MeshAnythingV2 / app.py
Yiwen-ntu's picture
Update app.py
67d7dba verified
raw
history blame
10.6 kB
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()
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))
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 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_ = '''
<h2><b>Official 🤗 Gradio Demo</b></h2><h2><a href='https://github.com/buaacyw/MeshAnything' target='_blank'><b>MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenization</b></a></h2>
**MeshAnythingV2** converts any 3D representation into meshes created by human artists, i.e., Artist-Created Meshes (AMs).
Code: <a href='https://github.com/buaacyw/MeshAnythingV2' target='_blank'>GitHub</a>. Arxiv Paper: <a href='https://arxiv.org/abs/2406.10163' target='_blank'>ArXiv</a>.
❗️❗️❗️**Important Notes:**
- Gradio doesn't support interactive wireframe rendering currently. For interactive mesh visualization, please use download the obj file and open it with MeshLab or https://3dviewer.net/.
- The input mesh will be normalized to a unit bounding box. The up vector of the input mesh should be +Y for better results. Click **Preprocess with Marching Cubes** if the input mesh is a manually created mesh.
- 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.
- For point cloud input, please refer to our github repo <a href='https://github.com/buaacyw/MeshAnythingV2' target='_blank'>GitHub</a>.
'''
_CITE_ = r"""
If MeshAnythingV2 is helpful, please help to ⭐ the <a href='https://github.com/buaacyw/MeshAnythingV2' target='_blank'>Github Repo</a>. Thanks!
---
📋 **License**
MIT LICENSE.
📧 **Contact**
If you have any questions, feel free to open a discussion or contact us at <b>yiwen002@e.ntu.edu.sg</b>.
"""
output_model_obj = gr.Model3D(
label="Generated Mesh (OBJ Format)",
clear_color=[1, 1, 1, 1],
)
preprocess_model_obj = gr.Model3D(
label="Processed Input Mesh (OBJ Format)",
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",
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_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''')
gr.Markdown(_CITE_)
mv_images = gr.State()
submit.click(
fn=do_inference,
inputs=[input_3d, sample_seed, do_sampling, do_marching_cubes],
outputs=[preprocess_model_obj, input_image_render, output_model_obj, output_image_render],
)
demo.launch(share=True)