xobjdetect documentation

modules/xobjdetect/doc/integral_channel_features.rst

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+.. highlight:: cpp
+
+Integral Channel Features Detector
+==================================
+
+This section describes classes for object detection using WaldBoost and Integral
+Channel Features from [Šochman05]_ and [Dollár09]_.
+
+computeChannels
+---------------
+
+Compute channels for integral channel features evaluation
+
+.. ocv:function:: void computeChannels(InputArray image, vector<Mat>& channels)
+
+    :param image: image for which channels should be computed
+    :param channels: output array for computed channels
+
+FeatureEvaluator
+----------------
+
+Feature evaluation interface
+
+.. ocv:class:: FeatureEvaluator : public Algorithm
+
+FeatureEvaluator::setChannels
+-----------------------------
+
+Set channels for feature evaluation
+
+.. ocv:function:: void FeatureEvaluator::setChannels(InputArrayOfArrays channels)
+
+    :param channels: array of channels to be set
+
+FeatureEvaluator::setPosition
+-----------------------------
+
+Set window position to sample features with shift. By default position is (0, 0).
+
+.. ocv:function:: void FeatureEvaluator::setPosition(Size position)
+
+    :param position: position to be set
+
+FeatureEvaluator::evaluate
+--------------------------
+
+Evaluate feature value with given index for current channels and window position.
+
+.. ocv:function:: int FeatureEvaluator::evaluate(size_t feature_ind) const
+
+    :param feature_ind: index of feature to be evaluated
+
+FeatureEvaluator::evaluateAll
+-----------------------------
+
+Evaluate all features for current channels and window position.
+
+.. ocv:function:: void FeatureEvaluator::evaluateAll(OutputArray feature_values)
+
+    :param feature_values: matrix-column of evaluated feature values
+
+
+generateFeatures
+----------------
+
+Generate integral features. Returns vector of features.
+
+.. ocv:function:: vector<vector<int> > generateFeatures(Size window_size, const string& type, int count=INT_MAX, int channel_count=10)
+
+    :param window_size: size of window in which features should be evaluated
+    :param type: feature type. Can be "icf" or "acf"
+    :param count: number of features to generate.
+    :param channel_count: number of feature channels
+
+createFeatureEvaluator
+----------------------
+
+Construct feature evaluator.
+
+.. ocv:function:: Ptr<FeatureEvaluator> createFeatureEvaluator(const vector<vector<int>>& features, const string& type)
+
+    :param features: features for evaluation
+    :param type: feature type. Can be "icf" or "acf"
+
+
+WaldBoostParams
+---------------
+
+Parameters for WaldBoost. weak_count — number of weak learners, alpha — cascade
+thresholding param.
+
+::
+
+    struct CV_EXPORTS WaldBoostParams
+    {
+        int weak_count;
+        float alpha;
+
+        WaldBoostParams(): weak_count(100), alpha(0.02f)
+        {}
+    };
+
+WaldBoost
+---------
+
+.. ocv:class:: WaldBoost : public Algorithm
+
+WaldBoost::train
+----------------
+
+Train WaldBoost cascade for given data. Returns feature indices chosen for
+cascade. Feature enumeration starts from 0.
+
+.. ocv:function:: vector<int> WaldBoost::train(const Mat& data, const Mat& labels)
+
+    :param data: matrix of feature values, size M x N, one feature per row
+    :param labels: matrix of samples class labels, size 1 x N. Labels can be from {-1, +1}
+
+WaldBoost::predict
+------------------
+
+Predict objects class given object that can compute object features. Returns
+unnormed confidence value — measure of confidence that object is from class +1.
+
+.. ocv:function:: float WaldBoost::predict(const Ptr<FeatureEvaluator>& feature_evaluator) const
+
+    :param feature_evaluator: object that can compute features by demand
+
+WaldBoost::write
+----------------
+
+Write WaldBoost to FileStorage
+
+.. ocv:function:: void WaldBoost::write(FileStorage& fs)
+
+    :param fs: FileStorage for output
+
+WaldBoost::read
+---------------
+
+Write WaldBoost to FileNode
+
+.. ocv:function:: void WaldBoost::read(const FileNode& node)
+
+    :param node: FileNode for reading
+
+createWaldBoost
+---------------
+
+Construct WaldBoost object.
+
+.. ocv:function:: Ptr<WaldBoost> createWaldBoost(const WaldBoostParams& params = WaldBoostParams())
+
+ICFDetectorParams
+-----------------
+
+Params for ICFDetector training.
+
+::
+
+    struct CV_EXPORTS ICFDetectorParams
+    {
+        int feature_count;
+        int weak_count;
+        int model_n_rows;
+        int model_n_cols;
+        int bg_per_image;
+
+        ICFDetectorParams(): feature_count(UINT_MAX), weak_count(100),
+            model_n_rows(56), model_n_cols(56), bg_per_image(5)
+        {}
+    };
+
+ICFDetector
+-----------
+
+.. ocv:class:: ICFDetector
+
+ICFDetector::train
+------------------
+
+Train detector.
+
+.. ocv:function:: void ICFDetector::train(const String& pos_path, const String& bg_path, ICFDetectorParams params = ICFDetectorParams())
+
+    :param pos_path: path to folder with images of objects
+    :param bg_path: path to folder with background images
+    :param params: parameters for detector training
+
+ICFDetector::detect
+-------------------
+
+Detect objects on image.
+
+.. ocv:function:: void ICFDetector::detect(const Mat& image, vector<Rect>& objects, float scaleFactor, Size minSize, Size maxSize, float threshold)
+
+    :param image: image for detection
+    :param objects: output array of bounding boxes
+    :param scaleFactor: scale between layers in detection pyramid
+    :param minSize: min size of objects in pixels
+    :param maxSize: max size of objects in pixels
+
+ICFDetector::write
+------------------
+
+Write detector to FileStorage.
+
+.. ocv:function:: void ICFDetector::write(FileStorage& fs) const
+
+    :param fs: FileStorage for output
+
+ICFDetector::read
+-----------------
+
+Write ICFDetector to FileNode
+
+.. ocv:function:: void ICFDetector::read(const FileNode& node)
+
+    :param node: FileNode for reading
+
+
+.. [Šochman05] J. Šochman and J. Matas. WaldBoost – Learning for Time Constrained Sequential Detection", CVPR, 2005. The paper is available `online <https://dspace.cvut.cz/bitstream/handle/10467/9494/2005-Waldboost-learning-for-time-constrained-sequential-detection.pdf?sequence=1>`__.
+
+.. [Dollár09] P. Dollár, Z. Tu, P. Perona and S. Belongie. "Integral Channel Features", BMCV 2009. The paper is available `online <http://vision.ucsd.edu/~pdollar/files/papers/DollarBMVC09ChnFtrs.pdf>`__.

modules/xobjdetect/doc/xobjdetect.rst

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+***************************************
+xobjdetect. Extended object detection.
+***************************************
+
+.. highlight:: cpp
+
+.. toctree::
+    :maxdepth: 2
+
+    integral_channel_features