84 lines
4.1 KiB
C++
84 lines
4.1 KiB
C++
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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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//
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// Copyright (C) 2021, Dr Seng Cheong Loke (lokesengcheong@gmail.com)
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#ifndef __OPENCV_XIMGPROC_SCANSEGMENT_HPP__
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#define __OPENCV_XIMGPROC_SCANSEGMENT_HPP__
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#include <opencv2/core.hpp>
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namespace cv { namespace ximgproc {
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/** @brief Class implementing the F-DBSCAN (Accelerated superpixel image segmentation with a parallelized DBSCAN algorithm) superpixels
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algorithm by Loke SC, et al. @cite loke2021accelerated for original paper.
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The algorithm uses a parallelised DBSCAN cluster search that is resistant to noise, competitive in segmentation quality, and faster than
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existing superpixel segmentation methods. When tested on the Berkeley Segmentation Dataset, the average processing speed is 175 frames/s
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with a Boundary Recall of 0.797 and an Achievable Segmentation Accuracy of 0.944. The computational complexity is quadratic O(n2) and
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more suited to smaller images, but can still process a 2MP colour image faster than the SEEDS algorithm in OpenCV. The output is deterministic
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when the number of processing threads is fixed, and requires the source image to be in Lab colour format.
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*/
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class CV_EXPORTS_W ScanSegment : public Algorithm
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{
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public:
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virtual ~ScanSegment();
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/** @brief Returns the actual superpixel segmentation from the last image processed using iterate.
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Returns zero if no image has been processed.
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*/
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CV_WRAP virtual int getNumberOfSuperpixels() = 0;
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/** @brief Calculates the superpixel segmentation on a given image with the initialized
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parameters in the ScanSegment object.
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This function can be called again for other images without the need of initializing the algorithm with createScanSegment().
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This save the computational cost of allocating memory for all the structures of the algorithm.
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@param img Input image. Supported format: CV_8UC3. Image size must match with the initialized
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image size with the function createScanSegment(). It MUST be in Lab color space.
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*/
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CV_WRAP virtual void iterate(InputArray img) = 0;
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/** @brief Returns the segmentation labeling of the image.
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Each label represents a superpixel, and each pixel is assigned to one superpixel label.
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@param labels_out Return: A CV_32UC1 integer array containing the labels of the superpixel
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segmentation. The labels are in the range [0, getNumberOfSuperpixels()].
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*/
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CV_WRAP virtual void getLabels(OutputArray labels_out) = 0;
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/** @brief Returns the mask of the superpixel segmentation stored in the ScanSegment object.
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The function return the boundaries of the superpixel segmentation.
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@param image Return: CV_8UC1 image mask where -1 indicates that the pixel is a superpixel border, and 0 otherwise.
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@param thick_line If false, the border is only one pixel wide, otherwise all pixels at the border are masked.
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*/
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CV_WRAP virtual void getLabelContourMask(OutputArray image, bool thick_line = false) = 0;
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};
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/** @brief Initializes a ScanSegment object.
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The function initializes a ScanSegment object for the input image. It stores the parameters of
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the image: image_width and image_height. It also sets the parameters of the F-DBSCAN superpixel
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algorithm, which are: num_superpixels, threads, and merge_small.
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@param image_width Image width.
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@param image_height Image height.
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@param num_superpixels Desired number of superpixels. Note that the actual number may be smaller
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due to restrictions (depending on the image size). Use getNumberOfSuperpixels() to
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get the actual number.
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@param slices Number of processing threads for parallelisation. Setting -1 uses the maximum number
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of threads. In practice, four threads is enough for smaller images and eight threads for larger ones.
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@param merge_small merge small segments to give the desired number of superpixels. Processing is
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much faster without merging, but many small segments will be left in the image.
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*/
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CV_EXPORTS_W cv::Ptr<ScanSegment> createScanSegment(int image_width, int image_height, int num_superpixels, int slices = 8, bool merge_small = true);
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}} // namespace
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#endif
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