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