def post_prediction(decodedStream, predictor, columns, colors, bits):
'''
Predictor function to obtain the real stream, removing the prediction (PDF Specification)
@param decodedStream: The decoded stream to be modified
@param predictor: The type of predictor to apply
@param columns: Number of samples per row
@param colors: Number of colors per sample
@param bits: Number of bits per color
@return: A tuple (status,statusContent), where statusContent is the modified decoded stream in case status = 0 or an error in case status = -1
'''
output = ''
bytesPerRow = (colors * bits * columns + 7) / 8
# TIFF - 2
# http://www.gnupdf.org/PNG_and_TIFF_Predictors_Filter#TIFF
if predictor == 2:
numRows = len(decodedStream) / bytesPerRow
bitmask = 2**bits - 1
outputBitsStream = ''
for rowIndex in range(numRows):
row = decodedStream[rowIndex * bytesPerRow:rowIndex * bytesPerRow +
bytesPerRow]
ret, colorNums = getNumsFromBytes(row, bits)
if ret == -1:
return (ret, colorNums)
pixel = [0 for x in range(colors)]
for i in range(columns):
for j in range(colors):
diffPixel = colorNums[i + j]
pixel[j] = (pixel[j] + diffPixel) & bitmask
ret, outputBits = getBitsFromNum(pixel[j], bits)
if ret == -1:
return (ret, outputBits)
outputBitsStream += outputBits
output = getBytesFromBits(outputBitsStream)
return output
# PNG prediction
# http://www.libpng.org/pub/png/spec/1.2/PNG-Filters.html
# http://www.gnupdf.org/PNG_and_TIFF_Predictors_Filter#TIFF
elif predictor >= 10 and predictor <= 15:
bytesPerRow += 1
numRows = (len(decodedStream) + bytesPerRow - 1) / bytesPerRow
numSamplesPerRow = columns + 1
bytesPerSample = (colors * bits + 7) / 8
upRowdata = (0, ) * numSamplesPerRow
for row in xrange(numRows):
rowdata = [
ord(x)
for x in decodedStream[(row * bytesPerRow):((row + 1) *
bytesPerRow)]
]
# PNG prediction can vary from row to row
filterByte = rowdata[0]
rowdata[0] = 0
if filterByte == 0:
# None
pass
elif filterByte == 1:
# Sub - 11
for i in range(1, numSamplesPerRow):
if i < bytesPerSample:
prevSample = 0
else:
prevSample = rowdata[i - bytesPerSample]
rowdata[i] = (rowdata[i] + prevSample) % 256
elif filterByte == 2:
# Up - 12
for i in range(1, numSamplesPerRow):
upSample = upRowdata[i]
rowdata[i] = (rowdata[i] + upSample) % 256
elif filterByte == 3:
# Average - 13
for i in range(1, numSamplesPerRow):
upSample = upRowdata[i]
if i < bytesPerSample:
prevSample = 0
else:
prevSample = rowdata[i - bytesPerSample]
rowdata[i] = (rowdata[i] +
((prevSample + upSample) / 2)) % 256
elif filterByte == 4:
# Paeth - 14
for i in range(1, numSamplesPerRow):
upSample = upRowdata[i]
if i < bytesPerSample:
prevSample = 0
upPrevSample = 0
else:
prevSample = rowdata[i - bytesPerSample]
upPrevSample = upRowdata[i - bytesPerSample]
p = prevSample + upSample - upPrevSample
pa = abs(p - prevSample)
pb = abs(p - upSample)
pc = abs(p - upPrevSample)
if pa <= pb and pa <= pc:
nearest = prevSample
elif pb <= pc:
nearest = upSample
else:
nearest = upPrevSample
rowdata[i] = (rowdata[i] + nearest) % 256
else:
# Optimum - 15
#return (-1,'Unsupported predictor')
pass
upRowdata = rowdata
output += (''.join([chr(x) for x in rowdata[1:]]))
return (0, output)
else:
return (-1, 'Wrong value for predictor')
def post_prediction(decodedStream, predictor, columns, colors, bits):
'''
Predictor function to obtain the real stream, removing the prediction (PDF Specification)
@param decodedStream: The decoded stream to be modified
@param predictor: The type of predictor to apply
@param columns: Number of samples per row
@param colors: Number of colors per sample
@param bits: Number of bits per color
@return: A tuple (status,statusContent), where statusContent is the modified decoded stream in case status = 0 or an error in case status = -1
'''
output = ''
bytesPerRow = (colors * bits * columns + 7) / 8
# TIFF - 2
# http://www.gnupdf.org/PNG_and_TIFF_Predictors_Filter#TIFF
if predictor == 2:
numRows = len(decodedStream) / bytesPerRow
bitmask = 2 ** bits - 1
outputBitsStream = ''
for rowIndex in range(numRows):
row = decodedStream[rowIndex*bytesPerRow:rowIndex*bytesPerRow+bytesPerRow]
ret,colorNums = getNumsFromBytes(row, bits)
if ret == -1:
return (ret,colorNums)
pixel = [0 for x in range(colors)]
for i in range(columns):
for j in range(colors):
diffPixel = colorNums[i+j]
pixel[j] = (pixel[j] + diffPixel) & bitmask
ret, outputBits = getBitsFromNum(pixel[j],bits)
if ret == -1:
return (ret,outputBits)
outputBitsStream += outputBits
output = getBytesFromBits(outputBitsStream)
return output
# PNG prediction
# http://www.libpng.org/pub/png/spec/1.2/PNG-Filters.html
# http://www.gnupdf.org/PNG_and_TIFF_Predictors_Filter#TIFF
elif predictor >= 10 and predictor <= 15:
bytesPerRow += 1
numRows = (len(decodedStream) + bytesPerRow -1) / bytesPerRow
numSamplesPerRow = columns + 1
bytesPerSample = (colors * bits + 7) / 8
upRowdata = (0,) * numSamplesPerRow
for row in xrange(numRows):
rowdata = [ord(x) for x in decodedStream[(row*bytesPerRow):((row+1)*bytesPerRow)]]
# PNG prediction can vary from row to row
filterByte = rowdata[0]
rowdata[0] = 0
if filterByte == 0:
# None
pass
elif filterByte == 1:
# Sub - 11
for i in range(1, numSamplesPerRow):
if i < bytesPerSample:
prevSample = 0
else:
prevSample = rowdata[i-bytesPerSample]
rowdata[i] = (rowdata[i] + prevSample) % 256
elif filterByte == 2:
# Up - 12
for i in range(1, numSamplesPerRow):
upSample = upRowdata[i]
rowdata[i] = (rowdata[i] + upSample) % 256
elif filterByte == 3:
# Average - 13
for i in range(1, numSamplesPerRow):
upSample = upRowdata[i]
if i < bytesPerSample:
prevSample = 0
else:
prevSample = rowdata[i-bytesPerSample]
rowdata[i] = (rowdata[i] + ((prevSample+upSample)/2)) % 256
elif filterByte == 4:
# Paeth - 14
for i in range(1, numSamplesPerRow):
upSample = upRowdata[i]
if i < bytesPerSample:
prevSample = 0
upPrevSample = 0
else:
prevSample = rowdata[i-bytesPerSample]
upPrevSample = upRowdata[i-bytesPerSample]
p = prevSample + upSample - upPrevSample
pa = abs(p - prevSample)
pb = abs(p - upSample)
pc = abs(p - upPrevSample)
if pa <= pb and pa <= pc:
nearest = prevSample
elif pb <= pc:
nearest = upSample
else:
nearest = upPrevSample
rowdata[i] = (rowdata[i] + nearest) % 256
else:
# Optimum - 15
#return (-1,'Unsupported predictor')
pass
upRowdata = rowdata
output += (''.join([chr(x) for x in rowdata[1:]]))
return (0,output)
else:
return (-1,'Wrong value for predictor')