vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "opencv2/core.hpp"
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namespace cv { namespace hfs {
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/** @defgroup hfs Hierarchical Feature Selection for Efficient Image Segmentation
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The opencv hfs module contains an efficient algorithm to segment an image.
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This module is implemented based on the paper Hierarchical Feature Selection for Efficient
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Image Segmentation, ECCV 2016. The original project was developed by
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Yun Liu(https://github.com/yun-liu/hfs).
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Introduction to Hierarchical Feature Selection
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----------------------------------------------
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This algorithm is executed in 3 stages:
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In the first stage, the algorithm uses SLIC (simple linear iterative clustering) algorithm
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to obtain the superpixel of the input image.
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In the second stage, the algorithm view each superpixel as a node in the graph.
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It will calculate a feature vector for each edge of the graph. It then calculates a weight
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for each edge based on the feature vector and trained SVM parameters. After obtaining
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weight for each edge, it will exploit EGB (Efficient Graph-based Image Segmentation)
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algorithm to merge some nodes in the graph thus obtaining a coarser segmentation
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After these operations, a post process will be executed to merge regions that are smaller
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then a specific number of pixels into their nearby region.
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In the third stage, the algorithm exploits the similar mechanism to further merge
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the small regions obtained in the second stage into even coarser segmentation.
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After these three stages, we can obtain the final segmentation of the image.
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For further details about the algorithm, please refer to the original paper:
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Hierarchical Feature Selection for Efficient Image Segmentation, ECCV 2016
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*/
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//! @addtogroup hfs
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//! @{
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class CV_EXPORTS_W HfsSegment : public Algorithm {
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public:
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/** @brief: set and get the parameter segEgbThresholdI.
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* This parameter is used in the second stage mentioned above.
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* It is a constant used to threshold weights of the edge when merging
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* adjacent nodes when applying EGB algorithm. The segmentation result
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* tends to have more regions remained if this value is large and vice versa.
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*/
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CV_WRAP virtual void setSegEgbThresholdI(float c) = 0;
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CV_WRAP virtual float getSegEgbThresholdI() = 0;
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/** @brief: set and get the parameter minRegionSizeI.
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* This parameter is used in the second stage
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* mentioned above. After the EGB segmentation, regions that have fewer
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* pixels then this parameter will be merged into it's adjacent region.
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*/
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CV_WRAP virtual void setMinRegionSizeI(int n) = 0;
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CV_WRAP virtual int getMinRegionSizeI() = 0;
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/** @brief: set and get the parameter segEgbThresholdII.
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* This parameter is used in the third stage
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* mentioned above. It serves the same purpose as segEgbThresholdI.
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* The segmentation result tends to have more regions remained if
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* this value is large and vice versa.
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*/
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CV_WRAP virtual void setSegEgbThresholdII(float c) = 0;
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CV_WRAP virtual float getSegEgbThresholdII() = 0;
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/** @brief: set and get the parameter minRegionSizeII.
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* This parameter is used in the third stage
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* mentioned above. It serves the same purpose as minRegionSizeI
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*/
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CV_WRAP virtual void setMinRegionSizeII(int n) = 0;
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CV_WRAP virtual int getMinRegionSizeII() = 0;
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/** @brief: set and get the parameter spatialWeight.
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* This parameter is used in the first stage
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* mentioned above(the SLIC stage). It describes how important is the role
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* of position when calculating the distance between each pixel and it's
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* center. The exact formula to calculate the distance is
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* \f$colorDistance + spatialWeight \times spatialDistance\f$.
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* The segmentation result tends to have more local consistency
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* if this value is larger.
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*/
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CV_WRAP virtual void setSpatialWeight(float w) = 0;
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CV_WRAP virtual float getSpatialWeight() = 0;
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/** @brief: set and get the parameter slicSpixelSize.
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* This parameter is used in the first stage mentioned
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* above(the SLIC stage). It describes the size of each
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* superpixel when initializing SLIC. Every superpixel
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* approximately has \f$slicSpixelSize \times slicSpixelSize\f$
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* pixels in the beginning.
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*/
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CV_WRAP virtual void setSlicSpixelSize(int n) = 0;
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CV_WRAP virtual int getSlicSpixelSize() = 0;
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/** @brief: set and get the parameter numSlicIter.
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* This parameter is used in the first stage. It
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* describes how many iteration to perform when executing SLIC.
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*/
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CV_WRAP virtual void setNumSlicIter(int n) = 0;
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CV_WRAP virtual int getNumSlicIter() = 0;
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/** @brief do segmentation gpu
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* @param src: the input image
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* @param ifDraw: if draw the image in the returned Mat. if this parameter is false,
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* then the content of the returned Mat is a matrix of index, describing the region
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* each pixel belongs to. And it's data type is CV_16U. If this parameter is true,
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* then the returned Mat is a segmented picture, and color of each region is the
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* average color of all pixels in that region. And it's data type is the same as
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* the input image
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*/
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CV_WRAP virtual Mat performSegmentGpu(InputArray src, bool ifDraw = true) = 0;
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/** @brief do segmentation with cpu
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* This method is only implemented for reference.
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* It is highly NOT recommanded to use it.
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*/
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CV_WRAP virtual Mat performSegmentCpu(InputArray src, bool ifDraw = true) = 0;
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/** @brief: create a hfs object
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* @param height: the height of the input image
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* @param width: the width of the input image
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* @param segEgbThresholdI: parameter segEgbThresholdI
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* @param minRegionSizeI: parameter minRegionSizeI
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* @param segEgbThresholdII: parameter segEgbThresholdII
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* @param minRegionSizeII: parameter minRegionSizeII
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* @param spatialWeight: parameter spatialWeight
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* @param slicSpixelSize: parameter slicSpixelSize
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* @param numSlicIter: parameter numSlicIter
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*/
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CV_WRAP static Ptr<HfsSegment> create(int height, int width,
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float segEgbThresholdI = 0.08f, int minRegionSizeI = 100,
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float segEgbThresholdII = 0.28f, int minRegionSizeII = 200,
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float spatialWeight = 0.6f, int slicSpixelSize = 8, int numSlicIter = 5);
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};
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//! @}
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}} // namespace cv { namespace hfs {
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