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/*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) 2013, Itseez Inc, 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 <stdio.h>
namespace cv
{
/* 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"
namespace haar_cvt
{
struct HaarFeature
{
enum { RECT_NUM = 3 };
HaarFeature()
{
tilted = false;
for( int i = 0; i < RECT_NUM; i++ )
{
rect[i].r = Rect(0,0,0,0);
rect[i].weight = 0.f;
}
}
bool tilted;
struct
{
Rect r;
float weight;
} rect[RECT_NUM];
};
struct HaarClassifierNode
{
HaarClassifierNode()
{
f = left = right = 0;
threshold = 0.f;
}
int f, left, right;
float threshold;
};
struct HaarClassifier
{
std::vector<HaarClassifierNode> nodes;
std::vector<float> leaves;
};
struct HaarStageClassifier
{
double threshold;
std::vector<HaarClassifier> weaks;
};
static bool convert(const String& oldcascade, const String& newcascade)
{
FileStorage oldfs(oldcascade, FileStorage::READ);
if( !oldfs.isOpened() )
return false;
FileNode oldroot = oldfs.getFirstTopLevelNode();
FileNode sznode = oldroot[ICV_HAAR_SIZE_NAME];
if( sznode.empty() )
return false;
Size cascadesize;
cascadesize.width = (int)sznode[0];
cascadesize.height = (int)sznode[1];
std::vector<HaarFeature> features;
int i, j, k, n;
FileNode stages_seq = oldroot[ICV_HAAR_STAGES_NAME];
int nstages = (int)stages_seq.size();
std::vector<HaarStageClassifier> stages(nstages);
for( i = 0; i < nstages; i++ )
{
FileNode stagenode = stages_seq[i];
HaarStageClassifier& stage = stages[i];
stage.threshold = (double)stagenode[ICV_HAAR_STAGE_THRESHOLD_NAME];
FileNode weaks_seq = stagenode[ICV_HAAR_TREES_NAME];
int nweaks = (int)weaks_seq.size();
stage.weaks.resize(nweaks);
for( j = 0; j < nweaks; j++ )
{
HaarClassifier& weak = stage.weaks[j];
FileNode weaknode = weaks_seq[j];
int nnodes = (int)weaknode.size();
for( n = 0; n < nnodes; n++ )
{
FileNode nnode = weaknode[n];
FileNode fnode = nnode[ICV_HAAR_FEATURE_NAME];
HaarFeature f;
HaarClassifierNode node;
node.f = (int)features.size();
f.tilted = (int)fnode[ICV_HAAR_TILTED_NAME] != 0;
FileNode rects_seq = fnode[ICV_HAAR_RECTS_NAME];
int nrects = (int)rects_seq.size();
for( k = 0; k < nrects; k++ )
{
FileNode rnode = rects_seq[k];
f.rect[k].r.x = (int)rnode[0];
f.rect[k].r.y = (int)rnode[1];
f.rect[k].r.width = (int)rnode[2];
f.rect[k].r.height = (int)rnode[3];
f.rect[k].weight = (float)rnode[4];
}
features.push_back(f);
node.threshold = nnode[ICV_HAAR_THRESHOLD_NAME];
FileNode leftValNode = nnode[ICV_HAAR_LEFT_VAL_NAME];
if( !leftValNode.empty() )
{
node.left = -(int)weak.leaves.size();
weak.leaves.push_back((float)leftValNode);
}
else
{
node.left = (int)nnode[ICV_HAAR_LEFT_NODE_NAME];
}
FileNode rightValNode = nnode[ICV_HAAR_RIGHT_VAL_NAME];
if( !rightValNode.empty() )
{
node.right = -(int)weak.leaves.size();
weak.leaves.push_back((float)rightValNode);
}
else
{
node.right = (int)nnode[ICV_HAAR_RIGHT_NODE_NAME];
}
weak.nodes.push_back(node);
}
}
}
FileStorage newfs(newcascade, FileStorage::WRITE);
if( !newfs.isOpened() )
return false;
int maxWeakCount = 0, nfeatures = (int)features.size();
for( i = 0; i < nstages; i++ )
maxWeakCount = std::max(maxWeakCount, (int)stages[i].weaks.size());
newfs << "cascade" << "{:opencv-cascade-classifier"
<< "stageType" << "BOOST"
<< "featureType" << "HAAR"
<< "height" << cascadesize.width
<< "width" << cascadesize.height
<< "stageParams" << "{"
<< "maxWeakCount" << (int)maxWeakCount
<< "}"
<< "featureParams" << "{"
<< "maxCatCount" << 0
<< "}"
<< "stageNum" << (int)nstages
<< "stages" << "[";
for( i = 0; i < nstages; i++ )
{
int nweaks = (int)stages[i].weaks.size();
newfs << "{" << "maxWeakCount" << (int)nweaks
<< "stageThreshold" << stages[i].threshold
<< "weakClassifiers" << "[";
for( j = 0; j < nweaks; j++ )
{
const HaarClassifier& c = stages[i].weaks[j];
newfs << "{" << "internalNodes" << "[";
int nnodes = (int)c.nodes.size(), nleaves = (int)c.leaves.size();
for( k = 0; k < nnodes; k++ )
newfs << c.nodes[k].left << c.nodes[k].right
<< c.nodes[k].f << c.nodes[k].threshold;
newfs << "]" << "leafValues" << "[";
for( k = 0; k < nleaves; k++ )
newfs << c.leaves[k];
newfs << "]" << "}";
}
newfs << "]" << "}";
}
newfs << "]"
<< "features" << "[";
for( i = 0; i < nfeatures; i++ )
{
const HaarFeature& f = features[i];
newfs << "{" << "rects" << "[";
for( j = 0; j < HaarFeature::RECT_NUM; j++ )
{
if( j >= 2 && fabs(f.rect[j].weight) < FLT_EPSILON )
break;
newfs << "[" << f.rect[j].r.x << f.rect[j].r.y <<
f.rect[j].r.width << f.rect[j].r.height << f.rect[j].weight << "]";
}
newfs << "]";
if( f.tilted )
newfs << "tilted" << 1;
newfs << "}";
}
newfs << "]" << "}";
return true;
}
}
bool CascadeClassifier::convert(const String& oldcascade, const String& newcascade)
{
bool ok = haar_cvt::convert(oldcascade, newcascade);
if( !ok && newcascade.size() > 0 )
remove(newcascade.c_str());
return ok;
}
}