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Update HOGDescriptor

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This commit is contained in:
Suleyman TURKMEN
2018-10-02 20:39:23 +03:00
parent 690fb0544c
commit abbb73e894
3 changed files with 41 additions and 139 deletions
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modules/objdetect/include/opencv2/objdetect.hpp
+15 -20
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@@ -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
@param filename Name of the file to read.
/** @brief loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.
@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.