766 lines
30 KiB
C++
766 lines
30 KiB
C++
/*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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// (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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/*
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This is a regression test for stereo matching algorithms. This test gets some quality metrics
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discribed in "A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms".
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Daniel Scharstein, Richard Szeliski
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*/
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#include "test_precomp.hpp"
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#include <limits>
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#include <cstdio>
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using namespace std;
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using namespace cv;
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const float EVAL_BAD_THRESH = 1.f;
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const int EVAL_TEXTURELESS_WIDTH = 3;
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const float EVAL_TEXTURELESS_THRESH = 4.f;
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const float EVAL_DISP_THRESH = 1.f;
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const float EVAL_DISP_GAP = 2.f;
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const int EVAL_DISCONT_WIDTH = 9;
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const int EVAL_IGNORE_BORDER = 10;
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const int ERROR_KINDS_COUNT = 6;
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//============================== quality measuring functions =================================================
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/*
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Calculate textureless regions of image (regions where the squared horizontal intensity gradient averaged over
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a square window of size=evalTexturelessWidth is below a threshold=evalTexturelessThresh) and textured regions.
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*/
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void computeTextureBasedMasks( const Mat& _img, Mat* texturelessMask, Mat* texturedMask,
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int texturelessWidth = EVAL_TEXTURELESS_WIDTH, float texturelessThresh = EVAL_TEXTURELESS_THRESH )
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{
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if( !texturelessMask && !texturedMask )
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return;
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if( _img.empty() )
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CV_Error( CV_StsBadArg, "img is empty" );
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Mat img = _img;
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if( _img.channels() > 1)
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{
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Mat tmp; cvtColor( _img, tmp, CV_BGR2GRAY ); img = tmp;
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}
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Mat dxI; Sobel( img, dxI, CV_32FC1, 1, 0, 3 );
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Mat dxI2; pow( dxI / 8.f/*normalize*/, 2, dxI2 );
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Mat avgDxI2; boxFilter( dxI2, avgDxI2, CV_32FC1, Size(texturelessWidth,texturelessWidth) );
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if( texturelessMask )
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*texturelessMask = avgDxI2 < texturelessThresh;
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if( texturedMask )
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*texturedMask = avgDxI2 >= texturelessThresh;
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}
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void checkTypeAndSizeOfDisp( const Mat& dispMap, const Size* sz )
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{
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if( dispMap.empty() )
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CV_Error( CV_StsBadArg, "dispMap is empty" );
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if( dispMap.type() != CV_32FC1 )
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CV_Error( CV_StsBadArg, "dispMap must have CV_32FC1 type" );
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if( sz && (dispMap.rows != sz->height || dispMap.cols != sz->width) )
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CV_Error( CV_StsBadArg, "dispMap has incorrect size" );
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}
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void checkTypeAndSizeOfMask( const Mat& mask, Size sz )
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{
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if( mask.empty() )
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CV_Error( CV_StsBadArg, "mask is empty" );
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if( mask.type() != CV_8UC1 )
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CV_Error( CV_StsBadArg, "mask must have CV_8UC1 type" );
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if( mask.rows != sz.height || mask.cols != sz.width )
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CV_Error( CV_StsBadArg, "mask has incorrect size" );
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}
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void checkDispMapsAndUnknDispMasks( const Mat& leftDispMap, const Mat& rightDispMap,
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const Mat& leftUnknDispMask, const Mat& rightUnknDispMask )
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{
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// check type and size of disparity maps
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checkTypeAndSizeOfDisp( leftDispMap, 0 );
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if( !rightDispMap.empty() )
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{
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Size sz = leftDispMap.size();
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checkTypeAndSizeOfDisp( rightDispMap, &sz );
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}
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// check size and type of unknown disparity maps
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if( !leftUnknDispMask.empty() )
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checkTypeAndSizeOfMask( leftUnknDispMask, leftDispMap.size() );
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if( !rightUnknDispMask.empty() )
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checkTypeAndSizeOfMask( rightUnknDispMask, rightDispMap.size() );
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// check values of disparity maps (known disparity values musy be positive)
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double leftMinVal = 0, rightMinVal = 0;
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if( leftUnknDispMask.empty() )
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minMaxLoc( leftDispMap, &leftMinVal );
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else
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minMaxLoc( leftDispMap, &leftMinVal, 0, 0, 0, ~leftUnknDispMask );
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if( !rightDispMap.empty() )
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{
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if( rightUnknDispMask.empty() )
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minMaxLoc( rightDispMap, &rightMinVal );
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else
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minMaxLoc( rightDispMap, &rightMinVal, 0, 0, 0, ~rightUnknDispMask );
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}
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if( leftMinVal < 0 || rightMinVal < 0)
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CV_Error( CV_StsBadArg, "known disparity values must be positive" );
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}
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/*
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Calculate occluded regions of reference image (left image) (regions that are occluded in the matching image (right image),
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i.e., where the forward-mapped disparity lands at a location with a larger (nearer) disparity) and non occluded regions.
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*/
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void computeOcclusionBasedMasks( const Mat& leftDisp, const Mat& _rightDisp,
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Mat* occludedMask, Mat* nonOccludedMask,
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const Mat& leftUnknDispMask = Mat(), const Mat& rightUnknDispMask = Mat(),
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float dispThresh = EVAL_DISP_THRESH )
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{
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if( !occludedMask && !nonOccludedMask )
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return;
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checkDispMapsAndUnknDispMasks( leftDisp, _rightDisp, leftUnknDispMask, rightUnknDispMask );
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Mat rightDisp;
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if( _rightDisp.empty() )
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{
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if( !rightUnknDispMask.empty() )
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CV_Error( CV_StsBadArg, "rightUnknDispMask must be empty if _rightDisp is empty" );
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rightDisp.create(leftDisp.size(), CV_32FC1);
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rightDisp.setTo(Scalar::all(0) );
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for( int leftY = 0; leftY < leftDisp.rows; leftY++ )
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{
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for( int leftX = 0; leftX < leftDisp.cols; leftX++ )
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{
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if( !leftUnknDispMask.empty() && leftUnknDispMask.at<uchar>(leftY,leftX) )
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continue;
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float leftDispVal = leftDisp.at<float>(leftY, leftX);
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int rightX = leftX - cvRound(leftDispVal), rightY = leftY;
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if( rightX >= 0)
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rightDisp.at<float>(rightY,rightX) = max(rightDisp.at<float>(rightY,rightX), leftDispVal);
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}
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}
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}
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else
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_rightDisp.copyTo(rightDisp);
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if( occludedMask )
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{
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occludedMask->create(leftDisp.size(), CV_8UC1);
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occludedMask->setTo(Scalar::all(0) );
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}
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if( nonOccludedMask )
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{
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nonOccludedMask->create(leftDisp.size(), CV_8UC1);
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nonOccludedMask->setTo(Scalar::all(0) );
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}
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for( int leftY = 0; leftY < leftDisp.rows; leftY++ )
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{
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for( int leftX = 0; leftX < leftDisp.cols; leftX++ )
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{
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if( !leftUnknDispMask.empty() && leftUnknDispMask.at<uchar>(leftY,leftX) )
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continue;
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float leftDispVal = leftDisp.at<float>(leftY, leftX);
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int rightX = leftX - cvRound(leftDispVal), rightY = leftY;
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if( rightX < 0 && occludedMask )
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occludedMask->at<uchar>(leftY, leftX) = 255;
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else
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{
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if( !rightUnknDispMask.empty() && rightUnknDispMask.at<uchar>(rightY,rightX) )
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continue;
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float rightDispVal = rightDisp.at<float>(rightY, rightX);
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if( rightDispVal > leftDispVal + dispThresh )
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{
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if( occludedMask )
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occludedMask->at<uchar>(leftY, leftX) = 255;
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}
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else
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{
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if( nonOccludedMask )
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nonOccludedMask->at<uchar>(leftY, leftX) = 255;
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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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Calculate depth discontinuty regions: pixels whose neiboring disparities differ by more than
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dispGap, dilated by window of width discontWidth.
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*/
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void computeDepthDiscontMask( const Mat& disp, Mat& depthDiscontMask, const Mat& unknDispMask = Mat(),
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float dispGap = EVAL_DISP_GAP, int discontWidth = EVAL_DISCONT_WIDTH )
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{
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if( disp.empty() )
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CV_Error( CV_StsBadArg, "disp is empty" );
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if( disp.type() != CV_32FC1 )
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CV_Error( CV_StsBadArg, "disp must have CV_32FC1 type" );
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if( !unknDispMask.empty() )
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checkTypeAndSizeOfMask( unknDispMask, disp.size() );
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Mat curDisp; disp.copyTo( curDisp );
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if( !unknDispMask.empty() )
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curDisp.setTo( Scalar(numeric_limits<float>::min()), unknDispMask );
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Mat maxNeighbDisp; dilate( curDisp, maxNeighbDisp, Mat(3, 3, CV_8UC1, Scalar(1)) );
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if( !unknDispMask.empty() )
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curDisp.setTo( Scalar(numeric_limits<float>::max()), unknDispMask );
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Mat minNeighbDisp; erode( curDisp, minNeighbDisp, Mat(3, 3, CV_8UC1, Scalar(1)) );
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depthDiscontMask = max( (Mat)(maxNeighbDisp-disp), (Mat)(disp-minNeighbDisp) ) > dispGap;
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if( !unknDispMask.empty() )
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depthDiscontMask &= ~unknDispMask;
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dilate( depthDiscontMask, depthDiscontMask, Mat(discontWidth, discontWidth, CV_8UC1, Scalar(1)) );
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}
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/*
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Get evaluation masks excluding a border.
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*/
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Mat getBorderedMask( Size maskSize, int border = EVAL_IGNORE_BORDER )
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{
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CV_Assert( border >= 0 );
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Mat mask(maskSize, CV_8UC1, Scalar(0));
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int w = maskSize.width - 2*border, h = maskSize.height - 2*border;
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if( w < 0 || h < 0 )
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mask.setTo(Scalar(0));
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else
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mask( Rect(Point(border,border),Size(w,h)) ).setTo(Scalar(255));
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return mask;
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}
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/*
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Calculate root-mean-squared error between the computed disparity map (computedDisp) and ground truth map (groundTruthDisp).
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*/
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float dispRMS( const Mat& computedDisp, const Mat& groundTruthDisp, const Mat& mask )
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{
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checkTypeAndSizeOfDisp( groundTruthDisp, 0 );
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Size sz = groundTruthDisp.size();
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checkTypeAndSizeOfDisp( computedDisp, &sz );
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int pointsCount = sz.height*sz.width;
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if( !mask.empty() )
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{
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checkTypeAndSizeOfMask( mask, sz );
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pointsCount = countNonZero(mask);
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}
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return 1.f/sqrt((float)pointsCount) * (float)norm(computedDisp, groundTruthDisp, NORM_L2, mask);
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}
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/*
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Calculate fraction of bad matching pixels.
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*/
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float badMatchPxlsFraction( const Mat& computedDisp, const Mat& groundTruthDisp, const Mat& mask,
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float _badThresh = EVAL_BAD_THRESH )
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{
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int badThresh = cvRound(_badThresh);
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checkTypeAndSizeOfDisp( groundTruthDisp, 0 );
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Size sz = groundTruthDisp.size();
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checkTypeAndSizeOfDisp( computedDisp, &sz );
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Mat badPxlsMap;
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absdiff( computedDisp, groundTruthDisp, badPxlsMap );
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badPxlsMap = badPxlsMap > badThresh;
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int pointsCount = sz.height*sz.width;
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if( !mask.empty() )
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{
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checkTypeAndSizeOfMask( mask, sz );
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badPxlsMap = badPxlsMap & mask;
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pointsCount = countNonZero(mask);
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}
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return 1.f/pointsCount * countNonZero(badPxlsMap);
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}
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//===================== regression test for stereo matching algorithms ==============================
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const string ALGORITHMS_DIR = "stereomatching/algorithms/";
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const string DATASETS_DIR = "stereomatching/datasets/";
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const string DATASETS_FILE = "datasets.xml";
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const string RUN_PARAMS_FILE = "_params.xml";
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const string RESULT_FILE = "_res.xml";
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const string LEFT_IMG_NAME = "im2.png";
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const string RIGHT_IMG_NAME = "im6.png";
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const string TRUE_LEFT_DISP_NAME = "disp2.png";
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const string TRUE_RIGHT_DISP_NAME = "disp6.png";
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string ERROR_PREFIXES[] = { "borderedAll",
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"borderedNoOccl",
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"borderedOccl",
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"borderedTextured",
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"borderedTextureless",
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"borderedDepthDiscont" }; // size of ERROR_KINDS_COUNT
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const string RMS_STR = "RMS";
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const string BAD_PXLS_FRACTION_STR = "BadPxlsFraction";
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class QualityEvalParams
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{
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public:
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QualityEvalParams() { setDefaults(); }
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QualityEvalParams( int _ignoreBorder )
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{
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setDefaults();
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ignoreBorder = _ignoreBorder;
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}
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void setDefaults()
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{
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badThresh = EVAL_BAD_THRESH;
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texturelessWidth = EVAL_TEXTURELESS_WIDTH;
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texturelessThresh = EVAL_TEXTURELESS_THRESH;
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dispThresh = EVAL_DISP_THRESH;
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dispGap = EVAL_DISP_GAP;
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discontWidth = EVAL_DISCONT_WIDTH;
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ignoreBorder = EVAL_IGNORE_BORDER;
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}
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float badThresh;
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int texturelessWidth;
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float texturelessThresh;
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float dispThresh;
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float dispGap;
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int discontWidth;
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int ignoreBorder;
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};
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class CV_StereoMatchingTest : public cvtest::BaseTest
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{
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public:
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CV_StereoMatchingTest()
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{ rmsEps.resize( ERROR_KINDS_COUNT, 0.01f ); fracEps.resize( ERROR_KINDS_COUNT, 1.e-6f ); }
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protected:
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// assumed that left image is a reference image
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virtual int runStereoMatchingAlgorithm( const Mat& leftImg, const Mat& rightImg,
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Mat& leftDisp, Mat& rightDisp, int caseIdx ) = 0; // return ignored border width
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int readDatasetsParams( FileStorage& fs );
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virtual int readRunParams( FileStorage& fs );
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void writeErrors( const string& errName, const vector<float>& errors, FileStorage* fs = 0 );
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void readErrors( FileNode& fn, const string& errName, vector<float>& errors );
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int compareErrors( const vector<float>& calcErrors, const vector<float>& validErrors,
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const vector<float>& eps, const string& errName );
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int processStereoMatchingResults( FileStorage& fs, int caseIdx, bool isWrite,
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const Mat& leftImg, const Mat& rightImg,
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const Mat& trueLeftDisp, const Mat& trueRightDisp,
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const Mat& leftDisp, const Mat& rightDisp,
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const QualityEvalParams& qualityEvalParams );
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void run( int );
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vector<float> rmsEps;
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vector<float> fracEps;
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struct DatasetParams
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{
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int dispScaleFactor;
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int dispUnknVal;
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};
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map<string, DatasetParams> datasetsParams;
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vector<string> caseNames;
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vector<string> caseDatasets;
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};
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void CV_StereoMatchingTest::run(int)
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{
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string dataPath = ts->get_data_path();
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string algorithmName = name;
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assert( !algorithmName.empty() );
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if( dataPath.empty() )
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{
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ts->printf( cvtest::TS::LOG, "dataPath is empty" );
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ARG_CHECK );
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return;
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}
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FileStorage datasetsFS( dataPath + DATASETS_DIR + DATASETS_FILE, FileStorage::READ );
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int code = readDatasetsParams( datasetsFS );
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if( code != cvtest::TS::OK )
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{
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ts->set_failed_test_info( code );
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return;
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}
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FileStorage runParamsFS( dataPath + ALGORITHMS_DIR + algorithmName + RUN_PARAMS_FILE, FileStorage::READ );
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code = readRunParams( runParamsFS );
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if( code != cvtest::TS::OK )
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{
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ts->set_failed_test_info( code );
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return;
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}
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string fullResultFilename = dataPath + ALGORITHMS_DIR + algorithmName + RESULT_FILE;
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FileStorage resFS( fullResultFilename, FileStorage::READ );
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bool isWrite = true; // write or compare results
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if( resFS.isOpened() )
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isWrite = false;
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else
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{
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resFS.open( fullResultFilename, FileStorage::WRITE );
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if( !resFS.isOpened() )
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{
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ts->printf( cvtest::TS::LOG, "file %s can not be read or written\n", fullResultFilename.c_str() );
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ARG_CHECK );
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return;
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}
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resFS << "stereo_matching" << "{";
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}
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int progress = 0, caseCount = (int)caseNames.size();
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for( int ci = 0; ci < caseCount; ci++)
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{
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progress = update_progress( progress, ci, caseCount, 0 );
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printf("progress: %d%%\n", progress);
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fflush(stdout);
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string datasetName = caseDatasets[ci];
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string datasetFullDirName = dataPath + DATASETS_DIR + datasetName + "/";
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Mat leftImg = imread(datasetFullDirName + LEFT_IMG_NAME);
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Mat rightImg = imread(datasetFullDirName + RIGHT_IMG_NAME);
|
|
Mat trueLeftDisp = imread(datasetFullDirName + TRUE_LEFT_DISP_NAME, 0);
|
|
Mat trueRightDisp = imread(datasetFullDirName + TRUE_RIGHT_DISP_NAME, 0);
|
|
|
|
if( leftImg.empty() || rightImg.empty() || trueLeftDisp.empty() )
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "images or left ground-truth disparities of dataset %s can not be read", datasetName.c_str() );
|
|
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
|
|
continue;
|
|
}
|
|
int dispScaleFactor = datasetsParams[datasetName].dispScaleFactor;
|
|
Mat tmp; trueLeftDisp.convertTo( tmp, CV_32FC1, 1.f/dispScaleFactor ); trueLeftDisp = tmp; tmp.release();
|
|
if( !trueRightDisp.empty() )
|
|
trueRightDisp.convertTo( tmp, CV_32FC1, 1.f/dispScaleFactor ); trueRightDisp = tmp; tmp.release();
|
|
|
|
Mat leftDisp, rightDisp;
|
|
int ignBorder = max(runStereoMatchingAlgorithm(leftImg, rightImg, leftDisp, rightDisp, ci), EVAL_IGNORE_BORDER);
|
|
leftDisp.convertTo( tmp, CV_32FC1 ); leftDisp = tmp; tmp.release();
|
|
rightDisp.convertTo( tmp, CV_32FC1 ); rightDisp = tmp; tmp.release();
|
|
|
|
int tempCode = processStereoMatchingResults( resFS, ci, isWrite,
|
|
leftImg, rightImg, trueLeftDisp, trueRightDisp, leftDisp, rightDisp, QualityEvalParams(ignBorder));
|
|
code = tempCode==cvtest::TS::OK ? code : tempCode;
|
|
}
|
|
|
|
if( isWrite )
|
|
resFS << "}"; // "stereo_matching"
|
|
|
|
ts->set_failed_test_info( code );
|
|
}
|
|
|
|
void calcErrors( const Mat& leftImg, const Mat& /*rightImg*/,
|
|
const Mat& trueLeftDisp, const Mat& trueRightDisp,
|
|
const Mat& trueLeftUnknDispMask, const Mat& trueRightUnknDispMask,
|
|
const Mat& calcLeftDisp, const Mat& /*calcRightDisp*/,
|
|
vector<float>& rms, vector<float>& badPxlsFractions,
|
|
const QualityEvalParams& qualityEvalParams )
|
|
{
|
|
Mat texturelessMask, texturedMask;
|
|
computeTextureBasedMasks( leftImg, &texturelessMask, &texturedMask,
|
|
qualityEvalParams.texturelessWidth, qualityEvalParams.texturelessThresh );
|
|
Mat occludedMask, nonOccludedMask;
|
|
computeOcclusionBasedMasks( trueLeftDisp, trueRightDisp, &occludedMask, &nonOccludedMask,
|
|
trueLeftUnknDispMask, trueRightUnknDispMask, qualityEvalParams.dispThresh);
|
|
Mat depthDiscontMask;
|
|
computeDepthDiscontMask( trueLeftDisp, depthDiscontMask, trueLeftUnknDispMask,
|
|
qualityEvalParams.dispGap, qualityEvalParams.discontWidth);
|
|
|
|
Mat borderedKnownMask = getBorderedMask( leftImg.size(), qualityEvalParams.ignoreBorder ) & ~trueLeftUnknDispMask;
|
|
|
|
nonOccludedMask &= borderedKnownMask;
|
|
occludedMask &= borderedKnownMask;
|
|
texturedMask &= nonOccludedMask; // & borderedKnownMask
|
|
texturelessMask &= nonOccludedMask; // & borderedKnownMask
|
|
depthDiscontMask &= nonOccludedMask; // & borderedKnownMask
|
|
|
|
rms.resize(ERROR_KINDS_COUNT);
|
|
rms[0] = dispRMS( calcLeftDisp, trueLeftDisp, borderedKnownMask );
|
|
rms[1] = dispRMS( calcLeftDisp, trueLeftDisp, nonOccludedMask );
|
|
rms[2] = dispRMS( calcLeftDisp, trueLeftDisp, occludedMask );
|
|
rms[3] = dispRMS( calcLeftDisp, trueLeftDisp, texturedMask );
|
|
rms[4] = dispRMS( calcLeftDisp, trueLeftDisp, texturelessMask );
|
|
rms[5] = dispRMS( calcLeftDisp, trueLeftDisp, depthDiscontMask );
|
|
|
|
badPxlsFractions.resize(ERROR_KINDS_COUNT);
|
|
badPxlsFractions[0] = badMatchPxlsFraction( calcLeftDisp, trueLeftDisp, borderedKnownMask, qualityEvalParams.badThresh );
|
|
badPxlsFractions[1] = badMatchPxlsFraction( calcLeftDisp, trueLeftDisp, nonOccludedMask, qualityEvalParams.badThresh );
|
|
badPxlsFractions[2] = badMatchPxlsFraction( calcLeftDisp, trueLeftDisp, occludedMask, qualityEvalParams.badThresh );
|
|
badPxlsFractions[3] = badMatchPxlsFraction( calcLeftDisp, trueLeftDisp, texturedMask, qualityEvalParams.badThresh );
|
|
badPxlsFractions[4] = badMatchPxlsFraction( calcLeftDisp, trueLeftDisp, texturelessMask, qualityEvalParams.badThresh );
|
|
badPxlsFractions[5] = badMatchPxlsFraction( calcLeftDisp, trueLeftDisp, depthDiscontMask, qualityEvalParams.badThresh );
|
|
}
|
|
|
|
int CV_StereoMatchingTest::processStereoMatchingResults( FileStorage& fs, int caseIdx, bool isWrite,
|
|
const Mat& leftImg, const Mat& rightImg,
|
|
const Mat& trueLeftDisp, const Mat& trueRightDisp,
|
|
const Mat& leftDisp, const Mat& rightDisp,
|
|
const QualityEvalParams& qualityEvalParams )
|
|
{
|
|
// rightDisp is not used in current test virsion
|
|
int code = cvtest::TS::OK;
|
|
assert( fs.isOpened() );
|
|
assert( trueLeftDisp.type() == CV_32FC1 && trueRightDisp.type() == CV_32FC1 );
|
|
assert( leftDisp.type() == CV_32FC1 && rightDisp.type() == CV_32FC1 );
|
|
|
|
// get masks for unknown ground truth disparity values
|
|
Mat leftUnknMask, rightUnknMask;
|
|
DatasetParams params = datasetsParams[caseDatasets[caseIdx]];
|
|
absdiff( trueLeftDisp, Scalar(params.dispUnknVal), leftUnknMask );
|
|
leftUnknMask = leftUnknMask < numeric_limits<float>::epsilon();
|
|
assert(leftUnknMask.type() == CV_8UC1);
|
|
if( !trueRightDisp.empty() )
|
|
{
|
|
absdiff( trueRightDisp, Scalar(params.dispUnknVal), rightUnknMask );
|
|
rightUnknMask = rightUnknMask < numeric_limits<float>::epsilon();
|
|
assert(leftUnknMask.type() == CV_8UC1);
|
|
}
|
|
|
|
// calculate errors
|
|
vector<float> rmss, badPxlsFractions;
|
|
calcErrors( leftImg, rightImg, trueLeftDisp, trueRightDisp, leftUnknMask, rightUnknMask,
|
|
leftDisp, rightDisp, rmss, badPxlsFractions, qualityEvalParams );
|
|
|
|
if( isWrite )
|
|
{
|
|
fs << caseNames[caseIdx] << "{";
|
|
cvWriteComment( fs.fs, RMS_STR.c_str(), 0 );
|
|
writeErrors( RMS_STR, rmss, &fs );
|
|
cvWriteComment( fs.fs, BAD_PXLS_FRACTION_STR.c_str(), 0 );
|
|
writeErrors( BAD_PXLS_FRACTION_STR, badPxlsFractions, &fs );
|
|
fs << "}"; // datasetName
|
|
}
|
|
else // compare
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "\nquality of case named %s\n", caseNames[caseIdx].c_str() );
|
|
ts->printf( cvtest::TS::LOG, "%s\n", RMS_STR.c_str() );
|
|
writeErrors( RMS_STR, rmss );
|
|
ts->printf( cvtest::TS::LOG, "%s\n", BAD_PXLS_FRACTION_STR.c_str() );
|
|
writeErrors( BAD_PXLS_FRACTION_STR, badPxlsFractions );
|
|
|
|
FileNode fn = fs.getFirstTopLevelNode()[caseNames[caseIdx]];
|
|
vector<float> validRmss, validBadPxlsFractions;
|
|
|
|
readErrors( fn, RMS_STR, validRmss );
|
|
readErrors( fn, BAD_PXLS_FRACTION_STR, validBadPxlsFractions );
|
|
int tempCode = compareErrors( rmss, validRmss, rmsEps, RMS_STR );
|
|
code = tempCode==cvtest::TS::OK ? code : tempCode;
|
|
tempCode = compareErrors( badPxlsFractions, validBadPxlsFractions, fracEps, BAD_PXLS_FRACTION_STR );
|
|
code = tempCode==cvtest::TS::OK ? code : tempCode;
|
|
}
|
|
return code;
|
|
}
|
|
|
|
int CV_StereoMatchingTest::readDatasetsParams( FileStorage& fs )
|
|
{
|
|
if( !fs.isOpened() )
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "datasetsParams can not be read " );
|
|
return cvtest::TS::FAIL_INVALID_TEST_DATA;
|
|
}
|
|
datasetsParams.clear();
|
|
FileNode fn = fs.getFirstTopLevelNode();
|
|
assert(fn.isSeq());
|
|
for( int i = 0; i < (int)fn.size(); i+=3 )
|
|
{
|
|
string _name = fn[i];
|
|
DatasetParams params;
|
|
string sf = fn[i+1]; params.dispScaleFactor = atoi(sf.c_str());
|
|
string uv = fn[i+2]; params.dispUnknVal = atoi(uv.c_str());
|
|
datasetsParams[_name] = params;
|
|
}
|
|
return cvtest::TS::OK;
|
|
}
|
|
|
|
int CV_StereoMatchingTest::readRunParams( FileStorage& fs )
|
|
{
|
|
if( !fs.isOpened() )
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "runParams can not be read " );
|
|
return cvtest::TS::FAIL_INVALID_TEST_DATA;
|
|
}
|
|
caseNames.clear();;
|
|
caseDatasets.clear();
|
|
return cvtest::TS::OK;
|
|
}
|
|
|
|
void CV_StereoMatchingTest::writeErrors( const string& errName, const vector<float>& errors, FileStorage* fs )
|
|
{
|
|
assert( (int)errors.size() == ERROR_KINDS_COUNT );
|
|
vector<float>::const_iterator it = errors.begin();
|
|
if( fs )
|
|
for( int i = 0; i < ERROR_KINDS_COUNT; i++, ++it )
|
|
*fs << ERROR_PREFIXES[i] + errName << *it;
|
|
else
|
|
for( int i = 0; i < ERROR_KINDS_COUNT; i++, ++it )
|
|
ts->printf( cvtest::TS::LOG, "%s = %f\n", string(ERROR_PREFIXES[i]+errName).c_str(), *it );
|
|
}
|
|
|
|
void CV_StereoMatchingTest::readErrors( FileNode& fn, const string& errName, vector<float>& errors )
|
|
{
|
|
errors.resize( ERROR_KINDS_COUNT );
|
|
vector<float>::iterator it = errors.begin();
|
|
for( int i = 0; i < ERROR_KINDS_COUNT; i++, ++it )
|
|
fn[ERROR_PREFIXES[i]+errName] >> *it;
|
|
}
|
|
|
|
int CV_StereoMatchingTest::compareErrors( const vector<float>& calcErrors, const vector<float>& validErrors,
|
|
const vector<float>& eps, const string& errName )
|
|
{
|
|
assert( (int)calcErrors.size() == ERROR_KINDS_COUNT );
|
|
assert( (int)validErrors.size() == ERROR_KINDS_COUNT );
|
|
assert( (int)eps.size() == ERROR_KINDS_COUNT );
|
|
vector<float>::const_iterator calcIt = calcErrors.begin(),
|
|
validIt = validErrors.begin(),
|
|
epsIt = eps.begin();
|
|
bool ok = true;
|
|
for( int i = 0; i < ERROR_KINDS_COUNT; i++, ++calcIt, ++validIt, ++epsIt )
|
|
if( *calcIt - *validIt > *epsIt )
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "bad accuracy of %s (valid=%f; calc=%f)\n", string(ERROR_PREFIXES[i]+errName).c_str(), *validIt, *calcIt );
|
|
ok = false;
|
|
}
|
|
return ok ? cvtest::TS::OK : cvtest::TS::FAIL_BAD_ACCURACY;
|
|
}
|
|
|
|
//----------------------------------- StereoBM test -----------------------------------------------------
|
|
|
|
class CV_StereoBMTest : public CV_StereoMatchingTest
|
|
{
|
|
public:
|
|
CV_StereoBMTest()
|
|
{
|
|
name = "stereobm";
|
|
fill(rmsEps.begin(), rmsEps.end(), 0.4f);
|
|
fill(fracEps.begin(), fracEps.end(), 0.022f);
|
|
}
|
|
|
|
protected:
|
|
struct RunParams
|
|
{
|
|
int ndisp;
|
|
int winSize;
|
|
};
|
|
vector<RunParams> caseRunParams;
|
|
|
|
virtual int readRunParams( FileStorage& fs )
|
|
{
|
|
int code = CV_StereoMatchingTest::readRunParams( fs );
|
|
FileNode fn = fs.getFirstTopLevelNode();
|
|
assert(fn.isSeq());
|
|
for( int i = 0; i < (int)fn.size(); i+=4 )
|
|
{
|
|
string caseName = fn[i], datasetName = fn[i+1];
|
|
RunParams params;
|
|
string ndisp = fn[i+2]; params.ndisp = atoi(ndisp.c_str());
|
|
string winSize = fn[i+3]; params.winSize = atoi(winSize.c_str());
|
|
caseNames.push_back( caseName );
|
|
caseDatasets.push_back( datasetName );
|
|
caseRunParams.push_back( params );
|
|
}
|
|
return code;
|
|
}
|
|
|
|
virtual int runStereoMatchingAlgorithm( const Mat& _leftImg, const Mat& _rightImg,
|
|
Mat& leftDisp, Mat& /*rightDisp*/, int caseIdx )
|
|
{
|
|
RunParams params = caseRunParams[caseIdx];
|
|
assert( params.ndisp%16 == 0 );
|
|
assert( _leftImg.type() == CV_8UC3 && _rightImg.type() == CV_8UC3 );
|
|
Mat leftImg; cvtColor( _leftImg, leftImg, CV_BGR2GRAY );
|
|
Mat rightImg; cvtColor( _rightImg, rightImg, CV_BGR2GRAY );
|
|
|
|
StereoBM bm( StereoBM::BASIC_PRESET, params.ndisp, params.winSize );
|
|
bm( leftImg, rightImg, leftDisp, CV_32F );
|
|
return params.winSize/2;
|
|
}
|
|
};
|
|
|
|
//----------------------------------- StereoSGBM test -----------------------------------------------------
|
|
|
|
class CV_StereoSGBMTest : public CV_StereoMatchingTest
|
|
{
|
|
public:
|
|
CV_StereoSGBMTest()
|
|
{
|
|
name = "stereosgbm";
|
|
fill(rmsEps.begin(), rmsEps.end(), 0.25f);
|
|
fill(fracEps.begin(), fracEps.end(), 0.01f);
|
|
}
|
|
|
|
protected:
|
|
struct RunParams
|
|
{
|
|
int ndisp;
|
|
int winSize;
|
|
bool fullDP;
|
|
};
|
|
vector<RunParams> caseRunParams;
|
|
|
|
virtual int readRunParams( FileStorage& fs )
|
|
{
|
|
int code = CV_StereoMatchingTest::readRunParams(fs);
|
|
FileNode fn = fs.getFirstTopLevelNode();
|
|
assert(fn.isSeq());
|
|
for( int i = 0; i < (int)fn.size(); i+=5 )
|
|
{
|
|
string caseName = fn[i], datasetName = fn[i+1];
|
|
RunParams params;
|
|
string ndisp = fn[i+2]; params.ndisp = atoi(ndisp.c_str());
|
|
string winSize = fn[i+3]; params.winSize = atoi(winSize.c_str());
|
|
string fullDP = fn[i+4]; params.fullDP = atoi(fullDP.c_str()) == 0 ? false : true;
|
|
caseNames.push_back( caseName );
|
|
caseDatasets.push_back( datasetName );
|
|
caseRunParams.push_back( params );
|
|
}
|
|
return code;
|
|
}
|
|
|
|
virtual int runStereoMatchingAlgorithm( const Mat& leftImg, const Mat& rightImg,
|
|
Mat& leftDisp, Mat& /*rightDisp*/, int caseIdx )
|
|
{
|
|
RunParams params = caseRunParams[caseIdx];
|
|
assert( params.ndisp%16 == 0 );
|
|
StereoSGBM sgbm( 0, params.ndisp, params.winSize, 10*params.winSize*params.winSize, 40*params.winSize*params.winSize,
|
|
1, 63, 10, 100, 32, params.fullDP );
|
|
sgbm( leftImg, rightImg, leftDisp );
|
|
assert( leftDisp.type() == CV_16SC1 );
|
|
leftDisp/=16;
|
|
return 0;
|
|
}
|
|
};
|
|
|
|
|
|
TEST(Calib3d_StereoBM, regression) { CV_StereoBMTest test; test.safe_run(); }
|
|
TEST(Calib3d_StereoSGBM, regression) { CV_StereoSGBMTest test; test.safe_run(); }
|