app.py

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

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

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 = """
# Generate 3D Assets for Roblox

With this Space, you can generate 3D Assets using AI for your Roblox game for free.

Simply follow the 4 steps below.

1. Generate a 3D Mesh using an image model as input.
2. Simplify the Mesh to get lower polygon number
3. (Optional) make the Mesh more smooth
4. Get the Material

We wrote a tutorial here

"""

STEP1_HEADER = """
## Step 1: Generate the 3D Mesh

For this step, we use TripoSR, an open-source model for **fast** feedforward 3D reconstruction from a single image, developed in collaboration between [Tripo AI](https://www.tripo3d.ai/) and [Stability AI](https://stability.ai/).

During this step, you need to upload an image of what you want to generate a 3D Model from.


## 💡 Tips

-  If there's a background, ✅ Remove background.

- If you find the result is unsatisfied, please try to change the foreground ratio. It might improve the results.

- To know more about what is the Marching Cubes Resolution check this : https://huggingface.co./learn/ml-for-3d-course/en/unit4/marching-cubes#marching-cubes

"""

STEP2_HEADER = """
## Step 2: Simplify the generated 3D Mesh

ADD ILLUSTRATION

The 3D Mesh Generated contains too much polygons, fortunately, we can use another AI model to help us optimize it.

The model we use is called [MeshAnythingV2]().


## 💡 Tips

- We don't click on Preprocess with marching Cubes, because in the last step the input mesh was produced by it.

- Limited by computational resources, MeshAnything is trained on meshes with fewer than 1600 faces and cannot generate meshes with more than 1600 faces. The shape of the input mesh should be sharp enough; otherwise, it will be challenging to represent it with only 1600 faces. Thus, feed-forward image-to-3D methods may often produce bad results due to insufficient shape quality.

"""

STEP3_HEADER = """
## Step 3 (optional): Shader Smooth

- The mesh simplified in step 2, looks low poly. One way to make it more smooth is to use Shader Smooth.
- You can usually do it in Blender, but we can do it directly here

ADD ILLUSTRATION

ADD SHADERSMOOTH
"""

STEP4_HEADER = """
## Step 4: Get the Mesh Material

"""


# These part of the code (check_input_image and preprocess were taken from https://huggingface.co./spaces/stabilityai/TripoSR/blob/main/app.py)
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


with gr.Blocks() as demo:
	gr.Markdown(HEADER)
	gr.Markdown(STEP1_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="Processed Image", interactive=False)
			with gr.Row():
				with gr.Group():
					do_remove_background = gr.Checkbox(
						label="Remove Background",
						value=True)
					foreground_ratio = gr.Slider(
						label="Foreground Ratio",
						minimum=0.5,
						maximum=1.0,
						value=0.85,
						step=0.05,
						)
					mc_resolution = gr.Slider(
						label="Marching Cubes Resolution",
						minimum=32,
						maximum=320,
						value=256,
						step=32
						)
			with gr.Row():
				step1_submit = gr.Button("Generate", 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("Note: Downloaded object will be flipped in case of .obj export. Export .glb instead or manually flip it before usage.")
			with gr.Tab("GLB"):
				output_model_glb = gr.Model3D(
                    label="Output Model (GLB Format)",
                    interactive=False,
				)
				gr.Markdown("Note: The model shown here has a darker appearance. Download to get correct results.")

	step1_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],
    )
	gr.Markdown(STEP2_HEADER)
	gr.Markdown(STEP3_HEADER)
	gr.Markdown(STEP4_HEADER)

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