topostats.grains#
Find grains in an image.
Attributes#
Classes#
Find grains in an image. |
Module Contents#
- topostats.grains.LOGGER#
- class topostats.grains.Grains(image: numpy.ndarray, filename: str, pixel_to_nm_scaling: float, threshold_method: str = None, otsu_threshold_multiplier: float = None, threshold_std_dev: dict = None, threshold_absolute: dict = None, absolute_area_threshold: dict = None, direction: str = None, smallest_grain_size_nm2: float = None, remove_edge_intersecting_grains: bool = True)[source]#
Find grains in an image.
- image#
- filename#
- pixel_to_nm_scaling#
- threshold_method = None#
- otsu_threshold_multiplier = None#
- threshold_std_dev = None#
- threshold_absolute = None#
- absolute_area_threshold = None#
- direction#
- smallest_grain_size_nm2 = None#
- remove_edge_intersecting_grains = True#
- thresholds = None#
- images#
- directions#
- minimum_grain_size = None#
- region_properties#
- bounding_boxes#
- grainstats = None#
- tidy_border(image: numpy.array, **kwargs) numpy.array[source]#
Remove grains touching the border.
- Parameters:
image (np.array) – Numpy array representing image.
- Returns:
Numpy array of image with borders tidied.
- Return type:
np.array
- label_regions(image: numpy.array) numpy.array[source]#
Label regions.
This method is used twice, once prior to removal of small regions, and again afterwards, hence requiring an argument of what image to label.
- Parameters:
image (np.array) – Numpy array representing image.
- Returns:
Numpy array of image with objects coloured.
- Return type:
np.array
- calc_minimum_grain_size(image: numpy.ndarray) float[source]#
Calculate the minimum grain size in pixels squared.
Very small objects are first removed via thresholding before calculating the below extreme.
- remove_noise(image: numpy.ndarray, **kwargs) numpy.ndarray[source]#
Remove noise which are objects smaller than the ‘smallest_grain_size_nm2’.
This ensures that the smallest objects ~1px are removed regardless of the size distribution of the grains.
- Parameters:
image (np.ndarray) – 2D Numpy image to be cleaned.
- Returns:
2D Numpy array of image with objects < smallest_grain_size_nm2 removed.
- Return type:
np.ndarray
- remove_small_objects(image: numpy.array, **kwargs)[source]#
Remove small objects from the input image.
Threshold determined by the minimum grain size, in pixels squared, of the classes initialisation.
- Parameters:
image (np.ndarray) – 2D Numpy image to remove small objects from.
- Returns:
2D Numpy array of image with objects < minimum_grain_size removed.
- Return type:
np.ndarray
- area_thresholding(image: numpy.ndarray, area_thresholds: list)[source]#
Remove objects larger and smaller than the specified thresholds.
- Parameters:
image (np.ndarray) – Image array where the background == 0 and grains are labelled as integers > 0.
area_thresholds (list) – List of area thresholds (in nanometres squared, not pixels squared), first should be the lower limit for size, and the second should be the upper limit for size.
- Returns:
Image where grains outside the thresholds have been removed, as a re-numbered labeled image.
- Return type:
np.ndarray
- colour_regions(image: numpy.array, **kwargs) numpy.array[source]#
Colour the regions.
- Parameters:
image (np.array) – Numpy array representing image.
- Returns:
Numpy array of image with objects coloured.
- Return type:
np.array
- static get_region_properties(image: numpy.array, **kwargs) list[source]#
Extract the properties of each region.
- Parameters:
image (np.array) – Numpy array representing image
- Returns:
List of region property objects.
- Return type:
List
- get_bounding_boxes(direction) dict[source]#
Derive a list of bounding boxes for each region from the derived region_properties.
- Parameters:
direction (str) – Direction of threshold for which bounding boxes are being calculated.
- Returns:
Dictionary of bounding boxes indexed by region area.
- Return type:
dict