import os
import shutil
import subprocess
import textwrap
from pathlib import Path

import gradio as gr
import torch
from huggingface_hub import hf_hub_download

REPO_ID = "kbrodt/sketch2pose"
API_TOKEN = os.environ["sketch2pose"]
ASSET_DIR = Path("./assets")
SAVE_DIR = "output"
CMD = textwrap.dedent("""
    python src/pose.py
        --save-path {}
        --img-path {}
""")
TITLE = "Sketch2Pose: Estimating a 3D Character Pose from a Bitmap Sketch"
DESCRIPTION = '''
<style>
figure {
    margin: 0;
    font-size: smaller;
    text-align: justify;
}
img {
    width: auto;
    max-width: 100%;
    height: auto;
}
video {
    width: 720;
    max-width: 100%;
    height: 405;
}
ul.horizontal {
    padding: 0;
}
ul.horizontal li {
    padding: 0 1em 0 0;
    display: inline-block;
}
table td {
    vertical-align: top;
}
</style>

<table>
<tr>
<td>
<figure>
<img src="http://www-labs.iro.umontreal.ca/~bmpix/sketch2pose/teaser.png" alt="sketch2pose">
<figcaption>
Given a single natural <b>bitmap</b> sketch of a character (a), our
learning-based approach allows to automatically, with no additional input,
recover the 3D pose consistent with the viewer expectation (b). This pose can
be then automatically copied a custom rigged and skinned 3D character (c) using
standard retargeting tools (d). Input image &copy;&nbsp;Olga Posukh.
</figcaption>
</figure>

<p>
<ul class="horizontal">
<li><a href="http://www-labs.iro.umontreal.ca/~bmpix/sketch2pose/">[project page]</a></li>
<li><a href="https://dl.acm.org/doi/10.1145/3528223.3530106">[paper acm siggraph 2022]</a></li>
<li><a href="https://github.com/kbrodt/sketch2pose">[code.git]</a></li>
</ul>
</p>
</td>

<td>
<video width="720" height="405" controls autoplay muted loop>
<source src="http://www-labs.iro.umontreal.ca/~bmpix/sketch2pose/sketch2pose.webm" type="video/mp4">
</video>
</td>
</tr>
<table>
'''


def prepare():
    filename = "models_smplx_v1_1.zip"
    smpl_path = hf_hub_download(
        repo_id=REPO_ID,
        repo_type="model",
        filename=filename,
        use_auth_token=API_TOKEN,
        cache_dir=ASSET_DIR,
    )
    if not (ASSET_DIR / filename).is_file():
        shutil.copy(smpl_path, ASSET_DIR)
    
    subprocess.run("bash ./scripts/download.sh".split())
    subprocess.run("bash ./scripts/prepare.sh".split())


def main():
    prepare()

    save_dir = Path(SAVE_DIR)
    save_dir.mkdir(parents=True, exist_ok=True)

    def pose(img_path, use_cos=True, use_angle_transf=True, use_contacts=False, use_natural=True):
        if use_cos == False:
            use_angle_transf = False

        cmd = CMD.format(save_dir, img_path)
        if use_cos:
            cmd = cmd + " --use-cos"
        if use_angle_transf:
            cmd = cmd + " --use-angle-transf"
        if use_contacts:
            cmd = cmd + " --use-contacts"
        if use_natural:
            cmd = cmd + " --use-natural"

        out_dir = (save_dir / Path(img_path).name).with_suffix("")
        mesh_path = out_dir / "us.glb"

        if not mesh_path.is_file():
            subprocess.call(cmd.split())

        return str(mesh_path)

    examples = []
    for img_path in Path("./data/images").glob("*"):
        examples.append([str(img_path), True, True, False, True])

    demo = gr.Interface(
        fn=pose,
        inputs=[
            gr.Image(type="filepath", label="Image"),
            gr.Checkbox(value=True, label="Bone lenghts"),
            gr.Checkbox(value=True, label="Foreshortening"),
            gr.Checkbox(value=False, label="Self-contacts (available with cuda)", interactive=torch.cuda.is_available()),
            gr.Checkbox(value=True, label="Pose naturalness"),
        ],
        outputs=gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="SMPL 3D pose"),
        examples=examples[:5],        
        title=TITLE,
        description=DESCRIPTION,
    )

    demo.launch()


if __name__ == "__main__":
    main()