| import os | |
| os.system("pip freeze") | |
| import torch | |
| import PIL | |
| import gradio as gr | |
| import torch | |
| from utils import align_face | |
| from torchvision import transforms | |
| from huggingface_hub import hf_hub_download | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| image_size = 512 | |
| transform_size = 1024 | |
| means = [0.5, 0.5, 0.5] | |
| stds = [0.5, 0.5, 0.5] | |
| img_transforms = transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Normalize(means, stds)]) | |
| model_path = hf_hub_download(repo_id="jjeamin/ArcaneStyleTransfer", filename="pytorch_model.bin") | |
| if 'cuda' in device: | |
| style_transfer = torch.jit.load(model_path).eval().cuda().half() | |
| t_stds = torch.tensor(stds).cuda().half()[:,None,None] | |
| t_means = torch.tensor(means).cuda().half()[:,None,None] | |
| else: | |
| style_transfer = torch.jit.load(model_path).eval().cpu() | |
| t_stds = torch.tensor(stds).cpu()[:,None,None] | |
| t_means = torch.tensor(means).cpu()[:,None,None] | |
| def tensor2im(var): | |
| return var.mul(t_stds).add(t_means).mul(255.).clamp(0,255).permute(1,2,0) | |
| def proc_pil_img(input_image): | |
| if 'cuda' in device: | |
| transformed_image = img_transforms(input_image)[None,...].cuda().half() | |
| else: | |
| transformed_image = img_transforms(input_image)[None,...].cpu() | |
| with torch.no_grad(): | |
| result_image = style_transfer(transformed_image)[0] | |
| output_image = tensor2im(result_image) | |
| output_image = output_image.detach().cpu().numpy().astype('uint8') | |
| output_image = PIL.Image.fromarray(output_image) | |
| return output_image | |
| def process(im, is_align): | |
| im = PIL.ImageOps.exif_transpose(im) | |
| if is_align == 'True': | |
| im = align_face(im, output_size=image_size, transform_size=transform_size) | |
| else: | |
| pass | |
| res = proc_pil_img(im) | |
| return res | |
| gr.Interface( | |
| process, | |
| inputs=[gr.inputs.Image(source="webcam",type="pil", label="Input", shape=(image_size, image_size)), gr.inputs.Radio(['True','False'], type="value", default='True', label='face align')], | |
| outputs=gr.outputs.Image(type="pil", label="Output"), | |
| title="Arcane Style Transfer WEBCAM", | |
| description="Gradio demo for Arcane Style Transfer with webcam input", | |
| article = "<p style='text-align: center'><a href='https://github.com/jjeamin/anime_style_transfer_pytorch' target='_blank'>Github Repo by jjeamin</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=jjeamin_arcane_st' alt='visitor badge'></center></p>", | |
| enable_queue=True, | |
| allow_flagging=False, | |
| allow_screenshot=False | |
| ).launch(enable_queue=True) |