数字图像处理笔记之HOG

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数据集INRIA介绍

• 下载地址，目录有点乱
• 正样本目录：96X160H96/Train/pos
• 负样本目录：Test/neg

HOG

实现步骤

• 裁剪生成分辨率64×128的正样本文件
• 随机生成分辨率64×128的负样本文件
• 对正样本、负样本进行SVM机器学习，获取原始模型
• 用上一步得到的原始模型对原始负样本文件检测，获取分辨率64×128的hardexample负样本文件
• 对正样本，负样本，hardexample负样本进行SVM机器学习，得到最终模型
• 虽然opencv自带了INRIA数据集训练出来的官方HOGDescriptor，但是不一定适用于你的场景，所以我们还是要自己动手实现一下HOG+SVM的训练流程
• winSize(64, 128)
• blockSize(16, 16)
• blockStride(8, 8)
• cellSize(8, 8)
• nbins(9)

/** @overload
    @param _winSize sets winSize with given value.
    @param _blockSize sets blockSize with given value.
    @param _blockStride sets blockStride with given value.
    @param _cellSize sets cellSize with given value.
    @param _nbins sets nbins with given value.
    @param _derivAperture sets derivAperture with given value.
    @param _winSigma sets winSigma with given value.
    @param _histogramNormType sets histogramNormType with given value.
    @param _L2HysThreshold sets L2HysThreshold with given value.
    @param _gammaCorrection sets gammaCorrection with given value.
    @param _nlevels sets nlevels with given value.
    @param _signedGradient sets signedGradient with given value.
    */
    CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,
                  Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1,
                  int _histogramNormType=HOGDescriptor::L2Hys,
                  double _L2HysThreshold=0.2, bool _gammaCorrection=false,
                  int _nlevels=HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient=false)
    : winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize),
    nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma),
    histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold),
    gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels), signedGradient(_signedGradient)
    {}

/**@example train_HOG.cpp
    */
    /** @brief Computes HOG descriptors of given image.
    @param img Matrix of the type CV_8U containing an image where HOG features will be calculated.
    @param descriptors Matrix of the type CV_32F
    @param winStride Window stride. It must be a multiple of block stride.
    @param padding Padding
    @param locations Vector of Point
    */
    CV_WRAP virtual void compute(InputArray img,
                         CV_OUT std::vector<float>& descriptors,
                         Size winStride = Size(), Size padding = Size(),
                         const std::vector<Point>& locations = std::vector<Point>()) const;

/** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
    of rectangles.
    @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    @param foundLocations Vector of rectangles where each rectangle contains the detected object.
    @param hitThreshold Threshold for the distance between features and SVM classifying plane.
    Usually it is 0 and should be specified 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 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 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;