import os
import time
import torch
import numpy as np
import gradio as gr
import urllib.parse
import tempfile
import subprocess
from dust3r.losses import L21
from spann3r.model import Spann3R
from spann3r.datasets import Demo
from torch.utils.data import DataLoader
import trimesh
from scipy.spatial.transform import Rotation
from transformers import AutoModelForImageSegmentation
from torchvision import transforms
from PIL import Image
import open3d as o3d
from backend_utils import improved_multiway_registration, pts2normal, point2mesh, combine_and_clean_point_clouds
# Default values
DEFAULT_CKPT_PATH = './checkpoints/spann3r.pth'
DEFAULT_DUST3R_PATH = 'https://huggingface.co/camenduru/dust3r/resolve/main/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth'
DEFAULT_DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'
OPENGL = np.array([[1, 0, 0, 0],
[0, -1, 0, 0],
[0, 0, -1, 0],
[0, 0, 0, 1]])
def export_geometry(geometry, as_pointcloud=False):
if as_pointcloud:
if not isinstance(geometry, o3d.geometry.PointCloud):
raise ValueError("Expected an Open3D PointCloud object when as_pointcloud is True")
output_path = tempfile.mktemp(suffix='.ply')
else:
if not isinstance(geometry, o3d.geometry.TriangleMesh):
raise ValueError("Expected an Open3D TriangleMesh object when as_pointcloud is False")
output_path = tempfile.mktemp(suffix='.obj')
# Apply rotation
rot = np.eye(4)
rot[:3, :3] = Rotation.from_euler('y', np.deg2rad(180)).as_matrix()
transform = np.linalg.inv(OPENGL @ rot)
geometry.transform(transform)
# Export the geometry
if as_pointcloud:
o3d.io.write_point_cloud(output_path, geometry, write_ascii=False, compressed=True)
else:
o3d.io.write_triangle_mesh(output_path, geometry, write_ascii=False, compressed=True)
return output_path
def extract_frames(video_path: str, duration: float = 20.0, fps: float = 3.0) -> str:
temp_dir = tempfile.mkdtemp()
output_path = os.path.join(temp_dir, "%03d.jpg")
filter_complex = f"select='if(lt(t,{duration}),1,0)',fps={fps}"
command = [
"ffmpeg",
"-i", video_path,
"-vf", filter_complex,
"-vsync", "0",
output_path
]
subprocess.run(command, check=True)
return temp_dir
def cat_meshes(meshes):
vertices, faces, colors = zip(*[(m['vertices'], m['faces'], m['face_colors']) for m in meshes])
n_vertices = np.cumsum([0]+[len(v) for v in vertices])
for i in range(len(faces)):
faces[i][:] += n_vertices[i]
vertices = np.concatenate(vertices)
colors = np.concatenate(colors)
faces = np.concatenate(faces)
return dict(vertices=vertices, face_colors=colors, faces=faces)
def load_ckpt(model_path_or_url, verbose=True):
if verbose:
print('... loading model from', model_path_or_url)
is_url = urllib.parse.urlparse(model_path_or_url).scheme in ('http', 'https')
if is_url:
ckpt = torch.hub.load_state_dict_from_url(model_path_or_url, map_location='cpu', progress=verbose)
else:
ckpt = torch.load(model_path_or_url, map_location='cpu')
return ckpt
def load_model(ckpt_path, device):
model = Spann3R(dus3r_name=DEFAULT_DUST3R_PATH,
use_feat=False).to(device)
model.load_state_dict(load_ckpt(ckpt_path)['model'])
model.eval()
return model
def pts3d_to_trimesh(img, pts3d, valid=None):
H, W, THREE = img.shape
assert THREE == 3
assert img.shape == pts3d.shape
vertices = pts3d.reshape(-1, 3)
# make squares: each pixel == 2 triangles
idx = np.arange(len(vertices)).reshape(H, W)
idx1 = idx[:-1, :-1].ravel() # top-left corner
idx2 = idx[:-1, +1:].ravel() # right-left corner
idx3 = idx[+1:, :-1].ravel() # bottom-left corner
idx4 = idx[+1:, +1:].ravel() # bottom-right corner
faces = np.concatenate((
np.c_[idx1, idx2, idx3],
np.c_[idx3, idx2, idx1], # same triangle, but backward (cheap solution to cancel face culling)
np.c_[idx2, idx3, idx4],
np.c_[idx4, idx3, idx2], # same triangle, but backward (cheap solution to cancel face culling)
), axis=0)
# prepare triangle colors
face_colors = np.concatenate((
img[:-1, :-1].reshape(-1, 3),
img[:-1, :-1].reshape(-1, 3),
img[+1:, +1:].reshape(-1, 3),
img[+1:, +1:].reshape(-1, 3)
), axis=0)
# remove invalid faces
if valid is not None:
assert valid.shape == (H, W)
valid_idxs = valid.ravel()
valid_faces = valid_idxs[faces].all(axis=-1)
faces = faces[valid_faces]
face_colors = face_colors[valid_faces]
assert len(faces) == len(face_colors)
return dict(vertices=vertices, face_colors=face_colors, faces=faces)
model = load_model(DEFAULT_CKPT_PATH, DEFAULT_DEVICE)
birefnet = AutoModelForImageSegmentation.from_pretrained('zhengpeng7/BiRefNet', trust_remote_code=True)
birefnet.to(DEFAULT_DEVICE)
birefnet.eval()
def extract_object(birefnet, image):
# Data settings
image_size = (1024, 1024)
transform_image = transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
input_images = transform_image(image).unsqueeze(0).to(DEFAULT_DEVICE)
# Prediction
with torch.no_grad():
preds = birefnet(input_images)[-1].sigmoid().cpu()
pred = preds[0].squeeze()
pred_pil = transforms.ToPILImage()(pred)
mask = pred_pil.resize(image.size)
return mask
def generate_mask(image: np.ndarray):
# Convert numpy array to PIL Image
pil_image = Image.fromarray((image * 255).astype(np.uint8))
# Extract object and get mask
mask = extract_object(birefnet, pil_image)
# Convert mask to numpy array
mask_np = np.array(mask) / 255.0
return mask_np
@torch.no_grad()
def reconstruct(video_path, conf_thresh, kf_every,
as_pointcloud=False, remove_background=False, refine=False):
# Extract frames from video
demo_path = extract_frames(video_path)
# Load dataset
dataset = Demo(ROOT=demo_path, resolution=224, full_video=True, kf_every=kf_every)
dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0)
batch = next(iter(dataloader))
for view in batch:
view['img'] = view['img'].to(DEFAULT_DEVICE, non_blocking=True)
demo_name = os.path.basename(video_path)
print(f'Started reconstruction for {demo_name}')
start = time.time()
preds, preds_all = model.forward(batch)
end = time.time()
fps = len(batch) / (end - start)
print(f'Finished reconstruction for {demo_name}, FPS: {fps:.2f}')
# Process results
pcds = []
for j, view in enumerate(batch):
image = view['img'].permute(0, 2, 3, 1).cpu().numpy()[0]
image = (image + 1) / 2
pts = preds[j]['pts3d' if j==0 else 'pts3d_in_other_view'].detach().cpu().numpy()[0]
pts_normal = pts2normal(preds[j]['pts3d' if j==0 else 'pts3d_in_other_view'][0]).cpu().numpy()
conf = preds[j]['conf'][0].cpu().data.numpy()
conf_sig = (conf - 1) / conf
if remove_background:
mask = generate_mask(image)
else:
mask = np.ones_like(conf)
combined_mask = (conf_sig > conf_thresh) & (mask > 0.5)
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(pts[combined_mask])
pcd.colors = o3d.utility.Vector3dVector(image[combined_mask])
pcd.normals = o3d.utility.Vector3dVector(pts_normal[combined_mask])
pcds.append(pcd)
pcd_combined = combine_and_clean_point_clouds(pcds, voxel_size=0.001)
if as_pointcloud:
o3d_geometry = pcd_combined
else:
o3d_geometry = point2mesh(pcd_combined)
# Create coarse result
coarse_output_path = export_geometry(o3d_geometry, as_pointcloud)
yield coarse_output_path, None
if refine:
# Perform global optimization
print("Performing global registration...")
transformed_pcds, _, _ = improved_multiway_registration(pcds, voxel_size=0.001)
if as_pointcloud:
o3d_geometry = transformed_pcds
else:
o3d_geometry = point2mesh(transformed_pcds)
# Create coarse result
refined_output_path = export_geometry(o3d_geometry, as_pointcloud)
yield coarse_output_path, refined_output_path
# Clean up temporary directory
os.system(f"rm -rf {demo_path}")
# Update the Gradio interface with improved layout
with gr.Blocks(
title="StableSpann3r: Making Spann3r stable with Odometry Backend",
css="""
#download {
height: 118px;
}
.slider .inner {
width: 5px;
background: #FFF;
}
.viewport {
aspect-ratio: 4/3;
}
.tabs button.selected {
font-size: 20px !important;
color: crimson !important;
}
h1 {
text-align: center;
display: block;
}
h2 {
text-align: center;
display: block;
}
h3 {
text-align: center;
display: block;
}
.md_feedback li {
margin-bottom: 0px !important;
}
""",
head="""
""",
) as iface:
gr.Markdown(
"""
# StableSpann3r: Making Spann3r stable with Odometry Backend
"""
)
with gr.Row():
with gr.Column(scale=1):
video_input = gr.Video(label="Input Video")
with gr.Row():
conf_thresh = gr.Slider(0, 1, value=1e-3, label="Confidence Threshold")
kf_every = gr.Slider(1, 30, step=1, value=1, label="Keyframe Interval")
with gr.Row():
remove_background = gr.Checkbox(label="Remove Background", value=False)
refine = gr.Checkbox(label="Enable Backend", value=False)
as_pointcloud = gr.Checkbox(label="As Pointcloud", value=False)
reconstruct_btn = gr.Button("Reconstruct")
with gr.Column(scale=2):
with gr.Tab("Coarse Model"):
coarse_model = gr.Model3D(label="Coarse 3D Model", display_mode="solid", clear_color=[0.0, 0.0, 0.0, 0.0])
with gr.Tab("Refined Model"):
refined_model = gr.Model3D(label="Refined 3D Model", display_mode="solid", clear_color=[0.0, 0.0, 0.0, 0.0])
reconstruct_btn.click(
fn=reconstruct,
inputs=[video_input, conf_thresh, kf_every, as_pointcloud, remove_background, refine],
outputs=[coarse_model, refined_model]
)
if __name__ == "__main__":
iface.launch(server_name="0.0.0.0")