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SLIC.hpp
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SLIC.hpp
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// This file is part of the LITIV framework; visit the original repository at
// https://github.com/plstcharles/litiv for more information.
//
// Copyright 2017 Pierre-Luc St-Charles; pierre-luc.st-charles<at>polymtl.ca
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
// //////////////////////////////////////////////////////////////////////////
//
// SLIC Superpixel Oversegmentation Algorithm
// CUDA implementation of Achanta et al.'s method (TPAMI 2012)
//
// Note: requires CUDA compute architecture >= 3.0
// Author: Francois-Xavier Derue
// Contact: francois.xavier.derue@gmail.com
// Source: https://github.com/fderue/SLIC_CUDA
//
// Copyright (c) 2016 fderue
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in all
// copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE.
//
#pragma once
#include "litiv/utils/cuda.hpp"
/// SLIC superpixel segmentation algorithm
struct SLIC {
/// algorithm initialization method type list
enum InitType {
SLIC_SIZE, ///< initialize with spx size
SLIC_NSPX ///< initialize with spx count
};
SLIC();
~SLIC();
/// set up the parameters and initalize all gpu buffer for faster video segmentation
void initialize(const cv::Size& size, const int diamSpxOrNbSpx = 15, const InitType initType = SLIC_SIZE, const float wc = 35, const int nbIteration = 2);
/// segment a frame in superpixel
void segment(const cv::Mat& frame);
/// returns computed superpixel labels for the previous frame
inline const cv::Mat& getLabels() const {
return m_oLabels;
}
/// discard orphan clusters (optional)
int enforceConnectivity();
/// returns a displayable version of the given input with overlying superpixels (cpu-side drawing)
static cv::Mat displayBound(const cv::Mat& image, const cv::Mat& labels, const cv::Scalar& colour=cv::Scalar(255,0,0), const int& boundWidth = 1);
/// returns a displayable version of the given input represented with RGB mean of superpixels (cpu-side drawing)
static cv::Mat displayMean(const cv::Mat& image, const cv::Mat& labels);
protected:
const int m_deviceId = 0;
cudaDeviceProp m_deviceProp;
int m_nbPx;
int m_nbSpx;
int m_SpxDiam;
int m_nbSpxPerRow;
int m_nbSpxPerCol;
int m_FrameWidth, m_FrameHeight;
float m_wc;
int m_nbIteration;
InitType m_InitType;
// cpu buffer
cv::Mat_<float> m_oLabels;
// gpu variable
float* d_fClusters;
float* d_fLabels;
float* d_fAccAtt;
// cudaArray
cudaArray* cuArrayFrameBGRA;
cudaArray* cuArrayFrameLab;
cudaArray* cuArrayLabels;
// texture and surface Object
cudaTextureObject_t oTexFrameBGRA;
cudaSurfaceObject_t oSurfFrameLab;
cudaSurfaceObject_t oSurfLabels;
/// assign the closest centroid to each pixel
void assignment();
/// update the clusters' centroids with the belonging pixels
void update();
};