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| 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: | |
| """read the content of target file | |
| """ | |
| with open(file_path, 'r', encoding='utf-8') as f: | |
| content = f.read() | |
| return content | |
| def initialize_and_load_models(): | |
| checkpoint_path = 'model/cloth_segm.pth' | |
| net = load_seg_model(checkpoint_path, device=device) | |
| return net | |
| 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 | |
| # Define input and output interfaces | |
| input_image = gr.inputs.Image(label="Input Image", type="pil") | |
| # Define the Gradio interface | |
| cloth_seg_image = gr.outputs.Image(label="Cloth Segmentation", type="pil") | |
| title = "Demo for Cloth Segmentation" | |
| description = "An app for Cloth Segmentation" | |
| inputs = [input_image] | |
| outputs = [cloth_seg_image] | |
| 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} | |
| #mask_radio .gr-form{background:transparent; border: none} | |
| #word_mask{margin-top: .75em !important} | |
| #word_mask textarea:disabled{opacity: 0.3} | |
| .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} | |
| @keyframes spin { | |
| from { | |
| transform: rotate(0deg); | |
| } | |
| to { | |
| transform: rotate(360deg); | |
| } | |
| } | |
| #share-btn-container { | |
| display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; | |
| } | |
| #share-btn { | |
| all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important; | |
| } | |
| #share-btn * { | |
| all: unset; | |
| } | |
| #share-btn-container div:nth-child(-n+2){ | |
| width: auto !important; | |
| min-height: 0px !important; | |
| } | |
| #share-btn-container .wrap { | |
| display: none !important; | |
| } | |
| ''' | |
| example={} | |
| image_dir='input' | |
| image_list=[os.path.join(image_dir,file) for file in os.listdir(image_dir)] | |
| image_list.sort() | |
| image_blocks = gr.Blocks(css=css) | |
| with image_blocks as demo: | |
| gr.HTML(read_content("header.html")) | |
| with gr.Group(): | |
| with gr.Box(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| image = gr.Image(source='upload', elem_id="image_upload", type="pil", label="Input Image") | |
| with gr.Column(): | |
| image_out = gr.Image(label="Output", elem_id="output-img").style(height=400) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Examples(image_list, inputs=[image],label="Examples - Input Images",examples_per_page=12) | |
| with gr.Column(): | |
| with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True): | |
| btn = gr.Button("Run!").style( | |
| margin=False, | |
| rounded=(False, True, True, False), | |
| full_width=True, | |
| ) | |
| btn.click(fn=run, inputs=[image], outputs=[image_out]) | |
| gr.HTML( | |
| """ | |
| <div class="footer"> | |
| <p>Model by <a href="" style="text-decoration: underline;" target="_blank">WildOctopus</a> - Gradio Demo by 🤗 Hugging Face | |
| </p> | |
| </div> | |
| <div class="acknowledgments"> | |
| <p><h4>ACKNOWLEDGEMENTS</h4></p> | |
| <p> | |
| U2net model is from original u2net repo. Thanks to <a href="https://github.com/xuebinqin/U-2-Net" style="text-decoration: underline;" target="_blank">Xuebin Qin</a> for amazing repo.</p> | |
| <p>Codes are modified from <a href="https://github.com/levindabhi/cloth-segmentation" style="text-decoration: underline;" target="_blank">levindabhi/cloth-segmentation</a> | |
| </p> | |
| """ | |
| ) | |
| image_blocks.launch() |