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- #coding=utf-8
- from keras.models import Sequential
- from keras.layers import Dense, Dropout, Activation, Flatten
- from keras.layers import Conv2D, MaxPool2D
- from keras.optimizers import SGD
- from keras import backend as K
-
- K.set_image_dim_ordering('tf')
-
-
- import cv2
- import numpy as np
-
-
- plateType = ["蓝牌","单层黄牌","新能源车牌","白色","黑色-港澳"]
- def Getmodel_tensorflow(nb_classes):
- # nb_classes = len(charset)
-
- img_rows, img_cols = 9, 34
- # number of convolutional filters to use
- nb_filters = 32
- # size of pooling area for max pooling
- nb_pool = 2
- # convolution kernel size
- nb_conv = 3
-
- # x = np.load('x.npy')
- # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
- # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
- # weight = dict(zip(range(3063), weight / weight.mean())) # 调整权重，高频字优先
-
- model = Sequential()
- model.add(Conv2D(16, (5, 5),input_shape=(img_rows, img_cols,3)))
- model.add(Activation('relu'))
- model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
- model.add(Flatten())
- model.add(Dense(64))
- model.add(Activation('relu'))
- model.add(Dropout(0.5))
- model.add(Dense(nb_classes))
- model.add(Activation('softmax'))
- model.compile(loss='categorical_crossentropy',
- optimizer='adam',
- metrics=['accuracy'])
- return model
-
- model = Getmodel_tensorflow(5)
- model.load_weights("./model/plate_type.h5")
- model.save("./model/plate_type.h5")
- def SimplePredict(image):
- image = cv2.resize(image, (34, 9))
- image = image.astype(np.float) / 255
- res = np.array(model.predict(np.array([image]))[0])
- return res.argmax()
-
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