app.py

import spaces
import subprocess
# Install flash attention, skipping CUDA build if necessary
subprocess.run(
    "pip install flash-attn --no-build-isolation",
    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
    shell=True,
)
import os
import torch
import trimesh
from accelerate.utils import set_seed
from accelerate import Accelerator
import numpy as np
import gradio as gr
from main import load_v2
from mesh_to_pc import process_mesh_to_pc
import time
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from PIL import Image
import io

model = load_v2()

device = torch.device('cuda')
accelerator = Accelerator(
    mixed_precision="fp16",
)
model = accelerator.prepare(model)
model.eval()
print("Model loaded to device")

def wireframe_render(mesh):
    views = [
        (90, 20), (270, 20)
    ]
    mesh.vertices = mesh.vertices[:, [0, 2, 1]]

    bounding_box = mesh.bounds
    center = mesh.centroid
    scale = np.ptp(bounding_box, axis=0).max()

    fig = plt.figure(figsize=(10, 10))

    # Function to render and return each view as an image
    def render_view(mesh, azimuth, elevation):
        ax = fig.add_subplot(111, projection='3d')
        ax.set_axis_off()

        # Extract vertices and faces for plotting
        vertices = mesh.vertices
        faces = mesh.faces

        # Plot faces
        ax.add_collection3d(Poly3DCollection(
            vertices[faces],
            facecolors=(0.8, 0.5, 0.2, 1.0),  # Brownish yellow
            edgecolors='k',
            linewidths=0.5,
        ))

        # Set limits and center the view on the object
        ax.set_xlim(center[0] - scale / 2, center[0] + scale / 2)
        ax.set_ylim(center[1] - scale / 2, center[1] + scale / 2)
        ax.set_zlim(center[2] - scale / 2, center[2] + scale / 2)

        # Set view angle
        ax.view_init(elev=elevation, azim=azimuth)

        # Save the figure to a buffer
        buf = io.BytesIO()
        plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0, dpi=300)
        plt.clf()
        buf.seek(0)

        return Image.open(buf)

    # Render each view and store in a list
    images = [render_view(mesh, az, el) for az, el in views]

    # Combine images horizontally
    widths, heights = zip(*(i.size for i in images))
    total_width = sum(widths)
    max_height = max(heights)

    combined_image = Image.new('RGBA', (total_width, max_height))

    x_offset = 0
    for img in images:
        combined_image.paste(img, (x_offset, 0))
        x_offset += img.width

    # Save the combined image
    save_path = f"combined_mesh_view_{int(time.time())}.png"
    combined_image.save(save_path)

    plt.close(fig)
    return save_path

@spaces.GPU(duration=360)
def do_inference(input_3d, sample_seed=0, do_sampling=False, do_marching_cubes=False):
    set_seed(sample_seed)
    print("Seed value:", sample_seed)

    input_mesh = trimesh.load(input_3d)
    pc_list, mesh_list = process_mesh_to_pc([input_mesh], marching_cubes = do_marching_cubes)
    pc_normal = pc_list[0] # 4096, 6
    mesh = mesh_list[0]
    vertices = mesh.vertices

    pc_coor = pc_normal[:, :3]
    normals = pc_normal[:, 3:]

    bounds = np.array([vertices.min(axis=0), vertices.max(axis=0)])
    # scale mesh and pc
    vertices = vertices - (bounds[0] + bounds[1])[None, :] / 2
    vertices = vertices / (bounds[1] - bounds[0]).max()
    mesh.vertices = vertices
    pc_coor = pc_coor - (bounds[0] + bounds[1])[None, :] / 2
    pc_coor = pc_coor / (bounds[1] - bounds[0]).max()

    mesh.merge_vertices()
    mesh.update_faces(mesh.nondegenerate_faces())
    mesh.update_faces(mesh.unique_faces())
    mesh.remove_unreferenced_vertices()
    mesh.fix_normals()
    try:
        if mesh.visual.vertex_colors is not None:
            orange_color = np.array([255, 165, 0, 255], dtype=np.uint8)
    
            mesh.visual.vertex_colors = np.tile(orange_color, (mesh.vertices.shape[0], 1))
        else:
            orange_color = np.array([255, 165, 0, 255], dtype=np.uint8)
            mesh.visual.vertex_colors = np.tile(orange_color, (mesh.vertices.shape[0], 1))
    except Exception as e:
        print(e)
    input_save_name = f"processed_input_{int(time.time())}.obj"
    mesh.export(input_save_name)
    input_render_res = wireframe_render(mesh)

    pc_coor = pc_coor / np.abs(pc_coor).max() * 0.99 # input should be from -1 to 1

    assert (np.linalg.norm(normals, axis=-1) > 0.99).all(), "normals should be unit vectors, something wrong"
    normalized_pc_normal = np.concatenate([pc_coor, normals], axis=-1, dtype=np.float16)

    input = torch.tensor(normalized_pc_normal, dtype=torch.float16, device=device)[None]
    print("Data loaded")

    # with accelerator.autocast():
    with accelerator.autocast():
        outputs = model(input, do_sampling)
    print("Model inference done")
    recon_mesh = outputs[0]

    valid_mask = torch.all(~torch.isnan(recon_mesh.reshape((-1, 9))), dim=1)
    recon_mesh = recon_mesh[valid_mask]  # nvalid_face x 3 x 3
    vertices = recon_mesh.reshape(-1, 3).cpu()
    vertices_index = np.arange(len(vertices))  # 0, 1, ..., 3 x face
    triangles = vertices_index.reshape(-1, 3)

    artist_mesh = trimesh.Trimesh(vertices=vertices, faces=triangles, force="mesh",
                                 merge_primitives=True)

    artist_mesh.merge_vertices()
    artist_mesh.update_faces(artist_mesh.nondegenerate_faces())
    artist_mesh.update_faces(artist_mesh.unique_faces())
    artist_mesh.remove_unreferenced_vertices()
    artist_mesh.fix_normals()

    if do_smooth_shading:
        smooth_shaded(artist_mesh)

    if artist_mesh.visual.vertex_colors is not None:
        orange_color = np.array([255, 165, 0, 255], dtype=np.uint8)

        artist_mesh.visual.vertex_colors = np.tile(orange_color, (artist_mesh.vertices.shape[0], 1))
    else:
        orange_color = np.array([255, 165, 0, 255], dtype=np.uint8)
        artist_mesh.visual.vertex_colors = np.tile(orange_color, (artist_mesh.vertices.shape[0], 1))

    num_faces = len(artist_mesh.faces)

    brown_color = np.array([165, 42, 42, 255], dtype=np.uint8)
    face_colors = np.tile(brown_color, (num_faces, 1))

    artist_mesh.visual.face_colors = face_colors
    # add time stamp to avoid cache
    save_name = f"output_{int(time.time())}.obj"
    artist_mesh.export(save_name)
    output_render = wireframe_render(artist_mesh)
    return input_save_name, input_render_res, save_name, output_render


_HEADER_ = """
## Transform your high poly mesh into a low poly mesh

➡️ You can optimize your high poly mesh, here, to the drawback is that you'll need to create a new material on Roblox.

- To optimize your high poly mesh, we use a tool called [MeshAnythingV2](https://huggingface.co/Yiwen-ntu/MeshAnythingV2).

### The Process:

1. Import the GLB model generated with the high poly mesh generator tool above.
2. Check on Preprocess with marching Cubes.
3. If you want the look of your object smooth, check  "Apply Smooth Shading".

<img src="https://huggingface.co/spaces/ThomasSimonini/MeshAnythingV2ForRoblox/resolve/main/assets/img/smooth-shading.png" alt="With or without smooth shading applied"/>


4. Click on generate
5. The 3D mesh is generated, and you can download the file (it's GLB format) using the ⬇️
6. Open Roblox Studio
7. In your Roblox Project, click on Import 3D and select the downloaded file.
8. You can now drag and drop your generated 3D file in your scene 🎉.
9. You can change the material and color by clicking on Color and Material in Roblox studio.
"""

output_model_obj = gr.Model3D(
    label="Generated Mesh (OBJ Format)",
    display_mode="wireframe",
    clear_color=[1, 1, 1, 1],
)
preprocess_model_obj = gr.Model3D(
    label="Processed Input Mesh (OBJ Format)",
    display_mode="wireframe",
    clear_color=[1, 1, 1, 1],
)
input_image_render = gr.Image(
    label="Wireframe Render of Processed Input Mesh",
)
output_image_render = gr.Image(
    label="Wireframe Render of Generated Mesh",
)
with (gr.Blocks() as demo):
    gr.Markdown(_HEADER_)
    with gr.Row(variant="panel"):
        with gr.Column():
            with gr.Row():
                input_3d = gr.Model3D(
                    label="Input Mesh",
                    display_mode="wireframe",
                    clear_color=[1,1,1,1],
                )

            with gr.Row():
                with gr.Group():
                    do_marching_cubes = gr.Checkbox(label="Preprocess with Marching Cubes", value=False)
                    do_smooth_shading = gr.Checkbox(label="Apply Smooth Shading", value=False)
                    do_sampling = gr.Checkbox(label="Random Sampling", value=False)
                    sample_seed = gr.Number(value=0, label="Seed Value", precision=0)

            with gr.Row():
                submit = gr.Button("Generate", elem_id="generate", variant="primary")

            with gr.Row(variant="panel"):
                mesh_examples = gr.Examples(
                    examples=[
                        os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
                    ],
                    inputs=input_3d,
                    outputs=[preprocess_model_obj, input_image_render, output_model_obj, output_image_render],
                    fn=do_inference,
                    cache_examples = False,
                    examples_per_page=10
                )
        with gr.Column():
            with gr.Row():
                input_image_render.render()
            with gr.Row():
                with gr.Tab("OBJ"):
                    preprocess_model_obj.render()
            with gr.Row():
                output_image_render.render()
            with gr.Row():
                with gr.Tab("OBJ"):
                    output_model_obj.render()
            with gr.Row():
                gr.Markdown('''Try click random sampling and different <b>Seed Value</b> if the result is unsatisfying''')

    mv_images = gr.State()

    submit.click(
        fn=do_inference,
        inputs=[input_3d, sample_seed, do_sampling, do_marching_cubes, do_smooth_shading],
        outputs=[preprocess_model_obj, input_image_render, output_model_obj, output_image_render],
    )

demo.launch(share=True)