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validate.py
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validate.py
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from keras.models import load_model
from keras.layers import Lambda
from keras.preprocessing import image
import keras
import tensorflow as tf
import numpy as np
#import matplotlib.pyplot as plt
from PIL import Image
from keras import backend as K
import os
import math
oti = 'adam'
lr = 0.0001
def get_cpsnr(RGB1,RGB2,b):
RGB1 = RGB1.astype('double');
RGB2 = RGB2.astype('double');
diff = RGB1[b:-1-b,b:-1-b,:]-RGB2[b:-1-b,b:-1-b,:];
num = np.size(diff[:,:,1]);
MSE_R = np.sum( np.power(diff[:,:,2],2) );
MSE_G = np.sum( np.power(diff[:,:,1],2) );
MSE_B = np.sum( np.power(diff[:,:,0],2) );
CMSE = (MSE_R + MSE_G + MSE_B)/(3*num);
CPSNR = 10*math.log(255*255/CMSE,10);
return CPSNR;
def create_model():
inputs = keras.Input(shape=(None,None,1))
##Subpixel Construction
sub_layer_2 = Lambda(lambda x:tf.nn.space_to_depth(x,2))
init = sub_layer_2(inputs=inputs)
##Learning Residual (DCNN)
####Conv 3x3x64x64 + PReLu
x = keras.layers.Conv2D(filters = 64, #feature map number
kernel_size = 3,
strides = 1, # 2
padding = 'same',
input_shape = (None,None,1))(init)
x = keras.layers.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=[1,2])(x)
####Residual Block
for i in range(6):
Conv1 = keras.layers.Conv2D(filters = 64, #feature map number
kernel_size = 3,
strides = 1, # 2
padding = 'same',
input_shape = (None,None,64))(x)
PReLu = keras.layers.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=[1,2])(Conv1)
Conv2 = keras.layers.Conv2D(filters = 64, #feature map number
kernel_size = 3,
strides = 1, # 2
padding = 'same',
input_shape = (None,None,64))(PReLu)
x = keras.layers.Add()([Conv2,x])
x = keras.layers.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=[1,2])(x)
####Conv 3x3x64x64 + PReLu
x = keras.layers.Conv2D(filters = 64, #feature map number
kernel_size = 3,
strides = 1, # 2
padding = 'same',
input_shape = (None,None,1))(x)
x = keras.layers.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=[1,2])(x)
####Conv 3x3x64x48
x = keras.layers.Conv2D(filters = 48, #feature map number
kernel_size = 3,
strides = 1,
padding = 'same',
input_shape = (None,None,64))(x)
###########Learning Residual (DCNN)############
##Recovery From Subpixel
sub_layer = Lambda(lambda x:tf.nn.depth_to_space(x,4))
Residual_Output = sub_layer(inputs=x)
##Initial Prediction
R = Lambda(lambda x: x[:,:,:,0])(init)
G = Lambda(lambda x: x[:,:,:,1:3])(init)
G = Lambda(lambda x: K.mean(x, axis=3))(G)
B = Lambda(lambda x: x[:,:,:,3])(init)
print(init.shape)
print(R.shape)
print(G.shape)
print(B.shape)
R = Lambda(lambda x: tf.expand_dims(x, -1))(R)
G = Lambda(lambda x: tf.expand_dims(x, -1))(G)
B = Lambda(lambda x: tf.expand_dims(x, -1))(B)
#rgb = tf.keras.backend.stack((R, G,B),axis = 3)
print(R.shape)
rg = keras.layers.Concatenate(axis = 3)([R , G])
rgb = keras.layers.Concatenate(axis = 3)([rg,B])
print(rgb.shape)
Coarse_Output = keras.layers.UpSampling2D(size=(4, 4))(rgb)
## +
outputs = keras.layers.Add()([Residual_Output,Coarse_Output])
model = keras.Model(inputs=inputs, outputs=outputs, name="mnist_model")
return model
model = create_model()
model.load_weights('./model.hdf5')
#model = keras.Model(inputs=(64,64,1), outputs=(128,128,3), name="mnist_model")
#model.load_weights('trashup.h5')
#model.compile(optimizer=keras.optimizers.Nadam(lr), loss = 'mean_squared_error', metrics = ['mse'])
# test_image = image.load_img('./p/im_002_0.png', target_size = (64, 64))
# test_image = image.img_to_array(test_image)
# print(test_image)
# test_image = test_image[:,:,0]
# test_image = test_image[np.newaxis,:,:,np.newaxis]
# result = model.predict(test_image)
# print(result.shape)
# out = image.array_to_img(result[0])
# plt.imshow(out)
# plt.show()
if not os.path.exists('kodaout'):
os.makedirs('kodaout')
sum = 0
entries = os.listdir('./kodap/')
for entry in entries:
path = './kodap/'+entry
test_image = image.load_img(path)
test_image = image.img_to_array(test_image)
test_image = test_image[:,:,0]
test_image = test_image[np.newaxis,:,:,np.newaxis]
out = model.predict(test_image)
path = './koda/'+entry
ori = image.load_img(path)
ori = image.img_to_array(ori)
out = out[0];
#out = out*255;
print(entry,get_cpsnr(out,ori,20) );
sum+=get_cpsnr(out,ori,20);
out = image.array_to_img(out)
path = './kodaout/'+entry
out.save(path)
# plt.imshow(out)
# plt.show()
print('avg')
print(sum/24)