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57 lines
1.7 KiB
Python
57 lines
1.7 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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plateType = ["蓝牌","单层黄牌","新能源车牌","白色","黑色-港澳"]
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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 = 9, 34
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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(16, (5, 5),input_shape=(img_rows, img_cols,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(Flatten())
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model.add(Dense(64))
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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(5)
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model.load_weights("./model/plate_type.h5")
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model.save("./model/plate_type.h5")
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def SimplePredict(image):
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image = cv2.resize(image, (34, 9))
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image = image.astype(np.float) / 255
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res = np.array(model.predict(np.array([image]))[0])
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return res.argmax()
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