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