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| # install | |
| import glob | |
| import gradio as gr | |
| import os | |
| import numpy as np | |
| import subprocess | |
| if os.getenv('SYSTEM') == 'spaces': | |
| subprocess.run('pip install pyembree'.split()) | |
| subprocess.run( | |
| 'pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html'.split()) | |
| subprocess.run( | |
| 'pip install https://download.is.tue.mpg.de/icon/HF/kaolin-0.11.0-cp38-cp38-linux_x86_64.whl'.split()) | |
| subprocess.run( | |
| 'pip install https://download.is.tue.mpg.de/icon/HF/pytorch3d-0.7.0-cp38-cp38-linux_x86_64.whl'.split()) | |
| subprocess.run( | |
| 'pip install git+https://github.com/YuliangXiu/neural_voxelization_layer.git'.split()) | |
| from apps.infer import generate_model | |
| # running | |
| description = '''''' | |
| def generate_image(seed, psi): | |
| iface = gr.Interface.load("spaces/hysts/StyleGAN-Human") | |
| img = iface(seed, psi) | |
| return img | |
| model_types = ['ICON', 'PIFu', 'PaMIR'] | |
| examples_names = glob.glob('examples/*.png') | |
| examples_types = np.random.choice( | |
| model_types, len(examples_names), p=[0.6, 0.2, 0.2]) | |
| examples = [list(item) for item in zip(examples_names, examples_types)] | |
| with gr.Blocks() as demo: | |
| gr.Markdown(description) | |
| out_lst = [] | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| seed = gr.inputs.Slider( | |
| 0, 1000, step=1, default=0, label='Seed (For Image Generation)') | |
| psi = gr.inputs.Slider( | |
| 0, 2, step=0.05, default=0.7, label='Truncation psi (For Image Generation)') | |
| radio_choice = gr.Radio( | |
| model_types, label='Method (For Reconstruction)', value='icon-filter') | |
| inp = gr.Image(type="filepath", label="Input Image") | |
| with gr.Row(): | |
| btn_sample = gr.Button("Generate Image") | |
| btn_submit = gr.Button("Submit Image") | |
| gr.Examples(examples=examples, | |
| inputs=[inp, radio_choice], | |
| cache_examples=False, | |
| fn=generate_model, | |
| outputs=out_lst) | |
| out_vid = gr.Video( | |
| label="Image + Normal + SMPL Body + Clothed Human") | |
| out_vid_download = gr.File( | |
| label="Download Video, welcome share on Twitter with #ICON") | |
| with gr.Column(): | |
| overlap_inp = gr.Image( | |
| type="filepath", label="Image Normal Overlap") | |
| out_final = gr.Model3D( | |
| clear_color=[0.0, 0.0, 0.0, 0.0], label="Clothed human") | |
| out_final_download = gr.File( | |
| label="Download clothed human mesh") | |
| out_smpl = gr.Model3D( | |
| clear_color=[0.0, 0.0, 0.0, 0.0], label="SMPL body") | |
| out_smpl_download = gr.File(label="Download SMPL body mesh") | |
| out_smpl_npy_download = gr.File(label="Download SMPL params") | |
| out_lst = [out_smpl, out_smpl_download, out_smpl_npy_download, | |
| out_final, out_final_download, out_vid, out_vid_download, overlap_inp] | |
| btn_submit.click(fn=generate_model, inputs=[ | |
| inp, radio_choice], outputs=out_lst) | |
| btn_sample.click(fn=generate_image, inputs=[seed, psi], outputs=inp) | |
| if __name__ == "__main__": | |
| # demo.launch(debug=False, enable_queue=False, | |
| # auth=(os.environ['USER'], os.environ['PASSWORD']), | |
| # auth_message="Register at icon.is.tue.mpg.de to get HuggingFace username and password.") | |
| demo.launch(debug=True, enable_queue=True) | |