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| from keras.models import Model
from keras.layers import Dropout, Conv2D, UpSampling2D, \
LeakyReLU, Input, Concatenate, Activation, BatchNormalization
from keras.optimizers import Adam
from keras.initializers import RandomNormal
import os
import cv2
import numpy as np
import random
class DataLoader:
def __init__(self, dir_A, dir_B, batch_size, img_shape):
self.dir_A = dir_A
self.dir_B = dir_B
self.flist = os.listdir(dir_A)
self.fnum = len(self.flist)
self.batch_size = batch_size
self.img_shape = img_shape
self.idx_cur = 0
def getNumberOfBatch(self):
num = self.fnum / self.batch_size
if self.fnum % self.batch_size != 0:
num += 1
return int(num)
def reset(self):
self.idx_cur = 0
random.shuffle(self.flist)
def __iter__(self):
return self
def __next__(self):
if self.idx_cur >= self.fnum:
self.reset()
raise StopIteration
if self.idx_cur+self.batch_size-1 < self.fnum:
length = self.batch_size
idx_nxt = self.idx_cur+self.batch_size
else:
length = self.fnum-self.idx_cur
idx_nxt = self.fnum
imgA = np.zeros((length, *self.img_shape))
imgB = np.zeros((length, *self.img_shape))
for k in range(length):
fpath_A = os.path.join(self.dir_A, self.flist[self.idx_cur+k])
fpath_B = os.path.join(self.dir_B, self.flist[self.idx_cur+k])
img_a = cv2.imread(fpath_A, 1)
img_b = cv2.imread(fpath_B, 1)
imgA[k] = (img_a.astype(np.float32) - 127.5) / 127.5
imgB[k] = (img_b.astype(np.float32) - 127.5) / 127.5
self.idx_cur = idx_nxt
return imgA, imgB
class Pix2Pix:
def __init__(self):
self.img_row = 256
self.img_col = 256
self.img_channels = 3
self.img_shape = (self.img_row, self.img_col, self.img_channels)
patch = int(self.img_row / 2 ** 3)
self.discPatch = (patch, patch, 1)
self.buildGAN()
def buildGenerator(self):
initWeight = RandomNormal(stddev=0.02)
def EnConv2D(inputs, filters, k_size=4, norm=True):
x = Conv2D(filters, kernel_size=k_size, strides=2, padding='same', kernel_initializer=initWeight)(inputs)
if norm:
x = BatchNormalization()(x)
outputs = LeakyReLU(alpha=0.2)(x)
return outputs
def DeConv2D(inputs, skipInputs, filters, k_size=4, drop_rate=0.0):
x = UpSampling2D()(inputs)
x = Conv2D(filters, kernel_size=k_size, padding='same', kernel_initializer=initWeight)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
outputs = Concatenate()([x, skipInputs])
if drop_rate:
outputs = Dropout(drop_rate)(outputs)
return outputs
img_input = Input(shape=self.img_shape)
encoder1 = EnConv2D(img_input, 64, norm=False)
encoder2 = EnConv2D(encoder1, 128)
encoder3 = EnConv2D(encoder2, 256)
encoder4 = EnConv2D(encoder3, 512)
encoder5 = EnConv2D(encoder4, 512)
encoder6 = EnConv2D(encoder5, 512)
encoder7 = EnConv2D(encoder6, 512)
decoder1 = DeConv2D(encoder7, encoder6, 512)
decoder2 = DeConv2D(decoder1, encoder5, 512)
decoder3 = DeConv2D(decoder2, encoder4, 512)
decoder4 = DeConv2D(decoder3, encoder3, 256)
decoder5 = DeConv2D(decoder4, encoder2, 128)
decoder6 = DeConv2D(decoder5, encoder1, 64)
decoder7 = UpSampling2D()(decoder6)
img_output = Conv2D(filters=self.img_channels, kernel_size=4, padding='same', activation='tanh', kernel_initializer=initWeight)(decoder7)
return Model(img_input, img_output)
def buildDiscriminator(self):
initWeight = RandomNormal(stddev=0.02)
def discLayer(inputs, filters, k_size=4, stride=2, norm=True):
x = Conv2D(filters, kernel_size=k_size, strides=stride, padding='same', kernel_initializer=initWeight)(inputs)
if norm:
x = BatchNormalization()(x)
outputs = LeakyReLU(alpha=0.2)(x)
return outputs
imgA = Input(shape=self.img_shape)
imgB = Input(shape=self.img_shape)
inputImg = Concatenate()([imgA, imgB])
disc1 = discLayer(inputImg, 64, norm=False)
disc2 = discLayer(disc1, 128)
disc3 = discLayer(disc2, 256)
disc4 = discLayer(disc3, 512, stride=1)
validity = Conv2D(filters=1, kernel_size=4, padding='same', activation='sigmoid', kernel_initializer=initWeight)(disc4)
return Model([imgA, imgB], validity)
def buildGAN(self):
self.generator = self.buildGenerator()
self.discriminator = self.buildDiscriminator()
optimizer = Adam(2e-4)
self.discriminator.compile(loss='mse', optimizer=optimizer, metrics=['accuracy'])
imgB = Input(shape=self.img_shape)
fakeA = self.generator(imgB)
self.discriminator.trainable = False
validity = self.discriminator([fakeA, imgB])
self.combined = Model(imgB, [validity, fakeA])
self.combined.compile(loss=['mse', 'mae'], loss_weights=[1, 100], optimizer=optimizer)
def trainModel(self, epochs, batch_size=1):
self.dataLoader = DataLoader(
dir_A='D:/wallpaper/datas/test/trainA',
dir_B='D:/wallpaper/datas/test/trainB',
batch_size=batch_size,
img_shape=self.img_shape
)
totalStep = self.dataLoader.getNumberOfBatch()
for epoch in range(epochs):
for step, (imgA, imgB) in enumerate(self.dataLoader):
valid = np.ones((imgA.shape[0],) + self.discPatch)
fake = np.zeros((imgA.shape[0],) + self.discPatch)
fakeA = self.generator.predict(imgB)
D_loss_real = self.discriminator.train_on_batch([imgA, imgB], valid)
D_loss_fake = self.discriminator.train_on_batch([fakeA, imgB], fake)
D_loss = 0.5 * np.add(D_loss_real, D_loss_fake)
G_loss = self.combined.train_on_batch(imgB, [valid, imgA])
step += 1
print('Epoch {}/{}, step {}/{} -- D loss: {:.4f}, acc: {:.2f}%, '
'G loss: {:.4f}, adver loss: {:.4f}, L1 loss: {:.4f}'.format(
epoch + 1, epochs, step, totalStep, D_loss[0], D_loss[1] * 100, G_loss[0], G_loss[1], G_loss[2]))
if step % 2 == 0:
fpath = 'D:/wallpaper/datas/sketch/testB/1047028.png'
fname = 'output{}.png'.format(epoch)
self.colorizeImage(fpath=fpath, outputDir='output', fname=fname)
def colorizeImage(self, fpath, outputDir, fname):
img_input = cv2.imread(fpath, 1)
img_input = cv2.resize(img_input, (256, 256))
img_input = np.expand_dims(img_input, 0)
img_input = (img_input.astype(np.float32) - 127.5) / 127.5
img_output = self.generator.predict(img_input)[0]
img_output = img_output * 127.5 + 127.5
img_output = img_output.astype(np.uint8)
outputPath = os.path.join(outputDir, fname)
if not os.path.exists(outputDir):
os.mkdir(outputDir)
cv2.imwrite(outputPath, img_output)
|
