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Merge pull request #9744 from sturkmen72:Update_HOGDescriptor_Documentation
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Vadim Pisarevsky
commit
49afb28738
@ -352,22 +352,54 @@ struct DetectionROI
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std::vector<double> confidences;
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};
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/**@example peopledetect.cpp
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/**@brief Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector.
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the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs @cite Dalal2005 .
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useful links:
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https://hal.inria.fr/inria-00548512/document/
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https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients
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https://software.intel.com/en-us/ipp-dev-reference-histogram-of-oriented-gradients-hog-descriptor
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http://www.learnopencv.com/histogram-of-oriented-gradients
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http://www.learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial
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*/
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struct CV_EXPORTS_W HOGDescriptor
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{
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public:
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enum { L2Hys = 0
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enum { L2Hys = 0 //!< Default histogramNormType
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};
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enum { DEFAULT_NLEVELS = 64
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enum { DEFAULT_NLEVELS = 64 //!< Default nlevels value.
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};
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/**@brief Creates the HOG descriptor and detector with default params.
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aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9, 1 )
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*/
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CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
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cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
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histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true),
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free_coef(-1.f), nlevels(HOGDescriptor::DEFAULT_NLEVELS), signedGradient(false)
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{}
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/** @overload
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@param _winSize sets winSize with given value.
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@param _blockSize sets blockSize with given value.
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@param _blockStride sets blockStride with given value.
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@param _cellSize sets cellSize with given value.
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@param _nbins sets nbins with given value.
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@param _derivAperture sets derivAperture with given value.
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@param _winSigma sets winSigma with given value.
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@param _histogramNormType sets histogramNormType with given value.
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@param _L2HysThreshold sets L2HysThreshold with given value.
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@param _gammaCorrection sets gammaCorrection with given value.
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@param _nlevels sets nlevels with given value.
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@param _signedGradient sets signedGradient with given value.
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*/
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CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,
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Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1,
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int _histogramNormType=HOGDescriptor::L2Hys,
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@ -379,97 +411,262 @@ public:
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gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels), signedGradient(_signedGradient)
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{}
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/** @overload
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@param filename the file name containing HOGDescriptor properties and coefficients of the trained classifier
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*/
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CV_WRAP HOGDescriptor(const String& filename)
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{
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load(filename);
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}
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/** @overload
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@param d the HOGDescriptor which cloned to create a new one.
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*/
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HOGDescriptor(const HOGDescriptor& d)
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{
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d.copyTo(*this);
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}
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/**@brief Default destructor.
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*/
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virtual ~HOGDescriptor() {}
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/**@brief Returns the number of coefficients required for the classification.
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*/
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CV_WRAP size_t getDescriptorSize() const;
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/** @brief Checks if detector size equal to descriptor size.
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*/
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CV_WRAP bool checkDetectorSize() const;
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/** @brief Returns winSigma value
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*/
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CV_WRAP double getWinSigma() const;
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/**@example peopledetect.cpp
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*/
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/**@brief Sets coefficients for the linear SVM classifier.
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@param _svmdetector coefficients for the linear SVM classifier.
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*/
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CV_WRAP virtual void setSVMDetector(InputArray _svmdetector);
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/** @brief Reads HOGDescriptor parameters from a file node.
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@param fn File node
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*/
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virtual bool read(FileNode& fn);
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/** @brief Stores HOGDescriptor parameters in a file storage.
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@param fs File storage
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@param objname Object name
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*/
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virtual void write(FileStorage& fs, const String& objname) const;
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/** @brief loads coefficients for the linear SVM classifier from a file
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@param filename Name of the file to read.
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@param objname The optional name of the node to read (if empty, the first top-level node will be used).
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*/
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CV_WRAP virtual bool load(const String& filename, const String& objname = String());
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/** @brief saves coefficients for the linear SVM classifier to a file
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@param filename File name
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@param objname Object name
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*/
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CV_WRAP virtual void save(const String& filename, const String& objname = String()) const;
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/** @brief clones the HOGDescriptor
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@param c cloned HOGDescriptor
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*/
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virtual void copyTo(HOGDescriptor& c) const;
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/**@example train_HOG.cpp
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*/
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/** @brief Computes HOG descriptors of given image.
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@param img Matrix of the type CV_8U containing an image where HOG features will be calculated.
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@param descriptors Matrix of the type CV_32F
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@param winStride Window stride. It must be a multiple of block stride.
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@param padding Padding
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@param locations Vector of Point
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*/
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CV_WRAP virtual void compute(InputArray img,
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CV_OUT std::vector<float>& descriptors,
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Size winStride = Size(), Size padding = Size(),
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const std::vector<Point>& locations = std::vector<Point>()) const;
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//! with found weights output
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/** @brief Performs object detection without a multi-scale window.
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@param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
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@param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries.
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@param weights Vector that will contain confidence values for each detected object.
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@param hitThreshold Threshold for the distance between features and SVM classifying plane.
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Usually it is 0 and should be specfied in the detector coefficients (as the last free coefficient).
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But if the free coefficient is omitted (which is allowed), you can specify it manually here.
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@param winStride Window stride. It must be a multiple of block stride.
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@param padding Padding
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@param searchLocations Vector of Point includes set of requrested locations to be evaluated.
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*/
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CV_WRAP virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
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CV_OUT std::vector<double>& weights,
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double hitThreshold = 0, Size winStride = Size(),
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Size padding = Size(),
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const std::vector<Point>& searchLocations = std::vector<Point>()) const;
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//! without found weights output
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/** @brief Performs object detection without a multi-scale window.
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@param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
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@param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries.
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@param hitThreshold Threshold for the distance between features and SVM classifying plane.
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Usually it is 0 and should be specfied in the detector coefficients (as the last free coefficient).
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But if the free coefficient is omitted (which is allowed), you can specify it manually here.
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@param winStride Window stride. It must be a multiple of block stride.
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@param padding Padding
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@param searchLocations Vector of Point includes locations to search.
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*/
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virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
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double hitThreshold = 0, Size winStride = Size(),
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Size padding = Size(),
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const std::vector<Point>& searchLocations=std::vector<Point>()) const;
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//! with result weights output
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/** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
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of rectangles.
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@param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
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@param foundLocations Vector of rectangles where each rectangle contains the detected object.
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@param foundWeights Vector that will contain confidence values for each detected object.
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@param hitThreshold Threshold for the distance between features and SVM classifying plane.
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Usually it is 0 and should be specfied in the detector coefficients (as the last free coefficient).
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But if the free coefficient is omitted (which is allowed), you can specify it manually here.
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@param winStride Window stride. It must be a multiple of block stride.
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@param padding Padding
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@param scale Coefficient of the detection window increase.
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@param finalThreshold Final threshold
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@param useMeanshiftGrouping indicates grouping algorithm
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*/
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CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
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CV_OUT std::vector<double>& foundWeights, double hitThreshold = 0,
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Size winStride = Size(), Size padding = Size(), double scale = 1.05,
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double finalThreshold = 2.0,bool useMeanshiftGrouping = false) const;
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//! without found weights output
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/** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
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of rectangles.
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@param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
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@param foundLocations Vector of rectangles where each rectangle contains the detected object.
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@param hitThreshold Threshold for the distance between features and SVM classifying plane.
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Usually it is 0 and should be specfied in the detector coefficients (as the last free coefficient).
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But if the free coefficient is omitted (which is allowed), you can specify it manually here.
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@param winStride Window stride. It must be a multiple of block stride.
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@param padding Padding
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@param scale Coefficient of the detection window increase.
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@param finalThreshold Final threshold
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@param useMeanshiftGrouping indicates grouping algorithm
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*/
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virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
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double hitThreshold = 0, Size winStride = Size(),
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Size padding = Size(), double scale = 1.05,
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double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const;
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/** @brief Computes gradients and quantized gradient orientations.
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@param img Matrix contains the image to be computed
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@param grad Matrix of type CV_32FC2 contains computed gradients
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@param angleOfs Matrix of type CV_8UC2 contains quantized gradient orientations
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@param paddingTL Padding from top-left
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@param paddingBR Padding from bottom-right
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*/
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CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
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Size paddingTL = Size(), Size paddingBR = Size()) const;
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/** @brief Returns coefficients of the classifier trained for people detection (for 64x128 windows).
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*/
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CV_WRAP static std::vector<float> getDefaultPeopleDetector();
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/**@example hog.cpp
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*/
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/** @brief Returns coefficients of the classifier trained for people detection (for 48x96 windows).
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*/
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CV_WRAP static std::vector<float> getDaimlerPeopleDetector();
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//! Detection window size. Align to block size and block stride. Default value is Size(64,128).
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CV_PROP Size winSize;
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//! Block size in pixels. Align to cell size. Default value is Size(16,16).
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CV_PROP Size blockSize;
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//! Block stride. It must be a multiple of cell size. Default value is Size(8,8).
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CV_PROP Size blockStride;
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//! Cell size. Default value is Size(8,8).
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CV_PROP Size cellSize;
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//! Number of bins used in the calculation of histogram of gradients. Default value is 9.
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CV_PROP int nbins;
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//! not documented
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CV_PROP int derivAperture;
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//! Gaussian smoothing window parameter.
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CV_PROP double winSigma;
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//! histogramNormType
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CV_PROP int histogramNormType;
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//! L2-Hys normalization method shrinkage.
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CV_PROP double L2HysThreshold;
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//! Flag to specify whether the gamma correction preprocessing is required or not.
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CV_PROP bool gammaCorrection;
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//! coefficients for the linear SVM classifier.
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CV_PROP std::vector<float> svmDetector;
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//! coefficients for the linear SVM classifier used when OpenCL is enabled
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UMat oclSvmDetector;
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//! not documented
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float free_coef;
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//! Maximum number of detection window increases. Default value is 64
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CV_PROP int nlevels;
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//! Indicates signed gradient will be used or not
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CV_PROP bool signedGradient;
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//! evaluate specified ROI and return confidence value for each location
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/** @brief evaluate specified ROI and return confidence value for each location
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@param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
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@param locations Vector of Point
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@param foundLocations Vector of Point where each Point is detected object's top-left point.
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@param confidences confidences
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@param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually
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it is 0 and should be specfied in the detector coefficients (as the last free coefficient). But if
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the free coefficient is omitted (which is allowed), you can specify it manually here
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@param winStride winStride
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@param padding padding
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*/
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virtual void detectROI(const cv::Mat& img, const std::vector<cv::Point> &locations,
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CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
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double hitThreshold = 0, cv::Size winStride = Size(),
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cv::Size padding = Size()) const;
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//! evaluate specified ROI and return confidence value for each location in multiple scales
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/** @brief evaluate specified ROI and return confidence value for each location in multiple scales
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@param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
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@param foundLocations Vector of rectangles where each rectangle contains the detected object.
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@param locations Vector of DetectionROI
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@param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specfied
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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.
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@param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.
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*/
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virtual void detectMultiScaleROI(const cv::Mat& img,
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CV_OUT std::vector<cv::Rect>& foundLocations,
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std::vector<DetectionROI>& locations,
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double hitThreshold = 0,
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int groupThreshold = 0) const;
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CV_OUT std::vector<cv::Rect>& foundLocations,
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std::vector<DetectionROI>& locations,
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double hitThreshold = 0,
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int groupThreshold = 0) const;
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//! read/parse Dalal's alt model file
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/** @brief read/parse Dalal's alt model file
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@param modelfile Path of Dalal's alt model file.
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*/
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void readALTModel(String modelfile);
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/** @brief Groups the object candidate rectangles.
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@param rectList Input/output vector of rectangles. Output vector includes retained and grouped rectangles. (The Python list is not modified in place.)
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@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.)
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@param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.
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@param eps Relative difference between sides of the rectangles to merge them into a group.
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*/
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void groupRectangles(std::vector<cv::Rect>& rectList, std::vector<double>& weights, int groupThreshold, double eps) const;
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};
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