Merge pull request #12450 from sturkmen72:update_HOGDescriptor

modules/objdetect/include/opencv2/objdetect.hpp

@@ -380,7 +380,7 @@ public:
 
     /**@brief Creates the HOG descriptor and detector with default params.
 
-    aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9, 1 )
+    aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 )
     */
     CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
         cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
@@ -414,7 +414,7 @@ public:
     {}
 
     /** @overload
-    @param filename the file name containing  HOGDescriptor properties and coefficients of the trained classifier
+    @param filename The file name containing HOGDescriptor properties and coefficients for the linear SVM classifier.
     */
     CV_WRAP HOGDescriptor(const String& filename)
     {
@@ -448,28 +448,28 @@ public:
     /**@example samples/cpp/peopledetect.cpp
     */
     /**@brief Sets coefficients for the linear SVM classifier.
-    @param _svmdetector coefficients for the linear SVM classifier.
+    @param svmdetector coefficients for the linear SVM classifier.
     */
-    CV_WRAP virtual void setSVMDetector(InputArray _svmdetector);
+    CV_WRAP virtual void setSVMDetector(InputArray svmdetector);
 
-    /** @brief Reads HOGDescriptor parameters from a file node.
+    /** @brief Reads HOGDescriptor parameters from a cv::FileNode.
     @param fn File node
     */
     virtual bool read(FileNode& fn);
 
-    /** @brief Stores HOGDescriptor parameters in a file storage.
+    /** @brief Stores HOGDescriptor parameters in a cv::FileStorage.
     @param fs File storage
     @param objname Object name
     */
     virtual void write(FileStorage& fs, const String& objname) const;
 
-    /** @brief loads coefficients for the linear SVM classifier from a file
+    /** @brief loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.
-    @param filename Name of the file to read.
+    @param filename Path of the file to read.
     @param objname The optional name of the node to read (if empty, the first top-level node will be used).
     */
     CV_WRAP virtual bool load(const String& filename, const String& objname = String());
 
-    /** @brief saves coefficients for the linear SVM classifier to a file
+    /** @brief saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file
     @param filename File name
     @param objname Object name
     */
@@ -505,7 +505,7 @@ public:
     @param padding Padding
     @param searchLocations Vector of Point includes set of requested locations to be evaluated.
     */
-    CV_WRAP virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
+    CV_WRAP virtual void detect(InputArray img, CV_OUT std::vector<Point>& foundLocations,
                         CV_OUT std::vector<double>& weights,
                         double hitThreshold = 0, Size winStride = Size(),
                         Size padding = Size(),
@@ -521,7 +521,7 @@ public:
     @param padding Padding
     @param searchLocations Vector of Point includes locations to search.
     */
-    virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
+    virtual void detect(InputArray img, CV_OUT std::vector<Point>& foundLocations,
                         double hitThreshold = 0, Size winStride = Size(),
                         Size padding = Size(),
                         const std::vector<Point>& searchLocations=std::vector<Point>()) const;
@@ -570,7 +570,7 @@ public:
     @param paddingTL Padding from top-left
     @param paddingBR Padding from bottom-right
     */
-    CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
+    CV_WRAP virtual void computeGradient(InputArray img, InputOutputArray grad, InputOutputArray angleOfs,
                                  Size paddingTL = Size(), Size paddingBR = Size()) const;
 
     /** @brief Returns coefficients of the classifier trained for people detection (for 64x128 windows).
@@ -639,7 +639,7 @@ public:
     @param winStride winStride
     @param padding padding
     */
-    virtual void detectROI(const cv::Mat& img, const std::vector<cv::Point> &locations,
+    virtual void detectROI(InputArray img, const std::vector<cv::Point> &locations,
                                    CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
                                    double hitThreshold = 0, cv::Size winStride = Size(),
                                    cv::Size padding = Size()) const;
@@ -652,17 +652,12 @@ public:
     in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.
     @param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.
     */
-    virtual void detectMultiScaleROI(const cv::Mat& img,
+    virtual void detectMultiScaleROI(InputArray img,
                                      CV_OUT std::vector<cv::Rect>& foundLocations,
                                      std::vector<DetectionROI>& locations,
                                      double hitThreshold = 0,
                                      int groupThreshold = 0) const;
 
-    /** @brief read/parse Dalal's alt model file
-    @param modelfile Path of Dalal's alt model file.
-    */
-    void readALTModel(String modelfile);
-
     /** @brief Groups the object candidate rectangles.
     @param rectList  Input/output vector of rectangles. Output vector includes retained and grouped rectangles. (The Python list is not modified in place.)
     @param weights Input/output vector of weights of rectangles. Output vector includes weights of retained and grouped rectangles. (The Python list is not modified in place.)
@@ -688,7 +683,7 @@ protected:
 };
 
 /** @brief Detect QR code in image and return minimum area of quadrangle that describes QR code.
-    @param in  Matrix of the type CV_8UC1 containing an image where QR code are detected.
+    @param in  Matrix of the type CV_8U containing an image where QR code are detected.
     @param points Output vector of vertices of a quadrangle of minimal area that describes QR code.
     @param eps_x Epsilon neighborhood, which allows you to determine the horizontal pattern of the scheme 1:1:3:1:1 according to QR code standard.
     @param eps_y Epsilon neighborhood, which allows you to determine the vertical pattern of the scheme 1:1:3:1:1 according to QR code standard.

modules/objdetect/src/hog.cpp

@@ -234,17 +234,20 @@ inline float32x4_t vsetq_f32(float f0, float f1, float f2, float f3)
     return a;
 }
 #endif
-void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
+void HOGDescriptor::computeGradient(InputArray _img, InputOutputArray _grad, InputOutputArray _qangle,
     Size paddingTL, Size paddingBR) const
 {
     CV_INSTRUMENT_REGION();
 
+    Mat img = _img.getMat();
     CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
 
     Size gradsize(img.cols + paddingTL.width + paddingBR.width,
         img.rows + paddingTL.height + paddingBR.height);
-    grad.create(gradsize, CV_32FC2);  // <magnitude*(1-alpha), magnitude*alpha>
+    _grad.create(gradsize, CV_32FC2);  // <magnitude*(1-alpha), magnitude*alpha>
-    qangle.create(gradsize, CV_8UC2); // [0..nbins-1] - quantized gradient orientation
+    _qangle.create(gradsize, CV_8UC2); // [0..nbins-1] - quantized gradient orientation
+    Mat grad = _grad.getMat();
+    Mat qangle = _qangle.getMat();
 
     Size wholeSize;
     Point roiofs;
@@ -1650,12 +1653,13 @@ void HOGDescriptor::compute(InputArray _img, std::vector<float>& descriptors,
     }
 }
 
-void HOGDescriptor::detect(const Mat& img,
+void HOGDescriptor::detect(InputArray _img,
     std::vector<Point>& hits, std::vector<double>& weights, double hitThreshold,
     Size winStride, Size padding, const std::vector<Point>& locations) const
 {
     CV_INSTRUMENT_REGION();
 
+    Mat img = _img.getMat();
     hits.clear();
     weights.clear();
     if( svmDetector.empty() )
@@ -1764,7 +1768,7 @@ void HOGDescriptor::detect(const Mat& img,
     }
 }
 
-void HOGDescriptor::detect(const Mat& img, std::vector<Point>& hits, double hitThreshold,
+void HOGDescriptor::detect(InputArray img, std::vector<Point>& hits, double hitThreshold,
     Size winStride, Size padding, const std::vector<Point>& locations) const
 {
     CV_INSTRUMENT_REGION();
@@ -3544,12 +3548,13 @@ public:
     Mutex* mtx;
 };
 
-void HOGDescriptor::detectROI(const cv::Mat& img, const std::vector<cv::Point> &locations,
+void HOGDescriptor::detectROI(InputArray _img, const std::vector<cv::Point> &locations,
     CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
     double hitThreshold, cv::Size winStride, cv::Size padding) const
 {
     CV_INSTRUMENT_REGION();
 
+    Mat img = _img.getMat();
     foundLocations.clear();
     confidences.clear();
 
@@ -3656,12 +3661,13 @@ void HOGDescriptor::detectROI(const cv::Mat& img, const std::vector<cv::Point> &
     }
 }
 
-void HOGDescriptor::detectMultiScaleROI(const cv::Mat& img,
+void HOGDescriptor::detectMultiScaleROI(InputArray _img,
     CV_OUT std::vector<cv::Rect>& foundLocations, std::vector<DetectionROI>& locations,
     double hitThreshold, int groupThreshold) const
 {
     CV_INSTRUMENT_REGION();
 
+    Mat img = _img.getMat();
     std::vector<Rect> allCandidates;
     Mutex mtx;
 
@@ -3674,110 +3680,6 @@ void HOGDescriptor::detectMultiScaleROI(const cv::Mat& img,
     cv::groupRectangles(foundLocations, groupThreshold, 0.2);
 }
 
-void HOGDescriptor::readALTModel(String modelfile)
-{
-    // read model from SVMlight format..
-    FILE *modelfl;
-    if ((modelfl = fopen(modelfile.c_str(), "rb")) == NULL)
-    {
-        String eerr("file not exist");
-        String efile(__FILE__);
-        String efunc(__FUNCTION__);
-        CV_THROW (Exception(Error::StsError, eerr, efile, efunc, __LINE__));
-    }
-    char version_buffer[10];
-    if (!fread (&version_buffer,sizeof(char),10,modelfl))
-    {
-        String eerr("version?");
-        String efile(__FILE__);
-        String efunc(__FUNCTION__);
-        fclose(modelfl);
-
-        CV_THROW (Exception(Error::StsError, eerr, efile, efunc, __LINE__));
-    }
-    if(strcmp(version_buffer,"V6.01")) {
-        String eerr("version does not match");
-        String efile(__FILE__);
-        String efunc(__FUNCTION__);
-        fclose(modelfl);
-
-        CV_THROW (Exception(Error::StsError, eerr, efile, efunc, __LINE__));
-    }
-    /* read version number */
-    int version = 0;
-    if (!fread (&version,sizeof(int),1,modelfl))
-    {
-        fclose(modelfl);
-        CV_THROW (Exception());
-    }
-    if (version < 200)
-    {
-        String eerr("version does not match");
-        String efile(__FILE__);
-        String efunc(__FUNCTION__);
-        fclose(modelfl);
-        CV_THROW (Exception());
-    }
-    int kernel_type;
-    size_t nread;
-    nread=fread(&(kernel_type),sizeof(int),1,modelfl);
-
-    {// ignore these
-        int poly_degree;
-        nread=fread(&(poly_degree),sizeof(int),1,modelfl);
-
-        double rbf_gamma;
-        nread=fread(&(rbf_gamma),sizeof(double), 1, modelfl);
-        double coef_lin;
-        nread=fread(&(coef_lin),sizeof(double),1,modelfl);
-        double coef_const;
-        nread=fread(&(coef_const),sizeof(double),1,modelfl);
-        int l;
-        nread=fread(&l,sizeof(int),1,modelfl);
-        CV_Assert(l >= 0 && l < 0xFFFF);
-        char* custom = new char[l];
-        nread=fread(custom,sizeof(char),l,modelfl);
-        delete[] custom;
-    }
-    int totwords;
-    nread=fread(&(totwords),sizeof(int),1,modelfl);
-    {// ignore these
-        int totdoc;
-        nread=fread(&(totdoc),sizeof(int),1,modelfl);
-        int sv_num;
-        nread=fread(&(sv_num), sizeof(int),1,modelfl);
-    }
-
-    double linearbias;
-    nread=fread(&linearbias, sizeof(double), 1, modelfl);
-
-    std::vector<float> detector;
-    detector.clear();
-    if(kernel_type == 0) { /* linear kernel */
-        /* save linear wts also */
-        CV_Assert(totwords + 1 > 0 && totwords < 0xFFFF);
-        double *linearwt = new double[totwords+1];
-        int length = totwords;
-        nread = fread(linearwt, sizeof(double), totwords + 1, modelfl);
-        if(nread != static_cast<size_t>(length) + 1) {
-            delete [] linearwt;
-            fclose(modelfl);
-            CV_THROW (Exception());
-        }
-
-        for(int i = 0; i < length; i++)
-            detector.push_back((float)linearwt[i]);
-
-        detector.push_back((float)-linearbias);
-        setSVMDetector(detector);
-        delete [] linearwt;
-    } else {
-        fclose(modelfl);
-        CV_THROW (Exception());
-    }
-    fclose(modelfl);
-}
-
 void HOGDescriptor::groupRectangles(std::vector<cv::Rect>& rectList, std::vector<double>& weights, int groupThreshold, double eps) const
 {
     CV_INSTRUMENT_REGION();

modules/objdetect/test/test_cascadeandhog.cpp

@@ -553,15 +553,15 @@ public:
         ts(cvtest::TS::ptr()), failed(false)
     { }
 
-    virtual void computeGradient(const Mat& img, Mat& grad, Mat& qangle,
+    virtual void computeGradient(InputArray img, InputOutputArray grad, InputOutputArray qangle,
         Size paddingTL, Size paddingBR) const;
 
-    virtual void detect(const Mat& img,
+    virtual void detect(InputArray img,
         vector<Point>& hits, vector<double>& weights, double hitThreshold = 0.0,
         Size winStride = Size(), Size padding = Size(),
         const vector<Point>& locations = vector<Point>()) const;
 
-    virtual void detect(const Mat& img, vector<Point>& hits, double hitThreshold = 0.0,
+    virtual void detect(InputArray img, vector<Point>& hits, double hitThreshold = 0.0,
         Size winStride = Size(), Size padding = Size(),
         const vector<Point>& locations = vector<Point>()) const;
 
@@ -985,7 +985,7 @@ inline bool HOGDescriptorTester::is_failed() const
 
 static inline int gcd(int a, int b) { return (a % b == 0) ? b : gcd (b, a % b); }
 
-void HOGDescriptorTester::detect(const Mat& img,
+void HOGDescriptorTester::detect(InputArray _img,
     vector<Point>& hits, vector<double>& weights, double hitThreshold,
     Size winStride, Size padding, const vector<Point>& locations) const
 {
@@ -996,6 +996,7 @@ void HOGDescriptorTester::detect(const Mat& img,
     if( svmDetector.empty() )
         return;
 
+    Mat img = _img.getMat();
     if( winStride == Size() )
         winStride = cellSize;
     Size cacheStride(gcd(winStride.width, blockStride.width),
@@ -1085,7 +1086,7 @@ void HOGDescriptorTester::detect(const Mat& img,
     }
 }
 
-void HOGDescriptorTester::detect(const Mat& img, vector<Point>& hits, double hitThreshold,
+void HOGDescriptorTester::detect(InputArray img, vector<Point>& hits, double hitThreshold,
     Size winStride, Size padding, const vector<Point>& locations) const
 {
     vector<double> weightsV;
@@ -1166,15 +1167,19 @@ void HOGDescriptorTester::compute(InputArray _img, vector<float>& descriptors,
     }
 }
 
-void HOGDescriptorTester::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
+void HOGDescriptorTester::computeGradient(InputArray _img, InputOutputArray _grad, InputOutputArray _qangle,
    Size paddingTL, Size paddingBR) const
 {
+    Mat img = _img.getMat();
     CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
 
     Size gradsize(img.cols + paddingTL.width + paddingBR.width,
        img.rows + paddingTL.height + paddingBR.height);
-    grad.create(gradsize, CV_32FC2);  // <magnitude*(1-alpha), magnitude*alpha>
+    _grad.create(gradsize, CV_32FC2);  // <magnitude*(1-alpha), magnitude*alpha>
-    qangle.create(gradsize, CV_8UC2); // [0..nbins-1] - quantized gradient orientation
+    _qangle.create(gradsize, CV_8UC2); // [0..nbins-1] - quantized gradient orientation
+    Mat grad = _grad.getMat();
+    Mat qangle = _qangle.getMat();
+
     Size wholeSize;
     Point roiofs;
     img.locateROI(wholeSize, roiofs);