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