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#!/usr/bin/env python2
# Compute the overall processing latency for an image.
# Brandon Amos
# 2016-01-19
#
# 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 cv2
import os
import numpy as np
np.set_printoptions(precision=2)
import openface
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')
parser = argparse.ArgumentParser()
parser.add_argument('img', type=str, help="Input image.")
parser.add_argument('--dlibFacePredictor', type=str, help="Path to dlib's face predictor.",
default=os.path.join(dlibModelDir, "shape_predictor_68_face_landmarks.dat"))
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('--numIters', type=int,
help="Number of iterations.", default=100)
args = parser.parse_args()
print("Argument parsing and loading libraries took {:0.4f} seconds.".format(
time.time() - start))
start = time.time()
align = openface.AlignDlib(args.dlibFacePredictor)
net = openface.TorchNeuralNet(args.networkModel, args.imgDim)
print("Loading the dlib and OpenFace models took {:0.4f} seconds.".format(
time.time() - start))
def getTimes(rgbImg):
start = time.time()
bb = align.getLargestFaceBoundingBox(rgbImg)
if bb is None:
raise Exception("Unable to find a face.")
detectionTime = time.time() - start
start = time.time()
alignedFace = align.align(args.imgDim, rgbImg, bb,
landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)
alignmentTime = time.time() - start
start = time.time()
net.forward(alignedFace)
repTime = time.time() - start
return (detectionTime, alignmentTime, repTime)
bgrImg = cv2.imread(args.img)
if bgrImg is None:
raise Exception("Unable to load image: {:0.4f}".format(args.img))
rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)
print("Image size: {}".format(rgbImg.shape))
detectionTimes = []
alignmentTimes = []
repTimes = []
totalTimes = []
for i in range(args.numIters):
(dTime, aTime, repTime) = getTimes(rgbImg)
detectionTimes.append(dTime)
alignmentTimes.append(aTime)
repTimes.append(repTime)
totalTimes.append(dTime + aTime + repTime)
print('Number of iterations: {}'.format(args.numIters))
avg = np.mean(detectionTimes)
std = np.std(detectionTimes)
print('Average Detection Time (seconds): {:0.4f} +/- {:0.4f}'.format(avg, std))
avg = np.mean(alignmentTimes)
std = np.std(alignmentTimes)
print('Average Alignment Time (seconds): {:0.4f} +/- {:0.4f}'.format(avg, std))
avg = np.mean(repTimes)
std = np.std(repTimes)
print('Average Neural Net Representation Time (seconds): {:0.4f} +/- {:0.4f}'.format(avg, std))
avg = np.mean(totalTimes)
std = np.std(totalTimes)
print('Average Total Time (seconds): {:0.4f} +/- {:0.4f}'.format(avg, std))