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))
help="Default image dimension.",
default=96)
parser.add_argument('--cuda', type=bool, default=False)
parser.add_argument('--unknown',
type=bool,
default=False,
help='Try to predict unknown people')
args = parser.parse_args()
sys.path.append(args.dlibRoot)
import dlib
from openface.alignment import NaiveDlib # Depends on dlib.
align = NaiveDlib(args.dlibFaceMean, args.dlibFacePredictor)
net = openface.TorchWrap(args.networkModel, imgDim=args.imgDim, cuda=args.cuda)
class Face:
def __init__(self, rep, identity):
self.rep = rep
self.identity = identity
def __repr__(self):
return "{{id: {}, rep[0:5]: {}}}".format(str(self.identity),
self.rep[0:5])
class OpenFaceServerProtocol(WebSocketServerProtocol):
def __init__(self):
self.images = {}
from openface.alignment import NaiveDlib # Depends on dlib.
from subprocess import Popen, PIPE
fileDir = os.path.dirname(os.path.realpath(__file__))
modelDir = os.path.join(fileDir, 'models')
dlibModelDir = os.path.join(modelDir, 'dlib')
openfaceModelDir = os.path.join(modelDir, 'openface')
dlibFacePredictor = os.path.join(dlibModelDir,
"shape_predictor_68_face_landmarks.dat")
networkModel = os.path.join(openfaceModelDir, 'nn4.v1.t7')
imgDim = 96
align = NaiveDlib(dlibFacePredictor)
net = openface.TorchWrap(networkModel, imgDim=imgDim)
def test_pipeline():
imgPath = os.path.join(fileDir, 'images', 'examples', 'lennon-1.jpg')
bgrImg = cv2.imread(imgPath)
if bgrImg is None:
raise Exception("Unable to load image: {}".format(imgPath))
rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)
assert np.isclose(norm(rgbImg), 11.1355)
bb = align.getLargestFaceBoundingBox(rgbImg)
assert bb.left() == 341
assert bb.right() == 1006
assert bb.top() == 193
assert bb.bottom() == 859
