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import numpy as np |
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import torch |
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import torch.nn as nn |
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import gradio as gr |
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from PIL import Image |
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import torchvision.transforms as transforms |
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from lle import SYELLENetS |
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kwargs = {'channels': 12} |
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model = SYELLENetS(**kwargs) |
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model.load_state_dict(torch.load('./model_best_slim.pkl', map_location='cpu')) |
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model.eval() |
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def predict(input_img, ver): |
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input_img = Image.open(input_img) |
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input_img = np.array(input_img).transpose([2, 0, 1]) |
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input_img = input_img.astype(np.float32) / 255.0 |
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input_img = torch.from_numpy(input_img).unsqueeze(0) |
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with torch.no_grad(): |
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outputs = model(input_img) |
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out_img = (outputs.clip(0, 1)[0] * 255).permute([1, 2, 0]).cpu().numpy().astype(np.uint8)[..., ::-1] |
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return transforms.ToPILImage()(out_img) |
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title="Image to Line Drawings - Complex and Simple Portraits and Landscapes" |
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examples=['./examples/1.png', './examples/22.png', './examples/23.png', './examples/55.png', './examples/79.png' |
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] |
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iface = gr.Interface(predict, inputs=gr.Image(type='filepath'), |
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outputs='image', |
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title=title, |
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examples=examples) |
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iface.launch() |
