idx int64 0 165k | question stringlengths 73 4.15k | target stringlengths 5 918 | len_question int64 21 890 | len_target int64 3 255 |
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27,100 | public static ImageGridPanel showGrid ( int numColumns , String title , BufferedImage ... images ) { JFrame frame = new JFrame ( title ) ; int numRows = images . length / numColumns + images . length % numColumns ; ImageGridPanel panel = new ImageGridPanel ( numRows , numColumns , images ) ; frame . add ( panel , BorderLayout . CENTER ) ; frame . pack ( ) ; frame . setVisible ( true ) ; return panel ; } | Shows a set of images in a grid pattern . | 108 | 11 |
27,101 | public static JFrame setupWindow ( final JComponent component , String title , final boolean closeOnExit ) { BoofSwingUtil . checkGuiThread ( ) ; final JFrame frame = new JFrame ( title ) ; frame . add ( component , BorderLayout . CENTER ) ; frame . pack ( ) ; frame . setLocationRelativeTo ( null ) ; // centers window in the monitor if ( closeOnExit ) frame . setDefaultCloseOperation ( JFrame . EXIT_ON_CLOSE ) ; return frame ; } | Sets up the window but doesn t show it . Must be called in a GUI thread | 112 | 18 |
27,102 | public static void applyBoxFilter ( GrayF32 input ) { // declare storage GrayF32 boxImage = new GrayF32 ( input . width , input . height ) ; InterleavedF32 boxTransform = new InterleavedF32 ( input . width , input . height , 2 ) ; InterleavedF32 transform = new InterleavedF32 ( input . width , input . height , 2 ) ; GrayF32 blurredImage = new GrayF32 ( input . width , input . height ) ; GrayF32 spatialBlur = new GrayF32 ( input . width , input . height ) ; DiscreteFourierTransform < GrayF32 , InterleavedF32 > dft = DiscreteFourierTransformOps . createTransformF32 ( ) ; // Make the image scaled from 0 to 1 to reduce overflow issues PixelMath . divide ( input , 255.0f , input ) ; // compute the Fourier Transform dft . forward ( input , transform ) ; // create the box filter which is centered around the pixel. Note that the filter gets wrapped around // the image edges for ( int y = 0 ; y < 15 ; y ++ ) { int yy = y - 7 < 0 ? boxImage . height + ( y - 7 ) : y - 7 ; for ( int x = 0 ; x < 15 ; x ++ ) { int xx = x - 7 < 0 ? boxImage . width + ( x - 7 ) : x - 7 ; // Set the value such that it doesn't change the image intensity boxImage . set ( xx , yy , 1.0f / ( 15 * 15 ) ) ; } } // compute the DFT for the box filter dft . forward ( boxImage , boxTransform ) ; // Visualize the Fourier Transform for the input image and the box filter displayTransform ( transform , "Input Image" ) ; displayTransform ( boxTransform , "Box Filter" ) ; // apply the filter. convolution in spacial domain is the same as multiplication in the frequency domain DiscreteFourierTransformOps . multiplyComplex ( transform , boxTransform , transform ) ; // convert the image back and display the results dft . inverse ( transform , blurredImage ) ; // undo change of scale PixelMath . multiply ( blurredImage , 255.0f , blurredImage ) ; PixelMath . multiply ( input , 255.0f , input ) ; // For sake of comparison, let's compute the box blur filter in the spatial domain // NOTE: The image border will be different since the frequency domain wraps around and this implementation // of the spacial domain adapts the kernel size BlurImageOps . mean ( input , spatialBlur , 7 , null , null ) ; // Convert to BufferedImage for output BufferedImage originOut = ConvertBufferedImage . convertTo ( input , null ) ; BufferedImage spacialOut = ConvertBufferedImage . convertTo ( spatialBlur , null ) ; BufferedImage blurredOut = ConvertBufferedImage . convertTo ( blurredImage , null ) ; ListDisplayPanel listPanel = new ListDisplayPanel ( ) ; listPanel . addImage ( originOut , "Original Image" ) ; listPanel . addImage ( spacialOut , "Spacial Domain Box" ) ; listPanel . addImage ( blurredOut , "Frequency Domain Box" ) ; ShowImages . showWindow ( listPanel , "Box Blur in Spacial and Frequency Domain of Input Image" ) ; } | Demonstration of how to apply a box filter in the frequency domain and compares the results to a box filter which has been applied in the spatial domain | 726 | 29 |
27,103 | public static void displayTransform ( InterleavedF32 transform , String name ) { // declare storage GrayF32 magnitude = new GrayF32 ( transform . width , transform . height ) ; GrayF32 phase = new GrayF32 ( transform . width , transform . height ) ; // Make a copy so that you don't modify the input transform = transform . clone ( ) ; // shift the zero-frequency into the image center, as is standard in image processing DiscreteFourierTransformOps . shiftZeroFrequency ( transform , true ) ; // Compute the transform's magnitude and phase DiscreteFourierTransformOps . magnitude ( transform , magnitude ) ; DiscreteFourierTransformOps . phase ( transform , phase ) ; // Convert it to a log scale for visibility PixelMath . log ( magnitude , magnitude ) ; // Display the results BufferedImage visualMag = VisualizeImageData . grayMagnitude ( magnitude , null , - 1 ) ; BufferedImage visualPhase = VisualizeImageData . colorizeSign ( phase , null , Math . PI ) ; ImageGridPanel dual = new ImageGridPanel ( 1 , 2 , visualMag , visualPhase ) ; ShowImages . showWindow ( dual , "Magnitude and Phase of " + name ) ; } | Display the fourier transform s magnitude and phase . | 263 | 10 |
27,104 | public static DMatrixRMaj robustFundamental ( List < AssociatedPair > matches , List < AssociatedPair > inliers , double inlierThreshold ) { ConfigRansac configRansac = new ConfigRansac ( ) ; configRansac . inlierThreshold = inlierThreshold ; configRansac . maxIterations = 1000 ; ConfigFundamental configFundamental = new ConfigFundamental ( ) ; configFundamental . which = EnumFundamental . LINEAR_7 ; configFundamental . numResolve = 2 ; configFundamental . errorModel = ConfigFundamental . ErrorModel . GEOMETRIC ; // geometric error is the most accurate error metric, but also the slowest to compute. See how the // results change if you switch to sampson and how much faster it is. You also should adjust // the inlier threshold. ModelMatcher < DMatrixRMaj , AssociatedPair > ransac = FactoryMultiViewRobust . fundamentalRansac ( configFundamental , configRansac ) ; // Estimate the fundamental matrix while removing outliers if ( ! ransac . process ( matches ) ) throw new IllegalArgumentException ( "Failed" ) ; // save the set of features that were used to compute the fundamental matrix inliers . addAll ( ransac . getMatchSet ( ) ) ; // Improve the estimate of the fundamental matrix using non-linear optimization DMatrixRMaj F = new DMatrixRMaj ( 3 , 3 ) ; ModelFitter < DMatrixRMaj , AssociatedPair > refine = FactoryMultiView . fundamentalRefine ( 1e-8 , 400 , EpipolarError . SAMPSON ) ; if ( ! refine . fitModel ( inliers , ransac . getModelParameters ( ) , F ) ) throw new IllegalArgumentException ( "Failed" ) ; // Return the solution return F ; } | Given a set of noisy observations compute the Fundamental matrix while removing the noise . | 410 | 15 |
27,105 | public static DMatrixRMaj simpleFundamental ( List < AssociatedPair > matches ) { // Use the 8-point algorithm since it will work with an arbitrary number of points Estimate1ofEpipolar estimateF = FactoryMultiView . fundamental_1 ( EnumFundamental . LINEAR_8 , 0 ) ; DMatrixRMaj F = new DMatrixRMaj ( 3 , 3 ) ; if ( ! estimateF . process ( matches , F ) ) throw new IllegalArgumentException ( "Failed" ) ; // while not done here, this initial linear estimate can be refined using non-linear optimization // as was done above. return F ; } | If the set of associated features are known to be correct then the fundamental matrix can be computed directly with a lot less code . The down side is that this technique is very sensitive to noise . | 140 | 38 |
27,106 | public boolean applyErrorCorrection ( QrCode qr ) { // System.out.println("decoder ver "+qr.version); // System.out.println("decoder mask "+qr.mask); // System.out.println("decoder error "+qr.error); QrCode . VersionInfo info = QrCode . VERSION_INFO [ qr . version ] ; QrCode . BlockInfo block = info . levels . get ( qr . error ) ; int wordsBlockAllA = block . codewords ; int wordsBlockDataA = block . dataCodewords ; int wordsEcc = wordsBlockAllA - wordsBlockDataA ; int numBlocksA = block . blocks ; int wordsBlockAllB = wordsBlockAllA + 1 ; int wordsBlockDataB = wordsBlockDataA + 1 ; int numBlocksB = ( info . codewords - wordsBlockAllA * numBlocksA ) / wordsBlockAllB ; int totalBlocks = numBlocksA + numBlocksB ; int totalDataBytes = wordsBlockDataA * numBlocksA + wordsBlockDataB * numBlocksB ; qr . corrected = new byte [ totalDataBytes ] ; ecc . resize ( wordsEcc ) ; rscodes . generator ( wordsEcc ) ; if ( ! decodeBlocks ( qr , wordsBlockDataA , numBlocksA , 0 , 0 , totalDataBytes , totalBlocks ) ) return false ; return decodeBlocks ( qr , wordsBlockDataB , numBlocksB , numBlocksA * wordsBlockDataA , numBlocksA , totalDataBytes , totalBlocks ) ; } | Reconstruct the data while applying error correction . | 345 | 10 |
27,107 | private QrCode . Mode updateModeLogic ( QrCode . Mode current , QrCode . Mode candidate ) { if ( current == candidate ) return current ; else if ( current == QrCode . Mode . UNKNOWN ) { return candidate ; } else { return QrCode . Mode . MIXED ; } } | If only one mode then that mode is used . If more than one mode is used then set to multiple | 69 | 21 |
27,108 | boolean checkPaddingBytes ( QrCode qr , int lengthBytes ) { boolean a = true ; for ( int i = lengthBytes ; i < qr . corrected . length ; i ++ ) { if ( a ) { if ( 0b00110111 != ( qr . corrected [ i ] & 0xFF ) ) return false ; } else { if ( 0b10001000 != ( qr . corrected [ i ] & 0xFF ) ) { // the pattern starts over at the beginning of a block. Strictly enforcing the standard // requires knowing size of a data chunk and where it starts. Possible but // probably not worth the effort the implement as a strict requirement. if ( 0b00110111 == ( qr . corrected [ i ] & 0xFF ) ) { a = true ; } else { return false ; } } } a = ! a ; } return true ; } | Makes sure the used bytes have the expected values | 194 | 10 |
27,109 | private int decodeNumeric ( QrCode qr , PackedBits8 data , int bitLocation ) { int lengthBits = QrCodeEncoder . getLengthBitsNumeric ( qr . version ) ; int length = data . read ( bitLocation , lengthBits , true ) ; bitLocation += lengthBits ; while ( length >= 3 ) { if ( data . size < bitLocation + 10 ) { qr . failureCause = QrCode . Failure . MESSAGE_OVERFLOW ; return - 1 ; } int chunk = data . read ( bitLocation , 10 , true ) ; bitLocation += 10 ; int valA = chunk / 100 ; int valB = ( chunk - valA * 100 ) / 10 ; int valC = chunk - valA * 100 - valB * 10 ; workString . append ( ( char ) ( valA + ' ' ) ) ; workString . append ( ( char ) ( valB + ' ' ) ) ; workString . append ( ( char ) ( valC + ' ' ) ) ; length -= 3 ; } if ( length == 2 ) { if ( data . size < bitLocation + 7 ) { qr . failureCause = QrCode . Failure . MESSAGE_OVERFLOW ; return - 1 ; } int chunk = data . read ( bitLocation , 7 , true ) ; bitLocation += 7 ; int valA = chunk / 10 ; int valB = chunk - valA * 10 ; workString . append ( ( char ) ( valA + ' ' ) ) ; workString . append ( ( char ) ( valB + ' ' ) ) ; } else if ( length == 1 ) { if ( data . size < bitLocation + 4 ) { qr . failureCause = QrCode . Failure . MESSAGE_OVERFLOW ; return - 1 ; } int valA = data . read ( bitLocation , 4 , true ) ; bitLocation += 4 ; workString . append ( ( char ) ( valA + ' ' ) ) ; } return bitLocation ; } | Decodes a numeric message | 441 | 5 |
27,110 | private int decodeAlphanumeric ( QrCode qr , PackedBits8 data , int bitLocation ) { int lengthBits = QrCodeEncoder . getLengthBitsAlphanumeric ( qr . version ) ; int length = data . read ( bitLocation , lengthBits , true ) ; bitLocation += lengthBits ; while ( length >= 2 ) { if ( data . size < bitLocation + 11 ) { qr . failureCause = QrCode . Failure . MESSAGE_OVERFLOW ; return - 1 ; } int chunk = data . read ( bitLocation , 11 , true ) ; bitLocation += 11 ; int valA = chunk / 45 ; int valB = chunk - valA * 45 ; workString . append ( valueToAlphanumeric ( valA ) ) ; workString . append ( valueToAlphanumeric ( valB ) ) ; length -= 2 ; } if ( length == 1 ) { if ( data . size < bitLocation + 6 ) { qr . failureCause = QrCode . Failure . MESSAGE_OVERFLOW ; return - 1 ; } int valA = data . read ( bitLocation , 6 , true ) ; bitLocation += 6 ; workString . append ( valueToAlphanumeric ( valA ) ) ; } return bitLocation ; } | Decodes alphanumeric messages | 283 | 6 |
27,111 | private int decodeByte ( QrCode qr , PackedBits8 data , int bitLocation ) { int lengthBits = QrCodeEncoder . getLengthBitsBytes ( qr . version ) ; int length = data . read ( bitLocation , lengthBits , true ) ; bitLocation += lengthBits ; if ( length * 8 > data . size - bitLocation ) { qr . failureCause = QrCode . Failure . MESSAGE_OVERFLOW ; return - 1 ; } byte rawdata [ ] = new byte [ length ] ; for ( int i = 0 ; i < length ; i ++ ) { rawdata [ i ] = ( byte ) data . read ( bitLocation , 8 , true ) ; bitLocation += 8 ; } // If ECI encoding is not specified use the default encoding. Unfortunately the specification is ignored // by most people here and UTF-8 is used. If an encoding is specified then that is used. String encoding = encodingEci == null ? ( forceEncoding != null ? forceEncoding : guessEncoding ( rawdata ) ) : encodingEci ; try { workString . append ( new String ( rawdata , encoding ) ) ; } catch ( UnsupportedEncodingException ignored ) { qr . failureCause = JIS_UNAVAILABLE ; return - 1 ; } return bitLocation ; } | Decodes byte messages | 290 | 4 |
27,112 | private int decodeKanji ( QrCode qr , PackedBits8 data , int bitLocation ) { int lengthBits = QrCodeEncoder . getLengthBitsKanji ( qr . version ) ; int length = data . read ( bitLocation , lengthBits , true ) ; bitLocation += lengthBits ; byte rawdata [ ] = new byte [ length * 2 ] ; for ( int i = 0 ; i < length ; i ++ ) { if ( data . size < bitLocation + 13 ) { qr . failureCause = QrCode . Failure . MESSAGE_OVERFLOW ; return - 1 ; } int letter = data . read ( bitLocation , 13 , true ) ; bitLocation += 13 ; letter = ( ( letter / 0x0C0 ) << 8 ) | ( letter % 0x0C0 ) ; if ( letter < 0x01F00 ) { // In the 0x8140 to 0x9FFC range letter += 0x08140 ; } else { // In the 0xE040 to 0xEBBF range letter += 0x0C140 ; } rawdata [ i * 2 ] = ( byte ) ( letter >> 8 ) ; rawdata [ i * 2 + 1 ] = ( byte ) letter ; } // Shift_JIS may not be supported in some environments: try { workString . append ( new String ( rawdata , "Shift_JIS" ) ) ; } catch ( UnsupportedEncodingException ignored ) { qr . failureCause = KANJI_UNAVAILABLE ; return - 1 ; } return bitLocation ; } | Decodes Kanji messages | 351 | 5 |
27,113 | NodeInfo selectSeedCorner ( ) { NodeInfo best = null ; double bestScore = 0 ; double minAngle = Math . PI + 0.1 ; for ( int i = 0 ; i < contour . size ; i ++ ) { NodeInfo info = contour . get ( i ) ; if ( info . angleBetween < minAngle ) continue ; Edge middleR = selectClosest ( info . right , info , true ) ; if ( middleR == null ) continue ; Edge middleL = selectClosest ( info , info . left , true ) ; if ( middleL == null ) continue ; if ( middleL . target != middleR . target ) continue ; // With no perspective distortion, at the correct corners difference should be zero // while the bad ones will be around 60 degrees double r = UtilAngle . bound ( middleR . angle + Math . PI ) ; double difference = UtilAngle . dist ( r , middleL . angle ) ; double score = info . angleBetween - difference ; if ( score > bestScore ) { best = info ; bestScore = score ; } } if ( best != null ) { best . marked = true ; } return best ; } | Pick a corner but avoid the pointy edges at the other end | 255 | 13 |
27,114 | static void bottomTwoColumns ( NodeInfo first , NodeInfo second , List < NodeInfo > column0 , List < NodeInfo > column1 ) { column0 . add ( first ) ; column0 . add ( second ) ; NodeInfo a = selectClosestN ( first , second ) ; if ( a == null ) { return ; } a . marked = true ; column1 . add ( a ) ; NodeInfo b = second ; while ( true ) { NodeInfo t = selectClosestN ( a , b ) ; if ( t == null ) break ; t . marked = true ; column1 . add ( t ) ; a = t ; t = selectClosestN ( a , b ) ; if ( t == null ) break ; t . marked = true ; column0 . add ( t ) ; b = t ; } } | Traverses along the first two columns and sets them up | 180 | 12 |
27,115 | static Edge selectClosest ( NodeInfo a , NodeInfo b , boolean checkSide ) { double bestScore = Double . MAX_VALUE ; Edge bestEdgeA = null ; Edge edgeAB = a . findEdge ( b ) ; double distAB = a . distance ( b ) ; if ( edgeAB == null ) { return null ; // TODO BUG! FIX! } for ( int i = 0 ; i < a . edges . size ; i ++ ) { Edge edgeA = a . edges . get ( i ) ; NodeInfo aa = a . edges . get ( i ) . target ; if ( aa . marked ) continue ; for ( int j = 0 ; j < b . edges . size ; j ++ ) { Edge edgeB = b . edges . get ( j ) ; NodeInfo bb = b . edges . get ( j ) . target ; if ( bb . marked ) continue ; if ( aa == bb ) { // System.out.println("center "+aa.ellipse.center); if ( checkSide && UtilAngle . distanceCW ( edgeAB . angle , edgeA . angle ) > Math . PI * 0.75 ) continue ; double angle = UtilAngle . dist ( edgeA . angle , edgeB . angle ) ; if ( angle < 0.3 ) continue ; double da = EllipsesIntoClusters . axisAdjustedDistanceSq ( a . ellipse , aa . ellipse ) ; double db = EllipsesIntoClusters . axisAdjustedDistanceSq ( b . ellipse , aa . ellipse ) ; da = Math . sqrt ( da ) ; db = Math . sqrt ( db ) ; // see if they are approximately the same distance double diffRatio = Math . abs ( da - db ) / Math . max ( da , db ) ; if ( diffRatio > 0.3 ) continue ; // TODO reject if too far double d = ( da + db ) / distAB + 0.1 * angle ; if ( d < bestScore ) { bestScore = d ; bestEdgeA = a . edges . get ( i ) ; } break ; } } } return bestEdgeA ; } | Finds the closest that is the same distance from the two nodes and part of an approximate equilateral triangle | 476 | 21 |
27,116 | static NodeInfo selectClosestSide ( NodeInfo a , NodeInfo b ) { double ratio = 1.7321 ; NodeInfo best = null ; double bestDistance = Double . MAX_VALUE ; Edge bestEdgeA = null ; Edge bestEdgeB = null ; for ( int i = 0 ; i < a . edges . size ; i ++ ) { NodeInfo aa = a . edges . get ( i ) . target ; if ( aa . marked ) continue ; for ( int j = 0 ; j < b . edges . size ; j ++ ) { NodeInfo bb = b . edges . get ( j ) . target ; if ( bb . marked ) continue ; if ( aa == bb ) { double da = EllipsesIntoClusters . axisAdjustedDistanceSq ( a . ellipse , aa . ellipse ) ; double db = EllipsesIntoClusters . axisAdjustedDistanceSq ( b . ellipse , aa . ellipse ) ; da = Math . sqrt ( da ) ; db = Math . sqrt ( db ) ; double max , min ; if ( da > db ) { max = da ; min = db ; } else { max = db ; min = da ; } // see how much it deviates from the ideal length with no distortion double diffRatio = Math . abs ( max - min * ratio ) / max ; if ( diffRatio > 0.25 ) continue ; // TODO reject if too far double d = da + db ; if ( d < bestDistance ) { bestDistance = d ; best = aa ; bestEdgeA = a . edges . get ( i ) ; bestEdgeB = b . edges . get ( j ) ; } break ; } } } // check the angles if ( best != null ) { double angleA = UtilAngle . distanceCW ( bestEdgeA . angle , bestEdgeB . angle ) ; if ( angleA < Math . PI * 0.25 ) // expected with zero distortion is 30 degrees return best ; else return null ; } return null ; } | Selects the closest node with the assumption that it s along the side of the grid . | 446 | 18 |
27,117 | public static void rgbToYuv ( double r , double g , double b , double yuv [ ] ) { double y = yuv [ 0 ] = 0.299 * r + 0.587 * g + 0.114 * b ; yuv [ 1 ] = 0.492 * ( b - y ) ; yuv [ 2 ] = 0.877 * ( r - y ) ; } | Conversion from RGB to YUV using same equations as Intel IPP . | 84 | 15 |
27,118 | public static void yuvToRgb ( double y , double u , double v , double rgb [ ] ) { rgb [ 0 ] = y + 1.13983 * v ; rgb [ 1 ] = y - 0.39465 * u - 0.58060 * v ; rgb [ 2 ] = y + 2.032 * u ; } | Conversion from YUV to RGB using same equations as Intel IPP . | 74 | 15 |
27,119 | public boolean process ( List < AssociatedTriple > observations , TrifocalTensor solution ) { if ( observations . size ( ) < 7 ) throw new IllegalArgumentException ( "At least 7 correspondences must be provided. Found " + observations . size ( ) ) ; // compute normalization to reduce numerical errors LowLevelMultiViewOps . computeNormalization ( observations , N1 , N2 , N3 ) ; // compute solution in normalized pixel coordinates createLinearSystem ( observations ) ; // solve for the trifocal tensor solveLinearSystem ( ) ; // enforce geometric constraints to improve solution extractEpipoles . setTensor ( solutionN ) ; extractEpipoles . extractEpipoles ( e2 , e3 ) ; enforce . process ( e2 , e3 , A ) ; enforce . extractSolution ( solutionN ) ; // undo normalization removeNormalization ( solution ) ; return true ; } | Estimates the trifocal tensor given the set of observations | 192 | 13 |
27,120 | protected void createLinearSystem ( List < AssociatedTriple > observations ) { int N = observations . size ( ) ; A . reshape ( 4 * N , 27 ) ; A . zero ( ) ; for ( int i = 0 ; i < N ; i ++ ) { AssociatedTriple t = observations . get ( i ) ; N1 . apply ( t . p1 , p1_norm ) ; N2 . apply ( t . p2 , p2_norm ) ; N3 . apply ( t . p3 , p3_norm ) ; insert ( i , 0 , p1_norm . x ) ; // tensor 1 insert ( i , 1 , p1_norm . y ) ; // tensor 2 insert ( i , 2 , 1 ) ; // tensor 3 } } | Constructs the linear matrix that describes from the 3 - point constraint with linear dependent rows removed | 169 | 18 |
27,121 | protected boolean solveLinearSystem ( ) { if ( ! svdNull . decompose ( A ) ) return false ; SingularOps_DDRM . nullVector ( svdNull , true , vectorizedSolution ) ; solutionN . convertFrom ( vectorizedSolution ) ; return true ; } | Computes the null space of the linear system to find the trifocal tensor | 62 | 17 |
27,122 | protected void removeNormalization ( TrifocalTensor solution ) { DMatrixRMaj N2_inv = N2 . matrixInv ( ) ; DMatrixRMaj N3_inv = N3 . matrixInv ( ) ; DMatrixRMaj N1 = this . N1 . matrix ( ) ; for ( int i = 0 ; i < 3 ; i ++ ) { DMatrixRMaj T = solution . getT ( i ) ; for ( int j = 0 ; j < 3 ; j ++ ) { for ( int k = 0 ; k < 3 ; k ++ ) { double sum = 0 ; for ( int r = 0 ; r < 3 ; r ++ ) { double n1 = N1 . get ( r , i ) ; DMatrixRMaj TN = solutionN . getT ( r ) ; for ( int s = 0 ; s < 3 ; s ++ ) { double n2 = N2_inv . get ( j , s ) ; for ( int t = 0 ; t < 3 ; t ++ ) { sum += n1 * n2 * N3_inv . get ( k , t ) * TN . get ( s , t ) ; } } } T . set ( j , k , sum ) ; } } } } | Translates the trifocal tensor back into regular coordinate system | 270 | 14 |
27,123 | public double computeAverageDerivative ( Point2D_F64 a , Point2D_F64 b , double tanX , double tanY ) { samplesInside = 0 ; averageUp = averageDown = 0 ; for ( int i = 0 ; i < numSamples ; i ++ ) { double x = ( b . x - a . x ) * i / ( numSamples - 1 ) + a . x ; double y = ( b . y - a . y ) * i / ( numSamples - 1 ) + a . y ; double x0 = x + tanX ; double y0 = y + tanY ; if ( ! BoofMiscOps . checkInside ( integralImage . getWidth ( ) , integralImage . getHeight ( ) , x0 , y0 ) ) continue ; double x1 = x - tanX ; double y1 = y - tanY ; if ( ! BoofMiscOps . checkInside ( integralImage . getWidth ( ) , integralImage . getHeight ( ) , x1 , y1 ) ) continue ; samplesInside ++ ; double up = integral . compute ( x , y , x0 , y0 ) ; double down = integral . compute ( x , y , x1 , y1 ) ; // don't take the abs here and require that a high score involves it being entirely black or white around // the edge. Otherwise a random image would score high averageUp += up ; averageDown += down ; } if ( samplesInside == 0 ) return 0 ; averageUp /= samplesInside ; averageDown /= samplesInside ; return averageUp - averageDown ; } | Returns average tangential derivative along the line segment . Derivative is computed in direction of tangent . A positive step in the tangent direction will have a positive value . If all samples go outside the image then zero is returned . | 342 | 47 |
27,124 | public static void rgbToXyz ( int r , int g , int b , double xyz [ ] ) { srgbToXyz ( r / 255.0 , g / 255.0 , b / 255.0 , xyz ) ; } | Conversion from 8 - bit RGB into XYZ . 8 - bit = range of 0 to 255 . | 53 | 21 |
27,125 | public void configureCamera ( CameraPinholeBrown intrinsic , Se3_F64 planeToCamera ) { this . planeToCamera = planeToCamera ; if ( ! selectOverhead . process ( intrinsic , planeToCamera ) ) throw new IllegalArgumentException ( "Can't find a reasonable overhead map. Can the camera view the plane?" ) ; overhead . centerX = selectOverhead . getCenterX ( ) ; overhead . centerY = selectOverhead . getCenterY ( ) ; createOverhead . configure ( intrinsic , planeToCamera , overhead . centerX , overhead . centerY , overhead . cellSize , selectOverhead . getOverheadWidth ( ) , selectOverhead . getOverheadHeight ( ) ) ; // used to counter act offset in overhead image origToMap . set ( overhead . centerX , overhead . centerY , 0 ) ; mapToOrigin . set ( - overhead . centerX , - overhead . centerY , 0 ) ; // fill it so there aren't any artifacts in the left over overhead . image . reshape ( selectOverhead . getOverheadWidth ( ) , selectOverhead . getOverheadHeight ( ) ) ; GImageMiscOps . fill ( overhead . image , 0 ) ; } | Camera the camera s intrinsic and extrinsic parameters . Can be called at any time . | 260 | 18 |
27,126 | public Se3_F64 getWorldToCurr3D ( ) { // 2D to 3D coordinates worldToCurr3D . getT ( ) . set ( - worldToCurr2D . T . y , 0 , worldToCurr2D . T . x ) ; DMatrixRMaj R = worldToCurr3D . getR ( ) ; // set rotation around Y axis. // Transpose the 2D transform since the rotation are pointing in opposite directions R . unsafe_set ( 0 , 0 , worldToCurr2D . c ) ; R . unsafe_set ( 0 , 2 , - worldToCurr2D . s ) ; R . unsafe_set ( 1 , 1 , 1 ) ; R . unsafe_set ( 2 , 0 , worldToCurr2D . s ) ; R . unsafe_set ( 2 , 2 , worldToCurr2D . c ) ; worldToCurr3D . concat ( planeToCamera , worldToCurrCam3D ) ; return worldToCurrCam3D ; } | 3D motion . | 231 | 4 |
27,127 | public void process ( T gray , GrayU8 binary ) { results . reset ( ) ; ellipseDetector . process ( binary ) ; if ( ellipseRefiner != null ) ellipseRefiner . setImage ( gray ) ; intensityCheck . setImage ( gray ) ; List < BinaryEllipseDetectorPixel . Found > found = ellipseDetector . getFound ( ) ; for ( BinaryEllipseDetectorPixel . Found f : found ) { if ( ! intensityCheck . process ( f . ellipse ) ) { if ( verbose ) System . out . println ( "Rejecting ellipse. Initial fit didn't have intense enough edge" ) ; continue ; } EllipseInfo r = results . grow ( ) ; r . contour = f . contour ; if ( ellipseRefiner != null ) { if ( ! ellipseRefiner . process ( f . ellipse , r . ellipse ) ) { if ( verbose ) System . out . println ( "Rejecting ellipse. Refined fit didn't have an intense enough edge" ) ; results . removeTail ( ) ; continue ; } else if ( ! intensityCheck . process ( f . ellipse ) ) { if ( verbose ) System . out . println ( "Rejecting ellipse. Refined fit didn't have an intense enough edge" ) ; continue ; } } else { r . ellipse . set ( f . ellipse ) ; } r . averageInside = intensityCheck . averageInside ; r . averageOutside = intensityCheck . averageOutside ; } } | Detects ellipses inside the binary image and refines the edges for all detections inside the gray image | 347 | 22 |
27,128 | public boolean refine ( EllipseRotated_F64 ellipse ) { if ( autoRefine ) throw new IllegalArgumentException ( "Autorefine is true, no need to refine again" ) ; if ( ellipseRefiner == null ) throw new IllegalArgumentException ( "Refiner has not been passed in" ) ; if ( ! ellipseRefiner . process ( ellipse , ellipse ) ) { return false ; } else { return true ; } } | If auto refine is turned off an ellipse can be refined after the fact using this function provided that the refinement algorithm was passed in to the constructor | 105 | 30 |
27,129 | public static void colorizeSign ( GrayF32 input , float maxAbsValue , Bitmap output , byte [ ] storage ) { shapeShape ( input , output ) ; if ( storage == null ) storage = declareStorage ( output , null ) ; if ( maxAbsValue < 0 ) maxAbsValue = ImageStatistics . maxAbs ( input ) ; int indexDst = 0 ; for ( int y = 0 ; y < input . height ; y ++ ) { int indexSrc = input . startIndex + y * input . stride ; for ( int x = 0 ; x < input . width ; x ++ ) { float value = input . data [ indexSrc ++ ] ; if ( value > 0 ) { storage [ indexDst ++ ] = ( byte ) ( 255f * value / maxAbsValue ) ; storage [ indexDst ++ ] = 0 ; storage [ indexDst ++ ] = 0 ; } else { storage [ indexDst ++ ] = 0 ; storage [ indexDst ++ ] = ( byte ) ( - 255f * value / maxAbsValue ) ; storage [ indexDst ++ ] = 0 ; } storage [ indexDst ++ ] = ( byte ) 0xFF ; } } output . copyPixelsFromBuffer ( ByteBuffer . wrap ( storage ) ) ; } | Renders positive and negative values as two different colors . | 273 | 11 |
27,130 | public static void grayMagnitude ( GrayS32 input , int maxAbsValue , Bitmap output , byte [ ] storage ) { shapeShape ( input , output ) ; if ( storage == null ) storage = declareStorage ( output , null ) ; if ( maxAbsValue < 0 ) maxAbsValue = ImageStatistics . maxAbs ( input ) ; int indexDst = 0 ; for ( int y = 0 ; y < input . height ; y ++ ) { int indexSrc = input . startIndex + y * input . stride ; for ( int x = 0 ; x < input . width ; x ++ ) { byte gray = ( byte ) ( 255 * Math . abs ( input . data [ indexSrc ++ ] ) / maxAbsValue ) ; storage [ indexDst ++ ] = gray ; storage [ indexDst ++ ] = gray ; storage [ indexDst ++ ] = gray ; storage [ indexDst ++ ] = ( byte ) 0xFF ; } } output . copyPixelsFromBuffer ( ByteBuffer . wrap ( storage ) ) ; } | Renders the image using its gray magnitude | 223 | 8 |
27,131 | public static void disparity ( GrayI disparity , int minValue , int maxValue , int invalidColor , Bitmap output , byte [ ] storage ) { shapeShape ( disparity , output ) ; if ( storage == null ) storage = declareStorage ( output , null ) ; int range = maxValue - minValue ; int indexDst = 0 ; for ( int y = 0 ; y < disparity . height ; y ++ ) { for ( int x = 0 ; x < disparity . width ; x ++ ) { int v = disparity . unsafe_get ( x , y ) ; int r , g , b ; if ( v > range ) { r = ( invalidColor >> 16 ) & 0xFF ; g = ( invalidColor >> 8 ) & 0xFF ; b = ( invalidColor ) & 0xFF ; } else { g = 0 ; if ( v == 0 ) { r = b = 0 ; } else { r = 255 * v / maxValue ; b = 255 * ( maxValue - v ) / maxValue ; } } storage [ indexDst ++ ] = ( byte ) r ; storage [ indexDst ++ ] = ( byte ) g ; storage [ indexDst ++ ] = ( byte ) b ; storage [ indexDst ++ ] = ( byte ) 0xFF ; } } output . copyPixelsFromBuffer ( ByteBuffer . wrap ( storage ) ) ; } | Colorizes a disparity image . | 292 | 6 |
27,132 | public static void drawEdgeContours ( List < EdgeContour > contours , int color , Bitmap output , byte [ ] storage ) { if ( output . getConfig ( ) != Bitmap . Config . ARGB_8888 ) throw new IllegalArgumentException ( "Only ARGB_8888 is supported" ) ; if ( storage == null ) storage = declareStorage ( output , null ) ; else Arrays . fill ( storage , ( byte ) 0 ) ; byte r = ( byte ) ( ( color >> 16 ) & 0xFF ) ; byte g = ( byte ) ( ( color >> 8 ) & 0xFF ) ; byte b = ( byte ) ( color ) ; for ( int i = 0 ; i < contours . size ( ) ; i ++ ) { EdgeContour e = contours . get ( i ) ; for ( int j = 0 ; j < e . segments . size ( ) ; j ++ ) { EdgeSegment s = e . segments . get ( j ) ; for ( int k = 0 ; k < s . points . size ( ) ; k ++ ) { Point2D_I32 p = s . points . get ( k ) ; int index = p . y * 4 * output . getWidth ( ) + p . x * 4 ; storage [ index ++ ] = b ; storage [ index ++ ] = g ; storage [ index ++ ] = r ; storage [ index ] = ( byte ) 0xFF ; } } } output . copyPixelsFromBuffer ( ByteBuffer . wrap ( storage ) ) ; } | Draws each contour using a single color . | 330 | 10 |
27,133 | public void massage ( T input , T output ) { if ( clip ) { T inputAdjusted = clipInput ( input , output ) ; // configure a simple change in scale for both axises transform . a11 = input . width / ( float ) output . width ; transform . a22 = input . height / ( float ) output . height ; // this change is automatically reflected in the distortion class. It is configured to cache nothing distort . apply ( inputAdjusted , output ) ; } else { // scale each axis independently. It will have the whole image but it will be distorted transform . a11 = input . width / ( float ) output . width ; transform . a22 = input . height / ( float ) output . height ; distort . apply ( input , output ) ; } } | Clipps and scales the input iamge as neccisary | 163 | 15 |
27,134 | T clipInput ( T input , T output ) { double ratioInput = input . width / ( double ) input . height ; double ratioOutput = output . width / ( double ) output . height ; T a = input ; if ( ratioInput > ratioOutput ) { // clip the width int width = input . height * output . width / output . height ; int x0 = ( input . width - width ) / 2 ; int x1 = x0 + width ; clipped = input . subimage ( x0 , 0 , x1 , input . height , clipped ) ; a = clipped ; } else if ( ratioInput < ratioOutput ) { // clip the height int height = input . width * output . height / output . width ; int y0 = ( input . height - height ) / 2 ; int y1 = y0 + height ; clipped = input . subimage ( 0 , y0 , input . width , y1 , clipped ) ; a = clipped ; } return a ; } | Clip the input image to ensure a constant aspect ratio | 207 | 11 |
27,135 | public static int nextPow2 ( int x ) { if ( x < 1 ) throw new IllegalArgumentException ( "x must be greater or equal 1" ) ; if ( ( x & ( x - 1 ) ) == 0 ) { if ( x == 1 ) return 2 ; return x ; // x is already a power-of-two number } x |= ( x >>> 1 ) ; x |= ( x >>> 2 ) ; x |= ( x >>> 4 ) ; x |= ( x >>> 8 ) ; x |= ( x >>> 16 ) ; x |= ( x >>> 32 ) ; return x + 1 ; } | Returns the closest power - of - two number greater than or equal to x . | 135 | 16 |
27,136 | public static void checkImageArguments ( ImageBase image , ImageInterleaved transform ) { InputSanityCheck . checkSameShape ( image , transform ) ; if ( 2 != transform . getNumBands ( ) ) throw new IllegalArgumentException ( "The transform must have two bands" ) ; } | Checks to see if the image and its transform are appropriate sizes . The transform should have twice the width and twice the height as the image . | 64 | 29 |
27,137 | public static Se3_F64 estimateCameraMotion ( CameraPinholeBrown intrinsic , List < AssociatedPair > matchedNorm , List < AssociatedPair > inliers ) { ModelMatcherMultiview < Se3_F64 , AssociatedPair > epipolarMotion = FactoryMultiViewRobust . baselineRansac ( new ConfigEssential ( ) , new ConfigRansac ( 200 , 0.5 ) ) ; epipolarMotion . setIntrinsic ( 0 , intrinsic ) ; epipolarMotion . setIntrinsic ( 1 , intrinsic ) ; if ( ! epipolarMotion . process ( matchedNorm ) ) throw new RuntimeException ( "Motion estimation failed" ) ; // save inlier set for debugging purposes inliers . addAll ( epipolarMotion . getMatchSet ( ) ) ; return epipolarMotion . getModelParameters ( ) ; } | Estimates the camera motion robustly using RANSAC and a set of associated points . | 183 | 18 |
27,138 | public static List < AssociatedPair > convertToNormalizedCoordinates ( List < AssociatedPair > matchedFeatures , CameraPinholeBrown intrinsic ) { Point2Transform2_F64 p_to_n = LensDistortionFactory . narrow ( intrinsic ) . undistort_F64 ( true , false ) ; List < AssociatedPair > calibratedFeatures = new ArrayList <> ( ) ; for ( AssociatedPair p : matchedFeatures ) { AssociatedPair c = new AssociatedPair ( ) ; p_to_n . compute ( p . p1 . x , p . p1 . y , c . p1 ) ; p_to_n . compute ( p . p2 . x , p . p2 . y , c . p2 ) ; calibratedFeatures . add ( c ) ; } return calibratedFeatures ; } | Convert a set of associated point features from pixel coordinates into normalized image coordinates . | 177 | 16 |
27,139 | public static < T extends ImageBase < T > > void rectifyImages ( T distortedLeft , T distortedRight , Se3_F64 leftToRight , CameraPinholeBrown intrinsicLeft , CameraPinholeBrown intrinsicRight , T rectifiedLeft , T rectifiedRight , GrayU8 rectifiedMask , DMatrixRMaj rectifiedK , DMatrixRMaj rectifiedR ) { RectifyCalibrated rectifyAlg = RectifyImageOps . createCalibrated ( ) ; // original camera calibration matrices DMatrixRMaj K1 = PerspectiveOps . pinholeToMatrix ( intrinsicLeft , ( DMatrixRMaj ) null ) ; DMatrixRMaj K2 = PerspectiveOps . pinholeToMatrix ( intrinsicRight , ( DMatrixRMaj ) null ) ; rectifyAlg . process ( K1 , new Se3_F64 ( ) , K2 , leftToRight ) ; // rectification matrix for each image DMatrixRMaj rect1 = rectifyAlg . getRect1 ( ) ; DMatrixRMaj rect2 = rectifyAlg . getRect2 ( ) ; rectifiedR . set ( rectifyAlg . getRectifiedRotation ( ) ) ; // New calibration matrix, rectifiedK . set ( rectifyAlg . getCalibrationMatrix ( ) ) ; // Adjust the rectification to make the view area more useful RectifyImageOps . fullViewLeft ( intrinsicLeft , rect1 , rect2 , rectifiedK ) ; // undistorted and rectify images FMatrixRMaj rect1_F32 = new FMatrixRMaj ( 3 , 3 ) ; FMatrixRMaj rect2_F32 = new FMatrixRMaj ( 3 , 3 ) ; ConvertMatrixData . convert ( rect1 , rect1_F32 ) ; ConvertMatrixData . convert ( rect2 , rect2_F32 ) ; // Extending the image prevents a harsh edge reducing false matches at the image border // SKIP is another option, possibly a tinny bit faster, but has a harsh edge which will need to be filtered ImageDistort < T , T > distortLeft = RectifyImageOps . rectifyImage ( intrinsicLeft , rect1_F32 , BorderType . EXTENDED , distortedLeft . getImageType ( ) ) ; ImageDistort < T , T > distortRight = RectifyImageOps . rectifyImage ( intrinsicRight , rect2_F32 , BorderType . EXTENDED , distortedRight . getImageType ( ) ) ; distortLeft . apply ( distortedLeft , rectifiedLeft , rectifiedMask ) ; distortRight . apply ( distortedRight , rectifiedRight ) ; } | Remove lens distortion and rectify stereo images | 569 | 8 |
27,140 | public static void drawInliers ( BufferedImage left , BufferedImage right , CameraPinholeBrown intrinsic , List < AssociatedPair > normalized ) { Point2Transform2_F64 n_to_p = LensDistortionFactory . narrow ( intrinsic ) . distort_F64 ( false , true ) ; List < AssociatedPair > pixels = new ArrayList <> ( ) ; for ( AssociatedPair n : normalized ) { AssociatedPair p = new AssociatedPair ( ) ; n_to_p . compute ( n . p1 . x , n . p1 . y , p . p1 ) ; n_to_p . compute ( n . p2 . x , n . p2 . y , p . p2 ) ; pixels . add ( p ) ; } // display the results AssociationPanel panel = new AssociationPanel ( 20 ) ; panel . setAssociation ( pixels ) ; panel . setImages ( left , right ) ; ShowImages . showWindow ( panel , "Inlier Features" , true ) ; } | Draw inliers for debugging purposes . Need to convert from normalized to pixel coordinates . | 220 | 17 |
27,141 | public static double euclideanSq ( TupleDesc_F64 a , TupleDesc_F64 b ) { final int N = a . value . length ; double total = 0 ; for ( int i = 0 ; i < N ; i ++ ) { double d = a . value [ i ] - b . value [ i ] ; total += d * d ; } return total ; } | Returns the Euclidean distance squared between the two descriptors . | 85 | 13 |
27,142 | private float iterationSorSafe ( GrayF32 image1 , int x , int y , int pixelIndex ) { float w = SOR_RELAXATION ; float uf ; float vf ; float ui = initFlowX . data [ pixelIndex ] ; float vi = initFlowY . data [ pixelIndex ] ; float u = flowX . data [ pixelIndex ] ; float v = flowY . data [ pixelIndex ] ; float I1 = image1 . data [ pixelIndex ] ; float I2 = warpImage2 . data [ pixelIndex ] ; float I2x = warpDeriv2X . data [ pixelIndex ] ; float I2y = warpDeriv2Y . data [ pixelIndex ] ; float AU = A_safe ( x , y , flowX ) ; float AV = A_safe ( x , y , flowY ) ; flowX . data [ pixelIndex ] = uf = ( 1 - w ) * u + w * ( ( I1 - I2 + I2x * ui - I2y * ( v - vi ) ) * I2x + alpha2 * AU ) / ( I2x * I2x + alpha2 ) ; flowY . data [ pixelIndex ] = vf = ( 1 - w ) * v + w * ( ( I1 - I2 + I2y * vi - I2x * ( uf - ui ) ) * I2y + alpha2 * AV ) / ( I2y * I2y + alpha2 ) ; return ( uf - u ) * ( uf - u ) + ( vf - v ) * ( vf - v ) ; } | SOR iteration for border pixels | 358 | 6 |
27,143 | protected static float A_safe ( int x , int y , GrayF32 flow ) { float u0 = safe ( x - 1 , y , flow ) ; float u1 = safe ( x + 1 , y , flow ) ; float u2 = safe ( x , y - 1 , flow ) ; float u3 = safe ( x , y + 1 , flow ) ; float u4 = safe ( x - 1 , y - 1 , flow ) ; float u5 = safe ( x + 1 , y - 1 , flow ) ; float u6 = safe ( x - 1 , y + 1 , flow ) ; float u7 = safe ( x + 1 , y + 1 , flow ) ; return ( 1.0f / 6.0f ) * ( u0 + u1 + u2 + u3 ) + ( 1.0f / 12.0f ) * ( u4 + u5 + u6 + u7 ) ; } | See equation 25 . Safe version | 201 | 6 |
27,144 | protected static float A ( int x , int y , GrayF32 flow ) { int index = flow . getIndex ( x , y ) ; float u0 = flow . data [ index - 1 ] ; float u1 = flow . data [ index + 1 ] ; float u2 = flow . data [ index - flow . stride ] ; float u3 = flow . data [ index + flow . stride ] ; float u4 = flow . data [ index - 1 - flow . stride ] ; float u5 = flow . data [ index + 1 - flow . stride ] ; float u6 = flow . data [ index - 1 + flow . stride ] ; float u7 = flow . data [ index + 1 + flow . stride ] ; return ( 1.0f / 6.0f ) * ( u0 + u1 + u2 + u3 ) + ( 1.0f / 12.0f ) * ( u4 + u5 + u6 + u7 ) ; } | See equation 25 . Fast unsafe version | 208 | 7 |
27,145 | protected static float safe ( int x , int y , GrayF32 image ) { if ( x < 0 ) x = 0 ; else if ( x >= image . width ) x = image . width - 1 ; if ( y < 0 ) y = 0 ; else if ( y >= image . height ) y = image . height - 1 ; return image . unsafe_get ( x , y ) ; } | Ensures pixel values are inside the image . If output it is assigned to the nearest pixel inside the image | 84 | 22 |
27,146 | public void search ( float cx , float cy ) { peakX = cx ; peakY = cy ; setRegion ( cx , cy ) ; for ( int i = 0 ; i < maxIterations ; i ++ ) { float total = 0 ; float sumX = 0 , sumY = 0 ; int kernelIndex = 0 ; // see if it can use fast interpolation otherwise use the safer technique if ( interpolate . isInFastBounds ( x0 , y0 ) && interpolate . isInFastBounds ( x0 + width - 1 , y0 + width - 1 ) ) { for ( int yy = 0 ; yy < width ; yy ++ ) { for ( int xx = 0 ; xx < width ; xx ++ ) { float w = weights . weightIndex ( kernelIndex ++ ) ; float weight = w * interpolate . get_fast ( x0 + xx , y0 + yy ) ; total += weight ; sumX += weight * ( xx + x0 ) ; sumY += weight * ( yy + y0 ) ; } } } else { for ( int yy = 0 ; yy < width ; yy ++ ) { for ( int xx = 0 ; xx < width ; xx ++ ) { float w = weights . weightIndex ( kernelIndex ++ ) ; float weight = w * interpolate . get ( x0 + xx , y0 + yy ) ; total += weight ; sumX += weight * ( xx + x0 ) ; sumY += weight * ( yy + y0 ) ; } } } cx = sumX / total ; cy = sumY / total ; setRegion ( cx , cy ) ; float dx = cx - peakX ; float dy = cy - peakY ; peakX = cx ; peakY = cy ; if ( Math . abs ( dx ) < convergenceTol && Math . abs ( dy ) < convergenceTol ) { break ; } } } | Performs a mean - shift search center at the specified coordinates | 407 | 12 |
27,147 | protected void setRegion ( float cx , float cy ) { x0 = cx - radius ; y0 = cy - radius ; if ( x0 < 0 ) { x0 = 0 ; } else if ( x0 + width > image . width ) { x0 = image . width - width ; } if ( y0 < 0 ) { y0 = 0 ; } else if ( y0 + width > image . height ) { y0 = image . height - width ; } } | Updates the location of the rectangular bounding box | 101 | 10 |
27,148 | public void gaussianDerivToDirectDeriv ( ) { T blur = GeneralizedImageOps . createSingleBand ( imageType , width , height ) ; T blurDeriv = GeneralizedImageOps . createSingleBand ( imageType , width , height ) ; T gaussDeriv = GeneralizedImageOps . createSingleBand ( imageType , width , height ) ; BlurStorageFilter < T > funcBlur = FactoryBlurFilter . gaussian ( ImageType . single ( imageType ) , sigma , radius ) ; ImageGradient < T , T > funcDeriv = FactoryDerivative . three ( imageType , imageType ) ; ImageGradient < T , T > funcGaussDeriv = FactoryDerivative . gaussian ( sigma , radius , imageType , imageType ) ; funcBlur . process ( input , blur ) ; funcDeriv . process ( blur , blurDeriv , derivY ) ; funcGaussDeriv . process ( input , gaussDeriv , derivY ) ; printIntensity ( "Blur->Deriv" , blurDeriv ) ; printIntensity ( "Gauss Deriv" , gaussDeriv ) ; } | Compare computing the image | 253 | 4 |
27,149 | public static PaperSize lookup ( String word ) { for ( PaperSize paper : values ) { if ( paper . name . compareToIgnoreCase ( word ) == 0 ) { return paper ; } } return null ; } | Sees if the specified work matches any of the units full name or short name . | 46 | 17 |
27,150 | public static < In extends ImageBase < In > , Out extends ImageBase < Out > , K extends Kernel1D , B extends ImageBorder < In > > void horizontal ( K kernel , In input , Out output , B border ) { switch ( input . getImageType ( ) . getFamily ( ) ) { case GRAY : { if ( input instanceof GrayF32 ) { ConvolveImage . horizontal ( ( Kernel1D_F32 ) kernel , ( GrayF32 ) input , ( GrayF32 ) output , ( ImageBorder_F32 ) border ) ; } else if ( input instanceof GrayU8 ) { if ( GrayI16 . class . isAssignableFrom ( output . getClass ( ) ) ) ConvolveImage . horizontal ( ( Kernel1D_S32 ) kernel , ( GrayU8 ) input , ( GrayI16 ) output , ( ImageBorder_S32 ) border ) ; else ConvolveImage . horizontal ( ( Kernel1D_S32 ) kernel , ( GrayU8 ) input , ( GrayS32 ) output , ( ImageBorder_S32 ) border ) ; } else if ( input instanceof GrayS16 ) { ConvolveImage . horizontal ( ( Kernel1D_S32 ) kernel , ( GrayS16 ) input , ( GrayI16 ) output , ( ImageBorder_S32 ) border ) ; } else { throw new IllegalArgumentException ( "Unknown image type: " + input . getClass ( ) . getName ( ) ) ; } } break ; case INTERLEAVED : { if ( input instanceof InterleavedF32 ) { ConvolveImage . horizontal ( ( Kernel1D_F32 ) kernel , ( InterleavedF32 ) input , ( InterleavedF32 ) output , ( ImageBorder_IL_F32 ) border ) ; } else if ( input instanceof InterleavedU8 ) { if ( InterleavedI16 . class . isAssignableFrom ( output . getClass ( ) ) ) ConvolveImage . horizontal ( ( Kernel1D_S32 ) kernel , ( InterleavedU8 ) input , ( InterleavedI16 ) output , ( ImageBorder_IL_S32 ) border ) ; else ConvolveImage . horizontal ( ( Kernel1D_S32 ) kernel , ( InterleavedU8 ) input , ( InterleavedS32 ) output , ( ImageBorder_IL_S32 ) border ) ; } else if ( input instanceof InterleavedS16 ) { ConvolveImage . horizontal ( ( Kernel1D_S32 ) kernel , ( InterleavedS16 ) input , ( InterleavedU16 ) output , ( ImageBorder_IL_S32 ) border ) ; } else { throw new IllegalArgumentException ( "Unknown image type: " + input . getClass ( ) . getName ( ) ) ; } } break ; case PLANAR : { Planar inp = ( Planar ) input ; Planar outp = ( Planar ) output ; for ( int i = 0 ; i < inp . getNumBands ( ) ; i ++ ) { horizontal ( kernel , inp . getBand ( i ) , outp . getBand ( i ) , ( ImageBorder ) border ) ; } } break ; } } | Performs a horizontal 1D convolution across the image . Borders are handled as specified by the border parameter . | 694 | 22 |
27,151 | public static < In extends ImageBase < In > , Out extends ImageBase < Out > , K extends Kernel1D > void horizontal ( K kernel , In input , Out output ) { switch ( input . getImageType ( ) . getFamily ( ) ) { case GRAY : { if ( input instanceof GrayF32 ) { ConvolveImageNoBorder . horizontal ( ( Kernel1D_F32 ) kernel , ( GrayF32 ) input , ( GrayF32 ) output ) ; } else if ( input instanceof GrayU8 ) { if ( GrayI16 . class . isAssignableFrom ( output . getClass ( ) ) ) ConvolveImageNoBorder . horizontal ( ( Kernel1D_S32 ) kernel , ( GrayU8 ) input , ( GrayI16 ) output ) ; else ConvolveImageNoBorder . horizontal ( ( Kernel1D_S32 ) kernel , ( GrayU8 ) input , ( GrayS32 ) output ) ; } else if ( input instanceof GrayS16 ) { ConvolveImageNoBorder . horizontal ( ( Kernel1D_S32 ) kernel , ( GrayS16 ) input , ( GrayI16 ) output ) ; } else { throw new IllegalArgumentException ( "Unknown image type: " + input . getClass ( ) . getName ( ) ) ; } } break ; case INTERLEAVED : { if ( output instanceof InterleavedF32 ) { ConvolveImageNoBorder . horizontal ( ( Kernel1D_F32 ) kernel , ( InterleavedF32 ) input , ( InterleavedF32 ) output ) ; } else if ( input instanceof InterleavedU8 ) { if ( InterleavedI16 . class . isAssignableFrom ( output . getClass ( ) ) ) ConvolveImageNoBorder . horizontal ( ( Kernel1D_S32 ) kernel , ( InterleavedU8 ) input , ( InterleavedI16 ) output ) ; else ConvolveImageNoBorder . horizontal ( ( Kernel1D_S32 ) kernel , ( InterleavedU8 ) input , ( InterleavedS32 ) output ) ; } else if ( input instanceof InterleavedS16 ) { ConvolveImageNoBorder . horizontal ( ( Kernel1D_S32 ) kernel , ( InterleavedS16 ) input , ( InterleavedI16 ) output ) ; } else { throw new IllegalArgumentException ( "Unknown image type: " + input . getClass ( ) . getName ( ) ) ; } } break ; case PLANAR : { Planar inp = ( Planar ) input ; Planar outp = ( Planar ) output ; for ( int i = 0 ; i < inp . getNumBands ( ) ; i ++ ) { horizontal ( kernel , inp . getBand ( i ) , outp . getBand ( i ) ) ; } } break ; default : throw new IllegalArgumentException ( "Unknown image family" ) ; } } | Performs a horizontal 1D convolution across the image . The horizontal border is not processed . | 629 | 19 |
27,152 | public static < In extends ImageBase , Out extends ImageBase , K extends Kernel1D > void horizontalNormalized ( K kernel , In input , Out output ) { switch ( input . getImageType ( ) . getFamily ( ) ) { case GRAY : { if ( input instanceof GrayF32 ) { ConvolveImageNormalized . horizontal ( ( Kernel1D_F32 ) kernel , ( GrayF32 ) input , ( GrayF32 ) output ) ; } else if ( input instanceof GrayF64 ) { ConvolveImageNormalized . horizontal ( ( Kernel1D_F64 ) kernel , ( GrayF64 ) input , ( GrayF64 ) output ) ; } else if ( input instanceof GrayU8 ) { ConvolveImageNormalized . horizontal ( ( Kernel1D_S32 ) kernel , ( GrayU8 ) input , ( GrayI8 ) output ) ; } else if ( input instanceof GrayS16 ) { ConvolveImageNormalized . horizontal ( ( Kernel1D_S32 ) kernel , ( GrayS16 ) input , ( GrayI16 ) output ) ; } else { throw new IllegalArgumentException ( "Unknown image type: " + input . getClass ( ) . getName ( ) ) ; } } break ; case INTERLEAVED : { if ( input instanceof InterleavedF32 ) { ConvolveImageNormalized . horizontal ( ( Kernel1D_F32 ) kernel , ( InterleavedF32 ) input , ( InterleavedF32 ) output ) ; } else if ( input instanceof InterleavedF64 ) { ConvolveImageNormalized . horizontal ( ( Kernel1D_F64 ) kernel , ( InterleavedF64 ) input , ( InterleavedF64 ) output ) ; } else if ( input instanceof InterleavedU8 ) { ConvolveImageNormalized . horizontal ( ( Kernel1D_S32 ) kernel , ( InterleavedU8 ) input , ( InterleavedI8 ) output ) ; } else if ( input instanceof InterleavedS16 ) { ConvolveImageNormalized . horizontal ( ( Kernel1D_S32 ) kernel , ( InterleavedS16 ) input , ( InterleavedI16 ) output ) ; } else { throw new IllegalArgumentException ( "Unknown image type: " + input . getClass ( ) . getName ( ) ) ; } } break ; case PLANAR : { Planar inp = ( Planar ) input ; Planar outp = ( Planar ) output ; for ( int i = 0 ; i < inp . getNumBands ( ) ; i ++ ) { horizontalNormalized ( kernel , inp . getBand ( i ) , outp . getBand ( i ) ) ; } } break ; default : throw new IllegalArgumentException ( "Unknown image family" ) ; } } | Performs a horizontal 1D convolution across the image while re - normalizing the kernel depending on its overlap with the image . | 605 | 26 |
27,153 | public static < T extends ImageBase < T > , K extends Kernel2D > void convolveNormalized ( K kernel , T input , T output ) { switch ( input . getImageType ( ) . getFamily ( ) ) { case GRAY : { if ( input instanceof GrayF32 ) { ConvolveImageNormalized . convolve ( ( Kernel2D_F32 ) kernel , ( GrayF32 ) input , ( GrayF32 ) output ) ; } else if ( input instanceof GrayF64 ) { ConvolveImageNormalized . convolve ( ( Kernel2D_F64 ) kernel , ( GrayF64 ) input , ( GrayF64 ) output ) ; } else if ( input instanceof GrayU8 ) { ConvolveImageNormalized . convolve ( ( Kernel2D_S32 ) kernel , ( GrayU8 ) input , ( GrayI8 ) output ) ; } else if ( input instanceof GrayS16 ) { ConvolveImageNormalized . convolve ( ( Kernel2D_S32 ) kernel , ( GrayS16 ) input , ( GrayI16 ) output ) ; } else { throw new IllegalArgumentException ( "Unknown image type: " + input . getClass ( ) . getName ( ) ) ; } } break ; case INTERLEAVED : { if ( input instanceof InterleavedF32 ) { ConvolveImageNormalized . convolve ( ( Kernel2D_F32 ) kernel , ( InterleavedF32 ) input , ( InterleavedF32 ) output ) ; } else if ( input instanceof InterleavedF64 ) { ConvolveImageNormalized . convolve ( ( Kernel2D_F64 ) kernel , ( InterleavedF64 ) input , ( InterleavedF64 ) output ) ; } else if ( input instanceof InterleavedU8 ) { ConvolveImageNormalized . convolve ( ( Kernel2D_S32 ) kernel , ( InterleavedU8 ) input , ( InterleavedI8 ) output ) ; } else if ( input instanceof InterleavedS16 ) { ConvolveImageNormalized . convolve ( ( Kernel2D_S32 ) kernel , ( InterleavedS16 ) input , ( InterleavedI16 ) output ) ; } else { throw new IllegalArgumentException ( "Unknown image type: " + input . getClass ( ) . getName ( ) ) ; } } break ; case PLANAR : { Planar inp = ( Planar ) input ; Planar outp = ( Planar ) output ; for ( int i = 0 ; i < inp . getNumBands ( ) ; i ++ ) { convolveNormalized ( kernel , inp . getBand ( i ) , outp . getBand ( i ) ) ; } } break ; default : throw new IllegalArgumentException ( "Unknown image family" ) ; } } | Performs a 2D convolution across the image while re - normalizing the kernel depending on its overlap with the image . | 613 | 25 |
27,154 | private void addFirstSegment ( int x , int y ) { Point2D_I32 p = queuePoints . grow ( ) ; p . set ( x , y ) ; EdgeSegment s = new EdgeSegment ( ) ; s . points . add ( p ) ; s . index = 0 ; s . parent = s . parentPixel = - 1 ; e . segments . add ( s ) ; open . add ( s ) ; } | Starts a new segment at the first point in the contour | 94 | 13 |
27,155 | public static < T extends ImageBase < T > > void abs ( T input , T output ) { if ( input instanceof ImageGray ) { if ( GrayS8 . class == input . getClass ( ) ) { PixelMath . abs ( ( GrayS8 ) input , ( GrayS8 ) output ) ; } else if ( GrayS16 . class == input . getClass ( ) ) { PixelMath . abs ( ( GrayS16 ) input , ( GrayS16 ) output ) ; } else if ( GrayS32 . class == input . getClass ( ) ) { PixelMath . abs ( ( GrayS32 ) input , ( GrayS32 ) output ) ; } else if ( GrayS64 . class == input . getClass ( ) ) { PixelMath . abs ( ( GrayS64 ) input , ( GrayS64 ) output ) ; } else if ( GrayF32 . class == input . getClass ( ) ) { PixelMath . abs ( ( GrayF32 ) input , ( GrayF32 ) output ) ; } else if ( GrayF64 . class == input . getClass ( ) ) { PixelMath . abs ( ( GrayF64 ) input , ( GrayF64 ) output ) ; } // otherwise assume it is an unsigned image type } else if ( input instanceof ImageInterleaved ) { if ( InterleavedS8 . class == input . getClass ( ) ) { PixelMath . abs ( ( InterleavedS8 ) input , ( InterleavedS8 ) output ) ; } else if ( InterleavedS16 . class == input . getClass ( ) ) { PixelMath . abs ( ( InterleavedS16 ) input , ( InterleavedS16 ) output ) ; } else if ( InterleavedS32 . class == input . getClass ( ) ) { PixelMath . abs ( ( InterleavedS32 ) input , ( InterleavedS32 ) output ) ; } else if ( InterleavedS64 . class == input . getClass ( ) ) { PixelMath . abs ( ( InterleavedS64 ) input , ( InterleavedS64 ) output ) ; } else if ( InterleavedF32 . class == input . getClass ( ) ) { PixelMath . abs ( ( InterleavedF32 ) input , ( InterleavedF32 ) output ) ; } else if ( InterleavedF64 . class == input . getClass ( ) ) { PixelMath . abs ( ( InterleavedF64 ) input , ( InterleavedF64 ) output ) ; } } else { Planar in = ( Planar ) input ; Planar out = ( Planar ) output ; for ( int i = 0 ; i < in . getNumBands ( ) ; i ++ ) { abs ( in . getBand ( i ) , out . getBand ( i ) ) ; } } } | Sets each pixel in the output image to be the absolute value of the input image . Both the input and output image can be the same instance . | 609 | 30 |
27,156 | public static < T extends ImageBase < T > > void boundImage ( T input , double min , double max ) { if ( input instanceof ImageGray ) { if ( GrayU8 . class == input . getClass ( ) ) { PixelMath . boundImage ( ( GrayU8 ) input , ( int ) min , ( int ) max ) ; } else if ( GrayS8 . class == input . getClass ( ) ) { PixelMath . boundImage ( ( GrayS8 ) input , ( int ) min , ( int ) max ) ; } else if ( GrayU16 . class == input . getClass ( ) ) { PixelMath . boundImage ( ( GrayU16 ) input , ( int ) min , ( int ) max ) ; } else if ( GrayS16 . class == input . getClass ( ) ) { PixelMath . boundImage ( ( GrayS16 ) input , ( int ) min , ( int ) max ) ; } else if ( GrayS32 . class == input . getClass ( ) ) { PixelMath . boundImage ( ( GrayS32 ) input , ( int ) min , ( int ) max ) ; } else if ( GrayS64 . class == input . getClass ( ) ) { PixelMath . boundImage ( ( GrayS64 ) input , ( long ) min , ( long ) max ) ; } else if ( GrayF32 . class == input . getClass ( ) ) { PixelMath . boundImage ( ( GrayF32 ) input , ( float ) min , ( float ) max ) ; } else if ( GrayF64 . class == input . getClass ( ) ) { PixelMath . boundImage ( ( GrayF64 ) input , min , max ) ; } else { throw new IllegalArgumentException ( "Unknown image Type: " + input . getClass ( ) . getSimpleName ( ) ) ; } } else if ( input instanceof Planar ) { Planar in = ( Planar ) input ; for ( int i = 0 ; i < in . getNumBands ( ) ; i ++ ) { boundImage ( in . getBand ( i ) , min , max ) ; } } } | Bounds image pixels to be between these two values . | 456 | 11 |
27,157 | private void updateTargetDescription ( ) { if ( targetPt != null ) { TupleDesc feature = describe . createDescription ( ) ; describe . process ( targetPt . x , targetPt . y , targetOrientation , targetRadius , feature ) ; tuplePanel . setDescription ( feature ) ; } else { tuplePanel . setDescription ( null ) ; } tuplePanel . repaint ( ) ; } | Extracts the target description and updates the panel . Should only be called from a swing thread | 88 | 19 |
27,158 | public static DMatrixRMaj inducedHomography13 ( TrifocalTensor tensor , Vector3D_F64 line2 , DMatrixRMaj output ) { if ( output == null ) output = new DMatrixRMaj ( 3 , 3 ) ; DMatrixRMaj T = tensor . T1 ; // H(:,0) = transpose(T1)*line output . data [ 0 ] = T . data [ 0 ] * line2 . x + T . data [ 3 ] * line2 . y + T . data [ 6 ] * line2 . z ; output . data [ 3 ] = T . data [ 1 ] * line2 . x + T . data [ 4 ] * line2 . y + T . data [ 7 ] * line2 . z ; output . data [ 6 ] = T . data [ 2 ] * line2 . x + T . data [ 5 ] * line2 . y + T . data [ 8 ] * line2 . z ; // H(:,1) = transpose(T2)*line T = tensor . T2 ; output . data [ 1 ] = T . data [ 0 ] * line2 . x + T . data [ 3 ] * line2 . y + T . data [ 6 ] * line2 . z ; output . data [ 4 ] = T . data [ 1 ] * line2 . x + T . data [ 4 ] * line2 . y + T . data [ 7 ] * line2 . z ; output . data [ 7 ] = T . data [ 2 ] * line2 . x + T . data [ 5 ] * line2 . y + T . data [ 8 ] * line2 . z ; // H(:,2) = transpose(T3)*line T = tensor . T3 ; output . data [ 2 ] = T . data [ 0 ] * line2 . x + T . data [ 3 ] * line2 . y + T . data [ 6 ] * line2 . z ; output . data [ 5 ] = T . data [ 1 ] * line2 . x + T . data [ 4 ] * line2 . y + T . data [ 7 ] * line2 . z ; output . data [ 8 ] = T . data [ 2 ] * line2 . x + T . data [ 5 ] * line2 . y + T . data [ 8 ] * line2 . z ; // Vector3D_F64 temp = new Vector3D_F64(); // // for( int i = 0; i < 3; i++ ) { // GeometryMath_F64.multTran(tensor.getT(i),line,temp); // output.unsafe_set(0,i,temp.x); // output.unsafe_set(1,i,temp.y); // output.unsafe_set(2,i,temp.z); // } return output ; } | Computes the homography induced from view 1 to 3 by a line in view 2 . The provided line in view 2 must contain the view 2 observation . | 627 | 31 |
27,159 | public static DMatrixRMaj inducedHomography12 ( TrifocalTensor tensor , Vector3D_F64 line3 , DMatrixRMaj output ) { if ( output == null ) output = new DMatrixRMaj ( 3 , 3 ) ; // H(:,0) = T1*line DMatrixRMaj T = tensor . T1 ; output . data [ 0 ] = T . data [ 0 ] * line3 . x + T . data [ 1 ] * line3 . y + T . data [ 2 ] * line3 . z ; output . data [ 3 ] = T . data [ 3 ] * line3 . x + T . data [ 4 ] * line3 . y + T . data [ 5 ] * line3 . z ; output . data [ 6 ] = T . data [ 6 ] * line3 . x + T . data [ 7 ] * line3 . y + T . data [ 8 ] * line3 . z ; // H(:,0) = T2*line T = tensor . T2 ; output . data [ 1 ] = T . data [ 0 ] * line3 . x + T . data [ 1 ] * line3 . y + T . data [ 2 ] * line3 . z ; output . data [ 4 ] = T . data [ 3 ] * line3 . x + T . data [ 4 ] * line3 . y + T . data [ 5 ] * line3 . z ; output . data [ 7 ] = T . data [ 6 ] * line3 . x + T . data [ 7 ] * line3 . y + T . data [ 8 ] * line3 . z ; // H(:,0) = T3*line T = tensor . T3 ; output . data [ 2 ] = T . data [ 0 ] * line3 . x + T . data [ 1 ] * line3 . y + T . data [ 2 ] * line3 . z ; output . data [ 5 ] = T . data [ 3 ] * line3 . x + T . data [ 4 ] * line3 . y + T . data [ 5 ] * line3 . z ; output . data [ 8 ] = T . data [ 6 ] * line3 . x + T . data [ 7 ] * line3 . y + T . data [ 8 ] * line3 . z ; // Vector3D_F64 temp = new Vector3D_F64(); // // for( int i = 0; i < 3; i++ ) { // GeometryMath_F64.mult(tensor.getT(i), line, temp); // output.unsafe_set(0,i,temp.x); // output.unsafe_set(1,i,temp.y); // output.unsafe_set(2,i,temp.z); // } return output ; } | Computes the homography induced from view 1 to 2 by a line in view 3 . The provided line in view 3 must contain the view 3 observation . | 616 | 31 |
27,160 | public static DMatrixRMaj homographyStereo3Pts ( DMatrixRMaj F , AssociatedPair p1 , AssociatedPair p2 , AssociatedPair p3 ) { HomographyInducedStereo3Pts alg = new HomographyInducedStereo3Pts ( ) ; alg . setFundamental ( F , null ) ; if ( ! alg . process ( p1 , p2 , p3 ) ) return null ; return alg . getHomography ( ) ; } | Computes the homography induced from a planar surface when viewed from two views using correspondences of three points . Observations must be on the planar surface . | 110 | 33 |
27,161 | public static DMatrixRMaj homographyStereoLinePt ( DMatrixRMaj F , PairLineNorm line , AssociatedPair point ) { HomographyInducedStereoLinePt alg = new HomographyInducedStereoLinePt ( ) ; alg . setFundamental ( F , null ) ; alg . process ( line , point ) ; return alg . getHomography ( ) ; } | Computes the homography induced from a planar surface when viewed from two views using correspondences of a line and a point . Observations must be on the planar surface . | 91 | 36 |
27,162 | public static DMatrixRMaj homographyStereo2Lines ( DMatrixRMaj F , PairLineNorm line0 , PairLineNorm line1 ) { HomographyInducedStereo2Line alg = new HomographyInducedStereo2Line ( ) ; alg . setFundamental ( F , null ) ; if ( ! alg . process ( line0 , line1 ) ) return null ; return alg . getHomography ( ) ; } | Computes the homography induced from a planar surface when viewed from two views using correspondences of two lines . Observations must be on the planar surface . | 99 | 33 |
27,163 | public static DMatrixRMaj createFundamental ( DMatrixRMaj E , CameraPinhole intrinsic ) { DMatrixRMaj K = PerspectiveOps . pinholeToMatrix ( intrinsic , ( DMatrixRMaj ) null ) ; return createFundamental ( E , K ) ; } | Computes a Fundamental matrix given an Essential matrix and the camera s intrinsic parameters . | 62 | 16 |
27,164 | public static void projectiveToMetric ( DMatrixRMaj cameraMatrix , DMatrixRMaj H , Se3_F64 worldToView , DMatrixRMaj K ) { DMatrixRMaj tmp = new DMatrixRMaj ( 3 , 4 ) ; CommonOps_DDRM . mult ( cameraMatrix , H , tmp ) ; MultiViewOps . decomposeMetricCamera ( tmp , K , worldToView ) ; } | Elevates a projective camera matrix into a metric one using the rectifying homography . Extracts calibration and Se3 pose . | 97 | 27 |
27,165 | public static void projectiveToMetricKnownK ( DMatrixRMaj cameraMatrix , DMatrixRMaj H , DMatrixRMaj K , Se3_F64 worldToView ) { DMatrixRMaj tmp = new DMatrixRMaj ( 3 , 4 ) ; CommonOps_DDRM . mult ( cameraMatrix , H , tmp ) ; DMatrixRMaj K_inv = new DMatrixRMaj ( 3 , 3 ) ; CommonOps_DDRM . invert ( K , K_inv ) ; DMatrixRMaj P = new DMatrixRMaj ( 3 , 4 ) ; CommonOps_DDRM . mult ( K_inv , tmp , P ) ; CommonOps_DDRM . extract ( P , 0 , 0 , worldToView . R ) ; worldToView . T . x = P . get ( 0 , 3 ) ; worldToView . T . y = P . get ( 1 , 3 ) ; worldToView . T . z = P . get ( 2 , 3 ) ; SingularValueDecomposition_F64 < DMatrixRMaj > svd = DecompositionFactory_DDRM . svd ( true , true , true ) ; DMatrixRMaj R = worldToView . R ; if ( ! svd . decompose ( R ) ) throw new RuntimeException ( "SVD Failed" ) ; CommonOps_DDRM . multTransB ( svd . getU ( null , false ) , svd . getV ( null , false ) , R ) ; // determinant should be +1 double det = CommonOps_DDRM . det ( R ) ; if ( det < 0 ) { CommonOps_DDRM . scale ( - 1 , R ) ; worldToView . T . scale ( - 1 ) ; } } | Convert the projective camera matrix into a metric transform given the rectifying homography and a known calibration matrix . | 396 | 23 |
27,166 | public static void rectifyHToAbsoluteQuadratic ( DMatrixRMaj H , DMatrixRMaj Q ) { int indexQ = 0 ; for ( int rowA = 0 ; rowA < 4 ; rowA ++ ) { for ( int colB = 0 ; colB < 4 ; colB ++ ) { int indexA = rowA * 4 ; int indexB = colB * 4 ; double sum = 0 ; for ( int i = 0 ; i < 3 ; i ++ ) { // sum += H.get(rowA,i)*H.get(colB,i); sum += H . data [ indexA ++ ] * H . data [ indexB ++ ] ; } // Q.set(rowA,colB,sum); Q . data [ indexQ ++ ] = sum ; } } } | Rectifying homography to dual absolute quadratic . | 177 | 11 |
27,167 | public static void intrinsicFromAbsoluteQuadratic ( DMatrixRMaj Q , DMatrixRMaj P , CameraPinhole intrinsic ) { DMatrixRMaj tmp = new DMatrixRMaj ( 3 , 4 ) ; DMatrixRMaj tmp2 = new DMatrixRMaj ( 3 , 3 ) ; CommonOps_DDRM . mult ( P , Q , tmp ) ; CommonOps_DDRM . multTransB ( tmp , P , tmp2 ) ; decomposeDiac ( tmp2 , intrinsic ) ; } | Extracts the intrinsic camera matrix from a view given its camera matrix and the dual absolute quadratic . | 117 | 22 |
27,168 | public static Tuple2 < List < Point2D_F64 > , List < Point2D_F64 > > split2 ( List < AssociatedPair > input ) { List < Point2D_F64 > list1 = new ArrayList <> ( ) ; List < Point2D_F64 > list2 = new ArrayList <> ( ) ; for ( int i = 0 ; i < input . size ( ) ; i ++ ) { list1 . add ( input . get ( i ) . p1 ) ; list2 . add ( input . get ( i ) . p2 ) ; } return new Tuple2 <> ( list1 , list2 ) ; } | Splits the associated pairs into two lists | 146 | 8 |
27,169 | public static Tuple3 < List < Point2D_F64 > , List < Point2D_F64 > , List < Point2D_F64 > > split3 ( List < AssociatedTriple > input ) { List < Point2D_F64 > list1 = new ArrayList <> ( ) ; List < Point2D_F64 > list2 = new ArrayList <> ( ) ; List < Point2D_F64 > list3 = new ArrayList <> ( ) ; for ( int i = 0 ; i < input . size ( ) ; i ++ ) { list1 . add ( input . get ( i ) . p1 ) ; list2 . add ( input . get ( i ) . p2 ) ; list3 . add ( input . get ( i ) . p3 ) ; } return new Tuple3 <> ( list1 , list2 , list3 ) ; } | Splits the associated triple into three lists | 195 | 8 |
27,170 | protected void performShrinkage ( I transform , int numLevels ) { // step through each layer in the pyramid. for ( int i = 0 ; i < numLevels ; i ++ ) { int w = transform . width ; int h = transform . height ; int ww = w / 2 ; int hh = h / 2 ; Number threshold ; I subband ; // HL subband = transform . subimage ( ww , 0 , w , hh , null ) ; threshold = computeThreshold ( subband ) ; rule . process ( subband , threshold ) ; // System.out.print("HL = "+threshold); // LH subband = transform . subimage ( 0 , hh , ww , h , null ) ; threshold = computeThreshold ( subband ) ; rule . process ( subband , threshold ) ; // System.out.print(" LH = "+threshold); // HH subband = transform . subimage ( ww , hh , w , h , null ) ; threshold = computeThreshold ( subband ) ; rule . process ( subband , threshold ) ; // System.out.println(" HH = "+threshold); transform = transform . subimage ( 0 , 0 , ww , hh , null ) ; } } | Performs wavelet shrinking using the specified rule and by computing a threshold for each subband . | 270 | 19 |
27,171 | @ Override public void denoise ( GrayF32 transform , int numLevels ) { int scale = UtilWavelet . computeScale ( numLevels ) ; final int h = transform . height ; final int w = transform . width ; // width and height of scaling image final int innerWidth = w / scale ; final int innerHeight = h / scale ; GrayF32 subbandHH = transform . subimage ( w / 2 , h / 2 , w , h , null ) ; float sigma = UtilDenoiseWavelet . estimateNoiseStdDev ( subbandHH , null ) ; float threshold = ( float ) UtilDenoiseWavelet . universalThreshold ( subbandHH , sigma ) ; // apply same threshold to all wavelet coefficients rule . process ( transform . subimage ( innerWidth , 0 , w , h , null ) , threshold ) ; rule . process ( transform . subimage ( 0 , innerHeight , innerWidth , h , null ) , threshold ) ; } | Applies VisuShrink denoising to the provided multilevel wavelet transform using the provided threshold . | 212 | 24 |
27,172 | public static WaveletDescription < WlCoef_F32 > generate_F32 ( int I ) { if ( I != 6 ) { throw new IllegalArgumentException ( "Only 6 is currently supported" ) ; } WlCoef_F32 coef = new WlCoef_F32 ( ) ; coef . offsetScaling = - 2 ; coef . offsetWavelet = - 2 ; coef . scaling = new float [ 6 ] ; coef . wavelet = new float [ 6 ] ; double sqrt7 = Math . sqrt ( 7 ) ; double div = 16.0 * Math . sqrt ( 2 ) ; coef . scaling [ 0 ] = ( float ) ( ( 1.0 - sqrt7 ) / div ) ; coef . scaling [ 1 ] = ( float ) ( ( 5.0 + sqrt7 ) / div ) ; coef . scaling [ 2 ] = ( float ) ( ( 14.0 + 2.0 * sqrt7 ) / div ) ; coef . scaling [ 3 ] = ( float ) ( ( 14.0 - 2.0 * sqrt7 ) / div ) ; coef . scaling [ 4 ] = ( float ) ( ( 1.0 - sqrt7 ) / div ) ; coef . scaling [ 5 ] = ( float ) ( ( - 3.0 + sqrt7 ) / div ) ; coef . wavelet [ 0 ] = coef . scaling [ 5 ] ; coef . wavelet [ 1 ] = - coef . scaling [ 4 ] ; coef . wavelet [ 2 ] = coef . scaling [ 3 ] ; coef . wavelet [ 3 ] = - coef . scaling [ 2 ] ; coef . wavelet [ 4 ] = coef . scaling [ 1 ] ; coef . wavelet [ 5 ] = - coef . scaling [ 0 ] ; WlBorderCoefStandard < WlCoef_F32 > inverse = new WlBorderCoefStandard <> ( coef ) ; return new WaveletDescription <> ( new BorderIndex1D_Wrap ( ) , coef , inverse ) ; } | Creates a description of a Coiflet of order I wavelet . | 460 | 15 |
27,173 | public static FitData < EllipseRotated_F64 > fitEllipse_F64 ( List < Point2D_F64 > points , int iterations , boolean computeError , FitData < EllipseRotated_F64 > outputStorage ) { if ( outputStorage == null ) { outputStorage = new FitData <> ( new EllipseRotated_F64 ( ) ) ; } // Compute the optimal algebraic error FitEllipseAlgebraic_F64 algebraic = new FitEllipseAlgebraic_F64 ( ) ; if ( ! algebraic . process ( points ) ) { // could be a line or some other weird case. Create a crude estimate instead FitData < Circle2D_F64 > circleData = averageCircle_F64 ( points , null , null ) ; Circle2D_F64 circle = circleData . shape ; outputStorage . shape . set ( circle . center . x , circle . center . y , circle . radius , circle . radius , 0 ) ; } else { UtilEllipse_F64 . convert ( algebraic . getEllipse ( ) , outputStorage . shape ) ; } // Improve the solution from algebraic into Euclidean if ( iterations > 0 ) { RefineEllipseEuclideanLeastSquares_F64 leastSquares = new RefineEllipseEuclideanLeastSquares_F64 ( ) ; leastSquares . setMaxIterations ( iterations ) ; leastSquares . refine ( outputStorage . shape , points ) ; outputStorage . shape . set ( leastSquares . getFound ( ) ) ; } // compute the average Euclidean error if the user requests it if ( computeError ) { ClosestPointEllipseAngle_F64 closestPoint = new ClosestPointEllipseAngle_F64 ( 1e-8 , 100 ) ; closestPoint . setEllipse ( outputStorage . shape ) ; double total = 0 ; for ( Point2D_F64 p : points ) { closestPoint . process ( p ) ; total += p . distance ( closestPoint . getClosest ( ) ) ; } outputStorage . error = total / points . size ( ) ; } else { outputStorage . error = 0 ; } return outputStorage ; } | Computes the best fit ellipse based on minimizing Euclidean distance . An estimate is initially provided using algebraic algorithm which is then refined using non - linear optimization . The amount of non - linear optimization can be controlled using iterations parameter . Will work with partial and complete contours of objects . | 496 | 60 |
27,174 | public static List < Point2D_F64 > convert_I32_F64 ( List < Point2D_I32 > points ) { return convert_I32_F64 ( points , null ) . toList ( ) ; } | Converts a list of I32 points into F64 | 51 | 11 |
27,175 | public static FitData < Circle2D_F64 > averageCircle_I32 ( List < Point2D_I32 > points , GrowQueue_F64 optional , FitData < Circle2D_F64 > outputStorage ) { if ( outputStorage == null ) { outputStorage = new FitData <> ( new Circle2D_F64 ( ) ) ; } if ( optional == null ) { optional = new GrowQueue_F64 ( ) ; } Circle2D_F64 circle = outputStorage . shape ; int N = points . size ( ) ; // find center of the circle by computing the mean x and y int sumX = 0 , sumY = 0 ; for ( int i = 0 ; i < N ; i ++ ) { Point2D_I32 p = points . get ( i ) ; sumX += p . x ; sumY += p . y ; } optional . reset ( ) ; double centerX = circle . center . x = sumX / ( double ) N ; double centerY = circle . center . y = sumY / ( double ) N ; double meanR = 0 ; for ( int i = 0 ; i < N ; i ++ ) { Point2D_I32 p = points . get ( i ) ; double dx = p . x - centerX ; double dy = p . y - centerY ; double r = Math . sqrt ( dx * dx + dy * dy ) ; optional . push ( r ) ; meanR += r ; } meanR /= N ; circle . radius = meanR ; // compute radius variance double variance = 0 ; for ( int i = 0 ; i < N ; i ++ ) { double diff = optional . get ( i ) - meanR ; variance += diff * diff ; } outputStorage . error = variance / N ; return outputStorage ; } | Computes a circle which has it s center at the mean position of the provided points and radius is equal to the average distance of each point from the center . While fast to compute the provided circle is not a best fit circle by any reasonable metric except for special cases . | 389 | 54 |
27,176 | public void fixate ( ) { ransac = FactoryMultiViewRobust . trifocalRansac ( configTriRansac , configError , configRansac ) ; sba = FactoryMultiView . bundleSparseProjective ( configSBA ) ; } | Must call if you change configurations . | 58 | 7 |
27,177 | boolean selectInitialTriplet ( View seed , GrowQueue_I32 motions , int selected [ ] ) { double bestScore = 0 ; for ( int i = 0 ; i < motions . size ; i ++ ) { View viewB = seed . connections . get ( i ) . other ( seed ) ; for ( int j = i + 1 ; j < motions . size ; j ++ ) { View viewC = seed . connections . get ( j ) . other ( seed ) ; double s = scoreTripleView ( seed , viewB , viewC ) ; if ( s > bestScore ) { bestScore = s ; selected [ 0 ] = i ; selected [ 1 ] = j ; } } } return bestScore != 0 ; } | Exhaustively look at all triplets that connect with the seed view | 155 | 14 |
27,178 | private void triangulateFeatures ( List < AssociatedTriple > inliers , DMatrixRMaj P1 , DMatrixRMaj P2 , DMatrixRMaj P3 ) { List < DMatrixRMaj > cameraMatrices = new ArrayList <> ( ) ; cameraMatrices . add ( P1 ) ; cameraMatrices . add ( P2 ) ; cameraMatrices . add ( P3 ) ; // need elements to be non-empty so that it can use set(). probably over optimization List < Point2D_F64 > triangObs = new ArrayList <> ( ) ; triangObs . add ( null ) ; triangObs . add ( null ) ; triangObs . add ( null ) ; Point4D_F64 X = new Point4D_F64 ( ) ; for ( int i = 0 ; i < inliers . size ( ) ; i ++ ) { AssociatedTriple t = inliers . get ( i ) ; triangObs . set ( 0 , t . p1 ) ; triangObs . set ( 1 , t . p2 ) ; triangObs . set ( 2 , t . p3 ) ; // triangulation can fail if all 3 views have the same pixel value. This has been observed in // simulated 3D scenes if ( triangulator . triangulate ( triangObs , cameraMatrices , X ) ) { structure . points [ i ] . set ( X . x , X . y , X . z , X . w ) ; } else { throw new RuntimeException ( "Failed to triangulate a point in the inlier set?! Handle if this is common" ) ; } } } | Triangulates the location of each features in homogenous space | 361 | 12 |
27,179 | private void initializeProjective3 ( FastQueue < AssociatedTriple > associated , FastQueue < AssociatedTripleIndex > associatedIdx , int totalViews , View viewA , View viewB , View viewC , int idxViewB , int idxViewC ) { ransac . process ( associated . toList ( ) ) ; List < AssociatedTriple > inliers = ransac . getMatchSet ( ) ; TrifocalTensor model = ransac . getModelParameters ( ) ; if ( verbose != null ) verbose . println ( "Remaining after RANSAC " + inliers . size ( ) + " / " + associated . size ( ) ) ; // projective camera matrices for each view DMatrixRMaj P1 = CommonOps_DDRM . identity ( 3 , 4 ) ; DMatrixRMaj P2 = new DMatrixRMaj ( 3 , 4 ) ; DMatrixRMaj P3 = new DMatrixRMaj ( 3 , 4 ) ; MultiViewOps . extractCameraMatrices ( model , P2 , P3 ) ; // Initialize the 3D scene structure, stored in a format understood by bundle adjustment structure . initialize ( totalViews , inliers . size ( ) ) ; // specify the found projective camera matrices db . lookupShape ( viewA . id , shape ) ; // The first view is assumed to be the coordinate system's origin and is identity by definition structure . setView ( 0 , true , P1 , shape . width , shape . height ) ; db . lookupShape ( viewB . id , shape ) ; structure . setView ( idxViewB , false , P2 , shape . width , shape . height ) ; db . lookupShape ( viewC . id , shape ) ; structure . setView ( idxViewC , false , P3 , shape . width , shape . height ) ; // triangulate homogenous coordinates for each point in the inlier set triangulateFeatures ( inliers , P1 , P2 , P3 ) ; // Update the list of common features by pruning features not in the inlier set seedToStructure . resize ( viewA . totalFeatures ) ; seedToStructure . fill ( - 1 ) ; // -1 indicates no match inlierToSeed . resize ( inliers . size ( ) ) ; for ( int i = 0 ; i < inliers . size ( ) ; i ++ ) { int inputIdx = ransac . getInputIndex ( i ) ; // table to go from inlier list into seed feature index inlierToSeed . data [ i ] = matchesTripleIdx . get ( inputIdx ) . a ; // seed feature index into the ouptut structure index seedToStructure . data [ inlierToSeed . data [ i ] ] = i ; } } | Initializes projective reconstruction from 3 - views . | 615 | 10 |
27,180 | boolean findRemainingCameraMatrices ( LookupSimilarImages db , View seed , GrowQueue_I32 motions ) { points3D . reset ( ) ; // points in 3D for ( int i = 0 ; i < structure . points . length ; i ++ ) { structure . points [ i ] . get ( points3D . grow ( ) ) ; } // contains associated pairs of pixel observations // save a call to db by using the previously loaded points assocPixel . reset ( ) ; for ( int i = 0 ; i < inlierToSeed . size ; i ++ ) { // inliers from triple RANSAC // each of these inliers was declared a feature in the world reference frame assocPixel . grow ( ) . p1 . set ( matchesTriple . get ( i ) . p1 ) ; } DMatrixRMaj cameraMatrix = new DMatrixRMaj ( 3 , 4 ) ; for ( int motionIdx = 0 ; motionIdx < motions . size ; motionIdx ++ ) { // skip views already in the scene's structure if ( motionIdx == selectedTriple [ 0 ] || motionIdx == selectedTriple [ 1 ] ) continue ; int connectionIdx = motions . get ( motionIdx ) ; Motion edge = seed . connections . get ( connectionIdx ) ; View viewI = edge . other ( seed ) ; // Lookup pixel locations of features in the connected view db . lookupPixelFeats ( viewI . id , featsB ) ; if ( ! computeCameraMatrix ( seed , edge , featsB , cameraMatrix ) ) { if ( verbose != null ) { verbose . println ( "Pose estimator failed! motionIdx=" + motionIdx ) ; } return false ; } db . lookupShape ( edge . other ( seed ) . id , shape ) ; structure . setView ( motionIdx , false , cameraMatrix , shape . width , shape . height ) ; } return true ; } | Uses the triangulated points and observations in the root view to estimate the camera matrix for all the views which are remaining | 420 | 25 |
27,181 | private boolean computeCameraMatrix ( View seed , Motion edge , FastQueue < Point2D_F64 > featsB , DMatrixRMaj cameraMatrix ) { boolean seedSrc = edge . src == seed ; int matched = 0 ; for ( int i = 0 ; i < edge . inliers . size ; i ++ ) { // need to go from i to index of detected features in view 'seed' to index index of feature in // the reconstruction AssociatedIndex a = edge . inliers . get ( i ) ; int featId = seedToStructure . data [ seedSrc ? a . src : a . dst ] ; if ( featId == - 1 ) continue ; assocPixel . get ( featId ) . p2 . set ( featsB . get ( seedSrc ? a . dst : a . src ) ) ; matched ++ ; } // All views should have matches for all features, simple sanity check if ( matched != assocPixel . size ) throw new RuntimeException ( "BUG! Didn't find all features in the view" ) ; // Estimate the camera matrix given homogenous pixel observations if ( poseEstimator . processHomogenous ( assocPixel . toList ( ) , points3D . toList ( ) ) ) { cameraMatrix . set ( poseEstimator . getProjective ( ) ) ; return true ; } else { return false ; } } | Computes camera matrix between the seed view and a connected view | 295 | 12 |
27,182 | private SceneObservations createObservationsForBundleAdjustment ( LookupSimilarImages db , View seed , GrowQueue_I32 motions ) { // seed view + the motions SceneObservations observations = new SceneObservations ( motions . size + 1 ) ; // Observations for the seed view are a special case SceneObservations . View obsView = observations . getView ( 0 ) ; for ( int i = 0 ; i < inlierToSeed . size ; i ++ ) { int id = inlierToSeed . data [ i ] ; Point2D_F64 o = featsA . get ( id ) ; // featsA is never modified after initially loaded id = seedToStructure . data [ id ] ; obsView . add ( id , ( float ) o . x , ( float ) o . y ) ; } // Now add observations for edges connected to the seed for ( int i = 0 ; i < motions . size ( ) ; i ++ ) { obsView = observations . getView ( i + 1 ) ; Motion m = seed . connections . get ( motions . get ( i ) ) ; View v = m . other ( seed ) ; boolean seedIsSrc = m . src == seed ; db . lookupPixelFeats ( v . id , featsB ) ; for ( int j = 0 ; j < m . inliers . size ; j ++ ) { AssociatedIndex a = m . inliers . get ( j ) ; int id = seedToStructure . data [ seedIsSrc ? a . src : a . dst ] ; if ( id < 0 ) continue ; Point2D_F64 o = featsB . get ( seedIsSrc ? a . dst : a . src ) ; obsView . add ( id , ( float ) o . x , ( float ) o . y ) ; } } return observations ; } | Convert observations into a format which bundle adjustment will understand | 399 | 11 |
27,183 | private boolean refineWithBundleAdjustment ( SceneObservations observations ) { if ( scaleSBA ) { scaler . applyScale ( structure , observations ) ; } sba . setVerbose ( verbose , verboseLevel ) ; sba . setParameters ( structure , observations ) ; sba . configure ( converge . ftol , converge . gtol , converge . maxIterations ) ; if ( ! sba . optimize ( structure ) ) { return false ; } if ( scaleSBA ) { // only undo scaling on camera matrices since observations are discarded for ( int i = 0 ; i < structure . views . length ; i ++ ) { DMatrixRMaj P = structure . views [ i ] . worldToView ; scaler . pixelScaling . get ( i ) . remove ( P , P ) ; } scaler . undoScale ( structure , observations ) ; } return true ; } | Last step is to refine the current initial estimate with bundle adjustment | 193 | 12 |
27,184 | public static void nv21ToBoof ( byte [ ] data , int width , int height , ImageBase output ) { if ( output instanceof Planar ) { Planar ms = ( Planar ) output ; if ( ms . getBandType ( ) == GrayU8 . class ) { ConvertNV21 . nv21TPlanarRgb_U8 ( data , width , height , ms ) ; } else if ( ms . getBandType ( ) == GrayF32 . class ) { ConvertNV21 . nv21ToPlanarRgb_F32 ( data , width , height , ms ) ; } else { throw new IllegalArgumentException ( "Unsupported output band format" ) ; } } else if ( output instanceof ImageGray ) { if ( output . getClass ( ) == GrayU8 . class ) { nv21ToGray ( data , width , height , ( GrayU8 ) output ) ; } else if ( output . getClass ( ) == GrayF32 . class ) { nv21ToGray ( data , width , height , ( GrayF32 ) output ) ; } else { throw new IllegalArgumentException ( "Unsupported output type" ) ; } } else if ( output instanceof ImageInterleaved ) { if ( output . getClass ( ) == InterleavedU8 . class ) { ConvertNV21 . nv21ToInterleaved ( data , width , height , ( InterleavedU8 ) output ) ; } else if ( output . getClass ( ) == InterleavedF32 . class ) { ConvertNV21 . nv21ToInterleaved ( data , width , height , ( InterleavedF32 ) output ) ; } else { throw new IllegalArgumentException ( "Unsupported output type" ) ; } } else { throw new IllegalArgumentException ( "Boofcv image type not yet supported" ) ; } } | Converts a NV21 encoded byte array into a BoofCV formatted image . | 405 | 16 |
27,185 | public static < T extends ImageGray < T > > T nv21ToGray ( byte [ ] data , int width , int height , T output , Class < T > outputType ) { if ( outputType == GrayU8 . class ) { return ( T ) nv21ToGray ( data , width , height , ( GrayU8 ) output ) ; } else if ( outputType == GrayF32 . class ) { return ( T ) nv21ToGray ( data , width , height , ( GrayF32 ) output ) ; } else { throw new IllegalArgumentException ( "Unsupported BoofCV Image Type " + outputType . getSimpleName ( ) ) ; } } | Converts an NV21 image into a gray scale image . Image type is determined at runtime . | 146 | 19 |
27,186 | public static GrayU8 nv21ToGray ( byte [ ] data , int width , int height , GrayU8 output ) { if ( output != null ) { output . reshape ( width , height ) ; } else { output = new GrayU8 ( width , height ) ; } if ( BoofConcurrency . USE_CONCURRENT ) { ImplConvertNV21_MT . nv21ToGray ( data , output ) ; } else { ImplConvertNV21 . nv21ToGray ( data , output ) ; } return output ; } | Converts an NV21 image into a gray scale U8 image . | 119 | 14 |
27,187 | public void generate ( long value , int gridWidth ) { renderer . init ( ) ; drawBorder ( ) ; double whiteBorder = whiteBorderDoc / markerWidth ; double X0 = whiteBorder + blackBorder ; double Y0 = whiteBorder + blackBorder ; double bw = ( 1.0 - 2 * X0 ) / gridWidth ; // Draw the black corner used to ID the orientation square ( X0 , 1.0 - whiteBorder - blackBorder - bw , bw ) ; final int bitCount = gridWidth * gridWidth - 4 ; for ( int j = 0 ; j < bitCount ; j ++ ) { if ( ( value & ( 1L << j ) ) != 0 ) { box ( bw , j , gridWidth ) ; } } // int s2 = (int)Math.round(ret.width*borderFraction); // int s5 = s2+square*(gridWidth-1); // // int N = gridWidth*gridWidth-4; // for (int i = 0; i < N; i++) { // if( (value& (1<<i)) != 0 ) // continue; // // int where = index(i, gridWidth); // int x = where%gridWidth; // int y = gridWidth-1-(where/gridWidth); // // x = s2 + square*x; // y = s2 + square*y; // // ImageMiscOps.fillRectangle(ret,0xFF,x,y,square,square); // } // ImageMiscOps.fillRectangle(ret,0xFF,s2,s2,square,square); // ImageMiscOps.fillRectangle(ret,0xFF,s5,s5,square,square); // ImageMiscOps.fillRectangle(ret,0xFF,s5,s2,square,square); } | Renders a binary square fiducial | 406 | 8 |
27,188 | public void setConfiguration ( Se3_F64 planeToCamera , CameraPinholeBrown intrinsic ) { this . planeToCamera = planeToCamera ; normToPixel = LensDistortionFactory . narrow ( intrinsic ) . distort_F64 ( false , true ) ; pixelToNorm = LensDistortionFactory . narrow ( intrinsic ) . undistort_F64 ( true , false ) ; planeToCamera . invert ( cameraToPlane ) ; } | Configures the camera s intrinsic and extrinsic parameters | 95 | 11 |
27,189 | public void setIntrinsic ( CameraPinholeBrown intrinsic ) { normToPixel = LensDistortionFactory . narrow ( intrinsic ) . distort_F64 ( false , true ) ; pixelToNorm = LensDistortionFactory . narrow ( intrinsic ) . undistort_F64 ( true , false ) ; } | Configures the camera s intrinsic parameters | 66 | 7 |
27,190 | public void setPlaneToCamera ( Se3_F64 planeToCamera , boolean computeInverse ) { this . planeToCamera = planeToCamera ; if ( computeInverse ) planeToCamera . invert ( cameraToPlane ) ; } | Specifies camera s extrinsic parameters . | 53 | 9 |
27,191 | public boolean planeToPixel ( double pointX , double pointY , Point2D_F64 pixel ) { // convert it into a 3D coordinate and transform into camera reference frame plain3D . set ( - pointY , 0 , pointX ) ; SePointOps_F64 . transform ( planeToCamera , plain3D , camera3D ) ; // if it's behind the camera it can't be seen if ( camera3D . z <= 0 ) return false ; // normalized image coordinates and convert into pixels double normX = camera3D . x / camera3D . z ; double normY = camera3D . y / camera3D . z ; normToPixel . compute ( normX , normY , pixel ) ; return true ; } | Given a point on the plane find the pixel in the image . | 159 | 13 |
27,192 | public boolean planeToNormalized ( double pointX , double pointY , Point2D_F64 normalized ) { // convert it into a 3D coordinate and transform into camera reference frame plain3D . set ( - pointY , 0 , pointX ) ; SePointOps_F64 . transform ( planeToCamera , plain3D , camera3D ) ; // if it's behind the camera it can't be seen if ( camera3D . z <= 0 ) return false ; // normalized image coordinates and convert into pixels normalized . x = camera3D . x / camera3D . z ; normalized . y = camera3D . y / camera3D . z ; return true ; } | Given a point on the plane find the normalized image coordinate | 145 | 11 |
27,193 | public void convert ( FeatureGraph2D graph ) { graph . nodes . resize ( corners . size ) ; graph . reset ( ) ; for ( int i = 0 ; i < corners . size ; i ++ ) { Node c = corners . get ( i ) ; FeatureGraph2D . Node n = graph . nodes . grow ( ) ; n . reset ( ) ; n . set ( c . x , c . y ) ; n . index = c . index ; } for ( int i = 0 ; i < corners . size ; i ++ ) { Node c = corners . get ( i ) ; for ( int j = 0 ; j < 4 ; j ++ ) { if ( c . edges [ j ] == null ) continue ; graph . connect ( c . index , c . edges [ j ] . index ) ; } } } | Convert into a generic graph . | 175 | 7 |
27,194 | public boolean process ( I frame ) { keyFrame = false ; // update the feature tracker tracker . process ( frame ) ; totalFramesProcessed ++ ; List < PointTrack > tracks = tracker . getActiveTracks ( null ) ; if ( tracks . size ( ) == 0 ) return false ; List < AssociatedPair > pairs = new ArrayList <> ( ) ; for ( PointTrack t : tracks ) { pairs . add ( ( AssociatedPair ) t . getCookie ( ) ) ; } // fit the motion model to the feature tracks if ( ! modelMatcher . process ( ( List ) pairs ) ) { return false ; } if ( modelRefiner != null ) { if ( ! modelRefiner . fitModel ( modelMatcher . getMatchSet ( ) , modelMatcher . getModelParameters ( ) , keyToCurr ) ) return false ; } else { keyToCurr . set ( modelMatcher . getModelParameters ( ) ) ; } // mark that the track is in the inlier set for ( AssociatedPair p : modelMatcher . getMatchSet ( ) ) { ( ( AssociatedPairTrack ) p ) . lastUsed = totalFramesProcessed ; } // prune tracks which aren't being used pruneUnusedTracks ( ) ; // Update the motion worldToKey . concat ( keyToCurr , worldToCurr ) ; return true ; } | Processes the next frame in the sequence . | 296 | 9 |
27,195 | public void changeKeyFrame ( ) { // drop all inactive tracks since their location is unknown in the current frame List < PointTrack > inactive = tracker . getInactiveTracks ( null ) ; for ( PointTrack l : inactive ) { tracker . dropTrack ( l ) ; } // set the keyframe for active tracks as their current location List < PointTrack > active = tracker . getActiveTracks ( null ) ; for ( PointTrack l : active ) { AssociatedPairTrack p = l . getCookie ( ) ; p . p1 . set ( l ) ; p . lastUsed = totalFramesProcessed ; } tracker . spawnTracks ( ) ; List < PointTrack > spawned = tracker . getNewTracks ( null ) ; for ( PointTrack l : spawned ) { AssociatedPairTrack p = l . getCookie ( ) ; if ( p == null ) { l . cookie = p = new AssociatedPairTrack ( ) ; // little bit of trickery here. Save the reference so that the point // in the current frame is updated for free as PointTrack is p . p2 = l ; } p . p1 . set ( l ) ; p . lastUsed = totalFramesProcessed ; } worldToKey . set ( worldToCurr ) ; keyToCurr . reset ( ) ; keyFrame = true ; } | Change the current frame into the keyframe . p1 location of existing tracks is set to their current location and new tracks are spawned . Reference frame transformations are also updated | 284 | 33 |
27,196 | public static double autoScale ( List < Point3D_F64 > cloud , double target ) { Point3D_F64 mean = new Point3D_F64 ( ) ; Point3D_F64 stdev = new Point3D_F64 ( ) ; statistics ( cloud , mean , stdev ) ; double scale = target / ( Math . max ( Math . max ( stdev . x , stdev . y ) , stdev . z ) ) ; int N = cloud . size ( ) ; for ( int i = 0 ; i < N ; i ++ ) { cloud . get ( i ) . scale ( scale ) ; } return scale ; } | Automatically rescales the point cloud based so that it has a standard deviation of target | 141 | 17 |
27,197 | public static void statistics ( List < Point3D_F64 > cloud , Point3D_F64 mean , Point3D_F64 stdev ) { final int N = cloud . size ( ) ; for ( int i = 0 ; i < N ; i ++ ) { Point3D_F64 p = cloud . get ( i ) ; mean . x += p . x / N ; mean . y += p . y / N ; mean . z += p . z / N ; } for ( int i = 0 ; i < N ; i ++ ) { Point3D_F64 p = cloud . get ( i ) ; double dx = p . x - mean . x ; double dy = p . y - mean . y ; double dz = p . z - mean . z ; stdev . x += dx * dx / N ; stdev . y += dy * dy / N ; stdev . z += dz * dz / N ; } stdev . x = Math . sqrt ( stdev . x ) ; stdev . y = Math . sqrt ( stdev . y ) ; stdev . z = Math . sqrt ( stdev . z ) ; } | Computes the mean and standard deviation of each axis in the point cloud computed in dependently | 254 | 18 |
27,198 | public static void prune ( List < Point3D_F64 > cloud , int minNeighbors , double radius ) { if ( minNeighbors < 0 ) throw new IllegalArgumentException ( "minNeighbors must be >= 0" ) ; NearestNeighbor < Point3D_F64 > nn = FactoryNearestNeighbor . kdtree ( new KdTreePoint3D_F64 ( ) ) ; NearestNeighbor . Search < Point3D_F64 > search = nn . createSearch ( ) ; nn . setPoints ( cloud , false ) ; FastQueue < NnData < Point3D_F64 > > results = new FastQueue ( NnData . class , true ) ; // It will always find itself minNeighbors += 1 ; // distance is Euclidean squared radius *= radius ; for ( int i = cloud . size ( ) - 1 ; i >= 0 ; i -- ) { search . findNearest ( cloud . get ( i ) , radius , minNeighbors , results ) ; if ( results . size < minNeighbors ) { cloud . remove ( i ) ; } } } | Prunes points from the point cloud if they have very few neighbors | 244 | 13 |
27,199 | public static void computeNormalizationLL ( List < List < Point2D_F64 > > points , NormalizationPoint2D normalize ) { double meanX = 0 ; double meanY = 0 ; int count = 0 ; for ( int i = 0 ; i < points . size ( ) ; i ++ ) { List < Point2D_F64 > l = points . get ( i ) ; for ( int j = 0 ; j < l . size ( ) ; j ++ ) { Point2D_F64 p = l . get ( j ) ; meanX += p . x ; meanY += p . y ; } count += l . size ( ) ; } meanX /= count ; meanY /= count ; double stdX = 0 ; double stdY = 0 ; for ( int i = 0 ; i < points . size ( ) ; i ++ ) { List < Point2D_F64 > l = points . get ( i ) ; for ( int j = 0 ; j < l . size ( ) ; j ++ ) { Point2D_F64 p = l . get ( j ) ; double dx = p . x - meanX ; double dy = p . y - meanY ; stdX += dx * dx ; stdY += dy * dy ; } } normalize . meanX = meanX ; normalize . meanY = meanY ; normalize . stdX = Math . sqrt ( stdX / count ) ; normalize . stdY = Math . sqrt ( stdY / count ) ; } | Computes normalization when points are contained in a list of lists | 327 | 13 |
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