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47 lines
1.6 KiB
47 lines
1.6 KiB
import numpy as np
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import cv2
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import os
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class SimplePreprocessor:
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def __init__(self, width, height, inter=cv2.INTER_AREA):
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self.width = width
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self.height = height
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self.inter = inter
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def preprocess(self, image):
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return cv2.resize(image, (self.width, self.height), interpolation=self.inter)
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class SimpleDatasetLoader:
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def __init__(self, preprocessors=None):
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self.preprocessors = preprocessors
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if self.preprocessors is None:
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self.preprocessors = []
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def load(self, imagePaths, verbose=-1):
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data = []
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labels = []
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for (i, imagePath) in enumerate(imagePaths):
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image = cv2.imread(imagePath)
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label = imagePath.split(os.path.sep)[-2]
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if self.preprocessors is not None:
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for p in self.preprocessors:
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if(image is None):
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print(i)
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os.remove(imagePaths[i])
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print('file: ')
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print(imagePaths[i])
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print('is removed.')
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continue
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image = p.preprocess(image)
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data.append(image)
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labels.append(label)
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if verbose > 0 and i > 0 and (i + 1 ) %verbose == 0:
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print('[INFO] processed {}/{}'.format( i +1, len(imagePaths)))
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return (np.array(data), np.array(labels))
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if __name__ == '__main__':
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imagePaths = 'D:/python/LearningMaterial/pet_sample/'
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sp = SimplePreprocessor(32, 32)
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sdl = SimpleDatasetLoader(preprocessors=[sp])
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