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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)
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