app.py

import logging
import os
import shlex
import subprocess
import tempfile
import time

import gradio as gr
import numpy as np
import rembg
import spaces
import torch
from PIL import Image
from functools import partial

subprocess.run(shlex.split('pip install wheel/torchmcubes-0.1.0-cp310-cp310-linux_x86_64.whl'))

from tsr.system import TSR
from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation


HEADER = """
** ARM <3 GoldExtra ** - 3D extrapolation from 2.5D images

--> 2.5D Bild hochladen und BG-Preprocessing aktivieren!
"""


if torch.cuda.is_available():
    device = "cuda:0"
else:
    device = "cpu"

model = TSR.from_pretrained(
    "stabilityai/TripoSR",
    config_name="config.yaml",
    weight_name="model.ckpt",
)
model.renderer.set_chunk_size(131072)
model.to(device)

rembg_session = rembg.new_session()


def check_input_image(input_image):
    if input_image is None:
        raise gr.Error("No image uploaded!")


def preprocess(input_image, do_remove_background, foreground_ratio):
    def fill_background(image):
        image = np.array(image).astype(np.float32) / 255.0
        image = image[:, :, :3] * image[:, :, 3:4] + (1 - image[:, :, 3:4]) * 0.5
        image = Image.fromarray((image * 255.0).astype(np.uint8))
        return image

    if do_remove_background:
        image = input_image.convert("RGB")
        image = remove_background(image, rembg_session)
        image = resize_foreground(image, foreground_ratio)
        image = fill_background(image)
    else:
        image = input_image
        if image.mode == "RGBA":
            image = fill_background(image)
    return image


@spaces.GPU
def generate(image, mc_resolution, formats=["obj", "glb"]):
    scene_codes = model(image, device=device)
    mesh = model.extract_mesh(scene_codes, resolution=mc_resolution)[0]
    mesh = to_gradio_3d_orientation(mesh)

    mesh_path_glb = tempfile.NamedTemporaryFile(suffix=f".glb", delete=False)
    mesh.export(mesh_path_glb.name)

    mesh_path_obj = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False)
    mesh.apply_scale([-1, 1, 1])  # Otherwise the visualized .obj will be flipped
    mesh.export(mesh_path_obj.name)
    
    return mesh_path_obj.name, mesh_path_glb.name

def run_example(image_pil):
    preprocessed = preprocess(image_pil, False, 0.9)
    mesh_name_obj, mesh_name_glb = generate(preprocessed, 256, ["obj", "glb"])
    return preprocessed, mesh_name_obj, mesh_name_glb

with gr.Blocks() as demo:
    gr.Markdown(HEADER)
    with gr.Row(variant="panel"):
        with gr.Column():
            with gr.Row():
                input_image = gr.Image(
                    label="Input Image",
                    image_mode="RGBA",
                    sources="upload",
                    type="pil",
                    elem_id="content_image",
                )
                processed_image = gr.Image(label="Preprocess uWu", interactive=False)
            with gr.Row():
                with gr.Group():
                    do_remove_background = gr.Checkbox(
                        label="Hintergrund entfernen", value=True
                    )
                    foreground_ratio = gr.Slider(
                        label="Vordergrund definieren",
                        minimum=0.5,
                        maximum=1.0,
                        value=0.85,
                        step=0.05,
                    )
                    mc_resolution = gr.Slider(
                        label="MC-Qualität (optional)",
                        minimum=32,
                        maximum=320,
                        value=256,
                        step=32
                     )
            with gr.Row():
                submit = gr.Button("Simsalabim", elem_id="generate", variant="primary")
        with gr.Column():
            with gr.Tab("OBJ"):
                output_model_obj = gr.Model3D(
                    label="Output Model (OBJ Format)",
                    interactive=False,
                )
                gr.Markdown(".obj muss gedreht werden! .glb sollte passen. Test this!")
            with gr.Tab("GLB"):
                output_model_glb = gr.Model3D(
                    label="Output Model (GLB Format)",
                    interactive=False,
                )
                gr.Markdown("GLB erwartet bereits das lighting vom ARM.")
#    with gr.Row(variant="panel"):
#        gr.Examples(
#            examples=[
#                os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
#            ],
#            inputs=[input_image],
#            outputs=[processed_image, output_model_obj, output_model_glb],
#            cache_examples=True,
#            fn=partial(run_example),
#            label="Examples",
#            examples_per_page=20
#        )
    submit.click(fn=check_input_image, inputs=[input_image]).success(
        fn=preprocess,
        inputs=[input_image, do_remove_background, foreground_ratio],
        outputs=[processed_image],
    ).success(
        fn=generate,
        inputs=[processed_image, mc_resolution],
        outputs=[output_model_obj, output_model_glb],
    )

demo.queue(max_size=10)
demo.launch()