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Open Source Computer Vision Library
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207 lines
7.0 KiB
207 lines
7.0 KiB
/*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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//M*/ |
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#include "precomp.hpp" |
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#include "opencv2/imgproc/imgproc_c.h" |
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#include "opencv2/calib3d/calib3d_c.h" |
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#include <vector> |
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#include <algorithm> |
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//#define DEBUG_WINDOWS |
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#if defined(DEBUG_WINDOWS) |
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# include "opencv2/opencv_modules.hpp" |
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# ifdef HAVE_OPENCV_HIGHGUI |
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# include "opencv2/highgui.hpp" |
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# else |
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# undef DEBUG_WINDOWS |
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# endif |
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#endif |
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static void icvGetQuadrangleHypotheses(CvSeq* contours, std::vector<std::pair<float, int> >& quads, int class_id) |
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{ |
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const float min_aspect_ratio = 0.3f; |
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const float max_aspect_ratio = 3.0f; |
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const float min_box_size = 10.0f; |
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for(CvSeq* seq = contours; seq != NULL; seq = seq->h_next) |
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{ |
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CvBox2D box = cvMinAreaRect2(seq); |
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float box_size = MAX(box.size.width, box.size.height); |
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if(box_size < min_box_size) |
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{ |
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continue; |
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} |
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float aspect_ratio = box.size.width/MAX(box.size.height, 1); |
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if(aspect_ratio < min_aspect_ratio || aspect_ratio > max_aspect_ratio) |
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{ |
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continue; |
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} |
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quads.push_back(std::pair<float, int>(box_size, class_id)); |
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} |
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} |
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static void countClasses(const std::vector<std::pair<float, int> >& pairs, size_t idx1, size_t idx2, std::vector<int>& counts) |
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{ |
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counts.assign(2, 0); |
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for(size_t i = idx1; i != idx2; i++) |
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{ |
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counts[pairs[i].second]++; |
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} |
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} |
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inline bool less_pred(const std::pair<float, int>& p1, const std::pair<float, int>& p2) |
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{ |
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return p1.first < p2.first; |
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} |
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// does a fast check if a chessboard is in the input image. This is a workaround to |
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// a problem of cvFindChessboardCorners being slow on images with no chessboard |
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// - src: input image |
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// - size: chessboard size |
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// Returns 1 if a chessboard can be in this image and findChessboardCorners should be called, |
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// 0 if there is no chessboard, -1 in case of error |
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int cvCheckChessboard(IplImage* src, CvSize size) |
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{ |
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if(src->nChannels > 1) |
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{ |
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cvError(CV_BadNumChannels, "cvCheckChessboard", "supports single-channel images only", |
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__FILE__, __LINE__); |
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} |
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if(src->depth != 8) |
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{ |
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cvError(CV_BadDepth, "cvCheckChessboard", "supports depth=8 images only", |
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__FILE__, __LINE__); |
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} |
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const int erosion_count = 1; |
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const float black_level = 20.f; |
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const float white_level = 130.f; |
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const float black_white_gap = 70.f; |
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#if defined(DEBUG_WINDOWS) |
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cvNamedWindow("1", 1); |
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cvShowImage("1", src); |
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cvWaitKey(0); |
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#endif //DEBUG_WINDOWS |
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CvMemStorage* storage = cvCreateMemStorage(); |
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IplImage* white = cvCloneImage(src); |
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IplImage* black = cvCloneImage(src); |
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cvErode(white, white, NULL, erosion_count); |
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cvDilate(black, black, NULL, erosion_count); |
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IplImage* thresh = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1); |
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int result = 0; |
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for(float thresh_level = black_level; thresh_level < white_level && !result; thresh_level += 20.0f) |
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{ |
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cvThreshold(white, thresh, thresh_level + black_white_gap, 255, CV_THRESH_BINARY); |
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#if defined(DEBUG_WINDOWS) |
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cvShowImage("1", thresh); |
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cvWaitKey(0); |
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#endif //DEBUG_WINDOWS |
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CvSeq* first = 0; |
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std::vector<std::pair<float, int> > quads; |
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cvFindContours(thresh, storage, &first, sizeof(CvContour), CV_RETR_CCOMP); |
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icvGetQuadrangleHypotheses(first, quads, 1); |
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cvThreshold(black, thresh, thresh_level, 255, CV_THRESH_BINARY_INV); |
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#if defined(DEBUG_WINDOWS) |
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cvShowImage("1", thresh); |
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cvWaitKey(0); |
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#endif //DEBUG_WINDOWS |
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cvFindContours(thresh, storage, &first, sizeof(CvContour), CV_RETR_CCOMP); |
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icvGetQuadrangleHypotheses(first, quads, 0); |
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const size_t min_quads_count = size.width*size.height/2; |
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std::sort(quads.begin(), quads.end(), less_pred); |
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// now check if there are many hypotheses with similar sizes |
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// do this by floodfill-style algorithm |
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const float size_rel_dev = 0.4f; |
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for(size_t i = 0; i < quads.size(); i++) |
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{ |
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size_t j = i + 1; |
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for(; j < quads.size(); j++) |
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{ |
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if(quads[j].first/quads[i].first > 1.0f + size_rel_dev) |
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{ |
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break; |
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} |
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} |
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if(j + 1 > min_quads_count + i) |
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{ |
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// check the number of black and white squares |
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std::vector<int> counts; |
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countClasses(quads, i, j, counts); |
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const int black_count = cvRound(ceil(size.width/2.0)*ceil(size.height/2.0)); |
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const int white_count = cvRound(floor(size.width/2.0)*floor(size.height/2.0)); |
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if(counts[0] < black_count*0.75 || |
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counts[1] < white_count*0.75) |
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{ |
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continue; |
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} |
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result = 1; |
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break; |
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} |
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} |
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} |
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cvReleaseImage(&thresh); |
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cvReleaseImage(&white); |
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cvReleaseImage(&black); |
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cvReleaseMemStorage(&storage); |
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return result; |
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}
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