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import logging
import os
import shutil
import time
from abc import ABC, abstractmethod
from pathlib import Path
import numpy as np
import open3d as o3d
import torch
from dust3r.cloud_opt import GlobalAlignerMode, global_aligner
from dust3r.image_pairs import make_pairs
from dust3r.inference import inference
from dust3r.utils.device import to_numpy
from dust3r.utils.geometry import inv
from mast3r.model import AsymmetricMASt3R
from utils.sfm_utils import (compute_co_vis_masks, get_sorted_image_files,
load_images, save_extrinsic, save_intrinsics,
save_points3D)
from .utils import prepare_input, prepare_output, storePly
class BaseEstimator(ABC):
@abstractmethod
def get_poses():
pass
class ColmapEstimator(BaseEstimator):
def __init__(self, cfg):
self.cfg = cfg
def get_poses(self, camera_model="OPENCV", use_gpu=True):
save_path = self.cfg.pipeline.data_path
database_path = os.path.join(save_path, "distorted", "database.db")
raw_img_path = os.path.join(save_path, "input")
sparse_path = os.path.join(save_path, "distorted", "sparse")
os.makedirs(os.path.join(save_path, "distorted"), exist_ok=True)
os.makedirs(sparse_path, exist_ok=True)
feat_extraction_cmd = [
"colmap", "feature_extractor",
"--database_path", database_path,
"--image_path", raw_img_path,
"--ImageReader.single_camera", "1",
"--ImageReader.camera_model", camera_model,
"--SiftExtraction.use_gpu", str(int(use_gpu))
]
feat_extraction_cmd = " ".join(feat_extraction_cmd)
exit_code = os.system(feat_extraction_cmd)
if exit_code != 0:
logging.error(f"Feature extraction failed with code {exit_code}. Exiting.")
exit(exit_code)
feat_matching_cmd = [
"colmap", "exhaustive_matcher",
"--database_path", database_path,
"--SiftMatching.use_gpu", str(int(use_gpu))
]
feat_matching_cmd = " ".join(feat_matching_cmd)
exit_code = os.system(feat_matching_cmd)
if exit_code != 0:
logging.error(f"Feature matching failed with code {exit_code}. Exiting.")
exit(exit_code)
mapper_cmd = [
"colmap", "mapper",
"--database_path", database_path,
"--image_path", raw_img_path,
"--output_path", sparse_path,
"--Mapper.ba_global_function_tolerance=0.000001"
]
mapper_cmd = " ".join(mapper_cmd)
exit_code = os.system(mapper_cmd)
if exit_code != 0:
logging.error(f"Mapper failed with code {exit_code}. Exiting.")
exit(exit_code)
img_undist_cmd = [
"colmap", "image_undistorter",
"--image_path", raw_img_path,
"--input_path", os.path.join(sparse_path, "0"),
"--output_path", save_path,
"--output_type", "COLMAP"
]
img_undist_cmd = " ".join(img_undist_cmd)
exit_code = os.system(img_undist_cmd)
if exit_code != 0:
logging.error(f"Mapper failed with code {exit_code}. Exiting.")
exit(exit_code)
# move data:
curr_path = os.path.join(save_path, "sparse")
dest_path = os.path.join(curr_path, "0")
os.makedirs(dest_path, exist_ok=True)
files = list(filter(lambda x: x != "0", os.listdir(curr_path)))
for file in files:
src_file = os.path.join(curr_path, file)
dest_file = os.path.join(dest_path, file)
shutil.move(src_file, dest_file)
class MASt3REstimator(BaseEstimator):
def __init__(self, cfg):
self.cfg = cfg
self.device = cfg.pose_estimator.device
self.model = AsymmetricMASt3R.from_pretrained(cfg.pose_estimator.model_path).to(self.device)
def get_poses(self):
save_path = self.cfg.pipeline.data_path
co_vis_dsp = self.cfg.pose_estimator.co_vis_dsp
sparse_path = os.path.join(save_path, "sparse", "0")
os.makedirs(sparse_path, exist_ok=True)
image_dir = Path(save_path) / "input"
image_files, image_suffix = get_sorted_image_files(image_dir)
n_views = len(image_files)
images, org_imgs_shape = load_images(image_files, size=512)
logging.info(">> Making pairs...")
pairs = make_pairs(images)
logging.info(">> Inference...")
output = inference(pairs, self.model, self.device, batch_size=1, verbose=True)
logging.info(f'>> Global alignment...')
scene = global_aligner(output, device=self.device, mode=GlobalAlignerMode.PointCloudOptimizer)
extrinsics_w2c = inv(to_numpy(scene.get_im_poses()))
intrinsics = to_numpy(scene.get_intrinsics())
focals = to_numpy(scene.get_focals())
imgs = np.array(scene.imgs)
pts3d = to_numpy(scene.get_pts3d())
pts3d = np.array(pts3d)
depthmaps = to_numpy(scene.im_depthmaps.detach().cpu().numpy())
values = [param.detach().cpu().numpy() for param in scene.im_conf]
confs = np.array(values)
logging.info(f'>> Confiden-aware Ranking...')
avg_conf_scores = confs.mean(axis=(1, 2))
sorted_conf_indices = np.argsort(avg_conf_scores)[::-1]
sorted_conf_avg_conf_scores = avg_conf_scores[sorted_conf_indices]
logging.info("Sorted indices:", str(sorted_conf_indices))
logging.info("Sorted average confidence scores:", str(sorted_conf_avg_conf_scores))
logging.info(f'>> Calculate the co-visibility mask...')
depth_thre = self.cfg.pose_estimator.depth_thre
if depth_thre > 0:
overlapping_masks = compute_co_vis_masks(sorted_conf_indices, depthmaps, pts3d, intrinsics, extrinsics_w2c, imgs.shape, depth_threshold=depth_thre)
overlapping_masks = ~overlapping_masks
else:
co_vis_dsp = False
overlapping_masks = None
focals = np.repeat(focals[0], n_views)
logging.info(f'>> Saving results...')
save_extrinsic(Path(sparse_path), extrinsics_w2c, image_files, image_suffix)
save_intrinsics(Path(sparse_path), focals, org_imgs_shape, imgs.shape, save_focals=True)
pts_num = save_points3D(Path(sparse_path), imgs, pts3d, confs.reshape(pts3d.shape[0], -1), overlapping_masks, use_masks=co_vis_dsp, save_all_pts=True, save_txt_path=save_path, depth_threshold=depth_thre)
# save_images_and_masks(Path(sparse_path), n_views, imgs, overlapping_masks, image_files, image_suffix)
logging.info(f'MASt3R Reconstruction is successfully converted to COLMAP files in: {sparse_path}')
logging.info(f'Number of points: {pts3d.reshape(-1, 3).shape[0]}')
logging.info(f'Number of points after downsampling: {pts_num}')
class CUT3REstimator(BaseEstimator):
def __init__(self, cfg):
self.cfg = cfg
self.device = cfg.pose_estimator.device
def get_poses(self):
cfg = self.cfg
if self.device == "cuda" and not torch.cuda.is_available():
print("cuda not available. switching to cpu.")
self.device = "cpu"
from cut3r.dust3r.inference import inference
from cut3r.dust3r.model import ARCroco3DStereo
save_path = self.cfg.pipeline.data_path
img_folder_path = os.path.join(save_path, "input")
img_paths = [os.path.join(img_folder_path, img_name) for img_name in os.listdir(img_folder_path)]
img_mask = [True] * len(img_paths)
views, orig_shape = prepare_input(
img_paths=img_paths,
img_mask=img_mask,
size=512,
revisit=1,
update=True,
)
model = ARCroco3DStereo.from_pretrained(cfg.pose_estimator.model_path).to(self.device)
model.eval()
logging.info("Running inference...")
start_time = time.time()
outputs, state_args = inference(views, model, self.device)
total_time = time.time() - start_time
per_frame_time = total_time / len(views)
print(
f"Inference completed in {total_time:.2f} seconds (average {per_frame_time:.2f} s per frame)."
)
pts3ds_other, colors, conf, cam_dict = prepare_output(
outputs, orig_shape, save_path, 1, True
)
conf = torch.cat(conf, dim=0)
if self.cfg.pipeline.selection:
conf_score = conf.mean(dim=(1, 2))
chunk_num = self.cfg.pipeline.chunk_num
keep_num_per_chunk = self.cfg.pipeline.keep_num_per_chunk
conf_scores_tuple = conf_score.chunk(chunk_num)
selected_idxs = []
total_conf_len = 0
for conf_scores_chunk in conf_scores_tuple:
_, idxs = conf_scores_chunk.sort(descending=True)
idxs = idxs[:keep_num_per_chunk]
selected_idxs += [(idx + total_conf_len).item() for idx in idxs]
total_conf_len += len(conf_scores_chunk)
self.cfg.pipeline.selected_idxs = sorted(selected_idxs)
pts3ds_to_save = [pts3ds_other[idx].cpu().numpy() for idx in self.cfg.pipeline.selected_idxs]
colors_to_save = [colors[idx].cpu().numpy() for idx in self.cfg.pipeline.selected_idxs]
all_pts3ds = np.stack(pts3ds_to_save).reshape(-1, 3)
all_colors = np.stack(colors_to_save).reshape(-1, 3)
storePly(os.path.join(save_path, "points3D.ply"), all_pts3ds, all_colors)
class VGGTEstimator(BaseEstimator):
def __init__(self, cfg):
self.cfg = cfg
self.device = cfg.pose_estimator.device
def get_poses(self):
from vggt.models.vggt import VGGT
from vggt.utils.geometry import unproject_depth_map_to_point_map
from vggt.utils.load_fn import load_and_preprocess_images
from vggt.utils.pose_enc import pose_encoding_to_extri_intri
cfg = self.cfg
if self.device == "cuda" and not torch.cuda.is_available():
print("cuda not available. switching to cpu.")
self.device = "cpu"
dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] >= 8 else torch.float16
logging.info("Loading vggt...")
model = VGGT.from_pretrained("facebook/VGGT-1B").to(self.device)
save_path = self.cfg.pipeline.data_path
img_folder_path = os.path.join(save_path, "input")
img_paths = [os.path.join(img_folder_path, img_name) for img_name in os.listdir(img_folder_path)]
images = load_and_preprocess_images(img_paths).to(self.device)
with torch.no_grad(), torch.amp.autocast("cuda", dtype=dtype):
images = images[None]
aggregated_tokens_list, ps_idx = model.aggregator(images)
pose_enc = model.camera_head(aggregated_tokens_list)[-1]
extrinsic, intrinsic = pose_encoding_to_extri_intri(pose_enc, images.shape[-2:])
depth_map, depth_conf = model.depth_head(aggregated_tokens_list, images, ps_idx)
point_map = unproject_depth_map_to_point_map(
depth_map.squeeze(0),
extrinsic.squeeze(0),
intrinsic.squeeze(0)
)
extrinsic, intrinsic = extrinsic.squeeze(), intrinsic.squeeze()
extrinsics_w2c = torch.eye(4)[None].repeat(len(extrinsic), 1, 1)
extrinsics_w2c[:, :3, :4] = extrinsic.cpu()
extrinsics_w2c = extrinsics_w2c.cpu().numpy()
intrinsics = intrinsic.cpu().numpy()
scaled_y, scaled_x = images.shape[-2:]
intrinsics[:, 0, 0] *= 720 / scaled_x
intrinsics[:, 1, 1] *= 480 / scaled_y
intrinsics[:, 0, 2] *= 720 / scaled_x
intrinsics[:, 1, 2] *= 480 / scaled_y
images = torch.stack([images[:, 0], images[:, -1]], dim=1)
point_map = np.stack([point_map[0], point_map[-1]], axis=0)
colors = images.permute(0, 1, 3, 4, 2).detach().cpu().numpy()
colors = colors.reshape(-1, 3)
point_map = point_map.reshape(-1, 3).astype(np.float32)
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(point_map)
pcd.colors = o3d.utility.Vector3dVector(colors)
o3d.io.write_point_cloud(os.path.join(save_path, "points3D.ply"), pcd)
camera_dir = os.path.join(save_path, "camera")
os.makedirs(camera_dir, exist_ok=True)
for i, (w2c, intrinsic) in enumerate(zip(extrinsics_w2c, intrinsics)):
c2w = np.eye(4)
c2w[:3, :3] = w2c[:3, :3].T
c2w[:3, 3] = - w2c[:3, :3].T @ w2c[:3, 3]
np.savez(
os.path.join(camera_dir, f"{i+1:04d}.npz"),
pose=c2w,
intrinsics=intrinsic
)
def get_pose_estimator(cfg):
POSE_ESTIMATOR = {
"colmap": ColmapEstimator,
"mast3r": MASt3REstimator,
"cut3r": CUT3REstimator,
"vggt": VGGTEstimator,
}
return POSE_ESTIMATOR[cfg.pose_estimator.type](cfg)
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