def get_detect_model():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
detect_model = MobileFaceNet(512).to(
device) # embeding size is 512 (feature vector)
detect_model.load_state_dict(
torch.load('Weights/MobileFace_Net.pt',
map_location=lambda storage, loc: storage))
print('MobileFaceNet face detection model generated')
detect_model.eval()
return detect_model
from torchvision import transforms as trans
import torch
import argparse
import sys
import os
import io
import json
from torchvision import models
from PIL import Image
from flask import Flask, jsonify, request
from flask_cors import CORS
device_0 = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
detect_model = MobileFaceNet(512).to(device_0)
detect_model.load_state_dict(
torch.load('Weights/MobileFace_Net',
map_location=lambda storage, loc: storage))
detect_model.eval()
target, name = load_facebank(path='facebank')
parser = argparse.ArgumentParser()
parser.add_argument('--miniface', default=10, type=int)
parser.add_argument('--scale', default=2, type=int)
parser.add_argument('--update', default=False, type=bool)
args = parser.parse_args()
if args.update:
targets, names = prepare_facebank(detect_model, path='facebank')
print('facebank updated')
else:
targets, names = load_facebank(path='facebank')
print('facebank loaded')
class VideoCamera(object):
def __init__(self,username='******',threshold=80,update_val=True,tta=False,c=True,scale=0.3,min_face=20,embedding=512,bank_path='media/account/facebank',model_path='Weights/model_final.pth'):
self.video = cv2.VideoCapture(0)
(self.grabbed, self.frame) = self.video.read()
self.thread=threading.Thread(target=self.update, args=())
self.flag=True
# recognition parameter
self.threshold=threshold
self.tta=tta
self.score=c
self.scale=scale
self.min_face=min_face
self.embedding=embedding
self.facebank='media/account/{}/facebank'.format(username)
self.model_path=model_path
self.up=update_val
self.device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print('using ',self.device)
self.face_detector=mtcnn_custom(device=self.device,p_model_path='MTCNN/weights/pnet_Weights',r_model_path='MTCNN/weights/rnet_Weights',o_model_path='MTCNN/weights/onet_Weights')
print('face detector created...')
#prepare pretrained model
self.detect_model = MobileFaceNet(self.embedding).to(self.device) # embeding size is 512 (feature vector)
# self.check_point=torch.load('Weights/model_final_t.pth',map_location=lambda storage, loc: storage)
# self.detect_model.load_state_dict(self.check_point['model_state_dict'])
self.detect_model.load_state_dict(torch.load('Weights/model_final.pth',map_location=lambda storage, loc: storage))
print('MobileFaceNet face detection model generated')
self.detect_model.eval()
#face bank update
if self.up:
self.targets, self.names = prepare_facebank(self.detect_model, path=self.facebank, tta=self.tta)
print('facebank updated')
else:
self.targets, self.names = load_facebank(path=self.facebank)
print('facebank loaded')
# targets: number of candidate x 512
def __del__(self):
self.video.release()
def update_facebank(self,img_list):
for img_path in img_list:
img=cv2.imread(img_path)
bboxes,landmarks=self.face_detector.detect_all_net(image=img,mini_face=self.min_face)
faces= Face_alignment(img,default_square=True,landmarks=landmarks)
try:
os.remove(img_path)
except:
print('fail to remove')
cv2.imwrite(img_path,faces[0])
self.targets,self.names=prepare_facebank(self.detect_model, path=self.facebank, tta=self.tta)
print('new facebank uploaded !!...')
def get_frame(self): #여기다가 face detection, recognition기능을 넣으면 문제없음.
frame = self.frame #thread가 update하는 이미지를 가져 옴.
if frame is not None and self.flag is True:
try:
start_time=time.time()
input=resize_image(frame,self.scale)# input size를 줄여줌으로 speed up 가능
#print('get bboxes')
# bboxes, landmarks = create_mtcnn_net(input, self.min_face, self.device, p_model_path='MTCNN/weights/pnet_Weights',
# r_model_path='MTCNN/weights/rnet_Weights',
# o_model_path='MTCNN/weights/onet_Weights')
#print('sucess bbox')
bboxes,landmarks=self.face_detector.detect_all_net(image=input,mini_face=self.min_face)
if bboxes != []:
bboxes=bboxes/self.scale
landmarks=landmarks/self.scale
faces= Face_alignment(frame,default_square=True,landmarks=landmarks)
embs=[]
test_transform = trans.Compose([
trans.ToTensor(),
trans.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
for img in faces:
if self.tta:
mirror = cv2.flip(img,1)
emb = self.detect_model(test_transform(img).to(self.device).unsqueeze(0))
emb_mirror = self.detect_model(test_transform(mirror).to(self.device).unsqueeze(0))
embs.append(l2_norm(emb + emb_mirror))
else:
embs.append(self.detect_model(test_transform(img).to(self.device).unsqueeze(0)))
source_embs=torch.cat(embs)
diff=source_embs.unsqueeze(-1) - self.targets.transpose(1, 0).unsqueeze(0) # i.e
dist = torch.sum(torch.pow(diff, 2), dim=1) # number of detected faces x numer of target faces
minimum, min_idx = torch.min(dist, dim=1) # min and idx for each row
min_idx[minimum > ((self.threshold-156)/(-80))] = -1 # if no match, set idx to -1
score = minimum
results = min_idx
score_100 = torch.clamp(score*-80+156,0,100)
FPS=1.0/(time.time()-start_time)
cv2.putText(frame,'FPS : {:.1f}'.format(FPS),(10,15),cv2.FONT_HERSHEY_DUPLEX,0.75,(255,0,255))
for i,b in enumerate(bboxes):
b=b.astype('uint32')
cv2.rectangle(frame,(b[0],b[1]),(b[2],b[3]),(0,255,0),1)
try:
if self.names[results[i]+1]=='Unknown': #mosic func
#print('detect unknwon')
face_region=frame[b[1]:b[3],b[0]:b[2]]
face_region=cv2.blur(face_region,(30,30))
frame[b[1]:b[3],b[0]:b[2]]=face_region
except:
pass
# development version
# if self.score:
# cv2.putText(frame,self.names[results[i]+1]+' score:{:.0f}'.format(score_100[i]),(int(b[0]),int(b[1]-25)),cv2.FONT_ITALIC,1,(255,255,0))
# else:
# cv2.putText(frame,self.names[results[i]+1],(int(b[0]),int(b[1]-25)),cv2.FONT_ITALIC,1,(255,255,0))
except:
pass
_, jpeg = cv2.imencode('.jpg',frame)
return jpeg.tobytes()
def update(self): # thread를 사용하여 지속적으로 cap.read반복
while True:
(self.grabbed, self.frame) = self.video.read()
if self.grabbed==False or self.flag==False:
break
def gen(self):
while True:
if self.flag==False:
break
frame = self.get_frame()
if frame is None:
continue
yield(b'--frame\r\n'b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')
type=float,
default=0,
help='drop path probability')
args = parser.parse_args()
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
from load_ms1m_lfw_agedb_cfp_dataset import *
dataloaders, dataset_sizes, dataset = load_glint_data_train_from_lmdb(
args.batch_size, dataset=args.dataset)
model = MobileFaceNet(args.feature_dim).to(
device) # embeding size is 512 (feature vector)
from face_model import ArcMarginProduct
margin = ArcMarginProduct(512, 180855).to(device)
model.load_state_dict(
torch.load(
'/data/face_recognition/Mobilefacenet/arc_center_focal/Iter_555000_model.ckpt'
)['net_state_dict'])
# margin.load_state_dict(torch.load('/data/face_recognition/Mobilefacenet/label_smooth_ckpt/Iter_290000_margin.ckpt')['net_state_dict'])
criterion_focal = FocalLoss().to(device) #FocalLoss()
criterion_center = CenterLoss(num_classes=180855,
feat_dim=512,
use_gpu=True).to(device)
optimizer_centloss = torch.optim.SGD(criterion_center.parameters(), lr=0.5)
# ！！！！！！！！！！！！！！只训练margin
optimizer_ft = optim.SGD([{
'params': margin.parameters(),
'weight_decay': 5e-4
}],
lr=0.001,
# model = Network(48, 85742, 20,False, PC_DARTS_image).to(device)
# print(model)
# model.load_state_dict(checkpoint['net_state_dict'])
# print(checkpoint)
print('MobileFaceNet face detection model loaded')
# margin = Arcface(embedding_size=args.feature_dim, classnum=int(dataset['train'].class_nums), s=32., m=0.5).to(device)
from face_model import ArcMarginProduct
margin = ArcMarginProduct(512, 85742).to(device)
# margin = Softmax(args.feature_dim,85742).to(device)
# model.load_state_dict(checkpoint['net_state_dict'])
checkpoint = torch.load(
'/data/face_recognition/Mobilefacenet/saving_Faces_emore_ckpt_arcface_64_randombrightness/Iter_125000_model.ckpt'
)
# /data/face_recognition/Mobilefacenet/saving_Faces_emore_ckpt/Iter_410000_margin.ckpt
model.load_state_dict(checkpoint['net_state_dict'])
margin.load_state_dict(
torch.load(
'/data/face_recognition/Mobilefacenet/saving_Faces_emore_ckpt_arcface_64_randombrightness/Iter_125000_margin.ckpt'
)['net_state_dict'])
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
margin = torch.nn.DataParallel(margin)
checkpoint = torch.load(
'./saving_Faces_emore_ckpt/Iter_410000_model.ckpt')
# /data/face_recognition/Mobilefacenet/saving_Faces_emore_ckpt/Iter_410000_margin.ckpt
model.load_state_dict(checkpoint['net_state_dict'])
margin.load_state_dict(
torch.load('./saving_Faces_emore_ckpt/Iter_410000_margin.ckpt')
['net_state_dict'])
parser.add_argument('--epoch', type=int, default=20, help='number of epoches for training')
parser.add_argument('--method', type=str, default='l2_distance',
help='methold to evaluate feature similarity, l2_distance, cos_distance')
parser.add_argument('--flip', type=str, default=True, help='if flip the image with time augmentation')
parser.add_argument('--drop_path_prob', type=float, default=0, help='drop path probability')
args = parser.parse_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
from load_ms1m_lfw_agedb_cfp_dataset import load_data_train_fix_from_lmdb
dataloaders , dataset_sizes, dataset = load_data_train_fix_from_lmdb(args.batch_size, dataset = args.dataset)
model = MobileFaceNet(args.feature_dim).to(device) # embeding size is 512 (feature vector)
from face_model import ArcMarginProduct
margin = ArcMarginProduct(512,85742).to(device)
model.load_state_dict(torch.load('/data/face_recognition/Mobilefacenet/saving_Faces_emore_ckpt/Iter_060000_model.ckpt')['net_state_dict'])
margin.load_state_dict(torch.load('/data/face_recognition/Mobilefacenet/saving_Faces_emore_ckpt/Iter_060000_margin.ckpt')['net_state_dict'])
if torch.cuda.device_count()>1:
model = torch.nn.DataParallel(model)
margin = torch.nn.DataParallel(margin)
checkpoint=torch.load('./saving_Faces_emore_ckpt/Iter_410000_model.ckpt')
# /data/face_recognition/Mobilefacenet/saving_Faces_emore_ckpt/Iter_410000_margin.ckpt
model.load_state_dict(checkpoint['net_state_dict'])
margin.load_state_dict(torch.load('./saving_Faces_emore_ckpt/Iter_410000_margin.ckpt')['net_state_dict'])
# else:
# model_state = torch.load('./saving_Faces_emore_ckpt/Iter_831000_model.ckpt')['net_state_dict']
# margin_state = torch.load('./saving_Faces_emore_ckpt/Iter_831000_margin.ckpt')['net_state_dict']
# from collections import OrderedDict
# new_model_state_dict = OrderedDict()
# for k, v in model_state.items():
'CFP_FP': len(dataset_CFP_FP), 'AgeDB30': len(dataset_AgeDB30)}
print('training and validation data loaded')
return dataloaders, dataset_sizes, dataset
if __name__ == '__main__':
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
from face_model import MobileFaceNet, l2_norm
dataloaders , dataset_sizes, dataset = load_test_data(50)
model = MobileFaceNet(512).to(device)
state_dict = torch.load('arc_center_focal\Iter_555000_model.ckpt')['net_state_dict']
# from collections import OrderedDict
# new_state_dict = OrderedDict()
# for k, v in state_dict.items():
# name = k[7:] # remove `module.`
# new_state_dict[name] = v
# load params
model.load_state_dict(state_dict)
# print(state_dict)
# model.drop_path_prob = 0
model.eval()
for phase in ['LFW', 'CFP_FP', 'AgeDB30','privacy']:
featureLs, featureRs = getFeature(model, dataloaders[phase], device, flip = True)
# ACCs, threshold = evaluation_10_fold(featureLs, featureRs, dataset[phase], method = 'l2_distance')
ACCs, threshold = evaluation_10_fold(featureLs, featureRs, dataset[phase], method = 'cos_distance')
print('Epoch {}/{}，{} average acc:{:.4f} average threshold:{:.4f}'.format(1, 1, phase, np.mean(ACCs) * 100, np.mean(threshold)))
type=str,
default=True,
help='if flip the image with time augmentation')
parser.add_argument('--drop_path_prob',
type=float,
default=0,
help='drop path probability')
args = parser.parse_args()
cpu_device = torch.device('cpu')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
dataloaders, dataset_sizes, dataset = load_glint_data_train_from_lmdb(
args.batch_size, dataset=args.dataset)
model = MobileFaceNet(args.feature_dim)
model.load_state_dict(
torch.load(
'/data/face_recognition/Mobilefacenet/acr_center_focal_0.03/Iter_455000_model.ckpt',
map_location='cpu')['net_state_dict'])
model = model.to(device)
margin = ArcMarginProduct(512, 180855).to(cpu_device)
margin.load_state_dict(
torch.load(
'/data/face_recognition/Mobilefacenet/acr_center_focal_0.03/Iter_glint_margin.ckpt',
map_location='cpu')['net_state_dict'])
margin = margin.to(device)
criterion_focal = FocalLoss().to(device) #FocalLoss()
criterion_center = CenterLoss(num_classes=180855,
feat_dim=512,
use_gpu=True).to(device)
if torch.cuda.device_count() > 1:
default=20,
type=int)
parser.add_argument('-e',
'--embedding',
type=int,
default=512,
help='embedding size')
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#prepare pretrained model
detect_model = MobileFaceNet(args.embedding).to(
device) # embeding size is 512 (feature vector)
check_point = torch.load('Weights/model_final.pth',
map_location=lambda storage, loc: storage)
detect_model.load_state_dict(check_point['model_state_dict'])
print('MobileFaceNet face detection model generated')
detect_model.eval()
#face bank update
if args.update:
targets, names = prepare_facebank(detect_model,
path='facebank',
tta=args.tta)
print('facebank updated')
else:
targets, names = load_facebank(path='facebank')
print('facebank loaded')
# targets: number of candidate x 512
cap = cv2.VideoCapture(0)