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Change of interface and multiple fixes
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Vlad Shakhuro
@@ -49,204 +49,28 @@ the use of this software, even if advised of the possibility of such damage.
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/** @defgroup xobjdetect Extended object detection
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*/
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namespace cv
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{
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namespace xobjdetect
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{
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//! @addtogroup xobjdetect
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//! @{
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/** @brief Compute channels for integral channel features evaluation
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@param image image for which channels should be computed
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@param channels output array for computed channels
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*/
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CV_EXPORTS void computeChannels(InputArray image, std::vector<Mat>& channels);
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/** @brief Feature evaluation interface
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*/
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class CV_EXPORTS FeatureEvaluator : public Algorithm
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{
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class CV_EXPORTS WBDetector {
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public:
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/** @brief Set channels for feature evaluation
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WBDetector(const std::string& model_filename);
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@param channels array of channels to be set
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*/
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virtual void setChannels(InputArrayOfArrays channels) = 0;
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void train(
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const std::string& pos_samples,
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const std::string& neg_imgs);
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/** @brief Set window position to sample features with shift. By default position is (0, 0).
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@param position position to be set
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*/
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virtual void setPosition(Size position) = 0;
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/** @brief Evaluate feature value with given index for current channels and window position.
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@param feature_ind index of feature to be evaluated
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*/
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virtual int evaluate(size_t feature_ind) const = 0;
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/** @brief Evaluate all features for current channels and window position.
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@param feature_values matrix-column of evaluated feature values
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*/
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virtual void evaluateAll(OutputArray feature_values) const = 0;
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virtual void assertChannels() = 0;
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};
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/** @brief Construct feature evaluator.
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@param features features for evaluation
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@param type feature type. Can be "icf" or "acf"
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*/
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CV_EXPORTS Ptr<FeatureEvaluator>
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createFeatureEvaluator(const std::vector<std::vector<int> >& features,
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const std::string& type);
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/** @brief Generate integral features. Returns vector of features.
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@param window_size size of window in which features should be evaluated
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@param type feature type. Can be "icf" or "acf"
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@param count number of features to generate.
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@param channel_count number of feature channels
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*/
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std::vector<std::vector<int> >
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generateFeatures(Size window_size, const std::string& type,
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int count = INT_MAX, int channel_count = 10);
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//sort in-place of columns of the input matrix
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void sort_columns_without_copy(Mat& m, Mat indices = Mat());
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/** @brief Parameters for WaldBoost. weak_count — number of weak learners, alpha — cascade thresholding param.
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*/
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struct CV_EXPORTS WaldBoostParams
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{
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int weak_count;
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float alpha;
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WaldBoostParams(): weak_count(100), alpha(0.02f)
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{}
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};
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/** @brief WaldBoost object detector from @cite Sochman05 .
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*/
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class CV_EXPORTS WaldBoost : public Algorithm
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{
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public:
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/** @brief Train WaldBoost cascade for given data.
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Returns feature indices chosen for cascade. Feature enumeration starts from 0.
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@param data matrix of feature values, size M x N, one feature per row
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@param labels matrix of samples class labels, size 1 x N. Labels can be from {-1, +1}
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@param use_fast_log
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*/
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virtual std::vector<int> train(Mat& data,
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const Mat& labels, bool use_fast_log=false) = 0;
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/** @brief Predict objects class given object that can compute object features.
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Returns unnormed confidence value — measure of confidence that object is from class +1.
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@param feature_evaluator object that can compute features by demand
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*/
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virtual float predict(
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const Ptr<FeatureEvaluator>& feature_evaluator) const = 0;
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};
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/** @brief Construct WaldBoost object.
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*/
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CV_EXPORTS Ptr<WaldBoost>
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createWaldBoost(const WaldBoostParams& params = WaldBoostParams());
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/** @brief Params for ICFDetector training.
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*/
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struct CV_EXPORTS ICFDetectorParams
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{
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int feature_count;
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int weak_count;
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int model_n_rows;
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int model_n_cols;
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int bg_per_image;
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std::string features_type;
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float alpha;
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bool is_grayscale;
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bool use_fast_log;
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ICFDetectorParams(): feature_count(UINT_MAX), weak_count(100),
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model_n_rows(56), model_n_cols(56), bg_per_image(5), alpha(0.02f), is_grayscale(false), use_fast_log(false)
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{}
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};
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/** @brief Integral Channel Features from @cite Dollar09 .
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*/
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class CV_EXPORTS ICFDetector
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{
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public:
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ICFDetector(): waldboost_(), features_(), ftype_() {}
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/** @brief Train detector.
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@param pos_filenames path to folder with images of objects (wildcards like /my/path/\*.png are allowed)
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@param bg_filenames path to folder with background images
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@param params parameters for detector training
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*/
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void train(const std::vector<String>& pos_filenames,
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const std::vector<String>& bg_filenames,
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ICFDetectorParams params = ICFDetectorParams());
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/** @brief Detect objects on image.
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@param image image for detection
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@param objects output array of bounding boxes
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@param scaleFactor scale between layers in detection pyramid
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@param minSize min size of objects in pixels
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@param maxSize max size of objects in pixels
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@param threshold
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@param slidingStep sliding window step
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@param values output vector with values of positive samples
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*/
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void detect(const Mat& image, std::vector<Rect>& objects,
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float scaleFactor, Size minSize, Size maxSize, float threshold, int slidingStep, std::vector<float>& values);
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/** @brief Detect objects on image.
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@param img image for detection
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@param objects output array of bounding boxes
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@param minScaleFactor min factor by which the image will be resized
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@param maxScaleFactor max factor by which the image will be resized
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@param factorStep scaling factor is incremented each pyramid layer according to this parameter
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@param threshold
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@param slidingStep sliding window step
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@param values output vector with values of positive samples
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*/
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void detect(const Mat& img, std::vector<Rect>& objects, float minScaleFactor, float maxScaleFactor, float factorStep, float threshold, int slidingStep, std::vector<float>& values);
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/** @brief Write detector to FileStorage.
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@param fs FileStorage for output
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*/
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void write(FileStorage &fs) const;
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/** @brief Write ICFDetector to FileNode
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@param node FileNode for reading
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*/
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void read(const FileNode &node);
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void detect(
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const Mat& img,
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std::vector<Rect> &bboxes,
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std::vector<double> &confidences);
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private:
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Ptr<WaldBoost> waldboost_;
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std::vector<std::vector<int> > features_;
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int model_n_rows_;
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int model_n_cols_;
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std::string ftype_;
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std::string model_filename_;
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};
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CV_EXPORTS void write(FileStorage& fs, String&, const ICFDetector& detector);
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CV_EXPORTS void read(const FileNode& node, ICFDetector& d,
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const ICFDetector& default_value = ICFDetector());
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//! @}
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} /* namespace xobjdetect */
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} /* namespace cv */
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@@ -1,88 +0,0 @@
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#ifndef __OPENCV_XOBJDETECT_PRIVATE_HPP__
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#define __OPENCV_XOBJDETECT_PRIVATE_HPP__
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#ifndef __OPENCV_BUILD
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# error this is a private header, do not include it outside OpenCV
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#endif
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#include <opencv2/core.hpp>
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namespace cv
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{
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namespace xobjdetect
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{
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class CV_EXPORTS Stump
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{
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public:
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/* Initialize zero stump */
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Stump(): threshold_(0), polarity_(1), pos_value_(1), neg_value_(-1) {}
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/* Initialize stump with given threshold, polarity
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and classification values */
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Stump(int threshold, int polarity, float pos_value, float neg_value):
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threshold_(threshold), polarity_(polarity),
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pos_value_(pos_value), neg_value_(neg_value) {}
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/* Train stump for given data
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data — matrix of feature values, size M x N, one feature per row
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labels — matrix of sample class labels, size 1 x N. Labels can be from
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{-1, +1}
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weights — matrix of sample weights, size 1 x N
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visited_features: vector of already visited features (ignored in successive calls)
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Returns chosen feature index. Feature enumeration starts from 0
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*/
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int train(const Mat& data, const Mat& labels, const Mat& weights, const std::vector<int>& visited_features, bool use_fast_log = false);
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/* Predict object class given
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value — feature value. Feature must be the same as was chosen
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during training stump
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Returns real value, sign(value) means class
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*/
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float predict(int value) const;
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/* Write stump in FileStorage */
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void write(FileStorage& fs) const
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{
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fs << "{"
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<< "threshold" << threshold_
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<< "polarity" << polarity_
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<< "pos_value" << pos_value_
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<< "neg_value" << neg_value_
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<< "}";
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}
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/* Read stump */
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void read(const FileNode& node)
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{
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threshold_ = (int)node["threshold"];
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polarity_ = (int)node["polarity"];
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pos_value_ = (float)node["pos_value"];
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neg_value_ = (float)node["neg_value"];
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}
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private:
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/* Stump decision threshold */
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int threshold_;
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/* Stump polarity, can be from {-1, +1} */
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int polarity_;
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/* Classification values for positive and negative classes */
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float pos_value_, neg_value_;
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};
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void read(const FileNode& node, Stump& s, const Stump& default_value=Stump());
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void write(FileStorage& fs, String&, const Stump& s);
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} /* namespace xobjdetect */
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} /* namespace cv */
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#endif // __OPENCV_XOBJDETECT_PRIVATE_HPP__
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