def calculateShiftsFFT(self, stack, reference, offsets=None, widths=None, crop=False):
if DEBUG:
print("Offsets = ", offsets)
print("Widths = ", widths)
data = stack.data
if offsets is None:
offsets = [0.0, 0.0]
if widths is None:
widths = [reference.shape[0], reference.shape[1]]
fft2Function = numpy.fft.fft2
if 1:
DTYPE = numpy.float32
else:
DTYPE = numpy.float64
image2 = numpy.zeros((widths[0], widths[1]), dtype=DTYPE)
shape = image2.shape
USE_APODIZATION_WINDOW = False
apo = [10, 10]
if USE_APODIZATION_WINDOW:
# use apodization window
window = numpy.outer(SpecfitFuns.slit([0.5, shape[0] * 0.5, shape[0] - 4 * apo[0], apo[0]],
numpy.arange(float(shape[0]))),
SpecfitFuns.slit([0.5, shape[1] * 0.5, shape[1] - 4 * apo[1], apo[1]],
numpy.arange(float(shape[1])))).astype(DTYPE)
else:
window = numpy.zeros((shape[0], shape[1]), dtype=DTYPE)
window[apo[0]:shape[0] - apo[0], apo[1]:shape[1] - apo[1]] = 1
image2[:,:] = window * reference[offsets[0]:offsets[0]+widths[0],
offsets[1]:offsets[1]+widths[1]]
image2fft2 = fft2Function(image2)
mcaIndex = stack.info.get('McaIndex')
shifts = numpy.zeros((data.shape[mcaIndex], 2), numpy.float)
image1 = numpy.zeros(image2.shape, dtype=DTYPE)
total = float(data.shape[mcaIndex])
if mcaIndex == 0:
for i in range(data.shape[mcaIndex]):
image1[:,:] = window * data[i][offsets[0]:offsets[0]+widths[0],
offsets[1]:offsets[1]+widths[1]]
image1fft2 = fft2Function(image1)
shifts[i] = ImageRegistration.measure_offset_from_ffts(image2fft2,
image1fft2)
if DEBUG:
print("Index = %d shift = %.4f, %.4f" % (i, shifts[i][0], shifts[i][1]))
self._progress = (100 * i) / total
elif mcaIndex in [2, -1]:
for i in range(data.shape[mcaIndex]):
image1[:,:] = window * data[:,:,i][offsets[0]:offsets[0]+widths[0],
offsets[1]:offsets[1]+widths[1]]
image1fft2 = fft2Function(image1)
shifts[i] = ImageRegistration.measure_offset_from_ffts(image2fft2,
image1fft2)
if DEBUG:
print("Index = %d shift = %.4f, %.4f" % (i, shifts[i][0], shifts[i][1]))
self._progress = (100 * i) / total
else:
raise IndexError("Only stacks of images or spectra supported. 1D index should be 0 or 2")
return shifts
def calculateShiftsFFT(self, stack, reference, offsets=None, widths=None, crop=False):
if DEBUG:
print("Offsets = ", offsets)
print("Widths = ", widths)
data = stack.data
if offsets is None:
offsets = [0.0, 0.0]
if widths is None:
widths = [reference.shape[0], reference.shape[1]]
fft2Function = numpy.fft.fft2
if 1:
DTYPE = numpy.float32
else:
DTYPE = numpy.float64
image2 = numpy.zeros((widths[0], widths[1]), dtype=DTYPE)
shape = image2.shape
USE_APODIZATION_WINDOW = False
apo = [10, 10]
if USE_APODIZATION_WINDOW:
# use apodization window
window = numpy.outer(SpecfitFuns.slit([0.5, shape[0] * 0.5, shape[0] - 4 * apo[0], apo[0]],
numpy.arange(float(shape[0]))),
SpecfitFuns.slit([0.5, shape[1] * 0.5, shape[1] - 4 * apo[1], apo[1]],
numpy.arange(float(shape[1])))).astype(DTYPE)
else:
window = numpy.zeros((shape[0], shape[1]), dtype=DTYPE)
window[apo[0]:shape[0] - apo[0], apo[1]:shape[1] - apo[1]] = 1
image2[:,:] = window * reference[offsets[0]:offsets[0]+widths[0],
offsets[1]:offsets[1]+widths[1]]
image2fft2 = fft2Function(image2)
mcaIndex = stack.info.get('McaIndex')
shifts = numpy.zeros((data.shape[mcaIndex], 2), numpy.float)
image1 = numpy.zeros(image2.shape, dtype=DTYPE)
total = float(data.shape[mcaIndex])
if mcaIndex == 0:
for i in range(data.shape[mcaIndex]):
image1[:,:] = window * data[i][offsets[0]:offsets[0]+widths[0],
offsets[1]:offsets[1]+widths[1]]
image1fft2 = fft2Function(image1)
shifts[i] = ImageRegistration.measure_offset_from_ffts(image2fft2,
image1fft2)
if DEBUG:
print("Index = %d shift = %.4f, %.4f" % (i, shifts[i][0], shifts[i][1]))
self._progress = (100 * i) / total
elif mcaIndex in [2, -1]:
for i in range(data.shape[mcaIndex]):
image1[:,:] = window * data[:,:,i][offsets[0]:offsets[0]+widths[0],
offsets[1]:offsets[1]+widths[1]]
image1fft2 = fft2Function(image1)
shifts[i] = ImageRegistration.measure_offset_from_ffts(image2fft2,
image1fft2)
if DEBUG:
print("Index = %d shift = %.4f, %.4f" % (i, shifts[i][0], shifts[i][1]))
self._progress = (100 * i) / total
else:
raise IndexError("Only stacks of images or spectra supported. 1D index should be 0 or 2")
return shifts
def shiftStack(self, stack, shifts, crop=False, filename=None):
"""
"""
data = stack.data
mcaIndex = stack.info['McaIndex']
if mcaIndex not in [0, 2, -1]:
raise IndexError(
"Only stacks of images or spectra supported. 1D index should be 0 or 2"
)
if mcaIndex == 0:
shape = data[mcaIndex].shape
else:
shape = data.shape[0], data.shape[1]
d0_start, d0_end, d1_start, d1_end = \
ImageRegistration.get_crop_indices(shape,
shifts[:, 0],
shifts[:, 1])
window = numpy.zeros(shape, numpy.float32)
window[d0_start:d0_end, d1_start:d1_end] = 1.0
self._progress = 0.0
total = float(data.shape[mcaIndex])
if filename is not None:
hdf = self.__hdf5
dataGroup = hdf['/entry_000/Data']
attributes = {}
attributes['interpretation'] = "image"
attributes['signal'] = numpy.int32(1)
outputStack = self.getHDF5BufferIntoGroup(
dataGroup,
shape=(data.shape[mcaIndex], shape[0], shape[1]),
name="data",
dtype=numpy.float32,
attributes=attributes)
for i in range(data.shape[mcaIndex]):
#tmpImage = ImageRegistration.shiftFFT(data[i], shifts[i])
if mcaIndex == 0:
tmpImage = ImageRegistration.shiftBilinear(data[i], shifts[i])
#tmpImage = ImageRegistration.shiftImage(data[i], -shifts[i], method="fft")
if filename is None:
stack.data[i] = tmpImage * window
else:
outputStack[i] = tmpImage * window
else:
tmpImage = ImageRegistration.shiftBilinear(
data[:, :, i], shifts[i])
if filename is None:
stack.data[:, :, i] = tmpImage * window
else:
outputStack[i] = tmpImage * window
if DEBUG:
print("Index %d bilinear shifted" % i)
self._progress = (100 * i) / total
def shiftStack(self, stack, shifts, crop=False, filename=None):
"""
"""
data = stack.data
mcaIndex = stack.info['McaIndex']
if mcaIndex not in [0, 2, -1]:
raise IndexError("Only stacks of images or spectra supported. 1D index should be 0 or 2")
if mcaIndex == 0:
shape = data[mcaIndex].shape
else:
shape = data.shape[0], data.shape[1]
d0_start, d0_end, d1_start, d1_end = \
ImageRegistration.get_crop_indices(shape,
shifts[:, 0],
shifts[:, 1])
window = numpy.zeros(shape, numpy.float32)
window[d0_start:d0_end, d1_start:d1_end] = 1.0
self._progress = 0.0
total = float(data.shape[mcaIndex])
if filename is not None:
hdf = self.__hdf5
dataGroup = hdf['/entry_000/Data']
attributes = {}
attributes['interpretation'] = "image"
attributes['signal'] = numpy.int32(1)
outputStack = self.getHDF5BufferIntoGroup(dataGroup,
shape=(data.shape[mcaIndex],
shape[0],
shape[1]),
name="data",
dtype=numpy.float32,
attributes=attributes)
for i in range(data.shape[mcaIndex]):
#tmpImage = ImageRegistration.shiftFFT(data[i], shifts[i])
if mcaIndex == 0:
tmpImage = ImageRegistration.shiftBilinear(data[i], shifts[i])
#tmpImage = ImageRegistration.shiftImage(data[i], -shifts[i], method="fft")
if filename is None:
stack.data[i] = tmpImage * window
else:
outputStack[i] = tmpImage * window
else:
tmpImage = ImageRegistration.shiftBilinear(data[:,:,i], shifts[i])
if filename is None:
stack.data[:, :, i] = tmpImage * window
else:
outputStack[i] = tmpImage * window
if DEBUG:
print("Index %d bilinear shifted" % i)
self._progress = (100 * i) / total
