topostats.grains#

Find grains in an image.

Attributes#

LOGGER

Classes#

Grains

Find grains in an image.

Module Contents#

topostats.grains.LOGGER#
class topostats.grains.Grains(image: numpy.typing.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.

Parameters:

  • image (npt.NDArray) – 2-D Numpy array of image.

  • filename (str) – File being processed (used in logging).

  • pixel_to_nm_scaling (float) – Scaling of pixels to nanometres.

  • threshold_method (str) – Method for determining thershold to mask values, default is ‘otsu’.

  • otsu_threshold_multiplier (float) – Factor by which the below threshold is to be scaled prior to masking.

  • threshold_std_dev (dict) – Dictionary of ‘below’ and ‘above’ factors by which standard deviation is multiplied to derive the threshold if threshold_method is ‘std_dev’.

  • threshold_absolute (dict) – Dictionary of absolute ‘below’ and ‘above’ thresholds for grain finding.

  • absolute_area_threshold (dict) – Dictionary of above and below grain’s area thresholds.

  • direction (str) – Direction for which grains are to be detected, valid values are ‘above’, ‘below’ and ‘both’.

  • smallest_grain_size_nm2 (float) – Whether or not to remove grains that intersect the edge of the image.

  • remove_edge_intersecting_grains (bool) – Direction for which grains are to be detected, valid values are ‘above’, ‘below’ and ‘both’.

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.typing.NDArray, **kwargs) → numpy.typing.NDArray[source]#

Remove grains touching the border.

Parameters:

  • image (npt.NDarray) – 2-D Numpy array representing the image.

  • **kwargs – Arguments passed to ‘skimage.segmentation.clear_border(**kwargs)’.

Returns:

2-D Numpy array of image without objects touching the border.

Return type:

npt.NDarray

label_regions(image: numpy.typing.NDArray, background: int = 0) → numpy.typing.NDArray[source]#

Label regions.

This method is used twice, once prior to removal of small regions and again afterwards which is why an image must be supplied rather than using ‘self’.

Parameters:

  • image (npt.NDArray) – 2-D Numpy array of image.

  • background (int) – Value used to indicate background of image. Default = 0.

Returns:

2-D Numpy array of image with regions numbered.

Return type:

npt.NDArray

calc_minimum_grain_size(image: numpy.typing.NDArray) → float[source]#

Calculate the minimum grain size in pixels squared.

Very small objects are first removed via thresholding before calculating the below extreme.

Parameters:

image (npt.NDArray) – 2-D Numpy image from which to calculate the minimum grain size.

Returns:

Minimum grains size in pixels squared. If there are areas a value of -1 is returned.

Return type:

float

remove_noise(image: numpy.typing.NDArray, **kwargs) → numpy.typing.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 (npt.NDArray) – 2-D Numpy array to be cleaned.

  • **kwargs – Arguments passed to ‘skimage.morphology.remove_small_objects(**kwargs)’.

Returns:

2-D Numpy array of image with objects < smallest_grain_size_nm2 removed.

Return type:

npt.NDArray

remove_small_objects(image: numpy.array, **kwargs) → numpy.typing.NDArray[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.array) – 2-D Numpy array to remove small objects from.

  • **kwargs – Arguments passed to ‘skimage.morphology.remove_small_objects(**kwargs)’.

Returns:

2-D Numpy array of image with objects < minimumm_grain_size removed.

Return type:

npt.NDArray

area_thresholding(image: numpy.typing.NDArray, area_thresholds: tuple) → numpy.typing.NDArray[source]#

Remove objects larger and smaller than the specified thresholds.

Parameters:

  • image (npt.NDArray) – Image array where the background == 0 and grains are labelled as integers >0.

  • area_thresholds (tuple) – List of area thresholds (in nanometres squared, not pixels squared), first is the lower limit for size, second is the upper.

Returns:

Array with small and large objects removed.

Return type:

npt.NDArray

colour_regions(image: numpy.typing.NDArray, **kwargs) → numpy.typing.NDArray[source]#

Colour the regions.

Parameters:

  • image (npt.NDArray) – 2-D array of labelled regions to be coloured.

  • **kwargs – Arguments passed to ‘skimage.color.label2rgb(**kwargs)’.

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.

  • **kwargs – Arguments passed to ‘skimage.measure.regionprops(**kwargs)’.

Returns:

List of region property objects.

Return type:

list

get_bounding_boxes(direction: str) → 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

find_grains()[source]#

Find grains.