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jackdoerner/bilateral_approximation.py

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  1. jackdoerner revised this gist Dec 24, 2014. 1 changed file with 25 additions and 0 deletions.
    25 changes: 25 additions & 0 deletions bilateral_approximation.py
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    """
    bilateral_approximation.py
    Fast Bilateral Filter Approximation Using a Signal Processing Approach in Python
    Copyright (c) 2014 Jack Doerner
    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal
    in the Software without restriction, including without limitation the rights
    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
    copies of the Software, and to permit persons to whom the Software is
    furnished to do so, subject to the following conditions:
    The above copyright notice and this permission notice shall be included in
    all copies or substantial portions of the Software.
    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
    THE SOFTWARE.
    """

    import numpy
    import math
    import scipy.signal, scipy.interpolate
  2. jackdoerner created this gist Dec 6, 2014.
    85 changes: 85 additions & 0 deletions bilateral_approximation.py
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    import numpy
    import math
    import scipy.signal, scipy.interpolate

    def bilateral_approximation(data, edge, sigmaS, sigmaR, samplingS=None, samplingR=None, edgeMin=None, edgeMax=None):
    # This function implements Durand and Dorsey's Signal Processing Bilateral Filter Approximation (2006)
    # It is derived from Jiawen Chen's matlab implementation
    # The original papers and matlab code are available at http://people.csail.mit.edu/sparis/bf/

    inputHeight = data.shape[0]
    inputWidth = data.shape[1]
    samplingS = sigmaS if (samplingS is None) else samplingS
    samplingR = sigmaR if (samplingR is None) else samplingR
    edgeMax = numpy.amax(edge) if (edgeMax is None) else edgeMax
    edgeMin = numpy.amin(edge) if (edgeMin is None) else edgeMin
    edgeDelta = edgeMax - edgeMin
    derivedSigmaS = sigmaS / samplingS;
    derivedSigmaR = sigmaR / samplingR;

    paddingXY = math.floor( 2 * derivedSigmaS ) + 1
    paddingZ = math.floor( 2 * derivedSigmaR ) + 1

    # allocate 3D grid
    downsampledWidth = math.floor( ( inputWidth - 1 ) / samplingS ) + 1 + 2 * paddingXY
    downsampledHeight = math.floor( ( inputHeight - 1 ) / samplingS ) + 1 + 2 * paddingXY
    downsampledDepth = math.floor( edgeDelta / samplingR ) + 1 + 2 * paddingZ

    gridData = numpy.zeros( (downsampledHeight, downsampledWidth, downsampledDepth) )
    gridWeights = numpy.zeros( (downsampledHeight, downsampledWidth, downsampledDepth) )

    # compute downsampled indices
    (jj, ii) = numpy.meshgrid( range(inputWidth), range(inputHeight) )

    di = numpy.around( ii / samplingS ) + paddingXY
    dj = numpy.around( jj / samplingS ) + paddingXY
    dz = numpy.around( ( edge - edgeMin ) / samplingR ) + paddingZ

    # perform scatter (there's probably a faster way than this)
    # normally would do downsampledWeights( di, dj, dk ) = 1, but we have to
    # perform a summation to do box downsampling
    for k in range(dz.size):

    dataZ = data.flat[k]
    if (not math.isnan( dataZ )):

    dik = di.flat[k]
    djk = dj.flat[k]
    dzk = dz.flat[k]

    gridData[ dik, djk, dzk ] += dataZ
    gridWeights[ dik, djk, dzk ] += 1

    # make gaussian kernel
    kernelWidth = 2 * derivedSigmaS + 1
    kernelHeight = kernelWidth
    kernelDepth = 2 * derivedSigmaR + 1

    halfKernelWidth = math.floor( kernelWidth / 2 )
    halfKernelHeight = math.floor( kernelHeight / 2 )
    halfKernelDepth = math.floor( kernelDepth / 2 )

    (gridX, gridY, gridZ) = numpy.meshgrid( range( int(kernelWidth) ), range( int(kernelHeight) ), range( int(kernelDepth) ) )
    gridX -= halfKernelWidth
    gridY -= halfKernelHeight
    gridZ -= halfKernelDepth
    gridRSquared = (( gridX * gridX + gridY * gridY ) / ( derivedSigmaS * derivedSigmaS )) + (( gridZ * gridZ ) / ( derivedSigmaR * derivedSigmaR ))
    kernel = numpy.exp( -0.5 * gridRSquared )

    # convolve
    blurredGridData = scipy.signal.fftconvolve( gridData, kernel, mode='same' )
    blurredGridWeights = scipy.signal.fftconvolve( gridWeights, kernel, mode='same' )

    # divide
    blurredGridWeights = numpy.where( blurredGridWeights == 0 , -2, blurredGridWeights) # avoid divide by 0, won't read there anyway
    normalizedBlurredGrid = blurredGridData / blurredGridWeights;
    normalizedBlurredGrid = numpy.where( blurredGridWeights < -1, 0, normalizedBlurredGrid ) # put 0s where it's undefined

    # upsample
    ( jj, ii ) = numpy.meshgrid( range( inputWidth ), range( inputHeight ) )
    # no rounding
    di = ( ii / samplingS ) + paddingXY
    dj = ( jj / samplingS ) + paddingXY
    dz = ( edge - edgeMin ) / samplingR + paddingZ

    return scipy.interpolate.interpn( (range(normalizedBlurredGrid.shape[0]),range(normalizedBlurredGrid.shape[1]),range(normalizedBlurredGrid.shape[2])), normalizedBlurredGrid, (di, dj, dz) )