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2.1 KiB
Python

#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"", u"", u"", u"", u"", u"", u"", u"", u"", u"", u"", u"", u"", u"", u"", u"", u"", u"", u"", u"", u"",
u"", u"", u"", u"", u"", u"", u"", u"", u"", u"", 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"",u"",u"使",u"",u"",u"",u"",u"",u"",u"广",u"",u"",u"",u"",u"",u"",u"",u""
];
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()