Dataset statistical calculation.
Project description
datastate
Dataset statistical calculation.
How to use
from cvds import calc_mean_std, SegLabel
# calculate mean and std from image dataset
msd = calc_mean_std("dataset/image")
print(msd)
"""
output:
{'mean': array([[0.4923597 , 0.4912278 , 0.44912583]], dtype=float32), 'std': array([[0.2124527 , 0.20088832, 0.22155836]], dtype=float32)}
"""
# check label's information
lab_infor = SegLabel("dataset/label", num_classes=4)
print(lab.area) # area of each category
print(lab.sample) # number of samples per category
print(lab.index([0, 1, 2, 3])) # list of path with index category
"""
output:
{'0': 18.079630533854164, '1': 42.78903537326389, '2': 18.866475423177086, '3': 20.26485866970486}
{'0': 5, '1': 8, '2': 4, '3': 6}
['dataset\\label\\45.png', 'dataset\\label\\47.png']
"""
TODO
-
calc neam and std (population).
-
calc area of each category in segmentation task.
-
calc number of samples per category in classification task.
-
calc anchor size and etc in detection task.
-
add other state calc about dataset.
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