mirror of
https://github.com/Shawn-Shan/fawkes.git
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889fd933e8
Former-commit-id: 268fb7e6825ddfc1165fa7adc7c216f9d61005da [formerly 06376993a831c060c337ec6e7540252f0b2dfe09] Former-commit-id: c4812d40187a76a878e7d215d22ee84811b41896
76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
import argparse
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import glob
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import os
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import pickle
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import random
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import sys
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import numpy as np
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from keras.applications.vgg16 import preprocess_input
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from keras.preprocessing import image
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sys.path.append("../fawkes")
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# from utils import load_extractor
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import keras
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def load_sample_dir(path, sample=10):
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x_ls = []
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image_paths = list(os.listdir(path))
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random.shuffle(image_paths)
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for i, file in enumerate(image_paths):
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if i > sample:
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break
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cur_path = os.path.join(path, file)
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im = image.load_img(cur_path, target_size=(224, 224))
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im = image.img_to_array(im)
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x_ls.append(im)
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raw_x = np.array(x_ls)
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return preprocess_input(raw_x)
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def normalize(x):
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return x / np.linalg.norm(x)
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def main():
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extractor = keras.models.load_model(args.feature_extractor)
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path2emb = {}
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model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
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for path in glob.glob(os.path.join(model_dir, "target_data/*")):
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print(path)
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idx = int(path.split("/")[-1])
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cur_image_paths = glob.glob(os.path.join(path, "*"))
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imgs = np.array([image.img_to_array(image.load_img(p, target_size=(224, 224))) for p in cur_image_paths])
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imgs = preprocess_input(imgs)
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cur_feature = extractor.predict(imgs)
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cur_feature = np.mean(cur_feature, axis=0)
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path2emb[idx] = cur_feature
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model_path = os.path.join(model_dir, "{}_extract.h5".format(args.feature_extractor_name))
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emb_path = os.path.join(model_dir, "{}_emb.p".format(args.feature_extractor_name))
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extractor.save(model_path)
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pickle.dump(path2emb, open(emb_path, "wb"))
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def parse_arguments(argv):
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parser = argparse.ArgumentParser()
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parser.add_argument('--gpu', type=str,
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help='GPU id', default='0')
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parser.add_argument('--candidate-datasets', nargs='+',
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help='path candidate datasets')
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parser.add_argument('--feature-extractor', type=str,
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help="path of the feature extractor used for optimization",
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default="/home/shansixioing/fawkes/feature_extractors/high2_extract.h5")
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parser.add_argument('--feature-extractor-name', type=str,
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help="name of the feature extractor used for optimization",
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default="high2")
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return parser.parse_args(argv)
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if __name__ == '__main__':
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args = parse_arguments(sys.argv[1:])
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main()
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