81 lines
2.5 KiB
Python
Executable File
81 lines
2.5 KiB
Python
Executable File
#!/usr/bin/env python2
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#
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# Copyright 2015 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 sys
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sys.path.append(".")
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import argparse
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import numpy as np
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import os
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import random
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import cv2
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import openface
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from openface.alignment import NaiveDlib
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from openface.data import iterImgs
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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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dlibModelDir = os.path.join(modelDir, 'dlib')
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openfaceModelDir = os.path.join(modelDir, 'openface')
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parser = argparse.ArgumentParser()
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parser.add_argument('imgDir', type=str, help="Input image directory.")
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parser.add_argument('--numImages', type=int, default=1000)
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parser.add_argument('--model', type=str, help="TODO",
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default="./models/openface/nn4.v1.t7")
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parser.add_argument('--dlibFacePredictor', type=str, help="Path to dlib's face predictor.",
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default=os.path.join(dlibModelDir, "shape_predictor_68_face_landmarks.dat"))
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parser.add_argument('--outputFile', type=str,
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help="Output file, stored in numpy serialized format.",
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default="./unknown.npy")
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parser.add_argument('--imgDim', type=int, help="Default image size.",
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default=96)
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args = parser.parse_args()
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align = NaiveDlib(args.dlibFacePredictor)
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net = openface.TorchWrap(args.model, imgDim=args.imgDim, cuda=False)
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def getRep(imgPath):
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bgrImg = cv2.imread(imgPath)
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if bgrImg is None:
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return None
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rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)
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bb = align.getLargestFaceBoundingBox(rgbImg)
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if bb is None:
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return None
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alignedFace = align.alignImg("affine", args.imgDim, rgbImg, bb)
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if alignedFace is None:
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return None
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rep = net.forwardImage(alignedFace)
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return rep
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if __name__ == '__main__':
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allImgs = list(iterImgs(args.imgDir))
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imgObjs = random.sample(allImgs, args.numImages)
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reps = []
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for imgObj in imgObjs:
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rep = getRep(imgObj.path)
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reps.append(rep)
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np.save(args.outputFile, np.row_stack(reps))
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