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155 lines
5.2 KiB
Python
155 lines
5.2 KiB
Python
#coding=utf-8
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Activation, Flatten
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from keras.layers import Conv2D,MaxPool2D
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from keras.optimizers import SGD
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from keras import backend as K
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K.set_image_dim_ordering('tf')
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import cv2
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import numpy as np
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index = {u"京": 0, u"沪": 1, u"津": 2, u"渝": 3, u"冀": 4, u"晋": 5, u"蒙": 6, u"辽": 7, u"吉": 8, u"黑": 9, u"苏": 10, u"浙": 11, u"皖": 12,
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u"闽": 13, u"赣": 14, u"鲁": 15, u"豫": 16, u"鄂": 17, u"湘": 18, u"粤": 19, u"桂": 20, u"琼": 21, u"川": 22, u"贵": 23, u"云": 24,
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u"藏": 25, u"陕": 26, u"甘": 27, u"青": 28, u"宁": 29, u"新": 30, u"0": 31, u"1": 32, u"2": 33, u"3": 34, u"4": 35, u"5": 36,
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u"6": 37, u"7": 38, u"8": 39, u"9": 40, u"A": 41, u"B": 42, u"C": 43, u"D": 44, u"E": 45, u"F": 46, u"G": 47, u"H": 48,
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u"J": 49, u"K": 50, u"L": 51, u"M": 52, u"N": 53, u"P": 54, u"Q": 55, u"R": 56, u"S": 57, u"T": 58, u"U": 59, u"V": 60,
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u"W": 61, u"X": 62, u"Y": 63, u"Z": 64,u"港":65,u"学":66 ,u"O":67 ,u"使":68,u"警":69,u"澳":70,u"挂":71};
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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",
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"Q", "R", "S", "T", "U", "V", "W", "X",
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"Y", "Z","港","学","O","使","警","澳","挂" ];
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def Getmodel_tensorflow(nb_classes):
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# nb_classes = len(charset)
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img_rows, img_cols = 23, 23
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# number of convolutional filters to use
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nb_filters = 32
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# size of pooling area for max pooling
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nb_pool = 2
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# convolution kernel size
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nb_conv = 3
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# x = np.load('x.npy')
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# y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
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# weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
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# weight = dict(zip(range(3063), weight / weight.mean())) # 调整权重，高频字优先
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model = Sequential()
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model.add(Conv2D(32, (5, 5),input_shape=(img_rows, img_cols,1)))
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model.add(Activation('relu'))
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model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
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model.add(Dropout(0.25))
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model.add(Conv2D(32, (3, 3)))
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model.add(Activation('relu'))
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model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
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model.add(Dropout(0.25))
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model.add(Conv2D(512, (3, 3)))
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# model.add(Activation('relu'))
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# model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
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# model.add(Dropout(0.25))
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model.add(Flatten())
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model.add(Dense(512))
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model.add(Activation('relu'))
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model.add(Dropout(0.5))
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model.add(Dense(nb_classes))
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model.add(Activation('softmax'))
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model.compile(loss='categorical_crossentropy',
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optimizer='adam',
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metrics=['accuracy'])
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return model
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def Getmodel_ch(nb_classes):
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# nb_classes = len(charset)
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img_rows, img_cols = 23, 23
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# number of convolutional filters to use
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nb_filters = 32
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# size of pooling area for max pooling
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nb_pool = 2
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# convolution kernel size
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nb_conv = 3
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# x = np.load('x.npy')
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# y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
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# weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
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# weight = dict(zip(range(3063), weight / weight.mean())) # 调整权重，高频字优先
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model = Sequential()
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model.add(Conv2D(32, (5, 5),input_shape=(img_rows, img_cols,1)))
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model.add(Activation('relu'))
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model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
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model.add(Dropout(0.25))
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model.add(Conv2D(32, (3, 3)))
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model.add(Activation('relu'))
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model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
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model.add(Dropout(0.25))
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model.add(Conv2D(512, (3, 3)))
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# model.add(Activation('relu'))
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# model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
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# model.add(Dropout(0.25))
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model.add(Flatten())
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model.add(Dense(756))
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model.add(Activation('relu'))
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model.add(Dropout(0.5))
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model.add(Dense(nb_classes))
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model.add(Activation('softmax'))
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model.compile(loss='categorical_crossentropy',
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optimizer='adam',
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metrics=['accuracy'])
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return model
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model = Getmodel_tensorflow(65)
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#构建网络
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model_ch = Getmodel_ch(31)
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model_ch.load_weights("./model/char_chi_sim.h5")
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# model_ch.save_weights("./model/char_chi_sim.h5")
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model.load_weights("./model/char_rec.h5")
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# model.save("./model/char_rec.h5")
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def SimplePredict(image,pos):
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image = cv2.resize(image, (23, 23))
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image = cv2.equalizeHist(image)
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image = image.astype(np.float) / 255
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image -= image.mean()
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image = np.expand_dims(image, 3)
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if pos!=0:
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res = np.array(model.predict(np.array([image]))[0])
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else:
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res = np.array(model_ch.predict(np.array([image]))[0])
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zero_add = 0 ;
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if pos==0:
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res = res[:31]
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elif pos==1:
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res = res[31+10:65]
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zero_add = 31+10
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else:
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res = res[31:]
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zero_add = 31
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max_id = res.argmax()
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return res.max(),chars[max_id+zero_add],max_id+zero_add
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