mirror of
https://github.com/opencv/opencv_contrib.git
synced 2025-10-19 02:16:34 +08:00
Change of interface and multiple fixes
This commit is contained in:
Vlad Shakhuro
@@ -49,204 +49,28 @@ the use of this software, even if advised of the possibility of such damage.
|
||||
|
||||
/** @defgroup xobjdetect Extended object detection
|
||||
*/
|
||||
|
||||
namespace cv
|
||||
{
|
||||
namespace xobjdetect
|
||||
{
|
||||
|
||||
//! @addtogroup xobjdetect
|
||||
//! @{
|
||||
|
||||
/** @brief Compute channels for integral channel features evaluation
|
||||
|
||||
@param image image for which channels should be computed
|
||||
@param channels output array for computed channels
|
||||
*/
|
||||
CV_EXPORTS void computeChannels(InputArray image, std::vector<Mat>& channels);
|
||||
|
||||
/** @brief Feature evaluation interface
|
||||
*/
|
||||
class CV_EXPORTS FeatureEvaluator : public Algorithm
|
||||
{
|
||||
class CV_EXPORTS WBDetector {
|
||||
public:
|
||||
/** @brief Set channels for feature evaluation
|
||||
WBDetector(const std::string& model_filename);
|
||||
|
||||
@param channels array of channels to be set
|
||||
*/
|
||||
virtual void setChannels(InputArrayOfArrays channels) = 0;
|
||||
void train(
|
||||
const std::string& pos_samples,
|
||||
const std::string& neg_imgs);
|
||||
|
||||
/** @brief Set window position to sample features with shift. By default position is (0, 0).
|
||||
|
||||
@param position position to be set
|
||||
*/
|
||||
virtual void setPosition(Size position) = 0;
|
||||
|
||||
/** @brief Evaluate feature value with given index for current channels and window position.
|
||||
|
||||
@param feature_ind index of feature to be evaluated
|
||||
*/
|
||||
virtual int evaluate(size_t feature_ind) const = 0;
|
||||
|
||||
/** @brief Evaluate all features for current channels and window position.
|
||||
|
||||
@param feature_values matrix-column of evaluated feature values
|
||||
*/
|
||||
virtual void evaluateAll(OutputArray feature_values) const = 0;
|
||||
|
||||
virtual void assertChannels() = 0;
|
||||
};
|
||||
|
||||
/** @brief Construct feature evaluator.
|
||||
|
||||
@param features features for evaluation
|
||||
@param type feature type. Can be "icf" or "acf"
|
||||
*/
|
||||
CV_EXPORTS Ptr<FeatureEvaluator>
|
||||
createFeatureEvaluator(const std::vector<std::vector<int> >& features,
|
||||
const std::string& type);
|
||||
|
||||
/** @brief Generate integral features. Returns vector of features.
|
||||
|
||||
@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
|
||||
*/
|
||||
std::vector<std::vector<int> >
|
||||
generateFeatures(Size window_size, const std::string& type,
|
||||
int count = INT_MAX, int channel_count = 10);
|
||||
|
||||
//sort in-place of columns of the input matrix
|
||||
void sort_columns_without_copy(Mat& m, Mat indices = Mat());
|
||||
|
||||
/** @brief 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)
|
||||
{}
|
||||
};
|
||||
|
||||
/** @brief WaldBoost object detector from @cite Sochman05 .
|
||||
*/
|
||||
class CV_EXPORTS WaldBoost : public Algorithm
|
||||
{
|
||||
public:
|
||||
/** @brief Train WaldBoost cascade for given data.
|
||||
|
||||
Returns feature indices chosen for cascade. Feature enumeration starts from 0.
|
||||
@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}
|
||||
@param use_fast_log
|
||||
*/
|
||||
virtual std::vector<int> train(Mat& data,
|
||||
const Mat& labels, bool use_fast_log=false) = 0;
|
||||
|
||||
/** @brief Predict objects class given object that can compute object features.
|
||||
|
||||
Returns unnormed confidence value — measure of confidence that object is from class +1.
|
||||
@param feature_evaluator object that can compute features by demand
|
||||
*/
|
||||
virtual float predict(
|
||||
const Ptr<FeatureEvaluator>& feature_evaluator) const = 0;
|
||||
};
|
||||
|
||||
/** @brief Construct WaldBoost object.
|
||||
*/
|
||||
CV_EXPORTS Ptr<WaldBoost>
|
||||
createWaldBoost(const WaldBoostParams& params = WaldBoostParams());
|
||||
|
||||
/** @brief 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;
|
||||
std::string features_type;
|
||||
float alpha;
|
||||
bool is_grayscale;
|
||||
bool use_fast_log;
|
||||
|
||||
ICFDetectorParams(): feature_count(UINT_MAX), weak_count(100),
|
||||
model_n_rows(56), model_n_cols(56), bg_per_image(5), alpha(0.02f), is_grayscale(false), use_fast_log(false)
|
||||
{}
|
||||
};
|
||||
|
||||
/** @brief Integral Channel Features from @cite Dollar09 .
|
||||
*/
|
||||
class CV_EXPORTS ICFDetector
|
||||
{
|
||||
public:
|
||||
|
||||
ICFDetector(): waldboost_(), features_(), ftype_() {}
|
||||
|
||||
/** @brief Train detector.
|
||||
|
||||
@param pos_filenames path to folder with images of objects (wildcards like /my/path/\*.png are allowed)
|
||||
@param bg_filenames path to folder with background images
|
||||
@param params parameters for detector training
|
||||
*/
|
||||
void train(const std::vector<String>& pos_filenames,
|
||||
const std::vector<String>& bg_filenames,
|
||||
ICFDetectorParams params = ICFDetectorParams());
|
||||
|
||||
/** @brief Detect objects on image.
|
||||
@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
|
||||
@param threshold
|
||||
@param slidingStep sliding window step
|
||||
@param values output vector with values of positive samples
|
||||
*/
|
||||
void detect(const Mat& image, std::vector<Rect>& objects,
|
||||
float scaleFactor, Size minSize, Size maxSize, float threshold, int slidingStep, std::vector<float>& values);
|
||||
|
||||
/** @brief Detect objects on image.
|
||||
@param img image for detection
|
||||
@param objects output array of bounding boxes
|
||||
@param minScaleFactor min factor by which the image will be resized
|
||||
@param maxScaleFactor max factor by which the image will be resized
|
||||
@param factorStep scaling factor is incremented each pyramid layer according to this parameter
|
||||
@param threshold
|
||||
@param slidingStep sliding window step
|
||||
@param values output vector with values of positive samples
|
||||
*/
|
||||
void detect(const Mat& img, std::vector<Rect>& objects, float minScaleFactor, float maxScaleFactor, float factorStep, float threshold, int slidingStep, std::vector<float>& values);
|
||||
|
||||
/** @brief Write detector to FileStorage.
|
||||
@param fs FileStorage for output
|
||||
*/
|
||||
void write(FileStorage &fs) const;
|
||||
|
||||
/** @brief Write ICFDetector to FileNode
|
||||
@param node FileNode for reading
|
||||
*/
|
||||
void read(const FileNode &node);
|
||||
void detect(
|
||||
const Mat& img,
|
||||
std::vector<Rect> &bboxes,
|
||||
std::vector<double> &confidences);
|
||||
|
||||
private:
|
||||
Ptr<WaldBoost> waldboost_;
|
||||
std::vector<std::vector<int> > features_;
|
||||
int model_n_rows_;
|
||||
int model_n_cols_;
|
||||
std::string ftype_;
|
||||
std::string model_filename_;
|
||||
};
|
||||
|
||||
CV_EXPORTS void write(FileStorage& fs, String&, const ICFDetector& detector);
|
||||
|
||||
CV_EXPORTS void read(const FileNode& node, ICFDetector& d,
|
||||
const ICFDetector& default_value = ICFDetector());
|
||||
|
||||
//! @}
|
||||
|
||||
} /* namespace xobjdetect */
|
||||
} /* namespace cv */
|
||||
|
||||
|
||||
Reference in New Issue
Block a user