add e2e model.
jackyu
8 years ago
parent
c48543095e
commit
3b955ff1e5
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#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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from captcha.image import ImageCaptcha
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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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import e2emodel as model
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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",
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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",
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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",
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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"
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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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@ -0,0 +1,35 @@
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from keras import backend as K
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from keras.models import *
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from keras.layers import *
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from captcha.image import ImageCaptcha
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import e2e
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def ctc_lambda_func(args):
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y_pred, labels, input_length, label_length = args
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y_pred = y_pred[:, 2:, :]
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return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
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def construct_model(model_path):
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input_tensor = Input((None, 40, 3))
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x = input_tensor
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base_conv = 32
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for i in range(3):
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x = Conv2D(base_conv * (2 ** (i)), (3, 3),padding="same")(x)
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x = BatchNormalization()(x)
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x = Activation('relu')(x)
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x = MaxPooling2D(pool_size=(2, 2))(x)
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x = Conv2D(256, (5, 5))(x)
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x = BatchNormalization()(x)
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x = Activation('relu')(x)
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x = Conv2D(1024, (1, 1))(x)
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x = BatchNormalization()(x)
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x = Activation('relu')(x)
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x = Conv2D(len(e2e.chars)+1, (1, 1))(x)
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x = Activation('softmax')(x)
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base_model = Model(inputs=input_tensor, outputs=x)
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base_model.load_weights(model_path)
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return base_model
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