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