import PIL import torch import gradio as gr import os from process import load_seg_model, get_palette, generate_mask device = 'cpu' def read_content(file_path: str) -> str: with open(file_path, 'r', encoding='utf-8') as f: return f.read() def initialize_and_load_models(): checkpoint_path = 'model/cloth_segm.pth' return load_seg_model(checkpoint_path, device=device) net = initialize_and_load_models() palette = get_palette(4) def run(img): cloth_seg = generate_mask(img, net=net, palette=palette, device=device) return cloth_seg # CSS styling css = ''' .container {max-width: 1150px;margin: auto;padding-top: 1.5rem} #image_upload{min-height:400px} #image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px} .footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5} .footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white} .dark .footer {border-color: #303030} .dark .footer>p {background: #0b0f19} .acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%} #image_upload .touch-none{display: flex} ''' # Collect example images image_dir = 'input' image_list = [os.path.join(image_dir, file) for file in os.listdir(image_dir)] image_list.sort() examples = [[img] for img in image_list] with gr.Blocks(css=css) as demo: gr.HTML(read_content("header.html")) with gr.Row(): with gr.Column(): image = gr.Image(elem_id="image_upload", type="pil", label="Input Image") with gr.Column(): image_out = gr.Image(label="Output", elem_id="output-img") with gr.Row(): gr.Examples( examples=examples, inputs=[image], label="Examples - Input Images", examples_per_page=12 ) btn = gr.Button("Run!") btn.click(fn=run, inputs=[image], outputs=[image_out]) gr.HTML( """

ACKNOWLEDGEMENTS

U2net model is from original u2net repo. Thanks to Xuebin Qin.

Codes modified from levindabhi/cloth-segmentation

""" ) # Ensure the app works in Hugging Face by sharing a public link demo.launch(share=True)