72 lines
1.8 KiB
Python
72 lines
1.8 KiB
Python
"""
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Author: Lukas Heiligenbrunner
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Matr.Nr.: K12104785
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Exercise 3
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"""
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import os.path
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from glob import glob
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import numpy as np
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from PIL import Image, ImageStat
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class ImageStandardizer:
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def __init__(self, input_dir: str) -> None:
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super().__init__()
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# scan input dir for files
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files = glob(input_dir + '/**/*.jpg', recursive=True)
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if len(files) == 0:
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raise ValueError
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# convert them into absolute paths
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files = [os.path.abspath(x) for x in files]
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# sort filenames
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files.sort()
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self.files = files
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self.mean = None
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self.std = None
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def analyze_images(self) -> (np.array, np.array):
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mymean = np.zeros((3,), dtype=np.float64)
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mystd = np.zeros((3,), dtype=np.float64)
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for file in self.files:
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img = Image.open(file)
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stats = ImageStat.Stat(img)
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mymean += stats.mean
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mystd += stats.stddev
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del img
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mymean /= len(self.files)
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mystd /= len(self.files)
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self.mean = mymean
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self.std = mystd
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return mymean, mystd
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def get_standardized_images(self):
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if self.mean is None or self.std is None:
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raise ValueError
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for file in self.files:
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img = Image.open(file)
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arr = np.asarray(img.getdata(), dtype=np.float32).reshape(
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(img.height, img.width, 3)) # and reshape into 3channel rgb image
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# standardize image
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arr = (arr - self.mean) / self.std
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yield np.array(arr, dtype=np.float32)
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if __name__ == '__main__':
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std = ImageStandardizer(input_dir='unittest/unittest_input_0')
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print(std.analyze_images())
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for i in std.get_standardized_images():
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print(i)
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