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
# Default values
DEFAULT_CKPT_PATH = 'https://huggingface.co/spaces/Stable-X/StableSpann3R/resolve/main/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 extract_frames(video_path: str) -> str:
temp_dir = tempfile.mkdtemp()
output_path = os.path.join(temp_dir, "%03d.jpg")
command = [
"ffmpeg",
"-i", video_path,
"-vf", "fps=1",
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)
@torch.no_grad()
def reconstruct(video_path, conf_thresh, kf_every, as_pointcloud=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
pts_all, images_all, conf_all = [], [], []
for j, view in enumerate(batch):
image = view['img'].permute(0, 2, 3, 1).cpu().numpy()[0]
pts = preds[j]['pts3d' if j==0 else 'pts3d_in_other_view'].detach().cpu().numpy()[0]
conf = preds[j]['conf'][0].cpu().data.numpy()
images_all.append((image[None, ...] + 1.0)/2.0)
pts_all.append(pts[None, ...])
conf_all.append(conf[None, ...])
images_all = np.concatenate(images_all, axis=0)
pts_all = np.concatenate(pts_all, axis=0) * 10
conf_all = np.concatenate(conf_all, axis=0)
# Create point cloud or mesh
conf_sig_all = (conf_all-1) / conf_all
mask = conf_sig_all > conf_thresh
scene = trimesh.Scene()
if as_pointcloud:
pcd = trimesh.PointCloud(
vertices=pts_all[mask].reshape(-1, 3),
colors=images_all[mask].reshape(-1, 3)
)
scene.add_geometry(pcd)
else:
meshes = []
for i in range(len(images_all)):
meshes.append(pts3d_to_trimesh(images_all[i], pts_all[i], mask[i]))
mesh = trimesh.Trimesh(**cat_meshes(meshes))
scene.add_geometry(mesh)
rot = np.eye(4)
rot[:3, :3] = Rotation.from_euler('y', np.deg2rad(180)).as_matrix()
scene.apply_transform(np.linalg.inv(OPENGL @ rot))
# Save the scene as GLB
output_path = tempfile.mktemp(suffix='.glb')
scene.export(output_path)
# Clean up temporary directory
os.system(f"rm -rf {demo_path}")
return output_path, f"Reconstruction completed. FPS: {fps:.2f}"
iface = gr.Interface(
fn=reconstruct,
inputs=[
gr.Video(label="Input Video"),
gr.Slider(0, 1, value=1e-3, label="Confidence Threshold"),
gr.Slider(1, 30, step=1, value=5, label="Keyframe Interval"),
gr.Checkbox(label="As Pointcloud", value=False)
],
outputs=[
gr.Model3D(label="3D Model (GLB)", display_mode="solid"),
gr.Textbox(label="Status")
],
title="3D Reconstruction with Spatial Memory",
)
if __name__ == "__main__":
iface.launch()