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predict.py
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predict.py
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import argparse
from time import time
import numpy as np
import cv2
import tensorflow as tf
from model import LPRNet
from loader import resize_and_normailze
classnames = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9",
"가", "나", "다", "라", "마", "거", "너", "더", "러",
"머", "버", "서", "어", "저", "고", "노", "도", "로",
"모", "보", "소", "오", "조", "구", "누", "두", "루",
"무", "부", "수", "우", "주", "허", "하", "호"
]
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--image", required=True, help="path to image file")
parser.add_argument("-w", "--weights", required=True, help="path to weights file")
args = vars(parser.parse_args())
tf.compat.v1.enable_eager_execution()
net = LPRNet(len(classnames) + 1)
net.load_weights(args["weights"])
img = cv2.imread(args["image"])
x = np.expand_dims(resize_and_normailze(img), axis=0)
t = time()
print(net.predict(x, classnames))
print(time() - t)
cv2.imshow("lp", img)
cv2.waitKey(0)
cv2.destroyAllWindows()