mirror of
https://github.com/deepinsight/insightface.git
synced 2026-01-18 15:40:18 +00:00
27 lines
1.1 KiB
Python
27 lines
1.1 KiB
Python
import face_embedding
|
|
import argparse
|
|
import cv2
|
|
import numpy as np
|
|
import datetime
|
|
|
|
parser = argparse.ArgumentParser(description='face model test')
|
|
# general
|
|
parser.add_argument('--image-size', default='112,112', help='')
|
|
parser.add_argument('--model', default='../models/model-r34-amf/model,0', help='path to load model.')
|
|
parser.add_argument('--gpu', default=0, type=int, help='gpu id')
|
|
parser.add_argument('--det', default=2, type=int, help='mtcnn option, 2 means using R+O, else using O')
|
|
parser.add_argument('--flip', default=0, type=int, help='whether do lr flip aug')
|
|
parser.add_argument('--threshold', default=1.24, type=float, help='ver dist threshold')
|
|
args = parser.parse_args()
|
|
|
|
model = face_embedding.FaceModel(args)
|
|
#img = cv2.imread('/raid5data/dplearn/lfw/Jude_Law/Jude_Law_0001.jpg')
|
|
img = cv2.imread('/raid5data/dplearn/megaface/facescrubr/112x112/Tom_Hanks/Tom_Hanks_54745.png')
|
|
|
|
time_now = datetime.datetime.now()
|
|
for i in xrange(3000):
|
|
f1 = model.get_feature(img)
|
|
time_now2 = datetime.datetime.now()
|
|
diff = time_now2 - time_now
|
|
print(diff.total_seconds()/3000)
|