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fawkes/fawkes_dev/prepare_feature_extractor.py
Shawn-Shan 889fd933e8 0.01
Former-commit-id: 268fb7e6825ddfc1165fa7adc7c216f9d61005da [formerly 06376993a831c060c337ec6e7540252f0b2dfe09]
Former-commit-id: c4812d40187a76a878e7d215d22ee84811b41896
2020-07-01 21:16:03 -05:00

76 lines
2.4 KiB
Python

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