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37 lines
1.3 KiB
Python
37 lines
1.3 KiB
Python
#coding=utf-8
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from keras.layers import Conv2D, Input,MaxPool2D, Reshape,Activation,Flatten, Dense
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from keras.models import Model, Sequential
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from keras.layers.advanced_activations import PReLU
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from keras.optimizers import adam
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import numpy as np
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import cv2
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def getModel():
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input = Input(shape=[12, 50, 3]) # change this shape to [None,None,3] to enable arbitraty shape input
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x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
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x = PReLU(shared_axes=[1, 2], name='prelu1')(x)
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x = MaxPool2D(pool_size=2)(x)
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x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
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x = PReLU(shared_axes=[1, 2], name='prelu2')(x)
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x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
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x = PReLU(shared_axes=[1, 2], name='prelu3')(x)
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x = Flatten()(x)
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output = Dense(2)(x)
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output = PReLU(name='prelu4')(output)
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model = Model([input], [output])
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return model
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model = getModel()
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model.load_weights("./model/model12.h5")
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def finemappingVertical(image):
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resized = cv2.resize(image,(50,12))
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resized = resized.astype(np.float)/255
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res= model.predict(np.array([resized]))[0]
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res =res*image.shape[1]
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res = res.astype(np.int)
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image = image[0:35,res[0]+4:res[1]]
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image = cv2.resize(image, (int(136), int(36)))
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return image |