create-unknown-vectors.py

#!/usr/bin/env python2
#
# Copyright 2015 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 sys
sys.path.append(".")
sys.path.append("/home/bamos/src/dlib-18.15/python_examples")

import argparse
import numpy as np
import os
import random

import cv2

from skimage import io

import openface
from openface.alignment import NaiveDlib
from openface.data import iterImgs

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('imgDir', type=str, help="Input image directory.")
    parser.add_argument('--numImages', type=int, default=1000)
    parser.add_argument('--model', type=str, help="TODO",
                        default="./models/openface/nn4.v1.t7")
    parser.add_argument('--outputFile', type=str,
                        help="Output file, stored in numpy serialized format.",
                        default="./unknown.npy")
    parser.add_argument('--imgDim', type=int, help="Default image size.",
                        default=96)
    args = parser.parse_args()

    align = NaiveDlib("models/dlib/",
                      "shape_predictor_68_face_landmarks.dat")
    openface = openface.TorchWrap(args.model, imgDim=args.imgDim, cuda=False)

    allImgs = list(iterImgs(args.imgDir))
    imgObjs = random.sample(allImgs, args.numImages)

    reps = []
    for imgObj in imgObjs:
        rep = openface.forward(imgObj.path)
        rep = np.array(rep)
        reps.append(rep)

    np.save(args.outputFile, np.row_stack(reps))