105 lines
3.5 KiB
Python
Executable File
105 lines
3.5 KiB
Python
Executable File
#!/usr/bin/env python2
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#
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# Example to compare the faces in two images.
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# Brandon Amos
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# 2015/09/29
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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 cv2
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import itertools
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import os
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import numpy as np
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np.set_printoptions(precision=2)
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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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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('imgs', type=str, nargs='+', help="Input images.")
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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('--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('--verbose', action='store_true')
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args = parser.parse_args()
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if args.verbose:
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print("Argument parsing and loading libraries took {} seconds.".format(
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time.time() - start))
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start = time.time()
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align = openface.AlignDlib(args.dlibFacePredictor)
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net = openface.TorchNeuralNet(args.networkModel, args.imgDim)
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if args.verbose:
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print("Loading the dlib and OpenFace models took {} seconds.".format(
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time.time() - start))
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def getRep(imgPath):
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if args.verbose:
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print("Processing {}.".format(imgPath))
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bgrImg = cv2.imread(imgPath)
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if bgrImg is None:
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raise Exception("Unable to load image: {}".format(imgPath))
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rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)
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if args.verbose:
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print(" + Original size: {}".format(rgbImg.shape))
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start = time.time()
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bb = align.getLargestFaceBoundingBox(rgbImg)
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if bb is None:
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raise Exception("Unable to find a face: {}".format(imgPath))
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if args.verbose:
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print(" + Face detection took {} seconds.".format(time.time() - start))
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start = time.time()
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alignedFace = align.align_v1(args.imgDim, rgbImg, bb,
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landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)
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if alignedFace is None:
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raise Exception("Unable to align image: {}".format(imgPath))
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if args.verbose:
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print(" + Face alignment took {} seconds.".format(time.time() - start))
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start = time.time()
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rep = net.forward(alignedFace)
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if args.verbose:
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print(" + OpenFace forward pass took {} seconds.".format(time.time() - start))
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print("Representation:")
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print(rep)
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print("-----\n")
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return rep
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for (img1, img2) in itertools.combinations(args.imgs, 2):
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d = getRep(img1) - getRep(img2)
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print("Comparing {} with {}.".format(img1, img2))
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print(
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" + Squared l2 distance between representations: {:0.3f}".format(np.dot(d, d)))
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