dicspecklequality.py

class DICSpeckleQuality(pattern, subset_size, subset_step, gray_level)

Bases: object

__init__(pattern, subset_size, subset_step, gray_level)

mean_intensity_gradient()

Mean Intensity Gradient. Based on the below: https://www.sciencedirect.com/science/article/abs/pii/S0143816613001103

Returns: mean_intensity_gradient (float): float value for mean_intensity gradient

shannon_entropy()

shannon entropy for speckle patterns. Based on the below: https://www.sciencedirect.com/science/article/abs/pii/S0030402615007950

Returns: shannon_entropy (float): float value for shannon entropy

gray_level_histogram()

Count the number of occurrences of each gray value. plot results as a histogram

speckle_size()

Calculates the Speckle sizes using a binary map calculaed from otsu threshholding (https://learnopencv.com/otsu-thresholding-with-opencv/)

Returns: tuple containing: num_speckles (int): total number of speckles identified in the binary map equivalent_diameters (np.ndarray): Speckle diameter if circle with same area labeled_speckles (np.ndarray): Label of the connected elements within speckle

speckle_size_plot()

balance_subset()

balance_image()

balance_subset_avg()

balance_subset_plot()

extract_subset(image, x, y, subset_size)

Parameters x (int): x-coord of subset center in image y (int): y-coord of subset center in image