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