Create app.py

app.py

@@ -1,158 +0,0 @@
-# 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 git+https://github.com/YuliangXiu/rembg.git@hf'.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 = '''
-# ICON Clothed Human Digitization 
-### ICON: Implicit Clothed humans Obtained from Normals (CVPR 2022)
-
-<table>
-<th>
-<ul>
-<li><strong>Homepage</strong> <a href="http://icon.is.tue.mpg.de">icon.is.tue.mpg.de</a></li>
-<li><strong>Code</strong> <a href="https://github.com/YuliangXiu/ICON">YuliangXiu/ICON</a></li>
-<li><strong>Paper</strong> <a href="https://arxiv.org/abs/2112.09127">arXiv</a>, <a href="https://readpaper.com/paper/4569785684533977089">ReadPaper</a></li>
-<li><strong>Chatroom</strong> <a href="https://discord.gg/Vqa7KBGRyk">Discord</a></li>
-<li><strong>Colab Notebook</strong> <a href="https://colab.research.google.com/drive/1-AWeWhPvCTBX0KfMtgtMk10uPU05ihoA?usp=sharing">Google Colab</a></li>
-</ul>
-<a href="https://twitter.com/yuliangxiu"><img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/yuliangxiu?style=social"></a>
-<iframe src="https://ghbtns.com/github-btn.html?user=yuliangxiu&repo=ICON&type=star&count=true&v=2&size=small" frameborder="0" scrolling="0" width="100" height="20"></iframe>
-<a href="https://youtu.be/hZd6AYin2DE"><img alt="YouTube Video Views" src="https://img.shields.io/youtube/views/hZd6AYin2DE?style=social"></a>
-</th>
-<th>
-<iframe width="560" height="315" src="https://www.youtube.com/embed/hZd6AYin2DE" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-</th>
-</table>
-
-<h4> The reconstruction + refinement + video take about 200 seconds for single image. <span style="color:red"> If ERROR, try "Submit Image" again.</span></h4>
-
-<details>
-
-<summary>More</summary>
-
-#### Citation
-```
-@inproceedings{xiu2022icon,
-  title     = {{ICON}: {I}mplicit {C}lothed humans {O}btained from {N}ormals},
-  author    = {Xiu, Yuliang and Yang, Jinlong and Tzionas, Dimitrios and Black, Michael J.},
-  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-  month     = {June},
-  year      = {2022},
-  pages     = {13296-13306}
-} 
-```
-
-#### Acknowledgments:
-
-- [StyleGAN-Human, ECCV 2022](https://stylegan-human.github.io/)
-- [nagolinc/styleGanHuman_and_PIFu](https://huggingface.co/spaces/nagolinc/styleGanHuman_and_PIFu)
-- [radames/PIFu-Clothed-Human-Digitization](https://huggingface.co/spaces/radames/PIFu-Clothed-Human-Digitization)
-
-#### Image Credits
-
-* [Pinterest](https://www.pinterest.com/search/pins/?q=parkour&rs=sitelinks_searchbox)
-
-#### Related works
-
-* [ICON @ MPI](https://icon.is.tue.mpg.de/)
-* [MonoPort @ USC](https://xiuyuliang.cn/monoport)
-* [Phorhum @ Google](https://phorhum.github.io/)
-* [PIFuHD @ Meta](https://shunsukesaito.github.io/PIFuHD/)
-* [PaMIR @ Tsinghua](http://www.liuyebin.com/pamir/pamir.html)
-
-</details>
-'''
-
-
-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)