def FindOffset(FixedImage, MovingImage, MinOverlap=0.0, MaxOverlap=1.0, FFT_Required=True, FixedImageShape=None, MovingImageShape=None):
'''return an alignment record describing how the images overlap. The alignment record indicates how much the
moving image must be rotated and translated to align perfectly with the FixedImage.
If adjusting control points the peak can be added to the fixed image's control point, or subtracted from the
warped image's control point (accounting for any transform used to create the warped image) to align the images.
:param ndarray FixedImage: Target space we are registering into
:param ndarray MovingImage: Source space we are coming from
:param float MinOverlap: The minimum amount of overlap by area the registration must have
:param float MaxOverlap: The maximum amount of overlap by area the registration must have
:param bool FFT_Required: True by default, if False the input images are in FFT space already
:param tuple FixedImageShape: Defaults to None, if specified it contains the size of the fixed image before padding. Used to calculate mask for valid overlap values.
:param tuple MovingImageShape: Defaults to None, if specified it contains the size of the moving image before padding. Used to calculate mask for valid overlap values.
'''
# Find peak requires both the fixed and moving images have equal size
assert((FixedImage.shape[0] == MovingImage.shape[0]) and (FixedImage.shape[1] == MovingImage.shape[1]))
#nornir_imageregistration.ShowGrayscale([FixedImage, MovingImage])
if FixedImageShape is None:
FixedImageShape = FixedImage.shape
if MovingImageShape is None:
MovingImageShape = MovingImage.shape
CorrelationImage = None
if FFT_Required:
CorrelationImage = ImagePhaseCorrelation(FixedImage, MovingImage)
else:
CorrelationImage = FFTPhaseCorrelation(FixedImage, MovingImage, delete_input=False)
CorrelationImage = fftpack.fftshift(CorrelationImage)
# Crop the areas that cannot overlap
CorrelationImage -= CorrelationImage.min()
CorrelationImage /= CorrelationImage.max()
# Timer.Start('Find Peak')
OverlapMask = nornir_imageregistration.GetOverlapMask(FixedImageShape, MovingImageShape, CorrelationImage.shape, MinOverlap, MaxOverlap)
(peak, weight) = FindPeak(CorrelationImage, OverlapMask)
del CorrelationImage
record = nornir_imageregistration.AlignmentRecord(peak=peak, weight=weight)
return record
def testSquareOverlapMask(self):
FixedImageSize = np.asarray((128, 128), dtype=np.int32)
MovingImageSize = np.asarray((128, 128), dtype=np.int32)
CorrelationImageSize = FixedImageSize + MovingImageSize
mask = nornir_imageregistration.GetOverlapMask(FixedImageSize,
MovingImageSize,
CorrelationImageSize,
MinOverlap=0.25,
MaxOverlap=0.75)
np.testing.assert_equal(CorrelationImageSize, mask.shape)
self.assertTrue(
nornir_imageregistration.ShowGrayscale(
[mask],
title="Square Overlap Mask: 25% Min overlap, 75% max overlap",
PassFail=True))
def testSquareOddHeightOverlapMask(self):
FixedImageSize = np.asarray((31, 64), dtype=np.int32)
MovingImageSize = np.asarray((32, 64), dtype=np.int32)
CorrelationImageSize = FixedImageSize + MovingImageSize
mask = nornir_imageregistration.GetOverlapMask(FixedImageSize,
MovingImageSize,
CorrelationImageSize,
MinOverlap=0.25,
MaxOverlap=0.75)
np.testing.assert_equal(CorrelationImageSize, mask.shape,
"Dimensions of output mask is incorrect")
self.assertTrue(
nornir_imageregistration.ShowGrayscale(
[mask],
title=
"Square Overlap Mask with odd height dimension: 25% Min overlap, 75% max overlap",
PassFail=True))
def testMismatchedOverlapMask_MovingLarger(self):
FixedImageSize = np.asarray((64, 64), dtype=np.int32)
MovingImageSize = np.asarray((64, 256), dtype=np.int32)
CorrelationImageSize = FixedImageSize + MovingImageSize
mask = nornir_imageregistration.GetOverlapMask(FixedImageSize,
MovingImageSize,
CorrelationImageSize,
MinOverlap=0.25,
MaxOverlap=0.75)
np.testing.assert_equal(CorrelationImageSize, mask.shape)
self.assertTrue(
nornir_imageregistration.ShowGrayscale(
[mask],
title=
"Mismatched Overlap Mask (Moving Larger): 25% Min overlap, 75% max overlap",
PassFail=True))
return