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Commit 3a4bc0d4 authored by Alexander Alekhin's avatar Alexander Alekhin
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Merge pull request #13055 from vpisarev:remove_old_haar

parents 687fa6a8 b8175f89
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modules/objdetect/include/opencv2/objdetect/objdetect_c.h deleted 100644 → 0
View file @ 687fa6a8
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_OBJDETECT_C_H
#define OPENCV_OBJDETECT_C_H
#include "opencv2/core/core_c.h"
#ifdef __cplusplus
#include <deque>
#include <vector>
extern "C" {
#endif
/** @addtogroup objdetect_c
@{
*/
/****************************************************************************************\
* Haar-like Object Detection functions *
\****************************************************************************************/
#define CV_HAAR_MAGIC_VAL 0x42500000
#define CV_TYPE_NAME_HAAR "opencv-haar-classifier"
#define CV_IS_HAAR_CLASSIFIER( haar ) \
((haar) != NULL && \
(((const CvHaarClassifierCascade*)(haar))->flags & CV_MAGIC_MASK)==CV_HAAR_MAGIC_VAL)
#define CV_HAAR_FEATURE_MAX 3
#define CV_HAAR_STAGE_MAX 1000
typedef struct CvHaarFeature
{
int tilted;
struct
{
CvRect r;
float weight;
} rect[CV_HAAR_FEATURE_MAX];
} CvHaarFeature;
typedef struct CvHaarClassifier
{
int count;
CvHaarFeature* haar_feature;
float* threshold;
int* left;
int* right;
float* alpha;
} CvHaarClassifier;
typedef struct CvHaarStageClassifier
{
int count;
float threshold;
CvHaarClassifier* classifier;
int next;
int child;
int parent;
} CvHaarStageClassifier;
typedef struct CvHidHaarClassifierCascade CvHidHaarClassifierCascade;
typedef struct CvHaarClassifierCascade
{
int flags;
int count;
CvSize orig_window_size;
CvSize real_window_size;
double scale;
CvHaarStageClassifier* stage_classifier;
CvHidHaarClassifierCascade* hid_cascade;
} CvHaarClassifierCascade;
typedef struct CvAvgComp
{
CvRect rect;
int neighbors;
} CvAvgComp;
/* Loads haar classifier cascade from a directory.
It is obsolete: convert your cascade to xml and use cvLoad instead */
CVAPI(CvHaarClassifierCascade*) cvLoadHaarClassifierCascade(
const char* directory, CvSize orig_window_size);
CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade );
#define CV_HAAR_DO_CANNY_PRUNING 1
#define CV_HAAR_SCALE_IMAGE 2
#define CV_HAAR_FIND_BIGGEST_OBJECT 4
#define CV_HAAR_DO_ROUGH_SEARCH 8
CVAPI(CvSeq*) cvHaarDetectObjects( const CvArr* image,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
double scale_factor CV_DEFAULT(1.1),
int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)));
/* sets images for haar classifier cascade */
CVAPI(void) cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade,
const CvArr* sum, const CvArr* sqsum,
const CvArr* tilted_sum, double scale );
/* runs the cascade on the specified window */
CVAPI(int) cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade,
CvPoint pt, int start_stage CV_DEFAULT(0));
/** @} objdetect_c */
#ifdef __cplusplus
}
CV_EXPORTS CvSeq* cvHaarDetectObjectsForROC( const CvArr* image,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
std::vector<int>& rejectLevels, std::vector<double>& levelWeightds,
double scale_factor = 1.1,
int min_neighbors = 3, int flags = 0,
CvSize min_size = cvSize(0, 0), CvSize max_size = cvSize(0, 0),
bool outputRejectLevels = false );
#endif
#endif /* OPENCV_OBJDETECT_C_H */
modules/objdetect/src/cascadedetect.cpp
View file @ 3a4bc0d4
......@@ -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();
......@@ -1384,15 +1362,6 @@ void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rec
if( empty() )
return;
if( isOldFormatCascade() )
{
Mat image = _image.getMat();
std::vector<CvAvgComp> fakeVecAvgComp;
detectMultiScaleOldFormat( image, oldCascade, objects, rejectLevels, levelWeights, fakeVecAvgComp, scaleFactor,
minNeighbors, flags, minObjectSize, maxObjectSize, outputRejectLevels );
}
else
{
detectMultiScaleNoGrouping( _image, objects, rejectLevels, levelWeights, scaleFactor, minObjectSize, maxObjectSize,
outputRejectLevels );
const double GROUP_EPS = 0.2;
......@@ -1404,7 +1373,6 @@ void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rec
{
groupRectangles( objects, minNeighbors, GROUP_EPS );
}
}
}
void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rect>& objects,
......@@ -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 );
}
}
......@@ -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()
{
}
......
modules/objdetect/src/haar.avx.cpp deleted 100644 → 0
View file @ 687fa6a8
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/* Haar features calculation */
#include "precomp.hpp"
#include "haar.hpp"
namespace cv_haar_avx
{
// AVX version icvEvalHidHaarClassifier. Process 8 CvHidHaarClassifiers per call. Check AVX support before invocation!!
#if CV_HAAR_USE_AVX
double icvEvalHidHaarClassifierAVX(CvHidHaarClassifier* classifier,
double variance_norm_factor, size_t p_offset)
{
int CV_DECL_ALIGNED(32) idxV[8] = { 0,0,0,0,0,0,0,0 };
uchar flags[8] = { 0,0,0,0,0,0,0,0 };
CvHidHaarTreeNode* nodes[8];
double res = 0;
uchar exitConditionFlag = 0;
for (;;)
{
float CV_DECL_ALIGNED(32) tmp[8] = { 0,0,0,0,0,0,0,0 };
nodes[0] = (classifier + 0)->node + idxV[0];
nodes[1] = (classifier + 1)->node + idxV[1];
nodes[2] = (classifier + 2)->node + idxV[2];
nodes[3] = (classifier + 3)->node + idxV[3];
nodes[4] = (classifier + 4)->node + idxV[4];
nodes[5] = (classifier + 5)->node + idxV[5];
nodes[6] = (classifier + 6)->node + idxV[6];
nodes[7] = (classifier + 7)->node + idxV[7];
__m256 t = _mm256_set1_ps(static_cast<float>(variance_norm_factor));
t = _mm256_mul_ps(t, _mm256_set_ps(nodes[7]->threshold,
nodes[6]->threshold,
nodes[5]->threshold,
nodes[4]->threshold,
nodes[3]->threshold,
nodes[2]->threshold,
nodes[1]->threshold,
nodes[0]->threshold));
__m256 offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[0], p_offset),
calc_sumf(nodes[6]->feature.rect[0], p_offset),
calc_sumf(nodes[5]->feature.rect[0], p_offset),
calc_sumf(nodes[4]->feature.rect[0], p_offset),
calc_sumf(nodes[3]->feature.rect[0], p_offset),
calc_sumf(nodes[2]->feature.rect[0], p_offset),
calc_sumf(nodes[1]->feature.rect[0], p_offset),
calc_sumf(nodes[0]->feature.rect[0], p_offset));
__m256 weight = _mm256_set_ps(nodes[7]->feature.rect[0].weight,
nodes[6]->feature.rect[0].weight,
nodes[5]->feature.rect[0].weight,
nodes[4]->feature.rect[0].weight,
nodes[3]->feature.rect[0].weight,
nodes[2]->feature.rect[0].weight,
nodes[1]->feature.rect[0].weight,
nodes[0]->feature.rect[0].weight);
__m256 sum = _mm256_mul_ps(offset, weight);
offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[1], p_offset),
calc_sumf(nodes[6]->feature.rect[1], p_offset),
calc_sumf(nodes[5]->feature.rect[1], p_offset),
calc_sumf(nodes[4]->feature.rect[1], p_offset),
calc_sumf(nodes[3]->feature.rect[1], p_offset),
calc_sumf(nodes[2]->feature.rect[1], p_offset),
calc_sumf(nodes[1]->feature.rect[1], p_offset),
calc_sumf(nodes[0]->feature.rect[1], p_offset));
weight = _mm256_set_ps(nodes[7]->feature.rect[1].weight,
nodes[6]->feature.rect[1].weight,
nodes[5]->feature.rect[1].weight,
nodes[4]->feature.rect[1].weight,
nodes[3]->feature.rect[1].weight,
nodes[2]->feature.rect[1].weight,
nodes[1]->feature.rect[1].weight,
nodes[0]->feature.rect[1].weight);
sum = _mm256_add_ps(sum, _mm256_mul_ps(offset, weight));
if (nodes[0]->feature.rect[2].p0)
tmp[0] = calc_sumf(nodes[0]->feature.rect[2], p_offset) * nodes[0]->feature.rect[2].weight;
if (nodes[1]->feature.rect[2].p0)
tmp[1] = calc_sumf(nodes[1]->feature.rect[2], p_offset) * nodes[1]->feature.rect[2].weight;
if (nodes[2]->feature.rect[2].p0)
tmp[2] = calc_sumf(nodes[2]->feature.rect[2], p_offset) * nodes[2]->feature.rect[2].weight;
if (nodes[3]->feature.rect[2].p0)
tmp[3] = calc_sumf(nodes[3]->feature.rect[2], p_offset) * nodes[3]->feature.rect[2].weight;
if (nodes[4]->feature.rect[2].p0)
tmp[4] = calc_sumf(nodes[4]->feature.rect[2], p_offset) * nodes[4]->feature.rect[2].weight;
if (nodes[5]->feature.rect[2].p0)
tmp[5] = calc_sumf(nodes[5]->feature.rect[2], p_offset) * nodes[5]->feature.rect[2].weight;
if (nodes[6]->feature.rect[2].p0)
tmp[6] = calc_sumf(nodes[6]->feature.rect[2], p_offset) * nodes[6]->feature.rect[2].weight;
if (nodes[7]->feature.rect[2].p0)
tmp[7] = calc_sumf(nodes[7]->feature.rect[2], p_offset) * nodes[7]->feature.rect[2].weight;
sum = _mm256_add_ps(sum, _mm256_load_ps(tmp));
__m256 left = _mm256_set_ps(static_cast<float>(nodes[7]->left), static_cast<float>(nodes[6]->left),
static_cast<float>(nodes[5]->left), static_cast<float>(nodes[4]->left),
static_cast<float>(nodes[3]->left), static_cast<float>(nodes[2]->left),
static_cast<float>(nodes[1]->left), static_cast<float>(nodes[0]->left));
__m256 right = _mm256_set_ps(static_cast<float>(nodes[7]->right), static_cast<float>(nodes[6]->right),
static_cast<float>(nodes[5]->right), static_cast<float>(nodes[4]->right),
static_cast<float>(nodes[3]->right), static_cast<float>(nodes[2]->right),
static_cast<float>(nodes[1]->right), static_cast<float>(nodes[0]->right));
_mm256_store_si256((__m256i*)idxV, _mm256_cvttps_epi32(_mm256_blendv_ps(right, left, _mm256_cmp_ps(sum, t, _CMP_LT_OQ))));
for (int i = 0; i < 8; i++)
{
if (idxV[i] <= 0)
{
if (!flags[i])
{
exitConditionFlag++;
flags[i] = 1;
res += (classifier + i)->alpha[-idxV[i]];
}
idxV[i] = 0;
}
}
if (exitConditionFlag == 8)
return res;
}
}
double icvEvalHidHaarStumpClassifierAVX(CvHidHaarClassifier* classifier,
double variance_norm_factor, size_t p_offset)
{
float CV_DECL_ALIGNED(32) tmp[8] = { 0,0,0,0,0,0,0,0 };
CvHidHaarTreeNode* nodes[8];
nodes[0] = classifier[0].node;
nodes[1] = classifier[1].node;
nodes[2] = classifier[2].node;
nodes[3] = classifier[3].node;
nodes[4] = classifier[4].node;
nodes[5] = classifier[5].node;
nodes[6] = classifier[6].node;
nodes[7] = classifier[7].node;
__m256 t = _mm256_set1_ps(static_cast<float>(variance_norm_factor));
t = _mm256_mul_ps(t, _mm256_set_ps(nodes[7]->threshold,
nodes[6]->threshold,
nodes[5]->threshold,
nodes[4]->threshold,
nodes[3]->threshold,
nodes[2]->threshold,
nodes[1]->threshold,
nodes[0]->threshold));
__m256 offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[0], p_offset),
calc_sumf(nodes[6]->feature.rect[0], p_offset),
calc_sumf(nodes[5]->feature.rect[0], p_offset),
calc_sumf(nodes[4]->feature.rect[0], p_offset),
calc_sumf(nodes[3]->feature.rect[0], p_offset),
calc_sumf(nodes[2]->feature.rect[0], p_offset),
calc_sumf(nodes[1]->feature.rect[0], p_offset),
calc_sumf(nodes[0]->feature.rect[0], p_offset));
__m256 weight = _mm256_set_ps(nodes[7]->feature.rect[0].weight,
nodes[6]->feature.rect[0].weight,
nodes[5]->feature.rect[0].weight,
nodes[4]->feature.rect[0].weight,
nodes[3]->feature.rect[0].weight,
nodes[2]->feature.rect[0].weight,
nodes[1]->feature.rect[0].weight,
nodes[0]->feature.rect[0].weight);
__m256 sum = _mm256_mul_ps(offset, weight);
offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[1], p_offset),
calc_sumf(nodes[6]->feature.rect[1], p_offset),
calc_sumf(nodes[5]->feature.rect[1], p_offset),
calc_sumf(nodes[4]->feature.rect[1], p_offset),
calc_sumf(nodes[3]->feature.rect[1], p_offset),
calc_sumf(nodes[2]->feature.rect[1], p_offset),
calc_sumf(nodes[1]->feature.rect[1], p_offset),
calc_sumf(nodes[0]->feature.rect[1], p_offset));
weight = _mm256_set_ps(nodes[7]->feature.rect[1].weight,
nodes[6]->feature.rect[1].weight,
nodes[5]->feature.rect[1].weight,
nodes[4]->feature.rect[1].weight,
nodes[3]->feature.rect[1].weight,
nodes[2]->feature.rect[1].weight,
nodes[1]->feature.rect[1].weight,
nodes[0]->feature.rect[1].weight);
sum = _mm256_add_ps(sum, _mm256_mul_ps(offset, weight));
if (nodes[0]->feature.rect[2].p0)
tmp[0] = calc_sumf(nodes[0]->feature.rect[2], p_offset) * nodes[0]->feature.rect[2].weight;
if (nodes[1]->feature.rect[2].p0)
tmp[1] = calc_sumf(nodes[1]->feature.rect[2], p_offset) * nodes[1]->feature.rect[2].weight;
if (nodes[2]->feature.rect[2].p0)
tmp[2] = calc_sumf(nodes[2]->feature.rect[2], p_offset) * nodes[2]->feature.rect[2].weight;
if (nodes[3]->feature.rect[2].p0)
tmp[3] = calc_sumf(nodes[3]->feature.rect[2], p_offset) * nodes[3]->feature.rect[2].weight;
if (nodes[4]->feature.rect[2].p0)
tmp[4] = calc_sumf(nodes[4]->feature.rect[2], p_offset) * nodes[4]->feature.rect[2].weight;
if (nodes[5]->feature.rect[2].p0)
tmp[5] = calc_sumf(nodes[5]->feature.rect[2], p_offset) * nodes[5]->feature.rect[2].weight;
if (nodes[6]->feature.rect[2].p0)
tmp[6] = calc_sumf(nodes[6]->feature.rect[2], p_offset) * nodes[6]->feature.rect[2].weight;
if (nodes[7]->feature.rect[2].p0)
tmp[7] = calc_sumf(nodes[7]->feature.rect[2], p_offset) * nodes[7]->feature.rect[2].weight;
sum = _mm256_add_ps(sum, _mm256_load_ps(tmp));
__m256 alpha0 = _mm256_set_ps(classifier[7].alpha[0],
classifier[6].alpha[0],
classifier[5].alpha[0],
classifier[4].alpha[0],
classifier[3].alpha[0],
classifier[2].alpha[0],
classifier[1].alpha[0],
classifier[0].alpha[0]);
__m256 alpha1 = _mm256_set_ps(classifier[7].alpha[1],
classifier[6].alpha[1],
classifier[5].alpha[1],
classifier[4].alpha[1],
classifier[3].alpha[1],
classifier[2].alpha[1],
classifier[1].alpha[1],
classifier[0].alpha[1]);
__m256 outBuf = _mm256_blendv_ps(alpha0, alpha1, _mm256_cmp_ps(t, sum, _CMP_LE_OQ));
outBuf = _mm256_hadd_ps(outBuf, outBuf);
outBuf = _mm256_hadd_ps(outBuf, outBuf);
_mm256_store_ps(tmp, outBuf);
return (tmp[0] + tmp[4]);
}
double icvEvalHidHaarStumpClassifierTwoRectAVX(CvHidHaarClassifier* classifier,
double variance_norm_factor, size_t p_offset)
{
float CV_DECL_ALIGNED(32) buf[8];
CvHidHaarTreeNode* nodes[8];
nodes[0] = classifier[0].node;
nodes[1] = classifier[1].node;
nodes[2] = classifier[2].node;
nodes[3] = classifier[3].node;
nodes[4] = classifier[4].node;
nodes[5] = classifier[5].node;
nodes[6] = classifier[6].node;
nodes[7] = classifier[7].node;
__m256 t = _mm256_set1_ps(static_cast<float>(variance_norm_factor));
t = _mm256_mul_ps(t, _mm256_set_ps(nodes[7]->threshold,
nodes[6]->threshold,
nodes[5]->threshold,
nodes[4]->threshold,
nodes[3]->threshold,
nodes[2]->threshold,
nodes[1]->threshold,
nodes[0]->threshold));
__m256 offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[0], p_offset),
calc_sumf(nodes[6]->feature.rect[0], p_offset),
calc_sumf(nodes[5]->feature.rect[0], p_offset),
calc_sumf(nodes[4]->feature.rect[0], p_offset),
calc_sumf(nodes[3]->feature.rect[0], p_offset),
calc_sumf(nodes[2]->feature.rect[0], p_offset),
calc_sumf(nodes[1]->feature.rect[0], p_offset),
calc_sumf(nodes[0]->feature.rect[0], p_offset));
__m256 weight = _mm256_set_ps(nodes[7]->feature.rect[0].weight,
nodes[6]->feature.rect[0].weight,
nodes[5]->feature.rect[0].weight,
nodes[4]->feature.rect[0].weight,
nodes[3]->feature.rect[0].weight,
nodes[2]->feature.rect[0].weight,
nodes[1]->feature.rect[0].weight,
nodes[0]->feature.rect[0].weight);
__m256 sum = _mm256_mul_ps(offset, weight);
offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[1], p_offset),
calc_sumf(nodes[6]->feature.rect[1], p_offset),
calc_sumf(nodes[5]->feature.rect[1], p_offset),
calc_sumf(nodes[4]->feature.rect[1], p_offset),
calc_sumf(nodes[3]->feature.rect[1], p_offset),
calc_sumf(nodes[2]->feature.rect[1], p_offset),
calc_sumf(nodes[1]->feature.rect[1], p_offset),
calc_sumf(nodes[0]->feature.rect[1], p_offset));
weight = _mm256_set_ps(nodes[7]->feature.rect[1].weight,
nodes[6]->feature.rect[1].weight,
nodes[5]->feature.rect[1].weight,
nodes[4]->feature.rect[1].weight,
nodes[3]->feature.rect[1].weight,
nodes[2]->feature.rect[1].weight,
nodes[1]->feature.rect[1].weight,
nodes[0]->feature.rect[1].weight);
sum = _mm256_add_ps(sum, _mm256_mul_ps(offset, weight));
__m256 alpha0 = _mm256_set_ps(classifier[7].alpha[0],
classifier[6].alpha[0],
classifier[5].alpha[0],
classifier[4].alpha[0],
classifier[3].alpha[0],
classifier[2].alpha[0],
classifier[1].alpha[0],
classifier[0].alpha[0]);
__m256 alpha1 = _mm256_set_ps(classifier[7].alpha[1],
classifier[6].alpha[1],
classifier[5].alpha[1],
classifier[4].alpha[1],
classifier[3].alpha[1],
classifier[2].alpha[1],
classifier[1].alpha[1],
classifier[0].alpha[1]);
_mm256_store_ps(buf, _mm256_blendv_ps(alpha0, alpha1, _mm256_cmp_ps(t, sum, _CMP_LE_OQ)));
return (buf[0] + buf[1] + buf[2] + buf[3] + buf[4] + buf[5] + buf[6] + buf[7]);
}
#endif //CV_HAAR_USE_AVX
}
/* End of file. */
modules/objdetect/src/haar.cpp deleted 100644 → 0
View file @ 687fa6a8
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/* Haar features calculation */
#include "precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/objdetect/objdetect_c.h"
#include <stdio.h>
#include "haar.hpp"
#if CV_HAAR_FEATURE_MAX_LOCAL != CV_HAAR_FEATURE_MAX
#error CV_HAAR_FEATURE_MAX definition changed. Adjust CV_HAAR_FEATURE_MAX_LOCAL value please.
#endif
#if CV_SSE2
# if 1 /*!CV_SSE4_1 && !CV_SSE4_2*/
# define _mm_blendv_pd(a, b, m) _mm_xor_pd(a, _mm_and_pd(_mm_xor_pd(b, a), m))
# define _mm_blendv_ps(a, b, m) _mm_xor_ps(a, _mm_and_ps(_mm_xor_ps(b, a), m))
# endif
#endif
#if CV_HAAR_USE_AVX
# if defined _MSC_VER
# pragma warning( disable : 4752 )
# endif
#else
# if CV_SSE2
# define CV_HAAR_USE_SSE 1
# endif
#endif
/* these settings affect the quality of detection: change with care */
#define CV_ADJUST_FEATURES 1
#define CV_ADJUST_WEIGHTS 0
typedef struct CvHidHaarStageClassifier
{
int count;
float threshold;
CvHidHaarClassifier* classifier;
int two_rects;
struct CvHidHaarStageClassifier* next;
struct CvHidHaarStageClassifier* child;
struct CvHidHaarStageClassifier* parent;
} CvHidHaarStageClassifier;
typedef struct CvHidHaarClassifierCascade
{
int count;
int has_tilted_features;
double inv_window_area;
CvMat sum, sqsum, tilted;
CvHidHaarStageClassifier* stage_classifier;
sqsumtype *pq0, *pq1, *pq2, *pq3;
sumtype *p0, *p1, *p2, *p3;
bool is_tree;
bool isStumpBased;
} CvHidHaarClassifierCascade;
const int icv_object_win_border = 1;
const float icv_stage_threshold_bias = 0.0001f;
static void
icvReleaseHidHaarClassifierCascade( CvHidHaarClassifierCascade** _cascade )
{
if( _cascade && *_cascade )
{
cvFree( _cascade );
}
}
/* create more efficient internal representation of haar classifier cascade */
static CvHidHaarClassifierCascade*
icvCreateHidHaarClassifierCascade( CvHaarClassifierCascade* cascade )
{
CvHidHaarClassifierCascade* out = 0;
int i, j, k, l;
int datasize;
int total_classifiers = 0;
int total_nodes = 0;
char errorstr[1000];
CvHidHaarClassifier* haar_classifier_ptr;
CvHidHaarTreeNode* haar_node_ptr;
cv::Size orig_window_size;
bool has_tilted_features = false;
int max_count = 0;
if( !CV_IS_HAAR_CLASSIFIER(cascade) )
CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" );
if( cascade->hid_cascade )
CV_Error( CV_StsError, "hid_cascade has been already created" );
if( !cascade->stage_classifier )
CV_Error( CV_StsNullPtr, "" );
if( cascade->count <= 0 )
CV_Error( CV_StsOutOfRange, "Negative number of cascade stages" );
orig_window_size = cascade->orig_window_size;
/* check input structure correctness and calculate total memory size needed for
internal representation of the classifier cascade */
for( i = 0; i < cascade->count; i++ )
{
CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i;
if( !stage_classifier->classifier ||
stage_classifier->count <= 0 )
{
sprintf( errorstr, "header of the stage classifier #%d is invalid "
"(has null pointers or non-positive classfier count)", i );
CV_Error( CV_StsError, errorstr );
}
max_count = MAX( max_count, stage_classifier->count );
total_classifiers += stage_classifier->count;
for( j = 0; j < stage_classifier->count; j++ )
{
CvHaarClassifier* classifier = stage_classifier->classifier + j;
total_nodes += classifier->count;
for( l = 0; l < classifier->count; l++ )
{
for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ )
{
if( classifier->haar_feature[l].rect[k].r.width )
{
CvRect r = classifier->haar_feature[l].rect[k].r;
int tilted = classifier->haar_feature[l].tilted;
has_tilted_features = has_tilted_features | (tilted != 0);
if( r.width < 0 || r.height < 0 || r.y < 0 ||
r.x + r.width > orig_window_size.width
||
(!tilted &&
(r.x < 0 || r.y + r.height > orig_window_size.height))
||
(tilted && (r.x - r.height < 0 ||
r.y + r.width + r.height > orig_window_size.height)))
{
sprintf( errorstr, "rectangle #%d of the classifier #%d of "
"the stage classifier #%d is not inside "
"the reference (original) cascade window", k, j, i );
CV_Error( CV_StsNullPtr, errorstr );
}
}
}
}
}
}
// this is an upper boundary for the whole hidden cascade size
datasize = sizeof(CvHidHaarClassifierCascade) +
sizeof(CvHidHaarStageClassifier)*cascade->count +
sizeof(CvHidHaarClassifier) * total_classifiers +
sizeof(CvHidHaarTreeNode) * total_nodes +
sizeof(void*)*(total_nodes + total_classifiers);
out = (CvHidHaarClassifierCascade*)cvAlloc( datasize );
memset( out, 0, sizeof(*out) );
/* init header */
out->count = cascade->count;
out->stage_classifier = (CvHidHaarStageClassifier*)(out + 1);
haar_classifier_ptr = (CvHidHaarClassifier*)(out->stage_classifier + cascade->count);
haar_node_ptr = (CvHidHaarTreeNode*)(haar_classifier_ptr + total_classifiers);
out->isStumpBased = true;
out->has_tilted_features = has_tilted_features;
out->is_tree = false;
/* initialize internal representation */
for( i = 0; i < cascade->count; i++ )
{
CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i;
CvHidHaarStageClassifier* hid_stage_classifier = out->stage_classifier + i;
hid_stage_classifier->count = stage_classifier->count;
hid_stage_classifier->threshold = stage_classifier->threshold - icv_stage_threshold_bias;
hid_stage_classifier->classifier = haar_classifier_ptr;
hid_stage_classifier->two_rects = 1;
haar_classifier_ptr += stage_classifier->count;
hid_stage_classifier->parent = (stage_classifier->parent == -1)
? NULL : out->stage_classifier + stage_classifier->parent;
hid_stage_classifier->next = (stage_classifier->next == -1)
? NULL : out->stage_classifier + stage_classifier->next;
hid_stage_classifier->child = (stage_classifier->child == -1)
? NULL : out->stage_classifier + stage_classifier->child;
out->is_tree = out->is_tree || (hid_stage_classifier->next != NULL);
for( j = 0; j < stage_classifier->count; j++ )
{
CvHaarClassifier* classifier = stage_classifier->classifier + j;
CvHidHaarClassifier* hid_classifier = hid_stage_classifier->classifier + j;
int node_count = classifier->count;
float* alpha_ptr = (float*)(haar_node_ptr + node_count);
hid_classifier->count = node_count;
hid_classifier->node = haar_node_ptr;
hid_classifier->alpha = alpha_ptr;
for( l = 0; l < node_count; l++ )
{
CvHidHaarTreeNode* node = hid_classifier->node + l;
CvHaarFeature* feature = classifier->haar_feature + l;
memset( node, -1, sizeof(*node) );
node->threshold = classifier->threshold[l];
node->left = classifier->left[l];
node->right = classifier->right[l];
if( fabs(feature->rect[2].weight) < DBL_EPSILON ||
feature->rect[2].r.width == 0 ||
feature->rect[2].r.height == 0 )
memset( &(node->feature.rect[2]), 0, sizeof(node->feature.rect[2]) );
else
hid_stage_classifier->two_rects = 0;
}
memcpy( alpha_ptr, classifier->alpha, (node_count+1)*sizeof(alpha_ptr[0]));
haar_node_ptr =
(CvHidHaarTreeNode*)cvAlignPtr(alpha_ptr+node_count+1, sizeof(void*));
out->isStumpBased = out->isStumpBased && (node_count == 1);
}
}
cascade->hid_cascade = out;
assert( (char*)haar_node_ptr - (char*)out <= datasize );
return out;
}
#define sum_elem_ptr(sum,row,col) \
((sumtype*)CV_MAT_ELEM_PTR_FAST((sum),(row),(col),sizeof(sumtype)))
#define sqsum_elem_ptr(sqsum,row,col) \
((sqsumtype*)CV_MAT_ELEM_PTR_FAST((sqsum),(row),(col),sizeof(sqsumtype)))
#define calc_sum(rect,offset) \
((rect).p0[offset] - (rect).p1[offset] - (rect).p2[offset] + (rect).p3[offset])
CV_IMPL void
cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* _cascade,
const CvArr* _sum,
const CvArr* _sqsum,
const CvArr* _tilted_sum,
double scale )
{
CvMat sum_stub, *sum = (CvMat*)_sum;
CvMat sqsum_stub, *sqsum = (CvMat*)_sqsum;
CvMat tilted_stub, *tilted = (CvMat*)_tilted_sum;
CvHidHaarClassifierCascade* cascade;
int coi0 = 0, coi1 = 0;
int i;
cv::Rect equRect;
double weight_scale;
if( !CV_IS_HAAR_CLASSIFIER(_cascade) )
CV_Error( !_cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" );
if( scale <= 0 )
CV_Error( CV_StsOutOfRange, "Scale must be positive" );
sum = cvGetMat( sum, &sum_stub, &coi0 );
sqsum = cvGetMat( sqsum, &sqsum_stub, &coi1 );
if( coi0 || coi1 )
CV_Error( CV_BadCOI, "COI is not supported" );
if( !CV_ARE_SIZES_EQ( sum, sqsum ))
CV_Error( CV_StsUnmatchedSizes, "All integral images must have the same size" );
if( CV_MAT_TYPE(sqsum->type) != CV_64FC1 ||
CV_MAT_TYPE(sum->type) != CV_32SC1 )
CV_Error( CV_StsUnsupportedFormat,
"Only (32s, 64f, 32s) combination of (sum,sqsum,tilted_sum) formats is allowed" );
if( !_cascade->hid_cascade )
icvCreateHidHaarClassifierCascade(_cascade);
cascade = _cascade->hid_cascade;
if( cascade->has_tilted_features )
{
tilted = cvGetMat( tilted, &tilted_stub, &coi1 );
if( CV_MAT_TYPE(tilted->type) != CV_32SC1 )
CV_Error( CV_StsUnsupportedFormat,
"Only (32s, 64f, 32s) combination of (sum,sqsum,tilted_sum) formats is allowed" );
if( sum->step != tilted->step )
CV_Error( CV_StsUnmatchedSizes,
"Sum and tilted_sum must have the same stride (step, widthStep)" );
if( !CV_ARE_SIZES_EQ( sum, tilted ))
CV_Error( CV_StsUnmatchedSizes, "All integral images must have the same size" );
cascade->tilted = *tilted;
}
_cascade->scale = scale;
_cascade->real_window_size.width = cvRound( _cascade->orig_window_size.width * scale );
_cascade->real_window_size.height = cvRound( _cascade->orig_window_size.height * scale );
cascade->sum = *sum;
cascade->sqsum = *sqsum;
equRect.x = equRect.y = cvRound(scale);
equRect.width = cvRound((_cascade->orig_window_size.width-2)*scale);
equRect.height = cvRound((_cascade->orig_window_size.height-2)*scale);
weight_scale = 1./(equRect.width*equRect.height);
cascade->inv_window_area = weight_scale;
cascade->p0 = sum_elem_ptr(*sum, equRect.y, equRect.x);
cascade->p1 = sum_elem_ptr(*sum, equRect.y, equRect.x + equRect.width );
cascade->p2 = sum_elem_ptr(*sum, equRect.y + equRect.height, equRect.x );
cascade->p3 = sum_elem_ptr(*sum, equRect.y + equRect.height,
equRect.x + equRect.width );
cascade->pq0 = sqsum_elem_ptr(*sqsum, equRect.y, equRect.x);
cascade->pq1 = sqsum_elem_ptr(*sqsum, equRect.y, equRect.x + equRect.width );
cascade->pq2 = sqsum_elem_ptr(*sqsum, equRect.y + equRect.height, equRect.x );
cascade->pq3 = sqsum_elem_ptr(*sqsum, equRect.y + equRect.height,
equRect.x + equRect.width );
/* init pointers in haar features according to real window size and
given image pointers */
for( i = 0; i < _cascade->count; i++ )
{
int j, k, l;
for( j = 0; j < cascade->stage_classifier[i].count; j++ )
{
for( l = 0; l < cascade->stage_classifier[i].classifier[j].count; l++ )
{
CvHaarFeature* feature =
&_cascade->stage_classifier[i].classifier[j].haar_feature[l];
/* CvHidHaarClassifier* classifier =
cascade->stage_classifier[i].classifier + j; */
CvHidHaarFeature* hidfeature =
&cascade->stage_classifier[i].classifier[j].node[l].feature;
double sum0 = 0, area0 = 0;
cv::Rect r[3];
int base_w = -1, base_h = -1;
int new_base_w = 0, new_base_h = 0;
int kx, ky;
int flagx = 0, flagy = 0;
int x0 = 0, y0 = 0;
int nr;
/* align blocks */
for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ )
{
if( !hidfeature->rect[k].p0 )
break;
r[k] = feature->rect[k].r;
base_w = (int)CV_IMIN( (unsigned)base_w, (unsigned)(r[k].width-1) );
base_w = (int)CV_IMIN( (unsigned)base_w, (unsigned)(r[k].x - r[0].x-1) );
base_h = (int)CV_IMIN( (unsigned)base_h, (unsigned)(r[k].height-1) );
base_h = (int)CV_IMIN( (unsigned)base_h, (unsigned)(r[k].y - r[0].y-1) );
}
nr = k;
base_w += 1;
base_h += 1;
kx = r[0].width / base_w;
ky = r[0].height / base_h;
if( kx <= 0 )
{
flagx = 1;
new_base_w = cvRound( r[0].width * scale ) / kx;
x0 = cvRound( r[0].x * scale );
}
if( ky <= 0 )
{
flagy = 1;
new_base_h = cvRound( r[0].height * scale ) / ky;
y0 = cvRound( r[0].y * scale );
}
for( k = 0; k < nr; k++ )
{
cv::Rect tr;
double correction_ratio;
if( flagx )
{
tr.x = (r[k].x - r[0].x) * new_base_w / base_w + x0;
tr.width = r[k].width * new_base_w / base_w;
}
else
{
tr.x = cvRound( r[k].x * scale );
tr.width = cvRound( r[k].width * scale );
}
if( flagy )
{
tr.y = (r[k].y - r[0].y) * new_base_h / base_h + y0;
tr.height = r[k].height * new_base_h / base_h;
}
else
{
tr.y = cvRound( r[k].y * scale );
tr.height = cvRound( r[k].height * scale );
}
#if CV_ADJUST_WEIGHTS
{
// RAINER START
const float orig_feature_size = (float)(feature->rect[k].r.width)*feature->rect[k].r.height;
const float orig_norm_size = (float)(_cascade->orig_window_size.width)*(_cascade->orig_window_size.height);
const float feature_size = float(tr.width*tr.height);
//const float normSize = float(equRect.width*equRect.height);
float target_ratio = orig_feature_size / orig_norm_size;
//float isRatio = featureSize / normSize;
//correctionRatio = targetRatio / isRatio / normSize;
correction_ratio = target_ratio / feature_size;
// RAINER END
}
#else
correction_ratio = weight_scale * (!feature->tilted ? 1 : 0.5);
#endif
if( !feature->tilted )
{
hidfeature->rect[k].p0 = sum_elem_ptr(*sum, tr.y, tr.x);
hidfeature->rect[k].p1 = sum_elem_ptr(*sum, tr.y, tr.x + tr.width);
hidfeature->rect[k].p2 = sum_elem_ptr(*sum, tr.y + tr.height, tr.x);
hidfeature->rect[k].p3 = sum_elem_ptr(*sum, tr.y + tr.height, tr.x + tr.width);
}
else
{
hidfeature->rect[k].p2 = sum_elem_ptr(*tilted, tr.y + tr.width, tr.x + tr.width);
hidfeature->rect[k].p3 = sum_elem_ptr(*tilted, tr.y + tr.width + tr.height,
tr.x + tr.width - tr.height);
hidfeature->rect[k].p0 = sum_elem_ptr(*tilted, tr.y, tr.x);
hidfeature->rect[k].p1 = sum_elem_ptr(*tilted, tr.y + tr.height, tr.x - tr.height);
}
hidfeature->rect[k].weight = (float)(feature->rect[k].weight * correction_ratio);
if( k == 0 )
area0 = tr.width * tr.height;
else
sum0 += hidfeature->rect[k].weight * tr.width * tr.height;
}
CV_Assert(area0 > 0);
hidfeature->rect[0].weight = (float)(-sum0/area0);
} /* l */
} /* j */
}
}
CV_INLINE
double icvEvalHidHaarClassifier( CvHidHaarClassifier* classifier,
double variance_norm_factor,
size_t p_offset )
{
int idx = 0;
/*#if CV_HAAR_USE_SSE && !CV_HAAR_USE_AVX
if(cv::checkHardwareSupport(CV_CPU_SSE2))//based on old SSE variant. Works slow
{
double CV_DECL_ALIGNED(16) temp[2];
__m128d zero = _mm_setzero_pd();
do
{
CvHidHaarTreeNode* node = classifier->node + idx;
__m128d t = _mm_set1_pd((node->threshold)*variance_norm_factor);
__m128d left = _mm_set1_pd(node->left);
__m128d right = _mm_set1_pd(node->right);
double _sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
_sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
if( node->feature.rect[2].p0 )
_sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight;
__m128d sum = _mm_set1_pd(_sum);
t = _mm_cmplt_sd(sum, t);
sum = _mm_blendv_pd(right, left, t);
_mm_store_pd(temp, sum);
idx = (int)temp[0];
}
while(idx > 0 );
}
else
#endif*/
{
do
{
CvHidHaarTreeNode* node = classifier->node + idx;
double t = node->threshold * variance_norm_factor;
double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
if( node->feature.rect[2].p0 )
sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight;
idx = sum < t ? node->left : node->right;
}
while( idx > 0 );
}
return classifier->alpha[-idx];
}
static int
cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
CvPoint pt, double& stage_sum, int start_stage )
{
#if CV_HAAR_USE_AVX
bool haveAVX = CV_CPU_HAS_SUPPORT_AVX;
#else
# ifdef CV_HAAR_USE_SSE
bool haveSSE2 = cv::checkHardwareSupport(CV_CPU_SSE2);
# endif
#endif
int p_offset, pq_offset;
int i, j;
double mean, variance_norm_factor;
CvHidHaarClassifierCascade* cascade;
if( !CV_IS_HAAR_CLASSIFIER(_cascade) )
CV_Error( !_cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid cascade pointer" );
cascade = _cascade->hid_cascade;
if( !cascade )
CV_Error( CV_StsNullPtr, "Hidden cascade has not been created.\n"
"Use cvSetImagesForHaarClassifierCascade" );
if( pt.x < 0 || pt.y < 0 ||
pt.x + _cascade->real_window_size.width >= cascade->sum.width ||
pt.y + _cascade->real_window_size.height >= cascade->sum.height )
return -1;
p_offset = pt.y * (cascade->sum.step/sizeof(sumtype)) + pt.x;
pq_offset = pt.y * (cascade->sqsum.step/sizeof(sqsumtype)) + pt.x;
mean = calc_sum(*cascade,p_offset)*cascade->inv_window_area;
variance_norm_factor = cascade->pq0[pq_offset] - cascade->pq1[pq_offset] -
cascade->pq2[pq_offset] + cascade->pq3[pq_offset];
variance_norm_factor = variance_norm_factor*cascade->inv_window_area - mean*mean;
if( variance_norm_factor >= 0. )
variance_norm_factor = std::sqrt(variance_norm_factor);
else
variance_norm_factor = 1.;
if( cascade->is_tree )
{
CvHidHaarStageClassifier* ptr = cascade->stage_classifier;
assert( start_stage == 0 );
while( ptr )
{
stage_sum = 0.0;
j = 0;
#if CV_HAAR_USE_AVX
if(haveAVX)
{
for( ; j <= ptr->count - 8; j += 8 )
{
stage_sum += cv_haar_avx::icvEvalHidHaarClassifierAVX(
ptr->classifier + j,
variance_norm_factor, p_offset );
}
}
#endif
for( ; j < ptr->count; j++ )
{
stage_sum += icvEvalHidHaarClassifier( ptr->classifier + j, variance_norm_factor, p_offset );
}
if( stage_sum >= ptr->threshold )
{
ptr = ptr->child;
}
else
{
while( ptr && ptr->next == NULL ) ptr = ptr->parent;
if( ptr == NULL )
return 0;
ptr = ptr->next;
}
}
}
else if( cascade->isStumpBased )
{
#if CV_HAAR_USE_AVX
if(haveAVX)
{
for( i = start_stage; i < cascade->count; i++ )
{
stage_sum = 0.0;
j = 0;
if( cascade->stage_classifier[i].two_rects )
{
for( ; j <= cascade->stage_classifier[i].count - 8; j += 8 )
{
stage_sum += cv_haar_avx::icvEvalHidHaarStumpClassifierTwoRectAVX(
cascade->stage_classifier[i].classifier + j,
variance_norm_factor, p_offset);
}
for( ; j < cascade->stage_classifier[i].count; j++ )
{
CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
CvHidHaarTreeNode* node = classifier->node;
double t = node->threshold*variance_norm_factor;
double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
stage_sum += classifier->alpha[sum >= t];
}
}
else
{
for( ; j <= (cascade->stage_classifier[i].count)-8; j+=8 )
{
stage_sum += cv_haar_avx::icvEvalHidHaarStumpClassifierAVX(
cascade->stage_classifier[i].classifier + j,
variance_norm_factor, p_offset);
}
for( ; j < cascade->stage_classifier[i].count; j++ )
{
CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
CvHidHaarTreeNode* node = classifier->node;
double t = node->threshold*variance_norm_factor;
double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
if( node->feature.rect[2].p0 )
sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight;
stage_sum += classifier->alpha[sum >= t];
}
}
if( stage_sum < cascade->stage_classifier[i].threshold )
return -i;
}
}
else
#elif defined CV_HAAR_USE_SSE //old SSE optimization
if(haveSSE2)
{
for( i = start_stage; i < cascade->count; i++ )
{
__m128d vstage_sum = _mm_setzero_pd();
if( cascade->stage_classifier[i].two_rects )
{
for( j = 0; j < cascade->stage_classifier[i].count; j++ )
{
CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
CvHidHaarTreeNode* node = classifier->node;
// ayasin - NHM perf optim. Avoid use of costly flaky jcc
__m128d t = _mm_set_sd(node->threshold*variance_norm_factor);
__m128d a = _mm_set_sd(classifier->alpha[0]);
__m128d b = _mm_set_sd(classifier->alpha[1]);
__m128d sum = _mm_set_sd(calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight +
calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight);
t = _mm_cmpgt_sd(t, sum);
vstage_sum = _mm_add_sd(vstage_sum, _mm_blendv_pd(b, a, t));
}
}
else
{
for( j = 0; j < cascade->stage_classifier[i].count; j++ )
{
CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
CvHidHaarTreeNode* node = classifier->node;
// ayasin - NHM perf optim. Avoid use of costly flaky jcc
__m128d t = _mm_set_sd(node->threshold*variance_norm_factor);
__m128d a = _mm_set_sd(classifier->alpha[0]);
__m128d b = _mm_set_sd(classifier->alpha[1]);
double _sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
_sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
if( node->feature.rect[2].p0 )
_sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight;
__m128d sum = _mm_set_sd(_sum);
t = _mm_cmpgt_sd(t, sum);
vstage_sum = _mm_add_sd(vstage_sum, _mm_blendv_pd(b, a, t));
}
}
__m128d i_threshold = _mm_set1_pd(cascade->stage_classifier[i].threshold);
if( _mm_comilt_sd(vstage_sum, i_threshold) )
return -i;
}
}
else
#endif // AVX or SSE
{
for( i = start_stage; i < cascade->count; i++ )
{
stage_sum = 0.0;
if( cascade->stage_classifier[i].two_rects )
{
for( j = 0; j < cascade->stage_classifier[i].count; j++ )
{
CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
CvHidHaarTreeNode* node = classifier->node;
double t = node->threshold*variance_norm_factor;
double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
stage_sum += classifier->alpha[sum >= t];
}
}
else
{
for( j = 0; j < cascade->stage_classifier[i].count; j++ )
{
CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
CvHidHaarTreeNode* node = classifier->node;
double t = node->threshold*variance_norm_factor;
double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
if( node->feature.rect[2].p0 )
sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight;
stage_sum += classifier->alpha[sum >= t];
}
}
if( stage_sum < cascade->stage_classifier[i].threshold )
return -i;
}
}
}
else
{
for( i = start_stage; i < cascade->count; i++ )
{
stage_sum = 0.0;
int k = 0;
#if CV_HAAR_USE_AVX
if(haveAVX)
{
for( ; k < cascade->stage_classifier[i].count - 8; k += 8 )
{
stage_sum += cv_haar_avx::icvEvalHidHaarClassifierAVX(
cascade->stage_classifier[i].classifier + k,
variance_norm_factor, p_offset );
}
}
#endif
for(; k < cascade->stage_classifier[i].count; k++ )
{
stage_sum += icvEvalHidHaarClassifier(
cascade->stage_classifier[i].classifier + k,
variance_norm_factor, p_offset );
}
if( stage_sum < cascade->stage_classifier[i].threshold )
return -i;
}
}
return 1;
}
CV_IMPL int
cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
CvPoint pt, int start_stage )
{
CV_INSTRUMENT_REGION();
double stage_sum;
return cvRunHaarClassifierCascadeSum(_cascade, pt, stage_sum, start_stage);
}
namespace cv
{
const size_t PARALLEL_LOOP_BATCH_SIZE = 100;
class HaarDetectObjects_ScaleImage_Invoker : public ParallelLoopBody
{
public:
HaarDetectObjects_ScaleImage_Invoker( const CvHaarClassifierCascade* _cascade,
int _stripSize, double _factor,
const Mat& _sum1, const Mat& _sqsum1, Mat* _norm1,
Mat* _mask1, Rect _equRect, std::vector<Rect>& _vec,
std::vector<int>& _levels, std::vector<double>& _weights,
bool _outputLevels, Mutex *_mtx )
{
cascade = _cascade;
stripSize = _stripSize;
factor = _factor;
sum1 = _sum1;
sqsum1 = _sqsum1;
norm1 = _norm1;
mask1 = _mask1;
equRect = _equRect;
vec = &_vec;
rejectLevels = _outputLevels ? &_levels : 0;
levelWeights = _outputLevels ? &_weights : 0;
mtx = _mtx;
}
void operator()(const Range& range) const CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
Size winSize0 = cascade->orig_window_size;
Size winSize(cvRound(winSize0.width*factor), cvRound(winSize0.height*factor));
int y1 = range.start*stripSize, y2 = std::min(range.end*stripSize, sum1.rows - 1 - winSize0.height);
if (y2 <= y1 || sum1.cols <= 1 + winSize0.width)
return;
Size ssz(sum1.cols - 1 - winSize0.width, y2 - y1);
int x, y, ystep = factor > 2 ? 1 : 2;
std::vector<Rect> vecLocal;
std::vector<int> rejectLevelsLocal;
std::vector<double> levelWeightsLocal;
for( y = y1; y < y2; y += ystep )
for( x = 0; x < ssz.width; x += ystep )
{
double gypWeight;
int result = cvRunHaarClassifierCascadeSum( cascade, cvPoint(x,y), gypWeight, 0 );
if( rejectLevels )
{
if( result == 1 )
result = -1*cascade->count;
if( cascade->count + result < 4 )
{
vecLocal.push_back(Rect(cvRound(x*factor), cvRound(y*factor),
winSize.width, winSize.height));
rejectLevelsLocal.push_back(-result);
levelWeightsLocal.push_back(gypWeight);
if (vecLocal.size() >= PARALLEL_LOOP_BATCH_SIZE)
{
mtx->lock();
vec->insert(vec->end(), vecLocal.begin(), vecLocal.end());
rejectLevels->insert(rejectLevels->end(), rejectLevelsLocal.begin(), rejectLevelsLocal.end());
levelWeights->insert(levelWeights->end(), levelWeightsLocal.begin(), levelWeightsLocal.end());
mtx->unlock();
vecLocal.clear();
rejectLevelsLocal.clear();
levelWeightsLocal.clear();
}
}
}
else
{
if( result > 0 )
{
vecLocal.push_back(Rect(cvRound(x*factor), cvRound(y*factor),
winSize.width, winSize.height));
if (vecLocal.size() >= PARALLEL_LOOP_BATCH_SIZE)
{
mtx->lock();
vec->insert(vec->end(), vecLocal.begin(), vecLocal.end());
mtx->unlock();
vecLocal.clear();
}
}
}
}
if (rejectLevelsLocal.size())
{
mtx->lock();
vec->insert(vec->end(), vecLocal.begin(), vecLocal.end());
rejectLevels->insert(rejectLevels->end(), rejectLevelsLocal.begin(), rejectLevelsLocal.end());
levelWeights->insert(levelWeights->end(), levelWeightsLocal.begin(), levelWeightsLocal.end());
mtx->unlock();
}
else
if (vecLocal.size())
{
mtx->lock();
vec->insert(vec->end(), vecLocal.begin(), vecLocal.end());
mtx->unlock();
}
}
const CvHaarClassifierCascade* cascade;
int stripSize;
double factor;
Mat sum1, sqsum1, *norm1, *mask1;
Rect equRect;
std::vector<Rect>* vec;
std::vector<int>* rejectLevels;
std::vector<double>* levelWeights;
Mutex* mtx;
};
class HaarDetectObjects_ScaleCascade_Invoker : public ParallelLoopBody
{
public:
HaarDetectObjects_ScaleCascade_Invoker( const CvHaarClassifierCascade* _cascade,
Size _winsize, const Range& _xrange, double _ystep,
size_t _sumstep, const int** _p, const int** _pq,
std::vector<Rect>& _vec, Mutex* _mtx )
{
cascade = _cascade;
winsize = _winsize;
xrange = _xrange;
ystep = _ystep;
sumstep = _sumstep;
p = _p; pq = _pq;
vec = &_vec;
mtx = _mtx;
}
void operator()(const Range& range) const CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
int iy, startY = range.start, endY = range.end;
const int *p0 = p[0], *p1 = p[1], *p2 = p[2], *p3 = p[3];
const int *pq0 = pq[0], *pq1 = pq[1], *pq2 = pq[2], *pq3 = pq[3];
bool doCannyPruning = p0 != 0;
int sstep = (int)(sumstep/sizeof(p0[0]));
std::vector<Rect> vecLocal;
for( iy = startY; iy < endY; iy++ )
{
int ix, y = cvRound(iy*ystep), ixstep = 1;
for( ix = xrange.start; ix < xrange.end; ix += ixstep )
{
int x = cvRound(ix*ystep); // it should really be ystep, not ixstep
if( doCannyPruning )
{
int offset = y*sstep + x;
int s = p0[offset] - p1[offset] - p2[offset] + p3[offset];
int sq = pq0[offset] - pq1[offset] - pq2[offset] + pq3[offset];
if( s < 100 || sq < 20 )
{
ixstep = 2;
continue;
}
}
int result = cvRunHaarClassifierCascade( cascade, cvPoint(x, y), 0 );
if( result > 0 )
{
vecLocal.push_back(Rect(x, y, winsize.width, winsize.height));
if (vecLocal.size() >= PARALLEL_LOOP_BATCH_SIZE)
{
mtx->lock();
vec->insert(vec->end(), vecLocal.begin(), vecLocal.end());
mtx->unlock();
vecLocal.clear();
}
}
ixstep = result != 0 ? 1 : 2;
}
}
if (vecLocal.size())
{
mtx->lock();
vec->insert(vec->end(), vecLocal.begin(), vecLocal.end());
mtx->unlock();
}
}
const CvHaarClassifierCascade* cascade;
double ystep;
size_t sumstep;
Size winsize;
Range xrange;
const int** p;
const int** pq;
std::vector<Rect>* vec;
Mutex* mtx;
};
}
CvSeq*
cvHaarDetectObjectsForROC( const CvArr* _img,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
double scaleFactor, int minNeighbors, int flags,
CvSize minSize, CvSize maxSize, bool outputRejectLevels )
{
CV_INSTRUMENT_REGION();
const double GROUP_EPS = 0.2;
CvMat stub, *img = (CvMat*)_img;
cv::Ptr<CvMat> temp, sum, tilted, sqsum, normImg, sumcanny, imgSmall;
CvSeq* result_seq = 0;
cv::Ptr<CvMemStorage> temp_storage;
std::vector<cv::Rect> allCandidates;
std::vector<cv::Rect> rectList;
std::vector<int> rweights;
double factor;
int coi;
bool doCannyPruning = (flags & CV_HAAR_DO_CANNY_PRUNING) != 0;
bool findBiggestObject = (flags & CV_HAAR_FIND_BIGGEST_OBJECT) != 0;
bool roughSearch = (flags & CV_HAAR_DO_ROUGH_SEARCH) != 0;
cv::Mutex mtx;
if( !CV_IS_HAAR_CLASSIFIER(cascade) )
CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier cascade" );
if( !storage )
CV_Error( CV_StsNullPtr, "Null storage pointer" );
img = cvGetMat( img, &stub, &coi );
if( coi )
CV_Error( CV_BadCOI, "COI is not supported" );
if( CV_MAT_DEPTH(img->type) != CV_8U )
CV_Error( CV_StsUnsupportedFormat, "Only 8-bit images are supported" );
if( scaleFactor <= 1 )
CV_Error( CV_StsOutOfRange, "scale factor must be > 1" );
if( findBiggestObject )
flags &= ~CV_HAAR_SCALE_IMAGE;
if( maxSize.height == 0 || maxSize.width == 0 )
{
maxSize.height = img->rows;
maxSize.width = img->cols;
}
temp.reset(cvCreateMat( img->rows, img->cols, CV_8UC1 ));
sum.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ));
sqsum.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_64FC1 ));
if( !cascade->hid_cascade )
icvCreateHidHaarClassifierCascade(cascade);
if( cascade->hid_cascade->has_tilted_features )
tilted.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ));
result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), storage );
if( CV_MAT_CN(img->type) > 1 )
{
cvCvtColor( img, temp, CV_BGR2GRAY );
img = temp;
}
if( findBiggestObject )
flags &= ~(CV_HAAR_SCALE_IMAGE|CV_HAAR_DO_CANNY_PRUNING);
if( flags & CV_HAAR_SCALE_IMAGE )
{
CvSize winSize0 = cascade->orig_window_size;
imgSmall.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_8UC1 ));
for( factor = 1; ; factor *= scaleFactor )
{
CvSize winSize = { cvRound(winSize0.width*factor),
cvRound(winSize0.height*factor) };
CvSize sz = { cvRound(img->cols/factor), cvRound(img->rows/factor) };
CvSize sz1 = { sz.width - winSize0.width + 1, sz.height - winSize0.height + 1 };
CvRect equRect = { icv_object_win_border, icv_object_win_border,
winSize0.width - icv_object_win_border*2, winSize0.height - icv_object_win_border*2 };
CvMat img1, sum1, sqsum1, norm1, tilted1, mask1;
CvMat* _tilted = 0;
if( sz1.width <= 0 || sz1.height <= 0 )
break;
if( winSize.width > maxSize.width || winSize.height > maxSize.height )
break;
if( winSize.width < minSize.width || winSize.height < minSize.height )
continue;
img1 = cvMat( sz.height, sz.width, CV_8UC1, imgSmall->data.ptr );
sum1 = cvMat( sz.height+1, sz.width+1, CV_32SC1, sum->data.ptr );
sqsum1 = cvMat( sz.height+1, sz.width+1, CV_64FC1, sqsum->data.ptr );
if( tilted )
{
tilted1 = cvMat( sz.height+1, sz.width+1, CV_32SC1, tilted->data.ptr );
_tilted = &tilted1;
}
norm1 = cvMat( sz1.height, sz1.width, CV_32FC1, normImg ? normImg->data.ptr : 0 );
mask1 = cvMat( sz1.height, sz1.width, CV_8UC1, temp->data.ptr );
cvResize( img, &img1, cv::INTER_LINEAR_EXACT );
cvIntegral( &img1, &sum1, &sqsum1, _tilted );
int ystep = factor > 2 ? 1 : 2;
const int LOCS_PER_THREAD = 1000;
int stripCount = ((sz1.width/ystep)*(sz1.height + ystep-1)/ystep + LOCS_PER_THREAD/2)/LOCS_PER_THREAD;
stripCount = std::min(std::max(stripCount, 1), 100);
cvSetImagesForHaarClassifierCascade( cascade, &sum1, &sqsum1, _tilted, 1. );
cv::Mat _norm1 = cv::cvarrToMat(&norm1), _mask1 = cv::cvarrToMat(&mask1);
cv::parallel_for_(cv::Range(0, stripCount),
cv::HaarDetectObjects_ScaleImage_Invoker(cascade,
(((sz1.height + stripCount - 1)/stripCount + ystep-1)/ystep)*ystep,
factor, cv::cvarrToMat(&sum1), cv::cvarrToMat(&sqsum1), &_norm1, &_mask1,
cv::Rect(equRect), allCandidates, rejectLevels, levelWeights, outputRejectLevels, &mtx));
}
}
else
{
int n_factors = 0;
cv::Rect scanROI;
cvIntegral( img, sum, sqsum, tilted );
if( doCannyPruning )
{
sumcanny.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ));
cvCanny( img, temp, 0, 50, 3 );
cvIntegral( temp, sumcanny );
}
for( n_factors = 0, factor = 1;
factor*cascade->orig_window_size.width < img->cols - 10 &&
factor*cascade->orig_window_size.height < img->rows - 10;
n_factors++, factor *= scaleFactor )
;
if( findBiggestObject )
{
scaleFactor = 1./scaleFactor;
factor *= scaleFactor;
}
else
factor = 1;
for( ; n_factors-- > 0; factor *= scaleFactor )
{
const double ystep = std::max( 2., factor );
cv::Size winSize(cvRound(cascade->orig_window_size.width * factor),
cvRound(cascade->orig_window_size.height * factor));
cv::Rect equRect;
int *p[4] = {0,0,0,0};
int *pq[4] = {0,0,0,0};
int startX = 0, startY = 0;
int endX = cvRound((img->cols - winSize.width) / ystep);
int endY = cvRound((img->rows - winSize.height) / ystep);
if( winSize.width < minSize.width || winSize.height < minSize.height )
{
if( findBiggestObject )
break;
continue;
}
if ( winSize.width > maxSize.width || winSize.height > maxSize.height )
{
if( !findBiggestObject )
break;
continue;
}
cvSetImagesForHaarClassifierCascade( cascade, sum, sqsum, tilted, factor );
cvZero( temp );
if( doCannyPruning )
{
equRect.x = cvRound(winSize.width*0.15);
equRect.y = cvRound(winSize.height*0.15);
equRect.width = cvRound(winSize.width*0.7);
equRect.height = cvRound(winSize.height*0.7);
p[0] = (int*)(sumcanny->data.ptr + equRect.y*sumcanny->step) + equRect.x;
p[1] = (int*)(sumcanny->data.ptr + equRect.y*sumcanny->step)
+ equRect.x + equRect.width;
p[2] = (int*)(sumcanny->data.ptr + (equRect.y + equRect.height)*sumcanny->step) + equRect.x;
p[3] = (int*)(sumcanny->data.ptr + (equRect.y + equRect.height)*sumcanny->step)
+ equRect.x + equRect.width;
pq[0] = (int*)(sum->data.ptr + equRect.y*sum->step) + equRect.x;
pq[1] = (int*)(sum->data.ptr + equRect.y*sum->step)
+ equRect.x + equRect.width;
pq[2] = (int*)(sum->data.ptr + (equRect.y + equRect.height)*sum->step) + equRect.x;
pq[3] = (int*)(sum->data.ptr + (equRect.y + equRect.height)*sum->step)
+ equRect.x + equRect.width;
}
if( !scanROI.empty() )
{
//adjust start_height and stop_height
startY = cvRound(scanROI.y / ystep);
endY = cvRound((scanROI.y + scanROI.height - winSize.height) / ystep);
startX = cvRound(scanROI.x / ystep);
endX = cvRound((scanROI.x + scanROI.width - winSize.width) / ystep);
}
cv::parallel_for_(cv::Range(startY, endY),
cv::HaarDetectObjects_ScaleCascade_Invoker(cascade, winSize, cv::Range(startX, endX),
ystep, sum->step, (const int**)p,
(const int**)pq, allCandidates, &mtx ));
if( findBiggestObject && !allCandidates.empty() && scanROI.empty() )
{
rectList.resize(allCandidates.size());
std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());
groupRectangles(rectList, std::max(minNeighbors, 1), GROUP_EPS);
if( !rectList.empty() )
{
size_t i, sz = rectList.size();
cv::Rect maxRect;
for( i = 0; i < sz; i++ )
{
if( rectList[i].area() > maxRect.area() )
maxRect = rectList[i];
}
allCandidates.push_back(maxRect);
scanROI = maxRect;
int dx = cvRound(maxRect.width*GROUP_EPS);
int dy = cvRound(maxRect.height*GROUP_EPS);
scanROI.x = std::max(scanROI.x - dx, 0);
scanROI.y = std::max(scanROI.y - dy, 0);
scanROI.width = std::min(scanROI.width + dx*2, img->cols-1-scanROI.x);
scanROI.height = std::min(scanROI.height + dy*2, img->rows-1-scanROI.y);
double minScale = roughSearch ? 0.6 : 0.4;
minSize.width = cvRound(maxRect.width*minScale);
minSize.height = cvRound(maxRect.height*minScale);
}
}
}
}
rectList.resize(allCandidates.size());
if(!allCandidates.empty())
std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());
if( minNeighbors != 0 || findBiggestObject )
{
if( outputRejectLevels )
{
groupRectangles(rectList, rejectLevels, levelWeights, minNeighbors, GROUP_EPS );
}
else
{
groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS);
}
}
else
rweights.resize(rectList.size(),0);
if( findBiggestObject && rectList.size() )
{
CvAvgComp result_comp = {{0, 0, 0, 0},0};
for( size_t i = 0; i < rectList.size(); i++ )
{
cv::Rect r = rectList[i];
if( r.area() > cv::Rect(result_comp.rect).area() )
{
result_comp.rect = cvRect(r);
result_comp.neighbors = rweights[i];
}
}
cvSeqPush( result_seq, &result_comp );
}
else
{
for( size_t i = 0; i < rectList.size(); i++ )
{
CvAvgComp c;
c.rect = cvRect(rectList[i]);
c.neighbors = !rweights.empty() ? rweights[i] : 0;
cvSeqPush( result_seq, &c );
}
}
return result_seq;
}
CV_IMPL CvSeq*
cvHaarDetectObjects( const CvArr* _img,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
double scaleFactor,
int minNeighbors, int flags, CvSize minSize, CvSize maxSize )
{
std::vector<int> fakeLevels;
std::vector<double> fakeWeights;
return cvHaarDetectObjectsForROC( _img, cascade, storage, fakeLevels, fakeWeights,
scaleFactor, minNeighbors, flags, minSize, maxSize, false );
}
CV_IMPL void
cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** _cascade )
{
if( _cascade && *_cascade )
{
int i, j;
CvHaarClassifierCascade* cascade = *_cascade;
for( i = 0; i < cascade->count; i++ )
{
for( j = 0; j < cascade->stage_classifier[i].count; j++ )
cvFree( &cascade->stage_classifier[i].classifier[j].haar_feature );
cvFree( &cascade->stage_classifier[i].classifier );
}
icvReleaseHidHaarClassifierCascade( &cascade->hid_cascade );
cvFree( _cascade );
}
}
CV_IMPL CvHaarClassifierCascade*
cvLoadHaarClassifierCascade( const char*, CvSize )
{
return 0;
}
#if 0
static CvHaarClassifierCascade*
icvLoadCascadeCART( const char** input_cascade, int n, CvSize orig_window_size )
{
int i;
CvHaarClassifierCascade* cascade = icvCreateHaarClassifierCascade(n);
cascade->orig_window_size = orig_window_size;
for( i = 0; i < n; i++ )
{
int j, count, l;
float threshold = 0;
const char* stage = input_cascade[i];
int dl = 0;
/* tree links */
int parent = -1;
int next = -1;
sscanf( stage, "%d%n", &count, &dl );
stage += dl;
CV_Assert( count > 0 && count < CV_HAAR_STAGE_MAX);
cascade->stage_classifier[i].count = count;
cascade->stage_classifier[i].classifier =
(CvHaarClassifier*)cvAlloc( count*sizeof(cascade->stage_classifier[i].classifier[0]));
for( j = 0; j < count; j++ )
{
CvHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
int k, rects = 0;
char str[100];
sscanf( stage, "%d%n", &classifier->count, &dl );
stage += dl;
CV_Assert( classifier->count > 0 && classifier->count< CV_HAAR_STAGE_MAX);
classifier->haar_feature = (CvHaarFeature*) cvAlloc(
classifier->count * ( sizeof( *classifier->haar_feature ) +
sizeof( *classifier->threshold ) +
sizeof( *classifier->left ) +
sizeof( *classifier->right ) ) +
(classifier->count + 1) * sizeof( *classifier->alpha ) );
classifier->threshold = (float*) (classifier->haar_feature+classifier->count);
classifier->left = (int*) (classifier->threshold + classifier->count);
classifier->right = (int*) (classifier->left + classifier->count);
classifier->alpha = (float*) (classifier->right + classifier->count);
for( l = 0; l < classifier->count; l++ )
{
sscanf( stage, "%d%n", &rects, &dl );
stage += dl;
CV_Assert( rects >= 2 && rects <= CV_HAAR_FEATURE_MAX );
for( k = 0; k < rects; k++ )
{
cv::Rect r;
int band = 0;
sscanf( stage, "%d%d%d%d%d%f%n",
&r.x, &r.y, &r.width, &r.height, &band,
&(classifier->haar_feature[l].rect[k].weight), &dl );
stage += dl;
classifier->haar_feature[l].rect[k].r = cvRect(r);
}
sscanf( stage, "%99s%n", str, &dl );
stage += dl;
classifier->haar_feature[l].tilted = strncmp( str, "tilted", 6 ) == 0;
for( k = rects; k < CV_HAAR_FEATURE_MAX; k++ )
{
memset( classifier->haar_feature[l].rect + k, 0,
sizeof(classifier->haar_feature[l].rect[k]) );
}
sscanf( stage, "%f%d%d%n", &(classifier->threshold[l]),
&(classifier->left[l]),
&(classifier->right[l]), &dl );
stage += dl;
}
for( l = 0; l <= classifier->count; l++ )
{
sscanf( stage, "%f%n", &(classifier->alpha[l]), &dl );
stage += dl;
}
}
sscanf( stage, "%f%n", &threshold, &dl );
stage += dl;
cascade->stage_classifier[i].threshold = threshold;
/* load tree links */
if( sscanf( stage, "%d%d%n", &parent, &next, &dl ) != 2 )
{
parent = i - 1;
next = -1;
}
stage += dl;
CV_Assert(parent >= 0 && parent < i);
cascade->stage_classifier[i].parent = parent;
cascade->stage_classifier[i].next = next;
cascade->stage_classifier[i].child = -1;
if( parent != -1 && cascade->stage_classifier[parent].child == -1 )
{
cascade->stage_classifier[parent].child = i;
}
}
return cascade;
}
#ifndef _MAX_PATH
#define _MAX_PATH 1024
#endif
CV_IMPL CvHaarClassifierCascade*
cvLoadHaarClassifierCascade( const char* directory, CvSize orig_window_size )
{
if( !directory )
CV_Error( CV_StsNullPtr, "Null path is passed" );
char name[_MAX_PATH];
int n = (int)strlen(directory)-1;
const char* slash = directory[n] == '\\' || directory[n] == '/' ? "" : "/";
int size = 0;
/* try to read the classifier from directory */
for( n = 0; ; n++ )
{
sprintf( name, "%s%s%d/AdaBoostCARTHaarClassifier.txt", directory, slash, n );
FILE* f = fopen( name, "rb" );
if( !f )
break;
fseek( f, 0, SEEK_END );
size += ftell( f ) + 1;
fclose(f);
}
if( n == 0 && slash[0] )
return (CvHaarClassifierCascade*)cvLoad( directory );
if( n == 0 )
CV_Error( CV_StsBadArg, "Invalid path" );
size += (n+1)*sizeof(char*);
const char** input_cascade = (const char**)cvAlloc( size );
if( !input_cascade )
CV_Error( CV_StsNoMem, "Could not allocate memory for input_cascade" );
char* ptr = (char*)(input_cascade + n + 1);
for( int i = 0; i < n; i++ )
{
sprintf( name, "%s/%d/AdaBoostCARTHaarClassifier.txt", directory, i );
FILE* f = fopen( name, "rb" );
if( !f )
CV_Error( CV_StsError, "" );
fseek( f, 0, SEEK_END );
size = (int)ftell( f );
fseek( f, 0, SEEK_SET );
size_t elements_read = fread( ptr, 1, size, f );
CV_Assert(elements_read == (size_t)(size));
fclose(f);
input_cascade[i] = ptr;
ptr += size;
*ptr++ = '\0';
}
input_cascade[n] = 0;
CvHaarClassifierCascade* cascade = icvLoadCascadeCART( input_cascade, n, orig_window_size );
if( input_cascade )
cvFree( &input_cascade );
return cascade;
}
/****************************************************************************************\
* Persistence functions *
\****************************************************************************************/
/* field names */
#define ICV_HAAR_SIZE_NAME "size"
#define ICV_HAAR_STAGES_NAME "stages"
#define ICV_HAAR_TREES_NAME "trees"
#define ICV_HAAR_FEATURE_NAME "feature"
#define ICV_HAAR_RECTS_NAME "rects"
#define ICV_HAAR_TILTED_NAME "tilted"
#define ICV_HAAR_THRESHOLD_NAME "threshold"
#define ICV_HAAR_LEFT_NODE_NAME "left_node"
#define ICV_HAAR_LEFT_VAL_NAME "left_val"
#define ICV_HAAR_RIGHT_NODE_NAME "right_node"
#define ICV_HAAR_RIGHT_VAL_NAME "right_val"
#define ICV_HAAR_STAGE_THRESHOLD_NAME "stage_threshold"
#define ICV_HAAR_PARENT_NAME "parent"
#define ICV_HAAR_NEXT_NAME "next"
static int
icvIsHaarClassifier( const void* struct_ptr )
{
return CV_IS_HAAR_CLASSIFIER( struct_ptr );
}
static CvHaarClassifierCascade*
icvCreateHaarClassifierCascade( int stage_count )
{
CvHaarClassifierCascade* cascade = 0;
int block_size = sizeof(*cascade) + stage_count*sizeof(*cascade->stage_classifier);
if( stage_count <= 0 )
CV_Error( CV_StsOutOfRange, "Number of stages should be positive" );
cascade = (CvHaarClassifierCascade*)cvAlloc( block_size );
memset( cascade, 0, block_size );
cascade->stage_classifier = (CvHaarStageClassifier*)(cascade + 1);
cascade->flags = CV_HAAR_MAGIC_VAL;
cascade->count = stage_count;
return cascade;
}
static void*
icvReadHaarClassifier( CvFileStorage* fs, CvFileNode* node )
{
CvHaarClassifierCascade* cascade = NULL;
char buf[256];
CvFileNode* seq_fn = NULL; /* sequence */
CvFileNode* fn = NULL;
CvFileNode* stages_fn = NULL;
CvSeqReader stages_reader;
int n;
int i, j, k, l;
int parent, next;
stages_fn = cvGetFileNodeByName( fs, node, ICV_HAAR_STAGES_NAME );
if( !stages_fn || !CV_NODE_IS_SEQ( stages_fn->tag) )
CV_Error( CV_StsError, "Invalid stages node" );
n = stages_fn->data.seq->total;
cascade = icvCreateHaarClassifierCascade(n);
/* read size */
seq_fn = cvGetFileNodeByName( fs, node, ICV_HAAR_SIZE_NAME );
if( !seq_fn || !CV_NODE_IS_SEQ( seq_fn->tag ) || seq_fn->data.seq->total != 2 )
CV_Error( CV_StsError, "size node is not a valid sequence." );
fn = (CvFileNode*) cvGetSeqElem( seq_fn->data.seq, 0 );
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0 )
CV_Error( CV_StsError, "Invalid size node: width must be positive integer" );
cascade->orig_window_size.width = fn->data.i;
fn = (CvFileNode*) cvGetSeqElem( seq_fn->data.seq, 1 );
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0 )
CV_Error( CV_StsError, "Invalid size node: height must be positive integer" );
cascade->orig_window_size.height = fn->data.i;
cvStartReadSeq( stages_fn->data.seq, &stages_reader );
for( i = 0; i < n; ++i )
{
CvFileNode* stage_fn;
CvFileNode* trees_fn;
CvSeqReader trees_reader;
stage_fn = (CvFileNode*) stages_reader.ptr;
if( !CV_NODE_IS_MAP( stage_fn->tag ) )
{
sprintf( buf, "Invalid stage %d", i );
CV_Error( CV_StsError, buf );
}
trees_fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_TREES_NAME );
if( !trees_fn || !CV_NODE_IS_SEQ( trees_fn->tag )
|| trees_fn->data.seq->total <= 0 )
{
sprintf( buf, "Trees node is not a valid sequence. (stage %d)", i );
CV_Error( CV_StsError, buf );
}
cascade->stage_classifier[i].classifier =
(CvHaarClassifier*) cvAlloc( trees_fn->data.seq->total
* sizeof( cascade->stage_classifier[i].classifier[0] ) );
for( j = 0; j < trees_fn->data.seq->total; ++j )
{
cascade->stage_classifier[i].classifier[j].haar_feature = NULL;
}
cascade->stage_classifier[i].count = trees_fn->data.seq->total;
cvStartReadSeq( trees_fn->data.seq, &trees_reader );
for( j = 0; j < trees_fn->data.seq->total; ++j )
{
CvFileNode* tree_fn;
CvSeqReader tree_reader;
CvHaarClassifier* classifier;
int last_idx;
classifier = &cascade->stage_classifier[i].classifier[j];
tree_fn = (CvFileNode*) trees_reader.ptr;
if( !CV_NODE_IS_SEQ( tree_fn->tag ) || tree_fn->data.seq->total <= 0 )
{
sprintf( buf, "Tree node is not a valid sequence."
" (stage %d, tree %d)", i, j );
CV_Error( CV_StsError, buf );
}
classifier->count = tree_fn->data.seq->total;
classifier->haar_feature = (CvHaarFeature*) cvAlloc(
classifier->count * ( sizeof( *classifier->haar_feature ) +
sizeof( *classifier->threshold ) +
sizeof( *classifier->left ) +
sizeof( *classifier->right ) ) +
(classifier->count + 1) * sizeof( *classifier->alpha ) );
classifier->threshold = (float*) (classifier->haar_feature+classifier->count);
classifier->left = (int*) (classifier->threshold + classifier->count);
classifier->right = (int*) (classifier->left + classifier->count);
classifier->alpha = (float*) (classifier->right + classifier->count);
cvStartReadSeq( tree_fn->data.seq, &tree_reader );
for( k = 0, last_idx = 0; k < tree_fn->data.seq->total; ++k )
{
CvFileNode* node_fn;
CvFileNode* feature_fn;
CvFileNode* rects_fn;
CvSeqReader rects_reader;
node_fn = (CvFileNode*) tree_reader.ptr;
if( !CV_NODE_IS_MAP( node_fn->tag ) )
{
sprintf( buf, "Tree node %d is not a valid map. (stage %d, tree %d)",
k, i, j );
CV_Error( CV_StsError, buf );
}
feature_fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_FEATURE_NAME );
if( !feature_fn || !CV_NODE_IS_MAP( feature_fn->tag ) )
{
sprintf( buf, "Feature node is not a valid map. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
rects_fn = cvGetFileNodeByName( fs, feature_fn, ICV_HAAR_RECTS_NAME );
if( !rects_fn || !CV_NODE_IS_SEQ( rects_fn->tag )
|| rects_fn->data.seq->total < 1
|| rects_fn->data.seq->total > CV_HAAR_FEATURE_MAX )
{
sprintf( buf, "Rects node is not a valid sequence. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
cvStartReadSeq( rects_fn->data.seq, &rects_reader );
for( l = 0; l < rects_fn->data.seq->total; ++l )
{
CvFileNode* rect_fn;
cv::Rect r;
rect_fn = (CvFileNode*) rects_reader.ptr;
if( !CV_NODE_IS_SEQ( rect_fn->tag ) || rect_fn->data.seq->total != 5 )
{
sprintf( buf, "Rect %d is not a valid sequence. "
"(stage %d, tree %d, node %d)", l, i, j, k );
CV_Error( CV_StsError, buf );
}
fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 0 );
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i < 0 )
{
sprintf( buf, "x coordinate must be non-negative integer. "
"(stage %d, tree %d, node %d, rect %d)", i, j, k, l );
CV_Error( CV_StsError, buf );
}
r.x = fn->data.i;
fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 1 );
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i < 0 )
{
sprintf( buf, "y coordinate must be non-negative integer. "
"(stage %d, tree %d, node %d, rect %d)", i, j, k, l );
CV_Error( CV_StsError, buf );
}
r.y = fn->data.i;
fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 2 );
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0
|| r.x + fn->data.i > cascade->orig_window_size.width )
{
sprintf( buf, "width must be positive integer and "
"(x + width) must not exceed window width. "
"(stage %d, tree %d, node %d, rect %d)", i, j, k, l );
CV_Error( CV_StsError, buf );
}
r.width = fn->data.i;
fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 3 );
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0
|| r.y + fn->data.i > cascade->orig_window_size.height )
{
sprintf( buf, "height must be positive integer and "
"(y + height) must not exceed window height. "
"(stage %d, tree %d, node %d, rect %d)", i, j, k, l );
CV_Error( CV_StsError, buf );
}
r.height = fn->data.i;
fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 4 );
if( !CV_NODE_IS_REAL( fn->tag ) )
{
sprintf( buf, "weight must be real number. "
"(stage %d, tree %d, node %d, rect %d)", i, j, k, l );
CV_Error( CV_StsError, buf );
}
classifier->haar_feature[k].rect[l].weight = (float) fn->data.f;
classifier->haar_feature[k].rect[l].r = cvRect(r);
CV_NEXT_SEQ_ELEM( sizeof( *rect_fn ), rects_reader );
} /* for each rect */
for( l = rects_fn->data.seq->total; l < CV_HAAR_FEATURE_MAX; ++l )
{
classifier->haar_feature[k].rect[l].weight = 0;
classifier->haar_feature[k].rect[l].r = cvRect( 0, 0, 0, 0 );
}
fn = cvGetFileNodeByName( fs, feature_fn, ICV_HAAR_TILTED_NAME);
if( !fn || !CV_NODE_IS_INT( fn->tag ) )
{
sprintf( buf, "tilted must be 0 or 1. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
classifier->haar_feature[k].tilted = ( fn->data.i != 0 );
fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_THRESHOLD_NAME);
if( !fn || !CV_NODE_IS_REAL( fn->tag ) )
{
sprintf( buf, "threshold must be real number. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
classifier->threshold[k] = (float) fn->data.f;
fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_LEFT_NODE_NAME);
if( fn )
{
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= k
|| fn->data.i >= tree_fn->data.seq->total )
{
sprintf( buf, "left node must be valid node number. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
/* left node */
classifier->left[k] = fn->data.i;
}
else
{
fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_LEFT_VAL_NAME );
if( !fn )
{
sprintf( buf, "left node or left value must be specified. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
if( !CV_NODE_IS_REAL( fn->tag ) )
{
sprintf( buf, "left value must be real number. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
/* left value */
if( last_idx >= classifier->count + 1 )
{
sprintf( buf, "Tree structure is broken: too many values. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
classifier->left[k] = -last_idx;
classifier->alpha[last_idx++] = (float) fn->data.f;
}
fn = cvGetFileNodeByName( fs, node_fn,ICV_HAAR_RIGHT_NODE_NAME);
if( fn )
{
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= k
|| fn->data.i >= tree_fn->data.seq->total )
{
sprintf( buf, "right node must be valid node number. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
/* right node */
classifier->right[k] = fn->data.i;
}
else
{
fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_RIGHT_VAL_NAME );
if( !fn )
{
sprintf( buf, "right node or right value must be specified. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
if( !CV_NODE_IS_REAL( fn->tag ) )
{
sprintf( buf, "right value must be real number. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
/* right value */
if( last_idx >= classifier->count + 1 )
{
sprintf( buf, "Tree structure is broken: too many values. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
classifier->right[k] = -last_idx;
classifier->alpha[last_idx++] = (float) fn->data.f;
}
CV_NEXT_SEQ_ELEM( sizeof( *node_fn ), tree_reader );
} /* for each node */
if( last_idx != classifier->count + 1 )
{
sprintf( buf, "Tree structure is broken: too few values. "
"(stage %d, tree %d)", i, j );
CV_Error( CV_StsError, buf );
}
CV_NEXT_SEQ_ELEM( sizeof( *tree_fn ), trees_reader );
} /* for each tree */
fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_STAGE_THRESHOLD_NAME);
if( !fn || !CV_NODE_IS_REAL( fn->tag ) )
{
sprintf( buf, "stage threshold must be real number. (stage %d)", i );
CV_Error( CV_StsError, buf );
}
cascade->stage_classifier[i].threshold = (float) fn->data.f;
parent = i - 1;
next = -1;
fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_PARENT_NAME );
if( !fn || !CV_NODE_IS_INT( fn->tag )
|| fn->data.i < -1 || fn->data.i >= cascade->count )
{
sprintf( buf, "parent must be integer number. (stage %d)", i );
CV_Error( CV_StsError, buf );
}
parent = fn->data.i;
fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_NEXT_NAME );
if( !fn || !CV_NODE_IS_INT( fn->tag )
|| fn->data.i < -1 || fn->data.i >= cascade->count )
{
sprintf( buf, "next must be integer number. (stage %d)", i );
CV_Error( CV_StsError, buf );
}
next = fn->data.i;
cascade->stage_classifier[i].parent = parent;
cascade->stage_classifier[i].next = next;
cascade->stage_classifier[i].child = -1;
if( parent != -1 && cascade->stage_classifier[parent].child == -1 )
{
cascade->stage_classifier[parent].child = i;
}
CV_NEXT_SEQ_ELEM( sizeof( *stage_fn ), stages_reader );
} /* for each stage */
return cascade;
}
static void
icvWriteHaarClassifier( CvFileStorage* fs, const char* name, const void* struct_ptr,
CvAttrList attributes )
{
int i, j, k, l;
char buf[256];
const CvHaarClassifierCascade* cascade = (const CvHaarClassifierCascade*) struct_ptr;
/* TODO: parameters check */
cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_HAAR, attributes );
cvStartWriteStruct( fs, ICV_HAAR_SIZE_NAME, CV_NODE_SEQ | CV_NODE_FLOW );
cvWriteInt( fs, NULL, cascade->orig_window_size.width );
cvWriteInt( fs, NULL, cascade->orig_window_size.height );
cvEndWriteStruct( fs ); /* size */
cvStartWriteStruct( fs, ICV_HAAR_STAGES_NAME, CV_NODE_SEQ );
for( i = 0; i < cascade->count; ++i )
{
cvStartWriteStruct( fs, NULL, CV_NODE_MAP );
sprintf( buf, "stage %d", i );
cvWriteComment( fs, buf, 1 );
cvStartWriteStruct( fs, ICV_HAAR_TREES_NAME, CV_NODE_SEQ );
for( j = 0; j < cascade->stage_classifier[i].count; ++j )
{
CvHaarClassifier* tree = &cascade->stage_classifier[i].classifier[j];
cvStartWriteStruct( fs, NULL, CV_NODE_SEQ );
sprintf( buf, "tree %d", j );
cvWriteComment( fs, buf, 1 );
for( k = 0; k < tree->count; ++k )
{
CvHaarFeature* feature = &tree->haar_feature[k];
cvStartWriteStruct( fs, NULL, CV_NODE_MAP );
if( k )
{
sprintf( buf, "node %d", k );
}
else
{
sprintf( buf, "root node" );
}
cvWriteComment( fs, buf, 1 );
cvStartWriteStruct( fs, ICV_HAAR_FEATURE_NAME, CV_NODE_MAP );
cvStartWriteStruct( fs, ICV_HAAR_RECTS_NAME, CV_NODE_SEQ );
for( l = 0; l < CV_HAAR_FEATURE_MAX && feature->rect[l].r.width != 0; ++l )
{
cvStartWriteStruct( fs, NULL, CV_NODE_SEQ | CV_NODE_FLOW );
cvWriteInt( fs, NULL, feature->rect[l].r.x );
cvWriteInt( fs, NULL, feature->rect[l].r.y );
cvWriteInt( fs, NULL, feature->rect[l].r.width );
cvWriteInt( fs, NULL, feature->rect[l].r.height );
cvWriteReal( fs, NULL, feature->rect[l].weight );
cvEndWriteStruct( fs ); /* rect */
}
cvEndWriteStruct( fs ); /* rects */
cvWriteInt( fs, ICV_HAAR_TILTED_NAME, feature->tilted );
cvEndWriteStruct( fs ); /* feature */
cvWriteReal( fs, ICV_HAAR_THRESHOLD_NAME, tree->threshold[k]);
if( tree->left[k] > 0 )
{
cvWriteInt( fs, ICV_HAAR_LEFT_NODE_NAME, tree->left[k] );
}
else
{
cvWriteReal( fs, ICV_HAAR_LEFT_VAL_NAME,
tree->alpha[-tree->left[k]] );
}
if( tree->right[k] > 0 )
{
cvWriteInt( fs, ICV_HAAR_RIGHT_NODE_NAME, tree->right[k] );
}
else
{
cvWriteReal( fs, ICV_HAAR_RIGHT_VAL_NAME,
tree->alpha[-tree->right[k]] );
}
cvEndWriteStruct( fs ); /* split */
}
cvEndWriteStruct( fs ); /* tree */
}
cvEndWriteStruct( fs ); /* trees */
cvWriteReal( fs, ICV_HAAR_STAGE_THRESHOLD_NAME, cascade->stage_classifier[i].threshold);
cvWriteInt( fs, ICV_HAAR_PARENT_NAME, cascade->stage_classifier[i].parent );
cvWriteInt( fs, ICV_HAAR_NEXT_NAME, cascade->stage_classifier[i].next );
cvEndWriteStruct( fs ); /* stage */
} /* for each stage */
cvEndWriteStruct( fs ); /* stages */
cvEndWriteStruct( fs ); /* root */
}
static void*
icvCloneHaarClassifier( const void* struct_ptr )
{
CvHaarClassifierCascade* cascade = NULL;
int i, j, k, n;
const CvHaarClassifierCascade* cascade_src =
(const CvHaarClassifierCascade*) struct_ptr;
n = cascade_src->count;
cascade = icvCreateHaarClassifierCascade(n);
cascade->orig_window_size = cascade_src->orig_window_size;
for( i = 0; i < n; ++i )
{
cascade->stage_classifier[i].parent = cascade_src->stage_classifier[i].parent;
cascade->stage_classifier[i].next = cascade_src->stage_classifier[i].next;
cascade->stage_classifier[i].child = cascade_src->stage_classifier[i].child;
cascade->stage_classifier[i].threshold = cascade_src->stage_classifier[i].threshold;
cascade->stage_classifier[i].count = 0;
cascade->stage_classifier[i].classifier =
(CvHaarClassifier*) cvAlloc( cascade_src->stage_classifier[i].count
* sizeof( cascade->stage_classifier[i].classifier[0] ) );
cascade->stage_classifier[i].count = cascade_src->stage_classifier[i].count;
for( j = 0; j < cascade->stage_classifier[i].count; ++j )
cascade->stage_classifier[i].classifier[j].haar_feature = NULL;
for( j = 0; j < cascade->stage_classifier[i].count; ++j )
{
const CvHaarClassifier* classifier_src =
&cascade_src->stage_classifier[i].classifier[j];
CvHaarClassifier* classifier =
&cascade->stage_classifier[i].classifier[j];
classifier->count = classifier_src->count;
classifier->haar_feature = (CvHaarFeature*) cvAlloc(
classifier->count * ( sizeof( *classifier->haar_feature ) +
sizeof( *classifier->threshold ) +
sizeof( *classifier->left ) +
sizeof( *classifier->right ) ) +
(classifier->count + 1) * sizeof( *classifier->alpha ) );
classifier->threshold = (float*) (classifier->haar_feature+classifier->count);
classifier->left = (int*) (classifier->threshold + classifier->count);
classifier->right = (int*) (classifier->left + classifier->count);
classifier->alpha = (float*) (classifier->right + classifier->count);
for( k = 0; k < classifier->count; ++k )
{
classifier->haar_feature[k] = classifier_src->haar_feature[k];
classifier->threshold[k] = classifier_src->threshold[k];
classifier->left[k] = classifier_src->left[k];
classifier->right[k] = classifier_src->right[k];
classifier->alpha[k] = classifier_src->alpha[k];
}
classifier->alpha[classifier->count] =
classifier_src->alpha[classifier->count];
}
}
return cascade;
}
CvType haar_type( CV_TYPE_NAME_HAAR, icvIsHaarClassifier,
(CvReleaseFunc)cvReleaseHaarClassifierCascade,
icvReadHaarClassifier, icvWriteHaarClassifier,
icvCloneHaarClassifier );
#endif
/* End of file. */
modules/objdetect/src/haar.hpp deleted 100644 → 0
View file @ 687fa6a8
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/* Haar features calculation */
#ifndef OPENCV_OBJDETECT_HAAR_HPP
#define OPENCV_OBJDETECT_HAAR_HPP
#define CV_HAAR_FEATURE_MAX_LOCAL 3
typedef int sumtype;
typedef double sqsumtype;
typedef struct CvHidHaarFeature
{
struct
{
sumtype *p0, *p1, *p2, *p3;
float weight;
}
rect[CV_HAAR_FEATURE_MAX_LOCAL];
} CvHidHaarFeature;
typedef struct CvHidHaarTreeNode
{
CvHidHaarFeature feature;
float threshold;
int left;
int right;
} CvHidHaarTreeNode;
typedef struct CvHidHaarClassifier
{
int count;
//CvHaarFeature* orig_feature;
CvHidHaarTreeNode* node;
float* alpha;
} CvHidHaarClassifier;
#define calc_sumf(rect,offset) \
static_cast<float>((rect).p0[offset] - (rect).p1[offset] - (rect).p2[offset] + (rect).p3[offset])
namespace cv_haar_avx
{
#if 0 /*CV_TRY_AVX*/
#define CV_HAAR_USE_AVX 1
#else
#define CV_HAAR_USE_AVX 0
#endif
#if CV_HAAR_USE_AVX
// AVX version icvEvalHidHaarClassifier. Process 8 CvHidHaarClassifiers per call. Check AVX support before invocation!!
double icvEvalHidHaarClassifierAVX(CvHidHaarClassifier* classifier, double variance_norm_factor, size_t p_offset);
double icvEvalHidHaarStumpClassifierAVX(CvHidHaarClassifier* classifier, double variance_norm_factor, size_t p_offset);
double icvEvalHidHaarStumpClassifierTwoRectAVX(CvHidHaarClassifier* classifier, double variance_norm_factor, size_t p_offset);
#endif
}
#endif
/* End of file. */
modules/objdetect/test/test_cascadeandhog.cpp
View file @ 3a4bc0d4
......@@ -404,7 +404,6 @@ protected:
virtual void readDetector( const FileNode& fn );
virtual void writeDetector( FileStorage& fs, int di );
virtual int detectMultiScale( int di, const Mat& img, vector<Rect>& objects );
virtual int detectMultiScale_C( const string& filename, int di, const Mat& img, vector<Rect>& objects );
vector<int> flags;
};
......@@ -434,36 +433,6 @@ void CV_CascadeDetectorTest::writeDetector( FileStorage& fs, int di )
fs << C_SCALE_CASCADE << sc;
}
int CV_CascadeDetectorTest::detectMultiScale_C( const string& filename,
int di, const Mat& img,
vector<Rect>& objects )
{
Ptr<CvHaarClassifierCascade> c_cascade(cvLoadHaarClassifierCascade(filename.c_str(), cvSize(0,0)));
Ptr<CvMemStorage> storage(cvCreateMemStorage());
if( !c_cascade )
{
ts->printf( cvtest::TS::LOG, "cascade %s can not be opened");
return cvtest::TS::FAIL_INVALID_TEST_DATA;
}
Mat grayImg;
cvtColor( img, grayImg, COLOR_BGR2GRAY );
equalizeHist( grayImg, grayImg );
CvMat c_gray = cvMat(grayImg);
CvSeq* rs = cvHaarDetectObjects(&c_gray, c_cascade, storage, 1.1, 3, flags[di] );
objects.clear();
for( int i = 0; i < rs->total; i++ )
{
Rect r = *(Rect*)cvGetSeqElem(rs, i);
objects.push_back(r);
}
return cvtest::TS::OK;
}
int CV_CascadeDetectorTest::detectMultiScale( int di, const Mat& img,
vector<Rect>& objects)
{
......@@ -471,11 +440,6 @@ int CV_CascadeDetectorTest::detectMultiScale( int di, const Mat& img,
filename = dataPath + detectorFilenames[di];
const string pattern = "haarcascade_frontalface_default.xml";
if( filename.size() >= pattern.size() &&
strcmp(filename.c_str() + (filename.size() - pattern.size()),
pattern.c_str()) == 0 )
return detectMultiScale_C(filename, di, img, objects);
CascadeClassifier cascade( filename );
if( cascade.empty() )
{
......
modules/objdetect/test/test_precomp.hpp
View file @ 3a4bc0d4
......@@ -6,6 +6,5 @@
#include "opencv2/ts.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/objdetect/objdetect_c.h"
#endif
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