from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, Dropout
from tensorflow.keras.layers import Conv2DTranspose, UpSampling2D, add
from skimage.transform import resize, rescale
from tensorflow.keras.models import Model
from tensorflow.keras import regularizers
import matplotlib.pyplot as plt
from scipy import ndimage, misc
from matplotlib import pyplot
import tensorflow as tf
import numpy as np
np.random.seed(0)
import re
import os
print(tf.__version__)2.3.0
input_img = Input(shape=(256, 256, 3))l1 = Conv2D(64, (3, 3), padding = 'same', activation = 'relu',
activity_regularizer = regularizers.l1(10e-10))(input_img)
l2 = Conv2D(64, (3, 3), padding = 'same', activation = 'relu',
activity_regularizer = regularizers.l1(10e-10))(l1)
l3 = MaxPooling2D(padding = 'same')(l2)
l3 = Dropout(0.3)(l3)
l4 = Conv2D(128, (3, 3), padding = 'same', activation = 'relu',
activity_regularizer = regularizers.l1(10e-10))(l3)
l5 = Conv2D(128, (3, 3), padding = 'same', activation = 'relu',
activity_regularizer = regularizers.l1(10e-10))(l4)
l6 = MaxPooling2D(padding = 'same')(l5)
l7 = Conv2D(256, (3, 3), padding = 'same', activation = 'relu',
activity_regularizer = regularizers.l1(10e-10))(l6)
encoder = Model(input_img, l7)encoder.summary()Model: "functional_7"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 256, 256, 3)] 0
_________________________________________________________________
conv2d_15 (Conv2D) (None, 256, 256, 64) 1792
_________________________________________________________________
conv2d_16 (Conv2D) (None, 256, 256, 64) 36928
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 128, 128, 64) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 128, 128, 64) 0
_________________________________________________________________
conv2d_17 (Conv2D) (None, 128, 128, 128) 73856
_________________________________________________________________
conv2d_18 (Conv2D) (None, 128, 128, 128) 147584
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 64, 64, 128) 0
_________________________________________________________________
conv2d_19 (Conv2D) (None, 64, 64, 256) 295168
=================================================================
Total params: 555,328
Trainable params: 555,328
Non-trainable params: 0
_________________________________________________________________
l1 = Conv2D(64, (3, 3), padding = 'same', activation = 'relu',
activity_regularizer = regularizers.l1(10e-10))(input_img)
l2 = Conv2D(64, (3, 3), padding = 'same', activation = 'relu',
activity_regularizer = regularizers.l1(10e-10))(l1)
l3 = MaxPooling2D(padding = 'same')(l2)
l3 = Dropout(0.3)(l3)
l4 = Conv2D(128, (3, 3), padding = 'same', activation = 'relu',
activity_regularizer = regularizers.l1(10e-10))(l3)
l5 = Conv2D(128, (3, 3), padding = 'same', activation = 'relu',
activity_regularizer = regularizers.l1(10e-10))(l4)
l6 = MaxPooling2D(padding = 'same')(l5)
l7 = Conv2D(256, (3, 3), padding = 'same', activation = 'relu',
activity_regularizer = regularizers.l1(10e-10))(l6)l8 = UpSampling2D()(l7)
l9 = Conv2D(128, (3, 3), padding = 'same', activation = 'relu',
activity_regularizer = regularizers.l1(10e-10))(l8)
l10 = Conv2D(128, (3, 3), padding = 'same', activation = 'relu',
activity_regularizer = regularizers.l1(10e-10))(l9)
l11 = add([l5, l10])
l12 = UpSampling2D()(l11)
l13 = Conv2D(64, (3, 3), padding = 'same', activation = 'relu',
activity_regularizer = regularizers.l1(10e-10))(l12)
l14 = Conv2D(64, (3, 3), padding = 'same', activation = 'relu',
activity_regularizer = regularizers.l1(10e-10))(l13)
l15 = add([l14, l2])
decoded = Conv2D(3, (3, 3), padding = 'same',
activation = 'relu', activity_regularizer = regularizers.l1(10e-10))(l15)
autoencoder = Model(input_img, decoded)
autoencoder_hfenn = Model(input_img, decoded)autoencoder.summary()Model: "functional_9"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 256, 256, 3) 0
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 256, 256, 64) 1792 input_2[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 256, 256, 64) 36928 conv2d_20[0][0]
__________________________________________________________________________________________________
max_pooling2d_6 (MaxPooling2D) (None, 128, 128, 64) 0 conv2d_21[0][0]
__________________________________________________________________________________________________
dropout_3 (Dropout) (None, 128, 128, 64) 0 max_pooling2d_6[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 128, 128, 128 73856 dropout_3[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 128, 128, 128 147584 conv2d_22[0][0]
__________________________________________________________________________________________________
max_pooling2d_7 (MaxPooling2D) (None, 64, 64, 128) 0 conv2d_23[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D) (None, 64, 64, 256) 295168 max_pooling2d_7[0][0]
__________________________________________________________________________________________________
up_sampling2d_2 (UpSampling2D) (None, 128, 128, 256 0 conv2d_24[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D) (None, 128, 128, 128 295040 up_sampling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_26 (Conv2D) (None, 128, 128, 128 147584 conv2d_25[0][0]
__________________________________________________________________________________________________
add_2 (Add) (None, 128, 128, 128 0 conv2d_23[0][0]
conv2d_26[0][0]
__________________________________________________________________________________________________
up_sampling2d_3 (UpSampling2D) (None, 256, 256, 128 0 add_2[0][0]
__________________________________________________________________________________________________
conv2d_27 (Conv2D) (None, 256, 256, 64) 73792 up_sampling2d_3[0][0]
__________________________________________________________________________________________________
conv2d_28 (Conv2D) (None, 256, 256, 64) 36928 conv2d_27[0][0]
__________________________________________________________________________________________________
add_3 (Add) (None, 256, 256, 64) 0 conv2d_28[0][0]
conv2d_21[0][0]
__________________________________________________________________________________________________
conv2d_29 (Conv2D) (None, 256, 256, 3) 1731 add_3[0][0]
==================================================================================================
Total params: 1,110,403
Trainable params: 1,110,403
Non-trainable params: 0
__________________________________________________________________________________________________
autoencoder.compile(optimizer = 'adadelta', loss = 'mean_squared_error')def train_batches(just_load_dataset=False):
batches = 256
batch = 0
batch_nb = 0
max_batches = -1
ep = 4
images = []
x_train_n = []
x_train_down = []
x_train_n2 = []
x_train_down2 = []
for root, dirnames, filenames in os.walk("data/cars_train"):
for filename in filenames:
if re.search("\.(jpg|jpeg|JPEG|png|bmp|tiff)$", filename):
if batch_nb == max_batches:
return x_train_n2, x_train_down2
filepath = os.path.join(root, filename)
image = pyplot.imread(filepath)
if len(image.shape) > 2:
image_resized = resize(image, (256, 256)) # Resize the image so that every image is the same size
x_train_n.append(image_resized) # Add this image to the high res dataset
dwn1 = rescale(image_resized, 2)
x_train_down.append(rescale(dwn1,0.5))
batch += 1
if batch == batches:
batch_nb += 1
x_train_n2 = np.array(x_train_n)
x_train_down2 = np.array(x_train_down)
if just_load_dataset:
return x_train_n2, x_train_down2
print('Training batch', batch_nb, '(', batches, ')')
autoencoder.fit(x_train_down2, x_train_n2,
epochs=ep,
batch_size=10,
shuffle=True,
validation_split=0.15)
x_train_n = []
x_train_down = []
batch = 0
return x_train_n2, x_train_down2x_train_n, x_train_down = train_batches(just_load_dataset = True)autoencoder.load_weights('data/sr.img_net.mse.final_model5.no_patch.weights.best.hdf5')encoder.load_weights('data/encoder_weights.hdf5')encoded_imgs = encoder.predict(x_train_down)encoded_imgs.shape(256, 64, 64, 256)
sr1 = np.clip(autoencoder.predict(x_train_down), 0.0, 1.0)image_index = 251plt.figure(figsize = (20, 20))
i = 1
ax = plt.subplot(10, 10, i)
plt.imshow(x_train_down[image_index])
i += 1
ax = plt.subplot(10, 10, i)
plt.imshow(x_train_down[image_index], interpolation = "bicubic")
i += 1
ax = plt.subplot(10, 10, i)
plt.imshow(encoded_imgs[image_index].reshape((64*64, 256)))
i += 1
ax = plt.subplot(10, 10, i)
plt.imshow(sr1[image_index])
i += 1
ax = plt.subplot(10, 10, i)
plt.imshow(x_train_n[image_index])
plt.show()