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| #!/usr/bin/env python | |
| import gradio as gr | |
| import torch | |
| from torch import Tensor | |
| from torchvision import transforms | |
| import numpy as np | |
| import sys | |
| model = None | |
| def load_model(): | |
| global model | |
| model = torch.jit.load("model.pt") | |
| def denormalize_and_clip_as_tensor (im: Tensor) -> Tensor: | |
| return torch.clip(im * 0.5 + 0.5, 0.0, 1.0) | |
| def denormalize_and_clip_as_numpy (im: Tensor) -> np.ndarray: | |
| im = im.squeeze(0) | |
| return np.ascontiguousarray(denormalize_and_clip_as_tensor(im).permute(1,2,0).detach().cpu().numpy()) | |
| def undo_antialiasing(im): | |
| im_torch = torch.from_numpy (im).permute(2,0,1).unsqueeze(0).float() / 255.0 | |
| im_torch = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(im_torch) | |
| with torch.no_grad(): | |
| output_torch = model(im_torch) | |
| output = denormalize_and_clip_as_numpy(output_torch.rgb) | |
| return (output*255.99).astype(np.uint8) | |
| load_model() | |
| iface = gr.Interface(fn=undo_antialiasing, | |
| inputs=gr.inputs.Image(), | |
| outputs=gr.outputs.Image(), | |
| examples=[['examples/Bowling.png'], ['examples/opencv.png']]) | |
| iface.launch() | |