Pixel2Pixel:卡通照片真人化
项目链接:Pixel2Pixel:卡通照片真人化
前言:
之前PaddleGAN的趣味应用如雨后春笋般地出现,非常多的项目都是xxx动漫化。当时就有一个很普通的想法为什么大家都会去搞动漫化,这很可能是因为二次元文化的原因,又或者是动漫化的应用、商业价值。就突然蹦出一个想法,为什么没人弄动漫真人化呢,然后我就去项目搜了,结果确实貌似没有人做这个项目。刚开始我以为我这个想法实现起来很难,到后面和大神们讨论后,其实觉得实现原理也很简单,就是把人像动漫化的数据集里面的标签互换。比如人像卡通化,就是A to B(A是真人,B是动漫,B是标签)。那么此次这个项目卡通人像化就是B to A(A是真人,B是动漫,A是标签).
先来看看实现效果
实现效果:
真人原图:
实现效果:
真人原图:
可以看到效果已经很逼真了!
1.下载安装包
import paddle
import paddle.nn as nn
from paddle.io import Dataset, DataLoaderimport os
import cv2
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt%matplotlib inline
2.解压数据
数据准备:
- 真人数据来自seeprettyface。
- 数据预处理(详情见photo2cartoon项目)。
- 使用photo2cartoon项目生成真人数据对应的卡通数据。
# 解压数据
!unzip -q data/data79149/cartoon_A2B.zip -d data/
3.数据可视化(已划分好数据集)
# 训练数据统计
train_names = os.listdir('data/cartoon_A2B/train')
print(f'训练集数据量: {len(train_names)}')# 测试数据统计
test_names = os.listdir('data/cartoon_A2B/test')
print(f'测试集数据量: {len(test_names)}')# 训练数据可视化
imgs = []
for img_name in np.random.choice(train_names, 3, replace=False):imgs.append(cv2.imread('data/cartoon_A2B/train/'+img_name))img_show = np.vstack(imgs)[:,:,::-1]
plt.figure(figsize=(10, 10))
plt.imshow(img_show)
plt.show()
注意:
A代表真人,B代表卡通。源参考代码 是A to B。本次实验项目是用 B to A
又因为数据集是把 真人照片和卡通图片拼接在一起,利用划分宽度来区别原图与标签。例如源程序 是用 宽度[ : 256]分成真人(即原图),[256 : ]分成卡通(即标签)
要实现这个项目因此要把他们调换过来。
class PairedData(Dataset):def __init__(self, phase):super(PairedData, self).__init__() self.img_path_list = self.load_A2B_data(phase) # 获取数据列表self.num_samples = len(self.img_path_list) # 数据量def __getitem__(self, idx):img_A2B = cv2.imread(self.img_path_list[idx]) # 读取数据img_A2B = img_A2B.astype('float32') / 127.5 - 1. # 归一化img_A2B = img_A2B.transpose(2, 0, 1) # HWC -> CHWimg_A = img_A2B[..., 256:] # 卡通图(原图) img_B = img_A2B[..., :256] # 真人图(标签)return img_A, img_Bdef __len__(self):return self.num_samples@staticmethoddef load_A2B_data(phase):assert phase in ['train', 'test'], "phase should be set within ['train', 'test']"# 读取数据集,数据中每张图像包含照片和对应的卡通画。data_path = 'data/cartoon_A2B/'+phasereturn [os.path.join(data_path, x) for x in os.listdir(data_path)]
paired_dataset_train = PairedData('train')
paired_dataset_test = PairedData('test')
4.定义生成器
class UnetGenerator(nn.Layer):def __init__(self, input_nc=3, output_nc=3, ngf=64):super(UnetGenerator, self).__init__()self.down1 = nn.Conv2D(input_nc, ngf, kernel_size=4, stride=2, padding=1)self.down2 = Downsample(ngf, ngf*2)self.down3 = Downsample(ngf*2, ngf*4)self.down4 = Downsample(ngf*4, ngf*8)self.down5 = Downsample(ngf*8, ngf*8)self.down6 = Downsample(ngf*8, ngf*8)self.down7 = Downsample(ngf*8, ngf*8)self.center = Downsample(ngf*8, ngf*8)self.up7 = Upsample(ngf*8, ngf*8, use_dropout=True)self.up6 = Upsample(ngf*8*2, ngf*8, use_dropout=True)self.up5 = Upsample(ngf*8*2, ngf*8, use_dropout=True)self.up4 = Upsample(ngf*8*2, ngf*8)self.up3 = Upsample(ngf*8*2, ngf*4)self.up2 = Upsample(ngf*4*2, ngf*2)self.up1 = Upsample(ngf*2*2, ngf)self.output_block = nn.Sequential(nn.ReLU(),nn.Conv2DTranspose(ngf*2, output_nc, kernel_size=4, stride=2, padding=1),nn.Tanh())def forward(self, x):d1 = self.down1(x)d2 = self.down2(d1)d3 = self.down3(d2)d4 = self.down4(d3)d5 = self.down5(d4)d6 = self.down6(d5)d7 = self.down7(d6)c = self.center(d7)x = self.up7(c, d7)x = self.up6(x, d6)x = self.up5(x, d5)x = self.up4(x, d4)x = self.up3(x, d3)x = self.up2(x, d2)x = self.up1(x, d1)x = self.output_block(x)return xclass Downsample(nn.Layer):# LeakyReLU => conv => batch normdef __init__(self, in_dim, out_dim, kernel_size=4, stride=2, padding=1):super(Downsample, self).__init__()self.layers = nn.Sequential(nn.LeakyReLU(0.2),nn.Conv2D(in_dim, out_dim, kernel_size, stride, padding, bias_attr=False),nn.BatchNorm2D(out_dim))def forward(self, x):x = self.layers(x)return xclass Upsample(nn.Layer):# ReLU => deconv => batch norm => dropoutdef __init__(self, in_dim, out_dim, kernel_size=4, stride=2, padding=1, use_dropout=False):super(Upsample, self).__init__()sequence = [nn.ReLU(),nn.Conv2DTranspose(in_dim, out_dim, kernel_size, stride, padding, bias_attr=False),nn.BatchNorm2D(out_dim)]if use_dropout:sequence.append(nn.Dropout(p=0.5))self.layers = nn.Sequential(*sequence)def forward(self, x, skip):x = self.layers(x)x = paddle.concat([x, skip], axis=1)return x
5.定义鉴别器
class NLayerDiscriminator(nn.Layer):def __init__(self, input_nc=6, ndf=64):super(NLayerDiscriminator, self).__init__()self.layers = nn.Sequential(nn.Conv2D(input_nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2),ConvBlock(ndf, ndf*2),ConvBlock(ndf*2, ndf*4),ConvBlock(ndf*4, ndf*8, stride=1),nn.Conv2D(ndf*8, 1, kernel_size=4, stride=1, padding=1),nn.Sigmoid())def forward(self, input):return self.layers(input)class ConvBlock(nn.Layer):# conv => batch norm => LeakyReLUdef __init__(self, in_dim, out_dim, kernel_size=4, stride=2, padding=1):super(ConvBlock, self).__init__()self.layers = nn.Sequential(nn.Conv2D(in_dim, out_dim, kernel_size, stride, padding, bias_attr=False),nn.BatchNorm2D(out_dim),nn.LeakyReLU(0.2))def forward(self, x):x = self.layers(x)return x
实例化生成器,鉴别器
generator = UnetGenerator()
discriminator = NLayerDiscriminator()
out = generator(paddle.ones([1, 3, 256, 256]))
print('生成器输出尺寸:', out.shape)out = discriminator(paddle.ones([1, 6, 256, 256]))
print('鉴别器输出尺寸:', out.shape)
6.定义训练各项超参数
# 超参数
LR = 1e-4
BATCH_SIZE = 8
EPOCHS = 100# 优化器
optimizerG = paddle.optimizer.Adam(learning_rate=LR,parameters=generator.parameters(),beta1=0.5,beta2=0.999)optimizerD = paddle.optimizer.Adam(learning_rate=LR,parameters=discriminator.parameters(), beta1=0.5,beta2=0.999)# 损失函数
bce_loss = nn.BCELoss()
l1_loss = nn.L1Loss()# dataloader
data_loader_train = DataLoader(paired_dataset_train,batch_size=BATCH_SIZE,shuffle=True,drop_last=True)data_loader_test = DataLoader(paired_dataset_test,batch_size=BATCH_SIZE)
训练效果
第一列是卡通(原图),第二列是真人图片(标签),第三列是学习出来的结果
刚开始学到的效果:
100epochs的效果:
我们可以看出已经有很好的效果
results_save_path = 'work/results'
os.makedirs(results_save_path, exist_ok=True) # 保存每个epoch的测试结果weights_save_path = 'work/weights'
os.makedirs(weights_save_path, exist_ok=True) # 保存模型for epoch in range(EPOCHS):for data in tqdm(data_loader_train):real_A, real_B = dataoptimizerD.clear_grad()# D(real)real_AB = paddle.concat((real_A, real_B), 1)d_real_predict = discriminator(real_AB)d_real_loss = bce_loss(d_real_predict, paddle.ones_like(d_real_predict))# D(fake)fake_B = generator(real_A).detach()fake_AB = paddle.concat((real_A, fake_B), 1)d_fake_predict = discriminator(fake_AB)d_fake_loss = bce_loss(d_fake_predict, paddle.zeros_like(d_fake_predict))# train Dd_loss = (d_real_loss + d_fake_loss) / 2.d_loss.backward()optimizerD.step()optimizerG.clear_grad()# D(fake)fake_B = generator(real_A)fake_AB = paddle.concat((real_A, fake_B), 1)g_fake_predict = discriminator(fake_AB)g_bce_loss = bce_loss(g_fake_predict, paddle.ones_like(g_fake_predict))g_l1_loss = l1_loss(fake_B, real_B) * 100.g_loss = g_bce_loss + g_l1_loss# train Gg_loss.backward()optimizerG.step()print(f'Epoch [{epoch+1}/{EPOCHS}] Loss D: {d_loss.numpy()}, Loss G: {g_loss.numpy()}')if (epoch+1) % 10 == 0:paddle.save(generator.state_dict(), os.path.join(weights_save_path, 'epoch'+str(epoch+1).zfill(3)+'.pdparams'))# testgenerator.eval()with paddle.no_grad():for data in data_loader_test:real_A, real_B = databreakfake_B = generator(real_A)result = paddle.concat([real_A[:3], real_B[:3], fake_B[:3]], 3)result = result.detach().numpy().transpose(0, 2, 3, 1)result = np.vstack(result)result = (result * 127.5 + 127.5).astype(np.uint8)cv2.imwrite(os.path.join(results_save_path, 'epoch'+str(epoch+1).zfill(3)+'.png'), result)generator.train()
7.测试
# 为生成器加载权重
last_weights_path = os.path.join(weights_save_path, sorted(os.listdir(weights_save_path))[-1])
print('加载权重:', last_weights_path)model_state_dict = paddle.load(last_weights_path)
generator.load_dict(model_state_dict)
generator.eval()
读取数据
test_names = os.listdir('data/cartoon_A2B/test')
# img_name = np.random.choice(test_names)
img_name = '01481.png'
img_A2B = cv2.imread('data/cartoon_A2B/test/'+img_name)
img_A = img_A2B[:, 256:] # 卡通图(即输入)
img_B = img_A2B[:, :256] # 真人图(即预测结果)# img_A= cv2.imread('data/test4.png')
# img_A = img_A[:, 256:]g_input = img_A.astype('float32') / 127.5 - 1 # 归一化
g_input = g_input[np.newaxis, ...].transpose(0, 3, 1, 2) # NHWC -> NCHW
g_input = paddle.to_tensor(g_input) # numpy -> tensorg_output = generator(g_input)
g_output = g_output.detach().numpy() # tensor -> numpy
g_output = g_output.transpose(0, 2, 3, 1)[0] # NCHW -> NHWC
g_output = g_output * 127.5 + 127.5 # 反归一化
g_output = g_output.astype(np.uint8)img_show = np.hstack([img_A, g_output])[:,:,::-1]
plt.figure(figsize=(8, 8))
plt.imshow(img_show)numpy
g_output = g_output.transpose(0, 2, 3, 1)[0] # NCHW -> NHWC
g_output = g_output * 127.5 + 127.5 # 反归一化
g_output = g_output.astype(np.uint8)img_show = np.hstack([img_A, g_output])[:,:,::-1]
plt.figure(figsize=(8, 8))
plt.imshow(img_show)
plt.show()
总结:
至此,动漫照片真人化项目就完成了,本次项目大部分基于参考项目,只是做了些许改动。
参考项目:
Pixel2Pixel:人像卡通化
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