摘要：本案例是 CartoonGAN: Generative Adversarial Networks for Photo Cartoonization的論文復現案例。

本文分享自華為雲社群《cartoongan 影象動漫化》，作者： HWCloudAI 。

本案例是 CartoonGAN: Generative Adversarial Networks for Photo Cartoonization的論文複習案例。在拷貝資料之後，將你想動漫化的影象放到cartoongan-pytorch/test_img/資料夾下，執行後面程式碼即可。

可以切換不同生成風格，Hosoda/Shinkai/Paprika/Hayao

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

拷貝程式碼和資料

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

%cd cartoongan-pytorch

執行程式碼

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") # 在這裡切換風格， 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)) # 顯示縮放比例
 plt.subplot(i+1,2,1)
 plt.imshow(original)
 plt.subplot(i+1,2,2)
 plt.imshow(style)
 plt.show()
print("Done!")

點選關注，第一時間瞭解華為雲新鮮技術~