VDSR-Keras/test_vdsr.py at master · GeorgeSeif/VDSR-Keras · GitHub

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from keras.models import load_model
from keras.models import Sequential, Model
from keras.layers import Dense, Activation
from keras.layers import Conv2D, MaxPooling2D, Input, Merge, ZeroPadding2D, merge
from keras.preprocessing import image
from scipy.misc import imsave, imread, imresize, toimage
import numpy as np
import matplotlib.pyplot as plt

img_shape = (41, 41, 1)

input_img = Input(shape=(img_shape))

model = Conv2D(64, (3, 3), padding='same', name='conv1')(input_img)
model = Activation('relu', name='act1')(model)
model = Conv2D(64, (3, 3), padding='same', name='conv2')(model)
model = Activation('relu', name='act2')(model)
model = Conv2D(64, (3, 3), padding='same', name='conv3')(model)
model = Activation('relu', name='act3')(model)
model = Conv2D(64, (3, 3), padding='same', name='conv4')(model)
model = Activation('relu', name='act4')(model)
model = Conv2D(64, (3, 3), padding='same', name='conv5')(model)
model = Activation('relu', name='act5')(model)

model = Conv2D(64, (3, 3), padding='same', name='conv6')(model)
model = Activation('relu', name='act6')(model)
model = Conv2D(64, (3, 3), padding='same', name='conv7')(model)
model = Activation('relu', name='act7')(model)
model = Conv2D(64, (3, 3), padding='same', name='conv8')(model)
model = Activation('relu', name='act8')(model)
model = Conv2D(64, (3, 3), padding='same', name='conv9')(model)
model = Activation('relu', name='act9')(model)
model = Conv2D(64, (3, 3), padding='same', name='conv10')(model)
model = Activation('relu', name='act10')(model)

model = Conv2D(64, (3, 3), padding='same', name='conv11')(model)
model = Activation('relu', name='act11')(model)
model = Conv2D(64, (3, 3), padding='same', name='conv12')(model)
model = Activation('relu', name='act12')(model)
model = Conv2D(64, (3, 3), padding='same', name='conv13')(model)
model = Activation('relu', name='act13')(model)
model = Conv2D(64, (3, 3), padding='same', name='conv14')(model)
model = Activation('relu', name='act14')(model)
model = Conv2D(64, (3, 3), padding='same', name='conv15')(model)
model = Activation('relu', name='act15')(model)

model = Conv2D(64, (3, 3), padding='same', name='conv16')(model)
model = Activation('relu', name='act16')(model)
model = Conv2D(64, (3, 3), padding='same', name='conv17')(model)
model = Activation('relu', name='act17')(model)
model = Conv2D(64, (3, 3), padding='same', name='conv18')(model)
model = Activation('relu', name='act18')(model)
model = Conv2D(64, (3, 3), padding='same', name='conv19')(model)
model = Activation('relu', name='act19')(model)
model = Conv2D(1, (3, 3), padding='same', name='conv20')(model)
model = Activation('relu', name='act20')(model)
res_img = model

output_img = merge([res_img, input_img])

model = Model(input_img, output_img)

model.load_weights('vdsr_model_edges.h5')

img = image.load_img('./patch.png', grayscale=True, target_size=(41, 41, 1))
x = image.img_to_array(img)
x = x.astype('float32') / 255
x = np.expand_dims(x, axis=0)

pred = model.predict(x)

test_img = np.reshape(pred, (41, 41))

imsave('test_img.png', test_img)