CartoonGAN论文复现：如何将图像动漫化

摘要：本案例是 CartoonGAN: Generative Adversarial Networks for Photo Cartoonization的论文复现案例。

本文分享自华为云社区《cartoongan 图像动漫化》，作者： HWCloudAI 。

本案例是 CartoonGAN: Generative Adversarial Networks for Photo Cartoonization的论文复习案例。在拷贝数据之后，将你想动漫化的图像放到cartoongan-pytorch/test_img/文件夹下，运行后面代码即可。

可以切换不同生成风格，Hosoda/Shinkai/Paprika/Hayao

参考：https://github.com/venture-anime/cartoongan-pytorch

拷贝代码和数据

import moxing as mox
mox.file.copy_parallel('obs://obs-aigallery-zc/clf/code/cartoongan-pytorch','cartoongan-pytorch')

%cd cartoongan-pytorch

运行代码

import torch
import os
import numpy as np
import torchvision.utils as vutils
from PIL import Image
import torchvision.transforms as transforms
from torch.autograd import Variable
import matplotlib.pyplot as plt
from network.Transformer import Transformer
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--input_dir", default="test_img")
parser.add_argument("--load_size", default=1280)
parser.add_argument("--model_path", default="./pretrained_model")
parser.add_argument("--style", default="Hosoda") # 在这里切换风格， Hosoda/Shinkai/Paprika/Hayao
parser.add_argument("--output_dir", default="test_output")
parser.add_argument("--gpu", type=int, default=0)
# opt = parser.parse_args()
opt, unknown = parser.parse_known_args()
valid_ext = [".jpg", ".png", ".jpeg"]
# setup
if not os.path.exists(opt.input_dir):
 os.makedirs(opt.input_dir)
if not os.path.exists(opt.output_dir):
 os.makedirs(opt.output_dir)
# load pretrained model
model = Transformer()
model.load_state_dict(
 torch.load(os.path.join(opt.model_path, opt.style + "_net_G_float.pth"))
)
model.eval()
disable_gpu = opt.gpu == -1 or not torch.cuda.is_available()
if disable_gpu:
 print("CPU mode")
 model.float()
else:
 print("GPU mode")
 model.cuda()
for i,files in enumerate(os.listdir(opt.input_dir)):
 ext = os.path.splitext(files)[1]
 if ext not in valid_ext:
 continue
 # load image
 input_image = Image.open(os.path.join(opt.input_dir, files)).convert("RGB")
 input_image = np.asarray(input_image)
 # RGB -> BGR
 input_image = input_image[:, :, [2, 1, 0]]
 input_image = transforms.ToTensor()(input_image).unsqueeze(0)
 # preprocess, (-1, 1)
 input_image = -1 + 2 * input_image
 if disable_gpu:
 input_image = Variable(input_image).float()
 else:
 input_image = Variable(input_image).cuda()
 # forward
 output_image = model(input_image)
 output_image = output_image[0]
 # BGR -> RGB
 output_image = output_image[[2, 1, 0], :, :]
 output_image = output_image.data.cpu().float() * 0.5 + 0.5
 # save
 vutils.save_image(
 output_image,
 os.path.join(opt.output_dir, files[:-4] + "_" + opt.style + ".jpg"),
 )
    original = np.array(Image.open(os.path.join(opt.input_dir, files)))
    style = np.array(Image.open(os.path.join(opt.output_dir, files[:-4] + "_" + opt.style + ".jpg")))
 plt.figure(figsize=(20,20)) # 显示缩放比例
 plt.subplot(i+1,2,1)
 plt.imshow(original)
 plt.subplot(i+1,2,2)
 plt.imshow(style)
 plt.show()
print("Done!")

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