def getSpatial(self,channel="Y"):
"""
This method returns one decompressed colour channel as a matrix.
The appropriate JPEG coefficient matrix is dequantised
(using the quantisation tables held by the object) and
inverse DCT transformed.
"""
X = self.getCoefMatrix(channel)
Q = self.getQMatrix(channel)
(M,N) = shape(X)
assert M % 8 == 0, "Image size not divisible by 8"
assert N % 8 == 0, "Image size not divisible by 8"
D = X * base.repmat( Q, (M/8, N/8) )
S = ibdct(D)
#assert max( abs(S).flatten() ) <=128, "Image colours out of range"
return (S + 128 ).astype(np.float32)
def getCalibrated(self,channel="Y",mode="all"):
"""
Return a calibrated coefficient matrix for the given channel.
Channel may be "Y", "Cb", or "Cr" for YCbCr format.
For Grayscale images, it may be None or "Y".
"""
S = self.getSpatial(channel)
(M,N) = shape(S)
assert M % 8 == 0, "Image size not divisible by 8"
assert N % 8 == 0, "Image size not divisible by 8"
if mode == "col":
S1 = S[:,4:(N-4)]
cShape = ( M/8, N/8-1 )
else:
S1 = S[4:(M-4),4:(N-4)]
cShape = ( (M-1)/8, (N-1)/8 )
D = bdct(S1 - 128)
X = D / base.repmat( self.getQMatrix(channel), cShape )
return np.round(X)
def getCalSpatial(self,channel="Y"):
"""
Return the decompressed, calibrated, grayscale image.
A different colour channel can be selected with the channel
argument.
"""
# We calibrate the image, obtaining a JPEG matrix.
# ::
C = self.getCalibrated(channel)
# The rest is straight forward JPEG decompression.
# ::
(M,N) = shape(C)
cShape = (M/8,N/8)
D = C * base.repmat( self.getQMatrix(channel), cShape )
S = np.round( ibdct(D) + 128 )
return S.astype(np.uint8)
def dequantise(C, Q):
"""Dequantise JPEG coefficients using the quantisation matrix Q."""
[k, l] = C.shape
[m, n] = Q.shape
rep = (k / m, l / n)
return C * repmat(Q, rep)
def quantise(C, Q):
"""Quantise DCT coefficients using the quantisation matrix Q."""
[k, l] = C.shape
[m, n] = Q.shape
rep = (k / m, l / n)
return C / repmat(Q, rep)