Motivated by Erik Zaborowski and Alex Drlica-Wagner, applying a Poisson resampling to image backgrounds for dataset diversity is not appropriate if the background images have been background-subtracted.
To remedy this behavior, Erik suggests removing the Poisson resampling. To ensure dataset diversity, we should also warn the user if the distribution of background images utilized could prove troublesome.
Consider a function placed in deeplenstronomy.py:
def check_background_indices(idx_list: list):
"""Issue a warning if an element occurs to frequently in the list.
Calculate number of elements in the list that deviate from a uniform distribution
by more than 1 standard deviation. If this number is more than 1/3 of the elements
in the list, print a warning.
Args:
idx_list (list): List of background image indices to use.
"""
values = np.unique(idx_list)
average_value, std = np.mean(values), np.std(values)
num_deviating = sum(np.abs(values - average_value) > np.std(values))
if num_deviating > len(idx_list) / 3:
print("WARNING: Non-uniform distribution of background images detected, check map.txt file.")
If this function is called on the output of utils.organize_image_backgrounds, then we should be safe to remove the Poisson resampling.
Motivated by Erik Zaborowski and Alex Drlica-Wagner, applying a Poisson resampling to image backgrounds for dataset diversity is not appropriate if the background images have been background-subtracted.
To remedy this behavior, Erik suggests removing the Poisson resampling. To ensure dataset diversity, we should also warn the user if the distribution of background images utilized could prove troublesome.
Consider a function placed in deeplenstronomy.py:
If this function is called on the output of utils.organize_image_backgrounds, then we should be safe to remove the Poisson resampling.