87 lines
2.7 KiB
Python
Executable File
87 lines
2.7 KiB
Python
Executable File
#!/usr/bin/env python2
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#
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# Detect outlier faces (not of the same person) in a directory
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# of aligned images.
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# Brandon Amos
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# 2016/02/14
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#
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# Copyright 2015-2016 Carnegie Mellon University
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import time
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start = time.time()
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import argparse
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import os
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import glob
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import numpy as np
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np.set_printoptions(precision=2)
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from sklearn.metrics.pairwise import euclidean_distances
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import cv2
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import openface
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fileDir = os.path.dirname(os.path.realpath(__file__))
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modelDir = os.path.join(fileDir, '..', 'models')
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openfaceModelDir = os.path.join(modelDir, 'openface')
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--networkModel', type=str, help="Path to Torch network model.",
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default=os.path.join(openfaceModelDir, 'nn4.small2.v1.t7'))
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parser.add_argument('--imgDim', type=int,
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help="Default image dimension.", default=96)
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parser.add_argument('--cuda', action='store_true')
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parser.add_argument('--threshold', type=float, default=0.9)
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parser.add_argument('--delete', action='store_true', help='Delete the outliers.')
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parser.add_argument('directory')
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args = parser.parse_args()
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net = openface.TorchNeuralNet(args.networkModel, args.imgDim, cuda=args.cuda)
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reps = []
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paths = sorted(list(glob.glob(os.path.join(args.directory, '*.png'))))
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print("=== {} ===".format(args.directory))
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for imgPath in paths:
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if cv2.imread(imgPath) is None:
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print("Warning: Skipping bad image file: {}".format(imgPath))
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if args.delete:
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# Remove the file if it's not a valid image.
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os.remove(imgPath)
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else:
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reps.append(net.forwardPath(imgPath))
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mean = np.mean(reps, axis=0)
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dists = euclidean_distances(reps, mean)
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outliers = []
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for path, dist in zip(paths, dists):
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dist = dist.take(0)
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if dist > args.threshold:
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outliers.append((path, dist))
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print("Found {} outlier(s) from {} images.".format(len(outliers), len(paths)))
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for path, dist in outliers:
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print(" + {} ({:0.2f})".format(path, dist))
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if args.delete:
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os.remove(path)
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if __name__ == '__main__':
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main()
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