def correct_drift(img1, img2, display_cc=False):
''' Returns two ImagePlus objects that are drift corrected to each other.
The first image is not changed.
The second image is a new image that is shifted using ImageJ's Translate.
:param img1: The reference image.
:param img2: The image to be shifted.
:param display_cc: Activate displaying the CrossCorrelation image (default False).
'''
img1_cc, img2_cc = cc.scale_to_power_of_two([img1, img2])
result = cc.perform_correlation(img1_cc, img2_cc)
x_off, y_off = cc.get_shift(result)
# style after maximum detection
if not display_cc:
result.hide()
else:
result.copyScale(img1)
cc.style_cc(result)
if x_off == 0 and y_off == 0:
print('No drift has been detected.')
return img1, img2
title = img2.getTitle()
img2_dk = Duplicator().run(img2)
img2_dk.setTitle('DK-' + title)
IJ.run(img2_dk, 'Translate...', 'x=%d y=%d interpolation=None' % (-x_off, -y_off))
img2_dk.copyScale(img2)
img2_dk.show()
return img1, img2_dk
def get_drift(i, j, _images):
'''Returns the drift of imagej compared to image i'''
cc_img = cc.perform_correlation(_images[i], _images[j])
offset = cc.get_drift(cc_img)
''' DEBUG
print 'Reference: %s at index %i' % (images[i],i)
print 'Drifted image: %s at index %i' % (images[j],j)
print 'Offset: %d,%d\n' % offset
'''
return offset
def get_drift_matrix_cc(images):
''' Returns the drift matrix of the given images.
Cross correlation is used for drift detection.
:param images: A list containing ImagePlus objects.
'''
def get_drift(i, j, _images):
'''Returns the drift of imagej compared to image i'''
cc_img = cc.perform_correlation(_images[i], _images[j])
offset = cc.get_drift(cc_img)
''' DEBUG
print 'Reference: %s at index %i' % (images[i],i)
print 'Drifted image: %s at index %i' % (images[j],j)
print 'Offset: %d,%d\n' % offset
'''
return offset
images_cc = cc.scale_to_power_of_two(images)
drift_matrix = [[]] * len(images)
for i, _ in enumerate(drift_matrix):
drift_matrix[i] = [(0, 0)] * len(images)
# print 'Initial matrix: ', drift_matrix
for i in range(len(images)):
# print 'i=%i: ' % i, range(i + 1, len(images))
for j in range(i + 1, len(images)):
img_drift = get_drift(i, j, images_cc)
''' DEBUG
print 'Appending to %i/%i:' % (i, j)
print shift
'''
# print 'Matrix element: ', i, j
# print 'Adding value: ', img_drift
drift_matrix[i][j] = img_drift
drift_matrix[j][i] = tuple([-val for val in img_drift])
# print 'Drift matrix:'
# pprint(drift_matrix)
return drift_matrix
def get_drift_vector_cc(images):
''' Returns the drift vector of the given images.
Cross correlation is used for drift detection.
:param images: A list containing ImagePlus objects.
'''
def get_drift(i, j, _images):
'''Returns the drift of imagej compared to image i'''
cc_img = cc.perform_correlation(_images[i], _images[j])
offset = cc.get_drift(cc_img)
''' DEBUG
print 'Reference: %s at index %i' % (images[i],i)
print 'Drifted image: %s at index %i' % (images[j],j)
print 'Offset: %d,%d\n' % offset
'''
return offset
images_cc = cc.scale_to_power_of_two(images)
drift_vector = [None] * len(images)
drift_vector[0] = (0., 0.)
# print 'Initial vector: ', drift_vector
for i in range(1, len(images)):
img_drift = get_drift(i - 1, i, images_cc)
# print 'Vector element: ', i
# print 'Adding value: ', img_drift
drift_vector[i] = img_drift
drift_vector[i] = tuple([a + b for a, b in zip(drift_vector[i - 1], drift_vector[i])])
# print 'Drift vector:'
# pprint(drift_vector)
return drift_vector
