobByID(BlobID);
return pF?pF->state:0.0f;
};
//cvNamedWindow("SVMMap",0);
//cvShowImage("SVMMap", pI);
cvReleaseImage(&pI);
} /* Debug output. */
};
float GetState(int BlobID)
{
DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlobByID(BlobID);
return pF?pF->state:0.0f;
};
/* Return 0 if trajectory is normal;
/* Return 0 if trajectory is normal;
return >0 if trajectory abnormal. */
virtual const char* GetStateDesc(int BlobID)
{
if(GetState(BlobID)>0.5) return "abnormal";
return NULL;
}
virtual const char* GetStateDesc(int BlobID)
{
if(GetState(BlobID)>0.5) return "abnormal";
return NULL;
}
virtual void SetFileName(char* DataBaseName)
{
if(m_pTrainData)SaveStatModel();
m_DataFileName[0] = m_DataFileName[1000] = 0;
if(DataBaseName)
{
strncpy(m_DataFileName,DataBaseName,1000);
strcat(m_DataFileName, ".yml");
}
LoadStatModel();
};
virtual void SetFileName(char* DataBaseName)
{
if(m_pTrainData)SaveStatModel();
m_DataFileName[0] = m_DataFileName[1000] = 0;
if(DataBaseName)
{
strncpy(m_DataFileName,DataBaseName,1000);
strcat(m_DataFileName, ".yml");
}
LoadStatModel();
};
virtual void Release(){ delete this; };
virtual void Release(){ delete this; };
}; /* CvBlobTrackAnalysisSVM. */
modules/legacy/src/blobtrackingauto.cpp
+13 -18
View File
@@ -206,7 +206,6 @@ CvBlobTrackerAuto1::~CvBlobTrackerAuto1()
void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask)
{
int CurBlobNum = 0;
int i;
IplImage* pFG = pMask;
/* Bump frame counter: */
@@ -268,15 +267,14 @@ void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask)
TIME_BEGIN()
if(m_pBT)
{
int i;
m_pBT->Process(pImg, pFG);
for(i=m_BlobList.GetBlobNum(); i>0; --i)
for(int i=m_BlobList.GetBlobNum(); i>0; --i)
{ /* Update data of tracked blob list: */
CvBlob* pB = m_BlobList.GetBlob(i-1);
int BlobID = CV_BLOB_ID(pB);
int i = m_pBT->GetBlobIndexByID(BlobID);
m_pBT->ProcessBlob(i, pB, pImg, pFG);
int idx = m_pBT->GetBlobIndexByID(BlobID);
m_pBT->ProcessBlob(idx, pB, pImg, pFG);
pB->ID = BlobID;
}
CurBlobNum = m_pBT->GetBlobNum();
@@ -286,9 +284,7 @@ void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask)
/* This part should be removed: */
if(m_BTReal && m_pBT)
{ /* Update blob list (detect new blob for real blob tracker): */
int i;
for(i=m_pBT->GetBlobNum(); i>0; --i)
for(int i=m_pBT->GetBlobNum(); i>0; --i)
{ /* Update data of tracked blob list: */
CvBlob* pB = m_pBT->GetBlob(i-1);
if(pB && m_BlobList.GetBlobByID(CV_BLOB_ID(pB)) == NULL )
@@ -301,7 +297,7 @@ void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask)
} /* Next blob. */
/* Delete blobs: */
for(i=m_BlobList.GetBlobNum(); i>0; --i)
for(int i=m_BlobList.GetBlobNum(); i>0; --i)
{ /* Update tracked-blob list: */
CvBlob* pB = m_BlobList.GetBlob(i-1);
if(pB && m_pBT->GetBlobByID(CV_BLOB_ID(pB)) == NULL )
@@ -315,15 +311,14 @@ void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask)
TIME_BEGIN()
if(m_pBTPostProc)
{ /* Post-processing module: */
int i;
for(i=m_BlobList.GetBlobNum(); i>0; --i)
for(int i=m_BlobList.GetBlobNum(); i>0; --i)
{ /* Update tracked-blob list: */
CvBlob* pB = m_BlobList.GetBlob(i-1);
m_pBTPostProc->AddBlob(pB);
}
m_pBTPostProc->Process();
for(i=m_BlobList.GetBlobNum(); i>0; --i)
for(int i=m_BlobList.GetBlobNum(); i>0; --i)
{ /* Update tracked-blob list: */
CvBlob* pB = m_BlobList.GetBlob(i-1);
int BlobID = CV_BLOB_ID(pB);
@@ -423,12 +418,12 @@ void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask)
if(m_pBD->DetectNewBlob(pImg, pFG, &NewBlobList, &m_BlobList))
{ /* Add new blob to tracker and blob list: */
int i;
IplImage* pMask = pFG;
IplImage* pmask = pFG;
/*if(0)if(NewBlobList.GetBlobNum()>0 && pFG )
{// erode FG mask (only for FG_0 and MS1||MS2)
pMask = cvCloneImage(pFG);
cvErode(pFG,pMask,NULL,2);
pmask = cvCloneImage(pFG);
cvErode(pFG,pmask,NULL,2);
}*/
for(i=0; i<NewBlobList.GetBlobNum(); ++i)
@@ -438,7 +433,7 @@ void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask)
if(pBN && pBN->w >= CV_BLOB_MINW && pBN->h >= CV_BLOB_MINH)
{
CvBlob* pB = m_pBT->AddBlob(pBN, pImg, pMask );
CvBlob* pB = m_pBT->AddBlob(pBN, pImg, pmask );
if(pB)
{
NewB.blob = pB[0];
@@ -449,7 +444,7 @@ void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask)
}
} /* Add next blob from list of detected blob. */
if(pMask != pFG) cvReleaseImage(&pMask);
if(pmask != pFG) cvReleaseImage(&pmask);
} /* Create and add new blobs and trackers. */
@@ -460,7 +455,7 @@ void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask)
TIME_BEGIN()
if(m_pBTGen)
{ /* Run track generator: */
for(i=m_BlobList.GetBlobNum(); i>0; --i)
for(int i=m_BlobList.GetBlobNum(); i>0; --i)
{ /* Update data of tracked blob list: */
CvBlob* pB = m_BlobList.GetBlob(i-1);
m_pBTGen->AddBlob(pB);
modules/legacy/src/blobtrackingccwithcr.cpp
+8 -12
View File
@@ -175,7 +175,6 @@ public:
{
CvSeq* cnts;
CvSeq* cnt;
int i;
//CvMat* pMC = NULL;
if(m_BlobList.GetBlobNum() <= 0 ) return;
@@ -219,7 +218,7 @@ public:
cvReleaseImage(&pBin);
}
for(i=m_BlobList.GetBlobNum(); i>0; --i)
for(int i=m_BlobList.GetBlobNum(); i>0; --i)
{ /* Predict new blob position. */
CvBlob* pB = NULL;
DefBlobTrackerCR* pBT = (DefBlobTrackerCR*)m_BlobList.GetBlob(i-1);
@@ -237,11 +236,10 @@ public:
if(m_BlobList.GetBlobNum()>0 && m_BlobListNew.GetBlobNum()>0)
{ /* Resolve new blob to old: */
int i,j;
int NOld = m_BlobList.GetBlobNum();
int NNew = m_BlobListNew.GetBlobNum();
for(i=0; i<NOld; i++)
for(int i=0; i<NOld; i++)
{ /* Set 0 collision and clear all hyp: */
DefBlobTrackerCR* pF = (DefBlobTrackerCR*)m_BlobList.GetBlob(i);
pF->Collision = 0;
@@ -249,12 +247,12 @@ public:
} /* Set 0 collision. */
/* Create correspondence records: */
for(j=0; j<NNew; ++j)
for(int j=0; j<NNew; ++j)
{
CvBlob* pB1 = m_BlobListNew.GetBlob(j);
DefBlobTrackerCR* pFLast = NULL;
for(i=0; i<NOld; i++)
for(int i=0; i<NOld; i++)
{ /* Check intersection: */
int Intersection = 0;
DefBlobTrackerCR* pF = (DefBlobTrackerCR*)m_BlobList.GetBlob(i);
@@ -276,14 +274,13 @@ public:
} /* Check next new blob. */
} /* Resolve new blob to old. */
for(i=m_BlobList.GetBlobNum(); i>0; --i)
for(int i=m_BlobList.GetBlobNum(); i>0; --i)
{ /* Track each blob. */
CvBlob* pB = m_BlobList.GetBlob(i-1);
DefBlobTrackerCR* pBT = (DefBlobTrackerCR*)pB;
int BlobID = CV_BLOB_ID(pB);
//CvBlob* pBBest = NULL;
//double DistBest = -1;
int j;
if(pBT->pResolver)
{
@@ -309,7 +306,7 @@ public:
CvBlob* pBBest = NULL;
double DistBest = -1;
double CMax = 0;
for(j=pBT->pBlobHyp->GetBlobNum();j>0;--j)
for(int j=pBT->pBlobHyp->GetBlobNum();j>0;--j)
{ /* Find best CC: */
CvBlob* pBNew = pBT->pBlobHyp->GetBlob(j-1);
if(pBT->pResolver)
@@ -354,8 +351,7 @@ public:
if(m_Wnd)
{
IplImage* pI = cvCloneImage(pImg);
int i;
for(i=m_BlobListNew.GetBlobNum(); i>0; --i)
for(int i=m_BlobListNew.GetBlobNum(); i>0; --i)
{ /* Draw each new CC: */
CvBlob* pB = m_BlobListNew.GetBlob(i-1);
CvPoint p = cvPointFrom32f(CV_BLOB_CENTER(pB));
@@ -369,7 +365,7 @@ public:
CV_RGB(255,255,0), 1 );
}
for(i=m_BlobList.GetBlobNum(); i>0; --i)
for(int i=m_BlobList.GetBlobNum(); i>0; --i)
{ /* Draw each new CC: */
DefBlobTrackerCR* pF = (DefBlobTrackerCR*)m_BlobList.GetBlob(i-1);
CvBlob* pB = &(pF->BlobPredict);
modules/legacy/src/blobtrackingmsfgs.cpp
+4 -4
View File
@@ -395,7 +395,7 @@ public:
{ /* Mean shift in scale space: */
float news = 0;
float sum = 0;
float sum1 = 0;
float scale;
Center = cvPoint(cvRound(m_Blob.x),cvRound(m_Blob.y));
@@ -407,13 +407,13 @@ public:
{
double W = cvDotProduct(m_Weights, m_KernelMeanShiftG[si]);;
int s = si-SCALE_RANGE;
sum += (float)fabs(W);
sum1 += (float)fabs(W);
news += (float)(s*W);
}
if(sum>0)
if(sum1>0)
{
news /= sum;
news /= sum1;
}
scale = (float)pow((double)SCALE_BASE,(double)news);
modules/legacy/src/blobtrackpostproclinear.cpp
+2 -2
View File
@@ -74,9 +74,9 @@ public:
{
float WSum = 0;
int i;
int index = m_Frame % TIME_WND;
int idx = m_Frame % TIME_WND;
int size = MIN((m_Frame+1), TIME_WND);
m_pBlobs[index] = pBlob[0];
m_pBlobs[idx] = pBlob[0];
m_Blob.x = m_Blob.y = m_Blob.w = m_Blob.h = 0;
for(i=0; i<size; ++i)
modules/legacy/src/calibfilter.cpp
+7 -11
View File
@@ -44,10 +44,6 @@
#undef quad
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4701 )
#endif
CvCalibFilter::CvCalibFilter()
{
/* etalon data */
@@ -93,7 +89,7 @@ CvCalibFilter::~CvCalibFilter()
bool CvCalibFilter::SetEtalon( CvCalibEtalonType type, double* params,
int pointCount, CvPoint2D32f* points )
int pointCount, CvPoint2D32f* _points )
{
int i, arrSize;
@@ -132,7 +128,7 @@ bool CvCalibFilter::SetEtalon( CvCalibEtalonType type, double* params,
case CV_CALIB_ETALON_USER:
etalonParamCount = 0;
if( !points || pointCount < 4 )
if( !_points || pointCount < 4 )
{
assert(0);
return false;
@@ -188,9 +184,9 @@ bool CvCalibFilter::SetEtalon( CvCalibEtalonType type, double* params,
{
memcpy( etalonParams, params, arrSize );
}
if (points != NULL)
if (_points != NULL)
{
memcpy( etalonPoints, points, arrSize );
memcpy( etalonPoints, _points, arrSize );
}
break;
@@ -205,7 +201,7 @@ bool CvCalibFilter::SetEtalon( CvCalibEtalonType type, double* params,
CvCalibEtalonType
CvCalibFilter::GetEtalon( int* paramCount, const double** params,
int* pointCount, const CvPoint2D32f** points ) const
int* pointCount, const CvPoint2D32f** _points ) const
{
if( paramCount )
*paramCount = etalonParamCount;
@@ -216,8 +212,8 @@ CvCalibFilter::GetEtalon( int* paramCount, const double** params,
if( pointCount )
*pointCount = etalonPointCount;
if( points )
*points = etalonPoints;
if( _points )
*_points = etalonPoints;
return etalonType;
}
modules/legacy/src/calonder.cpp
+11 -11
View File
@@ -311,19 +311,19 @@ int RandomizedTree::getIndex(uchar* patch_data) const
}
void RandomizedTree::train(std::vector<BaseKeypoint> const& base_set,
RNG &rng, int depth, int views, size_t reduced_num_dim,
RNG &rng, int _depth, int views, size_t reduced_num_dim,
int num_quant_bits)
{
PatchGenerator make_patch;
train(base_set, rng, make_patch, depth, views, reduced_num_dim, num_quant_bits);
train(base_set, rng, make_patch, _depth, views, reduced_num_dim, num_quant_bits);
}
void RandomizedTree::train(std::vector<BaseKeypoint> const& base_set,
RNG &rng, PatchGenerator &make_patch,
int depth, int views, size_t reduced_num_dim,
int _depth, int views, size_t reduced_num_dim,
int num_quant_bits)
{
init((int)base_set.size(), depth, rng);
init((int)base_set.size(), _depth, rng);
Mat patch;
@@ -381,10 +381,10 @@ void RandomizedTree::freePosteriors(int which)
classes_ = -1;
}
void RandomizedTree::init(int num_classes, int depth, RNG &rng)
void RandomizedTree::init(int num_classes, int _depth, RNG &rng)
{
depth_ = depth;
num_leaves_ = 1 << depth; // 2**d
depth_ = _depth;
num_leaves_ = 1 << _depth; // 2**d
int num_nodes = num_leaves_ - 1; // 2**d - 1
// Initialize probabilities and counts to 0
@@ -631,9 +631,9 @@ void RandomizedTree::savePosteriors(std::string url, bool append)
for (int i=0; i<num_leaves_; i++) {
float *post = posteriors_[i];
char buf[20];
for (int i=0; i<classes_; i++) {
for (int j=0; j<classes_; j++) {
sprintf(buf, "%.10e", *post++);
file << buf << ((i<classes_-1) ? " " : "");
file << buf << ((j<classes_-1) ? " " : "");
}
file << std::endl;
}
@@ -645,8 +645,8 @@ void RandomizedTree::savePosteriors2(std::string url, bool append)
std::ofstream file(url.c_str(), (append?std::ios::app:std::ios::out));
for (int i=0; i<num_leaves_; i++) {
uchar *post = posteriors2_[i];
for (int i=0; i<classes_; i++)
file << int(*post++) << (i<classes_-1?" ":"");
for (int j=0; j<classes_; j++)
file << int(*post++) << (j<classes_-1?" ":"");
file << std::endl;
}
file.close();
modules/legacy/src/dpstereo.cpp
+24 -24
View File
@@ -190,79 +190,79 @@ static void icvFindStereoCorrespondenceByBirchfieldDP( uchar* src1, uchar* src2,
for( x = 1; x < imgW; x++ )
{
int d = MIN( x + 1, maxDisparity + 1);
int dp = MIN( x + 1, maxDisparity + 1);
uchar* _edges = edges + y*imgW + x;
int e0 = _edges[0] & 1;
_CvDPCell* _cell = cells + x*dispH;
do
{
int s = dsi[d*imgW+x];
int _s = dsi[dp*imgW+x];
int sum[3];
//check left step
sum[0] = _cell[d-dispH].sum - param2;
sum[0] = _cell[dp-dispH].sum - param2;
//check up step
if( _cell[d+1].step != ICV_DP_STEP_DIAG && e0 )
if( _cell[dp+1].step != ICV_DP_STEP_DIAG && e0 )
{
sum[1] = _cell[d+1].sum + param1;
sum[1] = _cell[dp+1].sum + param1;
if( _cell[d-1-dispH].step != ICV_DP_STEP_UP && (_edges[1-d] & 2) )
if( _cell[dp-1-dispH].step != ICV_DP_STEP_UP && (_edges[1-dp] & 2) )
{
int t;
sum[2] = _cell[d-1-dispH].sum + param1;
sum[2] = _cell[dp-1-dispH].sum + param1;
t = sum[1] < sum[0];
//choose local-optimal pass
if( sum[t] <= sum[2] )
{
_cell[d].step = (uchar)t;
_cell[d].sum = sum[t] + s;
_cell[dp].step = (uchar)t;
_cell[dp].sum = sum[t] + _s;
}
else
{
_cell[d].step = ICV_DP_STEP_DIAG;
_cell[d].sum = sum[2] + s;
_cell[dp].step = ICV_DP_STEP_DIAG;
_cell[dp].sum = sum[2] + _s;
}
}
else
{
if( sum[0] <= sum[1] )
{
_cell[d].step = ICV_DP_STEP_LEFT;
_cell[d].sum = sum[0] + s;
_cell[dp].step = ICV_DP_STEP_LEFT;
_cell[dp].sum = sum[0] + _s;
}
else
{
_cell[d].step = ICV_DP_STEP_UP;
_cell[d].sum = sum[1] + s;
_cell[dp].step = ICV_DP_STEP_UP;
_cell[dp].sum = sum[1] + _s;
}
}
}
else if( _cell[d-1-dispH].step != ICV_DP_STEP_UP && (_edges[1-d] & 2) )
else if( _cell[dp-1-dispH].step != ICV_DP_STEP_UP && (_edges[1-dp] & 2) )
{
sum[2] = _cell[d-1-dispH].sum + param1;
sum[2] = _cell[dp-1-dispH].sum + param1;
if( sum[0] <= sum[2] )
{
_cell[d].step = ICV_DP_STEP_LEFT;
_cell[d].sum = sum[0] + s;
_cell[dp].step = ICV_DP_STEP_LEFT;
_cell[dp].sum = sum[0] + _s;
}
else
{
_cell[d].step = ICV_DP_STEP_DIAG;
_cell[d].sum = sum[2] + s;
_cell[dp].step = ICV_DP_STEP_DIAG;
_cell[dp].sum = sum[2] + _s;
}
}
else
{
_cell[d].step = ICV_DP_STEP_LEFT;
_cell[d].sum = sum[0] + s;
_cell[dp].step = ICV_DP_STEP_LEFT;
_cell[dp].sum = sum[0] + _s;
}
}
while( --d );
while( --dp );
}// for x
//extract optimal way and fill disparity image
modules/legacy/src/eigenobjects.cpp
+11 -12
View File
@@ -206,7 +206,7 @@ icvJacobiEigens_32f(float *A, float *V, float *E, int n, float eps)
//
// Returns: CV_NO_ERR or error code
//
// Notes:
// Notes:
//F*/
static CvStatus CV_STDCALL
icvCalcCovarMatrixEx_8u32fR( int nObjects, void *input, int objStep1,
@@ -539,8 +539,7 @@ icvCalcEigenObjects_8u32fR( int nObjects, void* input, int objStep,
/* Buffer size determination */
if( ioFlags )
{
int size = icvDefaultBufferSize();
ioBufSize = MIN( size, n );
ioBufSize = MIN( icvDefaultBufferSize(), n );
}
/* memory allocation (if necesseay) */
@@ -695,13 +694,13 @@ icvCalcEigenObjects_8u32fR( int nObjects, void* input, int objStep,
for( igr = 0; igr < ngr; igr++ )
{
int i, io, ie, imin = igr * nio, imax = imin + nio;
int io, ie, imin = igr * nio, imax = imin + nio;
if( imax > m1 )
imax = m1;
for( i = 0; i < eigSize.width * (imax - imin); i++ )
((float *) buffer)[i] = 0.f;
for(int k = 0; k < eigSize.width * (imax - imin); k++ )
((float *) buffer)[k] = 0.f;
for( io = 0; io < nObjects; io++ )
{
@@ -1313,7 +1312,7 @@ cvCalcEigenObjects( int nObjects,
int ioBufSize,
void* userData,
CvTermCriteria* calcLimit,
IplImage* avg,
IplImage* avg,
float* eigVals )
{
float *avg_data;
@@ -1570,7 +1569,7 @@ cvCalcDecompCoeff( IplImage * obj, IplImage * eigObj, IplImage * avg )
avg_data, avg_step, obj_size );
__END__;
return coeff;
}
@@ -1598,9 +1597,9 @@ CV_IMPL void
cvEigenDecomposite( IplImage* obj,
int nEigObjs,
void* eigInput,
int ioFlags,
void* userData,
IplImage* avg,
int ioFlags,
void* userData,
IplImage* avg,
float* coeffs )
{
float *avg_data;
@@ -1716,7 +1715,7 @@ cvEigenProjection( void* eigInput,
int nEigObjs,
int ioFlags,
void* userData,
float* coeffs,
float* coeffs,
IplImage* avg,
IplImage* proj )
{
modules/legacy/src/em.cpp
+5 -5
View File
@@ -158,7 +158,7 @@ bool CvEM::train( const CvMat* _samples, const CvMat* _sample_idx,
Mat samples = cvarrToMat(_samples), labels0, labels;
if( _labels )
labels0 = labels = cvarrToMat(_labels);
bool isOk = train(samples, Mat(), _params, _labels ? &labels : 0);
CV_Assert( labels0.data == labels.data );
@@ -202,8 +202,8 @@ bool CvEM::train( const Mat& _samples, const Mat& _sample_idx,
{
CV_Assert(_sample_idx.empty());
Mat prbs, weights, means, logLikelihoods;
std::vector<Mat> covsHdrs;
init_params(_params, prbs, weights, means, covsHdrs);
std::vector<Mat> covshdrs;
init_params(_params, prbs, weights, means, covshdrs);
emObj = EM(_params.nclusters, _params.cov_mat_type, _params.term_crit);
bool isOk = false;
@@ -211,14 +211,14 @@ bool CvEM::train( const Mat& _samples, const Mat& _sample_idx,
isOk = emObj.train(_samples,
logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs);
else if( _params.start_step == EM::START_E_STEP )
isOk = emObj.trainE(_samples, means, covsHdrs, weights,
isOk = emObj.trainE(_samples, means, covshdrs, weights,
logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs);
else if( _params.start_step == EM::START_M_STEP )
isOk = emObj.trainM(_samples, prbs,
logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs);
else
CV_Error(CV_StsBadArg, "Bad start type of EM algorithm");
if(isOk)
{
logLikelihood = sum(logLikelihoods).val[0];
modules/legacy/src/enteringblobdetection.cpp
+12 -12
View File
@@ -117,11 +117,11 @@ static void cvFindBlobsByCCClasters(IplImage* pFG, CvBlobSeq* pBlobs, CvMemStora
for(cnt_cur=0; cnt_cur<clasters->total; ++cnt_cur)
{
CvRect rect;
CvSeq* cnt;
CvSeq* cont;
int k = *(int*)cvGetSeqElem( clasters, cnt_cur );
if(k!=claster_cur) continue;
cnt = *(CvSeq**)cvGetSeqElem( cnt_list, cnt_cur );
rect = ((CvContour*)cnt)->rect;
cont = *(CvSeq**)cvGetSeqElem( cnt_list, cnt_cur );
rect = ((CvContour*)cont)->rect;
if(rect_res.height<0)
{
@@ -399,7 +399,7 @@ int CvBlobDetectorSimple::DetectNewBlob(IplImage* /*pImg*/, IplImage* pFGMask, C
if(Good)
do{ /* For each configuration: */
CvBlob* pBL[EBD_FRAME_NUM];
int Good = 1;
int good = 1;
double Error = 0;
CvBlob* pBNew = m_pBlobLists[EBD_FRAME_NUM-1]->GetBlob(pBLIndex[EBD_FRAME_NUM-1]);
@@ -408,7 +408,7 @@ int CvBlobDetectorSimple::DetectNewBlob(IplImage* /*pImg*/, IplImage* pFGMask, C
Count++;
/* Check intersection last blob with existed: */
if(Good && pOldBlobList)
if(good && pOldBlobList)
{ /* Check intersection last blob with existed: */
int k;
for(k=pOldBlobList->GetBlobNum(); k>0; --k)
@@ -416,22 +416,22 @@ int CvBlobDetectorSimple::DetectNewBlob(IplImage* /*pImg*/, IplImage* pFGMask, C
CvBlob* pBOld = pOldBlobList->GetBlob(k-1);
if((fabs(pBOld->x-pBNew->x) < (CV_BLOB_RX(pBOld)+CV_BLOB_RX(pBNew))) &&
(fabs(pBOld->y-pBNew->y) < (CV_BLOB_RY(pBOld)+CV_BLOB_RY(pBNew))))
Good = 0;
good = 0;
}
} /* Check intersection last blob with existed. */
/* Check distance to image border: */
if(Good)
if(good)
{ /* Check distance to image border: */
CvBlob* pB = pBNew;
float dx = MIN(pB->x,S.width-pB->x)/CV_BLOB_RX(pB);
float dy = MIN(pB->y,S.height-pB->y)/CV_BLOB_RY(pB);
if(dx < 1.1 || dy < 1.1) Good = 0;
if(dx < 1.1 || dy < 1.1) good = 0;
} /* Check distance to image border. */
/* Check uniform motion: */
if(Good)
if(good)
{
int N = EBD_FRAME_NUM;
float sum[2] = {0,0};
@@ -466,13 +466,13 @@ int CvBlobDetectorSimple::DetectNewBlob(IplImage* /*pImg*/, IplImage* pFGMask, C
if( Error > S.width*0.01 ||
fabs(a[0])>S.width*0.1 ||
fabs(a[1])>S.height*0.1)
Good = 0;
good = 0;
} /* Check configuration. */
/* New best trajectory: */
if(Good && (BestError == -1 || BestError > Error))
if(good && (BestError == -1 || BestError > Error))
{
for(i=0; i<EBD_FRAME_NUM; ++i)
{
@@ -491,7 +491,7 @@ int CvBlobDetectorSimple::DetectNewBlob(IplImage* /*pImg*/, IplImage* pFGMask, C
if(i==EBD_FRAME_NUM)finish=1;
} while(!finish); /* Check next time configuration of connected components. */
} while(!finish); /* Check next time configuration of connected components. */
#if 0
{/**/
modules/legacy/src/epilines.cpp
+3 -8
View File
@@ -2729,8 +2729,7 @@ static int icvSelectBestRt( int numImages,
/* allocate memory for 64d data */
int totalNum = 0;
int i;
for( i = 0; i < numImages; i++ )
for(int i = 0; i < numImages; i++ )
{
totalNum += numPoints[i];
}
@@ -2828,8 +2827,7 @@ static int icvSelectBestRt( int numImages,
points2 = (CvPoint3D64d*)calloc(numberPnt,sizeof(CvPoint3D64d));
/* Transform object points to first camera position */
int i;
for( i = 0; i < numberPnt; i++ )
for(int i = 0; i < numberPnt; i++ )
{
/* Create second camera point */
CvPoint3D64d tmpPoint;
@@ -2859,8 +2857,6 @@ static int icvSelectBestRt( int numImages,
dy = tmpPoint2.y - points1[i].y;
dz = tmpPoint2.z - points1[i].z;
err = sqrt(dx*dx + dy*dy + dz*dz);*/
}
#if 0
@@ -3094,8 +3090,7 @@ int icvComputeRestStereoParams(CvStereoCamera *stereoparams)
corns[3].x = 0;
corns[3].y = (float)(stereoparams->camera[0]->imgSize[1]-1);
int i;
for( i = 0; i < 4; i++ )
for(int i = 0; i < 4; i++ )
{
/* For first camera */
icvConvertWarpCoordinates( stereoparams->coeffs[0],
modules/legacy/src/facedetection.cpp
+41 -41
View File
@@ -67,7 +67,7 @@ FaceDetection::FaceDetection()
m_iNumLayers = 16;
assert(m_iNumLayers <= MAX_LAYERS);
m_pFaceList = new FaceDetectionList();
m_bBoosting = false;
@@ -87,7 +87,7 @@ FaceDetection::~FaceDetection()
if (m_mstgRects)
cvReleaseMemStorage(&m_mstgRects);
}// ~FaceDetection()
@@ -111,7 +111,7 @@ void FaceDetection::FindContours(IplImage* imgGray)
m_mstgRects = cvCreateMemStorage();
if (NULL == m_mstgRects)
return;
m_seqRects = cvCreateSeq(0, sizeof(CvSeq), sizeof(CvContourRect), m_mstgRects);
m_seqRects = cvCreateSeq(0, sizeof(CvSeq), sizeof(CvContourRect), m_mstgRects);
if (NULL == m_seqRects)
return;
// find contours
@@ -148,23 +148,23 @@ void FaceDetection::ThresholdingParam(IplImage *imgGray, int iNumLayers, int &iM
buffImg += imgGray->widthStep;
}
// params
for (i = 0; i <= GIST_NUM; i ++)
{
if (gistImg[i] >= GIST_MIN)
break;
}
iMinLevel = i * GIST_STEP;
for (i = GIST_NUM; i >= 0; i --)
{
if (gistImg[i] >= GIST_MIN)
break;
}
iMaxLevel = i * GIST_STEP;
int dLevels = iMaxLevel - iMinLevel;
if (dLevels <= 0)
{
@@ -191,12 +191,12 @@ void FaceDetection::ThresholdingParam(IplImage *imgGray, int iNumLayers, int &iM
void FaceDetection::CreateResults(CvSeq * lpSeq)
{
Face * tmp;
double Max = 0;
double CurStat = 0;
FaceData tmpData;
if (m_bBoosting)
{
@@ -218,12 +218,12 @@ void FaceDetection::CreateResults(CvSeq * lpSeq)
if (CurStat > Max)
Max = CurStat;
}
while ( (tmp = m_pFaceList->GetData()) != 0 )
{
tmp->CreateFace(&tmpData);
CurStat = tmp->GetWeight();
if (CurStat == Max)
{
CvFace tmpFace;
@@ -232,7 +232,7 @@ void FaceDetection::CreateResults(CvSeq * lpSeq)
tmpFace.RightEyeRect = tmpData.RightEyeRect;
cvSeqPush(lpSeq,&tmpFace);
}
}
}
@@ -265,7 +265,7 @@ void FaceDetection::AddContours2Rect(CvSeq *seq, int color, int iLayer)
cvSeqPush(m_seqRects, &cr);
for (CvSeq* internal = external->v_next; internal; internal = internal->h_next)
{
cr.r = cvContourBoundingRect(internal, 0);
cr.r = cvContourBoundingRect(internal, 0);
cr.pCenter.x = cr.r.x + cr.r.width / 2;
cr.pCenter.y = cr.r.y + cr.r.height / 2;
cr.iNumber = iLayer;
@@ -294,8 +294,8 @@ void FaceDetection::FindFace(IplImage *img)
if (m_bBoosting)
PostBoostingFindCandidats(img);
else
FindCandidats();
FindCandidats();
}// void FaceDetection::FindFace(IplImage *img)
@@ -306,7 +306,7 @@ void FaceDetection::FindCandidats()
RFace * lpFace1 = 0;
bool bInvalidRect1 = false;
CvRect * lpRect1 = NULL;
try
{
for (int i = 0; i < m_seqRects->total; i++)
@@ -320,38 +320,38 @@ void FaceDetection::FindCandidats()
3*(double)rect.width/(double)4,
(double)rect.width/(double)2,
(double)rect.width/(double)2);
lpFace1 = new RFace(lpFaceTemplate1);
for (int j = 0; j < m_seqRects->total; j++)
{
CvContourRect* pRect = (CvContourRect*)cvGetSeqElem(m_seqRects, j);
CvContourRect* prect = (CvContourRect*)cvGetSeqElem(m_seqRects, j);
if ( !bInvalidRect1 )
{
lpRect1 = NULL;
lpRect1 = new CvRect();
*lpRect1 = pRect->r;
*lpRect1 = prect->r;
}else
{
delete lpRect1;
lpRect1 = new CvRect();
*lpRect1 = pRect->r;
*lpRect1 = prect->r;
}
if ( lpFace1->isFeature(lpRect1) )
{
{
bFound1 = true;
bInvalidRect1 = false;
}else
bInvalidRect1 = true;
}
if (bFound1)
{
m_pFaceList->AddElem(lpFace1);
@@ -363,10 +363,10 @@ void FaceDetection::FindCandidats()
lpFace1 = NULL;
}
delete lpFaceTemplate1;
}
}
}
catch(...)
@@ -381,10 +381,10 @@ void FaceDetection::FindCandidats()
void FaceDetection::PostBoostingFindCandidats(IplImage * FaceImage)
{
BoostingFaceTemplate * lpFaceTemplate1 = 0;
RFace * lpFace1 = 0;
RFace * lpFace1 = 0;
bool bInvalidRect1 = false;
CvRect * lpRect1 = NULL;
try
{
if ( ( !FaceImage->roi ) )
@@ -392,13 +392,13 @@ void FaceDetection::PostBoostingFindCandidats(IplImage * FaceImage)
else
lpFaceTemplate1 = new BoostingFaceTemplate(3,cvRect(FaceImage->roi->xOffset,FaceImage->roi->yOffset,
FaceImage->roi->width,FaceImage->roi->height));
lpFace1 = new RFace(lpFaceTemplate1);
for (int i = 0; i < m_seqRects->total; i++)
{
CvContourRect* pRect = (CvContourRect*)cvGetSeqElem(m_seqRects, i);
if ( !bInvalidRect1 )
{
lpRect1 = NULL;
@@ -410,21 +410,21 @@ void FaceDetection::PostBoostingFindCandidats(IplImage * FaceImage)
lpRect1 = new CvRect();
*lpRect1 = pRect->r;
}
if ( lpFace1->isFeature(lpRect1) )
{
{
//bFound1 = true;
bInvalidRect1 = false;
}else
bInvalidRect1 = true;
}
m_pFaceList->AddElem(lpFace1);
lpFace1 = NULL;
delete lpFaceTemplate1;
}
catch(...)
modules/legacy/src/hmm.cpp
+335 -337
View File
@@ -49,12 +49,12 @@
#define _CV_CAUSAL 2
#define _CV_LAST_STATE 1
#define _CV_BEST_STATE 2
#define _CV_BEST_STATE 2
//*F///////////////////////////////////////////////////////////////////////////////////////
// Name: _cvCreateObsInfo
// Purpose: The function allocates memory for CvImgObsInfo structure
// Purpose: The function allocates memory for CvImgObsInfo structure
// and its inner stuff
// Context:
// Parameters: obs_info - addres of pointer to CvImgObsInfo structure
@@ -64,27 +64,27 @@
//
// Returns: error status
//
// Notes:
//F*/
static CvStatus CV_STDCALL icvCreateObsInfo( CvImgObsInfo** obs_info,
// Notes:
//F*/
static CvStatus CV_STDCALL icvCreateObsInfo( CvImgObsInfo** obs_info,
CvSize num_obs, int obs_size )
{
int total = num_obs.height * num_obs.width;
CvImgObsInfo* obs = (CvImgObsInfo*)cvAlloc( sizeof( CvImgObsInfo) );
obs->obs_x = num_obs.width;
obs->obs_y = num_obs.height;
obs->obs = (float*)cvAlloc( total * obs_size * sizeof(float) );
obs->state = (int*)cvAlloc( 2 * total * sizeof(int) );
obs->mix = (int*)cvAlloc( total * sizeof(int) );
obs->mix = (int*)cvAlloc( total * sizeof(int) );
obs->obs_size = obs_size;
obs_info[0] = obs;
return CV_NO_ERR;
}
@@ -94,23 +94,23 @@ static CvStatus CV_STDCALL icvReleaseObsInfo( CvImgObsInfo** p_obs_info )
cvFree( &(obs_info->obs) );
cvFree( &(obs_info->mix) );
cvFree( &(obs_info->state) );
cvFree( &(obs_info->state) );
cvFree( &(obs_info) );
p_obs_info[0] = NULL;
return CV_NO_ERR;
}
}
//*F///////////////////////////////////////////////////////////////////////////////////////
// Name: icvCreate2DHMM
// Purpose: The function allocates memory for 2-dimensional embedded HMM model
// Purpose: The function allocates memory for 2-dimensional embedded HMM model
// and its inner stuff
// Context:
// Parameters: hmm - addres of pointer to CvEHMM structure
// state_number - array of hmm sizes (size of array == state_number[0]+1 )
// num_mix - number of gaussian mixtures in low-level HMM states
// num_mix - number of gaussian mixtures in low-level HMM states
// size of array is defined by previous array values
// obs_size - length of observation vectors
//
@@ -118,7 +118,7 @@ static CvStatus CV_STDCALL icvReleaseObsInfo( CvImgObsInfo** p_obs_info )
//
// Notes: state_number[0] - number of states in external HMM.
// state_number[i] - number of states in embedded HMM
//
//
// example for face recognition: state_number = { 5 3 6 6 6 3 },
// length of num_mix array = 3+6+6+6+3 = 24//
//
@@ -142,11 +142,11 @@ static CvStatus CV_STDCALL icvCreate2DHMM( CvEHMM** this_hmm,
/* allocate memory for all hmms (from all levels) */
hmm = (CvEHMM*)cvAlloc( (state_number[0] + 1) * sizeof(CvEHMM) );
/* set number of superstates */
hmm[0].num_states = state_number[0];
hmm[0].level = 1;
/* allocate memory for all states */
all_states = (CvEHMMState *)cvAlloc( real_states * sizeof( CvEHMMState ) );
@@ -160,51 +160,51 @@ static CvStatus CV_STDCALL icvCreate2DHMM( CvEHMM** this_hmm,
for( i = 0; i < real_states; i++ )
{
total_mix += num_mix[i];
}
}
/* allocate memory for states stuff */
pointers = (float*)cvAlloc( total_mix * (2/*for mu invvar */ * obs_size +
pointers = (float*)cvAlloc( total_mix * (2/*for mu invvar */ * obs_size +
2/*for weight and log_var_val*/ ) * sizeof( float) );
/* organize memory */
for( i = 0; i < real_states; i++ )
{
all_states[i].mu = pointers; pointers += num_mix[i] * obs_size;
all_states[i].mu = pointers; pointers += num_mix[i] * obs_size;
all_states[i].inv_var = pointers; pointers += num_mix[i] * obs_size;
all_states[i].log_var_val = pointers; pointers += num_mix[i];
all_states[i].weight = pointers; pointers += num_mix[i];
}
}
/* set pointer to embedded hmm array */
hmm->u.ehmm = hmm + 1;
for( i = 0; i < hmm[0].num_states; i++ )
{
hmm[i+1].u.state = all_states;
all_states += state_number[i+1];
hmm[i+1].num_states = state_number[i+1];
}
}
for( i = 0; i <= state_number[0]; i++ )
{
hmm[i].transP = icvCreateMatrix_32f( hmm[i].num_states, hmm[i].num_states );
hmm[i].obsProb = NULL;
hmm[i].level = i ? 0 : 1;
}
/* if all ok - return pointer */
*this_hmm = hmm;
return CV_NO_ERR;
}
}
static CvStatus CV_STDCALL icvRelease2DHMM( CvEHMM** phmm )
{
CvEHMM* hmm = phmm[0];
CvEHMM* hmm = phmm[0];
int i;
for( i = 0; i < hmm[0].num_states + 1; i++ )
{
icvDeleteMatrix( hmm[i].transP );
}
}
if (hmm->obsProb != NULL)
{
@@ -222,7 +222,7 @@ static CvStatus CV_STDCALL icvRelease2DHMM( CvEHMM** phmm )
phmm[0] = NULL;
return CV_NO_ERR;
}
}
/* distance between 2 vectors */
static float icvSquareDistance( CvVect32f v1, CvVect32f v2, int len )
@@ -251,7 +251,7 @@ static float icvSquareDistance( CvVect32f v1, CvVect32f v2, int len )
}
return (float)(dist0 + dist1);
}
}
/*can be used in CHMM & DHMM */
static CvStatus CV_STDCALL
@@ -268,27 +268,27 @@ icvUniformImgSegm( CvImgObsInfo* obs_info, CvEHMM* hmm )
if ( !obs_info || !hmm ) return CV_NULLPTR_ERR;
first_state = hmm->u.ehmm->u.state;
for (i = 0; i < obs_info->obs_y; i++)
{
//bad line (division )
int superstate = (int)((i * hmm->num_states)*inv_y);/* /obs_info->obs_y; */
int index = (int)(hmm->u.ehmm[superstate].u.state - first_state);
for (j = 0; j < obs_info->obs_x; j++, counter++)
{
int state = (int)((j * hmm->u.ehmm[superstate].num_states)* inv_x); /* / obs_info->obs_x; */
obs_info->state[2 * counter] = superstate;
obs_info->state[2 * counter + 1] = state + index;
}
}
}
#else
//this is not ready yet
int i,j,k,m;
CvEHMMState* first_state = hmm->u.ehmm->u.state;
CvEHMMState* first_state = hmm->u.ehmm->u.state;
/* check bad arguments */
if ( hmm->num_states > obs_info->obs_y ) return CV_BADSIZE_ERR;
@@ -296,7 +296,7 @@ icvUniformImgSegm( CvImgObsInfo* obs_info, CvEHMM* hmm )
//compute vertical subdivision
float row_per_state = (float)obs_info->obs_y / hmm->num_states;
float col_per_state[1024]; /* maximum 1024 superstates */
//for every horizontal band compute subdivision
for( i = 0; i < hmm->num_states; i++ )
{
@@ -338,24 +338,24 @@ icvUniformImgSegm( CvImgObsInfo* obs_info, CvEHMM* hmm )
obs_info->state[row * obs_info->obs_x + 2 * k] = i;
obs_info->state[row * obs_info->obs_x + 2 * k + 1] = j + index;
}
col = es_bound[j];
col = es_bound[j];
}
//copy the same to other rows of superstate
for( m = row; m < ss_bound[i]; m++ )
{
memcpy( &(obs_info->state[m * obs_info->obs_x * 2]),
memcpy( &(obs_info->state[m * obs_info->obs_x * 2]),
&(obs_info->state[row * obs_info->obs_x * 2]), obs_info->obs_x * 2 * sizeof(int) );
}
row = ss_bound[i];
}
row = ss_bound[i];
}
#endif
return CV_NO_ERR;
}
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: InitMixSegm
@@ -364,59 +364,59 @@ icvUniformImgSegm( CvImgObsInfo* obs_info, CvEHMM* hmm )
// Context: used with the Viterbi training of the embedded HMM
// Function uses K-Means algorithm for clustering
//
// Parameters: obs_info_array - array of pointers to image observations
// Parameters: obs_info_array - array of pointers to image observations
// num_img - length of above array
// hmm - pointer to HMM structure
//
// hmm - pointer to HMM structure
//
// Returns: error status
//
// Notes:
// Notes:
//F*/
static CvStatus CV_STDCALL
icvInitMixSegm( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
{
int k, i, j;
{
int k, i, j;
int* num_samples; /* number of observations in every state */
int* counter; /* array of counters for every state */
int** a_class; /* for every state - characteristic array */
CvVect32f** samples; /* for every state - pointer to observation vectors */
int*** samples_mix; /* for every state - array of pointers to vectors mixtures */
int*** samples_mix; /* for every state - array of pointers to vectors mixtures */
CvTermCriteria criteria = cvTermCriteria( CV_TERMCRIT_EPS|CV_TERMCRIT_ITER,
1000, /* iter */
0.01f ); /* eps */
int total = 0;
CvEHMMState* first_state = hmm->u.ehmm->u.state;
CvEHMMState* first_state = hmm->u.ehmm->u.state;
for( i = 0 ; i < hmm->num_states; i++ )
{
total += hmm->u.ehmm[i].num_states;
}
}
/* for every state integer is allocated - number of vectors in state */
num_samples = (int*)cvAlloc( total * sizeof(int) );
/* integer counter is allocated for every state */
counter = (int*)cvAlloc( total * sizeof(int) );
samples = (CvVect32f**)cvAlloc( total * sizeof(CvVect32f*) );
samples_mix = (int***)cvAlloc( total * sizeof(int**) );
samples = (CvVect32f**)cvAlloc( total * sizeof(CvVect32f*) );
samples_mix = (int***)cvAlloc( total * sizeof(int**) );
/* clear */
memset( num_samples, 0 , total*sizeof(int) );
memset( counter, 0 , total*sizeof(int) );
/* for every state the number of vectors which belong to it is computed (smth. like histogram) */
for (k = 0; k < num_img; k++)
{
{
CvImgObsInfo* obs = obs_info_array[k];
int count = 0;
for (i = 0; i < obs->obs_y; i++)
{
for (j = 0; j < obs->obs_x; j++, count++)
@@ -425,21 +425,21 @@ icvInitMixSegm( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
num_samples[state] += 1;
}
}
}
}
/* for every state int* is allocated */
a_class = (int**)cvAlloc( total*sizeof(int*) );
for (i = 0; i < total; i++)
{
a_class[i] = (int*)cvAlloc( num_samples[i] * sizeof(int) );
samples[i] = (CvVect32f*)cvAlloc( num_samples[i] * sizeof(CvVect32f) );
samples_mix[i] = (int**)cvAlloc( num_samples[i] * sizeof(int*) );
}
/* for every state vectors which belong to state are gathered */
for (k = 0; k < num_img; k++)
{
{
CvImgObsInfo* obs = obs_info_array[k];
int num_obs = ( obs->obs_x ) * ( obs->obs_y );
float* vector = obs->obs;
@@ -447,35 +447,35 @@ icvInitMixSegm( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
for (i = 0; i < num_obs; i++, vector+=obs->obs_size )
{
int state = obs->state[2*i+1];
samples[state][counter[state]] = vector;
samples_mix[state][counter[state]] = &(obs->mix[i]);
counter[state]++;
counter[state]++;
}
}
}
/* clear counters */
memset( counter, 0, total*sizeof(int) );
/* do the actual clustering using the K Means algorithm */
for (i = 0; i < total; i++)
{
if ( first_state[i].num_mix == 1)
{
{
for (k = 0; k < num_samples[i]; k++)
{
{
/* all vectors belong to one mixture */
a_class[i][k] = 0;
}
}
}
else if( num_samples[i] )
{
/* clusterize vectors */
cvKMeans( first_state[i].num_mix, samples[i], num_samples[i],
cvKMeans( first_state[i].num_mix, samples[i], num_samples[i],
obs_info_array[0]->obs_size, criteria, a_class[i] );
}
}
}
/* for every vector number of mixture is assigned */
for( i = 0; i < total; i++ )
{
@@ -484,7 +484,7 @@ icvInitMixSegm( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
samples_mix[i][j][0] = a_class[i][j];
}
}
for (i = 0; i < total; i++)
{
cvFree( &(a_class[i]) );
@@ -496,28 +496,28 @@ icvInitMixSegm( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
cvFree( &samples );
cvFree( &samples_mix );
cvFree( &counter );
cvFree( &num_samples );
cvFree( &num_samples );
return CV_NO_ERR;
}
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: ComputeUniModeGauss
// Purpose: The function computes the Gaussian pdf for a sample vector
// Purpose: The function computes the Gaussian pdf for a sample vector
// Context:
// Parameters: obsVeq - pointer to the sample vector
// mu - pointer to the mean vector of the Gaussian pdf
// var - pointer to the variance vector of the Gaussian pdf
// VecSize - the size of sample vector
//
// Returns: the pdf of the sample vector given the specified Gaussian
//
// Notes:
// Returns: the pdf of the sample vector given the specified Gaussian
//
// Notes:
//F*/
/*static float icvComputeUniModeGauss(CvVect32f vect, CvVect32f mu,
CvVect32f inv_var, float log_var_val, int vect_size)
/*static float icvComputeUniModeGauss(CvVect32f vect, CvVect32f mu,
CvVect32f inv_var, float log_var_val, int vect_size)
{
int n;
int n;
double tmp;
double prob;
@@ -529,42 +529,42 @@ icvInitMixSegm( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
prob = prob - tmp * tmp;
}
//prob *= 0.5f;
return (float)prob;
}*/
}*/
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: ComputeGaussMixture
// Purpose: The function computes the mixture Gaussian pdf of a sample vector.
// Purpose: The function computes the mixture Gaussian pdf of a sample vector.
// Context:
// Parameters: obsVeq - pointer to the sample vector
// mu - two-dimensional pointer to the mean vector of the Gaussian pdf;
// the first dimension is indexed over the number of mixtures and
// the first dimension is indexed over the number of mixtures and
// the second dimension is indexed along the size of the mean vector
// var - two-dimensional pointer to the variance vector of the Gaussian pdf;
// the first dimension is indexed over the number of mixtures and
// the first dimension is indexed over the number of mixtures and
// the second dimension is indexed along the size of the variance vector
// VecSize - the size of sample vector
// weight - pointer to the wights of the Gaussian mixture
// NumMix - the number of Gaussian mixtures
//
// Returns: the pdf of the sample vector given the specified Gaussian mixture.
//
// Notes:
// Returns: the pdf of the sample vector given the specified Gaussian mixture.
//
// Notes:
//F*/
/* Calculate probability of observation at state in logarithmic scale*/
/*static float
icvComputeGaussMixture( CvVect32f vect, float* mu,
float* inv_var, float* log_var_val,
icvComputeGaussMixture( CvVect32f vect, float* mu,
float* inv_var, float* log_var_val,
int vect_size, float* weight, int num_mix )
{
{
double prob, l_prob;
prob = 0.0f;
prob = 0.0f;
if (num_mix == 1)
{
return icvComputeUniModeGauss( vect, mu, inv_var, log_var_val[0], vect_size);
return icvComputeUniModeGauss( vect, mu, inv_var, log_var_val[0], vect_size);
}
else
{
@@ -572,30 +572,30 @@ icvComputeGaussMixture( CvVect32f vect, float* mu,
for (m = 0; m < num_mix; m++)
{
if ( weight[m] > 0.0)
{
l_prob = icvComputeUniModeGauss(vect, mu + m*vect_size,
{
l_prob = icvComputeUniModeGauss(vect, mu + m*vect_size,
inv_var + m * vect_size,
log_var_val[m],
vect_size);
log_var_val[m],
vect_size);
prob = prob + weight[m]*exp((double)l_prob);
}
}
prob = log(prob);
}
return (float)prob;
}*/
}
prob = log(prob);
}
return (float)prob;
}*/
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: EstimateObsProb
// Purpose: The function computes the probability of every observation in every state
// Purpose: The function computes the probability of every observation in every state
// Context:
// Parameters: obs_info - observations
// hmm - hmm
// Returns: error status
// Returns: error status
//
// Notes:
// Notes:
//F*/
static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* hmm )
{
@@ -604,7 +604,7 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h
/* check if matrix exist and check current size
if not sufficient - realloc */
int status = 0; /* 1 - not allocated, 2 - allocated but small size,
int status = 0; /* 1 - not allocated, 2 - allocated but small size,
3 - size is enough, but distribution is bad, 0 - all ok */
for( j = 0; j < hmm->num_states; j++ )
@@ -612,7 +612,7 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h
total_states += hmm->u.ehmm[j].num_states;
}
if ( hmm->obsProb == NULL )
if ( hmm->obsProb == NULL )
{
/* allocare memory */
int need_size = ( obs_info->obs_x * obs_info->obs_y * total_states * sizeof(float) +
@@ -624,10 +624,10 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h
buffer[2] = obs_info->obs_x;
hmm->obsProb = (float**) (buffer + 3);
status = 3;
}
else
{
{
/* check current size */
int* total= (int*)(((int*)(hmm->obsProb)) - 3);
int need_size = ( obs_info->obs_x * obs_info->obs_y * total_states * sizeof(float) +
@@ -635,7 +635,7 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h
assert( sizeof(float*) == sizeof(int) );
if ( need_size > (*total) )
if ( need_size > (*total) )
{
int* buffer = ((int*)(hmm->obsProb)) - 3;
cvFree( &buffer);
@@ -645,22 +645,22 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h
buffer[2] = obs_info->obs_x;
hmm->obsProb = (float**)(buffer + 3);
status = 3;
}
}
}
if (!status)
{
int* obsx = ((int*)(hmm->obsProb)) - 1;
int* obsy = ((int*)(hmm->obsProb)) - 2;
assert( (*obsx > 0) && (*obsy > 0) );
/* is good distribution? */
if ( (obs_info->obs_x > (*obsx) ) || (obs_info->obs_y > (*obsy) ) )
status = 3;
if ( (obs_info->obs_x > (*obsx) ) || (obs_info->obs_y > (*obsy) ) )
status = 3;
}
/* if bad status - do reallocation actions */
assert( (status == 0) || (status == 3) );
@@ -672,7 +672,7 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h
/* distribute pointers of ehmm->obsProb */
for( i = 0; i < hmm->num_states; i++ )
{
hmm->u.ehmm[i].obsProb = tmp;
hmm->u.ehmm[i].obsProb = tmp;
tmp += obs_info->obs_y;
}
@@ -682,16 +682,16 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h
for( i = 0; i < hmm->num_states; i++ )
{
CvEHMM* ehmm = &( hmm->u.ehmm[i] );
for( j = 0; j < obs_info->obs_y; j++ )
{
ehmm->obsProb[j] = tmpf;
tmpf += ehmm->num_states * obs_info->obs_x;
}
}
}
}/* end of pointer distribution */
}/* end of pointer distribution */
#if 1
#if 1
{
#define MAX_BUF_SIZE 1200
float local_log_mix_prob[MAX_BUF_SIZE];
@@ -701,7 +701,7 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h
float* log_mix_prob = local_log_mix_prob;
double* mix_prob = local_mix_prob;
int max_size = 0;
int obs_x = obs_info->obs_x;
@@ -722,7 +722,7 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h
}
max_size *= obs_x * vect_size;
/* allocate buffer */
if( max_size > MAX_BUF_SIZE )
{
@@ -734,13 +734,13 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h
memset( log_mix_prob, 0, max_size*sizeof(float));
/*****************computing probabilities***********************/
/* loop through external states */
for( i = 0; i < hmm->num_states; i++ )
{
CvEHMM* ehmm = &(hmm->u.ehmm[i]);
CvEHMMState* state = ehmm->u.state;
int max_mix = 0;
int n_states = ehmm->num_states;
@@ -755,13 +755,13 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h
for( j = 0; j < obs_info->obs_y; j++ )
{
int m, n;
float* obs = obs_info->obs + j * obs_x * vect_size;
float* log_mp = max_mix > 1 ? log_mix_prob : ehmm->obsProb[j];
double* mp = mix_prob;
/* several passes are done below */
/* 1. calculate logarithms of probabilities for each mixture */
/* loop through mixtures */
@@ -845,21 +845,21 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h
for( j = 0; j < obs_info->obs_y; j++ )
{
int k,m;
int obs_index = j * obs_info->obs_x;
float* B = ehmm->obsProb[j];
/* cycles through obs and states */
for( k = 0; k < obs_info->obs_x; k++ )
{
CvVect32f vect = (obs_info->obs) + (obs_index + k) * vect_size;
float* matr_line = B + k * ehmm->num_states;
for( m = 0; m < ehmm->num_states; m++ )
{
matr_line[m] = icvComputeGaussMixture( vect, state[m].mu, state[m].inv_var,
matr_line[m] = icvComputeGaussMixture( vect, state[m].mu, state[m].inv_var,
state[m].log_var_val, vect_size, state[m].weight,
state[m].num_mix );
}
@@ -872,16 +872,16 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: EstimateTransProb
// Purpose: The function calculates the state and super state transition probabilities
// of the model given the images,
// Purpose: The function calculates the state and super state transition probabilities
// of the model given the images,
// the state segmentation and the input parameters
// Context:
// Parameters: obs_info_array - array of pointers to image observations
// Parameters: obs_info_array - array of pointers to image observations
// num_img - length of above array
// hmm - pointer to HMM structure
// hmm - pointer to HMM structure
// Returns: void
//
// Notes:
// Notes:
//F*/
static CvStatus CV_STDCALL
icvEstimateTransProb( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
@@ -890,47 +890,47 @@ icvEstimateTransProb( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
CvEHMMState* first_state = hmm->u.ehmm->u.state;
/* as a counter we will use transP matrix */
/* initialization */
/* clear transP */
icvSetZero_32f( hmm->transP, hmm->num_states, hmm->num_states );
for (i = 0; i < hmm->num_states; i++ )
{
icvSetZero_32f( hmm->u.ehmm[i].transP , hmm->u.ehmm[i].num_states, hmm->u.ehmm[i].num_states );
}
/* compute the counters */
for (i = 0; i < num_img; i++)
{
int counter = 0;
CvImgObsInfo* info = obs_info_array[i];
for (j = 0; j < info->obs_y; j++)
{
for (k = 0; k < info->obs_x; k++, counter++)
{
/* compute how many transitions from state to state
occured both in horizontal and vertical direction */
occured both in horizontal and vertical direction */
int superstate, state;
int nextsuperstate, nextstate;
int begin_ind;
superstate = info->state[2 * counter];
begin_ind = (int)(hmm->u.ehmm[superstate].u.state - first_state);
state = info->state[ 2 * counter + 1] - begin_ind;
state = info->state[ 2 * counter + 1] - begin_ind;
if (j < info->obs_y - 1)
{
int transP_size = hmm->num_states;
nextsuperstate = info->state[ 2*(counter + info->obs_x) ];
hmm->transP[superstate * transP_size + nextsuperstate] += 1;
}
if (k < info->obs_x - 1)
{
{
int transP_size = hmm->u.ehmm[superstate].num_states;
nextstate = info->state[2*(counter+1) + 1] - begin_ind;
@@ -951,15 +951,15 @@ icvEstimateTransProb( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
//assert( total );
inv_total = total ? 1.f/total : 0;
for( j = 0; j < hmm->num_states; j++)
{
hmm->transP[i * hmm->num_states + j] =
hmm->transP[i * hmm->num_states + j] ?
{
hmm->transP[i * hmm->num_states + j] =
hmm->transP[i * hmm->num_states + j] ?
(float)log( hmm->transP[i * hmm->num_states + j] * inv_total ) : -BIG_FLT;
}
}
/* estimate other matrices */
for( k = 0; k < hmm->num_states; k++ )
{
@@ -975,18 +975,18 @@ icvEstimateTransProb( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
}
//assert( total );
inv_total = total ? 1.f/total : 0;
for( j = 0; j < ehmm->num_states; j++)
{
ehmm->transP[i * ehmm->num_states + j] =
{
ehmm->transP[i * ehmm->num_states + j] =
(ehmm->transP[i * ehmm->num_states + j]) ?
(float)log( ehmm->transP[i * ehmm->num_states + j] * inv_total) : -BIG_FLT ;
}
}
}
return CV_NO_ERR;
}
}
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: MixSegmL2
@@ -994,24 +994,24 @@ icvEstimateTransProb( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
// embedded HMM
// Context: used with the Viterbi training of the embedded HMM
//
// Parameters:
// Parameters:
// obs_info_array
// num_img
// hmm
// Returns: void
//
// Notes:
// Notes:
//F*/
static CvStatus CV_STDCALL
icvMixSegmL2( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
{
int k, i, j, m;
CvEHMMState* state = hmm->u.ehmm[0].u.state;
for (k = 0; k < num_img; k++)
{
{
int counter = 0;
CvImgObsInfo* info = obs_info_array[k];
@@ -1021,11 +1021,11 @@ icvMixSegmL2( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
{
int e_state = info->state[2 * counter + 1];
float min_dist;
min_dist = icvSquareDistance((info->obs) + (counter * info->obs_size),
min_dist = icvSquareDistance((info->obs) + (counter * info->obs_size),
state[e_state].mu, info->obs_size);
info->mix[counter] = 0;
info->mix[counter] = 0;
for (m = 1; m < state[e_state].num_mix; m++)
{
float dist=icvSquareDistance( (info->obs) + (counter * info->obs_size),
@@ -1034,7 +1034,7 @@ icvMixSegmL2( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
if (dist < min_dist)
{
min_dist = dist;
/* assign mixture with smallest distance */
/* assign mixture with smallest distance */
info->mix[counter] = m;
}
}
@@ -1042,18 +1042,18 @@ icvMixSegmL2( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
}
}
return CV_NO_ERR;
}
}
/*
CvStatus icvMixSegmProb(CvImgObsInfo* obs_info, int num_img, CvEHMM* hmm )
{
int k, i, j, m;
CvEHMMState* state = hmm->ehmm[0].state_info;
for (k = 0; k < num_img; k++)
{
{
int counter = 0;
CvImgObsInfo* info = obs_info + k;
@@ -1063,32 +1063,32 @@ CvStatus icvMixSegmProb(CvImgObsInfo* obs_info, int num_img, CvEHMM* hmm )
{
int e_state = info->in_state[counter];
float max_prob;
max_prob = icvComputeUniModeGauss( info->obs[counter], state[e_state].mu[0],
state[e_state].inv_var[0],
max_prob = icvComputeUniModeGauss( info->obs[counter], state[e_state].mu[0],
state[e_state].inv_var[0],
state[e_state].log_var[0],
info->obs_size );
info->mix[counter] = 0;
info->mix[counter] = 0;
for (m = 1; m < state[e_state].num_mix; m++)
{
float prob=icvComputeUniModeGauss(info->obs[counter], state[e_state].mu[m],
state[e_state].inv_var[m],
state[e_state].inv_var[m],
state[e_state].log_var[m],
info->obs_size);
if (prob > max_prob)
{
max_prob = prob;
// assign mixture with greatest probability.
// assign mixture with greatest probability.
info->mix[counter] = m;
}
}
}
}
}
}
return CV_NO_ERR;
}
}
*/
static CvStatus CV_STDCALL
icvViterbiSegmentation( int num_states, int /*num_obs*/, CvMatr32f transP,
@@ -1096,45 +1096,45 @@ icvViterbiSegmentation( int num_states, int /*num_obs*/, CvMatr32f transP,
int** q, int min_num_obs, int max_num_obs,
float* prob )
{
// memory allocation
// memory allocation
int i, j, last_obs;
int m_HMMType = _CV_ERGODIC; /* _CV_CAUSAL or _CV_ERGODIC */
int m_ProbType = prob_type; /* _CV_LAST_STATE or _CV_BEST_STATE */
int m_minNumObs = min_num_obs; /*??*/
int m_maxNumObs = max_num_obs; /*??*/
int m_numStates = num_states;
float* m_pi = (float*)cvAlloc( num_states* sizeof(float) );
CvMatr32f m_a = transP;
// offset brobability matrix to starting observation
// offset brobability matrix to starting observation
CvMatr32f m_b = B + start_obs * num_states;
//so m_xl will not be used more
//m_xl = start_obs;
//m_xl = start_obs;
/* if (muDur != NULL){
/* if (muDur != NULL){
m_d = new int[m_numStates];
m_l = new double[m_numStates];
for (i = 0; i < m_numStates; i++){
m_l[i] = muDur[i];
m_l[i] = muDur[i];
}
}
}
else{
m_d = NULL;
m_l = NULL;
}
*/
CvMatr32f m_Gamma = icvCreateMatrix_32f( num_states, m_maxNumObs );
int* m_csi = (int*)cvAlloc( num_states * m_maxNumObs * sizeof(int) );
//stores maximal result for every ending observation */
CvVect32f m_MaxGamma = prob;
// assert( m_xl + max_num_obs <= num_obs );
@@ -1151,31 +1151,31 @@ icvViterbiSegmentation( int num_states, int /*num_obs*/, CvMatr32f transP,
m_pi[i] = -BIG_FLT;
}
m_pi[0] = 0.0f;
for (i = 0; i < num_states; i++)
{
m_Gamma[0 * num_states + i] = m_pi[i] + m_b[0 * num_states + i];
m_csi[0 * num_states + i] = 0;
m_csi[0 * num_states + i] = 0;
}
/******************************************************************/
/* Viterbi recursion */
if ( m_HMMType == _CV_CAUSAL ) //causal model
{
int t,j;
int t;
for (t = 1 ; t < m_maxNumObs; t++)
{
// evaluate self-to-self transition for state 0
m_Gamma[t * num_states + 0] = m_Gamma[(t-1) * num_states + 0] + m_a[0];
m_csi[t * num_states + 0] = 0;
for (j = 1; j < num_states; j++)
{
{
float self = m_Gamma[ (t-1) * num_states + j] + m_a[ j * num_states + j];
float prev = m_Gamma[ (t-1) * num_states +(j-1)] + m_a[ (j-1) * num_states + j];
if ( prev > self )
{
m_csi[t * num_states + j] = j-1;
@@ -1186,34 +1186,33 @@ icvViterbiSegmentation( int num_states, int /*num_obs*/, CvMatr32f transP,
m_csi[t * num_states + j] = j;
m_Gamma[t * num_states + j] = self;
}
m_Gamma[t * num_states + j] = m_Gamma[t * num_states + j] + m_b[t * num_states + j];
}
m_Gamma[t * num_states + j] = m_Gamma[t * num_states + j] + m_b[t * num_states + j];
}
}
}
else if ( m_HMMType == _CV_ERGODIC ) //ergodic model
{
else if ( m_HMMType == _CV_ERGODIC ) //ergodic model
{
int t;
for (t = 1 ; t < m_maxNumObs; t++)
{
{
for (j = 0; j < num_states; j++)
{
int i;
{
m_Gamma[ t*num_states + j] = m_Gamma[(t-1) * num_states + 0] + m_a[0*num_states+j];
m_csi[t *num_states + j] = 0;
for (i = 1; i < num_states; i++)
{
float currGamma = m_Gamma[(t-1) *num_states + i] + m_a[i *num_states + j];
float currGamma = m_Gamma[(t-1) *num_states + i] + m_a[i *num_states + j];
if (currGamma > m_Gamma[t *num_states + j])
{
{
m_Gamma[t * num_states + j] = currGamma;
m_csi[t * num_states + j] = i;
}
}
}
m_Gamma[t *num_states + j] = m_Gamma[t *num_states + j] + m_b[t * num_states + j];
}
}
}
}
}
for( last_obs = m_minNumObs-1, i = 0; last_obs < m_maxNumObs; last_obs++, i++ )
@@ -1222,7 +1221,7 @@ icvViterbiSegmentation( int num_states, int /*num_obs*/, CvMatr32f transP,
/******************************************************************/
/* Viterbi termination */
if ( m_ProbType == _CV_LAST_STATE )
{
m_MaxGamma[i] = m_Gamma[last_obs * num_states + num_states - 1];
@@ -1231,48 +1230,48 @@ icvViterbiSegmentation( int num_states, int /*num_obs*/, CvMatr32f transP,
else if( m_ProbType == _CV_BEST_STATE )
{
int k;
q[i][last_obs] = 0;
m_MaxGamma[i] = m_Gamma[last_obs * num_states + 0];
q[i][last_obs] = 0;
m_MaxGamma[i] = m_Gamma[last_obs * num_states + 0];
for(k = 1; k < num_states; k++)
{
{
if ( m_Gamma[last_obs * num_states + k] > m_MaxGamma[i] )
{
m_MaxGamma[i] = m_Gamma[last_obs * num_states + k];
q[i][last_obs] = k;
}
}
}
}
}
/******************************************************************/
/* Viterbi backtracking */
for (t = last_obs-1; t >= 0; t--)
{
q[i][t] = m_csi[(t+1) * num_states + q[i][t+1] ];
}
}
q[i][t] = m_csi[(t+1) * num_states + q[i][t+1] ];
}
}
/* memory free */
cvFree( &m_pi );
cvFree( &m_csi );
icvDeleteMatrix( m_Gamma );
icvDeleteMatrix( m_Gamma );
return CV_NO_ERR;
}
}
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: icvEViterbi
// Purpose: The function calculates the embedded Viterbi algorithm
// for 1 image
// for 1 image
// Context:
// Parameters:
// Parameters:
// obs_info - observations
// hmm - HMM
//
// Returns: the Embedded Viterbi probability (float)
//
// Returns: the Embedded Viterbi probability (float)
// and do state segmentation of observations
//
// Notes:
// Notes:
//F*/
static float CV_STDCALL icvEViterbi( CvImgObsInfo* obs_info, CvEHMM* hmm )
{
@@ -1282,70 +1281,70 @@ static float CV_STDCALL icvEViterbi( CvImgObsInfo* obs_info, CvEHMM* hmm )
float inv_obs_x = 1.f / obs_info->obs_x;
CvEHMMState* first_state = hmm->u.ehmm->u.state;
/* memory allocation for superB */
CvMatr32f superB = icvCreateMatrix_32f(hmm->num_states, obs_info->obs_y );
/* memory allocation for q */
int*** q = (int***)cvAlloc( hmm->num_states * sizeof(int**) );
int* super_q = (int*)cvAlloc( obs_info->obs_y * sizeof(int) );
for (i = 0; i < hmm->num_states; i++)
{
q[i] = (int**)cvAlloc( obs_info->obs_y * sizeof(int*) );
for (j = 0; j < obs_info->obs_y ; j++)
{
q[i][j] = (int*)cvAlloc( obs_info->obs_x * sizeof(int) );
}
}
}
/* start Viterbi segmentation */
for (i = 0; i < hmm->num_states; i++)
{
CvEHMM* ehmm = &(hmm->u.ehmm[i]);
for (j = 0; j < obs_info->obs_y; j++)
{
float max_gamma;
/* 1D HMM Viterbi segmentation */
icvViterbiSegmentation( ehmm->num_states, obs_info->obs_x,
ehmm->transP, ehmm->obsProb[j], 0,
icvViterbiSegmentation( ehmm->num_states, obs_info->obs_x,
ehmm->transP, ehmm->obsProb[j], 0,
_CV_LAST_STATE, &q[i][j], obs_info->obs_x,
obs_info->obs_x, &max_gamma);
superB[j * hmm->num_states + i] = max_gamma * inv_obs_x;
}
}
/* perform global Viterbi segmentation (i.e. process higher-level HMM) */
icvViterbiSegmentation( hmm->num_states, obs_info->obs_y,
hmm->transP, superB, 0,
icvViterbiSegmentation( hmm->num_states, obs_info->obs_y,
hmm->transP, superB, 0,
_CV_LAST_STATE, &super_q, obs_info->obs_y,
obs_info->obs_y, &log_likelihood );
log_likelihood /= obs_info->obs_y ;
log_likelihood /= obs_info->obs_y ;
counter = 0;
/* assign new state to observation vectors */
for (i = 0; i < obs_info->obs_y; i++)
{
{
for (j = 0; j < obs_info->obs_x; j++, counter++)
{
int superstate = super_q[i];
int state = (int)(hmm->u.ehmm[superstate].u.state - first_state);
obs_info->state[2 * counter] = superstate;
obs_info->state[2 * counter + 1] = state + q[superstate][i][j];
}
}
/* memory deallocation for superB */
icvDeleteMatrix( superB );
/*memory deallocation for q */
for (i = 0; i < hmm->num_states; i++)
{
@@ -1355,12 +1354,12 @@ static float CV_STDCALL icvEViterbi( CvImgObsInfo* obs_info, CvEHMM* hmm )
}
cvFree( &q[i] );
}
cvFree( &q );
cvFree( &super_q );
return log_likelihood;
}
}
static CvStatus CV_STDCALL
icvEstimateHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm )
@@ -1373,7 +1372,7 @@ icvEstimateHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* h
float start_log_var_val = LN2PI * vect_len;
CvVect32f tmp_vect = icvCreateVector_32f( vect_len );
CvEHMMState* first_state = hmm->u.ehmm[0].u.state;
assert( sizeof(float) == sizeof(int) );
@@ -1390,9 +1389,9 @@ icvEstimateHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* h
for (m = 0; m < first_state[i].num_mix; m++)
{
((int*)(first_state[i].weight))[m] = 0;
}
}
}
/* maybe gamma must be computed in mixsegm process ?? */
/* compute gamma */
@@ -1406,23 +1405,23 @@ icvEstimateHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* h
int state, mixture;
state = info->state[2*i + 1];
mixture = info->mix[i];
/* computes gamma - number of observations corresponding
to every mixture of every state */
((int*)(first_state[state].weight))[mixture] += 1;
/* computes gamma - number of observations corresponding
to every mixture of every state */
((int*)(first_state[state].weight))[mixture] += 1;
}
}
}
/***************Mean and Var***********************/
/* compute means and variances of every item */
/* initially variance placed to inv_var */
/* zero mean and variance */
for (i = 0; i < total; i++)
{
memset( (void*)first_state[i].mu, 0, first_state[i].num_mix * vect_len *
memset( (void*)first_state[i].mu, 0, first_state[i].num_mix * vect_len *
sizeof(float) );
memset( (void*)first_state[i].inv_var, 0, first_state[i].num_mix * vect_len *
memset( (void*)first_state[i].inv_var, 0, first_state[i].num_mix * vect_len *
sizeof(float) );
}
/* compute sums */
for (i = 0; i < num_img; i++)
{
@@ -1432,42 +1431,41 @@ icvEstimateHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* h
float* vector = info->obs;
for (j = 0; j < total_obs; j++, vector+=vect_len )
{
{
int state = info->state[2 * j + 1];
int mixture = info->mix[j];
int mixture = info->mix[j];
CvVect32f mean = first_state[state].mu + mixture * vect_len;
CvVect32f mean2 = first_state[state].inv_var + mixture * vect_len;
icvAddVector_32f( mean, vector, mean, vect_len );
for( k = 0; k < vect_len; k++ )
mean2[k] += vector[k]*vector[k];
}
}
}
/*compute the means and variances */
/* assume gamma already computed */
for (i = 0; i < total; i++)
{
{
CvEHMMState* state = &(first_state[i]);
for (m = 0; m < state->num_mix; m++)
{
int k;
CvVect32f mu = state->mu + m * vect_len;
CvVect32f invar = state->inv_var + m * vect_len;
CvVect32f invar = state->inv_var + m * vect_len;
if ( ((int*)state->weight)[m] > 1)
{
float inv_gamma = 1.f/((int*)(state->weight))[m];
icvScaleVector_32f( mu, mu, vect_len, inv_gamma);
icvScaleVector_32f( invar, invar, vect_len, inv_gamma);
}
icvMulVectors_32f(mu, mu, tmp_vect, vect_len);
icvSubVector_32f( invar, tmp_vect, invar, vect_len);
icvSubVector_32f( invar, tmp_vect, invar, vect_len);
/* low bound of variance - 100 (Ara's experimental result) */
for( k = 0; k < vect_len; k++ )
{
@@ -1479,7 +1477,7 @@ icvEstimateHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* h
for( k = 0; k < vect_len; k++ )
{
state->log_var_val[m] += (float)log( invar[k] );
}
}
/* SMOLI 27.10.2000 */
state->log_var_val[m] *= 0.5;
@@ -1490,32 +1488,32 @@ icvEstimateHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* h
cvbInvSqrt( invar, invar, vect_len );
}
}
/***************Weights***********************/
/* normilize gammas - i.e. compute mixture weights */
//compute weights
for (i = 0; i < total; i++)
{
{
int gamma_total = 0;
float norm;
for (m = 0; m < first_state[i].num_mix; m++)
{
gamma_total += ((int*)(first_state[i].weight))[m];
gamma_total += ((int*)(first_state[i].weight))[m];
}
norm = gamma_total ? (1.f/(float)gamma_total) : 0.f;
for (m = 0; m < first_state[i].num_mix; m++)
{
first_state[i].weight[m] = ((int*)(first_state[i].weight))[m] * norm;
}
}
}
}
icvDeleteVector( tmp_vect);
return CV_NO_ERR;
}
return CV_NO_ERR;
}
/*
CvStatus icvLightingCorrection8uC1R( uchar* img, CvSize roi, int src_step )
@@ -1523,28 +1521,28 @@ CvStatus icvLightingCorrection8uC1R( uchar* img, CvSize roi, int src_step )
int i, j;
int width = roi.width;
int height = roi.height;
float x1, x2, y1, y2;
int f[3] = {0, 0, 0};
float a[3] = {0, 0, 0};
float h1;
float h2;
float c1,c2;
float min = FLT_MAX;
float max = -FLT_MAX;
float correction;
float* float_img = icvAlloc( width * height * sizeof(float) );
x1 = width * (width + 1) / 2.0f; // Sum (1, ... , width)
x2 = width * (width + 1 ) * (2 * width + 1) / 6.0f; // Sum (1^2, ... , width^2)
y1 = height * (height + 1)/2.0f; // Sum (1, ... , width)
y2 = height * (height + 1 ) * (2 * height + 1) / 6.0f; // Sum (1^2, ... , width^2)
// extract grayvalues
for (i = 0; i < height; i++)
{
@@ -1555,38 +1553,38 @@ CvStatus icvLightingCorrection8uC1R( uchar* img, CvSize roi, int src_step )
f[0] = f[0] + img[i*src_step + j];
}
}
h1 = (float)f[0] * (float)x1 / (float)width;
h2 = (float)f[0] * (float)y1 / (float)height;
a[2] = ((float)f[2] - h1) / (float)(x2*height - x1*x1*height/(float)width);
a[1] = ((float)f[1] - h2) / (float)(y2*width - y1*y1*width/(float)height);
a[0] = (float)f[0]/(float)(width*height) - (float)y1*a[1]/(float)height -
a[0] = (float)f[0]/(float)(width*height) - (float)y1*a[1]/(float)height -
(float)x1*a[2]/(float)width;
for (i = 0; i < height; i++)
{
for (i = 0; i < height; i++)
{
for (j = 0; j < width; j++)
{
correction = a[0] + a[1]*(float)i + a[2]*(float)j;
float_img[i*width + j] = img[i*src_step + j] - correction;
if (float_img[i*width + j] < min) min = float_img[i*width+j];
if (float_img[i*width + j] > max) max = float_img[i*width+j];
}
}
//rescaling to the range 0:255
c2 = 0;
if (max == min)
c2 = 255.0f;
else
c2 = 255.0f/(float)(max - min);
c1 = (-(float)min)*c2;
for (i = 0; i < height; i++)
{
for (j = 0; j < width; j++)
@@ -1601,17 +1599,17 @@ CvStatus icvLightingCorrection8uC1R( uchar* img, CvSize roi, int src_step )
cvFree( &float_img );
return CV_NO_ERR;
}
CvStatus icvLightingCorrection( icvImage* img )
CvStatus icvLightingCorrection( icvImage* img )
{
CvSize roi;
if ( img->type != IPL_DEPTH_8U || img->channels != 1 )
return CV_BADFACTOR_ERR;
roi = _cvSize( img->roi.width, img->roi.height );
return _cvLightingCorrection8uC1R( img->data + img->roi.y * img->step + img->roi.x,
return _cvLightingCorrection8uC1R( img->data + img->roi.y * img->step + img->roi.x,
roi, img->step );
}
modules/legacy/src/image.cpp
+3 -3
View File
@@ -182,13 +182,13 @@ void CvImage::show( const char* window_name )
/////////////////////////////// CvMatrix implementation //////////////////////////////////
CvMatrix::CvMatrix( int rows, int cols, int type, CvMemStorage* storage, bool alloc_data )
CvMatrix::CvMatrix( int _rows, int _cols, int _type, CvMemStorage* storage, bool alloc_data )
{
if( storage )
{
matrix = (CvMat*)cvMemStorageAlloc( storage, sizeof(*matrix) );
cvInitMatHeader( matrix, rows, cols, type, alloc_data ?
cvMemStorageAlloc( storage, rows*cols*CV_ELEM_SIZE(type) ) : 0 );
cvInitMatHeader( matrix, _rows, _cols, _type, alloc_data ?
cvMemStorageAlloc( storage, _rows*_cols*CV_ELEM_SIZE(_type) ) : 0 );
}
else
matrix = 0;
modules/legacy/src/kdtree.cpp
-4
View File
@@ -48,10 +48,6 @@
#include "_kdtree.hpp"
#include "_featuretree.h"
#if defined _MSC_VER && _MSC_VER >= 1400
#pragma warning(disable:4996) // suppress "function call with parameters may be unsafe" in std::copy
#endif
class CvKDTreeWrap : public CvFeatureTree {
template <class __scalartype, int __cvtype>
struct deref {
modules/legacy/src/levmarprojbandle.cpp
+29 -41
View File
@@ -135,13 +135,11 @@ static void icvComputeDerivateProj(CvMat *points4D,CvMat *projMatr, CvMat *statu
}
/* ----- End test ----- */
int i;
/* Allocate memory for derivates */
double p[12];
/* Copy projection matrix */
for( i = 0; i < 12; i++ )
for(int i = 0; i < 12; i++ )
{
p[i] = cvmGet(projMatr,i/4,i%4);
}
@@ -168,7 +166,6 @@ static void icvComputeDerivateProj(CvMat *points4D,CvMat *projMatr, CvMat *statu
piX[1] = X[0]*p[4] + X[1]*p[5] + X[2]*p[6] + X[3]*p[7];
piX[2] = X[0]*p[8] + X[1]*p[9] + X[2]*p[10] + X[3]*p[11];
int i;
/* fill derivate by point */
double tmp3 = 1/(piX[2]*piX[2]);
@@ -177,7 +174,7 @@ static void icvComputeDerivateProj(CvMat *points4D,CvMat *projMatr, CvMat *statu
double tmp2 = -piX[1]*tmp3;
/* fill derivate by projection matrix */
for( i = 0; i < 4; i++ )
for(int i = 0; i < 4; i++ )
{
/* derivate for x */
cvmSet(derivProj,currVisPoint*2,i,X[i]/piX[2]);//x' p1i
@@ -291,8 +288,7 @@ static void icvComputeDerivatePoints(CvMat *points4D,CvMat *projMatr, CvMat *pre
/* Compute derivates by points */
double p[12];
int i;
for( i = 0; i < 12; i++ )
for(int i = 0; i < 12; i++ )
{
p[i] = cvmGet(projMatr,i/4,i%4);
}
@@ -316,13 +312,11 @@ static void icvComputeDerivatePoints(CvMat *points4D,CvMat *projMatr, CvMat *pre
piX[1] = X[0]*p[4] + X[1]*p[5] + X[2]*p[6] + X[3]*p[7];
piX[2] = X[0]*p[8] + X[1]*p[9] + X[2]*p[10] + X[3]*p[11];
int i,j;
double tmp3 = 1/(piX[2]*piX[2]);
for( j = 0; j < 2; j++ )//for x and y
for(int j = 0; j < 2; j++ )//for x and y
{
for( i = 0; i < 4; i++ )// for X,Y,Z,W
for(int i = 0; i < 4; i++ )// for X,Y,Z,W
{
cvmSet( derivPoint,
j, currVisPoint*4+i,
@@ -863,26 +857,25 @@ void icvReconstructPoints4DStatus(CvMat** projPoints, CvMat **projMatrs, CvMat**
double point3D_dat[3];
point3D = cvMat(3,1,CV_64F,point3D_dat);
int currPoint;
int numVis = 0;
double totalError = 0;
for( currPoint = 0; currPoint < numPoints; currPoint++ )
for(int curPoint = 0; curPoint < numPoints; curPoint++ )
{
if( cvmGet(presPoints[currImage],0,currPoint) > 0)
if( cvmGet(presPoints[currImage],0,curPoint) > 0)
{
double dx,dy;
cvGetCol(points4D,&point4D,currPoint);
cvGetCol(points4D,&point4D,curPoint);
cvmMul(projMatrs[currImage],&point4D,&point3D);
double w = point3D_dat[2];
double x = point3D_dat[0] / w;
double y = point3D_dat[1] / w;
dx = cvmGet(projPoints[currImage],0,currPoint) - x;
dy = cvmGet(projPoints[currImage],1,currPoint) - y;
dx = cvmGet(projPoints[currImage],0,curPoint) - x;
dy = cvmGet(projPoints[currImage],1,curPoint) - y;
if( projError )
{
cvmSet(projError[currImage],0,currPoint,dx);
cvmSet(projError[currImage],1,currPoint,dy);
cvmSet(projError[currImage],0,curPoint,dx);
cvmSet(projError[currImage],1,curPoint,dy);
}
totalError += sqrt(dx*dx+dy*dy);
numVis++;
@@ -1147,10 +1140,8 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
CV_CALL( changeVectorX_points4D = cvCreateMat(4,numPoints,CV_64F));
CV_CALL( changeVectorX_projMatrs = cvCreateMat(3,4,CV_64F));
int currImage;
/* ----- Test input params ----- */
for( currImage = 0; currImage < numImages; currImage++ )
for(int currImage = 0; currImage < numImages; currImage++ )
{
/* Test size of input initial and result projection matrices */
if( !CV_IS_MAT(projMatrs[currImage]) )
@@ -1194,7 +1185,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
/* ----- End test ----- */
/* Copy projection matrices to vectorX0 */
for( currImage = 0; currImage < numImages; currImage++ )
for(int currImage = 0; currImage < numImages; currImage++ )
{
CV_CALL( vectorX_projMatrs[currImage] = cvCreateMat(3,4,CV_64F));
CV_CALL( newVectorX_projMatrs[currImage] = cvCreateMat(3,4,CV_64F));
@@ -1230,7 +1221,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
CV_CALL( workMatrsInvVi[i] = cvCreateMat(4,4,CV_64F) );
}
for( currImage = 0; currImage < numImages; currImage++ )
for(int currImage = 0; currImage < numImages; currImage++ )
{
CV_CALL( matrsUk[currImage] = cvCreateMat(12,12,CV_64F) );
CV_CALL( workMatrsUk[currImage] = cvCreateMat(12,12,CV_64F) );
@@ -1299,7 +1290,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
/* Compute error with observed value and computed projection */
double prevError;
prevError = 0;
for( currImage = 0; currImage < numImages; currImage++ )
for(int currImage = 0; currImage < numImages; currImage++ )
{
cvSub(observVisPoints[currImage],projVisPoints[currImage],errorProjPoints[currImage]);
double currNorm = cvNorm(errorProjPoints[currImage]);
@@ -1325,8 +1316,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
fprintf(file,"projection errors\n");
/* Print all proejction errors */
int currImage;
for( currImage = 0; currImage < numImages; currImage++)
for(int currImage = 0; currImage < numImages; currImage++)
{
fprintf(file,"\nImage=%d\n",currImage);
int numPn = errorProjPoints[currImage]->cols;
@@ -1419,7 +1409,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
}
#endif
/* Copy matrices Uk to work matrices Uk */
for( currImage = 0; currImage < numImages; currImage++ )
for(int currImage = 0; currImage < numImages; currImage++ )
{
cvCopy(matrsUk[currImage],workMatrsUk[currImage]);
}
@@ -1459,7 +1449,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
{
cvCopy(matrsVi[currV],workMatrVi);
for( int i = 0; i < 4; i++ )
for( i = 0; i < 4; i++ )
{
cvmSet(workMatrVi,i,i,cvmGet(matrsVi[currV],i,i)*(1+alpha) );
}
@@ -1468,7 +1458,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
}
/* Add alpha to matrUk and make matrix workMatrsUk */
for( currImage = 0; currImage< numImages; currImage++ )
for(int currImage = 0; currImage< numImages; currImage++ )
{
for( i = 0; i < 12; i++ )
@@ -1485,7 +1475,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
int currRowV;
for( currRowV = 0; currRowV < 4; currRowV++ )
{
for( currImage = 0; currImage < numImages; currImage++ )
for(int currImage = 0; currImage < numImages; currImage++ )
{
for( int currCol = 0; currCol < 12; currCol++ )/* For each column of transposed matrix W */
{
@@ -1506,7 +1496,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
cvmMul(matrW,matrTmpSys1,matrSysDeltaP);
/* need to compute U-matrTmpSys2. But we compute matTmpSys2-U */
for( currImage = 0; currImage < numImages; currImage++ )
for(int currImage = 0; currImage < numImages; currImage++ )
{
CvMat subMatr;
cvGetSubRect(matrSysDeltaP,&subMatr,cvRect(currImage*12,currImage*12,12,12));
@@ -1536,8 +1526,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
FILE* file;
file = fopen( TRACK_BUNDLE_FILE_DELTAP ,"w");
int currImage;
for( currImage = 0; currImage < numImages; currImage++ )
for(int currImage = 0; currImage < numImages; currImage++ )
{
fprintf(file,"\nImage=%d\n",currImage);
int i;
@@ -1576,7 +1565,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
/* We know delta and compute new value of vector X: nextVectX = vectX + deltas */
/* Compute new P */
for( currImage = 0; currImage < numImages; currImage++ )
for(int currImage = 0; currImage < numImages; currImage++ )
{
for( i = 0; i < 3; i++ )
{
@@ -1604,7 +1593,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
icvProjPointsStatusFunc(numImages, newVectorX_points4D, newVectorX_projMatrs, pointsPres, projVisPoints);
/* Compute error with observed value and computed projection */
double newError = 0;
for( currImage = 0; currImage < numImages; currImage++ )
for(int currImage = 0; currImage < numImages; currImage++ )
{
cvSub(observVisPoints[currImage],projVisPoints[currImage],errorProjPoints[currImage]);
double currNorm = cvNorm(errorProjPoints[currImage]);
@@ -1643,8 +1632,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
/* Print all projection errors */
#if 0
fprintf(file,"projection errors\n");
int currImage;
for( currImage = 0; currImage < numImages; currImage++)
for(int currImage = 0; currImage < numImages; currImage++)
{
fprintf(file,"\nImage=%d\n",currImage);
int numPn = errorProjPoints[currImage]->cols;
@@ -1676,7 +1664,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
double currNorm1 = 0;
double currNorm2 = 0;
/* compute norm for projection matrices */
for( currImage = 0; currImage < numImages; currImage++ )
for(int currImage = 0; currImage < numImages; currImage++ )
{
currNorm1 = cvNorm(newVectorX_projMatrs[currImage],vectorX_projMatrs[currImage]);
currNorm2 = cvNorm(newVectorX_projMatrs[currImage]);
@@ -1713,7 +1701,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
}
alpha /= 10;
for( currImage = 0; currImage < numImages; currImage++ )
for(int currImage = 0; currImage < numImages; currImage++ )
{
cvCopy(newVectorX_projMatrs[currImage],vectorX_projMatrs[currImage]);
}
@@ -1745,7 +1733,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo
/*--------------------------------------------*/
/* Optimization complete copy computed params */
/* Copy projection matrices */
for( currImage = 0; currImage < numImages; currImage++ )
for(int currImage = 0; currImage < numImages; currImage++ )
{
cvCopy(newVectorX_projMatrs[currImage],resultProjMatrs[currImage]);
}
modules/legacy/src/oneway.cpp
+2 -3
View File
@@ -423,8 +423,8 @@ namespace cv{
}
cvAdd(pca_descriptors[0].GetPatch(i), m_samples[i], m_samples[i]);
double sum = cvSum(m_samples[i]).val[0];
cvConvertScale(m_samples[i], m_samples[i], 1.0/sum);
double sm = cvSum(m_samples[i]).val[0];
cvConvertScale(m_samples[i], m_samples[i], 1.0/sm);
#if 0
IplImage* test = cvCreateImage(cvSize(12, 12), IPL_DEPTH_8U, 1);
@@ -1671,7 +1671,6 @@ namespace cv{
if (! m_pca_descriptors[i].ReadByName(fn, buf))
{
char buf[1024];
sprintf(buf, "descriptor for pca component %d", i);
m_pca_descriptors[i].ReadByName(fn, buf);
}
modules/legacy/src/precomp.hpp
-4
View File
@@ -41,10 +41,6 @@
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4251 4710 4711 4514 4996 )
#endif
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif
modules/legacy/src/stereogc.cpp
+24 -24
View File
@@ -143,7 +143,7 @@ static int64 icvGCMaxFlow( GCVtx* vtx, int nvtx, GCEdge* edges, GCVtx**& _orphan
int norphans = 0, maxOrphans = _maxOrphans;
GCVtx** orphans = _orphans;
stub.next = nilNode;
// initialize the active queue and the graph vertices
for( i = 0; i < nvtx; i++ )
{
@@ -170,7 +170,7 @@ static int64 icvGCMaxFlow( GCVtx* vtx, int nvtx, GCEdge* edges, GCVtx**& _orphan
GCVtx* v, *u;
int e0 = -1, ei = 0, ej = 0, min_weight, weight;
uchar vt;
// grow S & T search trees, find an edge connecting them
while( first != nilNode )
{
@@ -262,7 +262,7 @@ static int64 icvGCMaxFlow( GCVtx* vtx, int nvtx, GCEdge* edges, GCVtx**& _orphan
v->parent = ORPHAN;
}
}
v->weight = (short)(v->weight + min_weight*(1-k*2));
if( v->weight == 0 )
{
@@ -277,12 +277,12 @@ static int64 icvGCMaxFlow( GCVtx* vtx, int nvtx, GCEdge* edges, GCVtx**& _orphan
curr_ts++;
while( norphans > 0 )
{
GCVtx* v = orphans[--norphans];
GCVtx* v1 = orphans[--norphans];
int d, min_dist = INT_MAX;
e0 = 0;
vt = v->t;
vt = v1->t;
for( ei = v->first; ei != 0; ei = edges[ei].next )
for( ei = v1->first; ei != 0; ei = edges[ei].next )
{
if( edges[ei^(vt^1)].weight == 0 )
continue;
@@ -329,16 +329,16 @@ static int64 icvGCMaxFlow( GCVtx* vtx, int nvtx, GCEdge* edges, GCVtx**& _orphan
}
}
if( (v->parent = e0) > 0 )
if( (v1->parent = e0) > 0 )
{
v->ts = curr_ts;
v->dist = min_dist;
v1->ts = curr_ts;
v1->dist = min_dist;
continue;
}
/* no parent is found */
v->ts = 0;
for( ei = v->first; ei != 0; ei = edges[ei].next )
v1->ts = 0;
for( ei = v1->first; ei != 0; ei = edges[ei].next )
{
u = edges[ei].dst;
ej = u->parent;
@@ -349,7 +349,7 @@ static int64 icvGCMaxFlow( GCVtx* vtx, int nvtx, GCEdge* edges, GCVtx**& _orphan
u->next = nilNode;
last = last->next = u;
}
if( ej > 0 && edges[ej].dst == v )
if( ej > 0 && edges[ej].dst == v1 )
{
if( norphans >= maxOrphans )
maxOrphans = icvGCResizeOrphansBuf( orphans, norphans );
@@ -387,7 +387,7 @@ CvStereoGCState* cvCreateStereoGCState( int numberOfDisparities, int maxIters )
void cvReleaseStereoGCState( CvStereoGCState** _state )
{
CvStereoGCState* state;
if( !_state && !*_state )
return;
@@ -438,7 +438,7 @@ static void icvInitGraySubpix( const CvMat* left, const CvMat* right,
CvMat* left3, CvMat* right3 )
{
int k, x, y, rows = left->rows, cols = left->cols;
for( k = 0; k < 2; k++ )
{
const CvMat* src = k == 0 ? left : right;
@@ -452,11 +452,11 @@ static void icvInitGraySubpix( const CvMat* left, const CvMat* right,
const uchar* sptr_next = y < rows-1 ? sptr + sstep : sptr;
uchar* dptr = dst->data.ptr + dst->step*y;
int v_prev = sptr[0];
for( x = 0; x < cols; x++, dptr += 3 )
{
int v = sptr[x], v1, minv = v, maxv = v;
v1 = (v + v_prev)/2;
minv = MIN(minv, v1); maxv = MAX(maxv, v1);
v1 = (v + sptr_prev[x])/2;
@@ -492,7 +492,7 @@ icvComputeK( CvStereoGCState* state )
{
const uchar* lptr = state->left->data.ptr + state->left->step*y;
const uchar* rptr = state->right->data.ptr + state->right->step*y;
for( x = 0; x < cols; x++ )
{
for( d = maxd-1, i = 0; d >= mind; d-- )
@@ -701,7 +701,7 @@ static int64 icvAlphaExpand( int64 Eprev, int alpha, CvStereoGCState* state, CvS
GCVtx** pright = pright0 + pstep*y;
const uchar* lr[] = { left, right };
const short* dlr[] = { dleft, dright };
GCVtx** plr[] = { pleft, pright };
GCVtx** plr[] = { pleft, pright };
for( k = 0; k < 2; k++ )
{
@@ -820,12 +820,12 @@ static int64 icvAlphaExpand( int64 Eprev, int alpha, CvStereoGCState* state, CvS
GCVtx** pright = pright0 + pstep*y;
for( x = 0; x < cols; x++ )
{
GCVtx* var = pleft[x];
if( var && var->parent && var->t )
dleft[x] = (short)alpha;
GCVtx* var2 = pleft[x];
if( var2 && var2->parent && var2->t )
dleft[x] = (short)alpha;
var = pright[x];
if( var && var->parent && var->t )
var2 = pright[x];
if( var2 && var2->parent && var2->t )
dright[x] = (short)-alpha;
}
}
@@ -903,7 +903,7 @@ CV_IMPL void cvFindStereoCorrespondenceGC( const CvArr* _left, const CvArr* _rig
icvInitStereoConstTabs();
icvInitGraySubpix( left, right, state->left, state->right );
std::vector<int> disp(state->numberOfDisparities);
CvMat _disp = cvMat( 1, (int)disp.size(), CV_32S, &disp[0] );
cvRange( &_disp, state->minDisparity, state->minDisparity + state->numberOfDisparities );
modules/legacy/src/testseq.cpp
+16 -18
View File
@@ -500,15 +500,15 @@ static CvTestSeqElem* icvTestSeqReadElemOne(CvTestSeq_* pTS, CvFileStorage* fs,
int y0=0, y1=pFG->height-1;
for(y0=0; y0<pFG->height; ++y0)
{
CvMat m;
CvScalar s = cvSum(cvGetRow(pFG, &m, y0));
CvMat tmp;
CvScalar s = cvSum(cvGetRow(pFG, &tmp, y0));
if(s.val[0] > 255*7) break;
}
for(y1=pFG->height-1; y1>0; --y1)
{
CvMat m;
CvScalar s = cvSum(cvGetRow(pFG, &m, y1));
CvMat tmp;
CvScalar s = cvSum(cvGetRow(pFG, &tmp, y1));
if(s.val[0] > 255*7) break;
}
@@ -573,8 +573,8 @@ static CvTestSeqElem* icvTestSeqReadElemOne(CvTestSeq_* pTS, CvFileStorage* fs,
p->FrameNum = cvReadIntByName( fs, node, "FrameNum", p->FrameNum );
p->FrameNum = cvReadIntByName( fs, node, "Dur", p->FrameNum );
{
int LastFrame = cvReadIntByName( fs, node, "LastFrame", p->FrameBegin+p->FrameNum-1 );
p->FrameNum = MIN(p->FrameNum,LastFrame - p->FrameBegin+1);
int lastFrame = cvReadIntByName( fs, node, "LastFrame", p->FrameBegin+p->FrameNum-1 );
p->FrameNum = MIN(p->FrameNum,lastFrame - p->FrameBegin+1);
}
icvTestSeqAllocTrans(p);
@@ -621,8 +621,8 @@ static CvTestSeqElem* icvTestSeqReadElemOne(CvTestSeq_* pTS, CvFileStorage* fs,
if(pTransSeq&&KeyFrameNum>1)
{
int i0,i1,i;
for(i=0; i<KeyFrameNum; ++i)
int i0,i1;
for(int i=0; i<KeyFrameNum; ++i)
{
CvFileNode* pTN = (CvFileNode*)cvGetSeqElem(pTransSeq,i);
KeyFrames[i] = cvReadIntByName(fs,pTN,"frame",-1);
@@ -633,14 +633,12 @@ static CvTestSeqElem* icvTestSeqReadElemOne(CvTestSeq_* pTS, CvFileStorage* fs,
for(i0=0, i1=1; i1<KeyFrameNum;)
{
int i;
for(i1=i0+1; i1<KeyFrameNum && KeyFrames[i1]<0; i1++);
assert(i1<KeyFrameNum);
assert(i1>i0);
for(i=i0+1; i<i1; ++i)
for(int i=i0+1; i<i1; ++i)
{
KeyFrames[i] = cvRound(KeyFrames[i0] + (float)(i-i0)*(float)(KeyFrames[i1] - KeyFrames[i0])/(float)(i1-i0));
}
@@ -665,9 +663,9 @@ static CvTestSeqElem* icvTestSeqReadElemOne(CvTestSeq_* pTS, CvFileStorage* fs,
{ /* Only one transform record: */
int i;
double val;
CvFileNode* node = cvGetFileNodeByName( fs, pTN,name);
if(node == NULL) continue;
val = cvReadReal(node,defv);
CvFileNode* fnode = cvGetFileNodeByName( fs, pTN,name);
if(fnode == NULL) continue;
val = cvReadReal(fnode,defv);
for(i=0; i<p->TransNum; ++i)
{
@@ -683,15 +681,15 @@ static CvTestSeqElem* icvTestSeqReadElemOne(CvTestSeq_* pTS, CvFileStorage* fs,
double v0;
double v1;
CvFileNode* pTN = (CvFileNode*)cvGetSeqElem(pTransSeq,0);
v0 = cvReadRealByName(fs, pTN,name,defv);
CvFileNode* pTN1 = (CvFileNode*)cvGetSeqElem(pTransSeq,0);
v0 = cvReadRealByName(fs, pTN1,name,defv);
for(i1=1,i0=0; i1<KeyFrameNum; ++i1)
{
int f0,f1;
int i;
CvFileNode* pTN = (CvFileNode*)cvGetSeqElem(pTransSeq,i1);
CvFileNode* pVN = cvGetFileNodeByName(fs,pTN,name);
CvFileNode* pTN2 = (CvFileNode*)cvGetSeqElem(pTransSeq,i1);
CvFileNode* pVN = cvGetFileNodeByName(fs,pTN2,name);
if(pVN)v1 = cvReadReal(pVN,defv);
else if(pVN == NULL && i1 == KeyFrameNum-1) v1 = defv;
modules/legacy/src/trifocal.cpp
+19 -27
View File
@@ -861,8 +861,7 @@ int icvComputeProjectMatricesNPoints( CvMat* points1,CvMat* points2,CvMat* poin
projMatrs[1] = projMatr2;
projMatrs[2] = projMatr3;
int i;
for( i = 0; i < 3; i++ )
for(int i = 0; i < 3; i++ )
{
if( projMatrs[i]->cols != 4 || projMatrs[i]->rows != 3 )
{
@@ -870,7 +869,7 @@ int icvComputeProjectMatricesNPoints( CvMat* points1,CvMat* points2,CvMat* poin
}
}
for( i = 0; i < 3; i++ )
for(int i = 0; i < 3; i++ )
{
if( points[i]->rows != 2)
{
@@ -951,10 +950,9 @@ int icvComputeProjectMatricesNPoints( CvMat* points1,CvMat* points2,CvMat* poin
icvProject4DPoints(recPoints4D,&proj6[2],tmpProjPoints[2]);
/* Compute distances and number of good points (inliers) */
int i;
int currImage;
numGoodPoints = 0;
for( i = 0; i < numPoints; i++ )
for(int i = 0; i < numPoints; i++ )
{
double dist=-1;
dist = 0;
@@ -1051,7 +1049,7 @@ int icvComputeProjectMatricesNPoints( CvMat* points1,CvMat* points2,CvMat* poin
CvMat *optStatus;
optStatus = cvCreateMat(1,numPoints,CV_64F);
int testNumber = 0;
for( i=0;i<numPoints;i++ )
for(int i=0;i<numPoints;i++ )
{
cvmSet(optStatus,0,i,(double)bestFlags[i]);
testNumber += bestFlags[i];
@@ -1063,7 +1061,7 @@ int icvComputeProjectMatricesNPoints( CvMat* points1,CvMat* points2,CvMat* poin
CvMat *gPresPoints;
gPresPoints = cvCreateMat(1,maxGoodPoints,CV_64F);
for( i = 0; i < maxGoodPoints; i++)
for(int i = 0; i < maxGoodPoints; i++)
{
cvmSet(gPresPoints,0,i,1.0);
}
@@ -1130,7 +1128,7 @@ int icvComputeProjectMatricesNPoints( CvMat* points1,CvMat* points2,CvMat* poin
int currImage;
finalGoodPoints = 0;
for( i = 0; i < numPoints; i++ )
for(int i = 0; i < numPoints; i++ )
{
double dist=-1;
/* Choose max distance for each of three points */
@@ -1178,7 +1176,7 @@ int icvComputeProjectMatricesNPoints( CvMat* points1,CvMat* points2,CvMat* poin
/* Create status */
CvMat *optStatus;
optStatus = cvCreateMat(1,numPoints,CV_64F);
for( i=0;i<numPoints;i++ )
for(int i=0;i<numPoints;i++ )
{
cvmSet(optStatus,0,i,(double)bestFlags[i]);
}
@@ -1236,7 +1234,7 @@ int icvComputeProjectMatricesNPoints( CvMat* points1,CvMat* points2,CvMat* poin
int currImage;
finalGoodPoints = 0;
for( i = 0; i < numPoints; i++ )
for(int i = 0; i < numPoints; i++ )
{
double dist=-1;
/* Choose max distance for each of three points */
@@ -1662,15 +1660,12 @@ void GetProjMatrFromReducedFundamental(CvMat* fundReduceCoefs,CvMat* projMatrCoe
matrA_dat[7] = s;
matrA_dat[8] = -(p+q+r+s+t);
CvMat matrU;
CvMat matrW;
CvMat matrV;
double matrU_dat[3*3];
double matrW_dat[3*3];
double matrV_dat[3*3];
matrU = cvMat(3,3,CV_64F,matrU_dat);
matrW = cvMat(3,3,CV_64F,matrW_dat);
matrV = cvMat(3,3,CV_64F,matrV_dat);
@@ -1731,27 +1726,24 @@ void GetProjMatrFromReducedFundamental(CvMat* fundReduceCoefs,CvMat* projMatrCoe
matrK_dat[4*6+5] = -B2;
matrK_dat[5*6+5] = -C2;
CvMat matrU;
CvMat matrW;
CvMat matrV;
CvMat matrW1;
CvMat matrV1;
double matrU_dat[36];
double matrW_dat[36];
double matrV_dat[36];
double matrW_dat1[36];
double matrV_dat1[36];
matrU = cvMat(6,6,CV_64F,matrU_dat);
matrW = cvMat(6,6,CV_64F,matrW_dat);
matrV = cvMat(6,6,CV_64F,matrV_dat);
matrW1 = cvMat(6,6,CV_64F,matrW_dat1);
matrV1 = cvMat(6,6,CV_64F,matrV_dat1);
/* From svd we need just last vector of V or last row V' */
/* We get transposed matrixes U and V */
cvSVD(&matrK,&matrW,0,&matrV,CV_SVD_V_T);
cvSVD(&matrK,&matrW1,0,&matrV1,CV_SVD_V_T);
a = matrV_dat[6*5+0];
b = matrV_dat[6*5+1];
c = matrV_dat[6*5+2];
d = matrV_dat[6*5+3];
a = matrV_dat1[6*5+0];
b = matrV_dat1[6*5+1];
c = matrV_dat1[6*5+2];
d = matrV_dat1[6*5+3];
/* we don't need last two coefficients. Because it just a k1,k2 */
cvmSet(projMatrCoefs,0,0,a);
modules/legacy/src/vecfacetracking.cpp
+4 -4
View File
@@ -137,7 +137,7 @@ struct CvFaceTracker
if (NULL != mstgContours)
cvReleaseMemStorage(&mstgContours);
};
int Init(CvRect* pRects, IplImage* imgGray)
int Init(CvRect* pRects, IplImage* imgray)
{
for (int i = 0; i < NUM_FACE_ELEMENTS; i++)
{
@@ -146,10 +146,10 @@ struct CvFaceTracker
ptTempl[i] = face[i].ptCenter;
rTempl[i] = face[i].r;
}
imgGray = cvCreateImage(cvSize(imgGray->width, imgGray->height), 8, 1);
imgThresh = cvCreateImage(cvSize(imgGray->width, imgGray->height), 8, 1);
imgray = cvCreateImage(cvSize(imgray->width, imgray->height), 8, 1);
imgThresh = cvCreateImage(cvSize(imgray->width, imgray->height), 8, 1);
mstgContours = cvCreateMemStorage();
if ((NULL == imgGray) ||
if ((NULL == imgray) ||
(NULL == imgThresh) ||
(NULL == mstgContours))
return FALSE;
modules/legacy/test/test_stereomatching.cpp
+2 -2
View File
@@ -593,11 +593,11 @@ int CV_StereoMatchingTest::readDatasetsParams( FileStorage& fs )
assert(fn.isSeq());
for( int i = 0; i < (int)fn.size(); i+=3 )
{
string name = fn[i];
string nm = fn[i];
DatasetParams params;
string sf = fn[i+1]; params.dispScaleFactor = atoi(sf.c_str());
string uv = fn[i+2]; params.dispUnknVal = atoi(uv.c_str());
datasetsParams[name] = params;
datasetsParams[nm] = params;
}
return cvtest::TS::OK;
}
modules/ml/src/precomp.hpp
-4
View File
@@ -41,10 +41,6 @@
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4251 4514 4710 4711 4710 )
#endif
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif
modules/ml/src/svm.cpp
-4
View File
@@ -88,10 +88,6 @@ using namespace cv;
#include <stdarg.h>
#include <ctype.h>
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4514 ) /* unreferenced inline functions */
#endif
#if 1
typedef float Qfloat;
#define QFLOAT_TYPE CV_32F
modules/nonfree/src/precomp.hpp
-4
View File
@@ -43,10 +43,6 @@
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4251 4512 4710 4711 4514 4996 )
#endif
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif
modules/objdetect/src/linemod.cpp
+3 -3
View File
@@ -416,7 +416,7 @@ void hysteresisGradient(Mat& magnitude, Mat& quantized_angle,
// Only accept the quantization if majority of pixels in the patch agree
static const int NEIGHBOR_THRESHOLD = 5;
if (max_votes >= NEIGHBOR_THRESHOLD)
quantized_angle.at<uchar>(r, c) = 1 << index;
quantized_angle.at<uchar>(r, c) = uchar(1 << index);
}
}
}
@@ -1252,7 +1252,7 @@ static void similarity(const std::vector<Mat>& linear_memories, const Template&
}
#endif
for ( ; j < template_positions; ++j)
dst_ptr[j] += lm_ptr[j];
dst_ptr[j] = uchar(dst_ptr[j] + lm_ptr[j]);
}
}
@@ -1334,7 +1334,7 @@ static void similarityLocal(const std::vector<Mat>& linear_memories, const Templ
for (int row = 0; row < 16; ++row)
{
for (int col = 0; col < 16; ++col)
dst_ptr[col] += lm_ptr[col];
dst_ptr[col] = uchar(dst_ptr[col] + lm_ptr[col]);
dst_ptr += 16;
lm_ptr += W;
}
modules/objdetect/src/precomp.hpp
-4
View File
@@ -43,10 +43,6 @@
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4251 4710 4711 4514 4996 )
#endif
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif
modules/photo/src/precomp.hpp
-4
View File
@@ -43,10 +43,6 @@
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4251 4512 4710 4711 4514 4996 )
#endif
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif
modules/python/src2/cv2.cv.hpp
+22 -2
View File
@@ -205,8 +205,18 @@ static CvMat *PyCvMat_AsCvMat(PyObject *o)
}
#define cvReleaseIplConvKernel(x) cvReleaseStructuringElement(x)
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( push )
#pragma warning( disable : 4244 )
#endif
#include "generated3.i"
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( pop )
#endif
/* iplimage */
static void iplimage_dealloc(PyObject *self)
@@ -388,7 +398,8 @@ static PyObject *cvmat_tostring(PyObject *self, PyObject *args)
bps = CV_MAT_CN(m->type) * 8;
break;
default:
return failmsg("Unrecognised depth %d", CV_MAT_DEPTH(m->type)), (PyObject*)0;
failmsg("Unrecognized depth %d", CV_MAT_DEPTH(m->type));
return (PyObject*)0;
}
int bpl = m->cols * bps; // bytes per line
@@ -3800,7 +3811,7 @@ static int zero = 0;
#define CVPY_VALIDATE_DrawChessboardCorners() do { \
if ((patternSize.width * patternSize.height) != corners.count) \
return (PyObject*)failmsg("Size is %dx%d, but corner list is length %d", patternSize.width, patternSize.height, corners.count); \
return (PyObject*)0; \
} while (0)
#define cvGetRotationMatrix2D cv2DRotationMatrix
@@ -3839,8 +3850,17 @@ static double cppKMeans(const CvArr* _samples, int cluster_count, CvArr* _labels
#define cvKMeans2(samples, nclusters, labels, termcrit, attempts, flags, centers) \
cppKMeans(samples, nclusters, labels, termcrit, attempts, flags, centers)
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( push )
#pragma warning( disable : 4244 )
#endif
#include "generated0.i"
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( pop )
#endif
static PyMethodDef old_methods[] = {
#if PYTHON_USE_NUMPY
modules/ts/CMakeLists.txt
+1 -4
View File
@@ -9,7 +9,7 @@ if(MINGW)
endif()
set(OPENCV_MODULE_IS_PART_OF_WORLD FALSE)
ocv_add_module(ts opencv_core)
ocv_glob_module_sources()
ocv_module_include_directories()
@@ -17,9 +17,6 @@ ocv_create_module()
if(BUILD_SHARED_LIBS AND NOT MINGW)
add_definitions(-DGTEST_CREATE_SHARED_LIBRARY=1)
if (MSVC AND NOT ENABLE_NOISY_WARNINGS)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4275")
endif()
else()
add_definitions(-DGTEST_CREATE_SHARED_LIBRARY=0)
endif()
modules/ts/include/opencv2/ts/ts.hpp
+2 -2
View File
@@ -19,8 +19,8 @@
#include <stdarg.h> // for va_list
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4251 4275 4355 4127 )
#ifdef _MSC_VER
#pragma warning( disable: 4127 )
#endif
#define GTEST_DONT_DEFINE_FAIL 0
modules/ts/include/opencv2/ts/ts_gtest.h
+1 -1
View File
@@ -16843,7 +16843,7 @@ class GTEST_API_ TestPartResultArray {
};
// This interface knows how to report a test part result.
class TestPartResultReporterInterface {
class GTEST_API_ TestPartResultReporterInterface {
public:
virtual ~TestPartResultReporterInterface() {}
modules/ts/src/precomp.hpp
-4
View File
@@ -1,7 +1,3 @@
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4127 4251)
#endif
#include "opencv2/core/core_c.h"
#include "opencv2/ts/ts.hpp"
modules/ts/src/ts_gtest.cpp
+5 -1
View File
@@ -36,7 +36,11 @@
// This line ensures that gtest.h can be compiled on its own, even
// when it's fused.
#include "opencv2/ts/ts.hpp"
#include "precomp.hpp"
#ifdef __GNUC__
# pragma GCC diagnostic ignored "-Wmissing-declarations"
#endif
// The following lines pull in the real gtest *.cc files.
// Copyright 2005, Google Inc.
modules/ts/src/ts_perf.cpp
+8
View File
@@ -584,9 +584,17 @@ int64 TestBase::_calibrate()
return (int64)compensation;
}
#ifdef _MSC_VER
# pragma warning(push)
# pragma warning(disable:4355) // 'this' : used in base member initializer list
#endif
TestBase::TestBase(): declare(this)
{
}
#ifdef _MSC_VER
# pragma warning(pop)
#endif
void TestBase::declareArray(SizeVector& sizes, cv::InputOutputArray a, int wtype)
{
modules/video/src/bgfg_gaussmix2.cpp
+2 -2
View File
@@ -455,7 +455,7 @@ struct MOG2Invoker
}
//set the number of modes
modesUsed[x] = nmodes;
modesUsed[x] = uchar(nmodes);
mask[x] = background ? 0 :
detectShadows && detectShadowGMM(data, nchannels, nmodes, gmm, mean, Tb, TB, tau) ?
shadowVal : 255;
@@ -571,7 +571,7 @@ void BackgroundSubtractorMOG2::operator()(InputArray _image, OutputArray _fgmask
bgmodelUsedModes.data, nmixtures, (float)learningRate,
(float)varThreshold,
backgroundRatio, varThresholdGen,
fVarInit, fVarMin, fVarMax, -learningRate*fCT, fTau,
fVarInit, fVarMin, fVarMax, float(-learningRate*fCT), fTau,
bShadowDetection, nShadowDetection));
}
}
modules/video/src/precomp.hpp
-4
View File
@@ -43,10 +43,6 @@
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4251 4710 4711 4514 4996 )
#endif
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif
modules/videostab/src/global_motion.cpp
+4 -4
View File
@@ -730,8 +730,8 @@ Mat KeypointBasedMotionEstimatorGpu::estimate(const gpu::GpuMat &frame0, const g
// perform outlier rejection
IOutlierRejector *outlierRejector = static_cast<IOutlierRejector*>(outlierRejector_);
if (!dynamic_cast<NullOutlierRejector*>(outlierRejector))
IOutlierRejector *rejector = static_cast<IOutlierRejector*>(outlierRejector_);
if (!dynamic_cast<NullOutlierRejector*>(rejector))
{
outlierRejector_->process(frame0.size(), hostPointsPrev_, hostPoints_, rejectionStatus_);
@@ -750,8 +750,8 @@ Mat KeypointBasedMotionEstimatorGpu::estimate(const gpu::GpuMat &frame0, const g
}
}
hostPointsPrev_ = Mat(1, hostPointsPrevTmp_.size(), CV_32FC2, &hostPointsPrevTmp_[0]);
hostPoints_ = Mat(1, hostPointsTmp_.size(), CV_32FC2, &hostPointsTmp_[0]);
hostPointsPrev_ = Mat(1, (int)hostPointsPrevTmp_.size(), CV_32FC2, &hostPointsPrevTmp_[0]);
hostPoints_ = Mat(1, (int)hostPointsTmp_.size(), CV_32FC2, &hostPointsTmp_[0]);
}
// estimate motion
modules/videostab/src/outlier_rejection.cpp
+1 -1
View File
@@ -52,7 +52,7 @@ namespace videostab
{
void NullOutlierRejector::process(
Size frameSize, InputArray points0, InputArray points1, OutputArray mask)
Size /*frameSize*/, InputArray points0, InputArray points1, OutputArray mask)
{
CV_Assert(points0.type() == points1.type());
CV_Assert(points0.getMat().checkVector(2) == points1.getMat().checkVector(2));
modules/videostab/src/wobble_suppression.cpp
+2 -2
View File
@@ -95,13 +95,13 @@ void MoreAccurateMotionWobbleSuppressor::suppress(int idx, const Mat &frame, Mat
yl = ML(1,0)*x + ML(1,1)*y + ML(1,2);
zl = ML(2,0)*x + ML(2,1)*y + ML(2,2);
xl /= zl; yl /= zl;
wl = idx - k1;
wl = float(idx - k1);
xr = MR(0,0)*x + MR(0,1)*y + MR(0,2);
yr = MR(1,0)*x + MR(1,1)*y + MR(1,2);
zr = MR(2,0)*x + MR(2,1)*y + MR(2,2);
xr /= zr; yr /= zr;
wr = k2 - idx;
wr = float(k2 - idx);
mapx_(y,x) = (wr * xl + wl * xr) / (wl + wr);
mapy_(y,x) = (wr * yl + wl * yr) / (wl + wr);
samples/c/find_obj.cpp
+10 -13
View File
@@ -32,23 +32,20 @@ static void help()
// define whether to use approximate nearest-neighbor search
#define USE_FLANN
IplImage* image = 0;
#ifdef USE_FLANN
static void
flannFindPairs( const CvSeq*, const CvSeq* objectDescriptors,
const CvSeq*, const CvSeq* imageDescriptors, vector<int>& ptpairs )
{
int length = (int)(objectDescriptors->elem_size/sizeof(float));
int length = (int)(objectDescriptors->elem_size/sizeof(float));
cv::Mat m_object(objectDescriptors->total, length, CV_32F);
cv::Mat m_image(imageDescriptors->total, length, CV_32F);
cv::Mat m_image(imageDescriptors->total, length, CV_32F);
// copy descriptors
// copy descriptors
CvSeqReader obj_reader;
float* obj_ptr = m_object.ptr<float>(0);
float* obj_ptr = m_object.ptr<float>(0);
cvStartReadSeq( objectDescriptors, &obj_reader );
for(int i = 0; i < objectDescriptors->total; i++ )
{
@@ -58,7 +55,7 @@ flannFindPairs( const CvSeq*, const CvSeq* objectDescriptors,
obj_ptr += length;
}
CvSeqReader img_reader;
float* img_ptr = m_image.ptr<float>(0);
float* img_ptr = m_image.ptr<float>(0);
cvStartReadSeq( imageDescriptors, &img_reader );
for(int i = 0; i < imageDescriptors->total; i++ )
{
@@ -77,10 +74,10 @@ flannFindPairs( const CvSeq*, const CvSeq* objectDescriptors,
int* indices_ptr = m_indices.ptr<int>(0);
float* dists_ptr = m_dists.ptr<float>(0);
for (int i=0;i<m_indices.rows;++i) {
if (dists_ptr[2*i]<0.6*dists_ptr[2*i+1]) {
ptpairs.push_back(i);
ptpairs.push_back(indices_ptr[2*i]);
}
if (dists_ptr[2*i]<0.6*dists_ptr[2*i+1]) {
ptpairs.push_back(i);
ptpairs.push_back(indices_ptr[2*i]);
}
}
}
#else
@@ -234,7 +231,7 @@ int main(int argc, char** argv)
cvNamedWindow("Object", 1);
cvNamedWindow("Object Correspond", 1);
static CvScalar colors[] =
static CvScalar colors[] =
{
{{0,0,255}},
{{0,128,255}},
samples/c/mushroom.cpp
+9 -14
View File
@@ -179,7 +179,7 @@ static const char* var_desc[] =
};
static void print_variable_importance( CvDTree* dtree, const char** var_desc )
static void print_variable_importance( CvDTree* dtree )
{
const CvMat* var_importance = dtree->get_var_importance();
int i;
@@ -201,21 +201,16 @@ static void print_variable_importance( CvDTree* dtree, const char** var_desc )
for( i = 0; i < var_importance->cols*var_importance->rows; i++ )
{
double val = var_importance->data.db[i];
if( var_desc )
{
char buf[100];
int len = (int)(strchr( var_desc[i], '(' ) - var_desc[i] - 1);
strncpy( buf, var_desc[i], len );
buf[len] = '\0';
printf( "%s", buf );
}
else
printf( "var #%d", i );
char buf[100];
int len = (int)(strchr( var_desc[i], '(' ) - var_desc[i] - 1);
strncpy( buf, var_desc[i], len );
buf[len] = '\0';
printf( "%s", buf );
printf( ": %g%%\n", val*100. );
}
}
static void interactive_classification( CvDTree* dtree, const char** var_desc )
static void interactive_classification( CvDTree* dtree )
{
char input[1000];
const CvDTreeNode* root;
@@ -319,8 +314,8 @@ int main( int argc, char** argv )
cvReleaseMat( &missing );
cvReleaseMat( &responses );
print_variable_importance( dtree, var_desc );
interactive_classification( dtree, var_desc );
print_variable_importance( dtree );
interactive_classification( dtree );
delete dtree;
return 0;
samples/cpp/OpenEXRimages_HighDynamicRange_Retina_toneMapping_video.cpp
+7 -6
View File
@@ -200,17 +200,18 @@ static void loadNewFrame(const std::string filenamePrototype, const int currentF
// TODO : take care of this step !!! maybe disable of do this in a nicer way ... each successive image should get the same transformation... but it depends on the initial image format
double maxInput, minInput;
minMaxLoc(inputImage, &minInput, &maxInput);
std::cout<<"ORIGINAL IMAGE pixels values range (max,min) : "<<maxInput<<", "<<minInput<<std::endl
;if (firstTimeread)
std::cout<<"ORIGINAL IMAGE pixels values range (max,min) : "<<maxInput<<", "<<minInput<<std::endl;
if (firstTimeread)
{
/* the first time, get the pixel values range and rougthly update scaling value
in order to center values around 128 and getting a range close to [0-255],
=> actually using a little less in order to let some more flexibility in range evolves...
*/
double maxInput, minInput;
minMaxLoc(inputImage, &minInput, &maxInput);
std::cout<<"FIRST IMAGE pixels values range (max,min) : "<<maxInput<<", "<<minInput<<std::endl;
globalRescalefactor=(float)(50.0/(maxInput-minInput)); // less than 255 for flexibility... experimental value to be carefull about
double maxInput1, minInput1;
minMaxLoc(inputImage, &minInput1, &maxInput1);
std::cout<<"FIRST IMAGE pixels values range (max,min) : "<<maxInput1<<", "<<minInput1<<std::endl;
globalRescalefactor=(float)(50.0/(maxInput1-minInput1)); // less than 255 for flexibility... experimental value to be carefull about
double channelOffset = -1.5*minInput;
globalOffset= cv::Scalar(channelOffset, channelOffset, channelOffset, channelOffset);
}
samples/cpp/bagofwords_classification.cpp
+3 -3
View File
@@ -803,9 +803,9 @@ void VocData::calcClassifierPrecRecall(const string& input_file, vector<float>&
std::sort(order.begin(),order.end(),orderingSorter());
/* 2. save ranking results to text file */
string input_file_std = checkFilenamePathsep(input_file);
size_t fnamestart = input_file_std.rfind("/");
string scoregt_file_str = input_file_std.substr(0,fnamestart+1) + "scoregt_" + class_name + ".txt";
string input_file_std1 = checkFilenamePathsep(input_file);
size_t fnamestart = input_file_std1.rfind("/");
string scoregt_file_str = input_file_std1.substr(0,fnamestart+1) + "scoregt_" + class_name + ".txt";
std::ofstream scoregt_file(scoregt_file_str.c_str());
if (scoregt_file.is_open())
{
samples/cpp/build3dmodel.cpp
+4 -4
View File
@@ -230,13 +230,13 @@ static void findConstrainedCorrespondences(const Mat& _F,
{
if( i1 == i )
continue;
Point2f p1 = keypoints1[i1].pt;
Point2f pt1 = keypoints1[i1].pt;
const float* d11 = descriptors1.ptr<float>(i1);
double dist = 0;
e = p2.x*(F[0]*p1.x + F[1]*p1.y + F[2]) +
p2.y*(F[3]*p1.x + F[4]*p1.y + F[5]) +
F[6]*p1.x + F[7]*p1.y + F[8];
e = p2.x*(F[0]*pt1.x + F[1]*pt1.y + F[2]) +
p2.y*(F[3]*pt1.x + F[4]*pt1.y + F[5]) +
F[6]*pt1.x + F[7]*pt1.y + F[8];
if( fabs(e) > eps )
continue;
samples/cpp/calibration.cpp
+3 -3
View File
@@ -173,9 +173,9 @@ static void saveCameraParams( const string& filename,
{
FileStorage fs( filename, FileStorage::WRITE );
time_t t;
time( &t );
struct tm *t2 = localtime( &t );
time_t tt;
time( &tt );
struct tm *t2 = localtime( &tt );
char buf[1024];
strftime( buf, sizeof(buf)-1, "%c", t2 );
samples/cpp/camshiftdemo.cpp
+22 -23
View File
@@ -48,46 +48,45 @@ static void onMouse( int event, int x, int y, int, void* )
static void help()
{
cout << "\nThis is a demo that shows mean-shift based tracking\n"
"You select a color objects such as your face and it tracks it.\n"
"This reads from video camera (0 by default, or the camera number the user enters\n"
"Usage: \n"
" ./camshiftdemo [camera number]\n";
"You select a color objects such as your face and it tracks it.\n"
"This reads from video camera (0 by default, or the camera number the user enters\n"
"Usage: \n"
" ./camshiftdemo [camera number]\n";
cout << "\n\nHot keys: \n"
"\tESC - quit the program\n"
"\tc - stop the tracking\n"
"\tb - switch to/from backprojection view\n"
"\th - show/hide object histogram\n"
"\tp - pause video\n"
"\tESC - quit the program\n"
"\tc - stop the tracking\n"
"\tb - switch to/from backprojection view\n"
"\th - show/hide object histogram\n"
"\tp - pause video\n"
"To initialize tracking, select the object with mouse\n";
}
const char* keys =
const char* keys =
{
"{1| | 0 | camera number}"
"{1| | 0 | camera number}"
};
int main( int argc, const char** argv )
{
help();
help();
VideoCapture cap;
Rect trackWindow;
RotatedRect trackBox;
int hsize = 16;
float hranges[] = {0,180};
const float* phranges = hranges;
CommandLineParser parser(argc, argv, keys);
int camNum = parser.get<int>("1");
cap.open(camNum);
CommandLineParser parser(argc, argv, keys);
int camNum = parser.get<int>("1");
cap.open(camNum);
if( !cap.isOpened() )
{
help();
help();
cout << "***Could not initialize capturing...***\n";
cout << "Current parameter's value: \n";
parser.printParams();
parser.printParams();
return -1;
}
@@ -100,7 +99,7 @@ int main( int argc, const char** argv )
Mat frame, hsv, hue, mask, hist, histimg = Mat::zeros(200, 320, CV_8UC3), backproj;
bool paused = false;
for(;;)
{
if( !paused )
@@ -111,7 +110,7 @@ int main( int argc, const char** argv )
}
frame.copyTo(image);
if( !paused )
{
cvtColor(image, hsv, CV_BGR2HSV);
@@ -131,7 +130,7 @@ int main( int argc, const char** argv )
Mat roi(hue, selection), maskroi(mask, selection);
calcHist(&roi, 1, 0, maskroi, hist, 1, &hsize, &phranges);
normalize(hist, hist, 0, 255, CV_MINMAX);
trackWindow = selection;
trackObject = 1;
@@ -141,7 +140,7 @@ int main( int argc, const char** argv )
for( int i = 0; i < hsize; i++ )
buf.at<Vec3b>(i) = Vec3b(saturate_cast<uchar>(i*180./hsize), 255, 255);
cvtColor(buf, buf, CV_HSV2BGR);
for( int i = 0; i < hsize; i++ )
{
int val = saturate_cast<int>(hist.at<float>(i)*histimg.rows/255);
samples/cpp/cout_mat.cpp
+3 -3
View File
@@ -28,9 +28,9 @@ static void help()
int main(int,char**)
{
help();
Mat i = Mat::eye(4, 4, CV_64F);
i.at<double>(1,1) = CV_PI;
cout << "i = " << i << ";" << endl;
Mat I = Mat::eye(4, 4, CV_64F);
I.at<double>(1,1) = CV_PI;
cout << "I = " << I << ";" << endl;
Mat r = Mat(10, 3, CV_8UC3);
randu(r, Scalar::all(0), Scalar::all(255));
samples/cpp/detector_descriptor_evaluation.cpp
+3 -3
View File
@@ -223,8 +223,8 @@ void BaseQualityEvaluator::readAllDatasetsRunParams()
isWriteParams = false;
FileNode topfn = fs.getFirstTopLevelNode();
FileNode fn = topfn[DEFAULT_PARAMS];
readDefaultRunParams(fn);
FileNode pfn = topfn[DEFAULT_PARAMS];
readDefaultRunParams(pfn);
for( int i = 0; i < DATASETS_COUNT; i++ )
{
@@ -280,7 +280,7 @@ bool BaseQualityEvaluator::readDataset( const string& datasetName, vector<Mat>&
if( !fs.isOpened() )
{
cout << "filename " << dirname + filename.str() << endl;
FileStorage fs( dirname + filename.str(), FileStorage::READ );
FileStorage fs2( dirname + filename.str(), FileStorage::READ );
return false;
}
fs.getFirstTopLevelNode() >> Hs[i];

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