mirror of https://github.com/opencv/opencv.git
Merge pull request #135 from nevion:master
Vadim Pisarevsky
12 years ago
committed by
OpenCV Buildbot
commit
b68df415a9
6 changed files with 590 additions and 22 deletions
@ -0,0 +1,411 @@ |
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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(CC_STAT_MAX, nlabels), cv::DataType<int>::type); |
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statsv = _mstatsv->getMat(); |
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_mcentroidsv->create(cv::Size(2, nlabels), 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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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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int cv::connectedComponents(InputArray _img, OutputArray _labels, int connectivity, int ltype){ |
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const cv::Mat img = _img.getMat(); |
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_labels.create(img.size(), CV_MAT_DEPTH(ltype)); |
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cv::Mat labels = _labels.getMat(); |
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connectedcomponents::NoOp sop; |
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if(ltype == CV_16U){ |
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return connectedComponents_sub1(img, labels, connectivity, sop); |
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}else if(ltype == CV_32S){ |
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return connectedComponents_sub1(img, labels, connectivity, sop); |
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}else{ |
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CV_Error(CV_StsUnsupportedFormat, "the type of labels must be 16u or 32s"); |
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return 0; |
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} |
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} |
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int cv::connectedComponentsWithStats(InputArray _img, OutputArray _labels, OutputArray statsv, |
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OutputArray centroids, int connectivity, int ltype) |
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{ |
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const cv::Mat img = _img.getMat(); |
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_labels.create(img.size(), CV_MAT_DEPTH(ltype)); |
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cv::Mat labels = _labels.getMat(); |
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connectedcomponents::CCStatsOp sop(statsv, centroids);
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if(ltype == CV_16U){ |
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return connectedComponents_sub1(img, labels, connectivity, sop); |
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}else if(ltype == CV_32S){ |
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return connectedComponents_sub1(img, labels, connectivity, sop); |
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}else{ |
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CV_Error(CV_StsUnsupportedFormat, "the type of labels must be 16u or 32s"); |
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return 0; |
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} |
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} |
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@ -0,0 +1,108 @@ |
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/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "test_precomp.hpp" |
||||
#include <string> |
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|
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using namespace cv; |
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using namespace std; |
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|
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class CV_ConnectedComponentsTest : public cvtest::BaseTest |
||||
{ |
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public: |
||||
CV_ConnectedComponentsTest(); |
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~CV_ConnectedComponentsTest(); |
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protected: |
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void run(int); |
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}; |
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|
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CV_ConnectedComponentsTest::CV_ConnectedComponentsTest() {} |
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CV_ConnectedComponentsTest::~CV_ConnectedComponentsTest() {} |
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|
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void CV_ConnectedComponentsTest::run( int /* start_from */) |
||||
{ |
||||
string exp_path = string(ts->get_data_path()) + "connectedcomponents/ccomp_exp.png"; |
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Mat exp = imread(exp_path, 0); |
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Mat orig = imread(string(ts->get_data_path()) + "connectedcomponents/concentric_circles.png", 0); |
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|
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if (orig.empty()) |
||||
{ |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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return; |
||||
} |
||||
|
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Mat bw = orig > 128; |
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Mat labelImage; |
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int nLabels = connectedComponents(bw, labelImage, 8, CV_32S); |
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|
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for(int r = 0; r < labelImage.rows; ++r){ |
||||
for(int c = 0; c < labelImage.cols; ++c){ |
||||
int l = labelImage.at<int>(r, c); |
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bool pass = l >= 0 && l <= nLabels; |
||||
if(!pass){ |
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
||||
return; |
||||
} |
||||
} |
||||
} |
||||
|
||||
if( exp.empty() || orig.size() != exp.size() ) |
||||
{ |
||||
imwrite(exp_path, labelImage); |
||||
exp = labelImage; |
||||
} |
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|
||||
if (0 != norm(labelImage > 0, exp > 0, NORM_INF)) |
||||
{ |
||||
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH ); |
||||
return; |
||||
} |
||||
if (nLabels != norm(labelImage, NORM_INF)+1) |
||||
{ |
||||
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH ); |
||||
return; |
||||
} |
||||
ts->set_failed_test_info(cvtest::TS::OK); |
||||
} |
||||
|
||||
TEST(Imgproc_ConnectedComponents, regression) { CV_ConnectedComponentsTest test; test.safe_run(); } |
||||
|
||||
Loading…
Reference in new issue