| // 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. | |
| 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 retrieved 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; | |
| struct Impl; | |
| inline Impl* getImpl() const { return impl.get(); } | |
| protected: | |
| std::shared_ptr<Impl> impl; | |
| }; | |
| //! @} | |
| } // namespace segmentation | |
| } // namespace cv | |