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opencv/modules/objdetect/test/test_cascadeandhog.cpp

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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.
//
//
// 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*/
#include "test_precomp.hpp"
namespace opencv_test { namespace {
//#define GET_STAT
#define DIST_E "distE"
#define S_E "sE"
#define NO_PAIR_E "noPairE"
//#define TOTAL_NO_PAIR_E "totalNoPairE"
#define DETECTOR_NAMES "detector_names"
#define DETECTORS "detectors"
#define IMAGE_FILENAMES "image_filenames"
#define VALIDATION "validation"
#define FILENAME "fn"
#define C_SCALE_CASCADE "scale_cascade"
class CV_DetectorTest : public cvtest::BaseTest
{
public:
CV_DetectorTest();
protected:
virtual int prepareData( FileStorage& fs );
virtual void run( int startFrom );
virtual string& getValidationFilename();
virtual void readDetector( const FileNode& fn ) = 0;
virtual void writeDetector( FileStorage& fs, int di ) = 0;
int runTestCase( int detectorIdx, vector<vector<Rect> >& objects );
virtual int detectMultiScale( int di, const Mat& img, vector<Rect>& objects ) = 0;
int validate( int detectorIdx, vector<vector<Rect> >& objects );
struct
{
float dist;
float s;
float noPair;
//float totalNoPair;
} eps;
vector<string> detectorNames;
vector<string> detectorFilenames;
vector<string> imageFilenames;
vector<Mat> images;
string validationFilename;
string configFilename;
FileStorage validationFS;
bool write_results;
};
CV_DetectorTest::CV_DetectorTest()
{
configFilename = "dummy";
write_results = false;
}
string& CV_DetectorTest::getValidationFilename()
{
return validationFilename;
}
int CV_DetectorTest::prepareData( FileStorage& _fs )
{
if( !_fs.isOpened() )
test_case_count = -1;
else
{
FileNode fn = _fs.getFirstTopLevelNode();
fn[DIST_E] >> eps.dist;
fn[S_E] >> eps.s;
fn[NO_PAIR_E] >> eps.noPair;
// fn[TOTAL_NO_PAIR_E] >> eps.totalNoPair;
// read detectors
if( fn[DETECTOR_NAMES].size() != 0 )
{
FileNodeIterator it = fn[DETECTOR_NAMES].begin();
for( ; it != fn[DETECTOR_NAMES].end(); )
{
String _name;
it >> _name;
detectorNames.push_back(_name);
readDetector(fn[DETECTORS][_name]);
}
}
test_case_count = (int)detectorNames.size();
// read images filenames and images
string dataPath = ts->get_data_path();
if( fn[IMAGE_FILENAMES].size() != 0 )
{
for( FileNodeIterator it = fn[IMAGE_FILENAMES].begin(); it != fn[IMAGE_FILENAMES].end(); )
{
String filename;
it >> filename;
imageFilenames.push_back(filename);
Mat img = imread( dataPath+filename, 1 );
images.push_back( img );
}
}
}
return cvtest::TS::OK;
}
void CV_DetectorTest::run( int )
{
string dataPath = ts->get_data_path();
string vs_filename = dataPath + getValidationFilename();
write_results = !validationFS.open( vs_filename, FileStorage::READ );
int code;
if( !write_results )
{
code = prepareData( validationFS );
}
else
{
FileStorage fs0(dataPath + configFilename, FileStorage::READ );
code = prepareData(fs0);
}
if( code < 0 )
{
ts->set_failed_test_info( code );
return;
}
if( write_results )
{
validationFS.release();
validationFS.open( vs_filename, FileStorage::WRITE );
validationFS << FileStorage::getDefaultObjectName(validationFilename) << "{";
validationFS << DIST_E << eps.dist;
validationFS << S_E << eps.s;
validationFS << NO_PAIR_E << eps.noPair;
// validationFS << TOTAL_NO_PAIR_E << eps.totalNoPair;
// write detector names
validationFS << DETECTOR_NAMES << "[";
vector<string>::const_iterator nit = detectorNames.begin();
for( ; nit != detectorNames.end(); ++nit )
{
validationFS << *nit;
}
validationFS << "]"; // DETECTOR_NAMES
// write detectors
validationFS << DETECTORS << "{";
CV_Assert( detectorNames.size() == detectorFilenames.size() );
nit = detectorNames.begin();
for( int di = 0; nit != detectorNames.end(); ++nit, di++ )
{
validationFS << *nit << "{";
writeDetector( validationFS, di );
validationFS << "}";
}
validationFS << "}";
// write image filenames
validationFS << IMAGE_FILENAMES << "[";
vector<string>::const_iterator it = imageFilenames.begin();
for( int ii = 0; it != imageFilenames.end(); ++it, ii++ )
{
//String buf = cv::format("img_%d", ii);
//cvWriteComment( validationFS.fs, buf, 0 );
validationFS << *it;
}
validationFS << "]"; // IMAGE_FILENAMES
validationFS << VALIDATION << "{";
}
int progress = 0;
for( int di = 0; di < test_case_count; di++ )
{
progress = update_progress( progress, di, test_case_count, 0 );
if( write_results )
validationFS << detectorNames[di] << "{";
vector<vector<Rect> > objects;
int temp_code = runTestCase( di, objects );
if (!write_results && temp_code == cvtest::TS::OK)
temp_code = validate( di, objects );
if (temp_code != cvtest::TS::OK)
code = temp_code;
if( write_results )
validationFS << "}"; // detectorNames[di]
}
if( write_results )
{
validationFS << "}"; // VALIDATION
validationFS << "}"; // getDefaultObjectName
}
if ( test_case_count <= 0 || imageFilenames.size() <= 0 )
{
ts->printf( cvtest::TS::LOG, "validation file is not determined or not correct" );
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
}
ts->set_failed_test_info( code );
}
int CV_DetectorTest::runTestCase( int detectorIdx, vector<vector<Rect> >& objects )
{
string dataPath = ts->get_data_path(), detectorFilename;
if( !detectorFilenames[detectorIdx].empty() )
detectorFilename = dataPath + detectorFilenames[detectorIdx];
printf("detector %s\n", detectorFilename.c_str());
for( int ii = 0; ii < (int)imageFilenames.size(); ++ii )
{
vector<Rect> imgObjects;
Mat image = images[ii];
if( image.empty() )
{
String msg = cv::format("image %d is empty", ii);
ts->printf( cvtest::TS::LOG, msg.c_str() );
return cvtest::TS::FAIL_INVALID_TEST_DATA;
}
int code = detectMultiScale( detectorIdx, image, imgObjects );
if( code != cvtest::TS::OK )
return code;
objects.push_back( imgObjects );
if( write_results )
{
String imageIdxStr = cv::format("img_%d", ii);
validationFS << imageIdxStr << "[:";
for( vector<Rect>::const_iterator it = imgObjects.begin();
it != imgObjects.end(); ++it )
{
validationFS << it->x << it->y << it->width << it->height;
}
validationFS << "]"; // imageIdxStr
}
}
return cvtest::TS::OK;
}
static bool isZero( uchar i ) {return i == 0;}
int CV_DetectorTest::validate( int detectorIdx, vector<vector<Rect> >& objects )
{
CV_Assert( imageFilenames.size() == objects.size() );
int imageIdx = 0;
int totalNoPair = 0, totalValRectCount = 0;
for( vector<vector<Rect> >::const_iterator it = objects.begin();
it != objects.end(); ++it, imageIdx++ ) // for image
{
Size imgSize = images[imageIdx].size();
float dist = min(imgSize.height, imgSize.width) * eps.dist;
float wDiff = imgSize.width * eps.s;
float hDiff = imgSize.height * eps.s;
int noPair = 0;
// read validation rectangles
String imageIdxStr = cv::format("img_%d", imageIdx);
FileNode node = validationFS.getFirstTopLevelNode()[VALIDATION][detectorNames[detectorIdx]][imageIdxStr];
vector<Rect> valRects;
if( node.size() != 0 )
{
for( FileNodeIterator it2 = node.begin(); it2 != node.end(); )
{
Rect r;
it2 >> r.x >> r.y >> r.width >> r.height;
valRects.push_back(r);
}
}
totalValRectCount += (int)valRects.size();
// compare rectangles
vector<uchar> map(valRects.size(), 0);
for( vector<Rect>::const_iterator cr = it->begin();
cr != it->end(); ++cr )
{
// find nearest rectangle
Point2f cp1 = Point2f( cr->x + (float)cr->width/2.0f, cr->y + (float)cr->height/2.0f );
int minIdx = -1, vi = 0;
float minDist = (float)cv::norm( Point(imgSize.width, imgSize.height) );
for( vector<Rect>::const_iterator vr = valRects.begin();
vr != valRects.end(); ++vr, vi++ )
{
Point2f cp2 = Point2f( vr->x + (float)vr->width/2.0f, vr->y + (float)vr->height/2.0f );
float curDist = (float)cv::norm(cp1-cp2);
if( curDist < minDist )
{
minIdx = vi;
minDist = curDist;
}
}
if( minIdx == -1 )
{
noPair++;
}
else
{
Rect vr = valRects[minIdx];
if( map[minIdx] != 0 || (minDist > dist) || (abs(cr->width - vr.width) > wDiff) ||
(abs(cr->height - vr.height) > hDiff) )
noPair++;
else
map[minIdx] = 1;
}
}
noPair += (int)count_if( map.begin(), map.end(), isZero );
totalNoPair += noPair;
EXPECT_LE(noPair, cvRound(valRects.size()*eps.noPair)+1)
<< "detector " << detectorNames[detectorIdx] << " has overrated count of rectangles without pair on "
<< imageFilenames[imageIdx] << " image";
if (::testing::Test::HasFailure())
break;
}
EXPECT_LE(totalNoPair, cvRound(totalValRectCount*eps./*total*/noPair)+1)
<< "detector " << detectorNames[detectorIdx] << " has overrated count of rectangles without pair on all images set";
if (::testing::Test::HasFailure())
return cvtest::TS::FAIL_BAD_ACCURACY;
return cvtest::TS::OK;
}
//----------------------------------------------- CascadeDetectorTest -----------------------------------
class CV_CascadeDetectorTest : public CV_DetectorTest
{
public:
CV_CascadeDetectorTest();
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;
};
CV_CascadeDetectorTest::CV_CascadeDetectorTest()
{
validationFilename = "cascadeandhog/cascade.xml";
configFilename = "cascadeandhog/_cascade.xml";
}
void CV_CascadeDetectorTest::readDetector( const FileNode& fn )
{
String filename;
int flag;
fn[FILENAME] >> filename;
detectorFilenames.push_back(filename);
fn[C_SCALE_CASCADE] >> flag;
if( flag )
flags.push_back( 0 );
else
flags.push_back( CASCADE_SCALE_IMAGE );
}
void CV_CascadeDetectorTest::writeDetector( FileStorage& fs, int di )
{
int sc = flags[di] & CASCADE_SCALE_IMAGE ? 0 : 1;
fs << FILENAME << detectorFilenames[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)
{
string dataPath = ts->get_data_path(), filename;
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() )
{
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 );
cascade.detectMultiScale( grayImg, objects, 1.1, 3, flags[di] );
return cvtest::TS::OK;
}
//----------------------------------------------- HOGDetectorTest -----------------------------------
class CV_HOGDetectorTest : public CV_DetectorTest
{
public:
CV_HOGDetectorTest();
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 );
};
CV_HOGDetectorTest::CV_HOGDetectorTest()
{
validationFilename = "cascadeandhog/hog.xml";
}
void CV_HOGDetectorTest::readDetector( const FileNode& fn )
{
String filename;
if( fn[FILENAME].size() != 0 )
fn[FILENAME] >> filename;
detectorFilenames.push_back( filename);
}
void CV_HOGDetectorTest::writeDetector( FileStorage& fs, int di )
{
fs << FILENAME << detectorFilenames[di];
}
int CV_HOGDetectorTest::detectMultiScale( int di, const Mat& img,
vector<Rect>& objects)
{
HOGDescriptor hog;
if( detectorFilenames[di].empty() )
hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
else
CV_Assert(0);
hog.detectMultiScale(img, objects);
return cvtest::TS::OK;
}
//----------------------------------------------- HOGDetectorReadWriteTest -----------------------------------
TEST(Objdetect_HOGDetectorReadWrite, regression)
{
// Inspired by bug #2607
Mat img;
img = imread(cvtest::TS::ptr()->get_data_path() + "/cascadeandhog/images/karen-and-rob.png");
ASSERT_FALSE(img.empty());
HOGDescriptor hog;
hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
string tempfilename = cv::tempfile(".xml");
FileStorage fs(tempfilename, FileStorage::WRITE);
hog.write(fs, "myHOG");
fs.open(tempfilename, FileStorage::READ);
remove(tempfilename.c_str());
FileNode n = fs["opencv_storage"]["myHOG"];
ASSERT_NO_THROW(hog.read(n));
}
TEST(Objdetect_CascadeDetector, regression) { CV_CascadeDetectorTest test; test.safe_run(); }
TEST(Objdetect_HOGDetector, regression) { CV_HOGDetectorTest test; test.safe_run(); }
//----------------------------------------------- HOG SSE2 compatible test -----------------------------------
class HOGDescriptorTester :
public cv::HOGDescriptor
{
HOGDescriptor* actual_hog;
cvtest::TS* ts;
mutable bool failed;
public:
HOGDescriptorTester(HOGDescriptor& instance) :
cv::HOGDescriptor(instance), actual_hog(&instance),
ts(cvtest::TS::ptr()), failed(false)
{ }
virtual void computeGradient(const Mat& img, Mat& grad, Mat& qangle,
Size paddingTL, Size paddingBR) const;
virtual void detect(const Mat& img,
vector<Point>& hits, vector<double>& weights, double hitThreshold = 0.0,
Size winStride = Size(), Size padding = Size(),
const vector<Point>& locations = vector<Point>()) const;
virtual void detect(const Mat& img, vector<Point>& hits, double hitThreshold = 0.0,
Size winStride = Size(), Size padding = Size(),
const vector<Point>& locations = vector<Point>()) const;
virtual void compute(InputArray img, vector<float>& descriptors,
Size winStride = Size(), Size padding = Size(),
const vector<Point>& locations = vector<Point>()) const;
bool is_failed() const;
};
struct HOGCacheTester
{
struct BlockData
{
BlockData() : histOfs(0), imgOffset() {}
int histOfs;
Point imgOffset;
};
struct PixData
{
size_t gradOfs, qangleOfs;
int histOfs[4];
float histWeights[4];
float gradWeight;
};
HOGCacheTester(const HOGDescriptorTester* descriptor,
const Mat& img, Size paddingTL, Size paddingBR,
bool useCache, Size cacheStride);
virtual ~HOGCacheTester() { }
virtual void init(const HOGDescriptorTester* descriptor,
const Mat& img, Size paddingTL, Size paddingBR,
bool useCache, Size cacheStride);
Size windowsInImage(Size imageSize, Size winStride) const;
Rect getWindow(Size imageSize, Size winStride, int idx) const;
const float* getBlock(Point pt, float* buf);
virtual void normalizeBlockHistogram(float* histogram) const;
vector<PixData> pixData;
vector<BlockData> blockData;
bool useCache;
vector<int> ymaxCached;
Size winSize, cacheStride;
Size nblocks, ncells;
int blockHistogramSize;
int count1, count2, count4;
Point imgoffset;
Mat_<float> blockCache;
Mat_<uchar> blockCacheFlags;
Mat grad, qangle;
const HOGDescriptorTester* descriptor;
private:
HOGCacheTester(); //= delete
};
HOGCacheTester::HOGCacheTester(const HOGDescriptorTester* _descriptor,
const Mat& _img, Size _paddingTL, Size _paddingBR,
bool _useCache, Size _cacheStride)
{
init(_descriptor, _img, _paddingTL, _paddingBR, _useCache, _cacheStride);
}
void HOGCacheTester::init(const HOGDescriptorTester* _descriptor,
const Mat& _img, Size _paddingTL, Size _paddingBR,
bool _useCache, Size _cacheStride)
{
descriptor = _descriptor;
cacheStride = _cacheStride;
useCache = _useCache;
descriptor->computeGradient(_img, grad, qangle, _paddingTL, _paddingBR);
imgoffset = _paddingTL;
winSize = descriptor->winSize;
Size blockSize = descriptor->blockSize;
Size blockStride = descriptor->blockStride;
Size cellSize = descriptor->cellSize;
int i, j, nbins = descriptor->nbins;
int rawBlockSize = blockSize.width*blockSize.height;
nblocks = Size((winSize.width - blockSize.width)/blockStride.width + 1,
(winSize.height - blockSize.height)/blockStride.height + 1);
ncells = Size(blockSize.width/cellSize.width, blockSize.height/cellSize.height);
blockHistogramSize = ncells.width*ncells.height*nbins;
if( useCache )
{
Size cacheSize((grad.cols - blockSize.width)/cacheStride.width+1,
(winSize.height/cacheStride.height)+1);
blockCache.create(cacheSize.height, cacheSize.width*blockHistogramSize);
blockCacheFlags.create(cacheSize);
size_t cacheRows = blockCache.rows;
ymaxCached.resize(cacheRows);
for(size_t ii = 0; ii < cacheRows; ii++ )
ymaxCached[ii] = -1;
}
Mat_<float> weights(blockSize);
float sigma = (float)descriptor->getWinSigma();
float scale = 1.f/(sigma*sigma*2);
for(i = 0; i < blockSize.height; i++)
for(j = 0; j < blockSize.width; j++)
{
float di = i - blockSize.height*0.5f;
float dj = j - blockSize.width*0.5f;
weights(i,j) = std::exp(-(di*di + dj*dj)*scale);
}
blockData.resize(nblocks.width*nblocks.height);
pixData.resize(rawBlockSize*3);
// Initialize 2 lookup tables, pixData & blockData.
// Here is why:
//
// The detection algorithm runs in 4 nested loops (at each pyramid layer):
// loop over the windows within the input image
// loop over the blocks within each window
// loop over the cells within each block
// loop over the pixels in each cell
//
// As each of the loops runs over a 2-dimensional array,
// we could get 8(!) nested loops in total, which is very-very slow.
//
// To speed the things up, we do the following:
// 1. loop over windows is unrolled in the HOGDescriptor::{compute|detect} methods;
// inside we compute the current search window using getWindow() method.
// Yes, it involves some overhead (function call + couple of divisions),
// but it's tiny in fact.
// 2. loop over the blocks is also unrolled. Inside we use pre-computed blockData[j]
// to set up gradient and histogram pointers.
// 3. loops over cells and pixels in each cell are merged
// (since there is no overlap between cells, each pixel in the block is processed once)
// and also unrolled. Inside we use PixData[k] to access the gradient values and
// update the histogram
//
count1 = count2 = count4 = 0;
for( j = 0; j < blockSize.width; j++ )
for( i = 0; i < blockSize.height; i++ )
{
PixData* data = 0;
float cellX = (j+0.5f)/cellSize.width - 0.5f;
float cellY = (i+0.5f)/cellSize.height - 0.5f;
int icellX0 = cvFloor(cellX);
int icellY0 = cvFloor(cellY);
int icellX1 = icellX0 + 1, icellY1 = icellY0 + 1;
cellX -= icellX0;
cellY -= icellY0;
if( (unsigned)icellX0 < (unsigned)ncells.width &&
(unsigned)icellX1 < (unsigned)ncells.width )
{
if( (unsigned)icellY0 < (unsigned)ncells.height &&
(unsigned)icellY1 < (unsigned)ncells.height )
{
data = &pixData[rawBlockSize*2 + (count4++)];
data->histOfs[0] = (icellX0*ncells.height + icellY0)*nbins;
data->histWeights[0] = (1.f - cellX)*(1.f - cellY);
data->histOfs[1] = (icellX1*ncells.height + icellY0)*nbins;
data->histWeights[1] = cellX*(1.f - cellY);
data->histOfs[2] = (icellX0*ncells.height + icellY1)*nbins;
data->histWeights[2] = (1.f - cellX)*cellY;
data->histOfs[3] = (icellX1*ncells.height + icellY1)*nbins;
data->histWeights[3] = cellX*cellY;
}
else
{
data = &pixData[rawBlockSize + (count2++)];
if( (unsigned)icellY0 < (unsigned)ncells.height )
{
icellY1 = icellY0;
cellY = 1.f - cellY;
}
data->histOfs[0] = (icellX0*ncells.height + icellY1)*nbins;
data->histWeights[0] = (1.f - cellX)*cellY;
data->histOfs[1] = (icellX1*ncells.height + icellY1)*nbins;
data->histWeights[1] = cellX*cellY;
data->histOfs[2] = data->histOfs[3] = 0;
data->histWeights[2] = data->histWeights[3] = 0;
}
}
else
{
if( (unsigned)icellX0 < (unsigned)ncells.width )
{
icellX1 = icellX0;
cellX = 1.f - cellX;
}
if( (unsigned)icellY0 < (unsigned)ncells.height &&
(unsigned)icellY1 < (unsigned)ncells.height )
{
data = &pixData[rawBlockSize + (count2++)];
data->histOfs[0] = (icellX1*ncells.height + icellY0)*nbins;
data->histWeights[0] = cellX*(1.f - cellY);
data->histOfs[1] = (icellX1*ncells.height + icellY1)*nbins;
data->histWeights[1] = cellX*cellY;
data->histOfs[2] = data->histOfs[3] = 0;
data->histWeights[2] = data->histWeights[3] = 0;
}
else
{
data = &pixData[count1++];
if( (unsigned)icellY0 < (unsigned)ncells.height )
{
icellY1 = icellY0;
cellY = 1.f - cellY;
}
data->histOfs[0] = (icellX1*ncells.height + icellY1)*nbins;
data->histWeights[0] = cellX*cellY;
data->histOfs[1] = data->histOfs[2] = data->histOfs[3] = 0;
data->histWeights[1] = data->histWeights[2] = data->histWeights[3] = 0;
}
}
data->gradOfs = (grad.cols*i + j)*2;
data->qangleOfs = (qangle.cols*i + j)*2;
data->gradWeight = weights(i,j);
}
CV_Assert( count1 + count2 + count4 == rawBlockSize );
// defragment pixData
for( j = 0; j < count2; j++ )
pixData[j + count1] = pixData[j + rawBlockSize];
for( j = 0; j < count4; j++ )
pixData[j + count1 + count2] = pixData[j + rawBlockSize*2];
count2 += count1;
count4 += count2;
// initialize blockData
for( j = 0; j < nblocks.width; j++ )
for( i = 0; i < nblocks.height; i++ )
{
BlockData& data = blockData[j*nblocks.height + i];
data.histOfs = (j*nblocks.height + i)*blockHistogramSize;
data.imgOffset = Point(j*blockStride.width,i*blockStride.height);
}
}
const float* HOGCacheTester::getBlock(Point pt, float* buf)
{
float* blockHist = buf;
CV_Assert(descriptor != 0);
Size blockSize = descriptor->blockSize;
pt += imgoffset;
CV_Assert( (unsigned)pt.x <= (unsigned)(grad.cols - blockSize.width) &&
(unsigned)pt.y <= (unsigned)(grad.rows - blockSize.height) );
if( useCache )
{
CV_Assert( pt.x % cacheStride.width == 0 &&
pt.y % cacheStride.height == 0 );
Point cacheIdx(pt.x/cacheStride.width,
(pt.y/cacheStride.height) % blockCache.rows);
if( pt.y != ymaxCached[cacheIdx.y] )
{
Mat_<uchar> cacheRow = blockCacheFlags.row(cacheIdx.y);
cacheRow = (uchar)0;
ymaxCached[cacheIdx.y] = pt.y;
}
blockHist = &blockCache[cacheIdx.y][cacheIdx.x*blockHistogramSize];
uchar& computedFlag = blockCacheFlags(cacheIdx.y, cacheIdx.x);
if( computedFlag != 0 )
return blockHist;
computedFlag = (uchar)1; // set it at once, before actual computing
}
int k, C1 = count1, C2 = count2, C4 = count4;
const float* gradPtr = grad.ptr<float>(pt.y) + pt.x*2;
const uchar* qanglePtr = qangle.ptr(pt.y) + pt.x*2;
CV_Assert( blockHist != 0 );
for( k = 0; k < blockHistogramSize; k++ )
blockHist[k] = 0.f;
const PixData* _pixData = &pixData[0];
for( k = 0; k < C1; k++ )
{
const PixData& pk = _pixData[k];
const float* a = gradPtr + pk.gradOfs;
float w = pk.gradWeight*pk.histWeights[0];
const uchar* h = qanglePtr + pk.qangleOfs;
int h0 = h[0], h1 = h[1];
float* hist = blockHist + pk.histOfs[0];
float t0 = hist[h0] + a[0]*w;
float t1 = hist[h1] + a[1]*w;
hist[h0] = t0; hist[h1] = t1;
}
for( ; k < C2; k++ )
{
const PixData& pk = _pixData[k];
const float* a = gradPtr + pk.gradOfs;
float w, t0, t1, a0 = a[0], a1 = a[1];
const uchar* h = qanglePtr + pk.qangleOfs;
int h0 = h[0], h1 = h[1];
float* hist = blockHist + pk.histOfs[0];
w = pk.gradWeight*pk.histWeights[0];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
hist = blockHist + pk.histOfs[1];
w = pk.gradWeight*pk.histWeights[1];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
}
for( ; k < C4; k++ )
{
const PixData& pk = _pixData[k];
const float* a = gradPtr + pk.gradOfs;
float w, t0, t1, a0 = a[0], a1 = a[1];
const uchar* h = qanglePtr + pk.qangleOfs;
int h0 = h[0], h1 = h[1];
float* hist = blockHist + pk.histOfs[0];
w = pk.gradWeight*pk.histWeights[0];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
hist = blockHist + pk.histOfs[1];
w = pk.gradWeight*pk.histWeights[1];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
hist = blockHist + pk.histOfs[2];
w = pk.gradWeight*pk.histWeights[2];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
hist = blockHist + pk.histOfs[3];
w = pk.gradWeight*pk.histWeights[3];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
}
normalizeBlockHistogram(blockHist);
return blockHist;
}
void HOGCacheTester::normalizeBlockHistogram(float* _hist) const
{
float* hist = &_hist[0], partSum[4] = { 0.0f, 0.0f, 0.0f, 0.0f };
size_t i, sz = blockHistogramSize;
for (i = 0; i <= sz - 4; i += 4)
{
partSum[0] += hist[i] * hist[i];
partSum[1] += hist[i+1] * hist[i+1];
partSum[2] += hist[i+2] * hist[i+2];
partSum[3] += hist[i+3] * hist[i+3];
}
float t0 = partSum[0] + partSum[1];
float t1 = partSum[2] + partSum[3];
float sum = t0 + t1;
for( ; i < sz; i++ )
sum += hist[i]*hist[i];
float scale = 1.f/(std::sqrt(sum)+sz*0.1f), thresh = (float)descriptor->L2HysThreshold;
partSum[0] = partSum[1] = partSum[2] = partSum[3] = 0.0f;
for(i = 0; i <= sz - 4; i += 4)
{
hist[i] = std::min(hist[i]*scale, thresh);
hist[i+1] = std::min(hist[i+1]*scale, thresh);
hist[i+2] = std::min(hist[i+2]*scale, thresh);
hist[i+3] = std::min(hist[i+3]*scale, thresh);
partSum[0] += hist[i]*hist[i];
partSum[1] += hist[i+1]*hist[i+1];
partSum[2] += hist[i+2]*hist[i+2];
partSum[3] += hist[i+3]*hist[i+3];
}
t0 = partSum[0] + partSum[1];
t1 = partSum[2] + partSum[3];
sum = t0 + t1;
for( ; i < sz; i++ )
{
hist[i] = std::min(hist[i]*scale, thresh);
sum += hist[i]*hist[i];
}
scale = 1.f/(std::sqrt(sum)+1e-3f);
for( i = 0; i < sz; i++ )
hist[i] *= scale;
}
Size HOGCacheTester::windowsInImage(Size imageSize, Size winStride) const
{
return Size((imageSize.width - winSize.width)/winStride.width + 1,
(imageSize.height - winSize.height)/winStride.height + 1);
}
Rect HOGCacheTester::getWindow(Size imageSize, Size winStride, int idx) const
{
int nwindowsX = (imageSize.width - winSize.width)/winStride.width + 1;
int y = idx / nwindowsX;
int x = idx - nwindowsX*y;
return Rect( x*winStride.width, y*winStride.height, winSize.width, winSize.height );
}
inline bool HOGDescriptorTester::is_failed() const
{
return failed;
}
static inline int gcd(int a, int b) { return (a % b == 0) ? b : gcd (b, a % b); }
void HOGDescriptorTester::detect(const Mat& img,
vector<Point>& hits, vector<double>& weights, double hitThreshold,
Size winStride, Size padding, const vector<Point>& locations) const
{
if (failed)
return;
hits.clear();
if( svmDetector.empty() )
return;
if( winStride == Size() )
winStride = cellSize;
Size cacheStride(gcd(winStride.width, blockStride.width),
gcd(winStride.height, blockStride.height));
size_t nwindows = locations.size();
padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);
HOGCacheTester cache(this, img, padding, padding, nwindows == 0, cacheStride);
if( !nwindows )
nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
const HOGCacheTester::BlockData* blockData = &cache.blockData[0];
int nblocks = cache.nblocks.area();
int blockHistogramSize = cache.blockHistogramSize;
size_t dsize = getDescriptorSize();
double rho = svmDetector.size() > dsize ? svmDetector[dsize] : 0;
vector<float> blockHist(blockHistogramSize);
for( size_t i = 0; i < nwindows; i++ )
{
Point pt0;
if( !locations.empty() )
{
pt0 = locations[i];
if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
continue;
}
else
{
pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
}
double s = rho;
const float* svmVec = &svmDetector[0];
int j, k;
for( j = 0; j < nblocks; j++, svmVec += blockHistogramSize )
{
const HOGCacheTester::BlockData& bj = blockData[j];
Point pt = pt0 + bj.imgOffset;
const float* vec = cache.getBlock(pt, &blockHist[0]);
for( k = 0; k <= blockHistogramSize - 4; k += 4 )
s += vec[k]*svmVec[k] + vec[k+1]*svmVec[k+1] +
vec[k+2]*svmVec[k+2] + vec[k+3]*svmVec[k+3];
for( ; k < blockHistogramSize; k++ )
s += vec[k]*svmVec[k];
}
if( s >= hitThreshold )
{
hits.push_back(pt0);
weights.push_back(s);
}
}
// validation
std::vector<Point> actual_find_locations;
std::vector<double> actual_weights;
actual_hog->detect(img, actual_find_locations, actual_weights,
hitThreshold, winStride, padding, locations);
if (!std::equal(hits.begin(), hits.end(),
actual_find_locations.begin()))
{
ts->printf(cvtest::TS::SUMMARY, "Found locations are not equal (see detect function)\n");
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
ts->set_gtest_status();
failed = true;
return;
}
const double eps = FLT_EPSILON * 100;
double diff_norm = cvtest::norm(actual_weights, weights, NORM_L2 + NORM_RELATIVE);
if (diff_norm > eps)
{
ts->printf(cvtest::TS::SUMMARY, "Weights for found locations aren't equal.\n"
"Norm of the difference is %lf\n", diff_norm);
ts->printf(cvtest::TS::LOG, "Channels: %d\n", img.channels());
failed = true;
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
ts->set_gtest_status();
}
}
void HOGDescriptorTester::detect(const Mat& img, vector<Point>& hits, double hitThreshold,
Size winStride, Size padding, const vector<Point>& locations) const
{
vector<double> weightsV;
detect(img, hits, weightsV, hitThreshold, winStride, padding, locations);
}
void HOGDescriptorTester::compute(InputArray _img, vector<float>& descriptors,
Size winStride, Size padding, const vector<Point>& locations) const
{
Mat img = _img.getMat();
if( winStride == Size() )
winStride = cellSize;
Size cacheStride(gcd(winStride.width, blockStride.width),
gcd(winStride.height, blockStride.height));
size_t nwindows = locations.size();
padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);
HOGCacheTester cache(this, img, padding, padding, nwindows == 0, cacheStride);
if( !nwindows )
nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
const HOGCacheTester::BlockData* blockData = &cache.blockData[0];
int nblocks = cache.nblocks.area();
int blockHistogramSize = cache.blockHistogramSize;
size_t dsize = getDescriptorSize();
descriptors.resize(dsize*nwindows);
for( size_t i = 0; i < nwindows; i++ )
{
float* descriptor = &descriptors[i*dsize];
Point pt0;
if( !locations.empty() )
{
pt0 = locations[i];
if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
continue;
}
else
{
pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
}
for( int j = 0; j < nblocks; j++ )
{
const HOGCacheTester::BlockData& bj = blockData[j];
Point pt = pt0 + bj.imgOffset;
float* dst = descriptor + bj.histOfs;
const float* src = cache.getBlock(pt, dst);
if( src != dst )
for( int k = 0; k < blockHistogramSize; k++ )
dst[k] = src[k];
}
}
// validation
std::vector<float> actual_descriptors;
actual_hog->compute(img, actual_descriptors, winStride, padding, locations);
double diff_norm = cvtest::norm(actual_descriptors, descriptors, NORM_L2 + NORM_RELATIVE);
const double eps = 2.0e-3;
if (diff_norm > eps)
{
ts->printf(cvtest::TS::SUMMARY, "Norm of the difference: %lf\n", diff_norm);
ts->printf(cvtest::TS::SUMMARY, "Found descriptors are not equal (see compute function)\n");
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
ts->printf(cvtest::TS::LOG, "Channels: %d\n", img.channels());
ts->set_gtest_status();
failed = true;
}
}
void HOGDescriptorTester::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
Size paddingTL, Size paddingBR) const
{
CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
Size gradsize(img.cols + paddingTL.width + paddingBR.width,
img.rows + paddingTL.height + paddingBR.height);
grad.create(gradsize, CV_32FC2); // <magnitude*(1-alpha), magnitude*alpha>
qangle.create(gradsize, CV_8UC2); // [0..nbins-1] - quantized gradient orientation
Size wholeSize;
Point roiofs;
img.locateROI(wholeSize, roiofs);
int i, x, y;
int cn = img.channels();
Mat_<float> _lut(1, 256);
const float* lut = &_lut(0,0);
if( gammaCorrection )
for( i = 0; i < 256; i++ )
_lut(0,i) = std::sqrt((float)i);
else
for( i = 0; i < 256; i++ )
_lut(0,i) = (float)i;
AutoBuffer<int> mapbuf(gradsize.width + gradsize.height + 4);
int* xmap = mapbuf.data() + 1;
int* ymap = xmap + gradsize.width + 2;
const int borderType = (int)BORDER_REFLECT_101;
for( x = -1; x < gradsize.width + 1; x++ )
xmap[x] = borderInterpolate(x - paddingTL.width + roiofs.x,
wholeSize.width, borderType) - roiofs.x;
for( y = -1; y < gradsize.height + 1; y++ )
ymap[y] = borderInterpolate(y - paddingTL.height + roiofs.y,
wholeSize.height, borderType) - roiofs.y;
// x- & y- derivatives for the whole row
int width = gradsize.width;
AutoBuffer<float> _dbuf(width*4);
float* dbuf = _dbuf.data();
Mat Dx(1, width, CV_32F, dbuf);
Mat Dy(1, width, CV_32F, dbuf + width);
Mat Mag(1, width, CV_32F, dbuf + width*2);
Mat Angle(1, width, CV_32F, dbuf + width*3);
int _nbins = nbins;
float angleScale = (float)(_nbins/CV_PI);
for( y = 0; y < gradsize.height; y++ )
{
const uchar* imgPtr = img.ptr(ymap[y]);
const uchar* prevPtr = img.ptr(ymap[y-1]);
const uchar* nextPtr = img.ptr(ymap[y+1]);
float* gradPtr = (float*)grad.ptr(y);
uchar* qanglePtr = (uchar*)qangle.ptr(y);
if( cn == 1 )
{
for( x = 0; x < width; x++ )
{
int x1 = xmap[x];
dbuf[x] = (float)(lut[imgPtr[xmap[x+1]]] - lut[imgPtr[xmap[x-1]]]);
dbuf[width + x] = (float)(lut[nextPtr[x1]] - lut[prevPtr[x1]]);
}
}
else
{
for( x = 0; x < width; x++ )
{
int x1 = xmap[x]*3;
float dx0, dy0, dx, dy, mag0, mag;
const uchar* p2 = imgPtr + xmap[x+1]*3;
const uchar* p0 = imgPtr + xmap[x-1]*3;
dx0 = lut[p2[2]] - lut[p0[2]];
dy0 = lut[nextPtr[x1+2]] - lut[prevPtr[x1+2]];
mag0 = dx0*dx0 + dy0*dy0;
dx = lut[p2[1]] - lut[p0[1]];
dy = lut[nextPtr[x1+1]] - lut[prevPtr[x1+1]];
mag = dx*dx + dy*dy;
if( mag0 < mag )
{
dx0 = dx;
dy0 = dy;
mag0 = mag;
}
dx = lut[p2[0]] - lut[p0[0]];
dy = lut[nextPtr[x1]] - lut[prevPtr[x1]];
mag = dx*dx + dy*dy;
if( mag0 < mag )
{
dx0 = dx;
dy0 = dy;
mag0 = mag;
}
dbuf[x] = dx0;
dbuf[x+width] = dy0;
}
}
cartToPolar( Dx, Dy, Mag, Angle, false );
for( x = 0; x < width; x++ )
{
float mag = dbuf[x+width*2], angle = dbuf[x+width*3]*angleScale - 0.5f;
int hidx = cvFloor(angle);
angle -= hidx;
gradPtr[x*2] = mag*(1.f - angle);
gradPtr[x*2+1] = mag*angle;
if( hidx < 0 )
hidx += _nbins;
else if( hidx >= _nbins )
hidx -= _nbins;
CV_Assert( (unsigned)hidx < (unsigned)_nbins );
qanglePtr[x*2] = (uchar)hidx;
hidx++;
hidx &= hidx < _nbins ? -1 : 0;
qanglePtr[x*2+1] = (uchar)hidx;
}
}
// validation
Mat actual_mats[2], reference_mats[2] = { grad, qangle };
const char* args[] = { "Gradient's", "Qangles's" };
actual_hog->computeGradient(img, actual_mats[0], actual_mats[1], paddingTL, paddingBR);
const double eps = 8.0e-3;
for (i = 0; i < 2; ++i)
{
double diff_norm = cvtest::norm(actual_mats[i], reference_mats[i], NORM_L2 + NORM_RELATIVE);
if (diff_norm > eps)
{
ts->printf(cvtest::TS::LOG, "%s matrices are not equal\n"
"Norm of the difference is %lf\n", args[i], diff_norm);
ts->printf(cvtest::TS::LOG, "Channels: %d\n", img.channels());
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
ts->set_gtest_status();
failed = true;
}
}
}
TEST(Objdetect_HOGDetector_Strict, accuracy)
{
cvtest::TS* ts = cvtest::TS::ptr();
RNG& rng = ts->get_rng();
HOGDescriptor actual_hog;
actual_hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
HOGDescriptorTester reference_hog(actual_hog);
const unsigned int test_case_count = 5;
for (unsigned int i = 0; i < test_case_count && !reference_hog.is_failed(); ++i)
{
// creating a matrix
Size ssize(rng.uniform(1, 10) * actual_hog.winSize.width,
rng.uniform(1, 10) * actual_hog.winSize.height);
int type = rng.uniform(0, 1) > 0 ? CV_8UC1 : CV_8UC3;
Mat image(ssize, type);
rng.fill(image, RNG::UNIFORM, 0, 256, true);
// checking detect
std::vector<Point> hits;
std::vector<double> weights;
reference_hog.detect(image, hits, weights);
// checking compute
std::vector<float> descriptors;
reference_hog.compute(image, descriptors);
}
}
TEST(Objdetect_CascadeDetector, small_img)
{
String root = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/cascades/";
String cascades[] =
{
root + "haarcascade_frontalface_alt.xml",
root + "lbpcascade_frontalface.xml",
String()
};
vector<Rect> objects;
RNG rng((uint64)-1);
for( int i = 0; !cascades[i].empty(); i++ )
{
printf("%d. %s\n", i, cascades[i].c_str());
CascadeClassifier cascade(cascades[i]);
for( int j = 0; j < 100; j++ )
{
int width = rng.uniform(1, 100);
int height = rng.uniform(1, 100);
Mat img(height, width, CV_8U);
randu(img, 0, 256);
cascade.detectMultiScale(img, objects);
}
}
}
}} // namespace