def main():
parser = argparse.ArgumentParser()
lfwDefault = os.path.expanduser(
"~/openface/data/lfw/dlib.affine.sz:96.OuterEyesAndNose")
parser.add_argument('--lfwAligned', type=str,
default=lfwDefault,
help='Location of aligned LFW images')
parser.add_argument('--networkModel', type=str, help="Path to Torch network model.",
default=os.path.join(openfaceModelDir, 'nn4.small2.v1.t7'))
parser.add_argument('--largeFont', action='store_true')
parser.add_argument('workDir', type=str,
help='The work directory where intermediate files and results are kept.')
args = parser.parse_args()
# print(args)
if args.largeFont:
font = {'family': 'normal', 'size': 20}
mpl.rc('font', **font)
mkdirP(args.workDir)
print("Getting lfwPpl")
lfwPplCache = os.path.join(args.workDir, 'lfwPpl.pkl')
lfwPpl = cacheToFile(lfwPplCache)(getLfwPplSorted)(args.lfwAligned)
print("Eigenfaces Experiment")
cls = cv2.createEigenFaceRecognizer()
cache = os.path.join(args.workDir, 'eigenFacesExp.pkl')
eigenFacesDf = cacheToFile(cache)(opencvExp)(lfwPpl, cls)
print("Fisherfaces Experiment")
cls = cv2.createFisherFaceRecognizer()
cache = os.path.join(args.workDir, 'fisherFacesExp.pkl')
fishFacesDf = cacheToFile(cache)(opencvExp)(lfwPpl, cls)
print("LBPH Experiment")
cls = cv2.createLBPHFaceRecognizer()
cache = os.path.join(args.workDir, 'lbphExp.pkl')
lbphFacesDf = cacheToFile(cache)(opencvExp)(lfwPpl, cls)
print("OpenFace CPU/SVM Experiment")
net = openface.TorchNeuralNet(args.networkModel, 96, cuda=False)
cls = SVC(kernel='linear', C=1)
cache = os.path.join(args.workDir, 'openface.cpu.svm.pkl')
openfaceCPUsvmDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
print("OpenFace GPU/SVM Experiment")
net = openface.TorchNeuralNet(args.networkModel, 96, cuda=True)
cache = os.path.join(args.workDir, 'openface.gpu.svm.pkl')
openfaceGPUsvmDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
plotAccuracy(args.workDir, args.largeFont,
eigenFacesDf, fishFacesDf, lbphFacesDf,
openfaceCPUsvmDf, openfaceGPUsvmDf)
plotTrainingTime(args.workDir, argrs.largeFont,
eigenFacesDf, fishFacesDf, lbphFacesDf,
openfaceCPUsvmDf, openfaceGPUsvmDf)
plotPredictionTime(args.workDir, args.largeFont,
eigenFacesDf, fishFacesDf, lbphFacesDf,
openfaceCPUsvmDf, openfaceGPUsvmDf)
def download(person, url, bb):
imgName = os.path.basename(url)
rawPersonPath = os.path.join(args.raw, person)
rawImgPath = os.path.join(rawPersonPath, imgName)
alignedPersonPath = os.path.join(args.aligned, person)
alignedImgPath = os.path.join(alignedPersonPath, hashlib.md5(imgName).hexdigest()+".png")
mkdirP(rawPersonPath)
mkdirP(alignedPersonPath)
if not os.path.isfile(rawImgPath):
urlF = urllib2.urlopen(url)
with open(rawImgPath, 'wb') as f:
f.write(urlF.read())
if not os.path.isfile(alignedImgPath):
bgr = cv2.imread(rawImgPath)
if bgr is None:
return
rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
dlibBB = dlib.rectangle(*bb)
outRgb = align.align(96, rgb,
bb=dlibBB,
landmarkIndices=landmarkIndices)
if outRgb is not None:
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(alignedImgPath, outBgr)
def download(person, url, bb):
imgName = os.path.basename(url)
rawPersonPath = os.path.join(args.raw, person)
rawImgPath = os.path.join(rawPersonPath, imgName)
alignedPersonPath = os.path.join(args.aligned, person)
alignedImgPath = os.path.join(alignedPersonPath,
hashlib.md5(imgName).hexdigest() + ".png")
mkdirP(rawPersonPath)
mkdirP(alignedPersonPath)
if not os.path.isfile(rawImgPath):
urlF = urllib2.urlopen(url, timeout=5)
with open(rawImgPath, 'wb') as f:
f.write(urlF.read())
if not os.path.isfile(alignedImgPath):
bgr = cv2.imread(rawImgPath)
if bgr is None:
return
rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
dlibBB = dlib.rectangle(*bb)
outRgb = align.align(96,
rgb,
bb=dlibBB,
landmarkIndices=landmarkIndices)
if outRgb is not None:
outBgr = cv2.cvtColor(outRgb, cv2.COLOR_RGB2BGR)
cv2.imwrite(alignedImgPath, outBgr)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--lfwDlibAligned', type=str,
help='Location of Dlib aligned LFW images')
parser.add_argument('--lfwMtcnnAligned', type=str,
help='Location of MTCNN aligned LFW images')
parser.add_argument('--largeFont', action='store_true')
parser.add_argument('workDir', type=str,
help='The work directory where intermediate files and results are kept.')
args = parser.parse_args()
# print(args)
if args.largeFont:
font = {'family': 'normal', 'size': 20}
mpl.rc('font', **font)
mkdirP(args.workDir)
# get ytf feature pickles
#print("Getting OpenFace YTF features")
#cache = os.path.join(args.workDir, 'facenet.ytf.features.pkl')
#openface_ytf = cacheToFile(cache)(getFacenetFeatures)(facenetModelDir, args.lfwMtcnnAligned)
#print("Getting Facenet YTF features")
#cache = os.path.join(args.workDir, 'openface.ytf.features.pkl')
#facenet_ytf = cacheToFile(cache)(getOpenfaceFeatures)(openfaceModelPath, args.lfwDlibAligned)
ytf_dlib = get_dataset(args.lfwDlibAligned)
ytf_mtcnn = get_dataset(args.lfwMtcnnAligned)
print("OpenFace SVM Experiment")
net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
cls = SVC(C=1, kernel='linear')
cache = os.path.join(args.workDir, 'openface.cpu.svm.pkl')
openfaceCPUsvmDf = cacheToFile(cache)(openfaceExp)(ytf_dlib, net, cls)
print("OpenFace LinearSVC Experiment")
net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
cls = LinearSVC(C=1, multi_class='ovr')
cache = os.path.join(args.workDir, 'openface.cpu.linearsvm.pkl')
openfaceCPUlinearsvmDf = cacheToFile(cache)(openfaceExp)(ytf_dlib, net, cls)
print("Facenet SVM Experiment")
cls = SVC(C=1, kernel='linear')
cache = os.path.join(args.workDir, 'facenet.svm.pkl')
facenetsvmDf = cacheToFile(cache)(facenetExp)(ytf_mtcnn, facenetModelDir, cls)
print("Facenet LinearSVC Experiment")
cls = LinearSVC(C=1, multi_class='ovr')
cache = os.path.join(args.workDir, 'facenet.linearsvm.pkl')
facenetlinearsvmDf = cacheToFile(cache)(facenetExp)(ytf_mtcnn, facenetModelDir, cls)
plotAccuracy(args.workDir, args.largeFont, openfaceCPUsvmDf, openfaceCPUlinearsvmDf, facenetsvmDf, facenetlinearsvmDf)
plotTrainingTime(args.workDir, args.largeFont, openfaceCPUsvmDf, openfaceCPUlinearsvmDf, facenetsvmDf, facenetlinearsvmDf)
plotPredictionTime(args.workDir, args.largeFont, openfaceCPUsvmDf, openfaceCPUlinearsvmDf, facenetsvmDf, facenetlinearsvmDf)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--lfwDlibAligned',
type=str,
help='Location of Dlib aligned LFW images')
parser.add_argument('--lfwMtcnnAligned',
type=str,
help='Location of MTCNN aligned LFW images')
parser.add_argument('--largeFont', action='store_true')
parser.add_argument(
'workDir',
type=str,
help='The work directory where intermediate files and results are kept.'
)
args = parser.parse_args()
# print(args)
if args.largeFont:
font = {'family': 'normal', 'size': 20}
mpl.rc('font', **font)
mkdirP(args.workDir)
print("Getting lfwPpl")
lfwPplCache = os.path.join(args.workDir, 'lfwPpl.pkl')
lfwPpl = cacheToFile(lfwPplCache)(getLfwPplSorted)(args.lfwDlibAligned)
print("OpenFace LinearSVC Experiment")
net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
cls = LinearSVC(C=1, multi_class='ovr')
cache = os.path.join(args.workDir, 'openface.cpu.linearsvm.pkl')
openfaceCPUlinearsvmDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
print("Getting lfwMtcnnPpl")
lfwMtcnnPplCache = os.path.join(args.workDir, 'lfwMtcnnPpl.pkl')
lfwMtcnnPpl = cacheToFile(lfwMtcnnPplCache)(getLfwPplSorted)(
args.lfwMtcnnAligned)
print("Facenet LinearSVC Experiment")
cls = LinearSVC(C=1, multi_class='ovr')
cache = os.path.join(args.workDir, 'facenet.linearsvm.pkl')
facenetlinearsvmDf = cacheToFile(cache)(facenetExp)(lfwMtcnnPpl,
facenetModelDir, cls)
plotAccuracy(args.workDir, args.largeFont, openfaceCPUlinearsvmDf,
facenetlinearsvmDf)
plotTrainingTime(args.workDir, args.largeFont, openfaceCPUlinearsvmDf,
facenetlinearsvmDf)
plotPredictionTime(args.workDir, args.largeFont, openfaceCPUlinearsvmDf,
facenetlinearsvmDf)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--lfwDlibAligned', type=str,
help='Location of Dlib aligned LFW images')
parser.add_argument('--lfwMtcnnAligned', type=str,
help='Location of MTCNN aligned LFW images')
parser.add_argument('--largeFont', action='store_true')
parser.add_argument('workDir', type=str,
help='The work directory where intermediate files and results are kept.')
args = parser.parse_args()
# print(args)
if args.largeFont:
font = {'family': 'normal', 'size': 20}
mpl.rc('font', **font)
mkdirP(args.workDir)
print("Getting lfwPpl")
lfwPplCache = os.path.join(args.workDir, 'lfwPpl.pkl')
lfwPpl = cacheToFile(lfwPplCache)(getLfwPplSorted)(args.lfwDlibAligned)
print("OpenFace LinearSVC Experiment")
net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
cls = LinearSVC(C=1, multi_class='ovr')
cache = os.path.join(args.workDir, 'openface.cpu.linearsvm.pkl')
openfaceCPUlinearsvmDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
print("Getting lfwMtcnnPpl")
lfwMtcnnPplCache = os.path.join(args.workDir, 'lfwMtcnnPpl.pkl')
lfwMtcnnPpl = cacheToFile(lfwMtcnnPplCache)(getLfwPplSorted)(args.lfwMtcnnAligned)
print("Facenet LinearSVC Experiment")
cls = LinearSVC(C=1, multi_class='ovr')
cache = os.path.join(args.workDir, 'facenet.linearsvm.pkl')
facenetlinearsvmDf = cacheToFile(cache)(facenetExp)(lfwMtcnnPpl, facenetModelDir, cls)
plotAccuracy(args.workDir, args.largeFont, openfaceCPUlinearsvmDf, facenetlinearsvmDf)
plotTrainingTime(args.workDir, args.largeFont, openfaceCPUlinearsvmDf, facenetlinearsvmDf)
plotPredictionTime(args.workDir, args.largeFont, openfaceCPUlinearsvmDf, facenetlinearsvmDf)
def peopleIntroducing(ros_srv):
name = ros_srv.name
num_images = ros_srv.num_images
NAME_DIR = os.path.join(DATASET_DIR, 'raw', name)
helper.mkdirP(NAME_DIR)
image_type = '.jpg'
image_labels = os.listdir(NAME_DIR)
add_image_labels = []
i = 1
k = 0
j = num_images
number = []
for label in image_labels:
number.append(int(label.replace(image_type, '')))
number.sort()
n = 1
while j > 0:
if k < len(number):
n = number[k] + 1
if number[k] == i:
k += 1
else:
add_image_labels.append((str(i) + image_type))
j -= 1
i += 1
else:
add_image_labels.append(str(n) + image_type)
j -= 1
n += 1
num_images = ros_srv.num_images
i = 0
while i < num_images:
try:
ros_image = rospy.wait_for_message(image_topic, Image, 1000)
except (ROSException, ROSInterruptException) as e:
print(e)
break
rgb_image = BRIDGE.imgmsg_to_cv2(ros_image, desired_encoding="bgr8")
face_recognition_ros.image_width = ros_image.width
face_recognition_ros.image_height = ros_image.height
face = face_recognition_ros.detectLargestFace(rgb_image)
s_rgb_image = rgb_image.copy()
if face != None:
bb = face_recognition_ros.dlibRectangle2RosBoundingBox(face)
color = (0, 255, 0)
cv2.rectangle(s_rgb_image, (bb.minX, bb.minY),
(bb.minX + bb.width, bb.minY + bb.height), color, 2)
cv2.imshow("Person", s_rgb_image)
regressiveCounter(3)
if face != None:
rospy.logwarn('Picture ' + add_image_labels[i] + ' was saved.')
cv2.imwrite(os.path.join(NAME_DIR, add_image_labels[i]), rgb_image)
i += 1
else:
rospy.logerr("The face was not detected.")
cv2.destroyAllWindows()
classifier_training = FaceClassifierTraining()
classifier_training.classifier_type = ros_srv.classifier_type
classifier_training.classifier_name = 'classifier_' + ros_srv.classifier_type + '_' + name + '.pkl'
return classifierTraining(classifier_training)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--lfwDlibAligned', type=str,
help='Location of Dlib aligned LFW images')
parser.add_argument('--lfwMtcnnAligned', type=str,
help='Location of MTCNN aligned LFW images')
parser.add_argument('--largeFont', action='store_true')
parser.add_argument('workDir', type=str,
help='The work directory where intermediate files and results are kept.')
args = parser.parse_args()
# print(args)
if args.largeFont:
font = {'family': 'normal', 'size': 20}
mpl.rc('font', **font)
mkdirP(args.workDir)
print("Getting lfwPpl")
lfwPplCache = os.path.join(args.workDir, 'lfwPpl.pkl')
lfwPpl = cacheToFile(lfwPplCache)(getLfwPplSorted)(args.lfwDlibAligned)
print("Eigenfaces Experiment")
cls = cv2.createEigenFaceRecognizer()
cache = os.path.join(args.workDir, 'eigenFacesExp.pkl')
eigenFacesDf = cacheToFile(cache)(opencvExp)(lfwPpl, cls)
print("Fisherfaces Experiment")
cls = cv2.createFisherFaceRecognizer()
cache = os.path.join(args.workDir, 'fisherFacesExp.pkl')
fishFacesDf = cacheToFile(cache)(opencvExp)(lfwPpl, cls)
print("LBPH Experiment")
cls = cv2.createLBPHFaceRecognizer()
cache = os.path.join(args.workDir, 'lbphExp.pkl')
lbphFacesDf = cacheToFile(cache)(opencvExp)(lfwPpl, cls)
print("OpenFace SVM Experiment")
net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
cls = SVC(C=1, kernel='linear')
cache = os.path.join(args.workDir, 'openface.cpu.svm.pkl')
openfaceCPUsvmDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
print("OpenFace LinearSVC Experiment")
net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
cls = LinearSVC(C=1, multi_class='ovr')
cache = os.path.join(args.workDir, 'openface.cpu.linearsvm.pkl')
openfaceCPUlinearsvmDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
#print("OpenFace GaussianNB Experiment")
#net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
#cls = GaussianNB()
#cache = os.path.join(args.workDir, 'openface.cpu.gaussiannb.pkl')
#openfaceCPUgaussiannbDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
print("Getting lfwMtcnnPpl")
lfwMtcnnPplCache = os.path.join(args.workDir, 'lfwMtcnnPpl.pkl')
lfwMtcnnPpl = cacheToFile(lfwMtcnnPplCache)(getLfwPplSorted)(args.lfwMtcnnAligned)
print("Facenet LinearSVC Experiment")
cls = LinearSVC(C=1, multi_class='ovr')
cache = os.path.join(args.workDir, 'facenet.linearsvm.pkl')
facenetlinearsvmDf = cacheToFile(cache)(facenetExp)(lfwMtcnnPpl, facenetModelDir, cls)
#print("OpenFace GPU/SVM Experiment")
#net = openface.TorchNeuralNet(args.networkModel, 96, cuda=True)
#cache = os.path.join(args.workDir, 'openface.gpu.svm.pkl')
#openfaceGPUsvmDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
# TODO add Facenet and other models, openbr?
plotAccuracy(args.workDir, args.largeFont,
eigenFacesDf, fishFacesDf, lbphFacesDf,
openfaceCPUsvmDf, openfaceCPUlinearsvmDf, facenetlinearsvmDf)
plotTrainingTime(args.workDir, args.largeFont,
eigenFacesDf, fishFacesDf, lbphFacesDf,
openfaceCPUsvmDf, openfaceCPUlinearsvmDf, facenetlinearsvmDf)
plotPredictionTime(args.workDir, args.largeFont,
eigenFacesDf, fishFacesDf, lbphFacesDf,
openfaceCPUsvmDf, openfaceCPUlinearsvmDf, facenetlinearsvmDf)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--lfwDlibAligned',
type=str,
help='Location of Dlib aligned LFW images')
parser.add_argument('--lfwMtcnnAligned',
type=str,
help='Location of MTCNN aligned LFW images')
parser.add_argument('--largeFont', action='store_true')
parser.add_argument(
'workDir',
type=str,
help='The work directory where intermediate files and results are kept.'
)
args = parser.parse_args()
# print(args)
if args.largeFont:
font = {'family': 'normal', 'size': 20}
mpl.rc('font', **font)
mkdirP(args.workDir)
print("Getting lfwPpl")
lfwPplCache = os.path.join(args.workDir, 'lfwPpl.pkl')
lfwPpl = cacheToFile(lfwPplCache)(getLfwPplSorted)(args.lfwDlibAligned)
print("Eigenfaces Experiment")
cls = cv2.createEigenFaceRecognizer()
cache = os.path.join(args.workDir, 'eigenFacesExp.pkl')
eigenFacesDf = cacheToFile(cache)(opencvExp)(lfwPpl, cls)
print("Fisherfaces Experiment")
cls = cv2.createFisherFaceRecognizer()
cache = os.path.join(args.workDir, 'fisherFacesExp.pkl')
fishFacesDf = cacheToFile(cache)(opencvExp)(lfwPpl, cls)
print("LBPH Experiment")
cls = cv2.createLBPHFaceRecognizer()
cache = os.path.join(args.workDir, 'lbphExp.pkl')
lbphFacesDf = cacheToFile(cache)(opencvExp)(lfwPpl, cls)
print("OpenFace SVM Experiment")
net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
cls = SVC(C=1, kernel='linear')
cache = os.path.join(args.workDir, 'openface.cpu.svm.pkl')
openfaceCPUsvmDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
print("OpenFace LinearSVC Experiment")
net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
cls = LinearSVC(C=1, multi_class='ovr')
cache = os.path.join(args.workDir, 'openface.cpu.linearsvm.pkl')
openfaceCPUlinearsvmDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
#print("OpenFace GaussianNB Experiment")
#net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
#cls = GaussianNB()
#cache = os.path.join(args.workDir, 'openface.cpu.gaussiannb.pkl')
#openfaceCPUgaussiannbDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
print("Getting lfwMtcnnPpl")
lfwMtcnnPplCache = os.path.join(args.workDir, 'lfwMtcnnPpl.pkl')
lfwMtcnnPpl = cacheToFile(lfwMtcnnPplCache)(getLfwPplSorted)(
args.lfwMtcnnAligned)
print("Facenet LinearSVC Experiment")
cls = LinearSVC(C=1, multi_class='ovr')
cache = os.path.join(args.workDir, 'facenet.linearsvm.pkl')
facenetlinearsvmDf = cacheToFile(cache)(facenetExp)(lfwMtcnnPpl,
facenetModelDir, cls)
#print("OpenFace GPU/SVM Experiment")
#net = openface.TorchNeuralNet(args.networkModel, 96, cuda=True)
#cache = os.path.join(args.workDir, 'openface.gpu.svm.pkl')
#openfaceGPUsvmDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
# TODO add Facenet and other models, openbr?
plotAccuracy(args.workDir, args.largeFont, eigenFacesDf, fishFacesDf,
lbphFacesDf, openfaceCPUsvmDf, openfaceCPUlinearsvmDf,
facenetlinearsvmDf)
plotTrainingTime(args.workDir, args.largeFont, eigenFacesDf, fishFacesDf,
lbphFacesDf, openfaceCPUsvmDf, openfaceCPUlinearsvmDf,
facenetlinearsvmDf)
plotPredictionTime(args.workDir, args.largeFont, eigenFacesDf, fishFacesDf,
lbphFacesDf, openfaceCPUsvmDf, openfaceCPUlinearsvmDf,
facenetlinearsvmDf)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--lfwDlibAligned', type=str,
help='Location of Dlib aligned LFW images')
parser.add_argument('--lfwMtcnnAligned', type=str,
help='Location of MTCNN aligned LFW images')
parser.add_argument('--largeFont', action='store_true')
parser.add_argument('workDir', type=str,
help='The work directory where intermediate files and results are kept.')
args = parser.parse_args()
# print(args)
if args.largeFont:
font = {'family': 'normal', 'size': 20}
mpl.rc('font', **font)
mkdirP(args.workDir)
eigenFacesDf = None
fishFacesDf = None
lbphFacesDf = None
openfaceCPUsvmDf = None
openfaceCPUlinearsvmDf = None
facenetlinearsvmDf = None
print("Getting lfwPpl")
lfwPplCache = os.path.join(args.workDir, 'lfwPpl.pkl')
lfwPpl = cacheToFile(lfwPplCache)(getLfwPplSorted)(args.lfwDlibAligned)
print("Getting lfwMtcnnPpl")
lfwMtcnnPplCache = os.path.join(args.workDir, 'lfwMtcnnPpl.pkl')
lfwMtcnnPpl = cacheToFile(lfwMtcnnPplCache)(getLfwPplSorted)(args.lfwMtcnnAligned)
def eigenfaces():
print("Eigenfaces Experiment")
global eigenFacesDf
cls = cv2.createEigenFaceRecognizer()
cache = os.path.join(args.workDir, 'eigenFacesExp.pkl')
eigenFacesDf = cacheToFile(cache)(opencvExp)(lfwPpl, cls)
def fisherfaces():
print("Fisherfaces Experiment")
global fishFacesDf
cls = cv2.createFisherFaceRecognizer()
cache = os.path.join(args.workDir, 'fisherFacesExp.pkl')
fishFacesDf = cacheToFile(cache)(opencvExp)(lfwPpl, cls)
def lbph():
print("LBPH Experiment")
global lbphFacesDf
cls = cv2.createLBPHFaceRecognizer()
cache = os.path.join(args.workDir, 'lbphExp.pkl')
lbphFacesDf = cacheToFile(cache)(opencvExp)(lfwPpl, cls)
def openfacesvm():
print("OpenFace SVM Experiment")
global openfaceCPUsvmDf
net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
cls = SVC(C=1, kernel='linear')
cache = os.path.join(args.workDir, 'openface.cpu.svm.pkl')
openfaceCPUsvmDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
def openfacelinsvm():
print("OpenFaceLinearSVC Experiment")
global openfaceCPUlinearsvmDf
net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
cls = LinearSVC(C=1, multi_class='ovr')
cache = os.path.join(args.workDir, 'openface.cpu.linearsvm.pkl')
openfaceCPUlinearsvmDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
def facenetlinsvm():
print("Facenet LinearSVC Experiment")
global facenetlinearsvmDf
cls = LinearSVC(C=1, multi_class='ovr')
cache = os.path.join(args.workDir, 'facenet.linearsvm.pkl')
facenetlinearsvmDf = cacheToFile(cache)(facenetExp)(lfwMtcnnPpl, facenetModelDir, cls)
threads = []
t1 = Thread(target=eigenfaces, args=())
t2 = Thread(target=fisherfaces, args=())
t3 = Thread(target=lbph, args=())
t4 = Thread(target=openfacesvm, args=())
t5 = Thread(target=openfacelinsvm, args=())
t6 = Thread(target=facenetlinsvm, args=())
threads.append(t1)
threads.append(t2)
threads.append(t3)
threads.append(t4)
threads.append(t5)
threads.append(t6)
# Start all threads
for x in threads:
x.start()
# Wait for all of them to finish
for x in threads:
x.join()
plotAccuracy(args.workDir, args.largeFont,
eigenFacesDf, fishFacesDf, lbphFacesDf,
openfaceCPUsvmDf, openfaceCPUlinearsvmDf, facenetlinearsvmDf)
plotTrainingTime(args.workDir, args.largeFont,
eigenFacesDf, fishFacesDf, lbphFacesDf,
openfaceCPUsvmDf, openfaceCPUlinearsvmDf, facenetlinearsvmDf)
plotPredictionTime(args.workDir, args.largeFont,
eigenFacesDf, fishFacesDf, lbphFacesDf,
openfaceCPUsvmDf, openfaceCPUlinearsvmDf, facenetlinearsvmDf)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--lfwDlibAligned', type=str,
help='Location of Dlib aligned LFW images')
parser.add_argument('--lfwMtcnnAligned', type=str,
help='Location of MTCNN aligned LFW images')
parser.add_argument('--largeFont', action='store_true')
parser.add_argument('workDir', type=str,
help='The work directory where intermediate files and results are kept.')
args = parser.parse_args()
# print(args)
if args.largeFont:
font = {'family': 'normal', 'size': 20}
mpl.rc('font', **font)
mkdirP(args.workDir)
openfaceCPUsvmDf = None
openfaceCPUlinearsvmDf = None
openfacegaussiannbDf = None
openfacedectreeDf = None
openfacelogregDf = None
facenetsvmDf = None
facenetlinearsvmDf = None
facenetgaussiannbDf = None
facenetdectreeDf = None
facenetlogregDf = None
print("Getting lfwPpl")
lfwPplCache = os.path.join(args.workDir, 'lfwPpl.pkl')
lfwPpl = cacheToFile(lfwPplCache)(getLfwPplSorted)(args.lfwDlibAligned)
print("Getting lfwMtcnnPpl")
lfwMtcnnPplCache = os.path.join(args.workDir, 'lfwMtcnnPpl.pkl')
lfwMtcnnPpl = cacheToFile(lfwMtcnnPplCache)(getLfwPplSorted)(args.lfwMtcnnAligned)
def openfacesvm():
print("OpenFace SVM Experiment")
global openfaceCPUsvmDf
net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
cls = SVC(C=1, kernel='linear')
cache = os.path.join(args.workDir, 'openface.cpu.svm.pkl')
openfaceCPUsvmDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
def openfacelinsvm():
print("OpenFace LinearSVC Experiment")
global openfaceCPUlinearsvmDf
net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
cls = LinearSVC(C=1, multi_class='ovr')
cache = os.path.join(args.workDir, 'openface.cpu.linearsvm.pkl')
openfaceCPUlinearsvmDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
def openfacegaussiannb():
print("OpenFace GaussianNB Experiment")
global openfacegaussiannbDf
net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
cls = GaussianNB()
cache = os.path.join(args.workDir, 'openface.cpu.gaussiannb.pkl')
openfacegaussiannbDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
def openfacedectree():
print("OpenFace DecisionTree Experiment")
global openfacedectreeDf
net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
cls = DecisionTreeClassifier(max_depth=20)
cache = os.path.join(args.workDir, 'openface.cpu.dectree.pkl')
openfacedectreeDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
def openfacelogreg():
print("OpenFace DecisionTree Experiment")
net = openface.TorchNeuralNet(openfaceModelPath, 96, cuda=False)
cls = LogisticRegression(C=1, multi_class ='ovr')
cache = os.path.join(args.workDir, 'openface.cpu.logreg.pkl')
openfacelogregDf = cacheToFile(cache)(openfaceExp)(lfwPpl, net, cls)
def facenetsvm():
print("Facenet SVM Experiment")
global facenetsvmDf
cls = SVC(C=1, kernel='linear')
cache = os.path.join(args.workDir, 'facenet.svm.pkl')
facenetsvmDf = cacheToFile(cache)(facenetExp)(lfwMtcnnPpl, facenetModelDir, cls)
def facenetlinsvm():
print("Facenet LinearSVC Experiment")
global facenetlinearsvmDf
cls = LinearSVC(C=1, multi_class='ovr')
cache = os.path.join(args.workDir, 'facenet.linearsvm.pkl')
facenetlinearsvmDf = cacheToFile(cache)(facenetExp)(lfwMtcnnPpl, facenetModelDir, cls)
def facenetgaussiannb():
print("Facenet GaussianNB Experiment")
global facenetgaussiannbDf
cls = GaussianNB()
cache = os.path.join(args.workDir, 'facenet.gaussiannb.pkl')
facenetgaussiannbDf = cacheToFile(cache)(facenetExp)(lfwMtcnnPpl, facenetModelDir, cls)
def facenetdectree():
print("Facenet DecisionTree Experiment")
global facenetdectreeDf
cls = DecisionTreeClassifier(max_depth=20)
cache = os.path.join(args.workDir, 'facenet.dectree.pkl')
facenetdectreeDf = cacheToFile(cache)(facenetExp)(lfwMtcnnPpl, facenetModelDir, cls)
def facenetlogreg():
print("Facenet LogisticRegression Experiment")
cls = LogisticRegression(C=1, multi_class ='ovr')
cache = os.path.join(args.workDir, 'facenet.logreg.pkl')
facenetlogregDf = cacheToFile(cache)(facenetExp)(lfwMtcnnPpl, facenetModelDir, cls)
threads = []
t1 = Thread(target=openfacesvm, args=())
t2 = Thread(target=openfacelinsvm, args=())
t3 = Thread(target=openfacegaussiannb, args=())
t4 = Thread(target=openfacedectree, args=())
t5 = Thread(target=openfacelogreg, args=())
t6 = Thread(target=facenetsvm, args=())
t7 = Thread(target=facenetlinsvm, args=())
t8 = Thread(target=facenetgaussiannb, args=())
t9 = Thread(target=facenetdectree, args=())
t10 = Thread(target=facenetlogreg, args=())
threads.append(t1)
threads.append(t2)
threads.append(t3)
threads.append(t4)
threads.append(t5)
threads.append(t6)
threads.append(t7)
threads.append(t8)
threads.append(t9)
threads.append(t10)
# Start all threads
for x in threads:
x.start()
# Wait for all of them to finish
for x in threads:
x.join()
plotAccuracy(args.workDir, args.largeFont,
openfaceCPUsvmDf, openfaceCPUlinearsvmDf, openfacegaussiannbDf,
openfacedectreeDf, openfacelogregDf, facenetsvmDf, facenetlinearsvmDf, facenetgaussiannbDf, facenetdectreeDf, facenetlogregDf)
plotTrainingTime(args.workDir, args.largeFont,
openfaceCPUsvmDf, openfaceCPUlinearsvmDf, openfacegaussiannbDf,
openfacedectreeDf, openfacelogregDf, facenetsvmDf, facenetlinearsvmDf, facenetgaussiannbDf, facenetdectreeDf, facenetlogregDf)
plotPredictionTime(args.workDir, args.largeFont,
openfaceCPUsvmDf, openfaceCPUlinearsvmDf, openfacegaussiannbDf,
openfacedectreeDf, openfacelogregDf, facenetsvmDf, facenetlinearsvmDf, facenetgaussiannbDf, facenetdectreeDf, facenetlogregDf)
