topostats.filters#
Contains filter functions that take a 2D array representing an image as an input, as well as necessary parameters, and return a 2D array of the same size representing the filtered image.
Attributes#
Classes#
Class for filtering scans. |
Module Contents#
- topostats.filters.LOGGER#
- class topostats.filters.Filters(image: numpy.ndarray, filename: str, pixel_to_nm_scaling: float, threshold_method: str = 'otsu', otsu_threshold_multiplier: float = 1.7, threshold_std_dev: dict = None, threshold_absolute: dict = None, gaussian_size: float = None, gaussian_mode: str = 'nearest', quiet: bool = False)[source]#
Class for filtering scans.
- filename#
- pixel_to_nm_scaling#
- gaussian_size#
- gaussian_mode#
- threshold_method#
- otsu_threshold_multiplier#
- threshold_std_dev#
- threshold_absolute#
- images#
- thresholds = None#
- medians#
- results#
- median_flatten(image: numpy.ndarray, mask: numpy.ndarray = None, img_name: str = None) numpy.ndarray[source]#
Uses the method of median differences to flatten the rows of an image, aligning the rows and centering the median around zero. When used with a mask, this has the effect of centering the background data on zero. Note this function does not handle scars.
- Parameters:
image (np.ndarray) – 2-D image of the data to align the rows of.
mask (np.ndarray) – Boolean array of points to mask out (ignore).
img_name (str) – Name of the image (to be able to print information in the console).
- Returns:
Returns a copy of the input image with rows aligned
- Return type:
np.ndarray
- remove_tilt(image: numpy.ndarray, mask: numpy.ndarray = None, img_name: str = None)[source]#
Removes planar tilt from an image (linear in 2D space). It uses a linear fit of the medians of the rows and columns to determine the linear slants in x and y directions and then subtracts the fit from the columns.
- Parameters:
image (np.ndarray) – 2-D image of the data to remove the planar tilt from.
mask (np.ndarray) – Boolean array of points to mask out (ignore).
img_name (str) – Name of the image (to be able to print information in the console).
- Returns:
Returns a copy of the input image with the planar tilt removed
- Return type:
np.ndarray
- remove_quadratic(image: numpy.ndarray, mask: numpy.ndarray = None, img_name: str = None)[source]#
Removes the quadratic bowing that can be seen in some large-scale AFM images. It uses a simple quadratic fit on the medians of the columns of the image and then subtracts the calculated quadratic from the columns.
- Parameters:
image (np.ndarray) – 2-D image of the data to remove the quadratic from.
mask (np.ndarray) – Boolean array of points to mask out (ignore).
img_name (str) – Name of the image (to be able to print information in the console).
- Returns:
Returns a copy of the input image with the quadratic bowing removed
- Return type:
np.ndarray
- calc_gradient(array: numpy.ndarray, shape: int) numpy.ndarray[source]#
Calculate the gradient of an array.
- average_background(image: numpy.ndarray, mask: numpy.ndarray = None) numpy.ndarray[source]#
Zero the background by subtracting the non-masked mean from all pixels.
- Parameters:
image (np.array) – Numpy array representing image.
mask (np.array) – Mask of the array, should have the same dimensions as image.
- Returns:
Numpy array of image zero averaged.
- Return type:
np.ndarray
- gaussian_filter(image: numpy.ndarray, **kwargs) numpy.array[source]#
Apply Gaussian filter to an image.
- Parameters:
image (np.array) – Numpy array representing image.
- Returns:
Numpy array of gaussian blurred image.
- Return type:
np.array
- filter_image() None[source]#
Process a single image, filtering, finding grains and calculating their statistics.
Example
from topostats.io import LoadScan from topostats.topotracing import Filter, process_scan
load_scan = LoadScan(“minicircle.spm”, channel=”Height”) load_scan.get_data() filter = Filter(image=load_scan.image, … pixel_to_nm_scaling=load_scan.pixel_to_nm_scaling, … filename=load_scan.filename, … threshold_method=’otsu’) filter.filter_image()