def read_image_data(self):
if self.imdtype == 'not_implemented':
raise AttributeError, "image data type: %s" % self.imdtype
if ('packed' in self.imdtype):
return self.read_packed_complex()
elif ('rgb' in self.imdtype):
return self.read_rgb()
else:
data = binIO.read_data_array(self.filename, self.imbytes,
self.byte_offset, self.imdtype)
imsize = self.imsize.tolist()
if self.order == 'F':
if self.record_by == 'spectrum':
swapelem(imsize, 0, 1)
data = data.reshape(imsize, order = self.order)
data = np.swapaxes(data, 0, 1).copy()
elif self.record_by == 'image':
data = data.reshape(imsize, order = 'C')
elif self.order == 'C':
if self.record_by == 'spectrum':
data = data.reshape(np.roll(self.imsize,1), order = self.order)
data = np.rollaxis(data, 0, self.dim).copy()
elif self.record_by == 'image':
data = data.reshape(self.imsize, order = self.order)
return data
def read_rgb(self):
self.imsize = list(self.imsize)
self.imsize.append(4)
self.imsize = tuple(self.imsize)
data = binIO.read_data_array(self.filename, self.imbytes, self.byte_offset)
data = data.reshape(self.imsize, order="C") # (B, G, R, A)
if self.imdtype == "rgb":
data = data[:, :, -2::-1] # (R, G, B)
self.mode += "rgb_"
self.imsize = list(self.imsize)
self.imsize[-1] = self.imsize[-1] - 1
self.imsize = tuple(self.imsize)
elif self.imdtype == "argb":
data = np.concatenate((data[:, :, -2::-1], data[:, :, -1:]), axis=2) # (R, G, B, A)
self.mode += "rgba_"
return data
def read_rgb(self):
self.imsize = list(self.imsize)
self.imsize.append(4)
self.imsize = tuple(self.imsize)
data = binIO.read_data_array(self.filename, self.imbytes,
self.byte_offset)
data = data.reshape(self.imsize, order='C') # (B, G, R, A)
if self.imdtype == 'rgb':
data = data[:, :, -2::-1] # (R, G, B)
self.mode += 'rgb_'
self.imsize = list(self.imsize)
self.imsize[-1] = self.imsize[-1] - 1
self.imsize = tuple(self.imsize)
elif self.imdtype == 'argb':
data = np.concatenate((data[:, :, -2::-1], data[:, :, -1:]),
axis=2) # (R, G, B, A)
self.mode += 'rgba_'
return data
def read_image_data(self):
if self.imdtype == "not_implemented":
raise AttributeError, "image data type: %s" % self.imdtype
if "packed" in self.imdtype:
return self.read_packed_complex()
elif "rgb" in self.imdtype:
return self.read_rgb()
else:
data = binIO.read_data_array(self.filename, self.imbytes, self.byte_offset, self.imdtype)
imsize = self.imsize.tolist()
if self.order == "F":
if self.record_by == "spectrum":
swapelem(imsize, 0, 1)
data = data.reshape(imsize, order=self.order)
data = np.swapaxes(data, 0, 1).copy()
elif self.record_by == "image":
data = data.reshape(imsize, order="C")
elif self.order == "C":
if self.record_by == "spectrum":
data = data.reshape(np.roll(self.imsize, 1), order=self.order)
data = np.rollaxis(data, 0, self.dim).copy()
elif self.record_by == "image":
data = data.reshape(self.imsize, order=self.order)
return data
def read_packed_complex(self):
if (self.imsize[0] != self.imsize[1]) or (len(self.imsize)>2):
msg = "Packed complex format works only for a 2Nx2N image"
msg += " -> width == height"
print msg
raise ImageModeError('FFT')
# print "This image is likely a FFT and each pixel is a complex number"
# print "You might want to display its complex norm"
# print "with a logarithmic intensity scale: log(abs(IMAGE))"
self.mode += 'FFT_'
N = int(self.imsize[0] / 2) # think about a 2Nx2N matrix
# read all the bytes as 1D array of 4-Byte float
tmpdata = binIO.read_data_array(self.filename, self.imbytes,
self.byte_offset, 'float32')
# create an empty 2Nx2N ndarray of complex
data = np.zeros(self.imsize, 'complex64', 'C')
# fill in the real values:
data[N, 0] = tmpdata[0]
data[0, 0] = tmpdata[1]
data[N, N] = tmpdata[2*N**2] # Nyquist frequency
data[0, N] = tmpdata[2*N**2+1] # Nyquist frequency
# fill in the non-redundant complex values:
# top right quarter, except 1st column
for i in xrange(N): # this could be optimized
start = 2 * i * N + 2
stop = start + 2 * (N - 1) - 1
step = 2
realpart = tmpdata[start:stop:step]
imagpart = tmpdata[start+1:stop+1:step]
data[i, N+1:2*N] = realpart + imagpart * 1j
# 1st column, bottom left quarter
start = 2 * N
stop = start + 2 * N * (N - 1) - 1
step = 2 * N
realpart = tmpdata[start:stop:step]
imagpart = tmpdata[start+1:stop+1:step]
data[N+1:2*N, 0] = realpart + imagpart * 1j
# 1st row, bottom right quarter
start = 2 * N**2 + 2
stop = start + 2 * (N - 1) - 1
step = 2
realpart = tmpdata[start:stop:step]
imagpart = tmpdata[start+1:stop+1:step]
data[N, N+1:2*N] = realpart + imagpart * 1j
# bottom right quarter, except 1st row
start = stop + 1
stop = start + 2 * N * (N - 1) - 1
step = 2
realpart = tmpdata[start:stop:step]
imagpart = tmpdata[start+1:stop+1:step]
complexdata = realpart + imagpart * 1j
data[N+1:2*N, N:2*N] = complexdata.reshape(N-1, N)
# fill in the empty pixels: A(i)(j) = A(2N-i)(2N-j)*
# 1st row, top left quarter, except 1st element
data[0, 1:N] = np.conjugate(data[0, -1:-N:-1])
# 1st row, bottom left quarter, except 1st element
data[N, 1:N] = np.conjugate(data[N, -1:-N:-1])
# 1st column, top left quarter, except 1st element
data[1:N, 0] = np.conjugate(data[-1:-N:-1, 0])
# 1st column, top right quarter, except 1st element
data[1:N, N] = np.conjugate(data[-1:-N:-1, N])
# top left quarter, except 1st row and 1st column
data[1:N, 1:N] = np.conjugate(data[-1:-N:-1, -1:-N:-1])
# bottom left quarter, except 1st row and 1st column
data[N+1:2*N, 1:N] = np.conjugate(data[-N-1:-2*N:-1, -1:-N:-1])
return data
def read_packed_complex(self):
if (self.imsize[0] != self.imsize[1]) or (len(self.imsize) > 2):
msg = "Packed complex format works only for a 2Nx2N image"
msg += " -> width == height"
print msg
raise ImageModeError("FFT")
# print "This image is likely a FFT and each pixel is a complex number"
# print "You might want to display its complex norm"
# print "with a logarithmic intensity scale: log(abs(IMAGE))"
self.mode += "FFT_"
N = int(self.imsize[0] / 2) # think about a 2Nx2N matrix
# read all the bytes as 1D array of 4-Byte float
tmpdata = binIO.read_data_array(self.filename, self.imbytes, self.byte_offset, "float32")
# create an empty 2Nx2N ndarray of complex
data = np.zeros(self.imsize, "complex64", "C")
# fill in the real values:
data[N, 0] = tmpdata[0]
data[0, 0] = tmpdata[1]
data[N, N] = tmpdata[2 * N ** 2] # Nyquist frequency
data[0, N] = tmpdata[2 * N ** 2 + 1] # Nyquist frequency
# fill in the non-redundant complex values:
# top right quarter, except 1st column
for i in xrange(N): # this could be optimized
start = 2 * i * N + 2
stop = start + 2 * (N - 1) - 1
step = 2
realpart = tmpdata[start:stop:step]
imagpart = tmpdata[start + 1 : stop + 1 : step]
data[i, N + 1 : 2 * N] = realpart + imagpart * 1j
# 1st column, bottom left quarter
start = 2 * N
stop = start + 2 * N * (N - 1) - 1
step = 2 * N
realpart = tmpdata[start:stop:step]
imagpart = tmpdata[start + 1 : stop + 1 : step]
data[N + 1 : 2 * N, 0] = realpart + imagpart * 1j
# 1st row, bottom right quarter
start = 2 * N ** 2 + 2
stop = start + 2 * (N - 1) - 1
step = 2
realpart = tmpdata[start:stop:step]
imagpart = tmpdata[start + 1 : stop + 1 : step]
data[N, N + 1 : 2 * N] = realpart + imagpart * 1j
# bottom right quarter, except 1st row
start = stop + 1
stop = start + 2 * N * (N - 1) - 1
step = 2
realpart = tmpdata[start:stop:step]
imagpart = tmpdata[start + 1 : stop + 1 : step]
complexdata = realpart + imagpart * 1j
data[N + 1 : 2 * N, N : 2 * N] = complexdata.reshape(N - 1, N)
# fill in the empty pixels: A(i)(j) = A(2N-i)(2N-j)*
# 1st row, top left quarter, except 1st element
data[0, 1:N] = np.conjugate(data[0, -1:-N:-1])
# 1st row, bottom left quarter, except 1st element
data[N, 1:N] = np.conjugate(data[N, -1:-N:-1])
# 1st column, top left quarter, except 1st element
data[1:N, 0] = np.conjugate(data[-1:-N:-1, 0])
# 1st column, top right quarter, except 1st element
data[1:N, N] = np.conjugate(data[-1:-N:-1, N])
# top left quarter, except 1st row and 1st column
data[1:N, 1:N] = np.conjugate(data[-1:-N:-1, -1:-N:-1])
# bottom left quarter, except 1st row and 1st column
data[N + 1 : 2 * N, 1:N] = np.conjugate(data[-N - 1 : -2 * N : -1, -1:-N:-1])
return data
