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update HOGDescriptor documentation

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This commit is contained in:
Suleyman TURKMEN 2022-02-13 11:18:30 +03:00
parent 1620a1e014
commit 44db2eea70
2 changed files with 19 additions and 15 deletions
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22
modules/objdetect/include/opencv2/objdetect.hpp
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@ -376,7 +376,7 @@ public:
};
enum { DEFAULT_NLEVELS = 64 //!< Default nlevels value.
};
/**@brief Creates the HOG descriptor and detector with default params.
/**@brief Creates the HOG descriptor and detector with default parameters.
aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9, 1 )
*/
@ -412,6 +412,8 @@ public:
{}
/** @overload
Creates the HOG descriptor and detector and loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.
@param filename the file name containing HOGDescriptor properties and coefficients of the trained classifier
*/
CV_WRAP HOGDescriptor(const String& filename)
@ -450,24 +452,24 @@ public:
*/
CV_WRAP virtual void setSVMDetector(InputArray _svmdetector);
/** @brief Reads HOGDescriptor parameters from a file node.
/** @brief Reads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file node.
@param fn File node
*/
virtual bool read(FileNode& fn);
/** @brief Stores HOGDescriptor parameters in a file storage.
/** @brief Stores HOGDescriptor parameters and coefficients for the linear SVM classifier in a file storage.
@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 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
*/
@ -535,13 +537,14 @@ public:
@param winStride Window stride. It must be a multiple of block stride.
@param padding Padding
@param scale Coefficient of the detection window increase.
@param finalThreshold Final threshold
@param groupThreshold Coefficient to regulate the similarity threshold. When detected, some objects can be covered
by many rectangles. 0 means not to perform grouping.
@param useMeanshiftGrouping indicates grouping algorithm
*/
CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
CV_OUT std::vector<double>& foundWeights, double hitThreshold = 0,
Size winStride = Size(), Size padding = Size(), double scale = 1.05,
double finalThreshold = 2.0,bool useMeanshiftGrouping = false) const;
double groupThreshold = 2.0, bool useMeanshiftGrouping = false) const;
/** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
of rectangles.
@ -553,13 +556,14 @@ public:
@param winStride Window stride. It must be a multiple of block stride.
@param padding Padding
@param scale Coefficient of the detection window increase.
@param finalThreshold Final threshold
@param groupThreshold Coefficient to regulate the similarity threshold. When detected, some objects can be covered
by many rectangles. 0 means not to perform grouping.
@param useMeanshiftGrouping indicates grouping algorithm
*/
virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
double hitThreshold = 0, Size winStride = Size(),
Size padding = Size(), double scale = 1.05,
double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const;
double groupThreshold = 2.0, bool useMeanshiftGrouping = false) const;
/** @brief Computes gradients and quantized gradient orientations.
@param img Matrix contains the image to be computed

12
modules/objdetect/src/hog.cpp
View File

@ -1884,7 +1884,7 @@ static bool ocl_detectMultiScale(InputArray _img, std::vector<Rect> &found_locat
void HOGDescriptor::detectMultiScale(
InputArray _img, std::vector<Rect>& foundLocations, std::vector<double>& foundWeights,
double hitThreshold, Size winStride, Size padding,
double scale0, double finalThreshold, bool useMeanshiftGrouping) const
double scale0, double groupThreshold, bool useMeanshiftGrouping) const
{
CV_INSTRUMENT_REGION();
@ -1910,7 +1910,7 @@ void HOGDescriptor::detectMultiScale(
CV_OCL_RUN(_img.dims() <= 2 && _img.type() == CV_8UC1 && scale0 > 1 && winStride.width % blockStride.width == 0 &&
winStride.height % blockStride.height == 0 && padding == Size(0,0) && _img.isUMat(),
ocl_detectMultiScale(_img, foundLocations, levelScale, hitThreshold, winStride, finalThreshold, oclSvmDetector,
ocl_detectMultiScale(_img, foundLocations, levelScale, hitThreshold, winStride, groupThreshold, oclSvmDetector,
blockSize, cellSize, nbins, blockStride, winSize, gammaCorrection, L2HysThreshold, (float)getWinSigma(), free_coef, signedGradient));
std::vector<Rect> allCandidates;
@ -1931,21 +1931,21 @@ void HOGDescriptor::detectMultiScale(
std::copy(tempWeights.begin(), tempWeights.end(), back_inserter(foundWeights));
if ( useMeanshiftGrouping )
groupRectangles_meanshift(foundLocations, foundWeights, foundScales, finalThreshold, winSize);
groupRectangles_meanshift(foundLocations, foundWeights, foundScales, groupThreshold, winSize);
else
groupRectangles(foundLocations, foundWeights, (int)finalThreshold, 0.2);
groupRectangles(foundLocations, foundWeights, (int)groupThreshold, 0.2);
clipObjects(imgSize, foundLocations, 0, &foundWeights);
}
void HOGDescriptor::detectMultiScale(InputArray img, std::vector<Rect>& foundLocations,
double hitThreshold, Size winStride, Size padding,
double scale0, double finalThreshold, bool useMeanshiftGrouping) const
double scale0, double groupThreshold, bool useMeanshiftGrouping) const
{
CV_INSTRUMENT_REGION();
std::vector<double> foundWeights;
detectMultiScale(img, foundLocations, foundWeights, hitThreshold, winStride,
padding, scale0, finalThreshold, useMeanshiftGrouping);
padding, scale0, groupThreshold, useMeanshiftGrouping);
}
template<typename _ClsName> struct RTTIImpl