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plate_judgment.py

+import cv2
+import numpy as np
+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
+import tensorflow as tf
+
+def constructmodel(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
+    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'])
+#     model.summary()
+
+    return model
+
+def load_model():
+    global model1
+    model1.load_weights("./model/two_type_judge.h5")
+    # this is key : save the graph after loading the model
+    global graph1
+    graph1 = tf.get_default_graph()
+    
+model1 = constructmodel(2)
+load_model()
+
+def SimplePredict(image):
+    image = cv2.resize(image, (34, 9))
+    image = image.astype(np.float) / 255
+    with graph1.as_default():
+        res = np.array(model1.predict(np.array([image]))[0])
+    return res.argmax()
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