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Commit e9aa6fa0 authored by Alexey Kazakov's avatar Alexey Kazakov
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Added ROC-curve calculating to the cascade detection algorithm

parent 06070dfc
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Showing with 98 additions and 136 deletions
modules/highgui/src/grfmt_tiff.cpp
View file @ e9aa6fa0
......@@ -301,11 +301,6 @@ TiffEncoder::~TiffEncoder()
{
}
bool TiffEncoder::isFormatSupported( int depth ) const
{
return depth == CV_8U || depth == CV_16U;
}
ImageEncoder TiffEncoder::newEncoder() const
{
return new TiffEncoder;
......@@ -326,13 +321,7 @@ bool TiffEncoder::write( const Mat& img, const vector<int>& )
{
int channels = img.channels();
int width = img.cols, height = img.rows;
int depth = img.depth();
if (depth != CV_8U && depth != CV_16U)
return false;
int bytesPerChannel = depth == CV_8U ? 1 : 2;
int fileStep = width * channels * bytesPerChannel;
int fileStep = width*channels;
WLByteStream strm;
if( m_buf )
......@@ -367,7 +356,7 @@ bool TiffEncoder::write( const Mat& img, const vector<int>& )
uchar* buffer = _buffer;
int stripOffsetsOffset = 0;
int stripCountsOffset = 0;
int bitsPerSample = 8 * bytesPerChannel;
int bitsPerSample = 8; // TODO support 16 bit
int y = 0;
strm.putBytes( fmtSignTiffII, 4 );
......@@ -387,15 +376,9 @@ bool TiffEncoder::write( const Mat& img, const vector<int>& )
for( ; y < limit; y++ )
{
if( channels == 3 )
if (depth == CV_8U)
icvCvt_BGR2RGB_8u_C3R( img.data + img.step*y, 0, buffer, 0, cvSize(width,1) );
else
icvCvt_BGR2RGB_16u_C3R( (const ushort*)(img.data + img.step*y), 0, (ushort*)buffer, 0, cvSize(width,1) );
icvCvt_BGR2RGB_8u_C3R( img.data + img.step*y, 0, buffer, 0, cvSize(width,1) );
else if( channels == 4 )
if (depth == CV_8U)
icvCvt_BGRA2RGBA_8u_C4R( img.data + img.step*y, 0, buffer, 0, cvSize(width,1) );
else
icvCvt_BGRA2RGBA_16u_C4R( (const ushort*)(img.data + img.step*y), 0, (ushort*)buffer, 0, cvSize(width,1) );
icvCvt_BGRA2RGBA_8u_C4R( img.data + img.step*y, 0, buffer, 0, cvSize(width,1) );
strm.putBytes( channels > 1 ? buffer : img.data + img.step*y, fileStep );
}
......@@ -433,13 +416,12 @@ bool TiffEncoder::write( const Mat& img, const vector<int>& )
if( channels > 1 )
{
int bitsPerSamplePos = strm.getPos();
strm.putWord(bitsPerSample);
strm.putWord(bitsPerSample);
strm.putWord(bitsPerSample);
bitsPerSample = strm.getPos();
strm.putWord(8);
strm.putWord(8);
strm.putWord(8);
if( channels == 4 )
strm.putWord(bitsPerSample);
bitsPerSample = bitsPerSamplePos;
strm.putWord(8);
}
directoryOffset = strm.getPos();
......
modules/highgui/src/grfmt_tiff.hpp
View file @ e9aa6fa0
......@@ -118,8 +118,6 @@ public:
TiffEncoder();
virtual ~TiffEncoder();
bool isFormatSupported( int depth ) const;
bool write( const Mat& img, const vector<int>& params );
ImageEncoder newEncoder() const;
......
modules/highgui/src/utils.cpp
View file @ e9aa6fa0
......@@ -192,25 +192,6 @@ void icvCvt_BGRA2RGBA_8u_C4R( const uchar* bgra, int bgra_step,
}
}
void icvCvt_BGRA2RGBA_16u_C4R( const ushort* bgra, int bgra_step,
ushort* rgba, int rgba_step, CvSize size )
{
int i;
for( ; size.height--; )
{
for( i = 0; i < size.width; i++, bgra += 4, rgba += 4 )
{
ushort t0 = bgra[0], t1 = bgra[1];
ushort t2 = bgra[2], t3 = bgra[3];
rgba[0] = t2; rgba[1] = t1;
rgba[2] = t0; rgba[3] = t3;
}
bgra += bgra_step/sizeof(bgra[0]) - size.width*4;
rgba += rgba_step/sizeof(rgba[0]) - size.width*4;
}
}
void icvCvt_BGR2RGB_8u_C3R( const uchar* bgr, int bgr_step,
uchar* rgb, int rgb_step, CvSize size )
......
modules/highgui/src/utils.hpp
View file @ e9aa6fa0
......@@ -88,10 +88,6 @@ void icvCvt_BGRA2RGBA_8u_C4R( const uchar* bgra, int bgra_step,
uchar* rgba, int rgba_step, CvSize size );
#define icvCvt_RGBA2BGRA_8u_C4R icvCvt_BGRA2RGBA_8u_C4R
void icvCvt_BGRA2RGBA_16u_C4R( const ushort* bgra, int bgra_step,
ushort* rgba, int rgba_step, CvSize size );
#define icvCvt_RGBA2BGRA_16u_C4R icvCvt_BGRA2RGBA_16u_C4R
void icvCvt_BGR5552Gray_8u_C2C1R( const uchar* bgr555, int bgr555_step,
uchar* gray, int gray_step, CvSize size );
void icvCvt_BGR5652Gray_8u_C2C1R( const uchar* bgr565, int bgr565_step,
......
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
View file @ e9aa6fa0
......@@ -125,9 +125,17 @@ CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade );
#define CV_HAAR_FIND_BIGGEST_OBJECT 4
#define CV_HAAR_DO_ROUGH_SEARCH 8
CVAPI(CvSeq*) cvHaarDetectObjectsForROC( const CvArr* image,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
std::vector<int>& rejectLevels, std::vector<double>& levelWeightds,
double scale_factor CV_DEFAULT(1.1),
int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)),
bool outputRejectLevels = false );
CVAPI(CvSeq*) cvHaarDetectObjects( const CvArr* image,
CvHaarClassifierCascade* cascade,
CvMemStorage* storage, double scale_factor CV_DEFAULT(1.1),
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
double scale_factor CV_DEFAULT(1.1),
int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)));
......@@ -275,7 +283,8 @@ namespace cv
CV_EXPORTS_W void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps=0.2);
CV_EXPORTS_W void groupRectangles(vector<Rect>& rectList, CV_OUT vector<int>& weights, int groupThreshold, double eps=0.2);
CV_EXPORTS void groupRectangles(vector<Rect>& rectList, vector<double>& resultWeights, int groupThreshold = 2, double eps=0.2);
CV_EXPORTS void groupRectangles(vector<Rect>& rectList, vector<int>& rejectLevels,
vector<double>& levelWeights, int groupThreshold, double eps=0.2);
CV_EXPORTS void groupRectangles_meanshift(vector<Rect>& rectList, vector<double>& foundWeights, vector<double>& foundScales,
double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
......@@ -352,11 +361,12 @@ public:
CV_WRAP virtual void detectMultiScale( const Mat& image,
CV_OUT vector<Rect>& objects,
vector<int>& rejectLevels,
vector<double>& levelWeights,
double scaleFactor=1.1,
int minNeighbors=3, int flags=0,
Size minSize=Size(),
Size maxSize=Size(),
bool outputRejectLevels = false );
bool outputRejectLevels=false );
bool isOldFormatCascade() const;
......@@ -370,7 +380,7 @@ protected:
virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
int stripSize, int yStep, double factor, vector<Rect>& candidates,
vector<int>& rejectLevels, bool outputRejectLevels = false);
vector<int>& rejectLevels, vector<double>& levelWeights, bool outputRejectLevels=false);
protected:
enum { BOOST = 0 };
......@@ -380,19 +390,19 @@ protected:
friend struct CascadeClassifierInvoker;
template<class FEval>
friend int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
friend int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
template<class FEval>
friend int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
friend int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
template<class FEval>
friend int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
friend int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
template<class FEval>
friend int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
friend int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
bool setImage( Ptr<FeatureEvaluator>&, const Mat& );
virtual int runAt( Ptr<FeatureEvaluator>&, Point );
virtual int runAt( Ptr<FeatureEvaluator>&, Point, double& weight );
class Data
{
......@@ -436,35 +446,6 @@ protected:
Data data;
Ptr<FeatureEvaluator> featureEvaluator;
Ptr<CvHaarClassifierCascade> oldCascade;
//!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
public:
int getNumStages()
{
int numStages;
if( !isOldFormatCascade() )
{
numStages = data.stages.size();
}
else
{
numStages = this->oldCascade->count;
}
return numStages;
}
void setNumStages(int stageCount)
{
if( !isOldFormatCascade() )
{
if( stageCount )
data.stages.resize(stageCount);
}
else
if( stageCount )
this->oldCascade->count = stageCount;
}
//!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
};
//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
......
modules/objdetect/src/cascadedetect.cpp
View file @ e9aa6fa0
This diff is collapsed.
modules/objdetect/src/datamatrix.cpp deleted 100644 → 0
View file @ 06070dfc
This diff is collapsed.
modules/objdetect/src/followblk.h deleted 100644 → 0
View file @ 06070dfc
unsigned char cblk[256] = { 34,19,36,36,51,19,51,51,66,19,36,36,66,19,66,66,49,19,36,36,51,19,51,51,49,19,36,36,49,19,49,49,32,19,36,36,51,19,51,51,66,19,36,36,66,19,66,66,32,19,36,36,51,19,51,51,32,19,36,36,32,19,32,32,17,19,36,36,51,19,51,51,66,19,36,36,66,19,66,66,49,19,36,36,51,19,51,51,49,19,36,36,49,19,49,49,17,19,36,36,51,19,51,51,66,19,36,36,66,19,66,66,17,19,36,36,51,19,51,51,17,19,36,36,17,19,17,17,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,66,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,49,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,66,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,32,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,66,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,49,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,66,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,34 };
unsigned char ccblk[256] = { 34,17,2,17,19,19,2,17,36,36,2,36,19,19,2,17,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,17,66,66,2,66,19,19,2,66,36,36,2,36,19,19,2,66,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,17,49,49,2,49,19,19,2,49,36,36,2,36,19,19,2,49,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,49,66,66,2,66,19,19,2,66,36,36,2,36,19,19,2,66,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,17,32,32,2,32,19,19,2,32,36,36,2,36,19,19,2,32,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,32,66,66,2,66,19,19,2,66,36,36,2,36,19,19,2,66,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,32,49,49,2,49,19,19,2,49,36,36,2,36,19,19,2,49,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,49,66,66,2,66,19,19,2,66,36,36,2,36,19,19,2,66,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,34 };
static const CvPoint pickup[64] = { {7,6},{8,6},{7,5},{8,5},{1,5},{7,4},{8,4},{1,4},{1,8},{2,8},{1,7},{2,7},{3,7},{1,6},{2,6},{3,6},{3,2},{4,2},{3,1},{4,1},{5,1},{3,8},{4,8},{5,8},{6,1},{7,1},{6,8},{7,8},{8,8},{6,7},{7,7},{8,7},{4,7},{5,7},{4,6},{5,6},{6,6},{4,5},{5,5},{6,5},{2,5},{3,5},{2,4},{3,4},{4,4},{2,3},{3,3},{4,3},{8,3},{1,3},{8,2},{1,2},{2,2},{8,1},{1,1},{2,1},{5,4},{6,4},{5,3},{6,3},{7,3},{5,2},{6,2},{7,2} };
static const uint8 Alog[256] = { 1,2,4,8,16,32,64,128,45,90,180,69,138,57,114,228,229,231,227,235,251,219,155,27,54,108,216,157,23,46,92,184,93,186,89,178,73,146,9,18,36,72,144,13,26,52,104,208,141,55,110,220,149,7,14,28,56,112,224,237,247,195,171,123,246,193,175,115,230,225,239,243,203,187,91,182,65,130,41,82,164,101,202,185,95,190,81,162,105,210,137,63,126,252,213,135,35,70,140,53,106,212,133,39,78,156,21,42,84,168,125,250,217,159,19,38,76,152,29,58,116,232,253,215,131,43,86,172,117,234,249,223,147,11,22,44,88,176,77,154,25,50,100,200,189,87,174,113,226,233,255,211,139,59,118,236,245,199,163,107,214,129,47,94,188,85,170,121,242,201,191,83,166,97,194,169,127,254,209,143,51,102,204,181,71,142,49,98,196,165,103,206,177,79,158,17,34,68,136,61,122,244,197,167,99,198,161,111,222,145,15,30,60,120,240,205,183,67,134,33,66,132,37,74,148,5,10,20,40,80,160,109,218,153,31,62,124,248,221,151,3,6,12,24,48,96,192,173,119,238,241,207,179,75,150,1 };
static const uint8 Log[256] = { -255,255,1,240,2,225,241,53,3,38,226,133,242,43,54,210,4,195,39,114,227,106,134,28,243,140,44,23,55,118,211,234,5,219,196,96,40,222,115,103,228,78,107,125,135,8,29,162,244,186,141,180,45,99,24,49,56,13,119,153,212,199,235,91,6,76,220,217,197,11,97,184,41,36,223,253,116,138,104,193,229,86,79,171,108,165,126,145,136,34,9,74,30,32,163,84,245,173,187,204,142,81,181,190,46,88,100,159,25,231,50,207,57,147,14,67,120,128,154,248,213,167,200,63,236,110,92,176,7,161,77,124,221,102,218,95,198,90,12,152,98,48,185,179,42,209,37,132,224,52,254,239,117,233,139,22,105,27,194,113,230,206,87,158,80,189,172,203,109,175,166,62,127,247,146,66,137,192,35,252,10,183,75,216,31,83,33,73,164,144,85,170,246,65,174,61,188,202,205,157,143,169,82,72,182,215,191,251,47,178,89,151,101,94,160,123,26,112,232,21,51,238,208,131,58,69,148,18,15,16,68,17,121,149,129,19,155,59,249,70,214,250,168,71,201,156,64,60,237,130,111,20,93,122,177,150 };
modules/objdetect/src/haar.cpp
View file @ e9aa6fa0
......@@ -654,8 +654,8 @@ double icvEvalHidHaarClassifier( CvHidHaarClassifier* classifier,
CV_IMPL int
cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
CvPoint pt, int start_stage )
cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
CvPoint pt, double& stage_sum, int start_stage )
{
int result = -1;
......@@ -698,7 +698,7 @@ cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
while( ptr )
{
double stage_sum = 0;
stage_sum = 0.0;
for( j = 0; j < ptr->count; j++ )
{
......@@ -724,7 +724,7 @@ cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
for( i = start_stage; i < cascade->count; i++ )
{
#ifndef CV_HAAR_USE_SSE
double stage_sum = 0;
stage_sum = 0.0;
#else
__m128d stage_sum = _mm_setzero_pd();
#endif
......@@ -796,7 +796,7 @@ cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
{
for( i = start_stage; i < cascade->count; i++ )
{
double stage_sum = 0;
stage_sum = 0.0;
for( j = 0; j < cascade->stage_classifier[i].count; j++ )
{
......@@ -809,10 +809,16 @@ cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
return -i;
}
}
return 1;
}
CV_IMPL int
cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
CvPoint pt, int start_stage )
{
double stage_sum;
return cvRunHaarClassifierCascadeSum(_cascade, pt, stage_sum, start_stage);
}
namespace cv
{
......@@ -822,7 +828,9 @@ struct HaarDetectObjects_ScaleImage_Invoker
HaarDetectObjects_ScaleImage_Invoker( const CvHaarClassifierCascade* _cascade,
int _stripSize, double _factor,
const Mat& _sum1, const Mat& _sqsum1, Mat* _norm1,
Mat* _mask1, Rect _equRect, ConcurrentRectVector& _vec )
Mat* _mask1, Rect _equRect, ConcurrentRectVector& _vec,
std::vector<int>& _levels, std::vector<double>& _weights,
bool _outputLevels )
{
cascade = _cascade;
stripSize = _stripSize;
......@@ -833,6 +841,8 @@ struct HaarDetectObjects_ScaleImage_Invoker
mask1 = _mask1;
equRect = _equRect;
vec = &_vec;
rejectLevels = _outputLevels ? &_levels : 0;
levelWeights = _outputLevels ? &_weights : 0;
}
void operator()( const BlockedRange& range ) const
......@@ -902,9 +912,26 @@ struct HaarDetectObjects_ScaleImage_Invoker
for( y = y1; y < y2; y += ystep )
for( x = 0; x < ssz.width; x += ystep )
{
if( cvRunHaarClassifierCascade( cascade, cvPoint(x,y), 0 ) > 0 )
vec->push_back(Rect(cvRound(x*factor), cvRound(y*factor),
winSize.width, winSize.height));
double gypWeight;
int result = cvRunHaarClassifierCascadeSum( cascade, cvPoint(x,y), gypWeight, 0 );
if( rejectLevels )
{
if( result == 1 )
result = -1*cascade->count;
if( cascade->count + result < 4 )
{
vec->push_back(Rect(cvRound(x*factor), cvRound(y*factor),
winSize.width, winSize.height));
rejectLevels->push_back(-result);
levelWeights->push_back(gypWeight);
}
}
else
{
if( result > 0 )
vec->push_back(Rect(cvRound(x*factor), cvRound(y*factor),
winSize.width, winSize.height));
}
}
}
......@@ -914,6 +941,8 @@ struct HaarDetectObjects_ScaleImage_Invoker
Mat sum1, sqsum1, *norm1, *mask1;
Rect equRect;
ConcurrentRectVector* vec;
std::vector<int>* rejectLevels;
std::vector<double>* levelWeights;
};
......@@ -983,10 +1012,11 @@ struct HaarDetectObjects_ScaleCascade_Invoker
CV_IMPL CvSeq*
cvHaarDetectObjects( const CvArr* _img,
CvHaarClassifierCascade* cascade,
CvMemStorage* storage, double scaleFactor,
int minNeighbors, int flags, CvSize minSize, CvSize maxSize )
cvHaarDetectObjectsForROC( const CvArr* _img,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
double scaleFactor, int minNeighbors, int flags,
CvSize minSize, CvSize maxSize, bool outputRejectLevels )
{
const double GROUP_EPS = 0.2;
CvMat stub, *img = (CvMat*)_img;
......@@ -1119,7 +1149,7 @@ cvHaarDetectObjects( const CvArr* _img,
cv::HaarDetectObjects_ScaleImage_Invoker(cascade,
(((sz1.height + stripCount - 1)/stripCount + ystep-1)/ystep)*ystep,
factor, cv::Mat(&sum1), cv::Mat(&sqsum1), &_norm1, &_mask1,
cv::Rect(equRect), allCandidates));
cv::Rect(equRect), allCandidates, rejectLevels, levelWeights, outputRejectLevels));
}
}
else
......@@ -1250,7 +1280,16 @@ cvHaarDetectObjects( const CvArr* _img,
std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());
if( minNeighbors != 0 || findBiggestObject )
groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS);
{
if( outputRejectLevels )
{
groupRectangles(rectList, rejectLevels, levelWeights, minNeighbors, GROUP_EPS );
}
else
{
groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS);
}
}
else
rweights.resize(rectList.size(),0);
......@@ -1275,7 +1314,7 @@ cvHaarDetectObjects( const CvArr* _img,
{
CvAvgComp c;
c.rect = rectList[i];
c.neighbors = rweights[i];
c.neighbors = !rweights.empty() ? rweights[i] : 0;
cvSeqPush( result_seq, &c );
}
}
......@@ -1283,6 +1322,19 @@ cvHaarDetectObjects( const CvArr* _img,
return result_seq;
}
CV_IMPL CvSeq*
cvHaarDetectObjects( const CvArr* _img,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
double scaleFactor,
int minNeighbors, int flags, CvSize minSize, CvSize maxSize )
{
std::vector<int> fakeLevels;
std::vector<double> fakeWeights;
return cvHaarDetectObjectsForROC( _img, cascade, storage, fakeLevels, fakeWeights,
scaleFactor, minNeighbors, flags, minSize, maxSize, false );
}
static CvHaarClassifierCascade*
icvLoadCascadeCART( const char** input_cascade, int n, CvSize orig_window_size )
......
modules/python/src/cv.cpp
View file @ e9aa6fa0
......@@ -3677,28 +3677,6 @@ static PyObject *pycvClipLine(PyObject *self, PyObject *args)
}
}
static PyObject *pyfinddatamatrix(PyObject *self, PyObject *args)
{
PyObject *pyim;
if (!PyArg_ParseTuple(args, "O", &pyim))
return NULL;
CvMat *image;
if (!convert_to_CvMat(pyim, &image, "image")) return NULL;
std::deque <DataMatrixCode> codes;
ERRWRAP(codes = cvFindDataMatrix(image));
PyObject *pycodes = PyList_New(codes.size());
int i;
for (i = 0; i < codes.size(); i++) {
DataMatrixCode *pc = &codes[i];
PyList_SetItem(pycodes, i, Py_BuildValue("(sOO)", pc->msg, FROM_CvMat(pc->corners), FROM_CvMat(pc->original)));
}
return pycodes;
}
static PyObject *temp_test(PyObject *self, PyObject *args)
{
#if 0
......@@ -3993,7 +3971,6 @@ static PyMethodDef methods[] = {
//{"_HOGDetect", (PyCFunction)pycvHOGDetect, METH_KEYWORDS, "_HOGDetect(image, svm_classifier, win_stride=block_stride, locations=None, padding=(0,0), win_size=(64,128), block_size=(16,16), block_stride=(8,8), cell_size=(8,8), nbins=9, gammaCorrection=true) -> list_of_points"},
//{"_HOGDetectMultiScale", (PyCFunction)pycvHOGDetectMultiScale, METH_KEYWORDS, "_HOGDetectMultiScale(image, svm_classifier, win_stride=block_stride, scale=1.05, group_threshold=2, padding=(0,0), win_size=(64,128), block_size=(16,16), block_stride=(8,8), cell_size=(8,8), nbins=9, gammaCorrection=true) -> list_of_points"},
{"FindDataMatrix", pyfinddatamatrix, METH_VARARGS},
{"temp_test", temp_test, METH_VARARGS},
#include "generated1.i"
......
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