ImageProcess/Archive/ios/opencv2.framework/Versions/A/Headers/imgproc/segmentation.hpp

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.

#ifndef OPENCV_IMGPROC_SEGMENTATION_HPP
#define OPENCV_IMGPROC_SEGMENTATION_HPP

#include "opencv2/imgproc.hpp"

namespace cv {

namespace segmentation {

//! @addtogroup imgproc_segmentation
//! @{


/** @brief Intelligent Scissors image segmentation
 *
 * This class is used to find the path (contour) between two points
 * which can be used for image segmentation.
 *
 * Usage example:
 * @snippet snippets/imgproc_segmentation.cpp usage_example_intelligent_scissors
 *
 * Reference: <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.138.3811&rep=rep1&type=pdf">"Intelligent Scissors for Image Composition"</a>
 * algorithm designed by Eric N. Mortensen and William A. Barrett, Brigham Young University
 * @cite Mortensen95intelligentscissors
 */
class CV_EXPORTS_W_SIMPLE IntelligentScissorsMB
{
public:
    CV_WRAP
    IntelligentScissorsMB();

    /** @brief Specify weights of feature functions
     *
     * Consider keeping weights normalized (sum of weights equals to 1.0)
     * Discrete dynamic programming (DP) goal is minimization of costs between pixels.
     *
     * @param weight_non_edge Specify cost of non-edge pixels (default: 0.43f)
     * @param weight_gradient_direction Specify cost of gradient direction function (default: 0.43f)
     * @param weight_gradient_magnitude Specify cost of gradient magnitude function (default: 0.14f)
     */
    CV_WRAP
    IntelligentScissorsMB& setWeights(float weight_non_edge, float weight_gradient_direction, float weight_gradient_magnitude);

    /** @brief Specify gradient magnitude max value threshold
     *
     * Zero limit value is used to disable gradient magnitude thresholding (default behavior, as described in original article).
     * Otherwize pixels with `gradient magnitude >= threshold` have zero cost.
     *
     * @note Thresholding should be used for images with irregular regions (to avoid stuck on parameters from high-contract areas, like embedded logos).
     *
     * @param gradient_magnitude_threshold_max Specify gradient magnitude max value threshold (default: 0, disabled)
     */
    CV_WRAP
    IntelligentScissorsMB& setGradientMagnitudeMaxLimit(float gradient_magnitude_threshold_max = 0.0f);

    /** @brief Switch to "Laplacian Zero-Crossing" edge feature extractor and specify its parameters
     *
     * This feature extractor is used by default according to article.
     *
     * Implementation has additional filtering for regions with low-amplitude noise.
     * This filtering is enabled through parameter of minimal gradient amplitude (use some small value 4, 8, 16).
     *
     * @note Current implementation of this feature extractor is based on processing of grayscale images (color image is converted to grayscale image first).
     *
     * @note Canny edge detector is a bit slower, but provides better results (especially on color images): use setEdgeFeatureCannyParameters().
     *
     * @param gradient_magnitude_min_value Minimal gradient magnitude value for edge pixels (default: 0, check is disabled)
     */
    CV_WRAP
    IntelligentScissorsMB& setEdgeFeatureZeroCrossingParameters(float gradient_magnitude_min_value = 0.0f);

    /** @brief Switch edge feature extractor to use Canny edge detector
     *
     * @note "Laplacian Zero-Crossing" feature extractor is used by default (following to original article)
     *
     * @sa Canny
     */
    CV_WRAP
    IntelligentScissorsMB& setEdgeFeatureCannyParameters(
            double threshold1, double threshold2,
            int apertureSize = 3, bool L2gradient = false
    );

    /** @brief Specify input image and extract image features
     *
     * @param image input image. Type is #CV_8UC1 / #CV_8UC3
     */
    CV_WRAP
    IntelligentScissorsMB& applyImage(InputArray image);

    /** @brief Specify custom features of input image
     *
     * Customized advanced variant of applyImage() call.
     *
     * @param non_edge Specify cost of non-edge pixels. Type is CV_8UC1. Expected values are `{0, 1}`.
     * @param gradient_direction Specify gradient direction feature. Type is CV_32FC2. Values are expected to be normalized: `x^2 + y^2 == 1`
     * @param gradient_magnitude Specify cost of gradient magnitude function: Type is CV_32FC1. Values should be in range `[0, 1]`.
     * @param image **Optional parameter**. Must be specified if subset of features is specified (non-specified features are calculated internally)
     */
    CV_WRAP
    IntelligentScissorsMB& applyImageFeatures(
            InputArray non_edge, InputArray gradient_direction, InputArray gradient_magnitude,
            InputArray image = noArray()
    );

    /** @brief Prepares a map of optimal paths for the given source point on the image
     *
     * @note applyImage() / applyImageFeatures() must be called before this call
     *
     * @param sourcePt The source point used to find the paths
     */
    CV_WRAP void buildMap(const Point& sourcePt);

    /** @brief Extracts optimal contour for the given target point on the image
     *
     * @note buildMap() must be called before this call
     *
     * @param targetPt The target point
     * @param[out] contour The list of pixels which contains optimal path between the source and the target points of the image. Type is CV_32SC2 (compatible with `std::vector<Point>`)
     * @param backward Flag to indicate reverse order of retrived pixels (use "true" value to fetch points from the target to the source point)
     */
    CV_WRAP void getContour(const Point& targetPt, OutputArray contour, bool backward = false) const;

#ifndef CV_DOXYGEN
    struct Impl;
    inline Impl* getImpl() const { return impl.get(); }
protected:
    std::shared_ptr<Impl> impl;
#endif
};

//! @}

}  // namespace segmentation
}  // namespace cv

#endif // OPENCV_IMGPROC_SEGMENTATION_HPP