image_coalignment

Functions

affine_alignment_quality(transform, im1, im2)
affine_params(origin, scale_x, scale_y, ...)	scale_x, scale_y, shear_x, shear_y, disp_x, disp_y, rot_angle = transform
alignment_quality(transform, im1, im2, ...)	Apply a chosen transform to Image 1 and cross-correlate with Image 2 to check coalignment Designed as a merit function for scipy.optimize routines
get_img_coords(img)
modified_affine(img, transform, origin[, ...])	scale_x, scale_y, shear_x, shear_y, disp_x, disp_y, rot_angle = transform
modified_affine_to_quadratic(transform, origin)	Since scipy.ndimage.affine_transform goes straight to an image transform instead of through map_coordinates this will convert the transform parameters used in modified affine for use with quadratic transform
normalized_cc(im1, im2[, mode])
quadratic_transform(img, transform, origin)	Apply a quadratic coordinate transformation of the form: x' = transform[0] + transform[1]*x + transform[2]*y + transform[3]*x*y + transform[4]*x^2 + transform[5]*y^2 y' = transform[6] + transform[7]*x + transform[8]*y + transform[9]*x*y + transform[10]*x^2 + transform[11]*y^2 about the specified origin = (x = origin[0], y = origin[1])
test_alignment_quality(transform, im1, im2, ...)	Apply a chosen transform to Image 1 and cross-correlate with Image 2 to check coalignment Designed as a merit function for scipy.optimize routines

Classes

ImageTransform(transform, origin, ...)	Class tracking transformations done to an image during co-alignment for repeating later or for use in a forward model
TransformCube(transform_cube)	For a given cube of images this class with contain a similar cube of ImageTransform Objects for transforming an entire cube of images.