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removed the old C API for Haar-based object detection; use CascadeClassifier from now on

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This commit is contained in:
Vadim Pisarevsky
2018-11-06 15:39:37 +03:00
parent 687fa6a8ca
commit b8175f8976
7 changed files with 12 additions and 2863 deletions
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modules/objdetect/src/cascadedetect.cpp
+12 -57
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@@ -44,7 +44,6 @@
#include <iostream>
#include "cascadedetect.hpp"
#include "opencv2/objdetect/objdetect_c.h"
#include "opencl_kernels_objdetect.hpp"
namespace cv
@@ -1071,9 +1070,6 @@ public:
};
struct getRect { Rect operator ()(const CvAvgComp& e) const { return e.rect; } };
struct getNeighbors { int operator ()(const CvAvgComp& e) const { return e.neighbors; } };
#ifdef HAVE_OPENCL
bool CascadeClassifierImpl::ocl_detectMultiScaleNoGrouping( const std::vector<float>& scales,
std::vector<Rect>& candidates )
@@ -1227,24 +1223,6 @@ void* CascadeClassifierImpl::getOldCascade()
return oldCascade;
}
static void detectMultiScaleOldFormat( const Mat& image, Ptr<CvHaarClassifierCascade> oldCascade,
std::vector<Rect>& objects,
std::vector<int>& rejectLevels,
std::vector<double>& levelWeights,
std::vector<CvAvgComp>& vecAvgComp,
double scaleFactor, int minNeighbors,
int flags, Size minObjectSize, Size maxObjectSize,
bool outputRejectLevels = false )
{
MemStorage storage(cvCreateMemStorage(0));
CvMat _image = cvMat(image);
CvSeq* _objects = cvHaarDetectObjectsForROC( &_image, oldCascade, storage, rejectLevels, levelWeights, scaleFactor,
minNeighbors, flags, cvSize(minObjectSize), cvSize(maxObjectSize), outputRejectLevels );
Seq<CvAvgComp>(_objects).copyTo(vecAvgComp);
objects.resize(vecAvgComp.size());
std::transform(vecAvgComp.begin(), vecAvgComp.end(), objects.begin(), getRect());
}
void CascadeClassifierImpl::detectMultiScaleNoGrouping( InputArray _image, std::vector<Rect>& candidates,
std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
double scaleFactor, Size minObjectSize, Size maxObjectSize,
@@ -1374,7 +1352,7 @@ void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rec
std::vector<int>& rejectLevels,
std::vector<double>& levelWeights,
double scaleFactor, int minNeighbors,
int flags, Size minObjectSize, Size maxObjectSize,
int /*flags*/, Size minObjectSize, Size maxObjectSize,
bool outputRejectLevels )
{
CV_INSTRUMENT_REGION();
@@ -1384,26 +1362,16 @@ void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rec
if( empty() )
return;
if( isOldFormatCascade() )
detectMultiScaleNoGrouping( _image, objects, rejectLevels, levelWeights, scaleFactor, minObjectSize, maxObjectSize,
outputRejectLevels );
const double GROUP_EPS = 0.2;
if( outputRejectLevels )
{
Mat image = _image.getMat();
std::vector<CvAvgComp> fakeVecAvgComp;
detectMultiScaleOldFormat( image, oldCascade, objects, rejectLevels, levelWeights, fakeVecAvgComp, scaleFactor,
minNeighbors, flags, minObjectSize, maxObjectSize, outputRejectLevels );
groupRectangles( objects, rejectLevels, levelWeights, minNeighbors, GROUP_EPS );
}
else
{
detectMultiScaleNoGrouping( _image, objects, rejectLevels, levelWeights, scaleFactor, minObjectSize, maxObjectSize,
outputRejectLevels );
const double GROUP_EPS = 0.2;
if( outputRejectLevels )
{
groupRectangles( objects, rejectLevels, levelWeights, minNeighbors, GROUP_EPS );
}
else
{
groupRectangles( objects, minNeighbors, GROUP_EPS );
}
groupRectangles( objects, minNeighbors, GROUP_EPS );
}
}
@@ -1421,7 +1389,7 @@ void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rec
void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rect>& objects,
std::vector<int>& numDetections, double scaleFactor,
int minNeighbors, int flags, Size minObjectSize,
int minNeighbors, int /*flags*/, Size minObjectSize,
Size maxObjectSize )
{
CV_INSTRUMENT_REGION();
@@ -1434,20 +1402,10 @@ void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rec
std::vector<int> fakeLevels;
std::vector<double> fakeWeights;
if( isOldFormatCascade() )
{
std::vector<CvAvgComp> vecAvgComp;
detectMultiScaleOldFormat( image, oldCascade, objects, fakeLevels, fakeWeights, vecAvgComp, scaleFactor,
minNeighbors, flags, minObjectSize, maxObjectSize );
numDetections.resize(vecAvgComp.size());
std::transform(vecAvgComp.begin(), vecAvgComp.end(), numDetections.begin(), getNeighbors());
}
else
{
detectMultiScaleNoGrouping( image, objects, fakeLevels, fakeWeights, scaleFactor, minObjectSize, maxObjectSize );
const double GROUP_EPS = 0.2;
groupRectangles( objects, numDetections, minNeighbors, GROUP_EPS );
}
detectMultiScaleNoGrouping( image, objects, fakeLevels, fakeWeights, scaleFactor, minObjectSize, maxObjectSize );
const double GROUP_EPS = 0.2;
groupRectangles( objects, numDetections, minNeighbors, GROUP_EPS );
}
@@ -1613,9 +1571,6 @@ bool CascadeClassifierImpl::read_(const FileNode& root)
return featureEvaluator->read(fn, data.origWinSize);
}
void DefaultDeleter<CvHaarClassifierCascade>::operator ()(CvHaarClassifierCascade* obj) const { cvReleaseHaarClassifierCascade(&obj); }
BaseCascadeClassifier::~BaseCascadeClassifier()
{
}