cascade.cpp
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/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
* Copyright 2012 The MITRE Corporation *
* *
* Licensed under the Apache License, Version 2.0 (the "License"); *
* you may not use this file except in compliance with the License. *
* You may obtain a copy of the License at *
* *
* http://www.apache.org/licenses/LICENSE-2.0 *
* *
* Unless required by applicable law or agreed to in writing, software *
* distributed under the License is distributed on an "AS IS" BASIS, *
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. *
* See the License for the specific language governing permissions and *
* limitations under the License. *
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
#include <QProcess>
#include <QTemporaryFile>
#include <opencv2/objdetect/objdetect.hpp>
#include <fstream>
#include <openbr/plugins/openbr_internal.h>
#include <openbr/core/opencvutils.h>
#include <openbr/core/resource.h>
#include <openbr/core/qtutils.h>
#include <openbr/core/cascade.h>
using namespace cv;
namespace br
{
class CascadeResourceMaker : public ResourceMaker<CascadeClassifier>
{
QString file;
public:
CascadeResourceMaker(const QString &model)
{
file = Globals->sdkPath + "/share/openbr/models/";
if (model == "Ear") file += "haarcascades/haarcascade_ear.xml";
else if (model == "Eye") file += "haarcascades/haarcascade_eye_tree_eyeglasses.xml";
else if (model == "FrontalFace") file += "haarcascades/haarcascade_frontalface_alt2.xml";
else if (model == "ProfileFace") file += "haarcascades/haarcascade_profileface.xml";
else {
// Create a directory for trainable cascades
file += "openbrcascades/"+model+"/cascade.xml";
QFile touchFile(file);
QtUtils::touchDir(touchFile);
}
}
private:
CascadeClassifier *make() const
{
CascadeClassifier *cascade = new CascadeClassifier();
if (!cascade->load(file.toStdString()))
qFatal("Failed to load: %s", qPrintable(file));
return cascade;
}
};
/*!
* \ingroup transforms
* \brief Wraps OpenCV cascade classifier
* \author Josh Klontz \cite jklontz
* \author David Crouse \cite dgcrouse
*/
class CascadeTransform : public MetaTransform
{
Q_OBJECT
Q_PROPERTY(QString model READ get_model WRITE set_model RESET reset_model STORED false)
Q_PROPERTY(int minSize READ get_minSize WRITE set_minSize RESET reset_minSize STORED false)
Q_PROPERTY(int minNeighbors READ get_minNeighbors WRITE set_minNeighbors RESET reset_minNeighbors STORED false)
Q_PROPERTY(bool ROCMode READ get_ROCMode WRITE set_ROCMode RESET reset_ROCMode STORED false)
// Training parameters
Q_PROPERTY(QString vecFile READ get_vecFile WRITE set_vecFile RESET reset_vecFile STORED false)
Q_PROPERTY(QString negFile READ get_negFile WRITE set_negFile RESET reset_negFile STORED false)
Q_PROPERTY(int winWidth READ get_winWidth WRITE set_winWidth RESET reset_winWidth STORED false)
Q_PROPERTY(int winHeight READ get_winHeight WRITE set_winHeight RESET reset_winHeight STORED false)
Q_PROPERTY(int numPos READ get_numPos WRITE set_numPos RESET reset_numPos STORED false)
Q_PROPERTY(int numNeg READ get_numNeg WRITE set_numNeg RESET reset_numNeg STORED false)
BR_PROPERTY(QString, model, "FrontalFace")
BR_PROPERTY(int, minSize, 64)
BR_PROPERTY(int, minNeighbors, 5)
BR_PROPERTY(bool, ROCMode, false)
BR_PROPERTY(QString, vecFile, "data.vec")
BR_PROPERTY(QString, negFile, "neg.txt")
BR_PROPERTY(int, winWidth, 24)
BR_PROPERTY(int, winHeight, 24)
BR_PROPERTY(int, numPos, 1000)
BR_PROPERTY(int, numNeg, 1000)
Resource<CascadeClassifier> cascadeResource;
void init()
{
cascadeResource.setResourceMaker(new CascadeResourceMaker(model));
if (model == "Ear" || model == "Eye" || model == "FrontalFace" || model == "ProfileFace")
this->trainable = false;
}
QList<Mat> getPos()
{
FILE *file = fopen(vecFile.toStdString().c_str(), "rb");
if ( !file )
qFatal("Couldn't open the file");
short* vec = 0;
int count, vecSize, last, base;
short tmp = 0;
if( fread( &count, sizeof( count ), 1, file ) != 1 ||
fread( &vecSize, sizeof( vecSize ), 1, file ) != 1 ||
fread( &tmp, sizeof( tmp ), 1, file ) != 1 ||
fread( &tmp, sizeof( tmp ), 1, file ) != 1 )
CV_Error_( CV_StsParseError, ("wrong file format for %s\n", qPrintable(vecFile)) );
base = sizeof( count ) + sizeof( vecSize ) + 2*sizeof( tmp );
last = 0;
vec = (short*) cvAlloc( sizeof( *vec ) * vecSize );
CV_Assert( vec );
QList<Mat> posImages;
for (int i = 0; i < 35770; i++) {
Mat pos(winHeight, winWidth, CV_8UC1);
CV_Assert( pos.rows * pos.cols == vecSize );
uchar tmp = 0;
size_t elements_read = fread( &tmp, sizeof( tmp ), 1, file );
if( elements_read != 1 )
CV_Error( CV_StsBadArg, "Can not get new positive sample. The most possible reason is "
"insufficient count of samples in given vec-file.\n");
elements_read = fread( vec, sizeof( vec[0] ), vecSize, file );
if( elements_read != (size_t)(vecSize) )
CV_Error( CV_StsBadArg, "Can not get new positive sample. Seems that vec-file has incorrect structure.\n");
if( feof( file ) || last++ >= count )
CV_Error( CV_StsBadArg, "Can not get new positive sample. vec-file is over.\n");
for( int r = 0; r < pos.rows; r++ )
for( int c = 0; c < pos.cols; c++ )
pos.ptr(r)[c] = (uchar)vec[r * pos.cols + c];
posImages.append(pos);
}
return posImages;
}
QList<Mat> getNeg()
{
QList<Mat> negs;
std::string dirname, str;
std::ifstream file(negFile.toStdString().c_str());
size_t pos = negFile.toStdString().rfind('\\');
char dlmrt = '\\';
if (pos == string::npos)
{
pos = negFile.toStdString().rfind('/');
dlmrt = '/';
}
dirname = pos == string::npos ? "" : negFile.toStdString().substr(0, pos) + dlmrt;
while( !file.eof() )
{
std::getline(file, str);
if (str.empty()) break;
if (str.at(0) == '#' ) continue;
negs.append(imread(dirname + str, CV_LOAD_IMAGE_GRAYSCALE));
}
file.close();
return negs;
}
// Train transform
void train(const TemplateList& data)
{
(void)data;
BrCascadeClassifier classifier;
QList<Mat> posImages = getPos();
QList<Mat> negImages = getNeg();
CascadeBoostParams stageParams(CvBoost::GENTLE, 0.999, 0.5, 0.95, 1, 200);
QString cascadeDir = Globals->sdkPath + "/share/openbr/models/openbrcascades/" + model;
classifier.train(cascadeDir.toStdString(),
posImages, negImages,
numPos, numNeg, 1024, 1024, 12,
Size(winWidth, winHeight),
stageParams);
}
void project(const Template &src, Template &dst) const
{
TemplateList temp;
project(TemplateList() << src, temp);
if (!temp.isEmpty()) dst = temp.first();
}
void project(const TemplateList &src, TemplateList &dst) const
{
CascadeClassifier *cascade = cascadeResource.acquire();
foreach (const Template &t, src) {
const bool enrollAll = t.file.getBool("enrollAll");
// Mirror the behavior of ExpandTransform in the special case
// of an empty template.
if (t.empty() && !enrollAll) {
dst.append(t);
continue;
}
for (int i=0; i<t.size(); i++) {
Mat m;
OpenCVUtils::cvtUChar(t[i], m);
std::vector<Rect> rects;
std::vector<int> rejectLevels;
std::vector<double> levelWeights;
if (ROCMode) cascade->detectMultiScale(m, rects, rejectLevels, levelWeights, 1.2, minNeighbors, (enrollAll ? 0 : CASCADE_FIND_BIGGEST_OBJECT) | CASCADE_SCALE_IMAGE, Size(minSize, minSize), Size(), true);
else cascade->detectMultiScale(m, rects, 1.2, minNeighbors, enrollAll ? 0 : CASCADE_FIND_BIGGEST_OBJECT, Size(minSize, minSize));
if (!enrollAll && rects.empty())
rects.push_back(Rect(0, 0, m.cols, m.rows));
for (size_t j=0; j<rects.size(); j++) {
Template u(t.file, m);
if (rejectLevels.size() > j)
u.file.set("Confidence", rejectLevels[j]*levelWeights[j]);
else
u.file.set("Confidence", 1);
const QRectF rect = OpenCVUtils::fromRect(rects[j]);
u.file.appendRect(rect);
u.file.set(model, rect);
dst.append(u);
}
}
}
cascadeResource.release(cascade);
}
// TODO: Remove this code when ready to break binary compatibility
void store(QDataStream &stream) const
{
int size = 1;
stream << size;
}
void load(QDataStream &stream)
{
int size;
stream >> size;
}
};
BR_REGISTER(Transform, CascadeTransform)
} // namespace br
#include "metadata/cascade.moc"