mirror of https://github.com/opencv/opencv.git
Ilya Lysenkov
14 years ago
parent
8f0d36b8b6
commit
dc9e5eda19
8 changed files with 279 additions and 224 deletions
@ -1,2 +1,2 @@ |
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define_opencv_module(features2d opencv_core opencv_imgproc opencv_calib3d opencv_highgui opencv_flann) |
define_opencv_module(features2d opencv_core opencv_imgproc opencv_highgui opencv_flann) |
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@ -1 +1 @@ |
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define_opencv_module(objdetect opencv_core opencv_imgproc opencv_highgui) |
define_opencv_module(objdetect opencv_core opencv_imgproc opencv_highgui opencv_features2d opencv_calib3d) |
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@ -0,0 +1,221 @@ |
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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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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., 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 the copyright holders 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 <stdio.h> |
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namespace cv |
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{ |
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////////////////////////////////////// Planar Object Detector ////////////////////////////////////
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PlanarObjectDetector::PlanarObjectDetector() |
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{ |
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} |
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PlanarObjectDetector::PlanarObjectDetector(const FileNode& node) |
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{ |
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read(node); |
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} |
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PlanarObjectDetector::PlanarObjectDetector(const vector<Mat>& pyr, int npoints, |
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int patchSize, int nstructs, int structSize, |
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int nviews, const LDetector& detector, |
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const PatchGenerator& patchGenerator) |
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{ |
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train(pyr, npoints, patchSize, nstructs, |
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structSize, nviews, detector, patchGenerator); |
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} |
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PlanarObjectDetector::~PlanarObjectDetector() |
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{ |
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} |
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vector<KeyPoint> PlanarObjectDetector::getModelPoints() const |
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{ |
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return modelPoints; |
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} |
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void PlanarObjectDetector::train(const vector<Mat>& pyr, int npoints, |
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int patchSize, int nstructs, int structSize, |
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int nviews, const LDetector& detector, |
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const PatchGenerator& patchGenerator) |
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{ |
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modelROI = Rect(0, 0, pyr[0].cols, pyr[0].rows); |
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ldetector = detector; |
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ldetector.setVerbose(verbose); |
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ldetector.getMostStable2D(pyr[0], modelPoints, npoints, patchGenerator); |
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npoints = (int)modelPoints.size(); |
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fernClassifier.setVerbose(verbose); |
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fernClassifier.trainFromSingleView(pyr[0], modelPoints, |
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patchSize, (int)modelPoints.size(), nstructs, structSize, nviews, |
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FernClassifier::COMPRESSION_NONE, patchGenerator); |
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} |
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void PlanarObjectDetector::train(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints, |
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int patchSize, int nstructs, int structSize, |
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int nviews, const LDetector& detector, |
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const PatchGenerator& patchGenerator) |
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{ |
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modelROI = Rect(0, 0, pyr[0].cols, pyr[0].rows); |
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ldetector = detector; |
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ldetector.setVerbose(verbose); |
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modelPoints.resize(keypoints.size()); |
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std::copy(keypoints.begin(), keypoints.end(), modelPoints.begin()); |
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fernClassifier.setVerbose(verbose); |
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fernClassifier.trainFromSingleView(pyr[0], modelPoints, |
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patchSize, (int)modelPoints.size(), nstructs, structSize, nviews, |
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FernClassifier::COMPRESSION_NONE, patchGenerator); |
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} |
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void PlanarObjectDetector::read(const FileNode& node) |
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{ |
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FileNodeIterator it = node["model-roi"].begin(), it_end; |
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it >> modelROI.x >> modelROI.y >> modelROI.width >> modelROI.height; |
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ldetector.read(node["detector"]); |
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fernClassifier.read(node["fern-classifier"]); |
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cv::read(node["model-points"], modelPoints); |
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CV_Assert(modelPoints.size() == (size_t)fernClassifier.getClassCount()); |
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} |
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void PlanarObjectDetector::write(FileStorage& fs, const String& objname) const |
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{ |
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WriteStructContext ws(fs, objname, CV_NODE_MAP); |
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{ |
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WriteStructContext wsroi(fs, "model-roi", CV_NODE_SEQ + CV_NODE_FLOW); |
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cv::write(fs, modelROI.x); |
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cv::write(fs, modelROI.y); |
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cv::write(fs, modelROI.width); |
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cv::write(fs, modelROI.height); |
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} |
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ldetector.write(fs, "detector"); |
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cv::write(fs, "model-points", modelPoints); |
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fernClassifier.write(fs, "fern-classifier"); |
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} |
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bool PlanarObjectDetector::operator()(const Mat& image, Mat& H, vector<Point2f>& corners) const |
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{ |
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vector<Mat> pyr; |
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buildPyramid(image, pyr, ldetector.nOctaves - 1); |
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vector<KeyPoint> keypoints; |
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ldetector(pyr, keypoints); |
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return (*this)(pyr, keypoints, H, corners); |
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} |
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bool PlanarObjectDetector::operator()(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints, |
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Mat& matH, vector<Point2f>& corners, vector<int>* pairs) const |
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{ |
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int i, j, m = (int)modelPoints.size(), n = (int)keypoints.size(); |
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vector<int> bestMatches(m, -1); |
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vector<float> maxLogProb(m, -FLT_MAX); |
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vector<float> signature; |
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vector<Point2f> fromPt, toPt; |
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for( i = 0; i < n; i++ ) |
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{ |
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KeyPoint kpt = keypoints[i]; |
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CV_Assert(0 <= kpt.octave && kpt.octave < (int)pyr.size()); |
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kpt.pt.x /= (float)(1 << kpt.octave); |
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kpt.pt.y /= (float)(1 << kpt.octave); |
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int k = fernClassifier(pyr[kpt.octave], kpt.pt, signature); |
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if( k >= 0 && (bestMatches[k] < 0 || signature[k] > maxLogProb[k]) ) |
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{ |
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maxLogProb[k] = signature[k]; |
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bestMatches[k] = i; |
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} |
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} |
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if(pairs) |
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pairs->resize(0); |
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for( i = 0; i < m; i++ ) |
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if( bestMatches[i] >= 0 ) |
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{ |
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fromPt.push_back(modelPoints[i].pt); |
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toPt.push_back(keypoints[bestMatches[i]].pt); |
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} |
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if( fromPt.size() < 4 ) |
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return false; |
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vector<uchar> mask; |
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matH = findHomography(Mat(fromPt), Mat(toPt), mask, RANSAC, 10); |
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if( matH.data ) |
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{ |
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const Mat_<double>& H = matH; |
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corners.resize(4); |
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for( i = 0; i < 4; i++ ) |
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{ |
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Point2f pt((float)(modelROI.x + (i == 0 || i == 3 ? 0 : modelROI.width)), |
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(float)(modelROI.y + (i <= 1 ? 0 : modelROI.height))); |
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double w = 1./(H(2,0)*pt.x + H(2,1)*pt.y + H(2,2)); |
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corners[i] = Point2f((float)((H(0,0)*pt.x + H(0,1)*pt.y + H(0,2))*w), |
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(float)((H(1,0)*pt.x + H(1,1)*pt.y + H(1,2))*w)); |
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} |
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} |
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if( pairs ) |
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{ |
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for( i = j = 0; i < m; i++ ) |
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if( bestMatches[i] >= 0 && mask[j++] ) |
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{ |
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pairs->push_back(i); |
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pairs->push_back(bestMatches[i]); |
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} |
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} |
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return matH.data != 0; |
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} |
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void PlanarObjectDetector::setVerbose(bool _verbose) |
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{ |
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verbose = _verbose; |
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} |
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} |
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