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787 lines
30 KiB
787 lines
30 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// Intel License Agreement |
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// For Open Source Computer Vision Library |
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// Copyright (C) 2000, Intel Corporation, all rights reserved. |
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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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#include <map> |
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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); |
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Mat trueLeftDisp = imread(datasetFullDirName + TRUE_LEFT_DISP_NAME, 0); |
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Mat trueRightDisp = imread(datasetFullDirName + TRUE_RIGHT_DISP_NAME, 0); |
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if( leftImg.empty() || rightImg.empty() || trueLeftDisp.empty() ) |
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{ |
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ts->printf( cvtest::TS::LOG, "images or left ground-truth disparities of dataset %s can not be read", datasetName.c_str() ); |
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code = cvtest::TS::FAIL_INVALID_TEST_DATA; |
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continue; |
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} |
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int dispScaleFactor = datasetsParams[datasetName].dispScaleFactor; |
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Mat tmp; |
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trueLeftDisp.convertTo( tmp, CV_32FC1, 1.f/dispScaleFactor ); |
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trueLeftDisp = tmp; |
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tmp.release(); |
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if( !trueRightDisp.empty() ) |
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{ |
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trueRightDisp.convertTo( tmp, CV_32FC1, 1.f/dispScaleFactor ); |
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trueRightDisp = tmp; |
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tmp.release(); |
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} |
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Mat leftDisp, rightDisp; |
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int ignBorder = max(runStereoMatchingAlgorithm(leftImg, rightImg, leftDisp, rightDisp, ci), EVAL_IGNORE_BORDER); |
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leftDisp.convertTo( tmp, CV_32FC1 ); |
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leftDisp = tmp; |
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tmp.release(); |
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rightDisp.convertTo( tmp, CV_32FC1 ); |
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rightDisp = tmp; |
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tmp.release(); |
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int tempCode = processStereoMatchingResults( resFS, ci, isWrite, |
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leftImg, rightImg, trueLeftDisp, trueRightDisp, leftDisp, rightDisp, QualityEvalParams(ignBorder)); |
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code = tempCode==cvtest::TS::OK ? code : tempCode; |
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} |
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if( isWrite ) |
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resFS << "}"; // "stereo_matching" |
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|
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ts->set_failed_test_info( code ); |
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} |
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void calcErrors( const Mat& leftImg, const Mat& /*rightImg*/, |
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const Mat& trueLeftDisp, const Mat& trueRightDisp, |
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const Mat& trueLeftUnknDispMask, const Mat& trueRightUnknDispMask, |
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const Mat& calcLeftDisp, const Mat& /*calcRightDisp*/, |
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vector<float>& rms, vector<float>& badPxlsFractions, |
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const QualityEvalParams& qualityEvalParams ) |
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{ |
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Mat texturelessMask, texturedMask; |
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computeTextureBasedMasks( leftImg, &texturelessMask, &texturedMask, |
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qualityEvalParams.texturelessWidth, qualityEvalParams.texturelessThresh ); |
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Mat occludedMask, nonOccludedMask; |
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computeOcclusionBasedMasks( trueLeftDisp, trueRightDisp, &occludedMask, &nonOccludedMask, |
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trueLeftUnknDispMask, trueRightUnknDispMask, qualityEvalParams.dispThresh); |
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Mat depthDiscontMask; |
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computeDepthDiscontMask( trueLeftDisp, depthDiscontMask, trueLeftUnknDispMask, |
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qualityEvalParams.dispGap, qualityEvalParams.discontWidth); |
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|
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Mat borderedKnownMask = getBorderedMask( leftImg.size(), qualityEvalParams.ignoreBorder ) & ~trueLeftUnknDispMask; |
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|
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nonOccludedMask &= borderedKnownMask; |
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occludedMask &= borderedKnownMask; |
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texturedMask &= nonOccludedMask; // & borderedKnownMask |
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texturelessMask &= nonOccludedMask; // & borderedKnownMask |
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depthDiscontMask &= nonOccludedMask; // & borderedKnownMask |
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|
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rms.resize(ERROR_KINDS_COUNT); |
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rms[0] = dispRMS( calcLeftDisp, trueLeftDisp, borderedKnownMask ); |
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rms[1] = dispRMS( calcLeftDisp, trueLeftDisp, nonOccludedMask ); |
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rms[2] = dispRMS( calcLeftDisp, trueLeftDisp, occludedMask ); |
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rms[3] = dispRMS( calcLeftDisp, trueLeftDisp, texturedMask ); |
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rms[4] = dispRMS( calcLeftDisp, trueLeftDisp, texturelessMask ); |
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rms[5] = dispRMS( calcLeftDisp, trueLeftDisp, depthDiscontMask ); |
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|
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badPxlsFractions.resize(ERROR_KINDS_COUNT); |
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badPxlsFractions[0] = badMatchPxlsFraction( calcLeftDisp, trueLeftDisp, borderedKnownMask, qualityEvalParams.badThresh ); |
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badPxlsFractions[1] = badMatchPxlsFraction( calcLeftDisp, trueLeftDisp, nonOccludedMask, qualityEvalParams.badThresh ); |
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badPxlsFractions[2] = badMatchPxlsFraction( calcLeftDisp, trueLeftDisp, occludedMask, qualityEvalParams.badThresh ); |
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badPxlsFractions[3] = badMatchPxlsFraction( calcLeftDisp, trueLeftDisp, texturedMask, qualityEvalParams.badThresh ); |
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badPxlsFractions[4] = badMatchPxlsFraction( calcLeftDisp, trueLeftDisp, texturelessMask, qualityEvalParams.badThresh ); |
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badPxlsFractions[5] = badMatchPxlsFraction( calcLeftDisp, trueLeftDisp, depthDiscontMask, qualityEvalParams.badThresh ); |
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} |
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|
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int CV_StereoMatchingTest::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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{ |
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// rightDisp is not used in current test virsion |
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int code = cvtest::TS::OK; |
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assert( fs.isOpened() ); |
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assert( trueLeftDisp.type() == CV_32FC1 ); |
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assert( trueRightDisp.empty() || trueRightDisp.type() == CV_32FC1 ); |
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assert( leftDisp.type() == CV_32FC1 && rightDisp.type() == CV_32FC1 ); |
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|
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// get masks for unknown ground truth disparity values |
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Mat leftUnknMask, rightUnknMask; |
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DatasetParams params = datasetsParams[caseDatasets[caseIdx]]; |
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absdiff( trueLeftDisp, Scalar(params.dispUnknVal), leftUnknMask ); |
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leftUnknMask = leftUnknMask < numeric_limits<float>::epsilon(); |
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assert(leftUnknMask.type() == CV_8UC1); |
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if( !trueRightDisp.empty() ) |
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{ |
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absdiff( trueRightDisp, Scalar(params.dispUnknVal), rightUnknMask ); |
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rightUnknMask = rightUnknMask < numeric_limits<float>::epsilon(); |
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assert(leftUnknMask.type() == CV_8UC1); |
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} |
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|
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// calculate errors |
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vector<float> rmss, badPxlsFractions; |
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calcErrors( leftImg, rightImg, trueLeftDisp, trueRightDisp, leftUnknMask, rightUnknMask, |
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leftDisp, rightDisp, rmss, badPxlsFractions, qualityEvalParams ); |
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|
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if( isWrite ) |
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{ |
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fs << caseNames[caseIdx] << "{"; |
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cvWriteComment( fs.fs, RMS_STR.c_str(), 0 ); |
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writeErrors( RMS_STR, rmss, &fs ); |
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cvWriteComment( fs.fs, BAD_PXLS_FRACTION_STR.c_str(), 0 ); |
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writeErrors( BAD_PXLS_FRACTION_STR, badPxlsFractions, &fs ); |
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fs << "}"; // datasetName |
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} |
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else // compare |
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{ |
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ts->printf( cvtest::TS::LOG, "\nquality of case named %s\n", caseNames[caseIdx].c_str() ); |
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ts->printf( cvtest::TS::LOG, "%s\n", RMS_STR.c_str() ); |
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writeErrors( RMS_STR, rmss ); |
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ts->printf( cvtest::TS::LOG, "%s\n", BAD_PXLS_FRACTION_STR.c_str() ); |
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writeErrors( BAD_PXLS_FRACTION_STR, badPxlsFractions ); |
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|
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FileNode fn = fs.getFirstTopLevelNode()[caseNames[caseIdx]]; |
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vector<float> validRmss, validBadPxlsFractions; |
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|
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readErrors( fn, RMS_STR, validRmss ); |
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readErrors( fn, BAD_PXLS_FRACTION_STR, validBadPxlsFractions ); |
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int tempCode = compareErrors( rmss, validRmss, rmsEps, RMS_STR ); |
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code = tempCode==cvtest::TS::OK ? code : tempCode; |
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tempCode = compareErrors( badPxlsFractions, validBadPxlsFractions, fracEps, BAD_PXLS_FRACTION_STR ); |
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code = tempCode==cvtest::TS::OK ? code : tempCode; |
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} |
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return code; |
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} |
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|
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int CV_StereoMatchingTest::readDatasetsParams( FileStorage& fs ) |
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{ |
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if( !fs.isOpened() ) |
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{ |
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ts->printf( cvtest::TS::LOG, "datasetsParams can not be read " ); |
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return cvtest::TS::FAIL_INVALID_TEST_DATA; |
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} |
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datasetsParams.clear(); |
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FileNode fn = fs.getFirstTopLevelNode(); |
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assert(fn.isSeq()); |
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for( int i = 0; i < (int)fn.size(); i+=3 ) |
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{ |
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String _name = fn[i]; |
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DatasetParams params; |
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String sf = fn[i+1]; params.dispScaleFactor = atoi(sf.c_str()); |
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String uv = fn[i+2]; params.dispUnknVal = atoi(uv.c_str()); |
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datasetsParams[_name] = params; |
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} |
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return cvtest::TS::OK; |
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} |
|
|
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int CV_StereoMatchingTest::readRunParams( FileStorage& fs ) |
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{ |
|
if( !fs.isOpened() ) |
|
{ |
|
ts->printf( cvtest::TS::LOG, "runParams can not be read " ); |
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return cvtest::TS::FAIL_INVALID_TEST_DATA; |
|
} |
|
caseNames.clear();; |
|
caseDatasets.clear(); |
|
return cvtest::TS::OK; |
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} |
|
|
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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(); |
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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 ); |
|
} |
|
|
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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 ); |
|
|
|
Ptr<StereoBM> bm = createStereoBM( params.ndisp, params.winSize ); |
|
Mat tempDisp; |
|
bm->compute( leftImg, rightImg, tempDisp ); |
|
tempDisp.convertTo(leftDisp, CV_32F, 1./StereoMatcher::DISP_SCALE); |
|
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 ); |
|
Ptr<StereoSGBM> sgbm = createStereoSGBM( 0, params.ndisp, params.winSize, |
|
10*params.winSize*params.winSize, |
|
40*params.winSize*params.winSize, |
|
1, 63, 10, 100, 32, params.fullDP ? |
|
StereoSGBM::MODE_HH : StereoSGBM::MODE_SGBM ); |
|
sgbm->compute( leftImg, rightImg, leftDisp ); |
|
CV_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(); }
|
|
|