help="whether perform update the facebank",
action="store_true")
parser.add_argument("-tta",
"--tta",
help="whether testCode time augmentation",
action="store_true")
parser.add_argument("-c",
"--score",
default=False,
help="whether show the confidence score",
action="store_true")
args = parser.parse_args()
conf = get_config(False)
mtcnn = MTCNN()
print('mtcnn loaded')
learner = face_learner(conf, True)
learner.threshold = args.threshold
if conf.device.type == 'cpu':
learner.load_state(conf, 'ir_se50.pth', True, True)
else:
learner.load_state(conf, 'ir_se50.pth', True, True)
learner.model.eval()
print('learner loaded')
if args.update:
targets, names = prepare_facebank(conf,
learner.model,
mtcnn,
def predict():
# image = Image.open(img_path)
# img=img_path#注意修改
parser = argparse.ArgumentParser(description='for face verification')
parser.add_argument("-s", "--save", help="whether save", action="store_true")
parser.add_argument('-th', '--threshold', help='threshold to decide identical faces', default=1.54,
type=float)
parser.add_argument("-u", "--update", default=True, help="whether perform update the facebank",
action="store_true")
parser.add_argument("-tta", "--tta", default=False, help="whether testCode time augmentation",
action="store_true")
parser.add_argument("-c", "--score", default=True, help="whether show the confidence score",
action="store_true")
parser.add_argument('--img_path', '-p', default='1.jpg', type=str,
help='input the name of the recording person')
args = parser.parse_args()
mtcnn = MTCNN()
conf = get_config(False)
learner = face_learner(conf, True)
learner.threshold = args.threshold
# image = Image.open(args.img_path)
if conf.device.type == 'cpu':
learner.load_state(conf, 'ir_se50.pth', True, True)
else:
learner.load_state(conf, 'ir_se50.pth', True, True)
learner.model.eval()
print('learner loaded')
if args.update:
targets, names = prepare_facebank(conf, learner.model, mtcnn, tta=args.tta)
print('facebank updated')
else:
targets, names = load_facebank(conf)
print('facebank loaded')
# inital camera
cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
cap.set(3, 1280)
cap.set(4, 720)
if args.save:
video_writer = cv2.VideoWriter('D:/code/Python/InsightFace_Pytorch-masterxin/InsightFace_Pytorch-master'
'/work_space/save/recording.avi', cv2.VideoWriter_fourcc(*'XVID'), 6,
(1280,
720))
# frame rate 6 due to my laptop is quite slow...
while cap.isOpened():
# 获取一帧一帧的三维图像
isSuccess, frame = cap.read()
if isSuccess:
try:
image = Image.fromarray(frame)
# 利用mtcnn网络，对齐
bboxes, faces = mtcnn.align_multi(image, conf.face_limit, conf.min_face_size)
bboxes = bboxes[:, :-1] # shape:[10,4],only keep 10 highest possibiity faces
bboxes = bboxes.astype(int)
bboxes = bboxes + [-1, -1, 1, 1] # personal choice
results, score = learner.infer(conf, faces, targets, args.tta)
print(results, score)
for idx, bbox in enumerate(bboxes):
if args.score:
frame = draw_box_name(bbox, names[results[idx] + 1] + '_{:.2f}'.format(
score[idx]), frame)
print(names[results[idx] + 1])
res = names[results[idx] + 1]
else:
frame = draw_box_name(bbox, names[results[idx] + 1], frame)
print(names[results[idx] + 1])
res = names[results[idx] + 1]
except:
print('detect')
res = "unknow"
cv2.imshow('face Capture', frame)
"""打开数据库"""
db = cx_Oracle.connect('fgos', 'bjrdfgos', '172.20.19.156:1521/fgosora')
# 使用cursor()方法获取操作游标
cursor = db.cursor()
# sql = "SELECT * FROM names where name='%res'"
# 使用execute方法执行SQL语句
cursor.execute("SELECT * FROM v$TD_VIP_TEST where VIPU_VIP_NAME='%s'" % (res))
# 使用 fetchone() 方法获取一条数据
dat = cursor.fetchone()
print(dat)
# 关闭数据库连接
db.close()
def predict(image):
# image = Image.open(img_path)
# img=img_path#注意修改
parser = argparse.ArgumentParser(description='for face verification')
parser.add_argument("-s",
"--save",
help="whether save",
action="store_true")
parser.add_argument('-th',
'--threshold',
help='threshold to decide identical faces',
default=1.54,
type=float)
parser.add_argument("-u",
"--update",
default=True,
help="whether perform update the facebank",
action="store_true")
parser.add_argument("-tta",
"--tta",
default=False,
help="whether testCode time augmentation",
action="store_true")
parser.add_argument("-c",
"--score",
default=True,
help="whether show the confidence score",
action="store_true")
parser.add_argument('--img_path',
'-p',
default='1.jpg',
type=str,
help='input the name of the recording person')
args = parser.parse_args()
mtcnn = MTCNN()
conf = get_config(False)
learner = face_learner(conf, True)
learner.threshold = args.threshold
# image = Image.open(args.img_path)
if conf.device.type == 'cpu':
learner.load_state(conf, 'ir_se50.pth', True, True)
else:
learner.load_state(conf, 'ir_se50.pth', True, True)
learner.model.eval()
print('learner loaded')
if args.update:
targets, names = prepare_facebank(conf,
learner.model,
mtcnn,
tta=args.tta)
print('facebank updated')
else:
targets, names = load_facebank(conf)
print('facebank loaded')
try:
# image = Image.fromarray(img)
# 利用mtcnn网络，对齐
bboxes, faces = mtcnn.align_multi(image, conf.face_limit,
conf.min_face_size)
bboxes = bboxes[:, :
-1] # shape:[10,4],only keep 10 highest possibiity faces
bboxes = bboxes.astype(int)
bboxes = bboxes + [-1, -1, 1, 1] # personal choice
results, score = learner.infer(conf, faces, targets, args.tta)
print(results, score)
for idx, bbox in enumerate(bboxes):
print(names[results[idx] + 1])
res = names[results[idx] + 1]
except:
print('detect')
res = "unknow"
"""打开数据库"""
# 主机 用户名 密码 建立的数据库 编码格式
db = pymysql.connect(host='203.176.95.7',
port=3306,
user='******',
passwd='123123ms@',
database='FaceBank',
charset='utf8')
# 使用cursor()方法获取操作游标
cursor = db.cursor()
# sql = "SELECT * FROM names where name='%res'"
# 使用execute方法执行SQL语句
cursor.execute("SELECT * FROM names where name='%s'" % (res))
# 使用 fetchone() 方法获取一条数据
dat = cursor.fetchone()
print(dat)
# 关闭数据库连接
db.close()
return dat
def predict(image):
# image = Image.open(img_path)
# img=img_path#注意修改
parser = argparse.ArgumentParser(description='for face verification')
parser.add_argument("-s",
"--save",
help="whether save",
action="store_true")
parser.add_argument('-th',
'--threshold',
help='threshold to decide identical faces',
default=1.54,
type=float)
parser.add_argument("-u",
"--update",
default=True,
help="whether perform update the facebank",
action="store_true")
parser.add_argument("-tta",
"--tta",
default=False,
help="whether testCode time augmentation",
action="store_true")
parser.add_argument("-c",
"--score",
default=True,
help="whether show the confidence score",
action="store_true")
parser.add_argument('--img_path',
'-p',
default='1.jpg',
type=str,
help='input the name of the recording person')
args = parser.parse_args()
mtcnn = MTCNN()
conf = get_config(False)
learner = face_learner(conf, True)
learner.threshold = args.threshold
# image = Image.open(args.img_path)
if conf.device.type == 'cpu':
learner.load_state(conf, 'ir_se50.pth', True, True)
else:
learner.load_state(conf, 'ir_se50.pth', True, True)
learner.model.eval()
print('learner loaded')
if args.update:
targets, names = prepare_facebank(conf,
learner.model,
mtcnn,
tta=args.tta)
print('facebank updated')
else:
targets, names = load_facebank(conf)
print('facebank loaded')
try:
# image = Image.fromarray(img)
# 利用mtcnn网络，对齐
bboxes, faces = mtcnn.align_multi(image, conf.face_limit,
conf.min_face_size)
bboxes = bboxes[:, :
-1] # shape:[10,4],only keep 10 highest possibiity faces
bboxes = bboxes.astype(int)
bboxes = bboxes + [-1, -1, 1, 1] # personal choice
results, score = learner.infer(conf, faces, targets, args.tta)
print(results, score)
for idx, bbox in enumerate(bboxes):
print(names[results[idx] + 1])
res = "name:" + names[results[idx] + 1]
except:
print('detect')
res = "unknow"
return res