README.md

@@ -1,2 +0,0 @@
-# gitproject
-

doc/文档1/复用代码/traincascade/CMakeLists.txt

@@ -1,5 +0,0 @@
-ocv_warnings_disable(CMAKE_CXX_FLAGS -Woverloaded-virtual -Winconsistent-missing-override -Wsuggest-override)
-file(GLOB SRCS *.cpp)
-ocv_add_application(opencv_traincascade
-    MODULES opencv_core opencv_imgproc opencv_objdetect opencv_imgcodecs opencv_highgui opencv_calib3d opencv_features2d
-    SRCS ${SRCS})

doc/文档1/复用代码/traincascade/HOGfeatures.cpp

@@ -1,250 +0,0 @@
-#include "opencv2/core.hpp"
-#include "opencv2/imgproc.hpp"
-
-#include "HOGfeatures.h"
-#include "cascadeclassifier.h"
-
-using namespace std;
-using namespace cv;
-
-CvHOGFeatureParams::CvHOGFeatureParams()
-{
-    maxCatCount = 0;
-    name = HOGF_NAME;
-    featSize = N_BINS * N_CELLS;
-}
-
-void CvHOGEvaluator::init(const CvFeatureParams *_featureParams, int _maxSampleCount, Size _winSize)
-{
-    CV_Assert( _maxSampleCount > 0);
-    int cols = (_winSize.width + 1) * (_winSize.height + 1);
-    for (int bin = 0; bin < N_BINS; bin++)
-    {
-        hist.push_back(Mat(_maxSampleCount, cols, CV_32FC1));
-    }
-    normSum.create( (int)_maxSampleCount, cols, CV_32FC1 );
-    CvFeatureEvaluator::init( _featureParams, _maxSampleCount, _winSize );
-}
-
-void CvHOGEvaluator::setImage(const Mat &img, uchar clsLabel, int idx)
-{
-    CV_DbgAssert( !hist.empty());
-    CvFeatureEvaluator::setImage( img, clsLabel, idx );
-    vector<Mat> integralHist;
-    for (int bin = 0; bin < N_BINS; bin++)
-    {
-        integralHist.push_back( Mat(winSize.height + 1, winSize.width + 1, hist[bin].type(), hist[bin].ptr<float>((int)idx)) );
-    }
-    Mat integralNorm(winSize.height + 1, winSize.width + 1, normSum.type(), normSum.ptr<float>((int)idx));
-    integralHistogram(img, integralHist, integralNorm, (int)N_BINS);
-}
-
-//void CvHOGEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
-//{
-//    _writeFeatures( features, fs, featureMap );
-//}
-
-void CvHOGEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
-{
-    int featIdx;
-    int componentIdx;
-    const Mat_<int>& featureMap_ = (const Mat_<int>&)featureMap;
-    fs << FEATURES << "[";
-    for ( int fi = 0; fi < featureMap.cols; fi++ )
-        if ( featureMap_(0, fi) >= 0 )
-        {
-            fs << "{";
-            featIdx = fi / getFeatureSize();
-            componentIdx = fi % getFeatureSize();
-            features[featIdx].write( fs, componentIdx );
-            fs << "}";
-        }
-    fs << "]";
-}
-
-void CvHOGEvaluator::generateFeatures()
-{
-    int offset = winSize.width + 1;
-    Size blockStep;
-    int x, y, t, w, h;
-
-    for (t = 8; t <= winSize.width/2; t+=8) //t = size of a cell. blocksize = 4*cellSize
-    {
-        blockStep = Size(4,4);
-        w = 2*t; //width of a block
-        h = 2*t; //height of a block
-        for (x = 0; x <= winSize.width - w; x += blockStep.width)
-        {
-            for (y = 0; y <= winSize.height - h; y += blockStep.height)
-            {
-                features.push_back(Feature(offset, x, y, t, t));
-            }
-        }
-        w = 2*t;
-        h = 4*t;
-        for (x = 0; x <= winSize.width - w; x += blockStep.width)
-        {
-            for (y = 0; y <= winSize.height - h; y += blockStep.height)
-            {
-                features.push_back(Feature(offset, x, y, t, 2*t));
-            }
-        }
-        w = 4*t;
-        h = 2*t;
-        for (x = 0; x <= winSize.width - w; x += blockStep.width)
-        {
-            for (y = 0; y <= winSize.height - h; y += blockStep.height)
-            {
-                features.push_back(Feature(offset, x, y, 2*t, t));
-            }
-        }
-    }
-
-    numFeatures = (int)features.size();
-}
-
-CvHOGEvaluator::Feature::Feature()
-{
-    for (int i = 0; i < N_CELLS; i++)
-    {
-        rect[i] = Rect(0, 0, 0, 0);
-    }
-}
-
-CvHOGEvaluator::Feature::Feature( int offset, int x, int y, int cellW, int cellH )
-{
-    rect[0] = Rect(x, y, cellW, cellH); //cell0
-    rect[1] = Rect(x+cellW, y, cellW, cellH); //cell1
-    rect[2] = Rect(x, y+cellH, cellW, cellH); //cell2
-    rect[3] = Rect(x+cellW, y+cellH, cellW, cellH); //cell3
-
-    for (int i = 0; i < N_CELLS; i++)
-    {
-        CV_SUM_OFFSETS(fastRect[i].p0, fastRect[i].p1, fastRect[i].p2, fastRect[i].p3, rect[i], offset);
-    }
-}
-
-void CvHOGEvaluator::Feature::write(FileStorage &fs) const
-{
-    fs << CC_RECTS << "[";
-    for( int i = 0; i < N_CELLS; i++ )
-    {
-        fs << "[:" << rect[i].x << rect[i].y << rect[i].width << rect[i].height << "]";
-    }
-    fs << "]";
-}
-
-//cell and bin idx writing
-//void CvHOGEvaluator::Feature::write(FileStorage &fs, int varIdx) const
-//{
-//    int featComponent = varIdx % (N_CELLS * N_BINS);
-//    int cellIdx = featComponent / N_BINS;
-//    int binIdx = featComponent % N_BINS;
-//
-//    fs << CC_RECTS << "[:" << rect[cellIdx].x << rect[cellIdx].y <<
-//        rect[cellIdx].width << rect[cellIdx].height << binIdx << "]";
-//}
-
-//cell[0] and featComponent idx writing. By cell[0] it's possible to recover all block
-//All block is necessary for block normalization
-void CvHOGEvaluator::Feature::write(FileStorage &fs, int featComponentIdx) const
-{
-    fs << CC_RECT << "[:" << rect[0].x << rect[0].y <<
-        rect[0].width << rect[0].height << featComponentIdx << "]";
-}
-
-
-void CvHOGEvaluator::integralHistogram(const Mat &img, vector<Mat> &histogram, Mat &norm, int nbins) const
-{
-    CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
-    int x, y, binIdx;
-
-    Size gradSize(img.size());
-    Size histSize(histogram[0].size());
-    Mat grad(gradSize, CV_32F);
-    Mat qangle(gradSize, CV_8U);
-
-    AutoBuffer<int> mapbuf(gradSize.width + gradSize.height + 4);
-    int* xmap = mapbuf.data() + 1;
-    int* ymap = xmap + gradSize.width + 2;
-
-    const int borderType = (int)BORDER_REPLICATE;
-
-    for( x = -1; x < gradSize.width + 1; x++ )
-        xmap[x] = borderInterpolate(x, gradSize.width, borderType);
-    for( y = -1; y < gradSize.height + 1; y++ )
-        ymap[y] = borderInterpolate(y, gradSize.height, borderType);
-
-    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);
-
-    float angleScale = (float)(nbins/CV_PI);
-
-    for( y = 0; y < gradSize.height; y++ )
-    {
-        const uchar* currPtr = img.ptr(ymap[y]);
-        const uchar* prevPtr = img.ptr(ymap[y-1]);
-        const uchar* nextPtr = img.ptr(ymap[y+1]);
-        float* gradPtr = grad.ptr<float>(y);
-        uchar* qanglePtr = qangle.ptr(y);
-
-        for( x = 0; x < width; x++ )
-        {
-            dbuf[x] = (float)(currPtr[xmap[x+1]] - currPtr[xmap[x-1]]);
-            dbuf[width + x] = (float)(nextPtr[xmap[x]] - prevPtr[xmap[x]]);
-        }
-        cartToPolar( Dx, Dy, Mag, Angle, false );
-        for( x = 0; x < width; x++ )
-        {
-            float mag = dbuf[x+width*2];
-            float angle = dbuf[x+width*3];
-            angle = angle*angleScale - 0.5f;
-            int bidx = cvFloor(angle);
-            angle -= bidx;
-            if( bidx < 0 )
-                bidx += nbins;
-            else if( bidx >= nbins )
-                bidx -= nbins;
-
-            qanglePtr[x] = (uchar)bidx;
-            gradPtr[x] = mag;
-        }
-    }
-    integral(grad, norm, grad.depth());
-
-    float* histBuf;
-    const float* magBuf;
-    const uchar* binsBuf;
-
-    int binsStep = (int)( qangle.step / sizeof(uchar) );
-    int histStep = (int)( histogram[0].step / sizeof(float) );
-    int magStep = (int)( grad.step / sizeof(float) );
-    for( binIdx = 0; binIdx < nbins; binIdx++ )
-    {
-        histBuf = histogram[binIdx].ptr<float>();
-        magBuf = grad.ptr<float>();
-        binsBuf = qangle.ptr();
-
-        memset( histBuf, 0, histSize.width * sizeof(histBuf[0]) );
-        histBuf += histStep + 1;
-        for( y = 0; y < qangle.rows; y++ )
-        {
-            histBuf[-1] = 0.f;
-            float strSum = 0.f;
-            for( x = 0; x < qangle.cols; x++ )
-            {
-                if( binsBuf[x] == binIdx )
-                    strSum += magBuf[x];
-                histBuf[x] = histBuf[-histStep + x] + strSum;
-            }
-            histBuf += histStep;
-            binsBuf += binsStep;
-            magBuf += magStep;
-        }
-    }
-}

doc/文档1/复用代码/traincascade/HOGfeatures.h

@@ -1,78 +0,0 @@
-#ifndef _OPENCV_HOGFEATURES_H_
-#define _OPENCV_HOGFEATURES_H_
-
-#include "traincascade_features.h"
-
-//#define TEST_INTHIST_BUILD
-//#define TEST_FEAT_CALC
-
-#define N_BINS 9
-#define N_CELLS 4
-
-#define HOGF_NAME "HOGFeatureParams"
-struct CvHOGFeatureParams : public CvFeatureParams
-{
-    CvHOGFeatureParams();
-};
-
-class CvHOGEvaluator : public CvFeatureEvaluator
-{
-public:
-    virtual ~CvHOGEvaluator() {}
-    virtual void init(const CvFeatureParams *_featureParams,
-        int _maxSampleCount, cv::Size _winSize );
-    virtual void setImage(const cv::Mat& img, uchar clsLabel, int idx);
-    virtual float operator()(int varIdx, int sampleIdx) const;
-    virtual void writeFeatures( cv::FileStorage &fs, const cv::Mat& featureMap ) const;
-protected:
-    virtual void generateFeatures();
-    virtual void integralHistogram(const cv::Mat &img, std::vector<cv::Mat> &histogram, cv::Mat &norm, int nbins) const;
-    class Feature
-    {
-    public:
-        Feature();
-        Feature( int offset, int x, int y, int cellW, int cellH );
-        float calc( const std::vector<cv::Mat> &_hists, const cv::Mat &_normSum, size_t y, int featComponent ) const;
-        void write( cv::FileStorage &fs ) const;
-        void write( cv::FileStorage &fs, int varIdx ) const;
-
-        cv::Rect rect[N_CELLS]; //cells
-
-        struct
-        {
-            int p0, p1, p2, p3;
-        } fastRect[N_CELLS];
-    };
-    std::vector<Feature> features;
-
-    cv::Mat normSum; //for normalization calculation (L1 or L2)
-    std::vector<cv::Mat> hist;
-};
-
-inline float CvHOGEvaluator::operator()(int varIdx, int sampleIdx) const
-{
-    int featureIdx = varIdx / (N_BINS * N_CELLS);
-    int componentIdx = varIdx % (N_BINS * N_CELLS);
-    //return features[featureIdx].calc( hist, sampleIdx, componentIdx);
-    return features[featureIdx].calc( hist, normSum, sampleIdx, componentIdx);
-}
-
-inline float CvHOGEvaluator::Feature::calc( const std::vector<cv::Mat>& _hists, const cv::Mat& _normSum, size_t y, int featComponent ) const
-{
-    float normFactor;
-    float res;
-
-    int binIdx = featComponent % N_BINS;
-    int cellIdx = featComponent / N_BINS;
-
-    const float *phist = _hists[binIdx].ptr<float>((int)y);
-    res = phist[fastRect[cellIdx].p0] - phist[fastRect[cellIdx].p1] - phist[fastRect[cellIdx].p2] + phist[fastRect[cellIdx].p3];
-
-    const float *pnormSum = _normSum.ptr<float>((int)y);
-    normFactor = (float)(pnormSum[fastRect[0].p0] - pnormSum[fastRect[1].p1] - pnormSum[fastRect[2].p2] + pnormSum[fastRect[3].p3]);
-    res = (res > 0.001f) ? ( res / (normFactor + 0.001f) ) : 0.f; //for cutting negative values, which appear due to floating precision
-
-    return res;
-}
-
-#endif // _OPENCV_HOGFEATURES_H_

doc/文档1/复用代码/traincascade/boost.h

@@ -1,86 +0,0 @@
-#ifndef _OPENCV_BOOST_H_
-#define _OPENCV_BOOST_H_
-
-#include "traincascade_features.h"
-#include "old_ml.hpp"
-
-struct CvCascadeBoostParams : CvBoostParams
-{
-    float minHitRate;
-    float maxFalseAlarm;
-
-    CvCascadeBoostParams();
-    CvCascadeBoostParams( int _boostType, float _minHitRate, float _maxFalseAlarm,
-                          double _weightTrimRate, int _maxDepth, int _maxWeakCount );
-    virtual ~CvCascadeBoostParams() {}
-    void write( cv::FileStorage &fs ) const;
-    bool read( const cv::FileNode &node );
-    virtual void printDefaults() const;
-    virtual void printAttrs() const;
-    virtual bool scanAttr( const std::string prmName, const std::string val);
-};
-
-struct CvCascadeBoostTrainData : CvDTreeTrainData
-{
-    CvCascadeBoostTrainData( const CvFeatureEvaluator* _featureEvaluator,
-                             const CvDTreeParams& _params );
-    CvCascadeBoostTrainData( const CvFeatureEvaluator* _featureEvaluator,
-                             int _numSamples, int _precalcValBufSize, int _precalcIdxBufSize,
-                             const CvDTreeParams& _params = CvDTreeParams() );
-    virtual void setData( const CvFeatureEvaluator* _featureEvaluator,
-                          int _numSamples, int _precalcValBufSize, int _precalcIdxBufSize,
-                          const CvDTreeParams& _params=CvDTreeParams() );
-    void precalculate();
-
-    virtual CvDTreeNode* subsample_data( const CvMat* _subsample_idx );
-
-    virtual const int* get_class_labels( CvDTreeNode* n, int* labelsBuf );
-    virtual const int* get_cv_labels( CvDTreeNode* n, int* labelsBuf);
-    virtual const int* get_sample_indices( CvDTreeNode* n, int* indicesBuf );
-
-    virtual void get_ord_var_data( CvDTreeNode* n, int vi, float* ordValuesBuf, int* sortedIndicesBuf,
-                                  const float** ordValues, const int** sortedIndices, int* sampleIndicesBuf );
-    virtual const int* get_cat_var_data( CvDTreeNode* n, int vi, int* catValuesBuf );
-    virtual float getVarValue( int vi, int si );
-    virtual void free_train_data();
-
-    const CvFeatureEvaluator* featureEvaluator;
-    cv::Mat valCache; // precalculated feature values (CV_32FC1)
-    CvMat _resp; // for casting
-    int numPrecalcVal, numPrecalcIdx;
-};
-
-class CvCascadeBoostTree : public CvBoostTree
-{
-public:
-    virtual CvDTreeNode* predict( int sampleIdx ) const;
-    void write( cv::FileStorage &fs, const cv::Mat& featureMap );
-    void read( const cv::FileNode &node, CvBoost* _ensemble, CvDTreeTrainData* _data );
-    void markFeaturesInMap( cv::Mat& featureMap );
-protected:
-    virtual void split_node_data( CvDTreeNode* n );
-};
-
-class CvCascadeBoost : public CvBoost
-{
-public:
-    virtual bool train( const CvFeatureEvaluator* _featureEvaluator,
-                        int _numSamples, int _precalcValBufSize, int _precalcIdxBufSize,
-                        const CvCascadeBoostParams& _params=CvCascadeBoostParams() );
-    virtual float predict( int sampleIdx, bool returnSum = false ) const;
-
-    float getThreshold() const { return threshold; }
-    void write( cv::FileStorage &fs, const cv::Mat& featureMap ) const;
-    bool read( const cv::FileNode &node, const CvFeatureEvaluator* _featureEvaluator,
-               const CvCascadeBoostParams& _params );
-    void markUsedFeaturesInMap( cv::Mat& featureMap );
-protected:
-    virtual bool set_params( const CvBoostParams& _params );
-    virtual void update_weights( CvBoostTree* tree );
-    virtual bool isErrDesired();
-
-    float threshold;
-    float minHitRate, maxFalseAlarm;
-};
-
-#endif

doc/文档1/复用代码/traincascade/cascadeclassifier.cpp

@@ -1,570 +0,0 @@
-#include "opencv2/core.hpp"
-
-#include "cascadeclassifier.h"
-#include <queue>
-
-using namespace std;
-using namespace cv;
-
-static const char* stageTypes[] = { CC_BOOST };
-static const char* featureTypes[] = { CC_HAAR, CC_LBP, CC_HOG };
-
-CvCascadeParams::CvCascadeParams() : stageType( defaultStageType ),
-    featureType( defaultFeatureType ), winSize( cvSize(24, 24) )
-{
-    name = CC_CASCADE_PARAMS;
-}
-CvCascadeParams::CvCascadeParams( int _stageType, int _featureType ) : stageType( _stageType ),
-    featureType( _featureType ), winSize( cvSize(24, 24) )
-{
-    name = CC_CASCADE_PARAMS;
-}
-
-//---------------------------- CascadeParams --------------------------------------
-
-void CvCascadeParams::write( FileStorage &fs ) const
-{
-    string stageTypeStr = stageType == BOOST ? CC_BOOST : string();
-    CV_Assert( !stageTypeStr.empty() );
-    fs << CC_STAGE_TYPE << stageTypeStr;
-    string featureTypeStr = featureType == CvFeatureParams::HAAR ? CC_HAAR :
-                            featureType == CvFeatureParams::LBP ? CC_LBP :
-                            featureType == CvFeatureParams::HOG ? CC_HOG :
-                            0;
-    CV_Assert( !stageTypeStr.empty() );
-    fs << CC_FEATURE_TYPE << featureTypeStr;
-    fs << CC_HEIGHT << winSize.height;
-    fs << CC_WIDTH << winSize.width;
-}
-
-bool CvCascadeParams::read( const FileNode &node )
-{
-    if ( node.empty() )
-        return false;
-    string stageTypeStr, featureTypeStr;
-    FileNode rnode = node[CC_STAGE_TYPE];
-    if ( !rnode.isString() )
-        return false;
-    rnode >> stageTypeStr;
-    stageType = !stageTypeStr.compare( CC_BOOST ) ? BOOST : -1;
-    if (stageType == -1)
-        return false;
-    rnode = node[CC_FEATURE_TYPE];
-    if ( !rnode.isString() )
-        return false;
-    rnode >> featureTypeStr;
-    featureType = !featureTypeStr.compare( CC_HAAR ) ? CvFeatureParams::HAAR :
-                  !featureTypeStr.compare( CC_LBP ) ? CvFeatureParams::LBP :
-                  !featureTypeStr.compare( CC_HOG ) ? CvFeatureParams::HOG :
-                  -1;
-    if (featureType == -1)
-        return false;
-    node[CC_HEIGHT] >> winSize.height;
-    node[CC_WIDTH] >> winSize.width;
-    return winSize.height > 0 && winSize.width > 0;
-}
-
-void CvCascadeParams::printDefaults() const
-{
-    CvParams::printDefaults();
-    cout << "  [-stageType <";
-    for( int i = 0; i < (int)(sizeof(stageTypes)/sizeof(stageTypes[0])); i++ )
-    {
-        cout << (i ? " | " : "") << stageTypes[i];
-        if ( i == defaultStageType )
-            cout << "(default)";
-    }
-    cout << ">]" << endl;
-
-    cout << "  [-featureType <{";
-    for( int i = 0; i < (int)(sizeof(featureTypes)/sizeof(featureTypes[0])); i++ )
-    {
-        cout << (i ? ", " : "") << featureTypes[i];
-        if ( i == defaultStageType )
-            cout << "(default)";
-    }
-    cout << "}>]" << endl;
-    cout << "  [-w <sampleWidth = " << winSize.width << ">]" << endl;
-    cout << "  [-h <sampleHeight = " << winSize.height << ">]" << endl;
-}
-
-void CvCascadeParams::printAttrs() const
-{
-    cout << "stageType: " << stageTypes[stageType] << endl;
-    cout << "featureType: " << featureTypes[featureType] << endl;
-    cout << "sampleWidth: " << winSize.width << endl;
-    cout << "sampleHeight: " << winSize.height << endl;
-}
-
-bool CvCascadeParams::scanAttr( const string prmName, const string val )
-{
-    bool res = true;
-    if( !prmName.compare( "-stageType" ) )
-    {
-        for( int i = 0; i < (int)(sizeof(stageTypes)/sizeof(stageTypes[0])); i++ )
-            if( !val.compare( stageTypes[i] ) )
-                stageType = i;
-    }
-    else if( !prmName.compare( "-featureType" ) )
-    {
-        for( int i = 0; i < (int)(sizeof(featureTypes)/sizeof(featureTypes[0])); i++ )
-            if( !val.compare( featureTypes[i] ) )
-                featureType = i;
-    }
-    else if( !prmName.compare( "-w" ) )
-    {
-        winSize.width = atoi( val.c_str() );
-    }
-    else if( !prmName.compare( "-h" ) )
-    {
-        winSize.height = atoi( val.c_str() );
-    }
-    else
-        res = false;
-    return res;
-}
-
-//---------------------------- CascadeClassifier --------------------------------------
-
-bool CvCascadeClassifier::train( const string _cascadeDirName,
-                                const string _posFilename,
-                                const string _negFilename,
-                                int _numPos, int _numNeg,
-                                int _precalcValBufSize, int _precalcIdxBufSize,
-                                int _numStages,
-                                const CvCascadeParams& _cascadeParams,
-                                const CvFeatureParams& _featureParams,
-                                const CvCascadeBoostParams& _stageParams,
-                                bool baseFormatSave,
-                                double acceptanceRatioBreakValue )
-{
-    // Start recording clock ticks for training time output
-    double time = (double)getTickCount();
-
-    if( _cascadeDirName.empty() || _posFilename.empty() || _negFilename.empty() )
-        CV_Error( cv::Error::StsBadArg, "_cascadeDirName or _bgfileName or _vecFileName is NULL" );
-
-    string dirName;
-    if (_cascadeDirName.find_last_of("/\\") == (_cascadeDirName.length() - 1) )
-        dirName = _cascadeDirName;
-    else
-        dirName = _cascadeDirName + '/';
-
-    numPos = _numPos;
-    numNeg = _numNeg;
-    numStages = _numStages;
-    if ( !imgReader.create( _posFilename, _negFilename, _cascadeParams.winSize ) )
-    {
-        cout << "Image reader can not be created from -vec " << _posFilename
-                << " and -bg " << _negFilename << "." << endl;
-        return false;
-    }
-    if ( !load( dirName ) )
-    {
-        cascadeParams = _cascadeParams;
-        featureParams = CvFeatureParams::create(cascadeParams.featureType);
-        featureParams->init(_featureParams);
-        stageParams = makePtr<CvCascadeBoostParams>();
-        *stageParams = _stageParams;
-        featureEvaluator = CvFeatureEvaluator::create(cascadeParams.featureType);
-        featureEvaluator->init( featureParams, numPos + numNeg, cascadeParams.winSize );
-        stageClassifiers.reserve( numStages );
-    }else{
-        // Make sure that if model parameters are preloaded, that people are aware of this,
-        // even when passing other parameters to the training command
-        cout << "---------------------------------------------------------------------------------" << endl;
-        cout << "Training parameters are pre-loaded from the parameter file in data folder!" << endl;
-        cout << "Please empty this folder if you want to use a NEW set of training parameters." << endl;
-        cout << "---------------------------------------------------------------------------------" << endl;
-    }
-    cout << "PARAMETERS:" << endl;
-    cout << "cascadeDirName: " << _cascadeDirName << endl;
-    cout << "vecFileName: " << _posFilename << endl;
-    cout << "bgFileName: " << _negFilename << endl;
-    cout << "numPos: " << _numPos << endl;
-    cout << "numNeg: " << _numNeg << endl;
-    cout << "numStages: " << numStages << endl;
-    cout << "precalcValBufSize[Mb] : " << _precalcValBufSize << endl;
-    cout << "precalcIdxBufSize[Mb] : " << _precalcIdxBufSize << endl;
-    cout << "acceptanceRatioBreakValue : " << acceptanceRatioBreakValue << endl;
-    cascadeParams.printAttrs();
-    stageParams->printAttrs();
-    featureParams->printAttrs();
-    cout << "Number of unique features given windowSize [" << _cascadeParams.winSize.width << "," << _cascadeParams.winSize.height << "] : " << featureEvaluator->getNumFeatures() << "" << endl;
-
-    int startNumStages = (int)stageClassifiers.size();
-    if ( startNumStages > 1 )
-        cout << endl << "Stages 0-" << startNumStages-1 << " are loaded" << endl;
-    else if ( startNumStages == 1)
-        cout << endl << "Stage 0 is loaded" << endl;
-
-    double requiredLeafFARate = pow( (double) stageParams->maxFalseAlarm, (double) numStages ) /
-                                (double)stageParams->max_depth;
-    double tempLeafFARate;
-
-    for( int i = startNumStages; i < numStages; i++ )
-    {
-        cout << endl << "===== TRAINING " << i << "-stage =====" << endl;
-        cout << "<BEGIN" << endl;
-
-        if ( !updateTrainingSet( requiredLeafFARate, tempLeafFARate ) )
-        {
-            cout << "Train dataset for temp stage can not be filled. "
-                    "Branch training terminated." << endl;
-            break;
-        }
-        if( tempLeafFARate <= requiredLeafFARate )
-        {
-            cout << "Required leaf false alarm rate achieved. "
-                    "Branch training terminated." << endl;
-            break;
-        }
-        if( (tempLeafFARate <= acceptanceRatioBreakValue) && (acceptanceRatioBreakValue >= 0) ){
-            cout << "The required acceptanceRatio for the model has been reached to avoid overfitting of trainingdata. "
-                    "Branch training terminated." << endl;
-            break;
-        }
-
-        Ptr<CvCascadeBoost> tempStage = makePtr<CvCascadeBoost>();
-        bool isStageTrained = tempStage->train( featureEvaluator,
-                                                curNumSamples, _precalcValBufSize, _precalcIdxBufSize,
-                                                *stageParams );
-        cout << "END>" << endl;
-
-        if(!isStageTrained)
-            break;
-
-        stageClassifiers.push_back( tempStage );
-
-        // save params
-        if( i == 0)
-        {
-            std::string paramsFilename = dirName + CC_PARAMS_FILENAME;
-            FileStorage fs( paramsFilename, FileStorage::WRITE);
-            if ( !fs.isOpened() )
-            {
-                cout << "Parameters can not be written, because file " << paramsFilename
-                        << " can not be opened." << endl;
-                return false;
-            }
-            fs << FileStorage::getDefaultObjectName(paramsFilename) << "{";
-            writeParams( fs );
-            fs << "}";
-        }
-        // save current stage
-        char buf[32];
-        snprintf(buf, sizeof(buf), "%s%d", "stage", i );
-        string stageFilename = dirName + buf + ".xml";
-        FileStorage fs( stageFilename, FileStorage::WRITE );
-        if ( !fs.isOpened() )
-        {
-            cout << "Current stage can not be written, because file " << stageFilename
-                    << " can not be opened." << endl;
-            return false;
-        }
-        fs << FileStorage::getDefaultObjectName(stageFilename) << "{";
-        tempStage->write( fs, Mat() );
-        fs << "}";
-
-        // Output training time up till now
-        double seconds = ( (double)getTickCount() - time)/ getTickFrequency();
-        int days = int(seconds) / 60 / 60 / 24;
-        int hours = (int(seconds) / 60 / 60) % 24;
-        int minutes = (int(seconds) / 60) % 60;
-        int seconds_left = int(seconds) % 60;
-        cout << "Training until now has taken " << days << " days " << hours << " hours " << minutes << " minutes " << seconds_left <<" seconds." << endl;
-    }
-
-    if(stageClassifiers.size() == 0)
-    {
-        cout << "Cascade classifier can't be trained. Check the used training parameters." << endl;
-        return false;
-    }
-
-    save( dirName + CC_CASCADE_FILENAME, baseFormatSave );
-
-    return true;
-}
-
-int CvCascadeClassifier::predict( int sampleIdx )
-{
-    CV_DbgAssert( sampleIdx < numPos + numNeg );
-    for (vector< Ptr<CvCascadeBoost> >::iterator it = stageClassifiers.begin();
-        it != stageClassifiers.end();++it )
-    {
-        if ( (*it)->predict( sampleIdx ) == 0.f )
-            return 0;
-    }
-    return 1;
-}
-
-bool CvCascadeClassifier::updateTrainingSet( double minimumAcceptanceRatio, double& acceptanceRatio)
-{
-    int64 posConsumed = 0, negConsumed = 0;
-    imgReader.restart();
-    int posCount = fillPassedSamples( 0, numPos, true, 0, posConsumed );
-    if( !posCount )
-        return false;
-    cout << "POS count : consumed   " << posCount << " : " << (int)posConsumed << endl;
-
-    int proNumNeg = cvRound( ( ((double)numNeg) * ((double)posCount) ) / numPos ); // apply only a fraction of negative samples. double is required since overflow is possible
-    int negCount = fillPassedSamples( posCount, proNumNeg, false, minimumAcceptanceRatio, negConsumed );
-    if ( !negCount )
-        if ( !(negConsumed > 0 && ((double)negCount+1)/(double)negConsumed <= minimumAcceptanceRatio) )
-            return false;
-
-    curNumSamples = posCount + negCount;
-    acceptanceRatio = negConsumed == 0 ? 0 : ( (double)negCount/(double)(int64)negConsumed );
-    cout << "NEG count : acceptanceRatio    " << negCount << " : " << acceptanceRatio << endl;
-    return true;
-}
-
-int CvCascadeClassifier::fillPassedSamples( int first, int count, bool isPositive, double minimumAcceptanceRatio, int64& consumed )
-{
-    int getcount = 0;
-    Mat img(cascadeParams.winSize, CV_8UC1);
-    for( int i = first; i < first + count; i++ )
-    {
-        for( ; ; )
-        {
-            if( consumed != 0 && ((double)getcount+1)/(double)(int64)consumed <= minimumAcceptanceRatio )
-                return getcount;
-
-            bool isGetImg = isPositive ? imgReader.getPos( img ) :
-                                           imgReader.getNeg( img );
-            if( !isGetImg )
-                return getcount;
-            consumed++;
-
-            featureEvaluator->setImage( img, isPositive ? 1 : 0, i );
-            if( predict( i ) == 1 )
-            {
-                getcount++;
-                printf("%s current samples: %d\r", isPositive ? "POS":"NEG", getcount);
-                fflush(stdout);
-                break;
-            }
-        }
-    }
-    return getcount;
-}
-
-void CvCascadeClassifier::writeParams( FileStorage &fs ) const
-{
-    cascadeParams.write( fs );
-    fs << CC_STAGE_PARAMS << "{"; stageParams->write( fs ); fs << "}";
-    fs << CC_FEATURE_PARAMS << "{"; featureParams->write( fs ); fs << "}";
-}
-
-void CvCascadeClassifier::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
-{
-    featureEvaluator->writeFeatures( fs, featureMap );
-}
-
-void CvCascadeClassifier::writeStages( FileStorage &fs, const Mat& featureMap ) const
-{
-    char cmnt[30];
-    int i = 0;
-    fs << CC_STAGES << "[";
-    for( vector< Ptr<CvCascadeBoost> >::const_iterator it = stageClassifiers.begin();
-        it != stageClassifiers.end();++it, ++i )
-    {
-        snprintf( cmnt, sizeof(cmnt), "stage %d", i );
-        fs.writeComment(cmnt);
-        fs << "{";
-        (*it)->write( fs, featureMap );
-        fs << "}";
-    }
-    fs << "]";
-}
-
-bool CvCascadeClassifier::readParams( const FileNode &node )
-{
-    if ( !node.isMap() || !cascadeParams.read( node ) )
-        return false;
-
-    stageParams = makePtr<CvCascadeBoostParams>();
-    FileNode rnode = node[CC_STAGE_PARAMS];
-    if ( !stageParams->read( rnode ) )
-        return false;
-
-    featureParams = CvFeatureParams::create(cascadeParams.featureType);
-    rnode = node[CC_FEATURE_PARAMS];
-    if ( !featureParams->read( rnode ) )
-        return false;
-    return true;
-}
-
-bool CvCascadeClassifier::readStages( const FileNode &node)
-{
-    FileNode rnode = node[CC_STAGES];
-    if (!rnode.empty() || !rnode.isSeq())
-        return false;
-    stageClassifiers.reserve(numStages);
-    FileNodeIterator it = rnode.begin();
-    for( int i = 0; i < min( (int)rnode.size(), numStages ); i++, it++ )
-    {
-        Ptr<CvCascadeBoost> tempStage = makePtr<CvCascadeBoost>();
-        if ( !tempStage->read( *it, featureEvaluator, *stageParams) )
-            return false;
-        stageClassifiers.push_back(tempStage);
-    }
-    return true;
-}
-
-// For old Haar Classifier file saving
-#define ICV_HAAR_TYPE_ID              "opencv-haar-classifier"
-#define ICV_HAAR_SIZE_NAME            "size"
-#define ICV_HAAR_STAGES_NAME          "stages"
-#define ICV_HAAR_TREES_NAME             "trees"
-#define ICV_HAAR_FEATURE_NAME             "feature"
-#define ICV_HAAR_RECTS_NAME                 "rects"
-#define ICV_HAAR_TILTED_NAME                "tilted"
-#define ICV_HAAR_THRESHOLD_NAME           "threshold"
-#define ICV_HAAR_LEFT_NODE_NAME           "left_node"
-#define ICV_HAAR_LEFT_VAL_NAME            "left_val"
-#define ICV_HAAR_RIGHT_NODE_NAME          "right_node"
-#define ICV_HAAR_RIGHT_VAL_NAME           "right_val"
-#define ICV_HAAR_STAGE_THRESHOLD_NAME   "stage_threshold"
-#define ICV_HAAR_PARENT_NAME            "parent"
-#define ICV_HAAR_NEXT_NAME              "next"
-
-void CvCascadeClassifier::save( const string filename, bool baseFormat )
-{
-    FileStorage fs( filename, FileStorage::WRITE );
-
-    if ( !fs.isOpened() )
-        return;
-
-    fs << FileStorage::getDefaultObjectName(filename);
-    if ( !baseFormat )
-    {
-        Mat featureMap;
-        getUsedFeaturesIdxMap( featureMap );
-        fs << "{";
-        writeParams( fs );
-        fs << CC_STAGE_NUM << (int)stageClassifiers.size();
-        writeStages( fs, featureMap );
-        writeFeatures( fs, featureMap );
-    }
-    else
-    {
-        //char buf[256];
-        CvSeq* weak;
-        if ( cascadeParams.featureType != CvFeatureParams::HAAR )
-            CV_Error( cv::Error::StsBadFunc, "old file format is used for Haar-like features only");
-        fs << "{:" ICV_HAAR_TYPE_ID;
-        fs << ICV_HAAR_SIZE_NAME << "[:" << cascadeParams.winSize.width <<
-            cascadeParams.winSize.height << "]";
-        fs << ICV_HAAR_STAGES_NAME << "[";
-        for( size_t si = 0; si < stageClassifiers.size(); si++ )
-        {
-            fs << "{"; //stage
-            /*snprintf( buf, sizeof(buf), "stage %d", si );
-            CV_CALL( cvWriteComment( fs, buf, 1 ) );*/
-            weak = stageClassifiers[si]->get_weak_predictors();
-            fs << ICV_HAAR_TREES_NAME << "[";
-            for( int wi = 0; wi < weak->total; wi++ )
-            {
-                int total_inner_node_idx = -1;
-                queue<const CvDTreeNode*> inner_nodes_queue;
-                CvCascadeBoostTree* tree = *((CvCascadeBoostTree**) cvGetSeqElem( weak, wi ));
-
-                fs << "[";
-                /*snprintf( buf, sizeof(buf), "tree %d", wi );
-                CV_CALL( cvWriteComment( fs, buf, 1 ) );*/
-
-                const CvDTreeNode* tempNode;
-
-                inner_nodes_queue.push( tree->get_root() );
-                total_inner_node_idx++;
-
-                while (!inner_nodes_queue.empty())
-                {
-                    tempNode = inner_nodes_queue.front();
-
-                    fs << "{";
-                    fs << ICV_HAAR_FEATURE_NAME << "{";
-                    ((CvHaarEvaluator*)featureEvaluator.get())->writeFeature( fs, tempNode->split->var_idx );
-                    fs << "}";
-
-                    fs << ICV_HAAR_THRESHOLD_NAME << tempNode->split->ord.c;
-
-                    if( tempNode->left->left || tempNode->left->right )
-                    {
-                        inner_nodes_queue.push( tempNode->left );
-                        total_inner_node_idx++;
-                        fs << ICV_HAAR_LEFT_NODE_NAME << total_inner_node_idx;
-                    }
-                    else
-                        fs << ICV_HAAR_LEFT_VAL_NAME << tempNode->left->value;
-
-                    if( tempNode->right->left || tempNode->right->right )
-                    {
-                        inner_nodes_queue.push( tempNode->right );
-                        total_inner_node_idx++;
-                        fs << ICV_HAAR_RIGHT_NODE_NAME << total_inner_node_idx;
-                    }
-                    else
-                        fs << ICV_HAAR_RIGHT_VAL_NAME << tempNode->right->value;
-                    fs << "}"; // ICV_HAAR_FEATURE_NAME
-                    inner_nodes_queue.pop();
-                }
-                fs << "]";
-            }
-            fs << "]"; //ICV_HAAR_TREES_NAME
-            fs << ICV_HAAR_STAGE_THRESHOLD_NAME << stageClassifiers[si]->getThreshold();
-            fs << ICV_HAAR_PARENT_NAME << (int)si-1 << ICV_HAAR_NEXT_NAME << -1;
-            fs << "}"; //stage
-        } /* for each stage */
-        fs << "]"; //ICV_HAAR_STAGES_NAME
-    }
-    fs << "}";
-}
-
-bool CvCascadeClassifier::load( const string cascadeDirName )
-{
-    FileStorage fs( cascadeDirName + CC_PARAMS_FILENAME, FileStorage::READ );
-    if ( !fs.isOpened() )
-        return false;
-    FileNode node = fs.getFirstTopLevelNode();
-    if ( !readParams( node ) )
-        return false;
-    featureEvaluator = CvFeatureEvaluator::create(cascadeParams.featureType);
-    featureEvaluator->init( featureParams, numPos + numNeg, cascadeParams.winSize );
-    fs.release();
-
-    char buf[5+10+1] = {0};
-    for ( int si = 0; si < numStages; si++ )
-    {
-        snprintf( buf, sizeof(buf), "%s%d", "stage", si);
-        fs.open( cascadeDirName + buf + ".xml", FileStorage::READ );
-        node = fs.getFirstTopLevelNode();
-        if ( !fs.isOpened() )
-            break;
-        Ptr<CvCascadeBoost> tempStage = makePtr<CvCascadeBoost>();
-
-        if ( !tempStage->read( node, featureEvaluator, *stageParams ))
-        {
-            fs.release();
-            break;
-        }
-        stageClassifiers.push_back(tempStage);
-    }
-    return true;
-}
-
-void CvCascadeClassifier::getUsedFeaturesIdxMap( Mat& featureMap )
-{
-    int varCount = featureEvaluator->getNumFeatures() * featureEvaluator->getFeatureSize();
-    featureMap.create( 1, varCount, CV_32SC1 );
-    featureMap.setTo(Scalar(-1));
-
-    for( vector< Ptr<CvCascadeBoost> >::const_iterator it = stageClassifiers.begin();
-        it != stageClassifiers.end();++it )
-        (*it)->markUsedFeaturesInMap( featureMap );
-
-    for( int fi = 0, idx = 0; fi < varCount; fi++ )
-        if ( featureMap.at<int>(0, fi) >= 0 )
-            featureMap.ptr<int>(0)[fi] = idx++;
-}

doc/文档1/复用代码/traincascade/cascadeclassifier.h

@@ -1,125 +0,0 @@
-#ifndef _OPENCV_CASCADECLASSIFIER_H_
-#define _OPENCV_CASCADECLASSIFIER_H_
-
-#include <ctime>
-#include "traincascade_features.h"
-#include "haarfeatures.h"
-#include "lbpfeatures.h"
-#include "HOGfeatures.h" //new
-#include "boost.h"
-
-#define CC_CASCADE_FILENAME "cascade.xml"
-#define CC_PARAMS_FILENAME "params.xml"
-
-#define CC_CASCADE_PARAMS "cascadeParams"
-#define CC_STAGE_TYPE "stageType"
-#define CC_FEATURE_TYPE "featureType"
-#define CC_HEIGHT "height"
-#define CC_WIDTH  "width"
-
-#define CC_STAGE_NUM    "stageNum"
-#define CC_STAGES       "stages"
-#define CC_STAGE_PARAMS "stageParams"
-
-#define CC_BOOST            "BOOST"
-#define CC_BOOST_TYPE       "boostType"
-#define CC_DISCRETE_BOOST   "DAB"
-#define CC_REAL_BOOST       "RAB"
-#define CC_LOGIT_BOOST      "LB"
-#define CC_GENTLE_BOOST     "GAB"
-#define CC_MINHITRATE       "minHitRate"
-#define CC_MAXFALSEALARM    "maxFalseAlarm"
-#define CC_TRIM_RATE        "weightTrimRate"
-#define CC_MAX_DEPTH        "maxDepth"
-#define CC_WEAK_COUNT       "maxWeakCount"
-#define CC_STAGE_THRESHOLD  "stageThreshold"
-#define CC_WEAK_CLASSIFIERS "weakClassifiers"
-#define CC_INTERNAL_NODES   "internalNodes"
-#define CC_LEAF_VALUES      "leafValues"
-
-#define CC_FEATURES       FEATURES
-#define CC_FEATURE_PARAMS "featureParams"
-#define CC_MAX_CAT_COUNT  "maxCatCount"
-#define CC_FEATURE_SIZE   "featSize"
-
-#define CC_HAAR        "HAAR"
-#define CC_MODE        "mode"
-#define CC_MODE_BASIC  "BASIC"
-#define CC_MODE_CORE   "CORE"
-#define CC_MODE_ALL    "ALL"
-#define CC_RECTS       "rects"
-#define CC_TILTED      "tilted"
-
-#define CC_LBP  "LBP"
-#define CC_RECT "rect"
-
-#define CC_HOG "HOG"
-
-#ifdef _WIN32
-#define TIME( arg ) (((double) clock()) / CLOCKS_PER_SEC)
-#else
-#define TIME( arg ) (time( arg ))
-#endif
-
-class CvCascadeParams : public CvParams
-{
-public:
-    enum { BOOST = 0 };
-    static const int defaultStageType = BOOST;
-    static const int defaultFeatureType = CvFeatureParams::HAAR;
-
-    CvCascadeParams();
-    CvCascadeParams( int _stageType, int _featureType );
-    void write( cv::FileStorage &fs ) const;
-    bool read( const cv::FileNode &node );
-
-    void printDefaults() const;
-    void printAttrs() const;
-    bool scanAttr( const std::string prmName, const std::string val );
-
-    int stageType;
-    int featureType;
-    cv::Size winSize;
-};
-
-class CvCascadeClassifier
-{
-public:
-    bool train( const std::string _cascadeDirName,
-                const std::string _posFilename,
-                const std::string _negFilename,
-                int _numPos, int _numNeg,
-                int _precalcValBufSize, int _precalcIdxBufSize,
-                int _numStages,
-                const CvCascadeParams& _cascadeParams,
-                const CvFeatureParams& _featureParams,
-                const CvCascadeBoostParams& _stageParams,
-                bool baseFormatSave = false,
-                double acceptanceRatioBreakValue = -1.0 );
-private:
-    int predict( int sampleIdx );
-    void save( const std::string cascadeDirName, bool baseFormat = false );
-    bool load( const std::string cascadeDirName );
-    bool updateTrainingSet( double minimumAcceptanceRatio, double& acceptanceRatio );
-    int fillPassedSamples( int first, int count, bool isPositive, double requiredAcceptanceRatio, int64& consumed );
-
-    void writeParams( cv::FileStorage &fs ) const;
-    void writeStages( cv::FileStorage &fs, const cv::Mat& featureMap ) const;
-    void writeFeatures( cv::FileStorage &fs, const cv::Mat& featureMap ) const;
-    bool readParams( const cv::FileNode &node );
-    bool readStages( const cv::FileNode &node );
-
-    void getUsedFeaturesIdxMap( cv::Mat& featureMap );
-
-    CvCascadeParams cascadeParams;
-    cv::Ptr<CvFeatureParams> featureParams;
-    cv::Ptr<CvCascadeBoostParams> stageParams;
-
-    cv::Ptr<CvFeatureEvaluator> featureEvaluator;
-    std::vector< cv::Ptr<CvCascadeBoost> > stageClassifiers;
-    CvCascadeImageReader imgReader;
-    int numStages, curNumSamples;
-    int numPos, numNeg;
-};
-
-#endif

doc/文档1/复用代码/traincascade/features.cpp

@@ -1,93 +0,0 @@
-#include "opencv2/core.hpp"
-
-#include "traincascade_features.h"
-#include "cascadeclassifier.h"
-
-using namespace std;
-using namespace cv;
-
-float calcNormFactor( const Mat& sum, const Mat& sqSum )
-{
-    CV_DbgAssert( sum.cols > 3 && sqSum.rows > 3 );
-    Rect normrect( 1, 1, sum.cols - 3, sum.rows - 3 );
-    size_t p0, p1, p2, p3;
-    CV_SUM_OFFSETS( p0, p1, p2, p3, normrect, sum.step1() )
-    double area = normrect.width * normrect.height;
-    const int *sp = sum.ptr<int>();
-    int valSum = sp[p0] - sp[p1] - sp[p2] + sp[p3];
-    const double *sqp = sqSum.ptr<double>();
-    double valSqSum = sqp[p0] - sqp[p1] - sqp[p2] + sqp[p3];
-    return (float) sqrt( (double) (area * valSqSum - (double)valSum * valSum) );
-}
-
-CvParams::CvParams() : name( "params" ) {}
-void CvParams::printDefaults() const
-{ cout << "--" << name << "--" << endl; }
-void CvParams::printAttrs() const {}
-bool CvParams::scanAttr( const string, const string ) { return false; }
-
-
-//---------------------------- FeatureParams --------------------------------------
-
-CvFeatureParams::CvFeatureParams() : maxCatCount( 0 ), featSize( 1 )
-{
-    name = CC_FEATURE_PARAMS;
-}
-
-void CvFeatureParams::init( const CvFeatureParams& fp )
-{
-    maxCatCount = fp.maxCatCount;
-    featSize = fp.featSize;
-}
-
-void CvFeatureParams::write( FileStorage &fs ) const
-{
-    fs << CC_MAX_CAT_COUNT << maxCatCount;
-    fs << CC_FEATURE_SIZE << featSize;
-}
-
-bool CvFeatureParams::read( const FileNode &node )
-{
-    if ( node.empty() )
-        return false;
-    maxCatCount = node[CC_MAX_CAT_COUNT];
-    featSize = node[CC_FEATURE_SIZE];
-    return ( maxCatCount >= 0 && featSize >= 1 );
-}
-
-Ptr<CvFeatureParams> CvFeatureParams::create( int featureType )
-{
-    return featureType == HAAR ? Ptr<CvFeatureParams>(new CvHaarFeatureParams) :
-        featureType == LBP ? Ptr<CvFeatureParams>(new CvLBPFeatureParams) :
-        featureType == HOG ? Ptr<CvFeatureParams>(new CvHOGFeatureParams) :
-        Ptr<CvFeatureParams>();
-}
-
-//------------------------------------- FeatureEvaluator ---------------------------------------
-
-void CvFeatureEvaluator::init(const CvFeatureParams *_featureParams,
-                              int _maxSampleCount, Size _winSize )
-{
-    CV_Assert(_maxSampleCount > 0);
-    featureParams = (CvFeatureParams *)_featureParams;
-    winSize = _winSize;
-    numFeatures = 0;
-    cls.create( (int)_maxSampleCount, 1, CV_32FC1 );
-    generateFeatures();
-}
-
-void CvFeatureEvaluator::setImage(const Mat &img, uchar clsLabel, int idx)
-{
-    CV_Assert(img.cols == winSize.width);
-    CV_Assert(img.rows == winSize.height);
-    CV_Assert(idx < cls.rows);
-    cls.ptr<float>(idx)[0] = clsLabel;
-}
-
-Ptr<CvFeatureEvaluator> CvFeatureEvaluator::create(int type)
-{
-    return type == CvFeatureParams::HAAR ? Ptr<CvFeatureEvaluator>(new CvHaarEvaluator) :
-        type == CvFeatureParams::LBP ? Ptr<CvFeatureEvaluator>(new CvLBPEvaluator) :
-        type == CvFeatureParams::HOG ? Ptr<CvFeatureEvaluator>(new CvHOGEvaluator) :
-        Ptr<CvFeatureEvaluator>();
-}

doc/文档1/复用代码/traincascade/haarfeatures.cpp

@@ -1,312 +0,0 @@
-#include "opencv2/core.hpp"
-#include "opencv2/imgproc.hpp"
-
-#include "haarfeatures.h"
-#include "cascadeclassifier.h"
-
-using namespace std;
-using namespace cv;
-
-CvHaarFeatureParams::CvHaarFeatureParams() : mode(BASIC)
-{
-    name = HFP_NAME;
-}
-
-CvHaarFeatureParams::CvHaarFeatureParams( int _mode ) : mode( _mode )
-{
-    name = HFP_NAME;
-}
-
-void CvHaarFeatureParams::init( const CvFeatureParams& fp )
-{
-    CvFeatureParams::init( fp );
-    mode = ((const CvHaarFeatureParams&)fp).mode;
-}
-
-void CvHaarFeatureParams::write( FileStorage &fs ) const
-{
-    CvFeatureParams::write( fs );
-    string modeStr = mode == BASIC ? CC_MODE_BASIC :
-                     mode == CORE ? CC_MODE_CORE :
-                     mode == ALL ? CC_MODE_ALL : string();
-    CV_Assert( !modeStr.empty() );
-    fs << CC_MODE << modeStr;
-}
-
-bool CvHaarFeatureParams::read( const FileNode &node )
-{
-    if( !CvFeatureParams::read( node ) )
-        return false;
-
-    FileNode rnode = node[CC_MODE];
-    if( !rnode.isString() )
-        return false;
-    string modeStr;
-    rnode >> modeStr;
-    mode = !modeStr.compare( CC_MODE_BASIC ) ? BASIC :
-           !modeStr.compare( CC_MODE_CORE ) ? CORE :
-           !modeStr.compare( CC_MODE_ALL ) ? ALL : -1;
-    return (mode >= 0);
-}
-
-void CvHaarFeatureParams::printDefaults() const
-{
-    CvFeatureParams::printDefaults();
-    cout << "  [-mode <" CC_MODE_BASIC << "(default) | "
-            << CC_MODE_CORE <<" | " << CC_MODE_ALL << endl;
-}
-
-void CvHaarFeatureParams::printAttrs() const
-{
-    CvFeatureParams::printAttrs();
-    string mode_str = mode == BASIC ? CC_MODE_BASIC :
-                       mode == CORE ? CC_MODE_CORE :
-                       mode == ALL ? CC_MODE_ALL : 0;
-    cout << "mode: " <<  mode_str << endl;
-}
-
-bool CvHaarFeatureParams::scanAttr( const string prmName, const string val)
-{
-    if ( !CvFeatureParams::scanAttr( prmName, val ) )
-    {
-        if( !prmName.compare("-mode") )
-        {
-            mode = !val.compare( CC_MODE_CORE ) ? CORE :
-                   !val.compare( CC_MODE_ALL ) ? ALL :
-                   !val.compare( CC_MODE_BASIC ) ? BASIC : -1;
-            if (mode == -1)
-                return false;
-        }
-        return false;
-    }
-    return true;
-}
-
-//--------------------- HaarFeatureEvaluator ----------------
-
-void CvHaarEvaluator::init(const CvFeatureParams *_featureParams,
-                           int _maxSampleCount, Size _winSize )
-{
-    CV_Assert(_maxSampleCount > 0);
-    int cols = (_winSize.width + 1) * (_winSize.height + 1);
-    sum.create((int)_maxSampleCount, cols, CV_32SC1);
-    tilted.create((int)_maxSampleCount, cols, CV_32SC1);
-    normfactor.create(1, (int)_maxSampleCount, CV_32FC1);
-    CvFeatureEvaluator::init( _featureParams, _maxSampleCount, _winSize );
-}
-
-void CvHaarEvaluator::setImage(const Mat& img, uchar clsLabel, int idx)
-{
-    CV_DbgAssert( !sum.empty() && !tilted.empty() && !normfactor.empty() );
-    CvFeatureEvaluator::setImage( img, clsLabel, idx);
-    Mat innSum(winSize.height + 1, winSize.width + 1, sum.type(), sum.ptr<int>((int)idx));
-    Mat innSqSum;
-    if (((const CvHaarFeatureParams*)featureParams)->mode == CvHaarFeatureParams::ALL)
-    {
-        Mat innTilted(winSize.height + 1, winSize.width + 1, tilted.type(), tilted.ptr<int>((int)idx));
-        integral(img, innSum, innSqSum, innTilted);
-    }
-    else
-        integral(img, innSum, innSqSum);
-    normfactor.ptr<float>(0)[idx] = calcNormFactor( innSum, innSqSum );
-}
-
-void CvHaarEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
-{
-    _writeFeatures( features, fs, featureMap );
-}
-
-void CvHaarEvaluator::writeFeature(FileStorage &fs, int fi) const
-{
-    CV_DbgAssert( fi < (int)features.size() );
-    features[fi].write(fs);
-}
-
-void CvHaarEvaluator::generateFeatures()
-{
-    int mode = ((const CvHaarFeatureParams*)((CvFeatureParams*)featureParams))->mode;
-    int offset = winSize.width + 1;
-    for( int x = 0; x < winSize.width; x++ )
-    {
-        for( int y = 0; y < winSize.height; y++ )
-        {
-            for( int dx = 1; dx <= winSize.width; dx++ )
-            {
-                for( int dy = 1; dy <= winSize.height; dy++ )
-                {
-                    // haar_x2
-                    if ( (x+dx*2 <= winSize.width) && (y+dy <= winSize.height) )
-                    {
-                        features.push_back( Feature( offset, false,
-                            x,    y, dx*2, dy, -1,
-                            x+dx, y, dx  , dy, +2 ) );
-                    }
-                    // haar_y2
-                    if ( (x+dx <= winSize.width) && (y+dy*2 <= winSize.height) )
-                    {
-                        features.push_back( Feature( offset, false,
-                            x,    y, dx, dy*2, -1,
-                            x, y+dy, dx, dy,   +2 ) );
-                    }
-                    // haar_x3
-                    if ( (x+dx*3 <= winSize.width) && (y+dy <= winSize.height) )
-                    {
-                        features.push_back( Feature( offset, false,
-                            x,    y, dx*3, dy, -1,
-                            x+dx, y, dx  , dy, +2 ) );
-                    }
-                    // haar_y3
-                    if ( (x+dx <= winSize.width) && (y+dy*3 <= winSize.height) )
-                    {
-                        features.push_back( Feature( offset, false,
-                            x, y,    dx, dy*3, -1,
-                            x, y+dy, dx, dy,   +2 ) );
-                    }
-                    if( mode != CvHaarFeatureParams::BASIC )
-                    {
-                        // haar_x4
-                        if ( (x+dx*4 <= winSize.width) && (y+dy <= winSize.height) )
-                        {
-                            features.push_back( Feature( offset, false,
-                                x,    y, dx*4, dy, -1,
-                                x+dx, y, dx*2, dy, +2 ) );
-                        }
-                        // haar_y4
-                        if ( (x+dx <= winSize.width ) && (y+dy*4 <= winSize.height) )
-                        {
-                            features.push_back( Feature( offset, false,
-                                x, y,    dx, dy*4, -1,
-                                x, y+dy, dx, dy*2, +2 ) );
-                        }
-                    }
-                    // x2_y2
-                    if ( (x+dx*2 <= winSize.width) && (y+dy*2 <= winSize.height) )
-                    {
-                        features.push_back( Feature( offset, false,
-                            x,    y,    dx*2, dy*2, -1,
-                            x,    y,    dx,   dy,   +2,
-                            x+dx, y+dy, dx,   dy,   +2 ) );
-                    }
-                    if (mode != CvHaarFeatureParams::BASIC)
-                    {
-                        if ( (x+dx*3 <= winSize.width) && (y+dy*3 <= winSize.height) )
-                        {
-                            features.push_back( Feature( offset, false,
-                                x   , y   , dx*3, dy*3, -1,
-                                x+dx, y+dy, dx  , dy  , +9) );
-                        }
-                    }
-                    if (mode == CvHaarFeatureParams::ALL)
-                    {
-                        // tilted haar_x2
-                        if ( (x+2*dx <= winSize.width) && (y+2*dx+dy <= winSize.height) && (x-dy>= 0) )
-                        {
-                            features.push_back( Feature( offset, true,
-                                x, y, dx*2, dy, -1,
-                                x, y, dx,   dy, +2 ) );
-                        }
-                        // tilted haar_y2
-                        if ( (x+dx <= winSize.width) && (y+dx+2*dy <= winSize.height) && (x-2*dy>= 0) )
-                        {
-                            features.push_back( Feature( offset, true,
-                                x, y, dx, 2*dy, -1,
-                                x, y, dx, dy,   +2 ) );
-                        }
-                        // tilted haar_x3
-                        if ( (x+3*dx <= winSize.width) && (y+3*dx+dy <= winSize.height) && (x-dy>= 0) )
-                        {
-                            features.push_back( Feature( offset, true,
-                                x,    y,    dx*3, dy, -1,
-                                x+dx, y+dx, dx,   dy, +3 ) );
-                        }
-                        // tilted haar_y3
-                        if ( (x+dx <= winSize.width) && (y+dx+3*dy <= winSize.height) && (x-3*dy>= 0) )
-                        {
-                            features.push_back( Feature( offset, true,
-                                x,    y,    dx, 3*dy, -1,
-                                x-dy, y+dy, dx, dy,   +3 ) );
-                        }
-                        // tilted haar_x4
-                        if ( (x+4*dx <= winSize.width) && (y+4*dx+dy <= winSize.height) && (x-dy>= 0) )
-                        {
-                            features.push_back( Feature( offset, true,
-                                x,    y,    dx*4, dy, -1,
-                                x+dx, y+dx, dx*2, dy, +2 ) );
-                        }
-                        // tilted haar_y4
-                        if ( (x+dx <= winSize.width) && (y+dx+4*dy <= winSize.height) && (x-4*dy>= 0) )
-                        {
-                            features.push_back( Feature( offset, true,
-                                x,    y,    dx, 4*dy, -1,
-                                x-dy, y+dy, dx, 2*dy, +2 ) );
-                        }
-                    }
-                }
-            }
-        }
-    }
-    numFeatures = (int)features.size();
-}
-
-CvHaarEvaluator::Feature::Feature()
-{
-    tilted = false;
-    rect[0].r = rect[1].r = rect[2].r = Rect(0,0,0,0);
-    rect[0].weight = rect[1].weight = rect[2].weight = 0;
-}
-
-CvHaarEvaluator::Feature::Feature( int offset, bool _tilted,
-                                          int x0, int y0, int w0, int h0, float wt0,
-                                          int x1, int y1, int w1, int h1, float wt1,
-                                          int x2, int y2, int w2, int h2, float wt2 )
-{
-    tilted = _tilted;
-
-    rect[0].r.x = x0;
-    rect[0].r.y = y0;
-    rect[0].r.width  = w0;
-    rect[0].r.height = h0;
-    rect[0].weight   = wt0;
-
-    rect[1].r.x = x1;
-    rect[1].r.y = y1;
-    rect[1].r.width  = w1;
-    rect[1].r.height = h1;
-    rect[1].weight   = wt1;
-
-    rect[2].r.x = x2;
-    rect[2].r.y = y2;
-    rect[2].r.width  = w2;
-    rect[2].r.height = h2;
-    rect[2].weight   = wt2;
-
-    if( !tilted )
-    {
-        for( int j = 0; j < CV_HAAR_FEATURE_MAX; j++ )
-        {
-            if( rect[j].weight == 0.0F )
-                break;
-            CV_SUM_OFFSETS( fastRect[j].p0, fastRect[j].p1, fastRect[j].p2, fastRect[j].p3, rect[j].r, offset )
-        }
-    }
-    else
-    {
-        for( int j = 0; j < CV_HAAR_FEATURE_MAX; j++ )
-        {
-            if( rect[j].weight == 0.0F )
-                break;
-            CV_TILTED_OFFSETS( fastRect[j].p0, fastRect[j].p1, fastRect[j].p2, fastRect[j].p3, rect[j].r, offset )
-        }
-    }
-}
-
-void CvHaarEvaluator::Feature::write( FileStorage &fs ) const
-{
-    fs << CC_RECTS << "[";
-    for( int ri = 0; ri < CV_HAAR_FEATURE_MAX && rect[ri].r.width != 0; ++ri )
-    {
-        fs << "[:" << rect[ri].r.x << rect[ri].r.y <<
-            rect[ri].r.width << rect[ri].r.height << rect[ri].weight << "]";
-    }
-    fs << "]" << CC_TILTED << tilted;
-}

doc/文档1/复用代码/traincascade/haarfeatures.h

@@ -1,89 +0,0 @@
-#ifndef _OPENCV_HAARFEATURES_H_
-#define _OPENCV_HAARFEATURES_H_
-
-#include "traincascade_features.h"
-
-#define CV_HAAR_FEATURE_MAX      3
-
-#define HFP_NAME "haarFeatureParams"
-class CvHaarFeatureParams : public CvFeatureParams
-{
-public:
-    enum { BASIC = 0, CORE = 1, ALL = 2 };
-     /* 0 - BASIC = Viola
-     *  1 - CORE  = All upright
-     *  2 - ALL   = All features */
-
-    CvHaarFeatureParams();
-    CvHaarFeatureParams( int _mode );
-
-    virtual void init( const CvFeatureParams& fp );
-    virtual void write( cv::FileStorage &fs ) const;
-    virtual bool read( const cv::FileNode &node );
-
-    virtual void printDefaults() const;
-    virtual void printAttrs() const;
-    virtual bool scanAttr( const std::string prm, const std::string val);
-
-    int mode;
-};
-
-class CvHaarEvaluator : public CvFeatureEvaluator
-{
-public:
-    virtual void init(const CvFeatureParams *_featureParams,
-        int _maxSampleCount, cv::Size _winSize );
-    virtual void setImage(const cv::Mat& img, uchar clsLabel, int idx);
-    virtual float operator()(int featureIdx, int sampleIdx) const;
-    virtual void writeFeatures( cv::FileStorage &fs, const cv::Mat& featureMap ) const;
-    void writeFeature( cv::FileStorage &fs, int fi ) const; // for old file fornat
-protected:
-    virtual void generateFeatures();
-
-    class Feature
-    {
-    public:
-        Feature();
-        Feature( int offset, bool _tilted,
-            int x0, int y0, int w0, int h0, float wt0,
-            int x1, int y1, int w1, int h1, float wt1,
-            int x2 = 0, int y2 = 0, int w2 = 0, int h2 = 0, float wt2 = 0.0F );
-        float calc( const cv::Mat &sum, const cv::Mat &tilted, size_t y) const;
-        void write( cv::FileStorage &fs ) const;
-
-        bool  tilted;
-        struct
-        {
-            cv::Rect r;
-            float weight;
-        } rect[CV_HAAR_FEATURE_MAX];
-
-        struct
-        {
-            int p0, p1, p2, p3;
-        } fastRect[CV_HAAR_FEATURE_MAX];
-    };
-
-    std::vector<Feature> features;
-    cv::Mat  sum;         /* sum images (each row represents image) */
-    cv::Mat  tilted;      /* tilted sum images (each row represents image) */
-    cv::Mat  normfactor;  /* normalization factor */
-};
-
-inline float CvHaarEvaluator::operator()(int featureIdx, int sampleIdx) const
-{
-    float nf = normfactor.at<float>(0, sampleIdx);
-    return !nf ? 0.0f : (features[featureIdx].calc( sum, tilted, sampleIdx)/nf);
-}
-
-inline float CvHaarEvaluator::Feature::calc( const cv::Mat &_sum, const cv::Mat &_tilted, size_t y) const
-{
-    const int* img = tilted ? _tilted.ptr<int>((int)y) : _sum.ptr<int>((int)y);
-    float ret = rect[0].weight * (img[fastRect[0].p0] - img[fastRect[0].p1] - img[fastRect[0].p2] + img[fastRect[0].p3] ) +
-        rect[1].weight * (img[fastRect[1].p0] - img[fastRect[1].p1] - img[fastRect[1].p2] + img[fastRect[1].p3] );
-    if( rect[2].weight != 0.0f )
-        ret += rect[2].weight * (img[fastRect[2].p0] - img[fastRect[2].p1] - img[fastRect[2].p2] + img[fastRect[2].p3] );
-    return ret;
-}
-
-#endif

doc/文档1/复用代码/traincascade/imagestorage.cpp

@@ -1,186 +0,0 @@
-#include "opencv2/core.hpp"
-#include "opencv2/core/core_c.h"
-#include "opencv2/imgproc.hpp"
-#include "opencv2/imgcodecs.hpp"
-
-#include "imagestorage.h"
-#include <stdio.h>
-#include <iostream>
-#include <fstream>
-
-using namespace std;
-using namespace cv;
-
-bool CvCascadeImageReader::create( const string _posFilename, const string _negFilename, Size _winSize )
-{
-    return posReader.create(_posFilename) && negReader.create(_negFilename, _winSize);
-}
-
-CvCascadeImageReader::NegReader::NegReader()
-{
-    src.create( 0, 0 , CV_8UC1 );
-    img.create( 0, 0, CV_8UC1 );
-    point = offset = Point( 0, 0 );
-    scale       = 1.0F;
-    scaleFactor = 1.4142135623730950488016887242097F;
-    stepFactor  = 0.5F;
-}
-
-bool CvCascadeImageReader::NegReader::create( const string _filename, Size _winSize )
-{
-    string str;
-    std::ifstream file(_filename.c_str());
-    if ( !file.is_open() )
-        return false;
-
-    while( !file.eof() )
-    {
-        std::getline(file, str);
-        str.erase(str.find_last_not_of(" \n\r\t")+1);
-        if (str.empty()) break;
-        if (str.at(0) == '#' ) continue; /* comment */
-        imgFilenames.push_back(str);
-    }
-    file.close();
-
-    winSize = _winSize;
-    last = round = 0;
-    return true;
-}
-
-bool CvCascadeImageReader::NegReader::nextImg()
-{
-    Point _offset = Point(0,0);
-    size_t count = imgFilenames.size();
-    for( size_t i = 0; i < count; i++ )
-    {
-        src = imread( imgFilenames[last++], IMREAD_GRAYSCALE );
-        if( src.empty() ){
-            last %= count;
-            continue;
-        }
-        round += last / count;
-        round = round % (winSize.width * winSize.height);
-        last %= count;
-
-        _offset.x = std::min( (int)round % winSize.width, src.cols - winSize.width );
-        _offset.y = std::min( (int)round / winSize.width, src.rows - winSize.height );
-        if( !src.empty() && src.type() == CV_8UC1
-                && _offset.x >= 0 && _offset.y >= 0 )
-            break;
-    }
-
-    if( src.empty() )
-        return false; // no appropriate image
-    point = offset = _offset;
-    scale = max( ((float)winSize.width + point.x) / ((float)src.cols),
-                 ((float)winSize.height + point.y) / ((float)src.rows) );
-
-    Size sz( (int)(scale*src.cols + 0.5F), (int)(scale*src.rows + 0.5F) );
-    resize( src, img, sz, 0, 0, INTER_LINEAR_EXACT );
-    return true;
-}
-
-bool CvCascadeImageReader::NegReader::get( Mat& _img )
-{
-    CV_Assert( !_img.empty() );
-    CV_Assert( _img.type() == CV_8UC1 );
-    CV_Assert( _img.cols == winSize.width );
-    CV_Assert( _img.rows == winSize.height );
-
-    if( img.empty() )
-        if ( !nextImg() )
-            return false;
-
-    Mat mat( winSize.height, winSize.width, CV_8UC1,
-        (void*)(img.ptr(point.y) + point.x * img.elemSize()), img.step );
-    mat.copyTo(_img);
-
-    if( (int)( point.x + (1.0F + stepFactor ) * winSize.width ) < img.cols )
-        point.x += (int)(stepFactor * winSize.width);
-    else
-    {
-        point.x = offset.x;
-        if( (int)( point.y + (1.0F + stepFactor ) * winSize.height ) < img.rows )
-            point.y += (int)(stepFactor * winSize.height);
-        else
-        {
-            point.y = offset.y;
-            scale *= scaleFactor;
-            if( scale <= 1.0F )
-                resize( src, img, Size( (int)(scale*src.cols), (int)(scale*src.rows) ), 0, 0, INTER_LINEAR_EXACT );
-            else
-            {
-                if ( !nextImg() )
-                    return false;
-            }
-        }
-    }
-    return true;
-}
-
-CvCascadeImageReader::PosReader::PosReader()
-{
-    file = 0;
-    vec = 0;
-}
-
-bool CvCascadeImageReader::PosReader::create( const string _filename )
-{
-    if ( file )
-        fclose( file );
-    file = fopen( _filename.c_str(), "rb" );
-
-    if( !file )
-        return false;
-    short tmp = 0;
-    if( fread( &count, sizeof( count ), 1, file ) != 1 ||
-        fread( &vecSize, sizeof( vecSize ), 1, file ) != 1 ||
-        fread( &tmp, sizeof( tmp ), 1, file ) != 1 ||
-        fread( &tmp, sizeof( tmp ), 1, file ) != 1 )
-        CV_Error_( cv::Error::StsParseError, ("wrong file format for %s\n", _filename.c_str()) );
-    base = sizeof( count ) + sizeof( vecSize ) + 2*sizeof( tmp );
-    if( feof( file ) )
-        return false;
-    last = 0;
-    vec = (short*) cvAlloc( sizeof( *vec ) * vecSize );
-    CV_Assert( vec );
-    return true;
-}
-
-bool CvCascadeImageReader::PosReader::get( Mat &_img )
-{
-    CV_Assert( _img.rows * _img.cols == vecSize );
-    uchar tmp = 0;
-    size_t elements_read = fread( &tmp, sizeof( tmp ), 1, file );
-    if( elements_read != 1 )
-        CV_Error( cv::Error::StsBadArg, "Can not get new positive sample. The most possible reason is "
-                                "insufficient count of samples in given vec-file.\n");
-    elements_read = fread( vec, sizeof( vec[0] ), vecSize, file );
-    if( elements_read != (size_t)(vecSize) )
-        CV_Error( cv::Error::StsBadArg, "Can not get new positive sample. Seems that vec-file has incorrect structure.\n");
-
-    if( feof( file ) || last++ >= count )
-        CV_Error( cv::Error::StsBadArg, "Can not get new positive sample. vec-file is over.\n");
-
-    for( int r = 0; r < _img.rows; r++ )
-    {
-        for( int c = 0; c < _img.cols; c++ )
-            _img.ptr(r)[c] = (uchar)vec[r * _img.cols + c];
-    }
-    return true;
-}
-
-void CvCascadeImageReader::PosReader::restart()
-{
-    CV_Assert( file );
-    last = 0;
-    fseek( file, base, SEEK_SET );
-}
-
-CvCascadeImageReader::PosReader::~PosReader()
-{
-    if (file)
-        fclose( file );
-    cvFree( &vec );
-}

doc/文档1/复用代码/traincascade/imagestorage.h

@@ -1,50 +0,0 @@
-#ifndef _OPENCV_IMAGESTORAGE_H_
-#define _OPENCV_IMAGESTORAGE_H_
-
-
-class CvCascadeImageReader
-{
-public:
-    bool create( const std::string _posFilename, const std::string _negFilename, cv::Size _winSize );
-    void restart() { posReader.restart(); }
-    bool getNeg(cv::Mat &_img) { return negReader.get( _img ); }
-    bool getPos(cv::Mat &_img) { return posReader.get( _img ); }
-
-private:
-    class PosReader
-    {
-    public:
-        PosReader();
-        virtual ~PosReader();
-        bool create( const std::string _filename );
-        bool get( cv::Mat &_img );
-        void restart();
-
-        short* vec;
-        FILE*  file;
-        int    count;
-        int    vecSize;
-        int    last;
-        int    base;
-    } posReader;
-
-    class NegReader
-    {
-    public:
-        NegReader();
-        bool create( const std::string _filename, cv::Size _winSize );
-        bool get( cv::Mat& _img );
-        bool nextImg();
-
-        cv::Mat     src, img;
-        std::vector<std::string> imgFilenames;
-        cv::Point   offset, point;
-        float   scale;
-        float   scaleFactor;
-        float   stepFactor;
-        size_t  last, round;
-        cv::Size    winSize;
-    } negReader;
-};
-
-#endif

doc/文档1/复用代码/traincascade/lbpfeatures.cpp

@@ -1,67 +0,0 @@
-#include "opencv2/core.hpp"
-#include "opencv2/imgproc.hpp"
-
-#include "lbpfeatures.h"
-#include "cascadeclassifier.h"
-
-using namespace cv;
-
-CvLBPFeatureParams::CvLBPFeatureParams()
-{
-    maxCatCount = 256;
-    name = LBPF_NAME;
-}
-
-void CvLBPEvaluator::init(const CvFeatureParams *_featureParams, int _maxSampleCount, Size _winSize)
-{
-    CV_Assert( _maxSampleCount > 0);
-    sum.create((int)_maxSampleCount, (_winSize.width + 1) * (_winSize.height + 1), CV_32SC1);
-    CvFeatureEvaluator::init( _featureParams, _maxSampleCount, _winSize );
-}
-
-void CvLBPEvaluator::setImage(const Mat &img, uchar clsLabel, int idx)
-{
-    CV_DbgAssert( !sum.empty() );
-    CvFeatureEvaluator::setImage( img, clsLabel, idx );
-    Mat innSum(winSize.height + 1, winSize.width + 1, sum.type(), sum.ptr<int>((int)idx));
-    integral( img, innSum );
-}
-
-void CvLBPEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
-{
-    _writeFeatures( features, fs, featureMap );
-}
-
-void CvLBPEvaluator::generateFeatures()
-{
-    int offset = winSize.width + 1;
-    for( int x = 0; x < winSize.width; x++ )
-        for( int y = 0; y < winSize.height; y++ )
-            for( int w = 1; w <= winSize.width / 3; w++ )
-                for( int h = 1; h <= winSize.height / 3; h++ )
-                    if ( (x+3*w <= winSize.width) && (y+3*h <= winSize.height) )
-                        features.push_back( Feature(offset, x, y, w, h ) );
-    numFeatures = (int)features.size();
-}
-
-CvLBPEvaluator::Feature::Feature()
-{
-    rect = cvRect(0, 0, 0, 0);
-}
-
-CvLBPEvaluator::Feature::Feature( int offset, int x, int y, int _blockWidth, int _blockHeight )
-{
-    Rect tr = rect = cvRect(x, y, _blockWidth, _blockHeight);
-    CV_SUM_OFFSETS( p[0], p[1], p[4], p[5], tr, offset )
-    tr.x += 2*rect.width;
-    CV_SUM_OFFSETS( p[2], p[3], p[6], p[7], tr, offset )
-    tr.y +=2*rect.height;
-    CV_SUM_OFFSETS( p[10], p[11], p[14], p[15], tr, offset )
-    tr.x -= 2*rect.width;
-    CV_SUM_OFFSETS( p[8], p[9], p[12], p[13], tr, offset )
-}
-
-void CvLBPEvaluator::Feature::write(FileStorage &fs) const
-{
-    fs << CC_RECT << "[:" << rect.x << rect.y << rect.width << rect.height << "]";
-}

doc/文档1/复用代码/traincascade/lbpfeatures.h

@@ -1,57 +0,0 @@
-#ifndef _OPENCV_LBPFEATURES_H_
-#define _OPENCV_LBPFEATURES_H_
-
-#include "traincascade_features.h"
-
-#define LBPF_NAME "lbpFeatureParams"
-struct CvLBPFeatureParams : CvFeatureParams
-{
-    CvLBPFeatureParams();
-
-};
-
-class CvLBPEvaluator : public CvFeatureEvaluator
-{
-public:
-    virtual ~CvLBPEvaluator() {}
-    virtual void init(const CvFeatureParams *_featureParams,
-        int _maxSampleCount, cv::Size _winSize );
-    virtual void setImage(const cv::Mat& img, uchar clsLabel, int idx);
-    virtual float operator()(int featureIdx, int sampleIdx) const
-    { return (float)features[featureIdx].calc( sum, sampleIdx); }
-    virtual void writeFeatures( cv::FileStorage &fs, const cv::Mat& featureMap ) const;
-protected:
-    virtual void generateFeatures();
-
-    class Feature
-    {
-    public:
-        Feature();
-        Feature( int offset, int x, int y, int _block_w, int _block_h  );
-        uchar calc( const cv::Mat& _sum, size_t y ) const;
-        void write( cv::FileStorage &fs ) const;
-
-        cv::Rect rect;
-        int p[16];
-    };
-    std::vector<Feature> features;
-
-    cv::Mat sum;
-};
-
-inline uchar CvLBPEvaluator::Feature::calc(const cv::Mat &_sum, size_t y) const
-{
-    const int* psum = _sum.ptr<int>((int)y);
-    int cval = psum[p[5]] - psum[p[6]] - psum[p[9]] + psum[p[10]];
-
-    return (uchar)((psum[p[0]] - psum[p[1]] - psum[p[4]] + psum[p[5]] >= cval ? 128 : 0) |   // 0
-        (psum[p[1]] - psum[p[2]] - psum[p[5]] + psum[p[6]] >= cval ? 64 : 0) |    // 1
-        (psum[p[2]] - psum[p[3]] - psum[p[6]] + psum[p[7]] >= cval ? 32 : 0) |    // 2
-        (psum[p[6]] - psum[p[7]] - psum[p[10]] + psum[p[11]] >= cval ? 16 : 0) |  // 5
-        (psum[p[10]] - psum[p[11]] - psum[p[14]] + psum[p[15]] >= cval ? 8 : 0) | // 8
-        (psum[p[9]] - psum[p[10]] - psum[p[13]] + psum[p[14]] >= cval ? 4 : 0) |  // 7
-        (psum[p[8]] - psum[p[9]] - psum[p[12]] + psum[p[13]] >= cval ? 2 : 0) |   // 6
-        (psum[p[4]] - psum[p[5]] - psum[p[8]] + psum[p[9]] >= cval ? 1 : 0));     // 3
-}
-
-#endif

doc/文档1/复用代码/traincascade/old_ml_data.cpp

@@ -1,792 +0,0 @@
-/*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
-//
-// 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 "old_ml_precomp.hpp"
-#include <ctype.h>
-
-#define MISS_VAL    FLT_MAX
-#define CV_VAR_MISS    0
-
-CvTrainTestSplit::CvTrainTestSplit()
-{
-    train_sample_part_mode = CV_COUNT;
-    train_sample_part.count = -1;
-    mix = false;
-}
-
-CvTrainTestSplit::CvTrainTestSplit( int _train_sample_count, bool _mix )
-{
-    train_sample_part_mode = CV_COUNT;
-    train_sample_part.count = _train_sample_count;
-    mix = _mix;
-}
-
-CvTrainTestSplit::CvTrainTestSplit( float _train_sample_portion, bool _mix )
-{
-    train_sample_part_mode = CV_PORTION;
-    train_sample_part.portion = _train_sample_portion;
-    mix = _mix;
-}
-
-////////////////
-
-CvMLData::CvMLData()
-{
-    values = missing = var_types = var_idx_mask = response_out = var_idx_out = var_types_out = 0;
-    train_sample_idx = test_sample_idx = 0;
-    header_lines_number = 0;
-    sample_idx = 0;
-    response_idx = -1;
-
-    train_sample_count = -1;
-
-    delimiter = ',';
-    miss_ch = '?';
-    //flt_separator = '.';
-
-    rng = &cv::theRNG();
-}
-
-CvMLData::~CvMLData()
-{
-    clear();
-}
-
-void CvMLData::free_train_test_idx()
-{
-    cvReleaseMat( &train_sample_idx );
-    cvReleaseMat( &test_sample_idx );
-    sample_idx = 0;
-}
-
-void CvMLData::clear()
-{
-    class_map.clear();
-
-    cvReleaseMat( &values );
-    cvReleaseMat( &missing );
-    cvReleaseMat( &var_types );
-    cvReleaseMat( &var_idx_mask );
-
-    cvReleaseMat( &response_out );
-    cvReleaseMat( &var_idx_out );
-    cvReleaseMat( &var_types_out );
-
-    free_train_test_idx();
-
-    total_class_count = 0;
-
-    response_idx = -1;
-
-    train_sample_count = -1;
-}
-
-
-void CvMLData::set_header_lines_number( int idx )
-{
-    header_lines_number = std::max(0, idx);
-}
-
-int CvMLData::get_header_lines_number() const
-{
-    return header_lines_number;
-}
-
-static char *fgets_chomp(char *str, int n, FILE *stream)
-{
-    char *head = fgets(str, n, stream);
-    if( head )
-    {
-        for(char *tail = head + strlen(head) - 1; tail >= head; --tail)
-        {
-            if( *tail != '\r'  && *tail != '\n' )
-                break;
-            *tail = '\0';
-        }
-    }
-    return head;
-}
-
-
-int CvMLData::read_csv(const char* filename)
-{
-    const int M = 1000000;
-    const char str_delimiter[3] = { ' ', delimiter, '\0' };
-    FILE* file = 0;
-    CvMemStorage* storage;
-    CvSeq* seq;
-    char *ptr;
-    float* el_ptr;
-    CvSeqReader reader;
-    int cols_count = 0;
-    uchar *var_types_ptr = 0;
-
-    clear();
-
-    file = fopen( filename, "rt" );
-
-    if( !file )
-        return -1;
-
-    std::vector<char> _buf(M);
-    char* buf = &_buf[0];
-
-    // skip header lines
-    for( int i = 0; i < header_lines_number; i++ )
-    {
-        if( fgets( buf, M, file ) == 0 )
-        {
-            fclose(file);
-            return -1;
-        }
-    }
-
-    // read the first data line and determine the number of variables
-    if( !fgets_chomp( buf, M, file ))
-    {
-        fclose(file);
-        return -1;
-    }
-
-    ptr = buf;
-    while( *ptr == ' ' )
-        ptr++;
-    for( ; *ptr != '\0'; )
-    {
-        if(*ptr == delimiter || *ptr == ' ')
-        {
-            cols_count++;
-            ptr++;
-            while( *ptr == ' ' ) ptr++;
-        }
-        else
-            ptr++;
-    }
-
-    cols_count++;
-
-    if ( cols_count == 0)
-    {
-        fclose(file);
-        return -1;
-    }
-
-    // create temporary memory storage to store the whole database
-    el_ptr = new float[cols_count];
-    storage = cvCreateMemStorage();
-    seq = cvCreateSeq( 0, sizeof(*seq), cols_count*sizeof(float), storage );
-
-    var_types = cvCreateMat( 1, cols_count, CV_8U );
-    cvZero( var_types );
-    var_types_ptr = var_types->data.ptr;
-
-    for(;;)
-    {
-        char *token = NULL;
-        int type;
-        token = strtok(buf, str_delimiter);
-        if (!token)
-            break;
-        for (int i = 0; i < cols_count-1; i++)
-        {
-            str_to_flt_elem( token, el_ptr[i], type);
-            var_types_ptr[i] |= type;
-            token = strtok(NULL, str_delimiter);
-            if (!token)
-            {
-                fclose(file);
-                delete [] el_ptr;
-                return -1;
-            }
-        }
-        str_to_flt_elem( token, el_ptr[cols_count-1], type);
-        var_types_ptr[cols_count-1] |= type;
-        cvSeqPush( seq, el_ptr );
-        if( !fgets_chomp( buf, M, file ) )
-            break;
-    }
-    fclose(file);
-
-    values = cvCreateMat( seq->total, cols_count, CV_32FC1 );
-    missing = cvCreateMat( seq->total, cols_count, CV_8U );
-    var_idx_mask = cvCreateMat( 1, values->cols, CV_8UC1 );
-    cvSet( var_idx_mask, cvRealScalar(1) );
-    train_sample_count = seq->total;
-
-    cvStartReadSeq( seq, &reader );
-    for(int i = 0; i < seq->total; i++ )
-    {
-        const float* sdata = (float*)reader.ptr;
-        float* ddata = values->data.fl + cols_count*i;
-        uchar* dm = missing->data.ptr + cols_count*i;
-
-        for( int j = 0; j < cols_count; j++ )
-        {
-            ddata[j] = sdata[j];
-            dm[j] = ( fabs( MISS_VAL - sdata[j] ) <= FLT_EPSILON );
-        }
-        CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
-    }
-
-    if ( cvNorm( missing, 0, CV_L1 ) <= FLT_EPSILON )
-        cvReleaseMat( &missing );
-
-    cvReleaseMemStorage( &storage );
-    delete []el_ptr;
-    return 0;
-}
-
-const CvMat* CvMLData::get_values() const
-{
-    return values;
-}
-
-const CvMat* CvMLData::get_missing() const
-{
-    CV_FUNCNAME( "CvMLData::get_missing" );
-    __BEGIN__;
-
-    if ( !values )
-        CV_ERROR( cv::Error::StsInternal, "data is empty" );
-
-    __END__;
-
-    return missing;
-}
-
-const std::map<cv::String, int>& CvMLData::get_class_labels_map() const
-{
-    return class_map;
-}
-
-void CvMLData::str_to_flt_elem( const char* token, float& flt_elem, int& type)
-{
-
-    char* stopstring = NULL;
-    flt_elem = (float)strtod( token, &stopstring );
-    assert( stopstring );
-    type = CV_VAR_ORDERED;
-    if ( *stopstring == miss_ch && strlen(stopstring) == 1 ) // missed value
-    {
-        flt_elem = MISS_VAL;
-        type = CV_VAR_MISS;
-    }
-    else
-    {
-        if ( (*stopstring != 0) && (*stopstring != '\n') && (strcmp(stopstring, "\r\n") != 0) ) // class label
-        {
-            int idx = class_map[token];
-            if ( idx == 0)
-            {
-                total_class_count++;
-                idx = total_class_count;
-                class_map[token] = idx;
-            }
-            flt_elem = (float)idx;
-            type = CV_VAR_CATEGORICAL;
-        }
-    }
-}
-
-void CvMLData::set_delimiter(char ch)
-{
-    CV_FUNCNAME( "CvMLData::set_delimited" );
-    __BEGIN__;
-
-    if (ch == miss_ch /*|| ch == flt_separator*/)
-        CV_ERROR(cv::Error::StsBadArg, "delimited, miss_character and flt_separator must be different");
-
-    delimiter = ch;
-
-    __END__;
-}
-
-char CvMLData::get_delimiter() const
-{
-    return delimiter;
-}
-
-void CvMLData::set_miss_ch(char ch)
-{
-    CV_FUNCNAME( "CvMLData::set_miss_ch" );
-    __BEGIN__;
-
-    if (ch == delimiter/* || ch == flt_separator*/)
-        CV_ERROR(cv::Error::StsBadArg, "delimited, miss_character and flt_separator must be different");
-
-    miss_ch = ch;
-
-    __END__;
-}
-
-char CvMLData::get_miss_ch() const
-{
-    return miss_ch;
-}
-
-void CvMLData::set_response_idx( int idx )
-{
-    CV_FUNCNAME( "CvMLData::set_response_idx" );
-    __BEGIN__;
-
-    if ( !values )
-        CV_ERROR( cv::Error::StsInternal, "data is empty" );
-
-    if ( idx >= values->cols)
-        CV_ERROR( cv::Error::StsBadArg, "idx value is not correct" );
-
-    if ( response_idx >= 0 )
-        chahge_var_idx( response_idx, true );
-    if ( idx >= 0 )
-        chahge_var_idx( idx, false );
-    response_idx = idx;
-
-    __END__;
-}
-
-int CvMLData::get_response_idx() const
-{
-    CV_FUNCNAME( "CvMLData::get_response_idx" );
-    __BEGIN__;
-
-    if ( !values )
-        CV_ERROR( cv::Error::StsInternal, "data is empty" );
-     __END__;
-    return response_idx;
-}
-
-void CvMLData::change_var_type( int var_idx, int type )
-{
-    CV_FUNCNAME( "CvMLData::change_var_type" );
-    __BEGIN__;
-
-    int var_count = 0;
-
-    if ( !values )
-        CV_ERROR( cv::Error::StsInternal, "data is empty" );
-
-     var_count = values->cols;
-
-    if ( var_idx < 0 || var_idx >= var_count)
-        CV_ERROR( cv::Error::StsBadArg, "var_idx is not correct" );
-
-    if ( type != CV_VAR_ORDERED && type != CV_VAR_CATEGORICAL)
-         CV_ERROR( cv::Error::StsBadArg, "type is not correct" );
-
-    assert( var_types );
-    if ( var_types->data.ptr[var_idx] == CV_VAR_CATEGORICAL && type == CV_VAR_ORDERED)
-        CV_ERROR( cv::Error::StsBadArg, "it`s impossible to assign CV_VAR_ORDERED type to categorical variable" );
-    var_types->data.ptr[var_idx] = (uchar)type;
-
-    __END__;
-
-    return;
-}
-
-void CvMLData::set_var_types( const char* str )
-{
-    CV_FUNCNAME( "CvMLData::set_var_types" );
-    __BEGIN__;
-
-    const char* ord = 0, *cat = 0;
-    int var_count = 0, set_var_type_count = 0;
-    if ( !values )
-        CV_ERROR( cv::Error::StsInternal, "data is empty" );
-
-    var_count = values->cols;
-
-    assert( var_types );
-
-    ord = strstr( str, "ord" );
-    cat = strstr( str, "cat" );
-    if ( !ord && !cat )
-        CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
-
-    if ( !ord && strlen(cat) == 3 ) // str == "cat"
-    {
-        cvSet( var_types, cvScalarAll(CV_VAR_CATEGORICAL) );
-        return;
-    }
-
-    if ( !cat && strlen(ord) == 3 ) // str == "ord"
-    {
-        cvSet( var_types, cvScalarAll(CV_VAR_ORDERED) );
-        return;
-    }
-
-    if ( ord ) // parse ord str
-    {
-        char* stopstring = NULL;
-        if ( ord[3] != '[')
-            CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
-
-        ord += 4; // pass "ord["
-        do
-        {
-            int b1 = (int)strtod( ord, &stopstring );
-            if ( *stopstring == 0 || (*stopstring != ',' && *stopstring != ']' && *stopstring != '-') )
-                CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
-            ord = stopstring + 1;
-            if ( (stopstring[0] == ',') || (stopstring[0] == ']'))
-            {
-                if ( var_types->data.ptr[b1] == CV_VAR_CATEGORICAL)
-                    CV_ERROR( cv::Error::StsBadArg, "it`s impossible to assign CV_VAR_ORDERED type to categorical variable" );
-                var_types->data.ptr[b1] = CV_VAR_ORDERED;
-                set_var_type_count++;
-            }
-            else
-            {
-                if ( stopstring[0] == '-')
-                {
-                    int b2 = (int)strtod( ord, &stopstring);
-                    if ( (*stopstring == 0) || (*stopstring != ',' && *stopstring != ']') )
-                        CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
-                    ord = stopstring + 1;
-                    for (int i = b1; i <= b2; i++)
-                    {
-                        if ( var_types->data.ptr[i] == CV_VAR_CATEGORICAL)
-                            CV_ERROR( cv::Error::StsBadArg, "it`s impossible to assign CV_VAR_ORDERED type to categorical variable" );
-                        var_types->data.ptr[i] = CV_VAR_ORDERED;
-                    }
-                    set_var_type_count += b2 - b1 + 1;
-                }
-                else
-                    CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
-
-            }
-        }
-        while (*stopstring != ']');
-
-        if ( stopstring[1] != '\0' && stopstring[1] != ',')
-            CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
-    }
-
-    if ( cat ) // parse cat str
-    {
-        char* stopstring = NULL;
-        if ( cat[3] != '[')
-            CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
-
-        cat += 4; // pass "cat["
-        do
-        {
-            int b1 = (int)strtod( cat, &stopstring );
-            if ( *stopstring == 0 || (*stopstring != ',' && *stopstring != ']' && *stopstring != '-') )
-                CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
-            cat = stopstring + 1;
-            if ( (stopstring[0] == ',') || (stopstring[0] == ']'))
-            {
-                var_types->data.ptr[b1] = CV_VAR_CATEGORICAL;
-                set_var_type_count++;
-            }
-            else
-            {
-                if ( stopstring[0] == '-')
-                {
-                    int b2 = (int)strtod( cat, &stopstring);
-                    if ( (*stopstring == 0) || (*stopstring != ',' && *stopstring != ']') )
-                        CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
-                    cat = stopstring + 1;
-                    for (int i = b1; i <= b2; i++)
-                        var_types->data.ptr[i] = CV_VAR_CATEGORICAL;
-                    set_var_type_count += b2 - b1 + 1;
-                }
-                else
-                    CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
-
-            }
-        }
-        while (*stopstring != ']');
-
-        if ( stopstring[1] != '\0' && stopstring[1] != ',')
-            CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
-    }
-
-    if (set_var_type_count != var_count)
-        CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
-
-     __END__;
-}
-
-const CvMat* CvMLData::get_var_types()
-{
-    CV_FUNCNAME( "CvMLData::get_var_types" );
-    __BEGIN__;
-
-    uchar *var_types_out_ptr = 0;
-    int avcount, vt_size;
-    if ( !values )
-        CV_ERROR( cv::Error::StsInternal, "data is empty" );
-
-    assert( var_idx_mask );
-
-    avcount = cvFloor( cvNorm( var_idx_mask, 0, CV_L1 ) );
-    vt_size = avcount + (response_idx >= 0);
-
-    if ( avcount == values->cols || (avcount == values->cols-1 && response_idx == values->cols-1) )
-        return var_types;
-
-    if ( !var_types_out || ( var_types_out && var_types_out->cols != vt_size ) )
-    {
-        cvReleaseMat( &var_types_out );
-        var_types_out = cvCreateMat( 1, vt_size, CV_8UC1 );
-    }
-
-    var_types_out_ptr = var_types_out->data.ptr;
-    for( int i = 0; i < var_types->cols; i++)
-    {
-        if (i == response_idx || !var_idx_mask->data.ptr[i]) continue;
-        *var_types_out_ptr = var_types->data.ptr[i];
-        var_types_out_ptr++;
-    }
-    if ( response_idx >= 0 )
-        *var_types_out_ptr = var_types->data.ptr[response_idx];
-
-    __END__;
-
-    return var_types_out;
-}
-
-int CvMLData::get_var_type( int var_idx ) const
-{
-    return var_types->data.ptr[var_idx];
-}
-
-const CvMat* CvMLData::get_responses()
-{
-    CV_FUNCNAME( "CvMLData::get_responses_ptr" );
-    __BEGIN__;
-
-    int var_count = 0;
-
-    if ( !values )
-        CV_ERROR( cv::Error::StsInternal, "data is empty" );
-    var_count = values->cols;
-
-    if ( response_idx < 0 || response_idx >= var_count )
-       return 0;
-    if ( !response_out )
-        response_out = cvCreateMatHeader( values->rows, 1, CV_32FC1 );
-    else
-        cvInitMatHeader( response_out, values->rows, 1, CV_32FC1);
-    cvGetCol( values, response_out, response_idx );
-
-    __END__;
-
-    return response_out;
-}
-
-void CvMLData::set_train_test_split( const CvTrainTestSplit * spl)
-{
-    CV_FUNCNAME( "CvMLData::set_division" );
-    __BEGIN__;
-
-    int sample_count = 0;
-
-    if ( !values )
-        CV_ERROR( cv::Error::StsInternal, "data is empty" );
-
-    sample_count = values->rows;
-
-    float train_sample_portion;
-
-    if (spl->train_sample_part_mode == CV_COUNT)
-    {
-        train_sample_count = spl->train_sample_part.count;
-        if (train_sample_count > sample_count)
-            CV_ERROR( cv::Error::StsBadArg, "train samples count is not correct" );
-        train_sample_count = train_sample_count<=0 ? sample_count : train_sample_count;
-    }
-    else // dtype.train_sample_part_mode == CV_PORTION
-    {
-        train_sample_portion = spl->train_sample_part.portion;
-        if ( train_sample_portion > 1)
-            CV_ERROR( cv::Error::StsBadArg, "train samples count is not correct" );
-        train_sample_portion = train_sample_portion <= FLT_EPSILON ||
-            1 - train_sample_portion <= FLT_EPSILON ? 1 : train_sample_portion;
-        train_sample_count = std::max(1, cvFloor( train_sample_portion * sample_count ));
-    }
-
-    if ( train_sample_count == sample_count )
-    {
-        free_train_test_idx();
-        return;
-    }
-
-    if ( train_sample_idx && train_sample_idx->cols != train_sample_count )
-        free_train_test_idx();
-
-    if ( !sample_idx)
-    {
-        int test_sample_count = sample_count- train_sample_count;
-        sample_idx = (int*)cvAlloc( sample_count * sizeof(sample_idx[0]) );
-        for (int i = 0; i < sample_count; i++ )
-            sample_idx[i] = i;
-        train_sample_idx = cvCreateMatHeader( 1, train_sample_count, CV_32SC1 );
-        *train_sample_idx = cvMat( 1, train_sample_count, CV_32SC1, &sample_idx[0] );
-
-        CV_Assert(test_sample_count > 0);
-        test_sample_idx = cvCreateMatHeader( 1, test_sample_count, CV_32SC1 );
-        *test_sample_idx = cvMat( 1, test_sample_count, CV_32SC1, &sample_idx[train_sample_count] );
-    }
-
-    mix = spl->mix;
-    if ( mix )
-        mix_train_and_test_idx();
-
-    __END__;
-}
-
-const CvMat* CvMLData::get_train_sample_idx() const
-{
-    CV_FUNCNAME( "CvMLData::get_train_sample_idx" );
-    __BEGIN__;
-
-    if ( !values )
-        CV_ERROR( cv::Error::StsInternal, "data is empty" );
-    __END__;
-
-    return train_sample_idx;
-}
-
-const CvMat* CvMLData::get_test_sample_idx() const
-{
-    CV_FUNCNAME( "CvMLData::get_test_sample_idx" );
-    __BEGIN__;
-
-    if ( !values )
-        CV_ERROR( cv::Error::StsInternal, "data is empty" );
-    __END__;
-
-    return test_sample_idx;
-}
-
-void CvMLData::mix_train_and_test_idx()
-{
-    CV_FUNCNAME( "CvMLData::mix_train_and_test_idx" );
-    __BEGIN__;
-
-    if ( !values )
-        CV_ERROR( cv::Error::StsInternal, "data is empty" );
-    __END__;
-
-    if ( !sample_idx)
-        return;
-
-    if ( train_sample_count > 0 && train_sample_count < values->rows )
-    {
-        int n = values->rows;
-        for (int i = 0; i < n; i++)
-        {
-            int a = (*rng)(n);
-            int b = (*rng)(n);
-            int t;
-            CV_SWAP( sample_idx[a], sample_idx[b], t );
-        }
-    }
-}
-
-const CvMat* CvMLData::get_var_idx()
-{
-     CV_FUNCNAME( "CvMLData::get_var_idx" );
-    __BEGIN__;
-
-    int avcount = 0;
-
-    if ( !values )
-        CV_ERROR( cv::Error::StsInternal, "data is empty" );
-
-    assert( var_idx_mask );
-
-    avcount = cvFloor( cvNorm( var_idx_mask, 0, CV_L1 ) );
-    int* vidx;
-
-    if ( avcount == values->cols )
-        return 0;
-
-    if ( !var_idx_out || ( var_idx_out && var_idx_out->cols != avcount ) )
-    {
-        cvReleaseMat( &var_idx_out );
-        var_idx_out = cvCreateMat( 1, avcount, CV_32SC1);
-        if ( response_idx >=0 )
-            var_idx_mask->data.ptr[response_idx] = 0;
-    }
-
-    vidx = var_idx_out->data.i;
-
-    for(int i = 0; i < var_idx_mask->cols; i++)
-        if ( var_idx_mask->data.ptr[i] )
-        {
-            *vidx = i;
-            vidx++;
-        }
-
-    __END__;
-
-    return var_idx_out;
-}
-
-void CvMLData::chahge_var_idx( int vi, bool state )
-{
-    change_var_idx( vi, state );
-}
-
-void CvMLData::change_var_idx( int vi, bool state )
-{
-     CV_FUNCNAME( "CvMLData::change_var_idx" );
-    __BEGIN__;
-
-    int var_count = 0;
-
-    if ( !values )
-        CV_ERROR( cv::Error::StsInternal, "data is empty" );
-
-    var_count = values->cols;
-
-    if ( vi < 0 || vi >= var_count)
-        CV_ERROR( cv::Error::StsBadArg, "variable index is not correct" );
-
-    assert( var_idx_mask );
-    var_idx_mask->data.ptr[vi] = state;
-
-    __END__;
-}
-
-/* End of file. */

doc/文档1/复用代码/traincascade/old_ml_precomp.hpp

@@ -1,376 +0,0 @@
-/*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
-//
-// 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*/
-
-#ifndef OPENCV_PRECOMP_H
-#define OPENCV_PRECOMP_H
-
-#include "opencv2/core.hpp"
-#include "old_ml.hpp"
-#include "opencv2/core/core_c.h"
-#include "opencv2/core/utility.hpp"
-
-#include "opencv2/core/private.hpp"
-
-#include <assert.h>
-#include <float.h>
-#include <limits.h>
-#include <math.h>
-#include <stdlib.h>
-#include <stdio.h>
-#include <string.h>
-#include <time.h>
-
-#define ML_IMPL CV_IMPL
-#define __BEGIN__ __CV_BEGIN__
-#define __END__ __CV_END__
-#define EXIT __CV_EXIT__
-
-#define CV_MAT_ELEM_FLAG( mat, type, comp, vect, tflag )    \
-    (( tflag == CV_ROW_SAMPLE )                             \
-    ? (CV_MAT_ELEM( mat, type, comp, vect ))                \
-    : (CV_MAT_ELEM( mat, type, vect, comp )))
-
-/* Convert matrix to vector */
-#define ICV_MAT2VEC( mat, vdata, vstep, num )      \
-    if( MIN( (mat).rows, (mat).cols ) != 1 )       \
-        CV_ERROR( cv::Error::StsBadArg, "" );              \
-    (vdata) = ((mat).data.ptr);                    \
-    if( (mat).rows == 1 )                          \
-    {                                              \
-        (vstep) = CV_ELEM_SIZE( (mat).type );      \
-        (num) = (mat).cols;                        \
-    }                                              \
-    else                                           \
-    {                                              \
-        (vstep) = (mat).step;                      \
-        (num) = (mat).rows;                        \
-    }
-
-/* get raw data */
-#define ICV_RAWDATA( mat, flags, rdata, sstep, cstep, m, n )         \
-    (rdata) = (mat).data.ptr;                                        \
-    if( CV_IS_ROW_SAMPLE( flags ) )                                  \
-    {                                                                \
-        (sstep) = (mat).step;                                        \
-        (cstep) = CV_ELEM_SIZE( (mat).type );                        \
-        (m) = (mat).rows;                                            \
-        (n) = (mat).cols;                                            \
-    }                                                                \
-    else                                                             \
-    {                                                                \
-        (cstep) = (mat).step;                                        \
-        (sstep) = CV_ELEM_SIZE( (mat).type );                        \
-        (n) = (mat).rows;                                            \
-        (m) = (mat).cols;                                            \
-    }
-
-#define ICV_IS_MAT_OF_TYPE( mat, mat_type) \
-    (CV_IS_MAT( mat ) && CV_MAT_TYPE( mat->type ) == (mat_type) &&   \
-    (mat)->cols > 0 && (mat)->rows > 0)
-
-/*
-    uchar* data; int sstep, cstep;      - trainData->data
-    uchar* classes; int clstep; int ncl;- trainClasses
-    uchar* tmask; int tmstep; int ntm;  - typeMask
-    uchar* missed;int msstep, mcstep;   -missedMeasurements...
-    int mm, mn;                         == m,n == size,dim
-    uchar* sidx;int sistep;             - sampleIdx
-    uchar* cidx;int cistep;             - compIdx
-    int k, l;                           == n,m == dim,size (length of cidx, sidx)
-    int m, n;                           == size,dim
-*/
-#define ICV_DECLARE_TRAIN_ARGS()                                                    \
-    uchar* data;                                                                    \
-    int sstep, cstep;                                                               \
-    uchar* classes;                                                                 \
-    int clstep;                                                                     \
-    int ncl;                                                                        \
-    uchar* tmask;                                                                   \
-    int tmstep;                                                                     \
-    int ntm;                                                                        \
-    uchar* missed;                                                                  \
-    int msstep, mcstep;                                                             \
-    int mm, mn;                                                                     \
-    uchar* sidx;                                                                    \
-    int sistep;                                                                     \
-    uchar* cidx;                                                                    \
-    int cistep;                                                                     \
-    int k, l;                                                                       \
-    int m, n;                                                                       \
-                                                                                    \
-    data = classes = tmask = missed = sidx = cidx = NULL;                           \
-    sstep = cstep = clstep = ncl = tmstep = ntm = msstep = mcstep = mm = mn = 0;    \
-    sistep = cistep = k = l = m = n = 0;
-
-#define ICV_TRAIN_DATA_REQUIRED( param, flags )                                     \
-    if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) )                                  \
-    {                                                                               \
-        CV_ERROR( cv::Error::StsBadArg, "Invalid " #param " parameter" );                   \
-    }                                                                               \
-    else                                                                            \
-    {                                                                               \
-        ICV_RAWDATA( *(param), (flags), data, sstep, cstep, m, n );                 \
-        k = n;                                                                      \
-        l = m;                                                                      \
-    }
-
-#define ICV_TRAIN_CLASSES_REQUIRED( param )                                         \
-    if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) )                                  \
-    {                                                                               \
-        CV_ERROR( cv::Error::StsBadArg, "Invalid " #param " parameter" );                   \
-    }                                                                               \
-    else                                                                            \
-    {                                                                               \
-        ICV_MAT2VEC( *(param), classes, clstep, ncl );                              \
-        if( m != ncl )                                                              \
-        {                                                                           \
-            CV_ERROR( cv::Error::StsBadArg, "Unmatched sizes" );                            \
-        }                                                                           \
-    }
-
-#define ICV_ARG_NULL( param )                                                       \
-    if( (param) != NULL )                                                           \
-    {                                                                               \
-        CV_ERROR( cv::Error::StsBadArg, #param " parameter must be NULL" );                 \
-    }
-
-#define ICV_MISSED_MEASUREMENTS_OPTIONAL( param, flags )                            \
-    if( param )                                                                     \
-    {                                                                               \
-        if( !ICV_IS_MAT_OF_TYPE( param, CV_8UC1 ) )                                 \
-        {                                                                           \
-            CV_ERROR( cv::Error::StsBadArg, "Invalid " #param " parameter" );               \
-        }                                                                           \
-        else                                                                        \
-        {                                                                           \
-            ICV_RAWDATA( *(param), (flags), missed, msstep, mcstep, mm, mn );       \
-            if( mm != m || mn != n )                                                \
-            {                                                                       \
-                CV_ERROR( cv::Error::StsBadArg, "Unmatched sizes" );                        \
-            }                                                                       \
-        }                                                                           \
-    }
-
-#define ICV_COMP_IDX_OPTIONAL( param )                                              \
-    if( param )                                                                     \
-    {                                                                               \
-        if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) )                                \
-        {                                                                           \
-            CV_ERROR( cv::Error::StsBadArg, "Invalid " #param " parameter" );               \
-        }                                                                           \
-        else                                                                        \
-        {                                                                           \
-            ICV_MAT2VEC( *(param), cidx, cistep, k );                               \
-            if( k > n )                                                             \
-                CV_ERROR( cv::Error::StsBadArg, "Invalid " #param " parameter" );           \
-        }                                                                           \
-    }
-
-#define ICV_SAMPLE_IDX_OPTIONAL( param )                                            \
-    if( param )                                                                     \
-    {                                                                               \
-        if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) )                                \
-        {                                                                           \
-            CV_ERROR( cv::Error::StsBadArg, "Invalid " #param " parameter" );               \
-        }                                                                           \
-        else                                                                        \
-        {                                                                           \
-            ICV_MAT2VEC( *sampleIdx, sidx, sistep, l );                             \
-            if( l > m )                                                             \
-                CV_ERROR( cv::Error::StsBadArg, "Invalid " #param " parameter" );           \
-        }                                                                           \
-    }
-
-/****************************************************************************************/
-#define ICV_CONVERT_FLOAT_ARRAY_TO_MATRICE( array, matrice )        \
-{                                                                   \
-    CvMat a, b;                                                     \
-    int dims = (matrice)->cols;                                     \
-    int nsamples = (matrice)->rows;                                 \
-    int type = CV_MAT_TYPE((matrice)->type);                        \
-    int i, offset = dims;                                           \
-                                                                    \
-    CV_ASSERT( type == CV_32FC1 || type == CV_64FC1 );              \
-    offset *= ((type == CV_32FC1) ? sizeof(float) : sizeof(double));\
-                                                                    \
-    b = cvMat( 1, dims, CV_32FC1 );                                 \
-    cvGetRow( matrice, &a, 0 );                                     \
-    for( i = 0; i < nsamples; i++, a.data.ptr += offset )           \
-    {                                                               \
-        b.data.fl = (float*)array[i];                               \
-        CV_CALL( cvConvert( &b, &a ) );                             \
-    }                                                               \
-}
-
-/****************************************************************************************\
-*                       Auxiliary functions declarations                                 *
-\****************************************************************************************/
-
-/* Generates a set of classes centers in quantity <num_of_clusters> that are generated as
-   uniform random vectors in parallelepiped, where <data> is concentrated. Vectors in
-   <data> should have horizontal orientation. If <centers> != NULL, the function doesn't
-   allocate any memory and stores generated centers in <centers>, returns <centers>.
-   If <centers> == NULL, the function allocates memory and creates the matrice. Centers
-   are supposed to be oriented horizontally. */
-CvMat* icvGenerateRandomClusterCenters( int seed,
-                                        const CvMat* data,
-                                        int num_of_clusters,
-                                        CvMat* centers CV_DEFAULT(0));
-
-/* Fills the <labels> using <probs> by choosing the maximal probability. Outliers are
-   fixed by <oulier_tresh> and have cluster label (-1). Function also controls that there
-   weren't "empty" clusters by filling empty clusters with the maximal probability vector.
-   If probs_sums != NULL, fills it with the sums of probabilities for each sample (it is
-   useful for normalizing probabilities' matrice of FCM) */
-void icvFindClusterLabels( const CvMat* probs, float outlier_thresh, float r,
-                           const CvMat* labels );
-
-typedef struct CvSparseVecElem32f
-{
-    int idx;
-    float val;
-}
-CvSparseVecElem32f;
-
-/* Prepare training data and related parameters */
-#define CV_TRAIN_STATMODEL_DEFRAGMENT_TRAIN_DATA    1
-#define CV_TRAIN_STATMODEL_SAMPLES_AS_ROWS          2
-#define CV_TRAIN_STATMODEL_SAMPLES_AS_COLUMNS       4
-#define CV_TRAIN_STATMODEL_CATEGORICAL_RESPONSE     8
-#define CV_TRAIN_STATMODEL_ORDERED_RESPONSE         16
-#define CV_TRAIN_STATMODEL_RESPONSES_ON_OUTPUT      32
-#define CV_TRAIN_STATMODEL_ALWAYS_COPY_TRAIN_DATA   64
-#define CV_TRAIN_STATMODEL_SPARSE_AS_SPARSE         128
-
-int
-cvPrepareTrainData( const char* /*funcname*/,
-                    const CvMat* train_data, int tflag,
-                    const CvMat* responses, int response_type,
-                    const CvMat* var_idx,
-                    const CvMat* sample_idx,
-                    bool always_copy_data,
-                    const float*** out_train_samples,
-                    int* _sample_count,
-                    int* _var_count,
-                    int* _var_all,
-                    CvMat** out_responses,
-                    CvMat** out_response_map,
-                    CvMat** out_var_idx,
-                    CvMat** out_sample_idx=0 );
-
-void
-cvSortSamplesByClasses( const float** samples, const CvMat* classes,
-                        int* class_ranges, const uchar** mask CV_DEFAULT(0) );
-
-void
-cvCombineResponseMaps (CvMat*  _responses,
-                 const CvMat*  old_response_map,
-                       CvMat*  new_response_map,
-                       CvMat** out_response_map);
-
-void
-cvPreparePredictData( const CvArr* sample, int dims_all, const CvMat* comp_idx,
-                      int class_count, const CvMat* prob, float** row_sample,
-                      int as_sparse CV_DEFAULT(0) );
-
-/* copies clustering [or batch "predict"] results
-   (labels and/or centers and/or probs) back to the output arrays */
-void
-cvWritebackLabels( const CvMat* labels, CvMat* dst_labels,
-                   const CvMat* centers, CvMat* dst_centers,
-                   const CvMat* probs, CvMat* dst_probs,
-                   const CvMat* sample_idx, int samples_all,
-                   const CvMat* comp_idx, int dims_all );
-#define cvWritebackResponses cvWritebackLabels
-
-#define XML_FIELD_NAME "_name"
-cv::FileNode icvFileNodeGetChild( cv::FileNode& father, const char* name );
-cv::FileNode icvFileNodeGetChildArrayElem( cv::FileNode& father, const char* name,int index );
-cv::FileNode icvFileNodeGetNext( cv::FileNode& n, const char* name );
-
-
-void cvCheckTrainData( const CvMat* train_data, int tflag,
-                       const CvMat* missing_mask,
-                       int* var_all, int* sample_all );
-
-CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, bool check_for_duplicates=false );
-
-CvMat* cvPreprocessVarType( const CvMat* type_mask, const CvMat* var_idx,
-                            int var_all, int* response_type );
-
-CvMat* cvPreprocessOrderedResponses( const CvMat* responses,
-                const CvMat* sample_idx, int sample_all );
-
-CvMat* cvPreprocessCategoricalResponses( const CvMat* responses,
-                const CvMat* sample_idx, int sample_all,
-                CvMat** out_response_map, CvMat** class_counts=0 );
-
-const float** cvGetTrainSamples( const CvMat* train_data, int tflag,
-                   const CvMat* var_idx, const CvMat* sample_idx,
-                   int* _var_count, int* _sample_count,
-                   bool always_copy_data=false );
-
-namespace cv
-{
-    struct DTreeBestSplitFinder
-    {
-        DTreeBestSplitFinder(){ splitSize = 0, tree = 0; node = 0; }
-        DTreeBestSplitFinder( CvDTree* _tree, CvDTreeNode* _node);
-        DTreeBestSplitFinder( const DTreeBestSplitFinder& finder, Split );
-        virtual ~DTreeBestSplitFinder() {}
-        virtual void operator()(const BlockedRange& range);
-        void join( DTreeBestSplitFinder& rhs );
-        Ptr<CvDTreeSplit> bestSplit;
-        Ptr<CvDTreeSplit> split;
-        int splitSize;
-        CvDTree* tree;
-        CvDTreeNode* node;
-    };
-
-    struct ForestTreeBestSplitFinder : DTreeBestSplitFinder
-    {
-        ForestTreeBestSplitFinder() : DTreeBestSplitFinder() {}
-        ForestTreeBestSplitFinder( CvForestTree* _tree, CvDTreeNode* _node );
-        ForestTreeBestSplitFinder( const ForestTreeBestSplitFinder& finder, Split );
-        virtual void operator()(const BlockedRange& range);
-    };
-}
-
-#endif /* __ML_H__ */

doc/文档1/复用代码/traincascade/traincascade.cpp

@@ -1,129 +0,0 @@
-#include "opencv2/core.hpp"
-#include "cascadeclassifier.h"
-
-using namespace std;
-using namespace cv;
-
-/*
-traincascade.cpp is the source file of the program used for cascade training.
-User has to provide training input in form of positive and negative training images,
-and other data related to training in form of command line argument.
-*/
-int main( int argc, char* argv[] )
-{
-    CvCascadeClassifier classifier;
-    string cascadeDirName, vecName, bgName;
-    int numPos    = 2000;
-    int numNeg    = 1000;
-    int numStages = 20;
-    int numThreads = getNumThreads();
-    int precalcValBufSize = 1024,
-        precalcIdxBufSize = 1024;
-    bool baseFormatSave = false;
-    double acceptanceRatioBreakValue = -1.0;
-
-    CvCascadeParams cascadeParams;
-    CvCascadeBoostParams stageParams;
-    Ptr<CvFeatureParams> featureParams[] = { makePtr<CvHaarFeatureParams>(),
-                                             makePtr<CvLBPFeatureParams>(),
-                                             makePtr<CvHOGFeatureParams>()
-                                           };
-    int fc = sizeof(featureParams)/sizeof(featureParams[0]);
-    if( argc == 1 )
-    {
-        cout << "Usage: " << argv[0] << endl;
-        cout << "  -data <cascade_dir_name>" << endl;
-        cout << "  -vec <vec_file_name>" << endl;
-        cout << "  -bg <background_file_name>" << endl;
-        cout << "  [-numPos <number_of_positive_samples = " << numPos << ">]" << endl;
-        cout << "  [-numNeg <number_of_negative_samples = " << numNeg << ">]" << endl;
-        cout << "  [-numStages <number_of_stages = " << numStages << ">]" << endl;
-        cout << "  [-precalcValBufSize <precalculated_vals_buffer_size_in_Mb = " << precalcValBufSize << ">]" << endl;
-        cout << "  [-precalcIdxBufSize <precalculated_idxs_buffer_size_in_Mb = " << precalcIdxBufSize << ">]" << endl;
-        cout << "  [-baseFormatSave]" << endl;
-        cout << "  [-numThreads <max_number_of_threads = " << numThreads << ">]" << endl;
-        cout << "  [-acceptanceRatioBreakValue <value> = " << acceptanceRatioBreakValue << ">]" << endl;
-        cascadeParams.printDefaults();
-        stageParams.printDefaults();
-        for( int fi = 0; fi < fc; fi++ )
-            featureParams[fi]->printDefaults();
-        return 0;
-    }
-
-    for( int i = 1; i < argc; i++ )
-    {
-        bool set = false;
-        if( !strcmp( argv[i], "-data" ) )
-        {
-            cascadeDirName = argv[++i];
-        }
-        else if( !strcmp( argv[i], "-vec" ) )
-        {
-            vecName = argv[++i];
-        }
-        else if( !strcmp( argv[i], "-bg" ) )
-        {
-            bgName = argv[++i];
-        }
-        else if( !strcmp( argv[i], "-numPos" ) )
-        {
-            numPos = atoi( argv[++i] );
-        }
-        else if( !strcmp( argv[i], "-numNeg" ) )
-        {
-            numNeg = atoi( argv[++i] );
-        }
-        else if( !strcmp( argv[i], "-numStages" ) )
-        {
-            numStages = atoi( argv[++i] );
-        }
-        else if( !strcmp( argv[i], "-precalcValBufSize" ) )
-        {
-            precalcValBufSize = atoi( argv[++i] );
-        }
-        else if( !strcmp( argv[i], "-precalcIdxBufSize" ) )
-        {
-            precalcIdxBufSize = atoi( argv[++i] );
-        }
-        else if( !strcmp( argv[i], "-baseFormatSave" ) )
-        {
-            baseFormatSave = true;
-        }
-        else if( !strcmp( argv[i], "-numThreads" ) )
-        {
-          numThreads = atoi(argv[++i]);
-        }
-        else if( !strcmp( argv[i], "-acceptanceRatioBreakValue" ) )
-        {
-          acceptanceRatioBreakValue = atof(argv[++i]);
-        }
-        else if ( cascadeParams.scanAttr( argv[i], argv[i+1] ) ) { i++; }
-        else if ( stageParams.scanAttr( argv[i], argv[i+1] ) ) { i++; }
-        else if ( !set )
-        {
-            for( int fi = 0; fi < fc; fi++ )
-            {
-                set = featureParams[fi]->scanAttr(argv[i], argv[i+1]);
-                if ( !set )
-                {
-                    i++;
-                    break;
-                }
-            }
-        }
-    }
-
-    setNumThreads( numThreads );
-    classifier.train( cascadeDirName,
-                      vecName,
-                      bgName,
-                      numPos, numNeg,
-                      precalcValBufSize, precalcIdxBufSize,
-                      numStages,
-                      cascadeParams,
-                      *featureParams[cascadeParams.featureType],
-                      stageParams,
-                      baseFormatSave,
-                      acceptanceRatioBreakValue );
-    return 0;
-}

doc/文档1/复用代码/traincascade/traincascade_features.h

@@ -1,101 +0,0 @@
-#ifndef _OPENCV_FEATURES_H_
-#define _OPENCV_FEATURES_H_
-
-#include "imagestorage.h"
-#include <stdio.h>
-
-#define FEATURES "features"
-
-#define CV_SUM_OFFSETS( p0, p1, p2, p3, rect, step )                      \
-    /* (x, y) */                                                          \
-    (p0) = (rect).x + (step) * (rect).y;                                  \
-    /* (x + w, y) */                                                      \
-    (p1) = (rect).x + (rect).width + (step) * (rect).y;                   \
-    /* (x + w, y) */                                                      \
-    (p2) = (rect).x + (step) * ((rect).y + (rect).height);                \
-    /* (x + w, y + h) */                                                  \
-    (p3) = (rect).x + (rect).width + (step) * ((rect).y + (rect).height);
-
-#define CV_TILTED_OFFSETS( p0, p1, p2, p3, rect, step )                   \
-    /* (x, y) */                                                          \
-    (p0) = (rect).x + (step) * (rect).y;                                  \
-    /* (x - h, y + h) */                                                  \
-    (p1) = (rect).x - (rect).height + (step) * ((rect).y + (rect).height);\
-    /* (x + w, y + w) */                                                  \
-    (p2) = (rect).x + (rect).width + (step) * ((rect).y + (rect).width);  \
-    /* (x + w - h, y + w + h) */                                          \
-    (p3) = (rect).x + (rect).width - (rect).height                        \
-           + (step) * ((rect).y + (rect).width + (rect).height);
-
-float calcNormFactor( const cv::Mat& sum, const cv::Mat& sqSum );
-
-template<class Feature>
-void _writeFeatures( const std::vector<Feature> features, cv::FileStorage &fs, const cv::Mat& featureMap )
-{
-    fs << FEATURES << "[";
-    const cv::Mat_<int>& featureMap_ = (const cv::Mat_<int>&)featureMap;
-    for ( int fi = 0; fi < featureMap.cols; fi++ )
-        if ( featureMap_(0, fi) >= 0 )
-        {
-            fs << "{";
-            features[fi].write( fs );
-            fs << "}";
-        }
-    fs << "]";
-}
-
-class CvParams
-{
-public:
-    CvParams();
-    virtual ~CvParams() {}
-    // from|to file
-    virtual void write( cv::FileStorage &fs ) const = 0;
-    virtual bool read( const cv::FileNode &node ) = 0;
-    // from|to screen
-    virtual void printDefaults() const;
-    virtual void printAttrs() const;
-    virtual bool scanAttr( const std::string prmName, const std::string val );
-    std::string name;
-};
-
-class CvFeatureParams : public CvParams
-{
-public:
-    enum { HAAR = 0, LBP = 1, HOG = 2 };
-    CvFeatureParams();
-    virtual void init( const CvFeatureParams& fp );
-    virtual void write( cv::FileStorage &fs ) const;
-    virtual bool read( const cv::FileNode &node );
-    static cv::Ptr<CvFeatureParams> create( int featureType );
-    int maxCatCount; // 0 in case of numerical features
-    int featSize; // 1 in case of simple features (HAAR, LBP) and N_BINS(9)*N_CELLS(4) in case of Dalal's HOG features
-};
-
-class CvFeatureEvaluator
-{
-public:
-    virtual ~CvFeatureEvaluator() {}
-    virtual void init(const CvFeatureParams *_featureParams,
-                      int _maxSampleCount, cv::Size _winSize );
-    virtual void setImage(const cv::Mat& img, uchar clsLabel, int idx);
-    virtual void writeFeatures( cv::FileStorage &fs, const cv::Mat& featureMap ) const = 0;
-    virtual float operator()(int featureIdx, int sampleIdx) const = 0;
-    static cv::Ptr<CvFeatureEvaluator> create(int type);
-
-    int getNumFeatures() const { return numFeatures; }
-    int getMaxCatCount() const { return featureParams->maxCatCount; }
-    int getFeatureSize() const { return featureParams->featSize; }
-    const cv::Mat& getCls() const { return cls; }
-    float getCls(int si) const { return cls.at<float>(si, 0); }
-protected:
-    virtual void generateFeatures() = 0;
-
-    int npos, nneg;
-    int numFeatures;
-    cv::Size winSize;
-    CvFeatureParams *featureParams;
-    cv::Mat cls;
-};
-
-#endif

doc/文档1/自主开发代码/11.py

@@ -1,19 +0,0 @@
-import csv
-
-def add_quotes_to_first_column(input_file, output_file):
-    with open(input_file, mode='r', newline='', encoding='utf-8') as infile, \
-            open(output_file, mode='w', newline='', encoding='utf-8') as outfile:
-
-        reader = csv.reader(infile)
-        writer = csv.writer(outfile)
-
-        for row in reader:
-            # 在第一列的每个内容两边加上引号
-            row[0] = f'"{row[0]}"'
-            writer.writerow(row)
-
-# 使用示例
-input_csv = 'E:\机器\论文分类.csv'  # 输入的CSV文件名
-output_csv = 'F:\桌面\论文分类1.csv'  # 输出的CSV文件名
-
-add_quotes_to_first_column(input_csv, output_csv)

doc/文档1/自主开发代码/CAM.py

@@ -1,60 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Form implementation generated from reading ui file 'CAM.ui'
-#
-# Created by: PyQt5 UI code generator 5.15.9
-#
-# WARNING: Any manual changes made to this file will be lost when pyuic5 is
-# run again.  Do not edit this file unless you know what you are doing.
-
-
-from PyQt5 import QtCore, QtGui, QtWidgets
-
-
-class Ui_MainWindow(object):
-    def setupUi(self, MainWindow):
-        MainWindow.setObjectName("MainWindow")
-        MainWindow.resize(690, 600)
-        self.centralwidget = QtWidgets.QWidget(MainWindow)
-        self.centralwidget.setObjectName("centralwidget")
-        self.pushButton = QtWidgets.QPushButton(self.centralwidget)
-        self.pushButton.setGeometry(QtCore.QRect(280, 190, 111, 41))
-        self.pushButton.setObjectName("pushButton")
-        self.pushButton_2 = QtWidgets.QPushButton(self.centralwidget)
-        self.pushButton_2.setGeometry(QtCore.QRect(280, 130, 111, 41))
-        self.pushButton_2.setObjectName("pushButton_2")
-        self.comboBox = QtWidgets.QComboBox(self.centralwidget)
-        self.comboBox.setGeometry(QtCore.QRect(280, 250, 111, 41))
-        self.comboBox.setObjectName("comboBox")
-        self.comboBox.addItem("")
-        self.comboBox.addItem("")
-        self.comboBox.addItem("")
-        self.comboBox.currentIndexChanged.connect(self.on_camera_selected)  # 添加信号
-        self.pushButton_3 = QtWidgets.QPushButton(self.centralwidget)
-        self.pushButton_3.setGeometry(QtCore.QRect(280, 310, 111, 41))
-        self.pushButton_3.setObjectName("pushButton_3")
-        MainWindow.setCentralWidget(self.centralwidget)
-        self.menubar = QtWidgets.QMenuBar(MainWindow)
-        self.menubar.setGeometry(QtCore.QRect(0, 0, 690, 22))
-        self.menubar.setObjectName("menubar")
-        MainWindow.setMenuBar(self.menubar)
-        self.statusbar = QtWidgets.QStatusBar(MainWindow)
-        self.statusbar.setObjectName("statusbar")
-        MainWindow.setStatusBar(self.statusbar)
-
-        self.retranslateUi(MainWindow)
-        QtCore.QMetaObject.connectSlotsByName(MainWindow)
-
-    def on_camera_selected(self, index):
-        # 这个函数会在下拉列表的选项改变时被调用
-        self.camera_index = index  # 保存选择的索引值
-
-    def retranslateUi(self, MainWindow):
-        _translate = QtCore.QCoreApplication.translate
-        MainWindow.setWindowTitle(_translate("MainWindow", "MainWindow"))
-        self.pushButton.setText(_translate("MainWindow", "打开摄像头"))
-        self.pushButton_2.setText(_translate("MainWindow", "设置"))
-        self.comboBox.setItemText(0, _translate("MainWindow", "本地相机"))
-        self.comboBox.setItemText(1, _translate("MainWindow", "外置相机"))
-        self.comboBox.setItemText(2, _translate("MainWindow", "其他"))
-        self.pushButton_3.setText(_translate("MainWindow", "退出登录"))

doc/文档1/自主开发代码/CAM.ui

@@ -1,96 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<ui version="4.0">
- <class>MainWindow</class>
- <widget class="QMainWindow" name="MainWindow">
-  <property name="geometry">
-   <rect>
-    <x>0</x>
-    <y>0</y>
-    <width>690</width>
-    <height>600</height>
-   </rect>
-  </property>
-  <property name="windowTitle">
-   <string>MainWindow</string>
-  </property>
-  <widget class="QWidget" name="centralwidget">
-   <widget class="QPushButton" name="pushButton">
-    <property name="geometry">
-     <rect>
-      <x>280</x>
-      <y>190</y>
-      <width>111</width>
-      <height>41</height>
-     </rect>
-    </property>
-    <property name="text">
-     <string>打开摄像头</string>
-    </property>
-   </widget>
-   <widget class="QPushButton" name="pushButton_2">
-    <property name="geometry">
-     <rect>
-      <x>280</x>
-      <y>130</y>
-      <width>111</width>
-      <height>41</height>
-     </rect>
-    </property>
-    <property name="text">
-     <string>设置</string>
-    </property>
-   </widget>
-   <widget class="QComboBox" name="comboBox">
-    <property name="geometry">
-     <rect>
-      <x>280</x>
-      <y>250</y>
-      <width>111</width>
-      <height>41</height>
-     </rect>
-    </property>
-    <item>
-     <property name="text">
-      <string>本地相机</string>
-     </property>
-    </item>
-    <item>
-     <property name="text">
-      <string>外置相机</string>
-     </property>
-    </item>
-    <item>
-     <property name="text">
-      <string>其他</string>
-     </property>
-    </item>
-   </widget>
-   <widget class="QPushButton" name="pushButton_3">
-    <property name="geometry">
-     <rect>
-      <x>280</x>
-      <y>310</y>
-      <width>111</width>
-      <height>41</height>
-     </rect>
-    </property>
-    <property name="text">
-     <string>退出登录</string>
-    </property>
-   </widget>
-  </widget>
-  <widget class="QMenuBar" name="menubar">
-   <property name="geometry">
-    <rect>
-     <x>0</x>
-     <y>0</y>
-     <width>690</width>
-     <height>22</height>
-    </rect>
-   </property>
-  </widget>
-  <widget class="QStatusBar" name="statusbar"/>
- </widget>
- <resources/>
- <connections/>
-</ui>

doc/文档1/自主开发代码/Canny算子.py

@@ -1,21 +0,0 @@
-import cv2
-import matplotlib.pyplot as plt
-
-# 读取图像
-image_path = r"F:\photos\p2.jpg"
-image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
-
-# 使用 Canny 算子进行边缘检测
-canny = cv2.Canny(image, 100, 200)
-
-# 显示结果
-plt.figure(figsize=(10, 5))
-plt.subplot(1, 2, 1)
-plt.title('Original Image')
-plt.imshow(image, cmap='gray')
-
-plt.subplot(1, 2, 2)
-plt.title('Canny Edge Detection')
-plt.imshow(canny, cmap='gray')
-
-plt.show()  

doc/文档1/自主开发代码/Harris.py

@@ -1,24 +0,0 @@
-import cv2
-import numpy as np
-
-# 读取图像
-img = cv2.imread( r"F:\photos\p2.jpg")  # 替换为你的图像文件名
-gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
-# 将图像转换为浮点型
-gray = np.float32(gray)
-
-# Harris 角点检测
-dst = cv2.cornerHarris(gray, blockSize=2, ksize=3, k=0.04)
-
-# 结果进行膨胀，便于显示
-dst = cv2.dilate(dst, None)
-
-# 设定阈值
-threshold = 0.01 * dst.max()
-img[dst > threshold] = [0, 0, 255]  # 将角点标记为红色
-
-# 显示结果
-cv2.imshow('Harris Corners', img)
-cv2.waitKey(0)
-cv2.destroyAllWindows()

doc/文档1/自主开发代码/Laplacian算子.py

@@ -1,25 +0,0 @@
-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-
-# 读取图像
-image_path = r"F:\photos\p2.jpg"
-image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
-
-# 使用 Laplacian 算子进行边缘检测
-laplacian = cv2.Laplacian(image, cv2.CV_64F)
-
-# 将结果转换为 8 位无符号整数
-laplacian = np.uint8(np.absolute(laplacian))
-
-# 显示结果
-plt.figure(figsize=(10, 5))
-plt.subplot(1, 2, 1)
-plt.title('Original Image')
-plt.imshow(image, cmap='gray')
-
-plt.subplot(1, 2, 2)
-plt.title('Laplacian Edge Detection')
-plt.imshow(laplacian, cmap='gray')
-
-plt.show()

doc/文档1/自主开发代码/a.qrc

@@ -1,5 +0,0 @@
-<RCC>
-  <qresource prefix="aaa">
-    <file>MainWindow.jpg</file>
-  </qresource>
-</RCC>

doc/文档1/自主开发代码/csv转换.py

@@ -1,13 +0,0 @@
-import csv
-
-# 打开 TSV 文件进行读取
-with open('F:\passage\savedrecs (1).txt', mode='r', encoding='utf-8') as tsv_file:
-    tsv_reader = csv.reader(tsv_file, delimiter='\t')
-
-    # 打开 CSV 文件进行写入
-    with open('D:\out\output.csv', mode='w', encoding='utf-8', newline='') as csv_file:
-        csv_writer = csv.writer(csv_file, delimiter=',')
-
-        # 逐行读取 TSV 数据并写入 CSV 文件
-        for row in tsv_reader:
-            csv_writer.writerow(row)

doc/文档1/自主开发代码/csv降维2.py

@@ -1,35 +0,0 @@
-import pandas as pd
-import numpy as np
-
-# 读取CSV文件
-df = pd.read_csv("F:\机器学习\output2.csv")
-
-# 获取数据（去掉最后一列作为标签）
-data = df.iloc[:, :-1]
-# 获取标签（最后一列）
-labels = df.iloc[:, -1]
-
-
-# 定义裁剪函数
-def reshape_and_crop(row):
-    # 将行数据转换为32x30的矩阵
-    image_32x30 = np.array(row).reshape(32, 30)
-
-    # 裁剪为28x26的矩阵
-    cropped_image = image_32x30[14:18, 13:16]
-
-    # 将裁剪后的矩阵展平为一维数组
-    cropped_image_flat = cropped_image.flatten()
-
-    return cropped_image_flat
-
-
-# 对每一行应用裁剪函数
-df_cropped = data.apply(reshape_and_crop, axis=1)
-
-# 将降维后的数据与标签合并
-df_result = pd.DataFrame(df_cropped.tolist())
-df_result['Label'] = labels.values  # 添加标签作为新一列
-
-# 保存降维后的数据到新的CSV文件
-df_result.to_csv("E:\机器\out4.csv", index=False)

doc/文档1/自主开发代码/face_descriptor_from_camera.py

@@ -1,92 +0,0 @@
-# Copyright (C) 2018-2021 coneypo
-# SPDX-License-Identifier: MIT
-
-# 摄像头实时人脸特征描述子计算 / Real-time face descriptor computing
-
-import dlib         # 人脸识别的库 Dlib
-import cv2          # 图像处理的库 OpenCV
-import time
-
-# 1. Dlib 正向人脸检测器
-detector = dlib.get_frontal_face_detector()
-
-# 2. Dlib 人脸 landmark 特征点检测器
-predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
-
-# 3. Dlib Resnet 人脸识别模型，提取 128D 的特征矢量
-face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
-
-
-class Face_Descriptor:
-    def __init__(self):
-        self.frame_time = 0
-        self.frame_start_time = 0
-        self.fps = 0
-        self.frame_cnt = 0
-
-    def update_fps(self):
-        now = time.time()
-        self.frame_time = now - self.frame_start_time
-        self.fps = 1.0 / self.frame_time
-        self.frame_start_time = now
-
-    def run(self):
-        cap = cv2.VideoCapture(0)
-        cap.set(3, 480)
-        self.process(cap)
-        cap.release()
-        cv2.destroyAllWindows()
-
-    def process(self, stream):
-        while stream.isOpened():
-            flag, img_rd = stream.read()
-            self.frame_cnt+=1
-            k = cv2.waitKey(1)
-
-            print('- Frame ', self.frame_cnt, " starts:")
-
-            timestamp1 = time.time()
-            faces = detector(img_rd, 0)
-            timestamp2 = time.time()
-            print("--- Time used to `detector`:                  %s seconds ---" % (timestamp2 - timestamp1))
-
-            font = cv2.FONT_HERSHEY_SIMPLEX
-
-            # 检测到人脸
-            if len(faces) != 0:
-                for face in faces:
-                    timestamp3 = time.time()
-                    face_shape = predictor(img_rd, face)
-                    timestamp4 = time.time()
-                    print("--- Time used to `predictor`:                 %s seconds ---" % (timestamp4 - timestamp3))
-
-                    timestamp5 = time.time()
-                    face_desc = face_reco_model.compute_face_descriptor(img_rd, face_shape)
-                    timestamp6 = time.time()
-                    print("--- Time used to `compute_face_descriptor：`   %s seconds ---" % (timestamp6 - timestamp5))
-
-            # 添加说明
-            cv2.putText(img_rd, "Face descriptor", (20, 40), font, 1, (255, 255, 255), 1, cv2.LINE_AA)
-            cv2.putText(img_rd, "FPS:   " + str(self.fps.__round__(2)), (20, 100), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
-            cv2.putText(img_rd, "Faces: " + str(len(faces)), (20, 140), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
-            cv2.putText(img_rd, "S: Save current face", (20, 400), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
-            cv2.putText(img_rd, "Q: Quit", (20, 450), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
-
-            # 按下 'q' 键退出
-            if k == ord('q'):
-                break
-
-            self.update_fps()
-
-            cv2.namedWindow("camera", 1)
-            cv2.imshow("camera", img_rd)
-            print('\n')
-
-
-def main():
-    Face_Descriptor_con = Face_Descriptor()
-    Face_Descriptor_con.run()
-
-
-if __name__ == '__main__':
-    main()

doc/文档1/自主开发代码/face_reco_from_camera.py

@@ -1,225 +0,0 @@
-# Copyright (C) 2018-2021 coneypo
-# SPDX-License-Identifier: MIT
-
-# Author:   coneypo
-# Blog:     http://www.cnblogs.com/AdaminXie
-# GitHub:   https://github.com/coneypo/Dlib_face_recognition_from_camera
-# Mail:     coneypo@foxmail.com
-
-# 摄像头实时人脸识别 / Real-time face detection and recognition
-
-import dlib
-import numpy as np
-import cv2
-import pandas as pd
-import os
-import time
-import logging
-from PIL import Image, ImageDraw, ImageFont
-
-# Dlib 正向人脸检测器 / Use frontal face detector of Dlib
-detector = dlib.get_frontal_face_detector()
-
-# Dlib 人脸 landmark 特征点检测器 / Get face landmarks
-predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
-
-# Dlib Resnet 人脸识别模型，提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor
-face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
-
-
-class Face_Recognizer:
-    def __init__(self):
-        self.face_feature_known_list = []                # 用来存放所有录入人脸特征的数组 / Save the features of faces in database
-        self.face_name_known_list = []                   # 存储录入人脸名字 / Save the name of faces in database
-
-        self.current_frame_face_cnt = 0                     # 存储当前摄像头中捕获到的人脸数 / Counter for faces in current frame
-        self.current_frame_face_feature_list = []           # 存储当前摄像头中捕获到的人脸特征 / Features of faces in current frame
-        self.current_frame_face_name_list = []              # 存储当前摄像头中捕获到的所有人脸的名字 / Names of faces in current frame
-        self.current_frame_face_name_position_list = []     # 存储当前摄像头中捕获到的所有人脸的名字坐标 / Positions of faces in current frame
-
-        # Update FPS
-        self.fps = 0                    # FPS of current frame
-        self.fps_show = 0               # FPS per second
-        self.frame_start_time = 0
-        self.frame_cnt = 0
-        self.start_time = time.time()
-
-        self.font = cv2.FONT_ITALIC
-        self.font_chinese = ImageFont.truetype("simsun.ttc", 30)
-
-    # 从 "features_all.csv" 读取录入人脸特征 / Read known faces from "features_all.csv"
-    def get_face_database(self):
-        if os.path.exists("data/features_all.csv"):
-            path_features_known_csv = "data/features_all.csv"
-            csv_rd = pd.read_csv(path_features_known_csv, header=None)
-            for i in range(csv_rd.shape[0]):
-                features_someone_arr = []
-                self.face_name_known_list.append(csv_rd.iloc[i][0])
-                for j in range(1, 129):
-                    if csv_rd.iloc[i][j] == '':
-                        features_someone_arr.append('0')
-                    else:
-                        features_someone_arr.append(csv_rd.iloc[i][j])
-                self.face_feature_known_list.append(features_someone_arr)
-            logging.info("Faces in Database：%d", len(self.face_feature_known_list))
-            return 1
-        else:
-            logging.warning("'features_all.csv' not found!")
-            logging.warning("Please run 'get_faces_from_camera.py' "
-                            "and 'features_extraction_to_csv.py' before 'face_reco_from_camera.py'")
-            return 0
-
-    # 计算两个128D向量间的欧式距离 / Compute the e-distance between two 128D features
-    @staticmethod
-    def return_euclidean_distance(feature_1, feature_2):
-        feature_1 = np.array(feature_1)
-        feature_2 = np.array(feature_2)
-        dist = np.sqrt(np.sum(np.square(feature_1 - feature_2)))
-        return dist
-
-    # 更新 FPS / Update FPS of Video stream
-    def update_fps(self):
-        now = time.time()
-        # 每秒刷新 fps / Refresh fps per second
-        if str(self.start_time).split(".")[0] != str(now).split(".")[0]:
-            self.fps_show = self.fps
-        self.start_time = now
-        self.frame_time = now - self.frame_start_time
-        self.fps = 1.0 / self.frame_time
-        self.frame_start_time = now
-
-    # 生成的 cv2 window 上面添加说明文字 / PutText on cv2 window
-    def draw_note(self, img_rd):
-        cv2.putText(img_rd, "Face Recognizer", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA)
-        cv2.putText(img_rd, "Frame:  " + str(self.frame_cnt), (20, 100), self.font, 0.8, (0, 255, 0), 1,
-                    cv2.LINE_AA)
-        cv2.putText(img_rd, "FPS:    " + str(self.fps_show.__round__(2)), (20, 130), self.font, 0.8, (0, 255, 0), 1,
-                    cv2.LINE_AA)
-        cv2.putText(img_rd, "Faces:  " + str(self.current_frame_face_cnt), (20, 160), self.font, 0.8, (0, 255, 0), 1,
-                    cv2.LINE_AA)
-        cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
-
-    def draw_name(self, img_rd):
-        # 在人脸框下面写人脸名字 / Write names under rectangle
-        img = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB))
-        draw = ImageDraw.Draw(img)
-        for i in range(self.current_frame_face_cnt):
-            # cv2.putText(img_rd, self.current_frame_face_name_list[i], self.current_frame_face_name_position_list[i], self.font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
-            draw.text(xy=self.current_frame_face_name_position_list[i], text=self.current_frame_face_name_list[i], font=self.font_chinese,
-                  fill=(255, 255, 0))
-            img_rd = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
-        return img_rd
-
-    # 修改显示人名 / Show names in chinese
-    def show_chinese_name(self):
-        # Default known name: person_1, person_2, person_3
-        if self.current_frame_face_cnt >= 1:
-            # 修改录入的人脸姓名 / Modify names in face_name_known_list to chinese name
-            self.face_name_known_list[0] = '张三'.encode('utf-8').decode()
-            # self.face_name_known_list[1] = '张四'.encode('utf-8').decode()
-
-    # 处理获取的视频流，进行人脸识别 / Face detection and recognition from input video stream
-    def process(self, stream):
-        # 1. 读取存放所有人脸特征的 csv / Read known faces from "features.all.csv"
-        if self.get_face_database():
-            while stream.isOpened():
-                self.frame_cnt += 1
-                logging.debug("Frame %d starts", self.frame_cnt)
-                flag, img_rd = stream.read()
-                faces = detector(img_rd, 0)
-                kk = cv2.waitKey(1)
-                # 按下 q 键退出 / Press 'q' to quit
-                if kk == ord('q'):
-                    break
-                else:
-                    self.draw_note(img_rd)
-                    self.current_frame_face_feature_list = []
-                    self.current_frame_face_cnt = 0
-                    self.current_frame_face_name_position_list = []
-                    self.current_frame_face_name_list = []
-
-                    # 2. 检测到人脸 / Face detected in current frame
-                    if len(faces) != 0:
-                        # 3. 获取当前捕获到的图像的所有人脸的特征 / Compute the face descriptors for faces in current frame
-                        for i in range(len(faces)):
-                            shape = predictor(img_rd, faces[i])
-                            self.current_frame_face_feature_list.append(face_reco_model.compute_face_descriptor(img_rd, shape))
-                        # 4. 遍历捕获到的图像中所有的人脸 / Traversal all the faces in the database
-                        for k in range(len(faces)):
-                            logging.debug("For face %d in camera:", k+1)
-                            # 先默认所有人不认识，是 unknown / Set the default names of faces with "unknown"
-                            self.current_frame_face_name_list.append("unknown")
-
-                            # 每个捕获人脸的名字坐标 / Positions of faces captured
-                            self.current_frame_face_name_position_list.append(tuple(
-                                [faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
-
-                            # 5. 对于某张人脸，遍历所有存储的人脸特征
-                            # For every faces detected, compare the faces in the database
-                            current_frame_e_distance_list = []
-                            for i in range(len(self.face_feature_known_list)):
-                                # 如果 person_X 数据不为空
-                                if str(self.face_feature_known_list[i][0]) != '0.0':
-                                    e_distance_tmp = self.return_euclidean_distance(self.current_frame_face_feature_list[k],
-                                                                                    self.face_feature_known_list[i])
-                                    logging.debug("  With person %s, the e-distance is %f", str(i + 1), e_distance_tmp)
-                                    current_frame_e_distance_list.append(e_distance_tmp)
-                                else:
-                                    # 空数据 person_X
-                                    current_frame_e_distance_list.append(999999999)
-                            # 6. 寻找出最小的欧式距离匹配 / Find the one with minimum e-distance
-                            similar_person_num = current_frame_e_distance_list.index(min(current_frame_e_distance_list))
-                            logging.debug("Minimum e-distance with %s: %f", self.face_name_known_list[similar_person_num], min(current_frame_e_distance_list))
-
-                            if min(current_frame_e_distance_list) < 0.4:
-                                self.current_frame_face_name_list[k] = self.face_name_known_list[similar_person_num]
-                                logging.debug("Face recognition result: %s", self.face_name_known_list[similar_person_num])
-                            else:
-                                logging.debug("Face recognition result: Unknown person")
-                            logging.debug("\n")
-
-                            # 矩形框 / Draw rectangle
-                            for kk, d in enumerate(faces):
-                                # 绘制矩形框
-                                cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]),
-                                              (255, 255, 255), 2)
-
-                        self.current_frame_face_cnt = len(faces)
-
-                        # 7. 在这里更改显示的人名 / Modify name if needed
-                        # self.show_chinese_name()
-
-                        # 8. 写名字 / Draw name
-                        img_with_name = self.draw_name(img_rd)
-
-                    else:
-                        img_with_name = img_rd
-
-                logging.debug("Faces in camera now: %s", self.current_frame_face_name_list)
-
-                cv2.imshow("camera", img_with_name)
-
-                # 9. 更新 FPS / Update stream FPS
-                self.update_fps()
-                logging.debug("Frame ends\n\n")
-
-    # OpenCV 调用摄像头并进行 process
-    def run(self):
-        # cap = cv2.VideoCapture("video.mp4")  # Get video stream from video file
-        cap = cv2.VideoCapture(0)              # Get video stream from camera
-        cap.set(3, 480)                        # 640x480
-        self.process(cap)
-
-        cap.release()
-        cv2.destroyAllWindows()
-
-
-def main():
-    # logging.basicConfig(level=logging.DEBUG) # Set log level to 'logging.DEBUG' to print debug info of every frame
-    logging.basicConfig(level=logging.INFO)
-    Face_Recognizer_con = Face_Recognizer()
-    Face_Recognizer_con.run()
-
-
-if __name__ == '__main__':
-    main()

doc/文档1/自主开发代码/face_reco_from_camera_ot.py

@@ -1,306 +0,0 @@
-# Copyright (C) 2018-2021 coneypo
-# SPDX-License-Identifier: MIT
-
-# Author:   coneypo
-# Blog:     http://www.cnblogs.com/AdaminXie
-# GitHub:   https://github.com/coneypo/Dlib_face_recognition_from_camera
-# Mail:     coneypo@foxmail.com
-
-# 利用 OT 人脸追踪, 进行人脸实时识别 / Real-time face detection and recognition via OT for multi faces
-# 检测 -> 识别人脸, 新人脸出现 -> 不需要识别, 而是利用质心追踪来判断识别结果 / Do detection -> recognize face, new face -> not do re-recognition
-# 人脸进行再识别需要花费大量时间, 这里用 OT 做跟踪 / Do re-recognition for multi faces will cost much time, OT will be used to instead it
-
-import dlib
-import numpy as np
-import cv2
-import os
-import pandas as pd
-import time
-import logging
-
-# Dlib 正向人脸检测器 / Use frontal face detector of Dlib
-detector = dlib.get_frontal_face_detector()
-
-# Dlib 人脸 landmark 特征点检测器 / Get face landmarks
-predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
-
-# Dlib Resnet 人脸识别模型, 提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor
-face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
-
-
-class Face_Recognizer:
-    def __init__(self):
-        self.font = cv2.FONT_ITALIC
-
-        # FPS
-        self.frame_time = 0
-        self.frame_start_time = 0
-        self.fps = 0
-        self.fps_show = 0
-        self.start_time = time.time()
-
-        # cnt for frame
-        self.frame_cnt = 0
-
-        # 用来存放所有录入人脸特征的数组 / Save the features of faces in the database
-        self.face_features_known_list = []
-        # 存储录入人脸名字 / Save the name of faces in the database
-        self.face_name_known_list = []
-
-        # 用来存储上一帧和当前帧 ROI 的质心坐标 / List to save centroid positions of ROI in frame N-1 and N
-        self.last_frame_face_centroid_list = []
-        self.current_frame_face_centroid_list = []
-
-        # 用来存储上一帧和当前帧检测出目标的名字 / List to save names of objects in frame N-1 and N
-        self.last_frame_face_name_list = []
-        self.current_frame_face_name_list = []
-
-        # 上一帧和当前帧中人脸数的计数器 / cnt for faces in frame N-1 and N
-        self.last_frame_face_cnt = 0
-        self.current_frame_face_cnt = 0
-
-        # 用来存放进行识别时候对比的欧氏距离 / Save the e-distance for faceX when recognizing
-        self.current_frame_face_X_e_distance_list = []
-
-        # 存储当前摄像头中捕获到的所有人脸的坐标名字 / Save the positions and names of current faces captured
-        self.current_frame_face_position_list = []
-        # 存储当前摄像头中捕获到的人脸特征 / Save the features of people in current frame
-        self.current_frame_face_feature_list = []
-
-        # e distance between centroid of ROI in last and current frame
-        self.last_current_frame_centroid_e_distance = 0
-
-        # 控制再识别的后续帧数 / Reclassify after 'reclassify_interval' frames
-        # 如果识别出 "unknown" 的脸, 将在 reclassify_interval_cnt 计数到 reclassify_interval 后, 对于人脸进行重新识别
-        self.reclassify_interval_cnt = 0
-        self.reclassify_interval = 10
-
-    # 从 "features_all.csv" 读取录入人脸特征 / Get known faces from "features_all.csv"
-    def get_face_database(self):
-        if os.path.exists("data/features_all.csv"):
-            path_features_known_csv = "data/features_all.csv"
-            csv_rd = pd.read_csv(path_features_known_csv, header=None)
-            for i in range(csv_rd.shape[0]):
-                features_someone_arr = []
-                self.face_name_known_list.append(csv_rd.iloc[i][0])
-                for j in range(1, 129):
-                    if csv_rd.iloc[i][j] == '':
-                        features_someone_arr.append('0')
-                    else:
-                        features_someone_arr.append(csv_rd.iloc[i][j])
-                self.face_features_known_list.append(features_someone_arr)
-            logging.info("Faces in Database： %d", len(self.face_features_known_list))
-            return 1
-        else:
-            logging.warning("'features_all.csv' not found!")
-            logging.warning("Please run 'get_faces_from_camera.py' "
-                            "and 'features_extraction_to_csv.py' before 'face_reco_from_camera.py'")
-            return 0
-
-    def update_fps(self):
-        now = time.time()
-        # 每秒刷新 fps / Refresh fps per second
-        if str(self.start_time).split(".")[0] != str(now).split(".")[0]:
-            self.fps_show = self.fps
-        self.start_time = now
-        self.frame_time = now - self.frame_start_time
-        self.fps = 1.0 / self.frame_time
-        self.frame_start_time = now
-
-    @staticmethod
-    # 计算两个128D向量间的欧式距离 / Compute the e-distance between two 128D features
-    def return_euclidean_distance(feature_1, feature_2):
-        feature_1 = np.array(feature_1)
-        feature_2 = np.array(feature_2)
-        dist = np.sqrt(np.sum(np.square(feature_1 - feature_2)))
-        return dist
-
-    # 使用质心追踪来识别人脸 / Use centroid tracker to link face_x in current frame with person_x in last frame
-    def centroid_tracker(self):
-        for i in range(len(self.current_frame_face_centroid_list)):
-            e_distance_current_frame_person_x_list = []
-            # 对于当前帧中的人脸1, 和上一帧中的 人脸1/2/3/4/.. 进行欧氏距离计算 / For object 1 in current_frame, compute e-distance with object 1/2/3/4/... in last frame
-            for j in range(len(self.last_frame_face_centroid_list)):
-                self.last_current_frame_centroid_e_distance = self.return_euclidean_distance(
-                    self.current_frame_face_centroid_list[i], self.last_frame_face_centroid_list[j])
-
-                e_distance_current_frame_person_x_list.append(
-                    self.last_current_frame_centroid_e_distance)
-
-            last_frame_num = e_distance_current_frame_person_x_list.index(
-                min(e_distance_current_frame_person_x_list))
-            self.current_frame_face_name_list[i] = self.last_frame_face_name_list[last_frame_num]
-
-    # 生成的 cv2 window 上面添加说明文字 / putText on cv2 window
-    def draw_note(self, img_rd):
-        # 添加说明 / Add some info on windows
-        cv2.putText(img_rd, "Face Recognizer with OT", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA)
-        cv2.putText(img_rd, "Frame:  " + str(self.frame_cnt), (20, 100), self.font, 0.8, (0, 255, 0), 1,
-                    cv2.LINE_AA)
-        cv2.putText(img_rd, "FPS:    " + str(self.fps.__round__(2)), (20, 130), self.font, 0.8, (0, 255, 0), 1,
-                    cv2.LINE_AA)
-        cv2.putText(img_rd, "Faces:  " + str(self.current_frame_face_cnt), (20, 160), self.font, 0.8, (0, 255, 0), 1,
-                    cv2.LINE_AA)
-        cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
-
-        for i in range(len(self.current_frame_face_name_list)):
-            img_rd = cv2.putText(img_rd, "Face_" + str(i + 1), tuple(
-                [int(self.current_frame_face_centroid_list[i][0]), int(self.current_frame_face_centroid_list[i][1])]),
-                                 self.font,
-                                 0.8, (255, 190, 0),
-                                 1,
-                                 cv2.LINE_AA)
-
-    # 处理获取的视频流, 进行人脸识别 / Face detection and recognition wit OT from input video stream
-    def process(self, stream):
-        # 1. 读取存放所有人脸特征的 csv / Get faces known from "features.all.csv"
-        if self.get_face_database():
-            while stream.isOpened():
-                self.frame_cnt += 1
-                logging.debug("Frame " + str(self.frame_cnt) + " starts")
-                flag, img_rd = stream.read()
-                kk = cv2.waitKey(1)
-
-                # 2. 检测人脸 / Detect faces for frame X
-                faces = detector(img_rd, 0)
-
-                # 3. 更新人脸计数器 / Update cnt for faces in frames
-                self.last_frame_face_cnt = self.current_frame_face_cnt
-                self.current_frame_face_cnt = len(faces)
-
-                # 4. 更新上一帧中的人脸列表 / Update the face name list in last frame
-                self.last_frame_face_name_list = self.current_frame_face_name_list[:]
-
-                # 5. 更新上一帧和当前帧的质心列表 / update frame centroid list
-                self.last_frame_face_centroid_list = self.current_frame_face_centroid_list
-                self.current_frame_face_centroid_list = []
-
-                # 6.1 如果当前帧和上一帧人脸数没有变化 / if cnt not changes
-                if (self.current_frame_face_cnt == self.last_frame_face_cnt) and (
-                        self.reclassify_interval_cnt != self.reclassify_interval):
-                    logging.debug("scene 1: 当前帧和上一帧相比没有发生人脸数变化 / No face cnt changes in this frame!!!")
-
-                    self.current_frame_face_position_list = []
-
-                    if "unknown" in self.current_frame_face_name_list:
-                        logging.debug("  有未知人脸, 开始进行 reclassify_interval_cnt 计数")
-                        self.reclassify_interval_cnt += 1
-
-                    if self.current_frame_face_cnt != 0:
-                        for k, d in enumerate(faces):
-                            self.current_frame_face_position_list.append(tuple(
-                                [faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
-                            self.current_frame_face_centroid_list.append(
-                                [int(faces[k].left() + faces[k].right()) / 2,
-                                 int(faces[k].top() + faces[k].bottom()) / 2])
-
-                            img_rd = cv2.rectangle(img_rd,
-                                                   tuple([d.left(), d.top()]),
-                                                   tuple([d.right(), d.bottom()]),
-                                                   (255, 255, 255), 2)
-
-                    # 如果当前帧中有多个人脸, 使用质心追踪 / Multi-faces in current frame, use centroid-tracker to track
-                    if self.current_frame_face_cnt != 1:
-                        self.centroid_tracker()
-
-                    for i in range(self.current_frame_face_cnt):
-                        # 6.2 Write names under ROI
-                        img_rd = cv2.putText(img_rd, self.current_frame_face_name_list[i],
-                                             self.current_frame_face_position_list[i], self.font, 0.8, (0, 255, 255), 1,
-                                             cv2.LINE_AA)
-                    self.draw_note(img_rd)
-
-                # 6.2 如果当前帧和上一帧人脸数发生变化 / If cnt of faces changes, 0->1 or 1->0 or ...
-                else:
-                    logging.debug("scene 2: 当前帧和上一帧相比人脸数发生变化 / Faces cnt changes in this frame")
-                    self.current_frame_face_position_list = []
-                    self.current_frame_face_X_e_distance_list = []
-                    self.current_frame_face_feature_list = []
-                    self.reclassify_interval_cnt = 0
-
-                    # 6.2.1 人脸数减少 / Face cnt decreases: 1->0, 2->1, ...
-                    if self.current_frame_face_cnt == 0:
-                        logging.debug("  scene 2.1 人脸消失, 当前帧中没有人脸 / No faces in this frame!!!")
-                        # clear list of names and features
-                        self.current_frame_face_name_list = []
-                    # 6.2.2 人脸数增加 / Face cnt increase: 0->1, 0->2, ..., 1->2, ...
-                    else:
-                        logging.debug("  scene 2.2 出现人脸, 进行人脸识别 / Get faces in this frame and do face recognition")
-                        self.current_frame_face_name_list = []
-                        for i in range(len(faces)):
-                            shape = predictor(img_rd, faces[i])
-                            self.current_frame_face_feature_list.append(
-                                face_reco_model.compute_face_descriptor(img_rd, shape))
-                            self.current_frame_face_name_list.append("unknown")
-
-                        # 6.2.2.1 遍历捕获到的图像中所有的人脸 / Traversal all the faces in the database
-                        for k in range(len(faces)):
-                            logging.debug("  For face %d in current frame:", k + 1)
-                            self.current_frame_face_centroid_list.append(
-                                [int(faces[k].left() + faces[k].right()) / 2,
-                                 int(faces[k].top() + faces[k].bottom()) / 2])
-
-                            self.current_frame_face_X_e_distance_list = []
-
-                            # 6.2.2.2 每个捕获人脸的名字坐标 / Positions of faces captured
-                            self.current_frame_face_position_list.append(tuple(
-                                [faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
-
-                            # 6.2.2.3 对于某张人脸, 遍历所有存储的人脸特征
-                            # For every faces detected, compare the faces in the database
-                            for i in range(len(self.face_features_known_list)):
-                                # 如果 q 数据不为空
-                                if str(self.face_features_known_list[i][0]) != '0.0':
-                                    e_distance_tmp = self.return_euclidean_distance(
-                                        self.current_frame_face_feature_list[k],
-                                        self.face_features_known_list[i])
-                                    logging.debug("      with person %d, the e-distance: %f", i + 1, e_distance_tmp)
-                                    self.current_frame_face_X_e_distance_list.append(e_distance_tmp)
-                                else:
-                                    # 空数据 person_X
-                                    self.current_frame_face_X_e_distance_list.append(999999999)
-
-                            # 6.2.2.4 寻找出最小的欧式距离匹配 / Find the one with minimum e distance
-                            similar_person_num = self.current_frame_face_X_e_distance_list.index(
-                                min(self.current_frame_face_X_e_distance_list))
-
-                            if min(self.current_frame_face_X_e_distance_list) < 0.4:
-                                self.current_frame_face_name_list[k] = self.face_name_known_list[similar_person_num]
-                                logging.debug("  Face recognition result: %s",
-                                              self.face_name_known_list[similar_person_num])
-                            else:
-                                logging.debug("  Face recognition result: Unknown person")
-
-                        # 7. 生成的窗口添加说明文字 / Add note on cv2 window
-                        self.draw_note(img_rd)
-
-                        # cv2.imwrite("debug/debug_" + str(self.frame_cnt) + ".png", img_rd) # Dump current frame image if needed
-
-                # 8. 按下 'q' 键退出 / Press 'q' to exit
-                if kk == ord('q'):
-                    break
-
-                self.update_fps()
-                cv2.namedWindow("camera", 1)
-                cv2.imshow("camera", img_rd)
-
-                logging.debug("Frame ends\n\n")
-
-    def run(self):
-        # cap = cv2.VideoCapture("video.mp4")  # Get video stream from video file
-        cap = cv2.VideoCapture(0)              # Get video stream from camera
-        self.process(cap)
-
-        cap.release()
-        cv2.destroyAllWindows()
-
-
-def main():
-    # logging.basicConfig(level=logging.DEBUG) # Set log level to 'logging.DEBUG' to print debug info of every frame
-    logging.basicConfig(level=logging.INFO)
-    Face_Recognizer_con = Face_Recognizer()
-    Face_Recognizer_con.run()
-
-
-if __name__ == '__main__':
-    main()

doc/文档1/自主开发代码/face_reco_from_camera_single_face.py

@@ -1,329 +0,0 @@
-# Copyright (C) 2018-2021 coneypo
-# SPDX-License-Identifier: MIT
-
-# Author:   coneypo
-# Blog:     http://www.cnblogs.com/AdaminXie
-# GitHub:   https://github.com/coneypo/Dlib_face_recognition_from_camera
-# Mail:     coneypo@foxmail.com
-
-# 单张人脸实时识别 / Real-time face detection and recognition for single face
-# 检测 -> 识别人脸, 新人脸出现 -> 再识别, 不会对于每一帧都进行识别 / Do detection -> recognize face, new face -> do re-recognition
-# 其实对于单张人脸, 不需要 OT 进行跟踪, 对于新出现的人脸, 再识别一次就好了 / No OT here, OT will be used only for multi faces
-
-import dlib
-import numpy as np
-import cv2
-import os
-import pandas as pd
-import time
-from PIL import Image, ImageDraw, ImageFont
-import logging
-
-# Dlib 正向人脸检测器 / Use frontal face detector of Dlib
-detector = dlib.get_frontal_face_detector()
-
-# Dlib 人脸 landmark 特征点检测器 / Get face landmarks
-predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
-
-# Dlib Resnet 人脸识别模型, 提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor
-face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
-
-
-class Face_Recognizer:
-    def __init__(self):
-        self.font = cv2.FONT_ITALIC
-        self.font_chinese = ImageFont.truetype("simsun.ttc", 30)
-
-        # 统计 FPS / For FPS
-        self.frame_time = 0
-        self.frame_start_time = 0
-        self.fps = 0
-        self.fps_show = 0
-        self.start_time = time.time()
-
-        # 统计帧数 / cnt for frame
-        self.frame_cnt = 0
-
-        # 用来存储所有录入人脸特征的数组 / Save the features of faces in the database
-        self.features_known_list = []
-        # 用来存储录入人脸名字 / Save the name of faces in the database
-        self.face_name_known_list = []
-
-        # 用来存储上一帧和当前帧 ROI 的质心坐标 / List to save centroid positions of ROI in frame N-1 and N
-        self.last_frame_centroid_list = []
-        self.current_frame_centroid_list = []
-
-        # 用来存储当前帧检测出目标的名字 / List to save names of objects in current frame
-        self.current_frame_name_list = []
-
-        # 上一帧和当前帧中人脸数的计数器 / cnt for faces in frame N-1 and N
-        self.last_frame_faces_cnt = 0
-        self.current_frame_face_cnt = 0
-
-        # 用来存放进行识别时候对比的欧氏距离 / Save the e-distance for faceX when recognizing
-        self.current_frame_face_X_e_distance_list = []
-
-        # 存储当前摄像头中捕获到的所有人脸的坐标名字 / Save the positions and names of current faces captured
-        self.current_frame_face_position_list = []
-        # 存储当前摄像头中捕获到的人脸特征 / Save the features of people in current frame
-        self.current_frame_face_feature_list = []
-
-        # 控制再识别的后续帧数 / Reclassify after 'reclassify_interval' frames
-        # 如果识别出 "unknown" 的脸, 将在 reclassify_interval_cnt 计数到 reclassify_interval 后, 对于人脸进行重新识别
-        self.reclassify_interval_cnt = 0
-        self.reclassify_interval = 10
-
-    # 从 "features_all.csv" 读取录入人脸特征 / Get known faces from "features_all.csv"
-    def get_face_database(self):
-        if os.path.exists("data/features_all.csv"):
-            path_features_known_csv = "data/features_all.csv"
-            csv_rd = pd.read_csv(path_features_known_csv, header=None)
-            for i in range(csv_rd.shape[0]):
-                features_someone_arr = []
-                self.face_name_known_list.append(csv_rd.iloc[i][0])
-                for j in range(1, 129):
-                    if csv_rd.iloc[i][j] == '':
-                        features_someone_arr.append('0')
-                    else:
-                        features_someone_arr.append(csv_rd.iloc[i][j])
-                self.features_known_list.append(features_someone_arr)
-            logging.info("Faces in Database： %d", len(self.features_known_list))
-            return 1
-        else:
-            logging.warning("'features_all.csv' not found!")
-            logging.warning("Please run 'get_faces_from_camera.py' "
-                            "and 'features_extraction_to_csv.py' before 'face_reco_from_camera.py'")
-            return 0
-
-    # 获取处理之后 stream 的帧数 / Update FPS of video stream
-    def update_fps(self):
-        now = time.time()
-        # 每秒刷新 fps / Refresh fps per second
-        if str(self.start_time).split(".")[0] != str(now).split(".")[0]:
-            self.fps_show = self.fps
-        self.start_time = now
-        self.frame_time = now - self.frame_start_time
-        self.fps = 1.0 / self.frame_time
-        self.frame_start_time = now
-
-    # 计算两个128D向量间的欧式距离 / Compute the e-distance between two 128D features
-    @staticmethod
-    def return_euclidean_distance(feature_1, feature_2):
-        feature_1 = np.array(feature_1)
-        feature_2 = np.array(feature_2)
-        dist = np.sqrt(np.sum(np.square(feature_1 - feature_2)))
-        return dist
-
-    # 生成的 cv2 window 上面添加说明文字 / putText on cv2 window
-    def draw_note(self, img_rd):
-        # 添加说明 (Add some statements
-        cv2.putText(img_rd, "Face Recognizer for single face", (20, 40), self.font, 1, (255, 255, 255), 1,
-                    cv2.LINE_AA)
-        cv2.putText(img_rd, "Frame:  " + str(self.frame_cnt), (20, 100), self.font, 0.8, (0, 255, 0), 1,
-                    cv2.LINE_AA)
-        cv2.putText(img_rd, "FPS:    " + str(self.fps_show.__round__(2)), (20, 130), self.font, 0.8, (0, 255, 0), 1,
-                    cv2.LINE_AA)
-        cv2.putText(img_rd, "Faces:  " + str(self.current_frame_face_cnt), (20, 160), self.font, 0.8, (0, 255, 0), 1,
-                    cv2.LINE_AA)
-        cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
-
-    def draw_name(self, img_rd):
-        # 在人脸框下面写人脸名字 / Write names under ROI
-        logging.debug(self.current_frame_name_list)
-        img = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB))
-        draw = ImageDraw.Draw(img)
-        draw.text(xy=self.current_frame_face_position_list[0], text=self.current_frame_name_list[0], font=self.font_chinese,
-                  fill=(255, 255, 0))
-        img_rd = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
-        return img_rd
-
-    def show_chinese_name(self):
-        if self.current_frame_face_cnt >= 1:
-            logging.debug(self.face_name_known_list)
-            # 修改录入的人脸姓名 / Modify names in face_name_known_list to chinese name
-            self.face_name_known_list[0] = '张三'.encode('utf-8').decode()
-            # self.face_name_known_list[1] = '张四'.encode('utf-8').decode()
-
-    # 处理获取的视频流, 进行人脸识别 / Face detection and recognition wit OT from input video stream
-    def process(self, stream):
-        # 1. 读取存放所有人脸特征的 csv / Get faces known from "features.all.csv"
-        if self.get_face_database():
-            while stream.isOpened():
-                self.frame_cnt += 1
-                logging.debug("Frame " + str(self.frame_cnt) + " starts")
-                flag, img_rd = stream.read()
-                kk = cv2.waitKey(1)
-
-                # 2. 检测人脸 / Detect faces for frame X
-                faces = detector(img_rd, 0)
-
-                # 3. 更新帧中的人脸数 / Update cnt for faces in frames
-                self.last_frame_faces_cnt = self.current_frame_face_cnt
-                self.current_frame_face_cnt = len(faces)
-
-                # 4.1 当前帧和上一帧相比没有发生人脸数变化 / If cnt not changes, 1->1 or 0->0
-                if self.current_frame_face_cnt == self.last_frame_faces_cnt:
-                    logging.debug("scene 1: 当前帧和上一帧相比没有发生人脸数变化 / No face cnt changes in this frame!!!")
-
-                    if "unknown" in self.current_frame_name_list:
-                        logging.debug("   >>> 有未知人脸, 开始进行 reclassify_interval_cnt 计数")
-                        self.reclassify_interval_cnt += 1
-
-                    # 4.1.1 当前帧一张人脸 / One face in this frame
-                    if self.current_frame_face_cnt == 1:
-                        if self.reclassify_interval_cnt == self.reclassify_interval:
-                            logging.debug("  scene 1.1 需要对于当前帧重新进行人脸识别 / Re-classify for current frame")
-
-                            self.reclassify_interval_cnt = 0
-                            self.current_frame_face_feature_list = []
-                            self.current_frame_face_X_e_distance_list = []
-                            self.current_frame_name_list = []
-
-                            for i in range(len(faces)):
-                                shape = predictor(img_rd, faces[i])
-                                self.current_frame_face_feature_list.append(
-                                    face_reco_model.compute_face_descriptor(img_rd, shape))
-
-                            # a. 遍历捕获到的图像中所有的人脸 / Traversal all the faces in the database
-                            for k in range(len(faces)):
-                                self.current_frame_name_list.append("unknown")
-
-                                # b. 每个捕获人脸的名字坐标 / Positions of faces captured
-                                self.current_frame_face_position_list.append(tuple(
-                                    [faces[k].left(),
-                                     int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
-
-                                # c. 对于某张人脸, 遍历所有存储的人脸特征 / For every face detected, compare it with all the faces in the database
-                                for i in range(len(self.features_known_list)):
-                                    # 如果 person_X 数据不为空 / If the data of person_X is not empty
-                                    if str(self.features_known_list[i][0]) != '0.0':
-                                        e_distance_tmp = self.return_euclidean_distance(
-                                            self.current_frame_face_feature_list[k],
-                                            self.features_known_list[i])
-                                        logging.debug("    with person %d, the e-distance: %f", i + 1, e_distance_tmp)
-                                        self.current_frame_face_X_e_distance_list.append(e_distance_tmp)
-                                    else:
-                                        # 空数据 person_X / For empty data
-                                        self.current_frame_face_X_e_distance_list.append(999999999)
-
-                                # d. 寻找出最小的欧式距离匹配 / Find the one with minimum e distance
-                                similar_person_num = self.current_frame_face_X_e_distance_list.index(
-                                    min(self.current_frame_face_X_e_distance_list))
-
-                                if min(self.current_frame_face_X_e_distance_list) < 0.4:
-                                    # 在这里更改显示的人名 / Modify name if needed
-                                    self.show_chinese_name()
-                                    self.current_frame_name_list[k] = self.face_name_known_list[similar_person_num]
-                                    logging.debug("    recognition result for face %d: %s", k + 1,
-                                                  self.face_name_known_list[similar_person_num])
-                                else:
-                                    logging.debug("    recognition result for face %d: %s", k + 1, "unknown")
-                        else:
-                            logging.debug(
-                                "  scene 1.2 不需要对于当前帧重新进行人脸识别 / No re-classification needed for current frame")
-                            # 获取特征框坐标 / Get ROI positions
-                            for k, d in enumerate(faces):
-                                cv2.rectangle(img_rd,
-                                              tuple([d.left(), d.top()]),
-                                              tuple([d.right(), d.bottom()]),
-                                              (255, 255, 255), 2)
-
-                                self.current_frame_face_position_list[k] = tuple(
-                                    [faces[k].left(),
-                                     int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)])
-
-                                img_rd = self.draw_name(img_rd)
-
-                # 4.2 当前帧和上一帧相比发生人脸数变化 / If face cnt changes, 1->0 or 0->1
-                else:
-                    logging.debug("scene 2: 当前帧和上一帧相比人脸数发生变化 / Faces cnt changes in this frame")
-                    self.current_frame_face_position_list = []
-                    self.current_frame_face_X_e_distance_list = []
-                    self.current_frame_face_feature_list = []
-
-                    # 4.2.1 人脸数从 0->1 / Face cnt 0->1
-                    if self.current_frame_face_cnt == 1:
-                        logging.debug("  scene 2.1 出现人脸, 进行人脸识别 / Get faces in this frame and do face recognition")
-                        self.current_frame_name_list = []
-
-                        for i in range(len(faces)):
-                            shape = predictor(img_rd, faces[i])
-                            self.current_frame_face_feature_list.append(
-                                face_reco_model.compute_face_descriptor(img_rd, shape))
-
-                        # a. 遍历捕获到的图像中所有的人脸 / Traversal all the faces in the database
-                        for k in range(len(faces)):
-                            self.current_frame_name_list.append("unknown")
-
-                            # b. 每个捕获人脸的名字坐标 / Positions of faces captured
-                            self.current_frame_face_position_list.append(tuple(
-                                [faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
-
-                            # c. 对于某张人脸, 遍历所有存储的人脸特征 / For every face detected, compare it with all the faces in database
-                            for i in range(len(self.features_known_list)):
-                                # 如果 person_X 数据不为空 / If data of person_X is not empty
-                                if str(self.features_known_list[i][0]) != '0.0':
-                                    e_distance_tmp = self.return_euclidean_distance(
-                                        self.current_frame_face_feature_list[k],
-                                        self.features_known_list[i])
-                                    logging.debug("    with person %d, the e-distance: %f", i + 1, e_distance_tmp)
-                                    self.current_frame_face_X_e_distance_list.append(e_distance_tmp)
-                                else:
-                                    # 空数据 person_X / Empty data for person_X
-                                    self.current_frame_face_X_e_distance_list.append(999999999)
-
-                            # d. 寻找出最小的欧式距离匹配 / Find the one with minimum e distance
-                            similar_person_num = self.current_frame_face_X_e_distance_list.index(
-                                min(self.current_frame_face_X_e_distance_list))
-
-                            if min(self.current_frame_face_X_e_distance_list) < 0.4:
-                                # 在这里更改显示的人名 / Modify name if needed
-                                self.show_chinese_name()
-                                self.current_frame_name_list[k] = self.face_name_known_list[similar_person_num]
-                                logging.debug("    recognition result for face %d: %s", k + 1,
-                                              self.face_name_known_list[similar_person_num])
-                            else:
-                                logging.debug("    recognition result for face %d: %s", k + 1, "unknown")
-
-                        if "unknown" in self.current_frame_name_list:
-                            self.reclassify_interval_cnt += 1
-
-                    # 4.2.1 人脸数从 1->0 / Face cnt 1->0
-                    elif self.current_frame_face_cnt == 0:
-                        logging.debug("  scene 2.2 人脸消失, 当前帧中没有人脸 / No face in this frame!!!")
-
-                        self.reclassify_interval_cnt = 0
-                        self.current_frame_name_list = []
-                        self.current_frame_face_feature_list = []
-
-                # 5. 生成的窗口添加说明文字 / Add note on cv2 window
-                self.draw_note(img_rd)
-
-                if kk == ord('q'):
-                    break
-
-                self.update_fps()
-
-                cv2.namedWindow("camera", 1)
-                cv2.imshow("camera", img_rd)
-
-                logging.debug("Frame ends\n\n")
-
-    def run(self):
-        # cap = cv2.VideoCapture("video.mp4")  # Get video stream from video file
-        cap = cv2.VideoCapture(0)              # Get video stream from camera
-        self.process(cap)
-
-        cap.release()
-        cv2.destroyAllWindows()
-
-
-def main():
-    # logging.basicConfig(level=logging.DEBUG) # Set log level to 'logging.DEBUG' to print debug info of every frame
-    logging.basicConfig(level=logging.INFO)
-    Face_Recognizer_con = Face_Recognizer()
-    Face_Recognizer_con.run()
-
-
-if __name__ == '__main__':
-    main()

doc/文档1/自主开发代码/features_extraction_to_csv.py

@@ -1,106 +0,0 @@
-# Copyright (C) 2018-2021 coneypo
-# SPDX-License-Identifier: MIT
-
-# Author:   coneypo
-# Blog:     http://www.cnblogs.com/AdaminXie
-# GitHub:   https://github.com/coneypo/Dlib_face_recognition_from_camera
-# Mail:     coneypo@foxmail.com
-
-# 从人脸图像文件中提取人脸特征存入 "features_all.csv" / Extract features from images and save into "features_all.csv"
-
-import os
-import dlib
-import csv
-import numpy as np
-import logging
-import cv2
-
-# 要读取人脸图像文件的路径 / Path of cropped faces
-path_images_from_camera = "data/data_faces_from_camera/"
-
-# Dlib 正向人脸检测器 / Use frontal face detector of Dlib
-detector = dlib.get_frontal_face_detector()
-
-# Dlib 人脸 landmark 特征点检测器 / Get face landmarks
-predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
-
-# Dlib Resnet 人脸识别模型，提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor
-face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
-
-
-# 返回单张图像的 128D 特征 / Return 128D features for single image
-# Input:    path_img           <class 'str'>
-# Output:   face_descriptor    <class 'dlib.vector'>
-def return_128d_features(path_img):
-    img_rd = cv2.imread(path_img)
-    faces = detector(img_rd, 1)
-
-    logging.info("%-40s %-20s", "检测到人脸的图像 / Image with faces detected:", path_img)
-
-    # 因为有可能截下来的人脸再去检测，检测不出来人脸了, 所以要确保是 检测到人脸的人脸图像拿去算特征
-    # For photos of faces saved, we need to make sure that we can detect faces from the cropped images
-    if len(faces) != 0:
-        shape = predictor(img_rd, faces[0])
-        face_descriptor = face_reco_model.compute_face_descriptor(img_rd, shape)
-    else:
-        face_descriptor = 0
-        logging.warning("no face")
-    return face_descriptor
-
-
-# 返回 personX 的 128D 特征均值 / Return the mean value of 128D face descriptor for person X
-# Input:    path_face_personX        <class 'str'>
-# Output:   features_mean_personX    <class 'numpy.ndarray'>
-def return_features_mean_personX(path_face_personX):
-    features_list_personX = []
-    photos_list = os.listdir(path_face_personX)
-    if photos_list:
-        for i in range(len(photos_list)):
-            # 调用 return_128d_features() 得到 128D 特征 / Get 128D features for single image of personX
-            logging.info("%-40s %-20s", "正在读的人脸图像 / Reading image:", path_face_personX + "/" + photos_list[i])
-            features_128d = return_128d_features(path_face_personX + "/" + photos_list[i])
-            # 遇到没有检测出人脸的图片跳过 / Jump if no face detected from image
-            if features_128d == 0:
-                i += 1
-            else:
-                features_list_personX.append(features_128d)
-    else:
-        logging.warning("文件夹内图像文件为空 / Warning: No images in%s/", path_face_personX)
-
-    # 计算 128D 特征的均值 / Compute the mean
-    # personX 的 N 张图像 x 128D -> 1 x 128D
-    if features_list_personX:
-        features_mean_personX = np.array(features_list_personX, dtype=object).mean(axis=0)
-    else:
-        features_mean_personX = np.zeros(128, dtype=object, order='C')
-    return features_mean_personX
-
-
-def main():
-    logging.basicConfig(level=logging.INFO)
-    # 获取已录入的最后一个人脸序号 / Get the order of latest person
-    person_list = os.listdir("data/data_faces_from_camera/")
-    person_list.sort()
-
-    with open("data/features_all.csv", "w", newline="") as csvfile:
-        writer = csv.writer(csvfile)
-        for person in person_list:
-            # Get the mean/average features of face/personX, it will be a list with a length of 128D
-            logging.info("%sperson_%s", path_images_from_camera, person)
-            features_mean_personX = return_features_mean_personX(path_images_from_camera + person)
-
-            if len(person.split('_', 2)) == 2:
-                # "person_x"
-                person_name = person
-            else:
-                # "person_x_tom"
-                person_name = person.split('_', 2)[-1]
-            features_mean_personX = np.insert(features_mean_personX, 0, person_name, axis=0)
-            # features_mean_personX will be 129D, person name + 128 features
-            writer.writerow(features_mean_personX)
-            logging.info('\n')
-        logging.info("所有录入人脸数据存入 / Save all the features of faces registered into: data/features_all.csv")
-
-
-if __name__ == '__main__':
-    main()

doc/文档1/自主开发代码/get_faces_from_camera.py

@@ -1,198 +0,0 @@
-# Copyright (C) 2018-2021 coneypo
-# SPDX-License-Identifier: MIT
-
-# Author:   coneypo
-# Blog:     http://www.cnblogs.com/AdaminXie
-# GitHub:   https://github.com/coneypo/Dlib_face_recognition_from_camera
-# Mail:     coneypo@foxmail.com
-
-# 进行人脸录入 / Face register
-
-import dlib
-import numpy as np
-import cv2
-import os
-import shutil
-import time
-import logging
-
-# Dlib 正向人脸检测器 / Use frontal face detector of Dlib
-detector = dlib.get_frontal_face_detector()
-
-
-class Face_Register:
-    def __init__(self):
-        self.path_photos_from_camera = "data/data_faces_from_camera/"
-        self.font = cv2.FONT_ITALIC
-
-        self.existing_faces_cnt = 0         # 已录入的人脸计数器 / cnt for counting saved faces
-        self.ss_cnt = 0                     # 录入 personX 人脸时图片计数器 / cnt for screen shots
-        self.current_frame_faces_cnt = 0    # 录入人脸计数器 / cnt for counting faces in current frame
-
-        self.save_flag = 1                  # 之后用来控制是否保存图像的 flag / The flag to control if save
-        self.press_n_flag = 0               # 之后用来检查是否先按 'n' 再按 's' / The flag to check if press 'n' before 's'
-
-        # FPS
-        self.frame_time = 0
-        self.frame_start_time = 0
-        self.fps = 0
-        self.fps_show = 0
-        self.start_time = time.time()
-
-    # 新建保存人脸图像文件和数据 CSV 文件夹 / Mkdir for saving photos and csv
-    def pre_work_mkdir(self):
-        # 新建文件夹 / Create folders to save face images and csv
-        if os.path.isdir(self.path_photos_from_camera):
-            pass
-        else:
-            os.mkdir(self.path_photos_from_camera)
-
-    # 删除之前存的人脸数据文件夹 / Delete old face folders
-    def pre_work_del_old_face_folders(self):
-        # 删除之前存的人脸数据文件夹, 删除 "/data_faces_from_camera/person_x/"...
-        folders_rd = os.listdir(self.path_photos_from_camera)
-        for i in range(len(folders_rd)):
-            shutil.rmtree(self.path_photos_from_camera+folders_rd[i])
-        if os.path.isfile("data/features_all.csv"):
-            os.remove("data/features_all.csv")
-
-    # 如果有之前录入的人脸, 在之前 person_x 的序号按照 person_x+1 开始录入 / Start from person_x+1
-    def check_existing_faces_cnt(self):
-        if os.listdir("data/data_faces_from_camera/"):
-            # 获取已录入的最后一个人脸序号 / Get the order of latest person
-            person_list = os.listdir("data/data_faces_from_camera/")
-            person_num_list = []
-            for person in person_list:
-                person_num_list.append(int(person.split('_')[-1]))
-            self.existing_faces_cnt = max(person_num_list)
-
-        # 如果第一次存储或者没有之前录入的人脸, 按照 person_1 开始录入 / Start from person_1
-        else:
-            self.existing_faces_cnt = 0
-
-    # 更新 FPS / Update FPS of Video stream
-    def update_fps(self):
-        now = time.time()
-        # 每秒刷新 fps / Refresh fps per second
-        if str(self.start_time).split(".")[0] != str(now).split(".")[0]:
-            self.fps_show = self.fps
-        self.start_time = now
-        self.frame_time = now - self.frame_start_time
-        self.fps = 1.0 / self.frame_time
-        self.frame_start_time = now
-
-    # 生成的 cv2 window 上面添加说明文字 / PutText on cv2 window
-    def draw_note(self, img_rd):
-        # 添加说明 / Add some notes
-        cv2.putText(img_rd, "Face Register", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA)
-        cv2.putText(img_rd, "FPS:   " + str(self.fps_show.__round__(2)), (20, 100), self.font, 0.8, (0, 255, 0), 1,
-                    cv2.LINE_AA)
-        cv2.putText(img_rd, "Faces: " + str(self.current_frame_faces_cnt), (20, 140), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
-        cv2.putText(img_rd, "N: Create 创建 face folder", (20, 350), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
-        cv2.putText(img_rd, "S: Save current face", (20, 400), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
-        cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
-
-    # 获取人脸 / Main process of face detection and saving
-    def process(self, stream):
-        # 1. 新建储存人脸图像文件目录 / Create folders to save photos
-        self.pre_work_mkdir()
-
-        # 2. 删除 "/data/data_faces_from_camera" 中已有人脸图像文件
-        # / Uncomment if want to delete the saved faces and start from person_1
-        # if os.path.isdir(self.path_photos_from_camera):
-        #     self.pre_work_del_old_face_folders()
-
-        # 3. 检查 "/data/data_faces_from_camera" 中已有人脸文件
-        self.check_existing_faces_cnt()
-
-        while stream.isOpened():
-            flag, img_rd = stream.read()        # Get camera video stream
-            kk = cv2.waitKey(1)
-            faces = detector(img_rd, 0)         # Use Dlib face detector
-
-            # 4. 按下 'n' 新建存储人脸的文件夹 / Press 'n' to create the folders for saving faces
-            if kk == ord('n'):
-                self.existing_faces_cnt += 1
-                current_face_dir = self.path_photos_from_camera + "person_" + str(self.existing_faces_cnt)
-                os.makedirs(current_face_dir)
-                logging.info("\n%-40s %s", "新建的人脸文件夹 / Create folders:", current_face_dir)
-
-                self.ss_cnt = 0                 # 将人脸计数器清零 / Clear the cnt of screen shots
-                self.press_n_flag = 1           # 已经按下 'n' / Pressed 'n' already
-
-            # 5. 检测到人脸 / Face detected
-            if len(faces) != 0:
-                # 矩形框 / Show the ROI of faces
-                for k, d in enumerate(faces):
-                    # 计算矩形框大小 / Compute the size of rectangle box
-                    height = (d.bottom() - d.top())
-                    width = (d.right() - d.left())
-                    hh = int(height/2)
-                    ww = int(width/2)
-
-                    # 6. 判断人脸矩形框是否超出 480x640 / If the size of ROI > 480x640
-                    if (d.right()+ww) > 640 or (d.bottom()+hh > 480) or (d.left()-ww < 0) or (d.top()-hh < 0):
-                        cv2.putText(img_rd, "OUT OF RANGE", (20, 300), self.font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
-                        color_rectangle = (0, 0, 255)
-                        save_flag = 0
-                        if kk == ord('s'):
-                            logging.warning("请调整位置 / Please adjust your position")
-                    else:
-                        color_rectangle = (255, 255, 255)
-                        save_flag = 1
-
-                    cv2.rectangle(img_rd,
-                                  tuple([d.left() - ww, d.top() - hh]),
-                                  tuple([d.right() + ww, d.bottom() + hh]),
-                                  color_rectangle, 2)
-
-                    # 7. 根据人脸大小生成空的图像 / Create blank image according to the size of face detected
-                    img_blank = np.zeros((int(height*2), width*2, 3), np.uint8)
-
-                    if save_flag:
-                        # 8. 按下 's' 保存摄像头中的人脸到本地 / Press 's' to save faces into local images
-                        if kk == ord('s'):
-                            # 检查有没有先按'n'新建文件夹 / Check if you have pressed 'n'
-                            if self.press_n_flag:
-                                self.ss_cnt += 1
-                                for ii in range(height*2):
-                                    for jj in range(width*2):
-                                        img_blank[ii][jj] = img_rd[d.top()-hh + ii][d.left()-ww + jj]
-                                cv2.imwrite(current_face_dir + "/img_face_" + str(self.ss_cnt) + ".jpg", img_blank)
-                                logging.info("%-40s %s/img_face_%s.jpg", "写入本地 / Save into：",
-                                             str(current_face_dir), str(self.ss_cnt))
-                            else:
-                                logging.warning("请先按 'N' 来建文件夹, 按 'S' / Please press 'N' and press 'S'")
-
-            self.current_frame_faces_cnt = len(faces)
-
-            # 9. 生成的窗口添加说明文字 / Add note on cv2 window
-            self.draw_note(img_rd)
-
-            # 10. 按下 'q' 键退出 / Press 'q' to exit
-            if kk == ord('q'):
-                break
-
-            # 11. Update FPS
-            self.update_fps()
-
-            cv2.namedWindow("camera", 1)
-            cv2.imshow("camera", img_rd)
-
-    def run(self):
-        # cap = cv2.VideoCapture("video.mp4")   # Get video stream from video file
-        cap = cv2.VideoCapture(0)               # Get video stream from camera
-        self.process(cap)
-
-        cap.release()
-        cv2.destroyAllWindows()
-
-
-def main():
-    logging.basicConfig(level=logging.INFO)
-    Face_Register_con = Face_Register()
-    Face_Register_con.run()
-
-
-if __name__ == '__main__':
-    main()

doc/文档1/自主开发代码/get_faces_from_camera_tkinter.py

@@ -1,305 +0,0 @@
-# Copyright (C) 2018-2021 coneypo
-# SPDX-License-Identifier: MIT
-
-# Author:   coneypo
-# Blog:     http://www.cnblogs.com/AdaminXie
-# GitHub:   https://github.com/coneypo/Dlib_face_recognition_from_camera
-# Mail:     coneypo@foxmail.com
-
-# 人脸录入 Tkinter GUI / Face register GUI with tkinter
-#改代码为简易ui界面，暂时无法运行
-import dlib
-import numpy as np
-import cv2
-import os
-import shutil
-import time
-import logging
-import tkinter as tk
-from tkinter import font as tkFont
-from PIL import Image, ImageTk
-
-# Dlib 正向人脸检测器 / Use frontal face detector of Dlib
-detector = dlib.get_frontal_face_detector()
-
-
-class Face_Register:
-    def __init__(self):
-
-        self.current_frame_faces_cnt = 0  # 当前帧中人脸计数器 / cnt for counting faces in current frame
-        self.existing_faces_cnt = 0  # 已录入的人脸计数器 / cnt for counting saved faces
-        self.ss_cnt = 0  # 录入 person_n 人脸时图片计数器 / cnt for screen shots
-
-        # Tkinter GUI
-        self.win = tk.Tk()
-        self.win.title("Face Register @coneypo")
-
-        # PLease modify window size here if needed
-        self.win.geometry("1300x550")
-
-        # GUI left part
-        self.frame_left_camera = tk.Frame(self.win)
-        self.label = tk.Label(self.win)
-        self.label.pack(side=tk.LEFT)
-        self.frame_left_camera.pack()
-
-        # GUI right part
-        self.frame_right_info = tk.Frame(self.win)
-        self.label_cnt_face_in_database = tk.Label(self.frame_right_info, text=str(self.existing_faces_cnt))
-        self.label_fps_info = tk.Label(self.frame_right_info, text="")
-        self.input_name = tk.Entry(self.frame_right_info)
-        self.input_name_char = ""
-        self.label_warning = tk.Label(self.frame_right_info)
-        self.label_face_cnt = tk.Label(self.frame_right_info, text="Faces in current frame: ")
-        self.log_all = tk.Label(self.frame_right_info)
-
-        self.font_title = tkFont.Font(family='Helvetica', size=20, weight='bold')
-        self.font_step_title = tkFont.Font(family='Helvetica', size=15, weight='bold')
-        self.font_warning = tkFont.Font(family='Helvetica', size=15, weight='bold')
-
-        self.path_photos_from_camera = "data/data_faces_from_camera/"
-        self.current_face_dir = ""
-        self.font = cv2.FONT_ITALIC
-
-        # Current frame and face ROI position
-        self.current_frame = np.ndarray
-        self.face_ROI_image = np.ndarray
-        self.face_ROI_width_start = 0
-        self.face_ROI_height_start = 0
-        self.face_ROI_width = 0
-        self.face_ROI_height = 0
-        self.ww = 0
-        self.hh = 0
-
-        self.out_of_range_flag = False
-        self.face_folder_created_flag = False
-
-        # FPS
-        self.frame_time = 0
-        self.frame_start_time = 0
-        self.fps = 0
-        self.fps_show = 0
-        self.start_time = time.time()
-
-        self.cap = cv2.VideoCapture(0)  # Get video stream from camera
-        # self.cap = cv2.VideoCapture("test.mp4")   # Input local video
-
-    # 删除之前存的人脸数据文件夹 / Delete old face folders
-    def GUI_clear_data(self):
-        # 删除之前存的人脸数据文件夹, 删除 "/data_faces_from_camera/person_x/"...
-        folders_rd = os.listdir(self.path_photos_from_camera)
-        for i in range(len(folders_rd)):
-            shutil.rmtree(self.path_photos_from_camera + folders_rd[i])
-        if os.path.isfile("data/features_all.csv"):
-            os.remove("data/features_all.csv")
-        self.label_cnt_face_in_database['text'] = "0"
-        self.existing_faces_cnt = 0
-        self.log_all["text"] = "Face images and `features_all.csv` removed!"
-
-    def GUI_get_input_name(self):
-        self.input_name_char = self.input_name.get()
-        self.create_face_folder()
-        self.label_cnt_face_in_database['text'] = str(self.existing_faces_cnt)
-
-    def GUI_info(self):
-        tk.Label(self.frame_right_info,
-                 text="Face register",
-                 font=self.font_title).grid(row=0, column=0, columnspan=3, sticky=tk.W, padx=2, pady=20)
-
-        tk.Label(self.frame_right_info,
-                 text="FPS: ").grid(row=1, column=0, columnspan=2, sticky=tk.W, padx=5, pady=2)
-        self.label_fps_info.grid(row=1, column=2, sticky=tk.W, padx=5, pady=2)
-
-        tk.Label(self.frame_right_info,
-                 text="Faces in database: ").grid(row=2, column=0, columnspan=2, sticky=tk.W, padx=5, pady=2)
-        self.label_cnt_face_in_database.grid(row=2, column=2, columnspan=3, sticky=tk.W, padx=5, pady=2)
-
-        tk.Label(self.frame_right_info,
-                 text="Faces in current frame: ").grid(row=3, column=0, columnspan=2, sticky=tk.W, padx=5, pady=2)
-        self.label_face_cnt.grid(row=3, column=2, columnspan=3, sticky=tk.W, padx=5, pady=2)
-
-        self.label_warning.grid(row=4, column=0, columnspan=3, sticky=tk.W, padx=5, pady=2)
-
-        # Step 1: Clear old data
-        tk.Label(self.frame_right_info,
-                 font=self.font_step_title,
-                 text="Step 1: Clear face photos").grid(row=5, column=0, columnspan=2, sticky=tk.W, padx=5, pady=20)
-        tk.Button(self.frame_right_info,
-                  text='Clear',
-                  command=self.GUI_clear_data).grid(row=6, column=0, columnspan=3, sticky=tk.W, padx=5, pady=2)
-
-        # Step 2: Input name and create folders for face
-        tk.Label(self.frame_right_info,
-                 font=self.font_step_title,
-                 text="Step 2: Input name").grid(row=7, column=0, columnspan=2, sticky=tk.W, padx=5, pady=20)
-
-        tk.Label(self.frame_right_info, text="Name: ").grid(row=8, column=0, sticky=tk.W, padx=5, pady=0)
-        self.input_name.grid(row=8, column=1, sticky=tk.W, padx=0, pady=2)
-
-        tk.Button(self.frame_right_info,
-                  text='Input',
-                  command=self.GUI_get_input_name).grid(row=8, column=2, padx=5)
-
-        # Step 3: Save current face in frame
-        tk.Label(self.frame_right_info,
-                 font=self.font_step_title,
-                 text="Step 3: Save face image").grid(row=9, column=0, columnspan=2, sticky=tk.W, padx=5, pady=20)
-
-        tk.Button(self.frame_right_info,
-                  text='Save current face',
-                  command=self.save_current_face).grid(row=10, column=0, columnspan=3, sticky=tk.W)
-
-        # Show log in GUI
-        self.log_all.grid(row=11, column=0, columnspan=20, sticky=tk.W, padx=5, pady=20)
-
-        self.frame_right_info.pack()
-
-    # 新建保存人脸图像文件和数据 CSV 文件夹 / Mkdir for saving photos and csv
-    def pre_work_mkdir(self):
-        # 新建文件夹 / Create folders to save face images and csv
-        if os.path.isdir(self.path_photos_from_camera):
-            pass
-        else:
-            os.mkdir(self.path_photos_from_camera)
-
-    # 如果有之前录入的人脸, 在之前 person_x 的序号按照 person_x+1 开始录入 / Start from person_x+1
-    def check_existing_faces_cnt(self):
-        if os.listdir("data/data_faces_from_camera/"):
-            # 获取已录入的最后一个人脸序号 / Get the order of latest person
-            person_list = os.listdir("data/data_faces_from_camera/")
-            person_num_list = []
-            for person in person_list:
-                person_order = person.split('_')[1].split('_')[0]
-                person_num_list.append(int(person_order))
-            self.existing_faces_cnt = max(person_num_list)
-
-        # 如果第一次存储或者没有之前录入的人脸, 按照 person_1 开始录入 / Start from person_1
-        else:
-            self.existing_faces_cnt = 0
-
-    # 更新 FPS / Update FPS of Video stream
-    def update_fps(self):
-        now = time.time()
-        # 每秒刷新 fps / Refresh fps per second
-        if str(self.start_time).split(".")[0] != str(now).split(".")[0]:
-            self.fps_show = self.fps
-        self.start_time = now
-        self.frame_time = now - self.frame_start_time
-        self.fps = 1.0 / self.frame_time
-        self.frame_start_time = now
-
-        self.label_fps_info["text"] = str(self.fps.__round__(2))
-
-    def create_face_folder(self):
-        # 新建存储人脸的文件夹 / Create the folders for saving faces
-        self.existing_faces_cnt += 1
-        if self.input_name_char:
-            self.current_face_dir = self.path_photos_from_camera + \
-                                    "person_" + str(self.existing_faces_cnt) + "_" + \
-                                    self.input_name_char
-        else:
-            self.current_face_dir = self.path_photos_from_camera + \
-                                    "person_" + str(self.existing_faces_cnt)
-        os.makedirs(self.current_face_dir)
-        self.log_all["text"] = "\"" + self.current_face_dir + "/\" created!"
-        logging.info("\n%-40s %s", "新建的人脸文件夹 / Create folders:", self.current_face_dir)
-
-        self.ss_cnt = 0  # 将人脸计数器清零 / Clear the cnt of screen shots
-        self.face_folder_created_flag = True  # Face folder already created
-
-    def save_current_face(self):
-        if self.face_folder_created_flag:
-            if self.current_frame_faces_cnt == 1:
-                if not self.out_of_range_flag:
-                    self.ss_cnt += 1
-                    # 根据人脸大小生成空的图像 / Create blank image according to the size of face detected
-                    self.face_ROI_image = np.zeros((int(self.face_ROI_height * 2), self.face_ROI_width * 2, 3),
-                                                   np.uint8)
-                    for ii in range(self.face_ROI_height * 2):
-                        for jj in range(self.face_ROI_width * 2):
-                            self.face_ROI_image[ii][jj] = self.current_frame[self.face_ROI_height_start - self.hh + ii][
-                                self.face_ROI_width_start - self.ww + jj]
-                    self.log_all["text"] = "\"" + self.current_face_dir + "/img_face_" + str(
-                        self.ss_cnt) + ".jpg\"" + " saved!"
-                    self.face_ROI_image = cv2.cvtColor(self.face_ROI_image, cv2.COLOR_BGR2RGB)
-
-                    cv2.imwrite(self.current_face_dir + "/img_face_" + str(self.ss_cnt) + ".jpg", self.face_ROI_image)
-                    logging.info("%-40s %s/img_face_%s.jpg", "写入本地 / Save into：",
-                                 str(self.current_face_dir), str(self.ss_cnt) + ".jpg")
-                else:
-                    self.log_all["text"] = "Please do not out of range!"
-            else:
-                self.log_all["text"] = "No face in current frame!"
-        else:
-            self.log_all["text"] = "Please run step 2!"
-
-    def get_frame(self):
-        try:
-            if self.cap.isOpened():
-                ret, frame = self.cap.read()
-                return ret, cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-        except:
-            print("Error: No video input!!!")
-
-    # 获取人脸 / Main process of face detection and saving
-    def process(self):
-        ret, self.current_frame = self.get_frame()
-        faces = detector(self.current_frame, 0)
-        # Get frame
-        if ret:
-            self.update_fps()
-            self.label_face_cnt["text"] = str(len(faces))
-            # 检测到人脸 / Face detected
-            if len(faces) != 0:
-                # 矩形框 / Show the ROI of faces
-                for k, d in enumerate(faces):
-                    self.face_ROI_width_start = d.left()
-                    self.face_ROI_height_start = d.top()
-                    # 计算矩形框大小 / Compute the size of rectangle box
-                    self.face_ROI_height = (d.bottom() - d.top())
-                    self.face_ROI_width = (d.right() - d.left())
-                    self.hh = int(self.face_ROI_height / 2)
-                    self.ww = int(self.face_ROI_width / 2)
-
-                    # 判断人脸矩形框是否超出 480x640 / If the size of ROI > 480x640
-                    if (d.right() + self.ww) > 640 or (d.bottom() + self.hh > 480) or (d.left() - self.ww < 0) or (
-                            d.top() - self.hh < 0):
-                        self.label_warning["text"] = "OUT OF RANGE"
-                        self.label_warning['fg'] = 'red'
-                        self.out_of_range_flag = True
-                        color_rectangle = (255, 0, 0)
-                    else:
-                        self.out_of_range_flag = False
-                        self.label_warning["text"] = ""
-                        color_rectangle = (255, 255, 255)
-                    self.current_frame = cv2.rectangle(self.current_frame,
-                                                       tuple([d.left() - self.ww, d.top() - self.hh]),
-                                                       tuple([d.right() + self.ww, d.bottom() + self.hh]),
-                                                       color_rectangle, 2)
-            self.current_frame_faces_cnt = len(faces)
-
-            # Convert PIL.Image.Image to PIL.Image.PhotoImage
-            img_Image = Image.fromarray(self.current_frame)
-            img_PhotoImage = ImageTk.PhotoImage(image=img_Image)
-            self.label.img_tk = img_PhotoImage
-            self.label.configure(image=img_PhotoImage)
-
-        # Refresh frame
-        self.win.after(20, self.process)
-
-    def run(self):
-        self.pre_work_mkdir()
-        self.check_existing_faces_cnt()
-        self.GUI_info()
-        self.process()
-        self.win.mainloop()
-
-
-def main():
-    logging.basicConfig(level=logging.INFO)
-    Face_Register_con = Face_Register()
-    Face_Register_con.run()
-
-
-if __name__ == '__main__':
-    main()

doc/文档1/自主开发代码/image.ui

@@ -1,63 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<ui version="4.0">
- <class>image</class>
- <widget class="QMainWindow" name="image">
-  <property name="geometry">
-   <rect>
-    <x>0</x>
-    <y>0</y>
-    <width>706</width>
-    <height>575</height>
-   </rect>
-  </property>
-  <property name="windowTitle">
-   <string>MainWindow</string>
-  </property>
-  <property name="styleSheet">
-   <string notr="true"/>
-  </property>
-  <widget class="QWidget" name="centralwidget">
-   <widget class="QLabel" name="label">
-    <property name="geometry">
-     <rect>
-      <x>40</x>
-      <y>30</y>
-      <width>621</width>
-      <height>401</height>
-     </rect>
-    </property>
-    <property name="styleSheet">
-     <string notr="true">background-color: rgb(0, 0, 0);</string>
-    </property>
-    <property name="text">
-     <string>TextLabel</string>
-    </property>
-   </widget>
-   <widget class="QPushButton" name="pushButton">
-    <property name="geometry">
-     <rect>
-      <x>40</x>
-      <y>490</y>
-      <width>111</width>
-      <height>21</height>
-     </rect>
-    </property>
-    <property name="text">
-     <string>打开文件</string>
-    </property>
-   </widget>
-   <widget class="QTextBrowser" name="textBrowser">
-    <property name="geometry">
-     <rect>
-      <x>230</x>
-      <y>490</y>
-      <width>256</width>
-      <height>21</height>
-     </rect>
-    </property>
-   </widget>
-  </widget>
- </widget>
- <resources/>
- <connections/>
-</ui>

doc/文档1/自主开发代码/image_ui.py

@@ -1,40 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Form implementation generated from reading ui file 'D:\pythonProjects\pyqt\image.ui'
-#
-# Created by: PyQt5 UI code generator 5.15.9
-#
-# WARNING: Any manual changes made to this file will be lost when pyuic5 is
-# run again.  Do not edit this file unless you know what you are doing.
-
-
-from PyQt5 import QtCore, QtGui, QtWidgets
-
-
-class Ui_image(object):
-    def setupUi(self, image):
-        image.setObjectName("image")
-        image.resize(706, 575)
-        image.setStyleSheet("")
-        self.centralwidget = QtWidgets.QWidget(image)
-        self.centralwidget.setObjectName("centralwidget")
-        self.label = QtWidgets.QLabel(self.centralwidget)
-        self.label.setGeometry(QtCore.QRect(40, 30, 621, 401))
-        self.label.setStyleSheet("background-color: rgb(0, 0, 0);")
-        self.label.setObjectName("label")
-        self.pushButton = QtWidgets.QPushButton(self.centralwidget)
-        self.pushButton.setGeometry(QtCore.QRect(40, 490, 75, 21))
-        self.pushButton.setObjectName("pushButton")
-        self.textBrowser = QtWidgets.QTextBrowser(self.centralwidget)
-        self.textBrowser.setGeometry(QtCore.QRect(140, 490, 256, 21))
-        self.textBrowser.setObjectName("textBrowser")
-        image.setCentralWidget(self.centralwidget)
-
-        self.retranslateUi(image)
-        QtCore.QMetaObject.connectSlotsByName(image)
-
-    def retranslateUi(self, image):
-        _translate = QtCore.QCoreApplication.translate
-        image.setWindowTitle(_translate("image", "MainWindow"))
-        self.label.setText(_translate("image", "TextLabel"))
-        self.pushButton.setText(_translate("image", "打开文件"))

doc/文档1/自主开发代码/login.ui

@@ -1,111 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<ui version="4.0">
- <class>LoginWindow</class>
- <widget class="QMainWindow" name="LoginWindow">
-  <property name="geometry">
-   <rect>
-    <x>0</x>
-    <y>0</y>
-    <width>800</width>
-    <height>600</height>
-   </rect>
-  </property>
-  <property name="windowTitle">
-   <string>MainWindow</string>
-  </property>
-  <widget class="QWidget" name="centralwidget">
-   <widget class="QFrame" name="frame">
-    <property name="geometry">
-     <rect>
-      <x>60</x>
-      <y>40</y>
-      <width>600</width>
-      <height>500</height>
-     </rect>
-    </property>
-    <property name="minimumSize">
-     <size>
-      <width>600</width>
-      <height>500</height>
-     </size>
-    </property>
-    <property name="styleSheet">
-     <string notr="true">background-color: rgb(217, 217, 217);</string>
-    </property>
-    <property name="frameShape">
-     <enum>QFrame::StyledPanel</enum>
-    </property>
-    <property name="frameShadow">
-     <enum>QFrame::Raised</enum>
-    </property>
-    <layout class="QVBoxLayout" name="verticalLayout">
-     <item alignment="Qt::AlignHCenter">
-      <widget class="QLineEdit" name="lineEdit">
-       <property name="minimumSize">
-        <size>
-         <width>250</width>
-         <height>25</height>
-        </size>
-       </property>
-       <property name="styleSheet">
-        <string notr="true">background-color: rgb(255, 255, 255);</string>
-       </property>
-       <property name="placeholderText">
-        <string>账号</string>
-       </property>
-      </widget>
-     </item>
-     <item alignment="Qt::AlignHCenter">
-      <widget class="QLineEdit" name="lineEdit_2">
-       <property name="minimumSize">
-        <size>
-         <width>250</width>
-         <height>25</height>
-        </size>
-       </property>
-       <property name="styleSheet">
-        <string notr="true">background-color: rgb(255, 255, 255);</string>
-       </property>
-       <property name="placeholderText">
-        <string>密码</string>
-       </property>
-      </widget>
-     </item>
-     <item alignment="Qt::AlignHCenter">
-      <widget class="QPushButton" name="pushButton_2">
-       <property name="styleSheet">
-        <string notr="true">background-color: rgb(255, 251, 208);</string>
-       </property>
-       <property name="text">
-        <string>登录</string>
-       </property>
-      </widget>
-     </item>
-     <item alignment="Qt::AlignHCenter">
-      <widget class="QPushButton" name="pushButton">
-       <property name="styleSheet">
-        <string notr="true">background-color: rgb(255, 251, 208);
-</string>
-       </property>
-       <property name="text">
-        <string>注册账号</string>
-       </property>
-      </widget>
-     </item>
-     <item alignment="Qt::AlignHCenter">
-      <widget class="QPushButton" name="pushButton_3">
-       <property name="styleSheet">
-        <string notr="true">background-color: rgb(255, 251, 208);</string>
-       </property>
-       <property name="text">
-        <string>找回密码</string>
-       </property>
-      </widget>
-     </item>
-    </layout>
-   </widget>
-  </widget>
- </widget>
- <resources/>
- <connections/>
-</ui>

doc/文档1/自主开发代码/login_ui.py

@@ -1,64 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Form implementation generated from reading ui file 'login.ui'
-#
-# Created by: PyQt5 UI code generator 5.15.9
-#
-# WARNING: Any manual changes made to this file will be lost when pyuic5 is
-# run again.  Do not edit this file unless you know what you are doing.
-
-
-from PyQt5 import QtCore, QtGui, QtWidgets
-
-
-class Ui_LoginWindow(object):
-    def setupUi(self, LoginWindow):
-        LoginWindow.setObjectName("LoginWindow")
-        LoginWindow.resize(800, 600)
-        self.centralwidget = QtWidgets.QWidget(LoginWindow)
-        self.centralwidget.setObjectName("centralwidget")
-        self.frame = QtWidgets.QFrame(self.centralwidget)
-        self.frame.setGeometry(QtCore.QRect(60, 40, 600, 500))
-        self.frame.setMinimumSize(QtCore.QSize(600, 500))
-        self.frame.setStyleSheet("background-color: rgb(217, 217, 217);")
-        self.frame.setFrameShape(QtWidgets.QFrame.StyledPanel)
-        self.frame.setFrameShadow(QtWidgets.QFrame.Raised)
-        self.frame.setObjectName("frame")
-        self.verticalLayout = QtWidgets.QVBoxLayout(self.frame)
-        self.verticalLayout.setObjectName("verticalLayout")
-        self.lineEdit = QtWidgets.QLineEdit(self.frame)
-        self.lineEdit.setMinimumSize(QtCore.QSize(250, 25))
-        self.lineEdit.setStyleSheet("background-color: rgb(255, 255, 255);")
-        self.lineEdit.setObjectName("lineEdit")
-        self.verticalLayout.addWidget(self.lineEdit, 0, QtCore.Qt.AlignHCenter)
-        self.lineEdit_2 = QtWidgets.QLineEdit(self.frame)
-        self.lineEdit_2.setMinimumSize(QtCore.QSize(250, 25))
-        self.lineEdit_2.setStyleSheet("background-color: rgb(255, 255, 255);")
-        self.lineEdit_2.setObjectName("lineEdit_2")
-        self.verticalLayout.addWidget(self.lineEdit_2, 0, QtCore.Qt.AlignHCenter)
-        self.pushButton_2 = QtWidgets.QPushButton(self.frame)
-        self.pushButton_2.setStyleSheet("background-color: rgb(255, 251, 208);")
-        self.pushButton_2.setObjectName("pushButton_2")
-        self.verticalLayout.addWidget(self.pushButton_2, 0, QtCore.Qt.AlignHCenter)
-        self.pushButton = QtWidgets.QPushButton(self.frame)
-        self.pushButton.setStyleSheet("background-color: rgb(255, 251, 208);\n"
-"")
-        self.pushButton.setObjectName("pushButton")
-        self.verticalLayout.addWidget(self.pushButton, 0, QtCore.Qt.AlignHCenter)
-        self.pushButton_3 = QtWidgets.QPushButton(self.frame)
-        self.pushButton_3.setStyleSheet("background-color: rgb(255, 251, 208);")
-        self.pushButton_3.setObjectName("pushButton_3")
-        self.verticalLayout.addWidget(self.pushButton_3, 0, QtCore.Qt.AlignHCenter)
-        LoginWindow.setCentralWidget(self.centralwidget)
-
-        self.retranslateUi(LoginWindow)
-        QtCore.QMetaObject.connectSlotsByName(LoginWindow)
-
-    def retranslateUi(self, LoginWindow):
-        _translate = QtCore.QCoreApplication.translate
-        LoginWindow.setWindowTitle(_translate("LoginWindow", "MainWindow"))
-        self.lineEdit.setPlaceholderText(_translate("LoginWindow", "账号"))
-        self.lineEdit_2.setPlaceholderText(_translate("LoginWindow", "密码"))
-        self.pushButton_2.setText(_translate("LoginWindow", "登录"))
-        self.pushButton.setText(_translate("LoginWindow", "注册账号"))
-        self.pushButton_3.setText(_translate("LoginWindow", "找回密码"))

doc/文档1/自主开发代码/main.py

@@ -1,463 +0,0 @@
-import sys
-from PyQt5.QtWidgets import QApplication, QMainWindow
-from login_ui import Ui_LoginWindow    # 这里需要根据实际的文件名字与类的名字导入
-from image_ui import Ui_image          # 这里需要根据实际的文件名字与类的名字导入
-from register_ui import Ui_RegWindow   # 这里需要根据实际的文件名字与类的名字导入
-from reset_ui import Ui_RetWindow      # 这里需要根据实际的文件名字与类的名字导入
-from PyQt5.QtWidgets import QMessageBox, QFileDialog, QLineEdit
-from PyQt5.QtGui import *
-from PyQt5.QtWidgets import *
-from PyQt5.QtCore import *
-from CAM import Ui_MainWindow
-import cv2
-import sqlite3
-from PyQt5.QtWidgets import QMessageBox
-from start import Ui_MainWindow as start_ui
-import time
-import os
-from datetime import datetime, timedelta
-from set import Ui_MainWindow as set_ui
-from test4 import calculate_required_time, is_within_time_range, create_training_directory
-from io import BytesIO
-
-class LoginWindow(QMainWindow):
-    def __init__(self, parent=None):
-        super().__init__(parent)
-        self.ui = Ui_LoginWindow()
-        self.ui.setupUi(self)
-        self.show()
-        self.ui.pushButton.clicked.connect(self.register)
-        self.ui.pushButton_2.clicked.connect(self.login)
-        self.ui.pushButton_3.clicked.connect(self.reset)
-
-    # 登录函数
-    def login(self):
-        # 连接数据库
-        conn1 = sqlite3.connect("F:\BaiduNetdiskDownload\pyqt\pyqt\mrsoft.db")
-        cursor1 = conn1.cursor()
-        user_id = self.ui.lineEdit.text()  # 获取账号
-        password = self.ui.lineEdit_2.text()  # 获取密码
-        # sql语句，判断数据库中是否拥有这账号和密码
-        sql = 'select user_id, password from user where user_id=? and password =?'
-        cursor1.execute(sql, (user_id, password))
-        # 获取一致的部分，存在表示输入正确，不存在提示错误
-        data = cursor1.fetchall()
-        conn1.commit()
-        cursor1.close()
-        conn1.close()
-        # 账号和密码不为空时候，才进入
-        if data :
-            self.close()
-            MainWindow.show()
-        else:
-            QMessageBox.information(self, "提示", "您的登录账号/密码错误或为空！",
-                                        QMessageBox.Close)
-
-# 跳转至注册函数
-    def register(self):
-        RegisterWindow.show()
-        self.close()
-
-# 跳转至重置函数
-    def reset(self):
-        ResetWindow.show()
-        self.close()
-
-    def video264(self):
-        MainWindow.show()
-        self.close()
-
-
-class RegisterWindow(QMainWindow, Ui_RegWindow):
-    def __init__(self, parent=None):
-        super(RegisterWindow, self).__init__(parent)
-        self.ui = Ui_RegWindow()
-        self.ui.setupUi(self)
-        self.ui.pushButton.clicked.connect(self.reg)
-        self.ui.pushButton_2.clicked.connect(self.back1)
-
-    def open(self):
-        self.show()
-
-    def back1(self):
-        self.close()
-        LoginWindow.show()
-
-    def reg(self):
-        conn2 = sqlite3.connect("F:\BaiduNetdiskDownload\pyqt\pyqt\mrsoft.db")
-        cursor2 = conn2.cursor()
-        user_id = self.ui.lineEdit.text()
-        password = self.ui.lineEdit_2.text()
-        confirm = self.ui.lineEdit_3.text()
-        if password == confirm and password and user_id:
-            # sql语句，符合条件的给数据库中加入新的账号和密码
-            sql = 'insert into user (user_id, password) values (?, ?)'
-            cursor2.execute(sql, (user_id, password))
-            self.back1()
-        # 如果两次输入密码不一致，发出提示
-        elif password != confirm:
-            QMessageBox.information(self, "提示", "两次输入的密码不一致，请重新输入！",
-                                    QMessageBox.Close)
-        else:
-            QMessageBox.information(self, "提示", "输入错误！",
-                                    QMessageBox.Close)
-        conn2.commit()
-        cursor2.close()
-        conn2.close()
-
-
-class ResetWindow(QMainWindow, Ui_RetWindow):
-    def __init__(self, parent=None):
-        super(ResetWindow, self).__init__(parent)
-        self.ui = Ui_RetWindow()
-        self.ui.setupUi(self)
-        self.ui.pushButton.clicked.connect(self.ret)
-        self.ui.pushButton_2.clicked.connect(self.back2)
-
-    def open(self):
-        self.show()
-
-    def back2(self):
-        self.close()
-        LoginWindow.show()
-
-    def ret(self):
-        conn3 = sqlite3.connect("F:\BaiduNetdiskDownload\pyqt\pyqt\mrsoft.db")
-        cursor3 = conn3.cursor()
-        user_id = self.ui.lineEdit.text()
-        password = self.ui.lineEdit_2.text()
-        confirm = self.ui.lineEdit_3.text()
-        if password == confirm and password and user_id:
-            sql = 'update user set password=? where user_id=?'
-            cursor3.execute(sql, (password, user_id))
-            self.back2()
-        elif password != confirm:
-            QMessageBox.information(self, "提示", "两次输入的密码不一致，请重新输入！",
-                                    QMessageBox.Close)
-        else:
-            QMessageBox.information(self, "提示", "输入错误！",
-                                    QMessageBox.Close)
-        conn3.commit()
-        cursor3.close()
-        conn3.close()
-
-
-global_background_image = None
-
-
-class StartWindow(QMainWindow, start_ui):
-    def __init__(self):
-        super(StartWindow, self).__init__()
-        self.setupUi(self)
-        self.startButton.clicked.connect(self.main)
-        self.closeButton.clicked.connect(self.back2)
-
-    def back2(self):
-        self.close()
-        MainWindow.show()  # 请确保 MainWindow 已经被实例化并可以显示
-
-    def main(self):  # 方法参数改为 self
-        # 获取输入值
-        start_time_str = self.lineEdit.text()
-        end_time_str = self.lineEdit_2.text()
-        percentage = float(self.lineEdit_3.text())  # 转换为浮点数
-
-        # 创建训练数据目录
-        def create_training_directory(path_name):
-            if not os.path.exists(path_name):
-                os.makedirs(path_name)
-
-        # 判断当前时间是否在指定的时间范围内
-        def is_within_time_range(start_time_str, end_time_str):
-            now = datetime.now().time()
-            start_time = datetime.strptime(start_time_str, "%H:%M").time()  # 24小时制
-            end_time = datetime.strptime(end_time_str, "%H:%M").time()
-            return start_time <= now <= end_time
-
-        # 计算所需的最少时间（分钟）
-        def calculate_required_time(start_time_str, end_time_str, percentage):
-            start_time = datetime.strptime(start_time_str, "%H:%M")
-            end_time = datetime.strptime(end_time_str, "%H:%M")
-            total_time = (end_time - start_time).total_seconds() / 60  # 总时长（分钟）
-            return total_time * (percentage / 100)
-
-        # 捕获人脸并计时
-        def capture_faces_with_timer(window_name, camera_id, path_name, max_num, required_time, start_time_str,
-                                     end_time_str):
-            cv2.namedWindow(window_name)
-            cap = cv2.VideoCapture(camera_id)
-            face_cascade = cv2.CascadeClassifier(
-                r"C:\Users\a'su's\AppData\Local\Programs\Python\Python310\Lib\site-packages\cv2\data\haarcascade_frontalface_alt.xml"
-            )
-
-            color = (0, 255, 0)
-            face_count = 0
-            countdown_started = False
-            remaining_time = required_time * 60  # 转换为秒
-
-            create_training_directory(path_name)
-
-            # 使用生成的唯一文件名
-            video_name = self.generate_unique_filename() + '.mp4'
-            fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-            out = cv2.VideoWriter(video_name, fourcc, 20.0, (640, 480))
-
-            while cap.isOpened() and face_count < max_num:
-                ret, frame = cap.read()
-                if not ret:
-                    break
-
-                gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-                faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-
-                # 检查当前时间是否在指定的时间范围内
-                if is_within_time_range(start_time_str, end_time_str):
-                    if len(faces) > 0:
-                        if not countdown_started:
-                            countdown_started = True
-                            start_time = time.time()
-
-                        remaining_time = required_time * 60 - int(time.time() - start_time)
-
-                        for (x, y, w, h) in faces:
-                            face_image = frame[y:y + h, x:x + w]
-                            image_name = os.path.join(path_name, f'face_{face_count}.jpg')
-                            cv2.imwrite(image_name, face_image)
-
-                            face_count += 1
-                            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
-
-                        # 显示倒计时
-                        cv2.putText(frame, f'Time Left: {remaining_time // 60}m {remaining_time % 60}s', (10, 30),
-                                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
-
-                        if remaining_time <= 0:
-                            break
-                else:
-                    countdown_started = False
-                    remaining_time = required_time * 60
-                    cv2.putText(frame, 'Outside of specified time range', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1,
-                                (0, 0, 255), 2)
-
-                cv2.imshow(window_name, frame)
-                out.write(frame)  # 写入视频帧
-
-                if cv2.waitKey(10) & 0xFF == ord('q'):
-                    break
-
-            cap.release()
-            out.release()
-            cv2.destroyAllWindows()
-            print('Finished capturing faces and video.')
-
-            # 将视频写入数据库
-            self.save_video_to_folder(video_name)
-
-            # 检查是否在规定时间内完成了任务
-            if countdown_started and remaining_time <= 0:
-                self.show_message_box("学习完成")
-            else:
-                self.show_message_box("学习未完成")
-
-        required_time = calculate_required_time(start_time_str, end_time_str, percentage)
-        print(f'Starting face capture, will monitor for at least {required_time} minutes...')
-        capture_faces_with_timer('FaceCapture', MainWindow.CAM_NUM, 'training_data_faces/', 1000, required_time, start_time_str,
-                                 end_time_str)
-
-    def save_video_to_folder(self, video_name):
-        print(f"Saving video: {video_name}")
-        save_directory = "F:\\BaiduNetdiskDownload\\pyqt\\pyqt\\videos"
-        video_path = os.path.join(save_directory, video_name)
-
-        if not os.path.exists(save_directory):
-            print("Directory does not exist. Creating...")
-            os.makedirs(save_directory)
-
-        # 使用cv2.VideoWriter将视频写入
-        os.rename(video_name, video_path)  # 将视频文件重命名并移动到目标路径
-        print(f"Video saved to {video_path}")
-
-    def generate_unique_filename(self):
-        # 生成唯一的文件名，例如使用时间戳
-        import time
-        return f"video_{int(time.time())}"
-
-    def show_message_box(self, message):
-        QMessageBox.information(self, "任务完成情况", message)
-
-# 请确保 MainWindow 已经被实例化并可以显示
-# 例如： MainWindow = QMainWindow()
-
-def set_background_image(window, image_path):
-    if os.path.exists(image_path):
-        pixmap = QPixmap(image_path)
-        palette = QPalette()
-        palette.setBrush(QPalette.Background, QBrush(pixmap))
-        window.setPalette(palette)
-        print(f"背景图片已设置: {image_path}")
-    else:
-        print(f"图片文件不存在: {image_path}")
-
-class SetWindow(QMainWindow, set_ui):
-    def __init__(self, MainWindow):
-        super(SetWindow, self).__init__()
-        self.setupUi(self)
-        self.MainWindow = MainWindow  # 保存 MainWindow 实例
-        self.backButton.clicked.connect(self.back2)
-        self.videoButton.clicked.connect(self.open_recorded_videos_folder)
-        self.fontButton.clicked.connect(self.change_font)
-        self.background.clicked.connect(self.change_background)  # 连接到改变背景的方法
-
-    def open_recorded_videos_folder(self):
-        # 定义文件夹路径
-        folder_path = r'F:\BaiduNetdiskDownload\pyqt\pyqt\videos'
-
-        try:
-            if isinstance(folder_path, str):
-                if os.path.exists(folder_path):
-                    os.startfile(folder_path)  # 打开指定的文件夹
-                    print(f"已打开文件夹: {folder_path}")
-                else:
-                    print(f"文件夹不存在: {folder_path}")
-            else:
-                print(f"传入的路径类型错误: {type(folder_path)}")
-        except Exception as e:
-            print(f"打开文件夹时出错: {e}")
-
-    def change_font(self):
-        # 打开字体选择对话框
-        font, ok = QFontDialog.getFont()
-
-        if ok:
-            # 应用字体到所有控件
-            self.apply_font_to_all_widgets(self, font)
-
-    def apply_font_to_all_widgets(self, widget, font):
-        # 遍历当前控件的所有子控件
-        for child in widget.findChildren(QWidget):
-            # 检查子控件是否支持设置字体
-            if hasattr(child, 'setFont'):
-                child.setFont(font)
-                print(f"字体已更改为: {font.toString()} 应用于: {child.objectName()}")
-            # 递归应用到子控件的子控件
-            self.apply_font_to_all_widgets(child, font)
-
-    def back2(self):
-        self.close()
-        MainWindow.show()
-
-    def change_background(self):
-        # 选择背景图片文件
-        options = QFileDialog.Options()
-        file_name, _ = QFileDialog.getOpenFileName(self, "选择背景图片", "",
-                                                   "Images (*.png *.xpm *.jpg);;All Files (*)", options=options)
-
-        if file_name:
-            # 设置窗口背景图片
-            set_background_image(self, file_name)
-            # 如果 MainWindow 存在，也设置背景图片
-            if hasattr(self, 'MainWindow'):
-                set_background_image(self.MainWindow, file_name)
-
-class MainWindow(QMainWindow, Ui_MainWindow):
-    def __init__(self, parent=None):
-        super(MainWindow, self).__init__(parent)
-        # UI界面
-        self.setupUi(self)
-        self.CAM_NUM = 0 # 默认camera_id为0
-        self.cap = cv2.VideoCapture()
-        self.background()
-        self.pushButton_2.clicked.connect(self.back2)
-        # 在label中播放视频
-        self.init_timer()
-        self.pushButton_3.clicked.connect(self.back3)
-        # 加载人脸检测分类器
-        self.face_cascade = cv2.CascadeClassifier(r"C:\Users\a'su's\AppData\Local\Programs\Python\Python310\Lib\site-packages\cv2\data\haarcascade_frontalface_alt.xml")
-
-    def background(self):
-        self.pushButton.clicked.connect(self.open_camera)
-        self.pushButton_2.clicked.connect(self.back2)
-
-        self.pushButton.setEnabled(True)
-
-    def back3(self):
-        LoginWindow.show()
-        self.close()
-
-    def back2(self):
-        SetWindow.show()
-        self.close()
-
-    def on_camera_selected(self, index):
-        # 根据下拉列表的选择设置camera_id
-        if index == 0:  # 本地相机
-            self.CAM_NUM = 0
-        elif index == 1:  # 外置相机
-            self.CAM_NUM = 1
-    # 打开相机采集视频
-    def open_camera(self):
-        StartWindow.show()
-        self.close()
-    # 关闭相机
-    def close_camera(self):
-        self.cap.release()
-        self.pushButton.setEnabled(True)
-        self.pushButton_2.setEnabled(False)
-        self.timer.stop()
-
-    # 播放视频画面
-    def init_timer(self):
-        self.timer = QTimer(self)
-        self.timer.timeout.connect(self.show_pic)
-
-    # 显示视频图像
-    def show_pic(self):
-        ret, img = self.cap.read()
-        if ret:
-            # 人脸检测
-            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-            faces = self.face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
-
-            # 在人脸上画框
-            for (x, y, w, h) in faces:
-                cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
-
-            cur_frame = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-            height, width = cur_frame.shape[:2]
-            pixmap = QImage(cur_frame, width, height, QImage.Format_RGB888)
-            pixmap = QPixmap.fromImage(pixmap)
-            ratio = max(width / self.label.width(), height / self.label.height())
-            pixmap.setDevicePixelRatio(ratio)
-            self.label.setAlignment(Qt.AlignCenter)
-            self.label.setPixmap(pixmap)
-
-if __name__ == '__main__':
-
-    # 连接到SQLite数据库，建立数据库文件,如果文件不存在，自动建立。
-    conn = sqlite3.connect('F:\BaiduNetdiskDownload\pyqt\pyqt\mrsoft.db')
-    # 创建一个cursor（游标）
-    cursor = conn.cursor()
-    # 执行一条SQL语句，创建user，包括账号user_id与密码password（只能执行一次，执行一次以后会报错，提示 table user已经存在，所以需要用#注释掉，但第一运行时请删除注释符#）
-#    cursor.execute('create table user (user_id varchar(30) primary key, password varchar)')
-    # 关闭游标
-    cursor.close()
-    # 提交事务
-    conn.commit()
-    # 关闭connection
-    conn.close()
-
-
-    # 将按钮与页面打开或者相应的功能函数连接起来，分别是注册账号按钮，注册按钮，登录按钮
-
-    app = QApplication(sys.argv)
-    MainWindow = MainWindow()
-    # 将CAM.py中的信号连接到main.py中的槽函数
-    MainWindow.comboBox.currentIndexChanged.connect(MainWindow.on_camera_selected)
-    ResetWindow = ResetWindow()
-    RegisterWindow = RegisterWindow()
-    StartWindow = StartWindow()
-    LoginWindow = LoginWindow()
-    LoginWindow.show()
-    SetWindow = SetWindow(MainWindow)
-    sys.exit(app.exec_())
-

doc/文档1/自主开发代码/main.spec

@@ -1,38 +0,0 @@
-# -*- mode: python ; coding: utf-8 -*-
-
-
-a = Analysis(
-    ['main.py'],
-    pathex=[],
-    binaries=[],
-    datas=[],
-    hiddenimports=[],
-    hookspath=[],
-    hooksconfig={},
-    runtime_hooks=[],
-    excludes=[],
-    noarchive=False,
-    optimize=0,
-)
-pyz = PYZ(a.pure)
-
-exe = EXE(
-    pyz,
-    a.scripts,
-    a.binaries,
-    a.datas,
-    [],
-    name='main',
-    debug=False,
-    bootloader_ignore_signals=False,
-    strip=False,
-    upx=True,
-    upx_exclude=[],
-    runtime_tmpdir=None,
-    console=True,
-    disable_windowed_traceback=False,
-    argv_emulation=False,
-    target_arch=None,
-    codesign_identity=None,
-    entitlements_file=None,
-)

doc/文档1/自主开发代码/register.ui

@@ -1,128 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<ui version="4.0">
- <class>RegWindow</class>
- <widget class="QMainWindow" name="RegWindow">
-  <property name="geometry">
-   <rect>
-    <x>0</x>
-    <y>0</y>
-    <width>800</width>
-    <height>600</height>
-   </rect>
-  </property>
-  <property name="minimumSize">
-   <size>
-    <width>650</width>
-    <height>500</height>
-   </size>
-  </property>
-  <property name="windowTitle">
-   <string>MainWindow</string>
-  </property>
-  <widget class="QWidget" name="centralwidget">
-   <widget class="QFrame" name="frame">
-    <property name="geometry">
-     <rect>
-      <x>20</x>
-      <y>40</y>
-      <width>631</width>
-      <height>500</height>
-     </rect>
-    </property>
-    <property name="minimumSize">
-     <size>
-      <width>631</width>
-      <height>500</height>
-     </size>
-    </property>
-    <property name="styleSheet">
-     <string notr="true">background-color: rgb(174, 200, 199);</string>
-    </property>
-    <property name="frameShape">
-     <enum>QFrame::StyledPanel</enum>
-    </property>
-    <property name="frameShadow">
-     <enum>QFrame::Raised</enum>
-    </property>
-    <layout class="QVBoxLayout" name="verticalLayout">
-     <item alignment="Qt::AlignHCenter">
-      <widget class="QLineEdit" name="lineEdit">
-       <property name="minimumSize">
-        <size>
-         <width>250</width>
-         <height>25</height>
-        </size>
-       </property>
-       <property name="styleSheet">
-        <string notr="true">background-color: rgb(255, 255, 255);</string>
-       </property>
-       <property name="placeholderText">
-        <string>账号：</string>
-       </property>
-      </widget>
-     </item>
-     <item alignment="Qt::AlignHCenter">
-      <widget class="QLineEdit" name="lineEdit_2">
-       <property name="minimumSize">
-        <size>
-         <width>250</width>
-         <height>25</height>
-        </size>
-       </property>
-       <property name="styleSheet">
-        <string notr="true">background-color: rgb(255, 255, 255);</string>
-       </property>
-       <property name="placeholderText">
-        <string>密码：</string>
-       </property>
-      </widget>
-     </item>
-     <item alignment="Qt::AlignHCenter">
-      <widget class="QLineEdit" name="lineEdit_3">
-       <property name="minimumSize">
-        <size>
-         <width>250</width>
-         <height>25</height>
-        </size>
-       </property>
-       <property name="styleSheet">
-        <string notr="true">background-color: rgb(255, 255, 255);</string>
-       </property>
-       <property name="placeholderText">
-        <string>确认密码</string>
-       </property>
-      </widget>
-     </item>
-     <item alignment="Qt::AlignHCenter">
-      <widget class="QPushButton" name="pushButton">
-       <property name="minimumSize">
-        <size>
-         <width>100</width>
-         <height>25</height>
-        </size>
-       </property>
-       <property name="font">
-        <font>
-         <weight>75</weight>
-         <bold>true</bold>
-        </font>
-       </property>
-       <property name="text">
-        <string>确认注册</string>
-       </property>
-      </widget>
-     </item>
-     <item alignment="Qt::AlignHCenter">
-      <widget class="QPushButton" name="pushButton_2">
-       <property name="text">
-        <string>返回</string>
-       </property>
-      </widget>
-     </item>
-    </layout>
-   </widget>
-  </widget>
- </widget>
- <resources/>
- <connections/>
-</ui>

doc/文档1/自主开发代码/register_ui.py

@@ -1,68 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Form implementation generated from reading ui file 'D:\pythonProjects\pyqt\register.ui'
-#
-# Created by: PyQt5 UI code generator 5.15.9
-#
-# WARNING: Any manual changes made to this file will be lost when pyuic5 is
-# run again.  Do not edit this file unless you know what you are doing.
-
-
-from PyQt5 import QtCore, QtGui, QtWidgets
-
-
-class Ui_RegWindow(object):
-    def setupUi(self, RegWindow):
-        RegWindow.setObjectName("RegWindow")
-        RegWindow.resize(800, 600)
-        RegWindow.setMinimumSize(QtCore.QSize(650, 500))
-        self.centralwidget = QtWidgets.QWidget(RegWindow)
-        self.centralwidget.setObjectName("centralwidget")
-        self.frame = QtWidgets.QFrame(self.centralwidget)
-        self.frame.setGeometry(QtCore.QRect(20, 40, 631, 500))
-        self.frame.setMinimumSize(QtCore.QSize(631, 500))
-        self.frame.setStyleSheet("background-color: rgb(174, 200, 199);")
-        self.frame.setFrameShape(QtWidgets.QFrame.StyledPanel)
-        self.frame.setFrameShadow(QtWidgets.QFrame.Raised)
-        self.frame.setObjectName("frame")
-        self.verticalLayout = QtWidgets.QVBoxLayout(self.frame)
-        self.verticalLayout.setObjectName("verticalLayout")
-        self.lineEdit = QtWidgets.QLineEdit(self.frame)
-        self.lineEdit.setMinimumSize(QtCore.QSize(250, 25))
-        self.lineEdit.setStyleSheet("background-color: rgb(255, 255, 255);")
-        self.lineEdit.setObjectName("lineEdit")
-        self.verticalLayout.addWidget(self.lineEdit, 0, QtCore.Qt.AlignHCenter)
-        self.lineEdit_2 = QtWidgets.QLineEdit(self.frame)
-        self.lineEdit_2.setMinimumSize(QtCore.QSize(250, 25))
-        self.lineEdit_2.setStyleSheet("background-color: rgb(255, 255, 255);")
-        self.lineEdit_2.setObjectName("lineEdit_2")
-        self.verticalLayout.addWidget(self.lineEdit_2, 0, QtCore.Qt.AlignHCenter)
-        self.lineEdit_3 = QtWidgets.QLineEdit(self.frame)
-        self.lineEdit_3.setMinimumSize(QtCore.QSize(250, 25))
-        self.lineEdit_3.setStyleSheet("background-color: rgb(255, 255, 255);")
-        self.lineEdit_3.setObjectName("lineEdit_3")
-        self.verticalLayout.addWidget(self.lineEdit_3, 0, QtCore.Qt.AlignHCenter)
-        self.pushButton = QtWidgets.QPushButton(self.frame)
-        self.pushButton.setMinimumSize(QtCore.QSize(100, 25))
-        font = QtGui.QFont()
-        font.setBold(True)
-        font.setWeight(75)
-        self.pushButton.setFont(font)
-        self.pushButton.setObjectName("pushButton")
-        self.verticalLayout.addWidget(self.pushButton, 0, QtCore.Qt.AlignHCenter)
-        self.pushButton_2 = QtWidgets.QPushButton(self.frame)
-        self.pushButton_2.setObjectName("pushButton_2")
-        self.verticalLayout.addWidget(self.pushButton_2, 0, QtCore.Qt.AlignHCenter)
-        RegWindow.setCentralWidget(self.centralwidget)
-
-        self.retranslateUi(RegWindow)
-        QtCore.QMetaObject.connectSlotsByName(RegWindow)
-
-    def retranslateUi(self, RegWindow):
-        _translate = QtCore.QCoreApplication.translate
-        RegWindow.setWindowTitle(_translate("RegWindow", "MainWindow"))
-        self.lineEdit.setPlaceholderText(_translate("RegWindow", "账号："))
-        self.lineEdit_2.setPlaceholderText(_translate("RegWindow", "密码："))
-        self.lineEdit_3.setPlaceholderText(_translate("RegWindow", "确认密码"))
-        self.pushButton.setText(_translate("RegWindow", "确认注册"))
-        self.pushButton_2.setText(_translate("RegWindow", "返回"))

doc/文档1/自主开发代码/reset.ui

@@ -1,128 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<ui version="4.0">
- <class>RetWindow</class>
- <widget class="QMainWindow" name="RetWindow">
-  <property name="geometry">
-   <rect>
-    <x>0</x>
-    <y>0</y>
-    <width>800</width>
-    <height>600</height>
-   </rect>
-  </property>
-  <property name="minimumSize">
-   <size>
-    <width>600</width>
-    <height>0</height>
-   </size>
-  </property>
-  <property name="windowTitle">
-   <string>MainWindow</string>
-  </property>
-  <widget class="QWidget" name="centralwidget">
-   <widget class="QFrame" name="frame">
-    <property name="geometry">
-     <rect>
-      <x>40</x>
-      <y>40</y>
-      <width>600</width>
-      <height>500</height>
-     </rect>
-    </property>
-    <property name="minimumSize">
-     <size>
-      <width>600</width>
-      <height>500</height>
-     </size>
-    </property>
-    <property name="styleSheet">
-     <string notr="true">background-color: rgb(187, 200, 190);</string>
-    </property>
-    <property name="frameShape">
-     <enum>QFrame::StyledPanel</enum>
-    </property>
-    <property name="frameShadow">
-     <enum>QFrame::Raised</enum>
-    </property>
-    <layout class="QVBoxLayout" name="verticalLayout">
-     <property name="spacing">
-      <number>0</number>
-     </property>
-     <property name="leftMargin">
-      <number>0</number>
-     </property>
-     <property name="topMargin">
-      <number>0</number>
-     </property>
-     <property name="rightMargin">
-      <number>0</number>
-     </property>
-     <property name="bottomMargin">
-      <number>0</number>
-     </property>
-     <item alignment="Qt::AlignHCenter">
-      <widget class="QLineEdit" name="lineEdit">
-       <property name="minimumSize">
-        <size>
-         <width>250</width>
-         <height>25</height>
-        </size>
-       </property>
-       <property name="placeholderText">
-        <string>账号：</string>
-       </property>
-      </widget>
-     </item>
-     <item alignment="Qt::AlignHCenter">
-      <widget class="QLineEdit" name="lineEdit_2">
-       <property name="minimumSize">
-        <size>
-         <width>250</width>
-         <height>25</height>
-        </size>
-       </property>
-       <property name="placeholderText">
-        <string>密码：</string>
-       </property>
-      </widget>
-     </item>
-     <item alignment="Qt::AlignHCenter">
-      <widget class="QLineEdit" name="lineEdit_3">
-       <property name="minimumSize">
-        <size>
-         <width>250</width>
-         <height>25</height>
-        </size>
-       </property>
-       <property name="placeholderText">
-        <string>确认密码：</string>
-       </property>
-      </widget>
-     </item>
-     <item alignment="Qt::AlignHCenter">
-      <widget class="QPushButton" name="pushButton">
-       <property name="minimumSize">
-        <size>
-         <width>80</width>
-         <height>25</height>
-        </size>
-       </property>
-       <property name="text">
-        <string>密码重置</string>
-       </property>
-      </widget>
-     </item>
-     <item alignment="Qt::AlignHCenter">
-      <widget class="QPushButton" name="pushButton_2">
-       <property name="text">
-        <string>返回</string>
-       </property>
-      </widget>
-     </item>
-    </layout>
-   </widget>
-  </widget>
- </widget>
- <resources/>
- <connections/>
-</ui>

doc/文档1/自主开发代码/reset_ui.py

@@ -1,63 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Form implementation generated from reading ui file 'D:\pythonProjects\pyqt\reset.ui'
-#
-# Created by: PyQt5 UI code generator 5.15.9
-#
-# WARNING: Any manual changes made to this file will be lost when pyuic5 is
-# run again.  Do not edit this file unless you know what you are doing.
-
-
-from PyQt5 import QtCore, QtGui, QtWidgets
-
-
-class Ui_RetWindow(object):
-    def setupUi(self, RetWindow):
-        RetWindow.setObjectName("RetWindow")
-        RetWindow.resize(800, 600)
-        RetWindow.setMinimumSize(QtCore.QSize(600, 0))
-        self.centralwidget = QtWidgets.QWidget(RetWindow)
-        self.centralwidget.setObjectName("centralwidget")
-        self.frame = QtWidgets.QFrame(self.centralwidget)
-        self.frame.setGeometry(QtCore.QRect(40, 40, 600, 500))
-        self.frame.setMinimumSize(QtCore.QSize(600, 500))
-        self.frame.setStyleSheet("background-color: rgb(187, 200, 190);")
-        self.frame.setFrameShape(QtWidgets.QFrame.StyledPanel)
-        self.frame.setFrameShadow(QtWidgets.QFrame.Raised)
-        self.frame.setObjectName("frame")
-        self.verticalLayout = QtWidgets.QVBoxLayout(self.frame)
-        self.verticalLayout.setContentsMargins(0, 0, 0, 0)
-        self.verticalLayout.setSpacing(0)
-        self.verticalLayout.setObjectName("verticalLayout")
-        self.lineEdit = QtWidgets.QLineEdit(self.frame)
-        self.lineEdit.setMinimumSize(QtCore.QSize(250, 25))
-        self.lineEdit.setObjectName("lineEdit")
-        self.verticalLayout.addWidget(self.lineEdit, 0, QtCore.Qt.AlignHCenter)
-        self.lineEdit_2 = QtWidgets.QLineEdit(self.frame)
-        self.lineEdit_2.setMinimumSize(QtCore.QSize(250, 25))
-        self.lineEdit_2.setObjectName("lineEdit_2")
-        self.verticalLayout.addWidget(self.lineEdit_2, 0, QtCore.Qt.AlignHCenter)
-        self.lineEdit_3 = QtWidgets.QLineEdit(self.frame)
-        self.lineEdit_3.setMinimumSize(QtCore.QSize(250, 25))
-        self.lineEdit_3.setObjectName("lineEdit_3")
-        self.verticalLayout.addWidget(self.lineEdit_3, 0, QtCore.Qt.AlignHCenter)
-        self.pushButton = QtWidgets.QPushButton(self.frame)
-        self.pushButton.setMinimumSize(QtCore.QSize(80, 25))
-        self.pushButton.setObjectName("pushButton")
-        self.verticalLayout.addWidget(self.pushButton, 0, QtCore.Qt.AlignHCenter)
-        self.pushButton_2 = QtWidgets.QPushButton(self.frame)
-        self.pushButton_2.setObjectName("pushButton_2")
-        self.verticalLayout.addWidget(self.pushButton_2, 0, QtCore.Qt.AlignHCenter)
-        RetWindow.setCentralWidget(self.centralwidget)
-
-        self.retranslateUi(RetWindow)
-        QtCore.QMetaObject.connectSlotsByName(RetWindow)
-
-    def retranslateUi(self, RetWindow):
-        _translate = QtCore.QCoreApplication.translate
-        RetWindow.setWindowTitle(_translate("RetWindow", "MainWindow"))
-        self.lineEdit.setPlaceholderText(_translate("RetWindow", "账号："))
-        self.lineEdit_2.setPlaceholderText(_translate("RetWindow", "密码："))
-        self.lineEdit_3.setPlaceholderText(_translate("RetWindow", "确认密码："))
-        self.pushButton.setText(_translate("RetWindow", "密码重置"))
-        self.pushButton_2.setText(_translate("RetWindow", "返回"))

doc/文档1/自主开发代码/set.py

@@ -1,46 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Form implementation generated from reading ui file 'set.ui'
-#
-# Created by: PyQt5 UI code generator 5.15.9
-#
-# WARNING: Any manual changes made to this file will be lost when pyuic5 is
-# run again.  Do not edit this file unless you know what you are doing.
-
-
-from PyQt5 import QtCore, QtGui, QtWidgets
-
-
-class Ui_MainWindow(object):
-    def setupUi(self, MainWindow):
-        MainWindow.setObjectName("MainWindow")
-        MainWindow.resize(1045, 640)
-        self.centralwidget = QtWidgets.QWidget(MainWindow)
-        self.centralwidget.setObjectName("centralwidget")
-        self.fontButton = QtWidgets.QPushButton(self.centralwidget)
-        self.fontButton.setGeometry(QtCore.QRect(390, 150, 91, 31))
-        self.fontButton.setObjectName("fontButton")
-        self.background = QtWidgets.QPushButton(self.centralwidget)
-        self.background.setGeometry(QtCore.QRect(390, 210, 91, 31))
-        self.background.setObjectName("pushButton_2")
-        self.backButton = QtWidgets.QPushButton(self.centralwidget)
-        self.backButton.setGeometry(QtCore.QRect(390, 320, 91, 31))
-        self.backButton.setObjectName("pushButton_3")
-        self.videoButton = QtWidgets.QPushButton(self.centralwidget)
-        self.videoButton.setGeometry(QtCore.QRect(390, 270, 91, 31))
-        self.videoButton.setObjectName("pushButton_4")
-        MainWindow.setCentralWidget(self.centralwidget)
-        self.statusbar = QtWidgets.QStatusBar(MainWindow)
-        self.statusbar.setObjectName("statusbar")
-        MainWindow.setStatusBar(self.statusbar)
-
-        self.retranslateUi(MainWindow)
-        QtCore.QMetaObject.connectSlotsByName(MainWindow)
-
-    def retranslateUi(self, MainWindow):
-        _translate = QtCore.QCoreApplication.translate
-        MainWindow.setWindowTitle(_translate("MainWindow", "MainWindow"))
-        self.fontButton.setText(_translate("MainWindow", "字体"))
-        self.background.setText(_translate("MainWindow", "背景图片"))
-        self.backButton.setText(_translate("MainWindow", "退回"))
-        self.videoButton.setText(_translate("MainWindow", "本地视频"))

doc/文档1/自主开发代码/set.ui

@@ -1,74 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<ui version="4.0">
- <class>MainWindow</class>
- <widget class="QMainWindow" name="MainWindow">
-  <property name="geometry">
-   <rect>
-    <x>0</x>
-    <y>0</y>
-    <width>1045</width>
-    <height>640</height>
-   </rect>
-  </property>
-  <property name="windowTitle">
-   <string>MainWindow</string>
-  </property>
-  <widget class="QWidget" name="centralwidget">
-   <widget class="QPushButton" name="pushButton">
-    <property name="geometry">
-     <rect>
-      <x>390</x>
-      <y>150</y>
-      <width>91</width>
-      <height>31</height>
-     </rect>
-    </property>
-    <property name="text">
-     <string>字体</string>
-    </property>
-   </widget>
-   <widget class="QPushButton" name="pushButton_2">
-    <property name="geometry">
-     <rect>
-      <x>390</x>
-      <y>210</y>
-      <width>91</width>
-      <height>31</height>
-     </rect>
-    </property>
-    <property name="text">
-     <string>背景图片</string>
-    </property>
-   </widget>
-   <widget class="QPushButton" name="pushButton_3">
-    <property name="geometry">
-     <rect>
-      <x>390</x>
-      <y>320</y>
-      <width>91</width>
-      <height>31</height>
-     </rect>
-    </property>
-    <property name="text">
-     <string>退回</string>
-    </property>
-   </widget>
-   <widget class="QPushButton" name="pushButton_4">
-    <property name="geometry">
-     <rect>
-      <x>390</x>
-      <y>270</y>
-      <width>91</width>
-      <height>31</height>
-     </rect>
-    </property>
-    <property name="text">
-     <string>本地视频</string>
-    </property>
-   </widget>
-  </widget>
-  <widget class="QStatusBar" name="statusbar"/>
- </widget>
- <resources/>
- <connections/>
-</ui>

doc/文档1/自主开发代码/sobel算子.py

@@ -1,29 +0,0 @@
-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-
-# 读取图像
-image_path = r"F:\photos\p2.jpg"
-image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
-
-# 使用 Sobel 算子进行边缘检测
-sobel_x = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=3)
-sobel_y = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=3)
-
-# 计算梯度幅值
-sobel_magnitude = np.sqrt(sobel_x**2 + sobel_y**2)
-
-# 将结果转换为 8 位无符号整数
-sobel_magnitude = np.uint8(sobel_magnitude)
-
-# 显示结果
-plt.figure(figsize=(10, 5))
-plt.subplot(1, 2, 1)
-plt.title('Original Image')
-plt.imshow(image, cmap='gray')
-
-plt.subplot(1, 2, 2)
-plt.title('Sobel Edge Detection')
-plt.imshow(sobel_magnitude, cmap='gray')
-
-plt.show()

doc/文档1/自主开发代码/start.py

@@ -1,56 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Form implementation generated from reading ui file 'start.ui'
-#
-# Created by: PyQt5 UI code generator 5.15.9
-#
-# WARNING: Any manual changes made to this file will be lost when pyuic5 is
-# run again.  Do not edit this file unless you know what you are doing.
-
-
-from PyQt5 import QtCore, QtGui, QtWidgets
-
-
-class Ui_MainWindow(object):
-    def setupUi(self, MainWindow):
-        MainWindow.setObjectName("MainWindow")
-        MainWindow.resize(698, 640)
-        self.centralwidget = QtWidgets.QWidget(MainWindow)
-        self.centralwidget.setObjectName("centralwidget")
-        self.label = QtWidgets.QLabel(self.centralwidget)
-        self.label.setGeometry(QtCore.QRect(190, 160, 71, 20))
-        self.label.setObjectName("label")
-        self.label_2 = QtWidgets.QLabel(self.centralwidget)
-        self.label_2.setGeometry(QtCore.QRect(190, 200, 71, 21))
-        self.label_2.setObjectName("label_2")
-        self.label_3 = QtWidgets.QLabel(self.centralwidget)
-        self.label_3.setGeometry(QtCore.QRect(190, 250, 71, 31))
-        self.label_3.setObjectName("label_3")
-        self.lineEdit = QtWidgets.QLineEdit(self.centralwidget)
-        self.lineEdit.setGeometry(QtCore.QRect(280, 160, 161, 20))
-        self.lineEdit.setObjectName("lineEdit")
-        self.lineEdit_2 = QtWidgets.QLineEdit(self.centralwidget)
-        self.lineEdit_2.setGeometry(QtCore.QRect(280, 200, 161, 20))
-        self.lineEdit_2.setObjectName("lineEdit_2")
-        self.lineEdit_3 = QtWidgets.QLineEdit(self.centralwidget)
-        self.lineEdit_3.setGeometry(QtCore.QRect(280, 260, 161, 20))
-        self.lineEdit_3.setObjectName("lineEdit_3")
-        self.startButton = QtWidgets.QPushButton(self.centralwidget)
-        self.startButton.setGeometry(QtCore.QRect(200, 320, 91, 31))
-        self.startButton.setObjectName("startButton")
-        self.closeButton = QtWidgets.QPushButton(self.centralwidget)
-        self.closeButton.setGeometry(QtCore.QRect(360, 320, 91, 31))
-        self.closeButton.setObjectName("closeButton")
-        MainWindow.setCentralWidget(self.centralwidget)
-
-        self.retranslateUi(MainWindow)
-        QtCore.QMetaObject.connectSlotsByName(MainWindow)
-
-    def retranslateUi(self, MainWindow):
-        _translate = QtCore.QCoreApplication.translate
-        MainWindow.setWindowTitle(_translate("MainWindow", "MainWindow"))
-        self.label.setText(_translate("MainWindow", "开始时间："))
-        self.label_2.setText(_translate("MainWindow", "结束时间："))
-        self.label_3.setText(_translate("MainWindow", "学习比例："))
-        self.startButton.setText(_translate("MainWindow", "开始学习"))
-        self.closeButton.setText(_translate("MainWindow", "取消"))

doc/文档1/自主开发代码/start.ui

@@ -1,116 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<ui version="4.0">
- <class>MainWindow</class>
- <widget class="QMainWindow" name="MainWindow">
-  <property name="geometry">
-   <rect>
-    <x>0</x>
-    <y>0</y>
-    <width>698</width>
-    <height>640</height>
-   </rect>
-  </property>
-  <property name="windowTitle">
-   <string>MainWindow</string>
-  </property>
-  <widget class="QWidget" name="centralwidget">
-   <widget class="QLabel" name="label">
-    <property name="geometry">
-     <rect>
-      <x>190</x>
-      <y>160</y>
-      <width>71</width>
-      <height>20</height>
-     </rect>
-    </property>
-    <property name="text">
-     <string>开始时间：</string>
-    </property>
-   </widget>
-   <widget class="QLabel" name="label_2">
-    <property name="geometry">
-     <rect>
-      <x>190</x>
-      <y>200</y>
-      <width>71</width>
-      <height>21</height>
-     </rect>
-    </property>
-    <property name="text">
-     <string>结束时间：</string>
-    </property>
-   </widget>
-   <widget class="QLabel" name="label_3">
-    <property name="geometry">
-     <rect>
-      <x>190</x>
-      <y>250</y>
-      <width>71</width>
-      <height>31</height>
-     </rect>
-    </property>
-    <property name="text">
-     <string>学习时间：</string>
-    </property>
-   </widget>
-   <widget class="QLineEdit" name="lineEdit">
-    <property name="geometry">
-     <rect>
-      <x>280</x>
-      <y>160</y>
-      <width>161</width>
-      <height>20</height>
-     </rect>
-    </property>
-   </widget>
-   <widget class="QLineEdit" name="lineEdit_2">
-    <property name="geometry">
-     <rect>
-      <x>280</x>
-      <y>200</y>
-      <width>161</width>
-      <height>20</height>
-     </rect>
-    </property>
-   </widget>
-   <widget class="QLineEdit" name="lineEdit_3">
-    <property name="geometry">
-     <rect>
-      <x>280</x>
-      <y>260</y>
-      <width>161</width>
-      <height>20</height>
-     </rect>
-    </property>
-   </widget>
-   <widget class="QPushButton" name="startButton">
-    <property name="geometry">
-     <rect>
-      <x>200</x>
-      <y>320</y>
-      <width>91</width>
-      <height>31</height>
-     </rect>
-    </property>
-    <property name="text">
-     <string>开始学习</string>
-    </property>
-   </widget>
-   <widget class="QPushButton" name="closeButton">
-    <property name="geometry">
-     <rect>
-      <x>360</x>
-      <y>320</y>
-      <width>91</width>
-      <height>31</height>
-     </rect>
-    </property>
-    <property name="text">
-     <string>取消</string>
-    </property>
-   </widget>
-  </widget>
- </widget>
- <resources/>
- <connections/>
-</ui>

doc/文档1/自主开发代码/test.py

@@ -1,25 +0,0 @@
-import sqlite3
-
-# 连接到SQLite数据库
-conn = sqlite3.connect('mrsoft.db')
-
-# 创建一个游标
-cursor = conn.cursor()
-
-# 查询所有用户账户
-cursor.execute('SELECT * FROM user')
-
-# 获取所有结果
-users = cursor.fetchall()
-
-# 检查是否有用户
-if users:
-    print("已创建的账户如下：")
-    for user in users:
-        print(f"User ID: {user[0]}, Password: {user[1]}")
-else:
-    print("没有找到任何账户。")
-
-# 关闭游标和连接
-cursor.close()
-conn.close()

doc/文档1/自主开发代码/test2.py

@@ -1,82 +0,0 @@
-import sys
-from PyQt5.QtWidgets import QMessageBox, QFileDialog, QLineEdit
-from PyQt5.QtGui import *
-from PyQt5.QtWidgets import *
-from PyQt5.QtCore import *
-from CAM import Ui_MainWindow
-import cv2
-
-
-class MainWindow(QMainWindow, Ui_MainWindow):
-    def __init__(self, parent=None):
-        super(MainWindow, self).__init__(parent)
-        # UI界面
-        self.setupUi(self)
-        self.CAM_NUM = 0
-        self.cap = cv2.VideoCapture()
-        self.background()
-        # 在label中播放视频
-        self.init_timer()
-
-        # 加载人脸检测分类器
-        self.face_cascade = cv2.CascadeClassifier(r"C:\Users\a'su's\AppData\Local\Programs\Python\Python310\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml")
-
-    def background(self):
-        self.pushButton.clicked.connect(self.open_camera)
-        self.pushButton_2.clicked.connect(self.close_camera)
-
-        self.pushButton.setEnabled(True)
-        self.pushButton_2.setEnabled(False)
-
-    # 打开相机采集视频
-    def open_camera(self):
-        index = self.comboBox.currentIndex()
-        self.CAM_NUM = index
-        flag = self.cap.open(self.CAM_NUM)
-        if flag is False:
-            QMessageBox.information(self, "警告", "该设备未正常连接", QMessageBox.Ok)
-        else:
-            self.label.setEnabled(True)
-            self.pushButton.setEnabled(False)
-            self.pushButton_2.setEnabled(True)
-            self.timer.start()
-            print("beginning！")
-
-    # 关闭相机
-    def close_camera(self):
-        self.cap.release()
-        self.pushButton.setEnabled(True)
-        self.pushButton_2.setEnabled(False)
-        self.timer.stop()
-
-    # 播放视频画面
-    def init_timer(self):
-        self.timer = QTimer(self)
-        self.timer.timeout.connect(self.show_pic)
-
-    # 显示视频图像
-    def show_pic(self):
-        ret, img = self.cap.read()
-        if ret:
-            # 人脸检测
-            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-            faces = self.face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
-
-            # 在人脸上画框
-            for (x, y, w, h) in faces:
-                cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
-
-            cur_frame = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-            height, width = cur_frame.shape[:2]
-            pixmap = QImage(cur_frame, width, height, QImage.Format_RGB888)
-            pixmap = QPixmap.fromImage(pixmap)
-            ratio = max(width / self.label.width(), height / self.label.height())
-            pixmap.setDevicePixelRatio(ratio)
-            self.label.setAlignment(Qt.AlignCenter)
-            self.label.setPixmap(pixmap)
-
-if __name__ == '__main__':
-    app = QApplication(sys.argv)
-    main = MainWindow()
-    main.show()
-    sys.exit(app.exec_())

doc/文档1/自主开发代码/test3.py

@@ -1,69 +0,0 @@
-import cv2
-import time
-import os
-
-def create_training_directory(path_name):
-    if not os.path.exists(path_name):
-        os.makedirs(path_name)
-
-def capture_faces_with_timer(window_name, camera_id, path_name, max_num, monitor_time):
-    cv2.namedWindow(window_name)
-    cap = cv2.VideoCapture(camera_id)
-    pathf = r"C:\Users\a'su's\AppData\Local\Programs\Python\Python310\Lib\site-packages\cv2\data\haarcascade_frontalface_alt.xml"
-
-    # 载入人脸检测器，眼睛检测器，微笑检测器
-    face_cascade = cv2.CascadeClassifier(pathf)
-
-
-
-    color = (0, 255, 0)
-    face_count = 0
-    countdown_started = False
-    remaining_time = monitor_time
-
-    create_training_directory(path_name)
-
-    while cap.isOpened() and face_count < max_num:
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-        faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-
-        if len(faces) > 0:
-            if not countdown_started:
-                countdown_started = True
-                start_time = time.time()
-
-            remaining_time = monitor_time - int(time.time() - start_time)
-
-            for (x, y, w, h) in faces:
-                face_image = frame[y:y + h, x:x + w]
-                image_name = os.path.join(path_name, f'face_{face_count}.jpg')
-                cv2.imwrite(image_name, face_image)
-
-                face_count += 1
-                cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
-
-            # Display countdown timer
-            cv2.putText(frame, f'Time Left: {remaining_time}s', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
-
-        else:
-            # If no faces detected, keep countdown paused
-            if countdown_started:
-                remaining_time = max(0, remaining_time)
-
-        cv2.imshow(window_name, frame)
-
-        if remaining_time <= 0 or (cv2.waitKey(10) & 0xFF == ord('q')):
-            break
-
-    cap.release()
-    cv2.destroyAllWindows()
-    print('Finished capturing faces.')
-
-if __name__ == '__main__':
-    monitor_time = int(input('Enter monitoring time in seconds: '))
-    print('Starting face capture...')
-    capture_faces_with_timer('FaceCapture', 0, 'training_data_faces/', 1000, monitor_time)

doc/文档1/自主开发代码/test4.py

@@ -1,96 +0,0 @@
-import cv2
-import time
-import os
-from datetime import datetime, timedelta
-
-
-def create_training_directory(path_name):
-    if not os.path.exists(path_name):
-        os.makedirs(path_name)
-
-
-def is_within_time_range(start_time_str, end_time_str):
-    """判断当前时间是否在指定的时间范围内"""
-    now = datetime.now().time()
-    start_time = datetime.strptime(start_time_str, "%H:%M").time()  # 24小时制
-    end_time = datetime.strptime(end_time_str, "%H:%M").time()
-    return start_time <= now <= end_time
-
-
-def calculate_required_time(start_time_str, end_time_str, percentage):
-    """计算所需的最少时间（分钟）"""
-    start_time = datetime.strptime(start_time_str, "%H:%M")
-    end_time = datetime.strptime(end_time_str, "%H:%M")
-    total_time = (end_time - start_time).total_seconds() / 60  # 总时长（分钟）
-    return total_time * (percentage / 100)
-
-
-def capture_faces_with_timer(window_name, camera_id, path_name, max_num, required_time, start_time_str, end_time_str):
-    cv2.namedWindow(window_name)
-    cap = cv2.VideoCapture(camera_id)
-    face_cascade = cv2.CascadeClassifier(
-         r"C:\Users\a'su's\AppData\Local\Programs\Python\Python310\Lib\site-packages\cv2\data\haarcascade_frontalface_alt.xml")
-
-    color = (0, 255, 0)
-    face_count = 0
-    countdown_started = False
-    remaining_time = required_time * 60  # 转换为秒
-
-    create_training_directory(path_name)
-
-    while cap.isOpened() and face_count < max_num:
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-        faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-
-        # 检查当前时间是否在指定的时间范围内
-        if is_within_time_range(start_time_str, end_time_str):
-            if len(faces) > 0:
-                if not countdown_started:
-                    countdown_started = True
-                    start_time = time.time()
-
-                remaining_time = required_time * 60 - int(time.time() - start_time)
-
-                for (x, y, w, h) in faces:
-                    face_image = frame[y:y + h, x:x + w]
-                    image_name = os.path.join(path_name, f'face_{face_count}.jpg')
-                    cv2.imwrite(image_name, face_image)
-
-                    face_count += 1
-                    cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
-
-                # 显示倒计时
-                cv2.putText(frame, f'Time Left: {remaining_time // 60}m {remaining_time % 60}s', (10, 30),
-                            cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
-
-                if remaining_time <= 0:
-                    break
-        else:
-            countdown_started = False
-            remaining_time = required_time * 60
-            cv2.putText(frame, 'Outside of specified time range', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
-
-        cv2.imshow(window_name, frame)
-
-        if cv2.waitKey(10) & 0xFF == ord('q'):
-            break
-
-    cap.release()
-    cv2.destroyAllWindows()
-    print('Finished capturing faces.')
-
-
-if __name__ == '__main__':
-    start_time_str = input("Enter the start time (e.g., 17:00 for 5pm): ")
-    end_time_str = input("Enter the end time (e.g., 18:00 for 6pm): ")
-    percentage = int(input("Enter the minimum percentage of the time to monitor (0-100): "))
-
-    required_time = calculate_required_time(start_time_str, end_time_str, percentage)
-
-    print(f'Starting face capture, will monitor for at least {required_time} minutes...')
-    capture_faces_with_timer('FaceCapture', 0, 'training_data_faces/', 1000, required_time, start_time_str,
-                             end_time_str)

doc/文档1/自主开发代码/test5.py

@@ -1,94 +0,0 @@
-import cv2
-import time
-import os
-from datetime import datetime, timedelta
-
-def main(start_time_str, end_time_str, percentage):
-    # 创建训练数据目录
-    def create_training_directory(path_name):
-        if not os.path.exists(path_name):
-            os.makedirs(path_name)
-
-    # 判断当前时间是否在指定的时间范围内
-    def is_within_time_range(start_time_str, end_time_str):
-        now = datetime.now().time()
-        start_time = datetime.strptime(start_time_str, "%H:%M").time()  # 24小时制
-        end_time = datetime.strptime(end_time_str, "%H:%M").time()
-        return start_time <= now <= end_time
-
-    # 计算所需的最少时间（分钟）
-    def calculate_required_time(start_time_str, end_time_str, percentage):
-        start_time = datetime.strptime(start_time_str, "%H:%M")
-        end_time = datetime.strptime(end_time_str, "%H:%M")
-        total_time = (end_time - start_time).total_seconds() / 60  # 总时长（分钟）
-        return total_time * (percentage / 100)
-
-    # 捕获人脸并计时
-    def capture_faces_with_timer(window_name, camera_id, path_name, max_num, required_time, start_time_str, end_time_str):
-        cv2.namedWindow(window_name)
-        cap = cv2.VideoCapture(camera_id)
-        face_cascade = cv2.CascadeClassifier(
-            r"C:\Users\a'su's\AppData\Local\Programs\Python\Python310\Lib\site-packages\cv2\data\haarcascade_frontalface_alt.xml")
-
-        color = (0, 255, 0)
-        face_count = 0
-        countdown_started = False
-        remaining_time = required_time * 60  # 转换为秒
-
-        create_training_directory(path_name)
-
-        while cap.isOpened() and face_count < max_num:
-            ret, frame = cap.read()
-            if not ret:
-                break
-
-            gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-            faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-
-            # 检查当前时间是否在指定的时间范围内
-            if is_within_time_range(start_time_str, end_time_str):
-                if len(faces) > 0:
-                    if not countdown_started:
-                        countdown_started = True
-                        start_time = time.time()
-
-                    remaining_time = required_time * 60 - int(time.time() - start_time)
-
-                    for (x, y, w, h) in faces:
-                        face_image = frame[y:y + h, x:x + w]
-                        image_name = os.path.join(path_name, f'face_{face_count}.jpg')
-                        cv2.imwrite(image_name, face_image)
-
-                        face_count += 1
-                        cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
-
-                    # 显示倒计时
-                    cv2.putText(frame, f'Time Left: {remaining_time // 60}m {remaining_time % 60}s', (10, 30),
-                                cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
-
-                    if remaining_time <= 0:
-                        break
-            else:
-                countdown_started = False
-                remaining_time = required_time * 60
-                cv2.putText(frame, 'Outside of specified time range', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
-
-            cv2.imshow(window_name, frame)
-
-            if cv2.waitKey(10) & 0xFF == ord('q'):
-                break
-
-        cap.release()
-        cv2.destroyAllWindows()
-        print('Finished capturing faces.')
-
-    required_time = calculate_required_time(start_time_str, end_time_str, percentage)
-    print(f'Starting face capture, will monitor for at least {required_time} minutes...')
-    capture_faces_with_timer('FaceCapture', 0, 'training_data_faces/', 1000, required_time, start_time_str, end_time_str)
-
-if __name__ == '__main__':
-    start_time_str = input("Enter the start time (e.g., 17:00 for 5pm): ")
-    end_time_str = input("Enter the end time (e.g., 18:00 for 6pm): ")
-    percentage = int(input("Enter the minimum percentage of the time to monitor (0-100): "))
-
-    main(start_time_str, end_time_str, percentage)

doc/文档1/自主开发代码/test6.py

@@ -1,199 +0,0 @@
-import cv2
-import time
-import os
-from datetime import datetime, timedelta
-import logging
-import yaml
-from PIL import Image, ImageFont, ImageDraw
-import numpy as np  # 添加导入
-
-# 初始化日志记录
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-
-def create_training_directory(path_name):
-    if not os.path.exists(path_name):
-        os.makedirs(path_name)
-
-
-def is_within_time_range(start_time_str, end_time_str):
-    """判断当前时间是否在指定的时间范围内"""
-    now = datetime.now().time()
-    start_time = datetime.strptime(start_time_str, "%H:%M").time()  # 24小时制
-    end_time = datetime.strptime(end_time_str, "%H:%M").time()
-    return start_time <= now <= end_time
-
-
-def calculate_required_time(start_time_str, end_time_str, percentage):
-    """计算所需的最少时间（分钟）"""
-    start_time = datetime.strptime(start_time_str, "%H:%M")
-    end_time = datetime.strptime(end_time_str, "%H:%M")
-    total_time = (end_time - start_time).total_seconds() / 60  # 总时长（分钟）
-    return total_time * (percentage / 100)
-
-
-def load_config(config_path='config.yaml'):
-    base_dir = os.path.dirname(os.path.abspath(__file__))  # 获取脚本所在目录
-    config_full_path = os.path.join(base_dir, config_path)  # 构建配置文件的绝对路径
-    with open(config_full_path, 'r', encoding='utf-8') as file:
-        return yaml.safe_load(file)
-
-
-def put_chinese_text(frame, text, position, font_path, font_size, color, font_cache={}):
-    """
-    使用 PIL 绘制中文文本并转换回 OpenCV 图像
-    """
-    # 检查字体文件是否存在
-    if not os.path.exists(font_path):
-        logging.error(f"字体文件未找到: {font_path}")
-        return frame
-
-    # 使用缓存的字体对象
-    if font_path in font_cache:
-        font = font_cache[font_path]
-    else:
-        try:
-            font = ImageFont.truetype(font_path, font_size)
-            font_cache[font_path] = font
-        except Exception as e:
-            logging.error(f"加载字体失败: {e}")
-            return frame
-
-    # 将 OpenCV 的 BGR 图像转换为 RGB
-    img_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-    draw = ImageDraw.Draw(img_pil)
-    # 绘制文本
-    draw.text(position, text, font=font, fill=color)
-    # 将 PIL 图像转换回 OpenCV 的 BGR 图像
-    frame = cv2.cvtColor(np.array(img_pil), cv2.COLOR_RGB2BGR)
-    return frame
-
-
-def capture_faces_with_timer(window_name, camera_id, path_name, max_num, required_time, start_time_str, end_time_str, font_path):
-    cv2.namedWindow(window_name)
-    try:
-        cap = cv2.VideoCapture(camera_id)  # 直接初始化 VideoCapture
-        face_cascade = cv2.CascadeClassifier(
-            config['face_cascade_path'])  # 使用配置文件中的路径
-
-        color = (0, 255, 0)
-        face_count = 0
-        countdown_started = False
-        remaining_time = required_time * 60  # 转换为秒
-        pause_time = None  # 初始化暂停时间
-
-        create_training_directory(path_name)
-
-        while cap.isOpened() and face_count < max_num:
-            ret, frame = cap.read()
-            if not ret:
-                break
-
-            gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-            faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-
-            # 检查当前时间是否在指定的时间范围内
-            if is_within_time_range(start_time_str, end_time_str):
-                if len(faces) > 0:
-                    if not countdown_started:
-                        countdown_started = True
-                        if pause_time is not None:
-                            # 调整 start_time 以考虑暂停的时长
-                            start_time += time.time() - pause_time
-                        else:
-                            start_time = time.time()
-
-                    remaining_time = required_time * 60 - int(time.time() - start_time)
-
-                    for (x, y, w, h) in faces:
-                        face_image = frame[y:y + h, x:x + w]
-                        image_name = os.path.join(path_name, f'face_{face_count}.jpg')
-                        cv2.imwrite(image_name, face_image)
-
-                        face_count += 1
-                        cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
-
-                    # 显示剩余学习时长
-                    text = f'剩余学习时长: {remaining_time // 60}m {remaining_time % 60}s'
-                    frame = put_chinese_text(
-                        frame,
-                        text,
-                        (10, 30),
-                        font_path,
-                        30,  # 字体大小
-                        (0, 0, 255)  # 颜色
-                    )
-
-                    if remaining_time <= 0:
-                        break
-                else:
-                    # 如果计时器已经暂停，继续显示剩余时间
-                    text = f'剩余学习时长: {remaining_time // 60}m {remaining_time % 60}s'
-                    frame = put_chinese_text(
-                        frame,
-                        text,
-                        (10, 30),
-                        font_path,
-                        30,  # 字体大小
-                        (0, 0, 255)  # 颜色
-                    )
-            else:
-                if countdown_started:
-                    logging.info('未检测到人脸，暂停计时。')
-                    countdown_started = False
-                    pause_time = time.time()  # 记录暂停时间
-                # 显示外部时间范围提示
-                frame = put_chinese_text(
-                    frame,
-                    '不在指定的时间范围内',
-                    (10, 30),
-                    font_path,
-                    30,  # 字体大小
-                    (0, 0, 255)  # 颜色
-                )
-
-            cv2.imshow(window_name, frame)
-
-            if cv2.waitKey(10) & 0xFF == ord('q'):
-                break
-
-    except Exception as e:
-        logging.error(f"发生错误: {e}")
-    finally:
-        cap.release()  # 确保摄像头资源被释放
-        cv2.destroyAllWindows()
-        logging.info('人脸捕捉完成。')
-
-
-def get_time_input(prompt):
-    while True:
-        time_str = input(prompt)
-        try:
-            datetime.strptime(time_str, "%H:%M")
-            return time_str
-        except ValueError:
-            print("时间格式错误，请输入如 17:00 这样的24小时制时间。")
-
-
-if __name__ == '__main__':
-    config = load_config()
-    start_time_str = get_time_input("Enter the start time (e.g., 17:00 for 5pm): ")
-    end_time_str = get_time_input("Enter the end time (e.g., 18:00 for 6pm): ")
-    while True:
-        try:
-            percentage = int(input("Enter the minimum percentage of the time to monitor (0-100): "))
-            if 0 <= percentage <= 100:
-                break
-            else:
-                print("请输入 0 到 100 之间的整数。")
-        except ValueError:
-            print("请输入有效的整数。")
-
-    required_time = calculate_required_time(start_time_str, end_time_str, percentage)
-
-    # 指定中文字体的路径
-    font_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'fonts', 'SimHei.ttf')  # 确保路径正确
-
-    logging.info(f'Starting face capture, will monitor for at least {required_time} minutes...')
-    capture_faces_with_timer('FaceCapture', config['camera_id'], config['path_name'], 1000, required_time, start_time_str,
-                             end_time_str, font_path)

doc/文档1/自主开发代码/图片转换.py

@@ -1,37 +0,0 @@
-import os
-import csv
-from PIL import Image
-
-def pgm_to_csv(pgm_dir, csv_file):
-    # 初始化CSV文件，并写入头部
-    with open(csv_file, 'w', newline='') as file:
-        writer = csv.writer(file)
-
-        # 假设图像大小为32x30，这里可根据实际情况调整
-        pixel_count = 960  # 32 * 30
-        # 写入CSV头部，其中filename是文件名列，后面是像素值列
-        header = ['filename'] + [f'pixel_{i}' for i in range(pixel_count)]
-        writer.writerow(header)
-
-    # 遍历文件夹中的所有PGM文件
-    for filename in os.listdir(pgm_dir):
-        if os.path.isdir(os.path.join(pgm_dir, filename)):  # 仅处理文件夹
-            for people in os.listdir(os.path.join(pgm_dir, filename)):
-                if people.endswith('.pgm'):
-                    img_path = os.path.join(pgm_dir, filename, people)
-                    # 读取PGM图像
-                    with Image.open(img_path) as img:
-                        img = img.convert('L')  # 转换为灰度图像
-                        pixels = list(img.getdata())
-                        # 将像素值写入CSV文件
-                        with open(csv_file, 'a', newline='') as file:
-                            writer = csv.writer(file)
-                            # 在每一行前添加文件名
-                            writer.writerow([people] + pixels)  # 写入文件名和像素值
-
-
-
-pgm_dir = 'F:\pr\orange'  # PGM文件所在的文件夹路径
-csv_file = 'F:\pr\change\change.csv'  # 输出CSV文件的路径
-pgm_to_csv(pgm_dir, csv_file)
-

doc/文档1/自主开发代码/图片转换2.py

@@ -1,42 +0,0 @@
-import os
-import csv
-from PIL import Image
-
-def png_to_pgm_and_csv(png_dir, pgm_dir, csv_file):
-    # 初始化CSV文件，并写入头部
-    with open(csv_file, 'w', newline='') as file:
-        writer = csv.writer(file)
-        # 假设图像大小为32x30，写入CSV头部
-        writer.writerow(['pixel_values'] + list(range(0, 4*3)) + ['filename'])
-
-    # 遍历文件夹中的所有PNG文件
-    for filename in os.listdir(png_dir):
-        if filename.endswith('.png'):
-            img_path = os.path.join(png_dir, filename)
-            # 读取PNG图像
-            with Image.open(img_path) as img:
-                img = img.convert('L')  # 转换为灰度图像
-                # 调整图像大小为32x30
-                img = img.resize((4, 3), resample=Image.Resampling.LANCZOS)
-                # 构造PGM文件路径
-                pgm_filename = filename.replace('.png', '.pgm')
-                pgm_path = os.path.join(pgm_dir, pgm_filename)
-                # 保存为PGM文件
-                img.save(pgm_path, format='PPM')  # 保存为PGM格式（PPM是PGM的一种）
-                # 获取像素值
-                pixels = list(img.getdata())
-                # 将像素值写入CSV文件
-                with open(csv_file, 'a', newline='') as file:
-                    writer = csv.writer(file)
-                    # 确保每一行有962个值（960个像素值 + 1个文件名）
-                    writer.writerow(pixels + [filename])
-
-# 使用示例
-png_dir = 'F:\\self'  # PNG文件所在的文件夹路径
-pgm_dir = 'F:\\self2\\43'  # 输出PGM文件的文件夹路径
-csv_file = 'F:\\self3\\43.csv'  # 输出CSV文件的路径
-
-# 确保PGM文件夹存在
-os.makedirs(pgm_dir, exist_ok=True)
-
-png_to_pgm_and_csv(png_dir, pgm_dir, csv_file)

doc/文档1/自主开发代码/库.py

@@ -1,199 +0,0 @@
-import cv2
-import time
-import os
-from datetime import datetime, timedelta
-import logging
-import yaml
-from PIL import Image, ImageFont, ImageDraw
-import numpy as np  # 添加导入
-
-# 初始化日志记录
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-
-def create_training_directory(path_name):
-    if not os.path.exists(path_name):
-        os.makedirs(path_name)
-
-
-def is_within_time_range(start_time_str, end_time_str):
-    """判断当前时间是否在指定的时间范围内"""
-    now = datetime.now().time()
-    start_time = datetime.strptime(start_time_str, "%H:%M").time()  # 24小时制
-    end_time = datetime.strptime(end_time_str, "%H:%M").time()
-    return start_time <= now <= end_time
-
-
-def calculate_required_time(start_time_str, end_time_str, percentage):
-    """计算所需的最少时间（分钟）"""
-    start_time = datetime.strptime(start_time_str, "%H:%M")
-    end_time = datetime.strptime(end_time_str, "%H:%M")
-    total_time = (end_time - start_time).total_seconds() / 60  # 总时长（分钟）
-    return total_time * (percentage / 100)
-
-
-def load_config(config_path='config.yaml'):
-    base_dir = os.path.dirname(os.path.abspath(__file__))  # 获取脚本所在目录
-    config_full_path = os.path.join(base_dir, config_path)  # 构建配置文件的绝对路径
-    with open(config_full_path, 'r', encoding='utf-8') as file:
-        return yaml.safe_load(file)
-
-
-def put_chinese_text(frame, text, position, font_path, font_size, color, font_cache={}):
-    """
-    使用 PIL 绘制中文文本并转换回 OpenCV 图像
-    """
-    # 检查字体文件是否存在
-    if not os.path.exists(font_path):
-        logging.error(f"字体文件未找到: {font_path}")
-        return frame
-
-    # 使用缓存的字体对象
-    if font_path in font_cache:
-        font = font_cache[font_path]
-    else:
-        try:
-            font = ImageFont.truetype(font_path, font_size)
-            font_cache[font_path] = font
-        except Exception as e:
-            logging.error(f"加载字体失败: {e}")
-            return frame
-
-    # 将 OpenCV 的 BGR 图像转换为 RGB
-    img_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-    draw = ImageDraw.Draw(img_pil)
-    # 绘制文本
-    draw.text(position, text, font=font, fill=color)
-    # 将 PIL 图像转换回 OpenCV 的 BGR 图像
-    frame = cv2.cvtColor(np.array(img_pil), cv2.COLOR_RGB2BGR)
-    return frame
-
-
-def capture_faces_with_timer(window_name, camera_id, path_name, max_num, required_time, start_time_str, end_time_str, font_path):
-    cv2.namedWindow(window_name)
-    try:
-        cap = cv2.VideoCapture(camera_id)  # 直接初始化 VideoCapture
-        face_cascade = cv2.CascadeClassifier(
-            config['face_cascade_path'])  # 使用配置文件中的路径
-
-        color = (0, 255, 0)
-        face_count = 0
-        countdown_started = False
-        remaining_time = required_time * 60  # 转换为秒
-        pause_time = None  # 初始化暂停时间
-
-        create_training_directory(path_name)
-
-        while cap.isOpened() and face_count < max_num:
-            ret, frame = cap.read()
-            if not ret:
-                break
-
-            gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-            faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-
-            # 检查当前时间是否在指定的时间范围内
-            if is_within_time_range(start_time_str, end_time_str):
-                if len(faces) > 0:
-                    if not countdown_started:
-                        countdown_started = True
-                        if pause_time is not None:
-                            # 调整 start_time 以考虑暂停的时长
-                            start_time += time.time() - pause_time
-                        else:
-                            start_time = time.time()
-
-                    remaining_time = required_time * 60 - int(time.time() - start_time)
-
-                    for (x, y, w, h) in faces:
-                        face_image = frame[y:y + h, x:x + w]
-                        image_name = os.path.join(path_name, f'face_{face_count}.jpg')
-                        cv2.imwrite(image_name, face_image)
-
-                        face_count += 1
-                        cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
-
-                    # 显示剩余学习时长
-                    text = f'剩余学习时长: {remaining_time // 60}m {remaining_time % 60}s'
-                    frame = put_chinese_text(
-                        frame,
-                        text,
-                        (10, 30),
-                        font_path,
-                        30,  # 字体大小
-                        (0, 0, 255)  # 颜色
-                    )
-
-                    if remaining_time <= 0:
-                        break
-                else:
-                    # 如果计时器已经暂停，继续显示剩余时间
-                    text = f'剩余学习时长: {remaining_time // 60}m {remaining_time % 60}s'
-                    frame = put_chinese_text(
-                        frame,
-                        text,
-                        (10, 30),
-                        font_path,
-                        30,  # 字体大小
-                        (0, 0, 255)  # 颜色
-                    )
-            else:
-                if countdown_started:
-                    logging.info('未检测到人脸，暂停计时。')
-                    countdown_started = False
-                    pause_time = time.time()  # 记录暂停时间
-                # 显示外部时间范围提示
-                frame = put_chinese_text(
-                    frame,
-                    '不在指定的时间范围内',
-                    (10, 30),
-                    font_path,
-                    30,  # 字体大小
-                    (0, 0, 255)  # 颜色
-                )
-
-            cv2.imshow(window_name, frame)
-
-            if cv2.waitKey(10) & 0xFF == ord('q'):
-                break
-
-    except Exception as e:
-        logging.error(f"发生错误: {e}")
-    finally:
-        cap.release()  # 确保摄像头资源被释放
-        cv2.destroyAllWindows()
-        logging.info('人脸捕捉完成。')
-
-
-def get_time_input(prompt):
-    while True:
-        time_str = input(prompt)
-        try:
-            datetime.strptime(time_str, "%H:%M")
-            return time_str
-        except ValueError:
-            print("时间格式错误，请输入如 17:00 这样的24小时制时间。")
-
-
-if __name__ == '__main__':
-    config = load_config()
-    start_time_str = get_time_input("Enter the start time (e.g., 17:00 for 5pm): ")
-    end_time_str = get_time_input("Enter the end time (e.g., 18:00 for 6pm): ")
-    while True:
-        try:
-            percentage = int(input("Enter the minimum percentage of the time to monitor (0-100): "))
-            if 0 <= percentage <= 100:
-                break
-            else:
-                print("请输入 0 到 100 之间的整数。")
-        except ValueError:
-            print("请输入有效的整数。")
-
-    required_time = calculate_required_time(start_time_str, end_time_str, percentage)
-
-    # 指定中文字体的路径
-    font_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'fonts', 'SimHei.ttf')  # 确保路径正确
-
-    logging.info(f'Starting face capture, will monitor for at least {required_time} minutes...')
-    capture_faces_with_timer('FaceCapture', config['camera_id'], config['path_name'], 1000, required_time, start_time_str,
-                             end_time_str, font_path)

doc/文档1/自主开发代码/数据增强.py

@@ -1,21 +0,0 @@
-# 示例代码：使用数据增强
-from sklearn.utils import resample
-import pandas as pd
-
-# 加载你自己的数据集
-data = pd.read_csv("F:\机器学习\\28-26.csv")
-# 假设 `data` 是包含所有类别的数据集
-# `self_data` 是 `self` 类别的数据
-self_data = data[data['a961'] == 'self']
-
-# 使用 resample 增加样本数量
-self_data_upsampled = resample(self_data,
-                               replace=True,            # 放回采样
-                               n_samples=20,          # 增加到10个样本
-                               random_state=42)         # 设置随机种子
-
-# 将增强后的数据合并回原始数据集
-data_upsampled = pd.concat([data[data['a961'] != 'self'], self_data_upsampled])
-# 查看每类样本的数量
-class_counts = data_upsampled['a961'].value_counts()
-print(class_counts)

doc/文档1/自主开发代码/文件转换.py

@@ -1,20 +0,0 @@
-import pandas as pd
-from scipy.io import arff
-
-# 读取 CSV 文件
-data = pd.read_csv('F:\test\test2.csv')
-
-# 保存为 ARFF 文件
-with open('F:\test\test2.arff', 'w') as f:
-    # 写入 @relation
-    f.write("@RELATION your_relation_name\n\n")
-
-    # 写入 @attribute
-    for column in data.columns:
-        f.write(f"@ATTRIBUTE {column} STRING\n")  # 可以根据需要调整属性类型
-
-    f.write("\n@DATA\n")
-
-    # 写入数据
-    for index, row in data.iterrows():
-        f.write(','.join(map(str, row.values)) + '\n')

doc/文档1/自主开发代码/视频流存储.py

@@ -1,152 +0,0 @@
-import os
-import sqlite3
-import time
-from datetime import datetime
-
-import cv2
-from PyQt5.QtWidgets import QMainWindow
-
-class StartWindow(QMainWindow, start_ui):
-    def __init__(self):
-        super(StartWindow, self).__init__()
-        self.setupUi(self)
-        self.startButton.clicked.connect(self.main)
-        self.closeButton.clicked.connect(self.back2)
-
-    def back2(self):
-        self.close()
-        MainWindow.show()  # 请确保 MainWindow 已经被实例化并可以显示
-
-    def main(self):  # 方法参数改为 self
-        # 获取输入值
-        start_time_str = self.lineEdit.text()
-        end_time_str = self.lineEdit_2.text()
-        percentage = float(self.lineEdit_3.text())  # 转换为浮点数
-
-        # 创建训练数据目录
-        def create_training_directory(path_name):
-            if not os.path.exists(path_name):
-                os.makedirs(path_name)
-
-        # 判断当前时间是否在指定的时间范围内
-        def is_within_time_range(start_time_str, end_time_str):
-            now = datetime.now().time()
-            start_time = datetime.strptime(start_time_str, "%H:%M").time()  # 24小时制
-            end_time = datetime.strptime(end_time_str, "%H:%M").time()
-            return start_time <= now <= end_time
-
-        # 计算所需的最少时间（分钟）
-        def calculate_required_time(start_time_str, end_time_str, percentage):
-            start_time = datetime.strptime(start_time_str, "%H:%M")
-            end_time = datetime.strptime(end_time_str, "%H:%M")
-            total_time = (end_time - start_time).total_seconds() / 60  # 总时长（分钟）
-            return total_time * (percentage / 100)
-
-        # 捕获人脸并计时
-        def capture_faces_with_timer(window_name, camera_id, path_name, max_num, required_time, start_time_str, end_time_str):
-            cv2.namedWindow(window_name)
-            cap = cv2.VideoCapture(camera_id)
-            face_cascade = cv2.CascadeClassifier(
-                r"C:\Users\a'su's\AppData\Local\Programs\Python\Python310\Lib\site-packages\cv2\data\haarcascade_frontalface_alt.xml"
-            )
-
-            color = (0, 255, 0)
-            face_count = 0
-            countdown_started = False
-            remaining_time = required_time * 60  # 转换为秒
-
-            create_training_directory(path_name)
-
-            # 视频存储设置
-            video_name = 'captured_video.mp4'
-            fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-            out = cv2.VideoWriter(video_name, fourcc, 20.0, (640, 480))
-
-            while cap.isOpened() and face_count < max_num:
-                ret, frame = cap.read()
-                if not ret:
-                    break
-
-                gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-                faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-
-                # 检查当前时间是否在指定的时间范围内
-                if is_within_time_range(start_time_str, end_time_str):
-                    if len(faces) > 0:
-                        if not countdown_started:
-                            countdown_started = True
-                            start_time = time.time()
-
-                        remaining_time = required_time * 60 - int(time.time() - start_time)
-
-                        for (x, y, w, h) in faces:
-                            face_image = frame[y:y + h, x:x + w]
-                            image_name = os.path.join(path_name, f'face_{face_count}.jpg')
-                            cv2.imwrite(image_name, face_image)
-
-                            face_count += 1
-                            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
-
-                        # 显示倒计时
-                        cv2.putText(frame, f'Time Left: {remaining_time // 60}m {remaining_time % 60}s', (10, 30),
-                                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
-
-                        if remaining_time <= 0:
-                            break
-                else:
-                    countdown_started = False
-                    remaining_time = required_time * 60
-                    cv2.putText(frame, 'Outside of specified time range', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1,
-                                (0, 0, 255), 2)
-
-                cv2.imshow(window_name, frame)
-                out.write(frame)  # 写入视频帧
-
-                if cv2.waitKey(10) & 0xFF == ord('q'):
-                    break
-
-            cap.release()
-            out.release()
-            cv2.destroyAllWindows()
-            print('Finished capturing faces and video.')
-
-            # 将视频写入数据库
-            self.save_video_to_db(video_name)
-
-        required_time = calculate_required_time(start_time_str, end_time_str, percentage)
-        print(f'Starting face capture, will monitor for at least {required_time} minutes...')
-        capture_faces_with_timer('FaceCapture', 0, 'training_data_faces/', 1000, required_time, start_time_str, end_time_str)
-
-    def save_video_to_db(self, video_file_path):
-        db_path = r'F:\BaiduNetdiskDownload\pyqt\pyqt\user.db'
-        # 连接到SQLite数据库
-        conn = sqlite3.connect(db_path)
-        cursor = conn.cursor()
-
-        # 创建表（如果尚未创建）
-        cursor.execute('''
-            CREATE TABLE IF NOT EXISTS videos (
-                id INTEGER PRIMARY KEY AUTOINCREMENT,
-                name TEXT NOT NULL,
-                data BLOB NOT NULL
-            )
-        ''')
-
-        # 读取视频文件
-        with open(video_file_path, 'rb') as video_file:
-            video_data = video_file.read()
-            # 获取文件名
-            video_name = video_file_path.split('\\')[-1]  # Windows路径分隔符
-
-            # 插入数据
-            cursor.execute('''
-                INSERT INTO videos (name, data) VALUES (?, ?)
-            ''', (video_name, video_data))
-
-        # 提交更改并关闭连接
-        conn.commit()
-        conn.close()
-        print(f"视频 '{video_name}' 已成功插入到数据库中。")
-
-# 请确保 MainWindow 已经被实例化并可以显示
-# 例如： MainWindow = QMainWindow()

gitproject

@@ -1 +1 @@
-Subproject commit fe6b02d92d839f094d2e8a0e9c71b8c30b15c7cb
+Subproject commit ed300fd741015b5fe0411443cc4f4527b6421290