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import PIL |
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import torch |
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import gradio as gr |
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import os |
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from process import load_seg_model, get_palette, generate_mask |
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device = 'cuda' if torch.cuda.is_available() else 'cpu' |
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def read_content(file_path: str) -> str: |
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"""Read file content with error handling""" |
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try: |
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with open(file_path, 'r', encoding='utf-8') as f: |
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return f.read() |
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except FileNotFoundError: |
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print(f"Warning: File {file_path} not found") |
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return "" |
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except Exception as e: |
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print(f"Error reading file {file_path}: {str(e)}") |
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return "" |
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def initialize_and_load_models(): |
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"""Initialize and load models with error handling""" |
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try: |
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checkpoint_path = 'model/cloth_segm.pth' |
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if not os.path.exists(checkpoint_path): |
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raise FileNotFoundError(f"Model checkpoint not found at {checkpoint_path}") |
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return load_seg_model(checkpoint_path, device=device) |
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except Exception as e: |
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print(f"Error loading model: {str(e)}") |
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return None |
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net = initialize_and_load_models() |
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if net is None: |
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raise RuntimeError("Failed to load model - check logs for details") |
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palette = get_palette(4) |
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def run(img): |
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"""Process image with error handling""" |
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if img is None: |
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raise gr.Error("No image uploaded") |
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try: |
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cloth_seg = generate_mask(img, net=net, palette=palette, device=device) |
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if cloth_seg is None: |
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raise gr.Error("Failed to generate mask") |
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return cloth_seg |
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except Exception as e: |
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raise gr.Error(f"Error processing image: {str(e)}") |
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css = ''' |
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.container {max-width: 1150px;margin: auto;padding-top: 1.5rem} |
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#image_upload{min-height:400px} |
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#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px} |
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.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5} |
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.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white} |
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.dark .footer {border-color: #303030} |
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.dark .footer>p {background: #0b0f19} |
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.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%} |
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#image_upload .touch-none{display: flex} |
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''' |
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image_dir = 'input' |
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image_list = [] |
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if os.path.exists(image_dir): |
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image_list = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.lower().endswith(('.png', '.jpg', '.jpeg'))] |
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image_list.sort() |
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examples = [[img] for img in image_list] |
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with gr.Blocks(css=css) as demo: |
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gr.HTML(read_content("header.html")) |
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with gr.Row(): |
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with gr.Column(): |
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image = gr.Image(elem_id="image_upload", type="pil", label="Input Image") |
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with gr.Column(): |
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image_out = gr.Image(label="Output", elem_id="output-img") |
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with gr.Row(): |
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gr.Examples( |
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examples=examples, |
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inputs=[image], |
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label="Examples - Input Images", |
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examples_per_page=12 |
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) |
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btn = gr.Button("Run!", variant="primary") |
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btn.click(fn=run, inputs=[image], outputs=[image_out]) |
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gr.HTML( |
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""" |
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<div class="footer"> |
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<p>Model by <a href="" style="text-decoration: underline;" target="_blank">WildOctopus</a> - Gradio Demo by 🤗 Hugging Face</p> |
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</div> |
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<div class="acknowledgments"> |
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<p><h4>ACKNOWLEDGEMENTS</h4></p> |
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<p>U2net model is from original u2net repo. Thanks to <a href="https://github.com/xuebinqin/U-2-Net" target="_blank">Xuebin Qin</a>.</p> |
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<p>Codes modified from <a href="https://github.com/levindabhi/cloth-segmentation" target="_blank">levindabhi/cloth-segmentation</a></p> |
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</div> |
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""" |
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) |
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demo.launch() |
