ai_ct_scans.phase_correlation_image_processing module
- ai_ct_scans.phase_correlation_image_processing.circle(array_size, radius)
Get a solid circle of True on a False background, centred at [0, 0]
- Parameters
array_size (list of ints) – The [number of rows, number of columns] to generate the circle on
radius (float) – The radius of the circle
- Returns
A boolean solid circle of True on a False background centred at [0, 0]
- Return type
(ndarray)
- ai_ct_scans.phase_correlation_image_processing.convolve(image_1, image_2)
Perform a convolution of two 2D images of equal shape via convolution theorem
- Parameters
image_1 (ndarray) – An image
image_2 (ndarray) – An image
- Returns
An image
- Return type
(ndarray)
- ai_ct_scans.phase_correlation_image_processing.generate_overlay_2d(images, normalize=True)
Take two grayscale images and overlay them in R and G channels of an output image, where output image is large enough to fit all data from both images
- Parameters
images (list of ndarrays) – list of 2D images
normalize (bool) – whether to divide by max of each image while assigning to a layer of output array
- Returns
3D array, [rows, cols, channels], first two channels filled by input images
- Return type
(ndarray)
- ai_ct_scans.phase_correlation_image_processing.lmr(image, filter_type=None, radius=None)
Perform local mean removal. Default to using a circle of radius radius, otherwise use the kernel provided in filter. The kernel must be the same shape as image if not None.
- Parameters
image (ndarray) – A 2D image
filter_type (None or ndarray) – The filter to apply to find the local mean, typically a shape centred at [0, 0]
ndarray (in an) –
radius (float) – The radius of a circle to use as default filter if filter is None
- Returns
A 2D image
- Return type
(ndarray)
- ai_ct_scans.phase_correlation_image_processing.max_shape_from_image_list(images)
Get the maximum size in each dimension from a list of images, useful for defining an empty image that can be used to overlay every image
- Parameters
images (list of ndarrays) – list of images, all of same dimensionality
- Returns
The maximum shape element along each dimension across all images
- Return type
(tuple of ints)
- ai_ct_scans.phase_correlation_image_processing.pad_nd(image, shape)
Pad an image up to shape with its mean value, where padding is added at end of each axis
- Parameters
image (ndarray) – An n-dimensional image
shape (list of ints) – expected output shape, must but greater than image.shape in each dimension
- Returns
Padded image
- Return type
(ndarray)
- ai_ct_scans.phase_correlation_image_processing.sphere(array_size, radius)
Get a solid circle of True on a False background, centred at [0, 0]
- Parameters
array_size (list of ints) – The [number of layers, number of rows, number of columns] to generate the sphere on
radius (float) – The radius of the sphere
- Returns
A boolean solid circle of True on a False background centred at [0, 0]
- Return type
(ndarray)
- ai_ct_scans.phase_correlation_image_processing.zero_crossings(image, thresh=0)
Get a binary array the shape of image, with True where zero crossing points occur
- Parameters
image (ndarray) – A 2D image
thresh (float or 'auto') – The amount by which adjacent pixel values must differ while crossing 0 to be counted
'auto' (as a crossing point. If) –
histogram (chooses the bin edge value prior to the maximum counts in the smoothed) –
values (of pixel) –
- Returns
Binary array the shape of image, with True where zero crossing points occur
- Return type
(ndarray)