opencv/modules/objdetect/test/test_cascadeandhog.cpp

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#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
        FileNode fn_names = fn[DETECTOR_NAMES];
        if( fn_names.size() != 0 )
        {
            FileNodeIterator it = fn_names.begin(), it_end = fn_names.end();
            for( ; it != it_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;

        /*if( noPair > cvRound(valRects.size()*eps.noPair)+1 )
        {
            printf("Problem discovered: imageIdx = %d, cascade=%s: %d vs %d rects\n", imageIdx, detectorNames[detectorIdx].c_str(), (int)it->size(), (int)valRects.size());
            Mat image = images[imageIdx].clone();
            for( int k = 0; k < 2; k++ )
            {
                const std::vector<Rect>& imgObjects = k == 0 ? *it : valRects;
                Scalar color = k == 0 ? Scalar(0, 255, 0) : Scalar(0, 0, 255);
                for( size_t i = 0; i < imgObjects.size(); i++ )
                {
                    Rect r = imgObjects[i];
                    rectangle(image, r, color, 3);
                    if( k == 1 )
                        putText(image, format("%d", (int)i), Point(r.x + r.width/4, r.y + r.height*3/4), FONT_HERSHEY_PLAIN, 2, Scalar(0, 0, 255), 3);
                }
            }
            imshow("results", image);
            waitKey();
        }*/

        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)
        << "In total, detector " << detectorNames[detectorIdx] << " has overrated count of rectangles without pair on the whole image 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 );
    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( 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";

    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["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(InputArray img, InputOutputArray grad, InputOutputArray qangle,
        Size paddingTL, Size paddingBR) const;

    virtual void detect(InputArray 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(InputArray 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(InputArray _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;

    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();

    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(InputArray 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(InputArray _img, InputOutputArray _grad, InputOutputArray _qangle,
   Size paddingTL, Size paddingBR) const
{
    Mat img = _img.getMat();
    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
    Mat grad = _grad.getMat();
    Mat qangle = _qangle.getMat();

    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