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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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// 2011 Jason Newton <nevion@gmail.com>
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//M*/
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//
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#include "precomp.hpp"
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#include <vector>
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namespace cv{
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namespace connectedcomponents{
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struct NoOp{
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NoOp(){
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}
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void init(int /*labels*/){
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}
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inline
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void operator()(int r, int c, int l){
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(void) r;
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(void) c;
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(void) l;
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}
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void finish(){}
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};
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struct Point2ui64{
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uint64 x, y;
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Point2ui64(uint64 _x, uint64 _y):x(_x), y(_y){}
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};
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struct CCStatsOp{
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const _OutputArray* _mstatsv;
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cv::Mat statsv;
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const _OutputArray* _mcentroidsv;
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cv::Mat centroidsv;
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std::vector<Point2ui64> integrals;
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CCStatsOp(OutputArray _statsv, OutputArray _centroidsv): _mstatsv(&_statsv), _mcentroidsv(&_centroidsv){
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}
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inline
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void init(int nlabels){
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_mstatsv->create(cv::Size(nlabels, CC_STAT_MAX), cv::DataType<int>::type);
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statsv = _mstatsv->getMat();
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_mcentroidsv->create(cv::Size(nlabels, 2), cv::DataType<double>::type);
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centroidsv = _mcentroidsv->getMat();
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for(int l = 0; l < (int) nlabels; ++l){
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int *row = (int *) &statsv.at<int>(l, 0);
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row[CC_STAT_LEFT] = INT_MAX;
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row[CC_STAT_TOP] = INT_MAX;
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row[CC_STAT_WIDTH] = INT_MIN;
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row[CC_STAT_HEIGHT] = INT_MIN;
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row[CC_STAT_AREA] = 0;
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}
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integrals.resize(nlabels, Point2ui64(0, 0));
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}
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void operator()(int r, int c, int l){
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int *row = &statsv.at<int>(l, 0);
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if(c > row[CC_STAT_WIDTH]){
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row[CC_STAT_WIDTH] = c;
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}else{
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if(c < row[CC_STAT_LEFT]){
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row[CC_STAT_LEFT] = c;
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}
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}
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if(r > row[CC_STAT_HEIGHT]){
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row[CC_STAT_HEIGHT] = r;
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}else{
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if(r < row[CC_STAT_TOP]){
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row[CC_STAT_TOP] = r;
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}
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}
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row[CC_STAT_AREA]++;
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Point2ui64 &integral = integrals[l];
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integral.x += c;
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integral.y += r;
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}
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void finish(){
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for(int l = 0; l < statsv.rows; ++l){
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int *row = &statsv.at<int>(l, 0);
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row[CC_STAT_LEFT] = std::min(row[CC_STAT_LEFT], row[CC_STAT_WIDTH]);
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row[CC_STAT_WIDTH] = row[CC_STAT_WIDTH] - row[CC_STAT_LEFT] + 1;
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row[CC_STAT_TOP] = std::min(row[CC_STAT_TOP], row[CC_STAT_HEIGHT]);
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row[CC_STAT_HEIGHT] = row[CC_STAT_HEIGHT] - row[CC_STAT_TOP] + 1;
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Point2ui64 &integral = integrals[l];
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double *centroid = ¢roidsv.at<double>(l, 0);
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double area = ((unsigned*)row)[CC_STAT_AREA];
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centroid[0] = double(integral.x) / area;
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centroid[1] = double(integral.y) / area;
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}
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}
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};
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//Find the root of the tree of node i
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template<typename LabelT>
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inline static
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LabelT findRoot(const LabelT *P, LabelT i){
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LabelT root = i;
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while(P[root] < root){
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root = P[root];
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}
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return root;
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}
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//Make all nodes in the path of node i point to root
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template<typename LabelT>
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inline static
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void setRoot(LabelT *P, LabelT i, LabelT root){
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while(P[i] < i){
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LabelT j = P[i];
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P[i] = root;
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i = j;
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}
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P[i] = root;
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}
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//Find the root of the tree of the node i and compress the path in the process
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template<typename LabelT>
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inline static
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LabelT find(LabelT *P, LabelT i){
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LabelT root = findRoot(P, i);
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setRoot(P, i, root);
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return root;
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}
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//unite the two trees containing nodes i and j and return the new root
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template<typename LabelT>
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inline static
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LabelT set_union(LabelT *P, LabelT i, LabelT j){
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LabelT root = findRoot(P, i);
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if(i != j){
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LabelT rootj = findRoot(P, j);
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if(root > rootj){
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root = rootj;
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}
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setRoot(P, j, root);
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}
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setRoot(P, i, root);
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return root;
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}
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//Flatten the Union Find tree and relabel the components
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template<typename LabelT>
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inline static
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LabelT flattenL(LabelT *P, LabelT length){
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LabelT k = 1;
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for(LabelT i = 1; i < length; ++i){
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if(P[i] < i){
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P[i] = P[P[i]];
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}else{
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P[i] = k; k = k + 1;
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}
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}
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return k;
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}
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//Based on "Two Strategies to Speed up Connected Components Algorithms", the SAUF (Scan array union find) variant
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//using decision trees
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//Kesheng Wu, et al
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//Note: rows are encoded as position in the "rows" array to save lookup times
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//reference for 4-way: {{-1, 0}, {0, -1}};//b, d neighborhoods
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const int G4[2][2] = {{1, 0}, {0, -1}};//b, d neighborhoods
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//reference for 8-way: {{-1, -1}, {-1, 0}, {-1, 1}, {0, -1}};//a, b, c, d neighborhoods
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const int G8[4][2] = {{1, -1}, {1, 0}, {1, 1}, {0, -1}};//a, b, c, d neighborhoods
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template<typename LabelT, typename PixelT, typename StatsOp = NoOp >
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struct LabelingImpl{
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LabelT operator()(const cv::Mat &I, cv::Mat &L, int connectivity, StatsOp &sop){
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CV_Assert(L.rows == I.rows);
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CV_Assert(L.cols == I.cols);
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CV_Assert(connectivity == 8 || connectivity == 4);
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const int rows = L.rows;
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const int cols = L.cols;
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size_t Plength = (size_t(rows + 3 - 1)/3) * (size_t(cols + 3 - 1)/3);
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if(connectivity == 4){
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Plength = 4 * Plength;//a quick and dirty upper bound, an exact answer exists if you want to find it
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//the 4 comes from the fact that a 3x3 block can never have more than 4 unique labels
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}
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LabelT *P = (LabelT *) fastMalloc(sizeof(LabelT) * Plength);
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P[0] = 0;
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LabelT lunique = 1;
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//scanning phase
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for(int r_i = 0; r_i < rows; ++r_i){
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LabelT *Lrow = (LabelT *)(L.data + L.step.p[0] * r_i);
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LabelT *Lrow_prev = (LabelT *)(((char *)Lrow) - L.step.p[0]);
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const PixelT *Irow = (PixelT *)(I.data + I.step.p[0] * r_i);
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const PixelT *Irow_prev = (const PixelT *)(((char *)Irow) - I.step.p[0]);
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LabelT *Lrows[2] = {
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Lrow,
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Lrow_prev
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};
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const PixelT *Irows[2] = {
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Irow,
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Irow_prev
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};
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if(connectivity == 8){
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const int a = 0;
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const int b = 1;
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const int c = 2;
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const int d = 3;
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const bool T_a_r = (r_i - G8[a][0]) >= 0;
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const bool T_b_r = (r_i - G8[b][0]) >= 0;
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const bool T_c_r = (r_i - G8[c][0]) >= 0;
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for(int c_i = 0; Irows[0] != Irow + cols; ++Irows[0], c_i++){
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if(!*Irows[0]){
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Lrow[c_i] = 0;
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continue;
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}
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Irows[1] = Irow_prev + c_i;
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Lrows[0] = Lrow + c_i;
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Lrows[1] = Lrow_prev + c_i;
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const bool T_a = T_a_r && (c_i + G8[a][1]) >= 0 && *(Irows[G8[a][0]] + G8[a][1]);
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const bool T_b = T_b_r && *(Irows[G8[b][0]] + G8[b][1]);
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const bool T_c = T_c_r && (c_i + G8[c][1]) < cols && *(Irows[G8[c][0]] + G8[c][1]);
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const bool T_d = (c_i + G8[d][1]) >= 0 && *(Irows[G8[d][0]] + G8[d][1]);
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//decision tree
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if(T_b){
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//copy(b)
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*Lrows[0] = *(Lrows[G8[b][0]] + G8[b][1]);
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}else{//not b
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if(T_c){
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if(T_a){
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//copy(c, a)
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*Lrows[0] = set_union(P, *(Lrows[G8[c][0]] + G8[c][1]), *(Lrows[G8[a][0]] + G8[a][1]));
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}else{
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if(T_d){
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//copy(c, d)
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*Lrows[0] = set_union(P, *(Lrows[G8[c][0]] + G8[c][1]), *(Lrows[G8[d][0]] + G8[d][1]));
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}else{
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//copy(c)
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*Lrows[0] = *(Lrows[G8[c][0]] + G8[c][1]);
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}
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}
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}else{//not c
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if(T_a){
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//copy(a)
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*Lrows[0] = *(Lrows[G8[a][0]] + G8[a][1]);
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}else{
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if(T_d){
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//copy(d)
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*Lrows[0] = *(Lrows[G8[d][0]] + G8[d][1]);
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}else{
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//new label
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*Lrows[0] = lunique;
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P[lunique] = lunique;
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lunique = lunique + 1;
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}
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}
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}
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}
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}
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}else{
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//B & D only
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const int b = 0;
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const int d = 1;
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const bool T_b_r = (r_i - G4[b][0]) >= 0;
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for(int c_i = 0; Irows[0] != Irow + cols; ++Irows[0], c_i++){
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if(!*Irows[0]){
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Lrow[c_i] = 0;
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continue;
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}
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Irows[1] = Irow_prev + c_i;
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Lrows[0] = Lrow + c_i;
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Lrows[1] = Lrow_prev + c_i;
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const bool T_b = T_b_r && *(Irows[G4[b][0]] + G4[b][1]);
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const bool T_d = (c_i + G4[d][1]) >= 0 && *(Irows[G4[d][0]] + G4[d][1]);
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if(T_b){
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if(T_d){
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//copy(d, b)
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*Lrows[0] = set_union(P, *(Lrows[G4[d][0]] + G4[d][1]), *(Lrows[G4[b][0]] + G4[b][1]));
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}else{
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//copy(b)
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*Lrows[0] = *(Lrows[G4[b][0]] + G4[b][1]);
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}
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}else{
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if(T_d){
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//copy(d)
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*Lrows[0] = *(Lrows[G4[d][0]] + G4[d][1]);
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}else{
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//new label
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*Lrows[0] = lunique;
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P[lunique] = lunique;
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lunique = lunique + 1;
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}
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}
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}
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}
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}
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//analysis
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LabelT nLabels = flattenL(P, lunique);
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sop.init(nLabels);
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for(int r_i = 0; r_i < rows; ++r_i){
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LabelT *Lrow_start = (LabelT *)(L.data + L.step.p[0] * r_i);
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LabelT *Lrow_end = Lrow_start + cols;
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LabelT *Lrow = Lrow_start;
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for(int c_i = 0; Lrow != Lrow_end; ++Lrow, ++c_i){
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const LabelT l = P[*Lrow];
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*Lrow = l;
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sop(r_i, c_i, l);
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}
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}
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sop.finish();
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fastFree(P);
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return nLabels;
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}//End function LabelingImpl operator()
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};//End struct LabelingImpl
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}//end namespace connectedcomponents
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//L's type must have an appropriate depth for the number of pixels in I
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template<typename StatsOp>
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static
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int connectedComponents_sub1(const cv::Mat &I, cv::Mat &L, int connectivity, StatsOp &sop){
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CV_Assert(L.channels() == 1 && I.channels() == 1);
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CV_Assert(connectivity == 8 || connectivity == 4);
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int lDepth = L.depth();
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int iDepth = I.depth();
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using connectedcomponents::LabelingImpl;
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//warn if L's depth is not sufficient?
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CV_Assert(iDepth == CV_8U || iDepth == CV_8S);
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if(lDepth == CV_8U){
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return (int) LabelingImpl<uchar, uchar, StatsOp>()(I, L, connectivity, sop);
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}else if(lDepth == CV_16U){
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return (int) LabelingImpl<ushort, uchar, StatsOp>()(I, L, connectivity, sop);
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}else if(lDepth == CV_32S){
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|
//note that signed types don't really make sense here and not being able to use unsigned matters for scientific projects
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//OpenCV: how should we proceed? .at<T> typechecks in debug mode
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return (int) LabelingImpl<int, uchar, StatsOp>()(I, L, connectivity, sop);
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|
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|
}
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CV_Error(CV_StsUnsupportedFormat, "unsupported label/image type");
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|
return -1;
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}
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|
|
|
|
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|
}
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|
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|
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|
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int cv::connectedComponents(InputArray _I, OutputArray _L, int connectivity, int ltype){
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const cv::Mat I = _I.getMat();
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_L.create(I.size(), CV_MAT_DEPTH(ltype));
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|
|
cv::Mat L = _L.getMat();
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|
|
connectedcomponents::NoOp sop;
|
|
|
|
if(ltype == CV_16U){
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|
return connectedComponents_sub1(I, L, connectivity, sop);
|
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|
|
}else if(ltype == CV_32S){
|
|
|
|
return connectedComponents_sub1(I, L, connectivity, sop);
|
|
|
|
}else{
|
|
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|
CV_Error(CV_StsUnsupportedFormat, "the type of labels must be 16u or 32s");
|
|
|
|
return 0;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
int cv::connectedComponentsWithStats(InputArray _I, OutputArray _L, OutputArray statsv,
|
|
|
|
OutputArray centroids, int connectivity, int ltype)
|
|
|
|
{
|
|
|
|
const cv::Mat I = _I.getMat();
|
|
|
|
_L.create(I.size(), CV_MAT_DEPTH(ltype));
|
|
|
|
cv::Mat L = _L.getMat();
|
|
|
|
connectedcomponents::CCStatsOp sop(statsv, centroids);
|
|
|
|
if(ltype == CV_16U){
|
|
|
|
return connectedComponents_sub1(I, L, connectivity, sop);
|
|
|
|
}else if(ltype == CV_32S){
|
|
|
|
return connectedComponents_sub1(I, L, connectivity, sop);
|
|
|
|
}else{
|
|
|
|
CV_Error(CV_StsUnsupportedFormat, "the type of labels must be 16u or 32s");
|
|
|
|
return 0;
|
|
|
|
}
|
|
|
|
}
|