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dbz.py
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dbz.py
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from __future__ import print_function, division
import scipy
import scipy.misc
from glob import glob
import numpy as np
from tensorflow_addons.layers import InstanceNormalization
#from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, Concatenate
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
import imageio
import streamlit as st
from PIL import Image
fav = Image.open("shenron.ico")
st.set_page_config(
page_title="dbs2dbz: CycleGAN",
page_icon = fav,
)
st.title("dbs2dbz")
class DataLoader():
def __init__(self, dataset_name, img_res=(512, 512)):
self.dataset_name = dataset_name
self.img_res = img_res
def load_data(self, domain, batch_size=1, is_testing=False):
data_type = "train%s" % domain if not is_testing else "test%s" % domain
path = glob('/content/%s/*' % ( data_type))
batch_images = np.random.choice(path, size=batch_size)
imgs = []
for img_path in batch_images:
img = self.imread(img_path)
img = Image.fromarray(img).resize(self.img_res)
if not is_testing and np.random.random() > 0.5:
img = np.fliplr(img)
imgs.append(np.array(img))
imgs = np.array(imgs)/127.5 - 1.
return imgs
def load_batch(self, batch_size=1, is_testing=False):
data_type = "train" if not is_testing else "val"
path_A = glob('/content/%sA/*' % ( data_type))
path_B = glob('/content/%sB/*' % ( data_type))
self.n_batches = int(min(len(path_A), len(path_B)) / batch_size)
total_samples = self.n_batches * batch_size
# Sample n_batches * batch_size from each path list so that model sees all
# samples from both domains
path_A = np.random.choice(path_A, total_samples, replace=False)
path_B = np.random.choice(path_B, total_samples, replace=False)
for i in range(self.n_batches-1):
batch_A = path_A[i*batch_size:(i+1)*batch_size]
batch_B = path_B[i*batch_size:(i+1)*batch_size]
imgs_A, imgs_B = [], []
for img_A, img_B in zip(batch_A, batch_B):
img_A = self.imread(img_A)
img_B = self.imread(img_B)
img_A = scipy.misc.imresize(img_A, self.img_res)
img_B = scipy.misc.imresize(img_B, self.img_res)
if not is_testing and np.random.random() > 0.5:
img_A = np.fliplr(img_A)
img_B = np.fliplr(img_B)
imgs_A.append(img_A)
imgs_B.append(img_B)
imgs_A = np.array(imgs_A)/127.5 - 1.
imgs_B = np.array(imgs_B)/127.5 - 1.
yield imgs_A, imgs_B
def imread(self, path):
return imageio.imread(path, pilmode='RGB').astype(np.float)
#return scipy.misc.imread(path, mode='RGB').astype(np.float)
class CycleGAN():
def __init__(self):
# Input shape
self.img_rows = 512
self.img_cols = 512
self.channels = 3
self.img_shape = (self.img_rows, self.img_cols, self.channels)
# Configure data loader
self.dataset_name = 'dbs2dbz'
# Use the DataLoader object to import a preprocessed dataset
self.data_loader = DataLoader(dataset_name=self.dataset_name,
img_res=(self.img_rows, self.img_cols))
# Calculate output shape of D (PatchGAN)
patch = int(self.img_rows / 2**4)
self.disc_patch = (patch, patch, 1)
# Number of filters in the first layer of G and D
self.gf = 32
self.df = 64
# Loss weights
self.lambda_cycle = 10.0 # Cycle-consistency loss
self.lambda_id = 0.9 * self.lambda_cycle # Identity loss
optimizer = Adam(0.0002, 0.5)
# Build and compile the discriminators
self.d_A = self.build_discriminator()
self.d_B = self.build_discriminator()
self.d_A.compile(loss='mse',
optimizer=optimizer,
metrics=['accuracy'])
self.d_B.compile(loss='mse',
optimizer=optimizer,
metrics=['accuracy'])
# Build the generators
self.g_AB = self.build_generator()
self.g_BA = self.build_generator()
# Input images from both domains
img_A = Input(shape=self.img_shape)
img_B = Input(shape=self.img_shape)
# Translate images to the other domain
fake_B = self.g_AB(img_A)
fake_A = self.g_BA(img_B)
# Translate images back to original domain
reconstr_A = self.g_BA(fake_B)
reconstr_B = self.g_AB(fake_A)
# Identity mapping of images
img_A_id = self.g_BA(img_A)
img_B_id = self.g_AB(img_B)
# For the combined model we will only train the generators
self.d_A.trainable = False
self.d_B.trainable = False
# Discriminators determines validity of translated images
valid_A = self.d_A(fake_A)
valid_B = self.d_B(fake_B)
# Combined model trains generators to fool discriminators
self.combined = Model(inputs=[img_A, img_B],
outputs=[valid_A, valid_B,
reconstr_A, reconstr_B,
img_A_id, img_B_id])
self.combined.compile(loss=['mse', 'mse',
'mae', 'mae',
'mae', 'mae'],
loss_weights=[1, 1,
self.lambda_cycle, self.lambda_cycle,
self.lambda_id, self.lambda_id],
optimizer=optimizer)
class CycleGAN(CycleGAN):
@staticmethod
def conv2d(layer_input, filters, f_size=4, normalization=True):
"""Discriminator layer"""
d = Conv2D(filters, kernel_size=f_size,
strides=2, padding='same')(layer_input)
d = LeakyReLU(alpha=0.2)(d)
if normalization:
d = InstanceNormalization()(d)
#d = tfa.layers.InstanceNormalization()(d)
return d
@staticmethod
def deconv2d(layer_input, skip_input, filters, f_size=4, dropout_rate=0):
"""Layers used during upsampling"""
u = UpSampling2D(size=2)(layer_input)
u = Conv2D(filters, kernel_size=f_size, strides=1,
padding='same', activation='relu')(u)
if dropout_rate:
u = Dropout(dropout_rate)(u)
u = InstanceNormalization()(u)
#u = tfa.layers.InstanceNormalization()(u)
u = Concatenate()([u, skip_input])
return u
def build_generator(self):
"""U-Net Generator"""
# Image input
d0 = Input(shape=self.img_shape)
# Downsampling
d1 = self.conv2d(d0, self.gf)
d2 = self.conv2d(d1, self.gf * 2)
d3 = self.conv2d(d2, self.gf * 4)
d4 = self.conv2d(d3, self.gf * 8)
# Upsampling
u1 = self.deconv2d(d4, d3, self.gf * 4)
u2 = self.deconv2d(u1, d2, self.gf * 2)
u3 = self.deconv2d(u2, d1, self.gf)
u4 = UpSampling2D(size=2)(u3)
output_img = Conv2D(self.channels, kernel_size=4,
strides=1, padding='same', activation='tanh')(u4)
return Model(d0, output_img)
def build_discriminator(self):
img = Input(shape=self.img_shape)
d1 = self.conv2d(img, self.df, normalization=False)
d2 = self.conv2d(d1, self.df * 2)
d3 = self.conv2d(d2, self.df * 4)
d4 = self.conv2d(d3, self.df * 8)
validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(d4)
return Model(img, validity)
def sample_images(self, epoch, batch_i):
r, c = 1, 2
imgs_A = self.data_loader.load_data(domain="A", batch_size=1, is_testing=True)
imgs_B = self.data_loader.load_data(domain="B", batch_size=1, is_testing=True)
# Translate images to the other domain
fake_B = self.g_AB.predict(imgs_A)
fake_A = self.g_BA.predict(imgs_B)
# Translate back to original domain
reconstr_A = self.g_BA.predict(fake_B)
reconstr_B = self.g_AB.predict(fake_A)
#gen_imgs = np.concatenate([imgs_A, fake_B, reconstr_A, imgs_B, fake_A, reconstr_B])
gen_imgs = np.concatenate([imgs_A, fake_B])
# Rescale images 0 - 1
gen_imgs = 0.5 * gen_imgs + 0.5
titles = ['Original', 'Translated']
fig, axs = plt.subplots(r, c)
cnt = 0
for i in range(r):
for j in range(c):
axs[j].imshow(gen_imgs[cnt])
axs[j].set_title(titles[j])
axs[j].axis('off')
cnt += 1
fig.savefig("/content/%d_%d.png" % ( epoch, batch_i))
plt.show()
def train(self, epochs, batch_size=1, sample_interval=50):
valid = np.ones((batch_size,) + self.disc_patch)
fake = np.zeros((batch_size,) + self.disc_patch)
for epoch in range(epochs):
for batch_i, (imgs_A, imgs_B) in enumerate(self.data_loader.load_batch(batch_size)):
# Translate images to opposite domain
fake_B = self.g_AB.predict(imgs_A)
fake_A = self.g_BA.predict(imgs_B)
# Train the discriminators (original images = real / translated = Fake)
dA_loss_real = self.d_A.train_on_batch(imgs_A, valid)
dA_loss_fake = self.d_A.train_on_batch(fake_A, fake)
dA_loss = 0.5 * np.add(dA_loss_real, dA_loss_fake)
dB_loss_real = self.d_B.train_on_batch(imgs_B, valid)
dB_loss_fake = self.d_B.train_on_batch(fake_B, fake)
dB_loss = 0.5 * np.add(dB_loss_real, dB_loss_fake)
# Total discriminator loss
d_loss = 0.5 * np.add(dA_loss, dB_loss)
# Train the generators
g_loss = self.combined.train_on_batch([imgs_A, imgs_B],
[valid, valid, imgs_A, imgs_B, imgs_A, imgs_B])
# If at save interval => plot the generated image samples
if batch_i % sample_interval == 0:
self.sample_images(epoch, batch_i)
cycle_gan = CycleGAN()
#cycle_gan = pickle.load(open('dbz.pickle', 'rb'))
cycle_gan.g_AB.load_weights('s2z_03_08_100.h5')
uploaded_file = st.file_uploader("Choose an image...", type="jpg")
if uploaded_file is not None:
uimg = Image.open(uploaded_file)
uimg = uimg.save('im.jpg')
#st.image(uimg, caption='Uploaded Image', use_column_width=True)
if st.button('Transform'):
img_res=(512, 512)
imgs = []
img = imageio.imread('im.jpg', pilmode = "RGB")
img = np.array(Image.fromarray(img).resize(img_res))
imgs.append(img)
imgs = np.array(imgs)/127.5 - 1.
imgs_A = imgs
fake_B = cycle_gan.g_AB.predict(imgs_A)
gen_imgs = np.concatenate([imgs_A, fake_B])
gen_imgs = 0.5 * gen_imgs + 0.5
#st.image(gen_imgs[0], use_column_width=True)
r, c = 1, 2
plt.style.use("dark_background")
titles = ['Original', 'Z-style']
fig, axs = plt.subplots(r, c)
cnt = 0
for i in range(r):
for j in range(c):
axs[j].imshow(gen_imgs[cnt], interpolation = 'hamming')
axs[j].set_title(titles[j])
axs[j].axis('off')
cnt += 1
#fig.savefig('dbz.jpg', dpi=600)
#dimg = Image.open('dbz.jpg')
#dimg.save('dbz.jpg', quality = 95)
#st.image(dimg)
#dim.show()
st.pyplot(fig)
#os.remove('im.jpg')