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| import os | |
| import sys | |
| import torch | |
| import gradio as gr | |
| import numpy as np | |
| import torchvision.transforms as transforms | |
| from torch.autograd import Variable | |
| from network.Transformer import Transformer | |
| import logging | |
| logger = logging.getLogger(__name__) | |
| MAX_DIMENSION = 1280 | |
| MODEL_PATH = "models" | |
| COLOUR_MODEL = "RGB" | |
| STYLE_SHINKAI = "Makoto Shinkai" | |
| STYLE_HOSODA = "Mamoru Hosoda" | |
| STYLE_MIYAZAKI = "Hayao Miyazaki" | |
| STYLE_KON = "Satoshi Kon" | |
| DEFAULT_STYLE = STYLE_SHINKAI | |
| STYLE_CHOICE_LIST = [STYLE_SHINKAI, STYLE_HOSODA, STYLE_MIYAZAKI, STYLE_KON] | |
| shinkai_model = Transformer() | |
| hosoda_model = Transformer() | |
| miyazaki_model = Transformer() | |
| kon_model = Transformer() | |
| shinkai_model.load_state_dict( | |
| torch.load(os.path.join(MODEL_PATH, "shinkai_makoto.pth")) | |
| ) | |
| hosoda_model.load_state_dict( | |
| torch.load(os.path.join(MODEL_PATH, "hosoda_mamoru.pth")) | |
| ) | |
| miyazaki_model.load_state_dict( | |
| torch.load(os.path.join(MODEL_PATH, "miyazaki_hayao.pth")) | |
| ) | |
| kon_model.load_state_dict( | |
| torch.load(os.path.join(MODEL_PATH, "kon_satoshi.pth")) | |
| ) | |
| shinkai_model.eval() | |
| hosoda_model.eval() | |
| miyazaki_model.eval() | |
| kon_model.eval() | |
| disable_gpu = True | |
| def get_model(style): | |
| if style == STYLE_SHINKAI: | |
| return shinkai_model | |
| elif style == STYLE_HOSODA: | |
| return hosoda_model | |
| elif style == STYLE_MIYAZAKI: | |
| return miyazaki_model | |
| elif style == STYLE_KON: | |
| return kon_model | |
| else: | |
| logger.warning( | |
| f"Style {style} not found. Defaulting to Makoto Shinkai" | |
| ) | |
| return shinkai_model | |
| def validate_image_size(img): | |
| logger.info(f"Image Height: {img.height}, Image Width: {img.width}") | |
| if img.height > MAX_DIMENSION or img.width > MAX_DIMENSION: | |
| raise RuntimeError( | |
| "Image size is too large. Please use an image less than {MAX_DIMENSION}px on both width and height" | |
| ) | |
| def inference(img, style): | |
| validate_image_size(img) | |
| # load image | |
| input_image = img.convert(COLOUR_MODEL) | |
| 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 | |
| model = get_model(style) | |
| 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 | |
| return transforms.ToPILImage()(output_image) | |
| title = "Anime Background GAN" | |
| description = "Gradio Demo for CartoonGAN by Chen Et. Al. Models are Shinkai Makoto, Hosoda Mamoru, Kon Satoshi, and Miyazaki Hayao." | |
| article = "<p style='text-align: center'><a href='http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/2205.pdf' target='_blank'>CartoonGAN Whitepaper from Chen et.al</a></p><p style='text-align: center'><a href='https://github.com/venture-anime/cartoongan-pytorch' target='_blank'>Github Repo</a></p><p style='text-align: center'><a href='https://github.com/Yijunmaverick/CartoonGAN-Test-Pytorch-Torch' target='_blank'>Original Implementation from Yijunmaverick</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akiyamasho' alt='visitor badge'></center></p>" | |
| examples = [ | |
| ["examples/garden_in.jpg", STYLE_SHINKAI], | |
| ["examples/library_in.jpg", STYLE_KON], | |
| ] | |
| gr.Interface( | |
| fn=inference, | |
| inputs=[ | |
| gr.inputs.Image( | |
| type="pil", | |
| label="Input Photo (less than 1280px on both width and height)", | |
| ), | |
| gr.inputs.Dropdown( | |
| STYLE_CHOICE_LIST, | |
| type="value", | |
| default=DEFAULT_STYLE, | |
| label="Style", | |
| ), | |
| ], | |
| outputs=gr.outputs.Image( | |
| type="pil", | |
| label="Output Image", | |
| ), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| allow_flagging="never", | |
| allow_screenshot=False, | |
| ).launch(enable_queue=True) | |