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64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
#coding=utf-8
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from keras import backend as K
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from keras.models import load_model
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from keras.layers import *
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import numpy as np
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import random
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import string
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import cv2
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from . import e2emodel as model
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chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
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"琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
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"B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X",
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"Y", "Z","港","学","使","警","澳","挂","军","北","南","广","沈","兰","成","济","海","民","航","空"
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];
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pred_model = model.construct_model("./model/ocr_plate_all_w_rnn_2.h5")
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import time
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def fastdecode(y_pred):
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results = ""
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confidence = 0.0
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table_pred = y_pred.reshape(-1, len(chars)+1)
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res = table_pred.argmax(axis=1)
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for i,one in enumerate(res):
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if one<len(chars) and (i==0 or (one!=res[i-1])):
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results+= chars[one]
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confidence+=table_pred[i][one]
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confidence/= len(results)
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return results,confidence
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def recognizeOne(src):
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# x_tempx= cv2.imread(src)
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x_tempx = src
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# x_tempx = cv2.bitwise_not(x_tempx)
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x_temp = cv2.resize(x_tempx,( 160,40))
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x_temp = x_temp.transpose(1, 0, 2)
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t0 = time.time()
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y_pred = pred_model.predict(np.array([x_temp]))
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y_pred = y_pred[:,2:,:]
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# plt.imshow(y_pred.reshape(16,66))
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# plt.show()
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#
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# cv2.imshow("x_temp",x_tempx)
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# cv2.waitKey(0)
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return fastdecode(y_pred)
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#
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#
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# import os
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#
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# path = "/Users/yujinke/PycharmProjects/HyperLPR_Python_web/cache/finemapping"
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# for filename in os.listdir(path):
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# if filename.endswith(".png") or filename.endswith(".jpg") or filename.endswith(".bmp"):
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# x = os.path.join(path,filename)
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# recognizeOne(x)
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# # print time.time() - t0
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#
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# # cv2.imshow("x",x)
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# # cv2.waitKey()
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