core.mask Module¶
This module is for functions specific to mask or threshold an image basically to clean images
-
skbeam.core.mask.bad_to_nan_gen(images, bad)¶ Convert the images marked as “bad” in bad by their index in images into a np.nan array
- Parameters
- imagesiterable
Iterable of 2-D arrays
- badlist
List of integer indices into the images parameter that mark those images as “bad”.
- Yields
- imgarray
if image is bad it will convert to np.nan array otherwise no change to the array
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skbeam.core.mask.binned_outlier(img, r, alpha, bins, mask=None)¶ Generates a mask by identifying outlier pixels in bins and masks any pixels which have a value greater or less than alpha * std away from the mean
- Parameters
- img: 2darray
The image
- r: 2darray
The array which maps pixels to bins
- alpha: float or tuple or, 1darray
Then number of acceptable standard deviations, if tuple then we use a linear distribution of alphas from alpha[0] to alpha[1], if array then we just use that as the distribution of alphas
- bins: list
The bin edges
- mask: 1darray, bool
A starting flattened mask
- Returns
- 2darray:
The mask
-
skbeam.core.mask.margin(img_shape, edge_size)¶ Mask the edge of an image
- Parameters
- img_shape: tuple
The shape of the image
- edge_size: int
Number of pixels to mask from the edge
- Returns
- 2darray:
The mask array, bad pixels are 0
-
skbeam.core.mask.threshold(images, threshold, mask=None)¶ This generator sets all pixels whose value is greater than threshold to 0 and yields the thresholded images out
- Parameters
- imagesiterable
Iterable of 2-D arrays
- thresholdfloat
threshold value to remove the hot spots in the image
- maskarray
array with values above the threshold marked as 0 and values below marked as 1. shape is (num_columns, num_rows) of the image, optional None
- Yields
- maskarray
array with values above the threshold marked as 0 and values below marked as 1. shape is (num_columns, num_rows) of the image