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app.py
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import os
import argparse
import imageio
import time
import mcubes
import cv2
import numpy as np
import torch
import trimesh
import rembg
from PIL import Image
from torchvision.transforms import v2
from pytorch_lightning import seed_everything
from omegaconf import OmegaConf
from einops import rearrange, repeat
from tqdm import tqdm
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
from src.utils.train_util import instantiate_from_config
from src.utils.camera_util import (
FOV_to_intrinsics,
get_zero123plus_input_cameras,
get_circular_camera_poses,
)
from src.utils.mesh_util import save_obj
from src.utils.infer_util import remove_background, resize_foreground, images_to_video
import tempfile
from functools import partial
def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False):
"""
Get the rendering camera parameters.
"""
c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation)
if is_flexicubes:
cameras = torch.linalg.inv(c2ws)
cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1)
else:
extrinsics = c2ws.flatten(-2)
intrinsics = FOV_to_intrinsics(50.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2)
cameras = torch.cat([extrinsics, intrinsics], dim=-1)
cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1)
return cameras
def images_to_video(images, output_path, fps=30):
# images: (N, C, H, W)
os.makedirs(os.path.dirname(output_path), exist_ok=True)
frames = []
for i in range(images.shape[0]):
frame = (images[i].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8).clip(0, 255)
assert frame.shape[0] == images.shape[2] and frame.shape[1] == images.shape[3], \
f"Frame shape mismatch: {frame.shape} vs {images.shape}"
assert frame.min() >= 0 and frame.max() <= 255, \
f"Frame value out of range: {frame.min()} ~ {frame.max()}"
frames.append(frame)
imageio.mimwrite(output_path, np.stack(frames), fps=fps, codec='h264')
###############################################################################
# Configuration.
###############################################################################
seed_everything(0)
config_path = 'configs/instant-mesh-large-eval.yaml'
config = OmegaConf.load(config_path)
config_name = os.path.basename(config_path).replace('.yaml', '')
model_config = config.model_config
infer_config = config.infer_config
IS_FLEXICUBES = True if config_name.startswith('instant-mesh') else False
device = torch.device('cuda')
# load diffusion model
print('Loading diffusion model ...')
pipeline = DiffusionPipeline.from_pretrained(
"sudo-ai/zero123plus-v1.2",
custom_pipeline="zero123plus",
torch_dtype=torch.float16,
)
pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
pipeline.scheduler.config, timestep_spacing='trailing'
)
# load custom white-background UNet
state_dict = torch.load(infer_config.unet_path, map_location='cpu')
pipeline.unet.load_state_dict(state_dict, strict=True)
pipeline = pipeline.to(device)
# load reconstruction model
print('Loading reconstruction model ...')
model = instantiate_from_config(model_config)
state_dict = torch.load(infer_config.model_path, map_location='cpu')['state_dict']
state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.') and 'source_camera' not in k}
model.load_state_dict(state_dict, strict=True)
model = model.to(device)
if IS_FLEXICUBES:
model.init_flexicubes_geometry(device)
model = model.eval()
print('Loading Finished!')
def check_input_image(input_image):
if input_image is None:
raise gr.Error("No image uploaded!")
def preprocess(input_image, do_remove_background):
rembg_session = rembg.new_session() if do_remove_background else None
#input_image = Image.open(image_file)
if do_remove_background:
input_image = remove_background(input_image, rembg_session)
return input_image
def generate_mvs(input_image, sample_steps, sample_seed):
seed_everything(sample_seed)
# sampling
generator = torch.Generator(device=device)
z123_image = pipeline(
input_image,
num_inference_steps=sample_steps,
generator=generator,
).images[0]
show_image = np.asarray(z123_image, dtype=np.uint8)
show_image = torch.from_numpy(show_image) # (960, 640, 3)
show_image = rearrange(show_image, '(n h) (m w) c -> (m h) (n w) c', n=3, m=2)
show_image = Image.fromarray(show_image.numpy())
return z123_image, show_image
def make_mesh(mesh_fpath, planes):
mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
mesh_dirname = os.path.dirname(mesh_fpath)
mesh_vis_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb")
with torch.no_grad():
# get mesh
mesh_out = model.extract_mesh(
planes,
use_texture_map=False,
**infer_config,
)
vertices, faces, vertex_colors = mesh_out
vertices = vertices[:, [0, 2, 1]]
vertices[:, -1] *= -1
save_obj(vertices, faces, vertex_colors, mesh_fpath)
print(f"Mesh saved to {mesh_fpath}")
return mesh_fpath
def make3d(images):
images = np.asarray(images, dtype=np.float32) / 255.0
images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float() # (3, 960, 640)
images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2) # (6, 3, 320, 320)
input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=2.5).to(device)
render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=IS_FLEXICUBES).to(device)
images = images.unsqueeze(0).to(device)
images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1)
mesh_fpath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name
print(mesh_fpath)
mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
mesh_dirname = os.path.dirname(mesh_fpath)
video_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.mp4")
with torch.no_grad():
# get triplane
planes = model.forward_planes(images, input_cameras)
# get video
chunk_size = 20 if IS_FLEXICUBES else 1
render_size = 384
frames = []
for i in tqdm(range(0, render_cameras.shape[1], chunk_size)):
if IS_FLEXICUBES:
frame = model.forward_geometry(
planes,
render_cameras[:, i:i+chunk_size],
render_size=render_size,
)['img']
else:
frame = model.synthesizer(
planes,
cameras=render_cameras[:, i:i+chunk_size],
render_size=render_size,
)['images_rgb']
frames.append(frame)
frames = torch.cat(frames, dim=1)
images_to_video(
frames[0],
video_fpath,
fps=30,
)
print(f"Video saved to {video_fpath}")
mesh_fpath = make_mesh(mesh_fpath, planes)
return video_fpath, mesh_fpath
def run_example(image_file):
preprocessed = preprocess(image_file, False, 0.85)
mv_images, _ = generate_mvs(preprocessed, 20, 0)
video_name, mesh_fpath, planes = make3d(mv_images)
mesh_name = make_mesh(mesh_fpath, planes)
return preprocessed, mesh_name, video_name
import gradio as gr
HEADER = '''
<h3>
<b>Official 🤗 Gradio demo</b> for
<a href='https://github.com/TencentARC/InstantMesh' target='_blank'>
<b>InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models</b>
</a>.
</h3>
<br>
* If the output is unsatisfying, try to use a different seed.
'''
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",
width=256,
height=256,
type="pil",
elem_id="content_image",
)
processed_image = gr.Image(
label="Processed Image",
image_mode="RGBA",
width=256,
height=256,
type="pil",
interactive=False
)
with gr.Row():
with gr.Group():
do_remove_background = gr.Checkbox(
label="Remove Background", value=True
)
sample_seed = gr.Number(value=42, label="Seed", precision=0)
sample_steps = gr.Slider(
label="Sample Steps",
minimum=30,
maximum=75,
value=75,
step=5
)
with gr.Row():
submit = gr.Button("Generate", elem_id="generate", variant="primary")
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_video],
# fn=partial(run_example),
# cache_examples=True,
label="Examples",
examples_per_page=20
)
with gr.Column():
with gr.Row():
with gr.Column():
mv_show_images = gr.Image(
label="Generated Multi-views",
type="pil",
width=379,
interactive=False
)
with gr.Column():
output_video = gr.Video(
label="video", format="mp4",
width=379,
autoplay=True,
interactive=False
)
with gr.Row():
output_model_obj = gr.Model3D(
label="Output Model (OBJ Format)",
width=768,
interactive=False,
)
mv_images = gr.State()
submit.click(fn=check_input_image, inputs=[input_image]).success(
fn=preprocess,
inputs=[input_image, do_remove_background],
outputs=[processed_image],
).success(
fn=generate_mvs,
inputs=[processed_image, sample_steps, sample_seed],
outputs=[mv_images, mv_show_images],
).success(
fn=make3d,
inputs=[mv_images],
outputs=[output_video, output_model_obj]
)
demo.queue(max_size=10)
demo.launch(server_name="0.0.0.0", server_port=43839)
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