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"""
Spring Dataset using WAI format data.
"""
import os
import cv2
import numpy as np
from mapanything.datasets.base.base_dataset import BaseDataset
from mapanything.utils.wai.core import load_data, load_frame
class SpringWAI(BaseDataset):
"""
Spring dataset containing high-quality large-scale in-the-wild scenes with unique animated objects.
"""
def __init__(
self,
*args,
ROOT,
dataset_metadata_dir,
split,
overfit_num_sets=None,
sample_specific_scene: bool = False,
specific_scene_name: str = None,
**kwargs,
):
"""
Initialize the dataset attributes.
Args:
ROOT: Root directory of the dataset.
dataset_metadata_dir: Path to the dataset metadata directory.
split: Dataset split (train, val, test).
overfit_num_sets: If None, use all sets. Else, the dataset will be truncated to this number of sets.
sample_specific_scene: Whether to sample a specific scene from the dataset.
specific_scene_name: Name of the specific scene to sample.
"""
# Initialize the dataset attributes
super().__init__(*args, **kwargs)
self.ROOT = ROOT
self.dataset_metadata_dir = dataset_metadata_dir
self.split = split
self.overfit_num_sets = overfit_num_sets
self.sample_specific_scene = sample_specific_scene
self.specific_scene_name = specific_scene_name
self._load_data()
# Define the dataset type flags
self.is_metric_scale = True
self.is_synthetic = True
def _load_data(self):
"Load the precomputed dataset metadata"
# Load the dataset metadata corresponding to the split
split_metadata_path = os.path.join(
self.dataset_metadata_dir,
self.split,
f"spring_scene_list_{self.split}.npy",
)
split_scene_list = np.load(split_metadata_path, allow_pickle=True)
# Get the list of all scenes
if not self.sample_specific_scene:
self.scenes = list(split_scene_list)
else:
self.scenes = [self.specific_scene_name]
self.num_of_scenes = len(self.scenes)
def _get_views(self, sampled_idx, num_views_to_sample, resolution):
# Get the scene name of the sampled index
scene_index = sampled_idx
scene_name = self.scenes[scene_index]
# Get the metadata corresponding to the scene
scene_root = os.path.join(self.ROOT, scene_name)
scene_meta = load_data(
os.path.join(scene_root, "scene_meta.json"), "scene_meta"
)
scene_file_names = list(scene_meta["frame_names"].keys())
num_views_in_scene = len(scene_file_names)
# Load the scene pairwise covisibility mmap
covisibility_version_key = "v0"
covisibility_map_dir = os.path.join(
scene_root, "covisibility", covisibility_version_key
)
covisibility_map_name = next(
f for f in os.listdir(covisibility_map_dir) if f.endswith(".npy")
) # Assumes only npy file in directory is covisbility map
covisibility_map_path = os.path.join(
scene_root, "covisibility", covisibility_version_key, covisibility_map_name
)
pairwise_covisibility = load_data(covisibility_map_path, "mmap")
# Get the indices of the N views in the scene
view_indices = self._sample_view_indices(
num_views_to_sample, num_views_in_scene, pairwise_covisibility
)
# Get the views corresponding to the selected view indices
views = []
for view_index in view_indices:
# Load the data corresponding to the view
view_file_name = scene_file_names[view_index]
view_data = load_frame(
scene_root,
view_file_name,
modalities=["image", "depth", "skymask", "pred_mask/moge2"],
scene_meta=scene_meta,
)
# Convert necessary data to numpy
image = view_data["image"].permute(1, 2, 0).numpy()
image = (image * 255).astype(np.uint8)
depthmap = view_data["depth"].numpy().astype(np.float32)
intrinsics = view_data["intrinsics"].numpy().astype(np.float32)
c2w_pose = view_data["extrinsics"].numpy().astype(np.float32)
# Get the sky mask and mask out GT depth
sky_mask = view_data["skymask"].numpy().astype(int)
depthmap = np.where(sky_mask, 0, depthmap)
# Ensure that the depthmap has all valid values
depthmap = np.nan_to_num(depthmap, nan=0.0, posinf=0.0, neginf=0.0)
# Get the non_ambiguous_mask and ensure it matches image resolution
non_ambiguous_mask = view_data["pred_mask/moge2"].numpy().astype(int)
non_ambiguous_mask = cv2.resize(
non_ambiguous_mask,
(image.shape[1], image.shape[0]),
interpolation=cv2.INTER_NEAREST,
)
# Mask out the GT depth using the non_ambiguous_mask
depthmap = np.where(non_ambiguous_mask, depthmap, 0)
# Resize the data to match the desired resolution
additional_quantities_to_resize = [non_ambiguous_mask]
image, depthmap, intrinsics, additional_quantities_to_resize = (
self._crop_resize_if_necessary(
image=image,
resolution=resolution,
depthmap=depthmap,
intrinsics=intrinsics,
additional_quantities=additional_quantities_to_resize,
)
)
non_ambiguous_mask = additional_quantities_to_resize[0]
# Append the view dictionary to the list of views
views.append(
dict(
img=image,
depthmap=depthmap,
camera_pose=c2w_pose, # cam2world
camera_intrinsics=intrinsics,
non_ambiguous_mask=non_ambiguous_mask,
dataset="Spring",
label=scene_name,
instance=os.path.join("images", str(view_file_name)),
)
)
return views
def get_parser():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"-rd", "--root_dir", default="/fsx/xrtech/data/spring", type=str
)
parser.add_argument(
"-dmd",
"--dataset_metadata_dir",
default="/fsx/nkeetha/mapanything_dataset_metadata",
type=str,
)
parser.add_argument(
"-nv",
"--num_of_views",
default=2,
type=int,
)
parser.add_argument("--viz", action="store_true")
return parser
if __name__ == "__main__":
import rerun as rr
from tqdm import tqdm
from mapanything.datasets.base.base_dataset import view_name
from mapanything.utils.image import rgb
from mapanything.utils.viz import script_add_rerun_args
parser = get_parser()
script_add_rerun_args(
parser
) # Options: --headless, --connect, --serve, --addr, --save, --stdout
args = parser.parse_args()
dataset = SpringWAI(
num_views=args.num_of_views,
split="train",
covisibility_thres=0.25,
ROOT=args.root_dir,
dataset_metadata_dir=args.dataset_metadata_dir,
resolution=(518, 294),
aug_crop=16,
transform="colorjitter+grayscale+gaublur",
data_norm_type="dinov2",
)
# dataset = SpringWAI(
# num_views=args.num_of_views,
# split="val",
# covisibility_thres=0.25,
# ROOT=args.root_dir,
# dataset_metadata_dir=args.dataset_metadata_dir,
# resolution=(518, 294),
# seed=777,
# transform="imgnorm",
# data_norm_type="dinov2",
# )
print(dataset.get_stats())
if args.viz:
rr.script_setup(args, "Spring_Dataloader")
rr.set_time("stable_time", sequence=0)
rr.log("world", rr.ViewCoordinates.RDF, static=True)
sampled_indices = np.random.choice(len(dataset), size=10, replace=False)
for num, idx in enumerate(tqdm(sampled_indices)):
views = dataset[idx]
assert len(views) == args.num_of_views
sample_name = f"{idx}"
for view_idx in range(args.num_of_views):
sample_name += f" {view_name(views[view_idx])}"
print(sample_name)
for view_idx in range(args.num_of_views):
image = rgb(
views[view_idx]["img"], norm_type=views[view_idx]["data_norm_type"]
)
depthmap = views[view_idx]["depthmap"]
pose = views[view_idx]["camera_pose"]
intrinsics = views[view_idx]["camera_intrinsics"]
pts3d = views[view_idx]["pts3d"]
valid_mask = views[view_idx]["valid_mask"]
if "non_ambiguous_mask" in views[view_idx]:
non_ambiguous_mask = views[view_idx]["non_ambiguous_mask"]
else:
non_ambiguous_mask = None
if "prior_depth_along_ray" in views[view_idx]:
prior_depth_along_ray = views[view_idx]["prior_depth_along_ray"]
else:
prior_depth_along_ray = None
if args.viz:
rr.set_time("stable_time", sequence=num)
base_name = f"world/view_{view_idx}"
pts_name = f"world/view_{view_idx}_pointcloud"
# Log camera info and loaded data
height, width = image.shape[0], image.shape[1]
rr.log(
base_name,
rr.Transform3D(
translation=pose[:3, 3],
mat3x3=pose[:3, :3],
),
)
rr.log(
f"{base_name}/pinhole",
rr.Pinhole(
image_from_camera=intrinsics,
height=height,
width=width,
camera_xyz=rr.ViewCoordinates.RDF,
),
)
rr.log(
f"{base_name}/pinhole/rgb",
rr.Image(image),
)
rr.log(
f"{base_name}/pinhole/depth",
rr.DepthImage(depthmap),
)
if prior_depth_along_ray is not None:
rr.log(
f"prior_depth_along_ray_{view_idx}",
rr.DepthImage(prior_depth_along_ray),
)
if non_ambiguous_mask is not None:
rr.log(
f"{base_name}/pinhole/non_ambiguous_mask",
rr.SegmentationImage(non_ambiguous_mask.astype(int)),
)
# Log points in 3D
filtered_pts = pts3d[valid_mask]
filtered_pts_col = image[valid_mask]
rr.log(
pts_name,
rr.Points3D(
positions=filtered_pts.reshape(-1, 3),
colors=filtered_pts_col.reshape(-1, 3),
),
)
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