Add new ARKitScenes example

examples/python/arkitscenes/.gitignore

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+dataset/**

examples/python/arkitscenes/download_dataset.py

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+# Copied from https://github.com/apple/ARKitScenes/blob/main/download_data.py
+# Licensing information: https://github.com/apple/ARKitScenes/blob/main/LICENSE
+import math
+import os
+import subprocess
+from pathlib import Path
+from typing import Final, List, Optional
+
+import pandas as pd
+
+ARkitscense_url = "https://docs-assets.developer.apple.com/ml-research/datasets/arkitscenes/v1"
+TRAINING: Final = "Training"
+VALIDATION: Final = "Validation"
+HIGRES_DEPTH_ASSET_NAME: Final = "highres_depth"
+POINT_CLOUDS_FOLDER: Final = "laser_scanner_point_clouds"
+
+AVAILABLE_RECORDINGS: Final = ["48458663", "42444949", "41069046", "41125722", "41125763", "42446167"]
+DATASET_DIR: Final = Path(os.path.dirname(__file__)) / "dataset"
+
+default_raw_dataset_assets = [
+    "mov",
+    "annotation",
+    "mesh",
+    "confidence",
+    "highres_depth",
+    "lowres_depth",
+    "lowres_wide.traj",
+    "lowres_wide",
+    "lowres_wide_intrinsics",
+    "ultrawide",
+    "ultrawide_intrinsics",
+    "vga_wide",
+    "vga_wide_intrinsics",
+]
+
+missing_3dod_assets_video_ids = [
+    "47334522",
+    "47334523",
+    "42897421",
+    "45261582",
+    "47333152",
+    "47333155",
+    "48458535",
+    "48018733",
+    "47429677",
+    "48458541",
+    "42897848",
+    "47895482",
+    "47333960",
+    "47430089",
+    "42899148",
+    "42897612",
+    "42899153",
+    "42446164",
+    "48018149",
+    "47332198",
+    "47334515",
+    "45663223",
+    "45663226",
+    "45663227",
+]
+
+
+def raw_files(video_id: str, assets: List[str], metadata: pd.DataFrame) -> List[str]:
+    file_names = []
+    for asset in assets:
+        if HIGRES_DEPTH_ASSET_NAME == asset:
+            in_upsampling = metadata.loc[metadata["video_id"] == float(video_id), ["is_in_upsampling"]].iat[0, 0]
+            if not in_upsampling:
+                print(f"Skipping asset {asset} for video_id {video_id} - Video not in upsampling dataset")
+                continue  # highres_depth asset only available for video ids from upsampling dataset
+
+        if asset in [
+            "confidence",
+            "highres_depth",
+            "lowres_depth",
+            "lowres_wide",
+            "lowres_wide_intrinsics",
+            "ultrawide",
+            "ultrawide_intrinsics",
+            "wide",
+            "wide_intrinsics",
+            "vga_wide",
+            "vga_wide_intrinsics",
+        ]:
+            file_names.append(asset + ".zip")
+        elif asset == "mov":
+            file_names.append(f"{video_id}.mov")
+        elif asset == "mesh":
+            if video_id not in missing_3dod_assets_video_ids:
+                file_names.append(f"{video_id}_3dod_mesh.ply")
+        elif asset == "annotation":
+            if video_id not in missing_3dod_assets_video_ids:
+                file_names.append(f"{video_id}_3dod_annotation.json")
+        elif asset == "lowres_wide.traj":
+            if video_id not in missing_3dod_assets_video_ids:
+                file_names.append("lowres_wide.traj")
+        else:
+            raise Exception(f"No asset = {asset} in raw dataset")
+    return file_names
+
+
+def download_file(url: str, file_name: str, dst: Path) -> bool:
+    os.makedirs(dst, exist_ok=True)
+    filepath = os.path.join(dst, file_name)
+
+    if not os.path.isfile(filepath):
+        command = f"curl {url} -o {file_name}.tmp --fail"
+        print(f"Downloading file {filepath}")
+        try:
+            subprocess.check_call(command, shell=True, cwd=dst)
+        except Exception as error:
+            print(f"Error downloading {url}, error: {error}")
+            return False
+        os.rename(filepath + ".tmp", filepath)
+    else:
+        print(f"WARNING: skipping download of existing file: {filepath}")
+    return True
+
+
+def unzip_file(file_name: str, dst: Path, keep_zip: bool = True) -> bool:
+    filepath = os.path.join(dst, file_name)
+    print(f"Unzipping zip file {filepath}")
+    command = f"unzip -oq {filepath} -d {dst}"
+    try:
+        subprocess.check_call(command, shell=True)
+    except Exception as error:
+        print(f"Error unzipping {filepath}, error: {error}")
+        return False
+    if not keep_zip:
+        os.remove(filepath)
+    return True
+
+
+def download_laser_scanner_point_clouds_for_video(video_id: str, metadata: pd.DataFrame, download_dir: Path) -> None:
+    video_metadata = metadata.loc[metadata["video_id"] == float(video_id)]
+    visit_id = video_metadata["visit_id"].iat[0]
+    has_laser_scanner_point_clouds = video_metadata["has_laser_scanner_point_clouds"].iat[0]
+
+    if not has_laser_scanner_point_clouds:
+        print(f"Warning: Laser scanner point clouds for video {video_id} are not available")
+        return
+
+    if math.isnan(visit_id) or not visit_id.is_integer():
+        print(f"Warning: Downloading laser scanner point clouds for video {video_id} failed - Bad visit id {visit_id}")
+        return
+
+    visit_id = int(visit_id)  # Expecting an 8 digit integer
+    laser_scanner_point_clouds_ids = laser_scanner_point_clouds_for_visit_id(visit_id, download_dir)
+
+    for point_cloud_id in laser_scanner_point_clouds_ids:
+        download_laser_scanner_point_clouds(point_cloud_id, visit_id, download_dir)
+
+
+def laser_scanner_point_clouds_for_visit_id(visit_id: int, download_dir: Path) -> List[str]:
+    point_cloud_to_visit_id_mapping_filename = "laser_scanner_point_clouds_mapping.csv"
+    if not os.path.exists(point_cloud_to_visit_id_mapping_filename):
+        point_cloud_to_visit_id_mapping_url = (
+            f"{ARkitscense_url}/raw/laser_scanner_point_clouds/{point_cloud_to_visit_id_mapping_filename}"
+        )
+        if not download_file(
+            point_cloud_to_visit_id_mapping_url,
+            point_cloud_to_visit_id_mapping_filename,
+            download_dir,
+        ):
+            print(
+                f"Error downloading point cloud for visit_id {visit_id} at location "
+                f"{point_cloud_to_visit_id_mapping_url}"
+            )
+            return []
+
+    point_cloud_to_visit_id_mapping_filepath = os.path.join(download_dir, point_cloud_to_visit_id_mapping_filename)
+    point_cloud_to_visit_id_mapping = pd.read_csv(point_cloud_to_visit_id_mapping_filepath)
+    point_cloud_ids = point_cloud_to_visit_id_mapping.loc[
+        point_cloud_to_visit_id_mapping["visit_id"] == visit_id,
+        ["laser_scanner_point_clouds_id"],
+    ]
+    point_cloud_ids_list = [scan_id[0] for scan_id in point_cloud_ids.values]
+
+    return point_cloud_ids_list
+
+
+def download_laser_scanner_point_clouds(laser_scanner_point_cloud_id: str, visit_id: int, download_dir: Path) -> None:
+    laser_scanner_point_clouds_folder_path = download_dir / POINT_CLOUDS_FOLDER / str(visit_id)
+    os.makedirs(laser_scanner_point_clouds_folder_path, exist_ok=True)
+
+    for extension in [".ply", "_pose.txt"]:
+        filename = f"{laser_scanner_point_cloud_id}{extension}"
+        filepath = os.path.join(laser_scanner_point_clouds_folder_path, filename)
+        if os.path.exists(filepath):
+            return
+        file_url = f"{ARkitscense_url}/raw/laser_scanner_point_clouds/{visit_id}/{filename}"
+        download_file(file_url, filename, laser_scanner_point_clouds_folder_path)
+
+
+def get_metadata(dataset: str, download_dir: Path) -> pd.DataFrame:
+    filename = "metadata.csv"
+    url = f"{ARkitscense_url}/threedod/{filename}" if "3dod" == dataset else f"{ARkitscense_url}/{dataset}/{filename}"
+    dst_folder = download_dir / dataset
+    dst_file = dst_folder / filename
+
+    if not download_file(url, filename, dst_folder):
+        return
+
+    metadata = pd.read_csv(dst_file)
+    return metadata
+
+
+def download_data(
+    dataset: str,
+    video_ids: List[str],
+    dataset_splits: List[str],
+    download_dir: Path,
+    keep_zip: bool,
+    raw_dataset_assets: Optional[List[str]] = None,
+    should_download_laser_scanner_point_cloud: bool = False,
+) -> None:
+    """
+    Downloads data from the specified dataset and video IDs to the given download directory.
+
+    Args:
+    ----
+    dataset: the name of the dataset to download from (raw, 3dod, or upsampling)
+    video_ids: the list of video IDs to download data for
+    dataset_splits: the list of splits for each video ID (train, validation, or test)
+    download_dir: the directory to download data to
+    keep_zip: whether to keep the downloaded zip files after extracting them
+    raw_dataset_assets: a list of asset types to download from the raw dataset, if dataset is "raw"
+    should_download_laser_scanner_point_cloud: whether to download the laser scanner point cloud data, if available
+
+    Returns: None
+    """
+    metadata = get_metadata(dataset, download_dir)
+    if None is metadata:
+        print(f"Error retrieving metadata for dataset {dataset}")
+        return
+
+    for video_id in sorted(set(video_ids)):
+        split = dataset_splits[video_ids.index(video_id)]
+        dst_dir = download_dir / dataset / split
+        if dataset == "raw":
+            url_prefix = ""
+            file_names = []
+            if not raw_dataset_assets:
+                print(f"Warning: No raw assets given for video id {video_id}")
+            else:
+                dst_dir = dst_dir / str(video_id)
+                url_prefix = f"{ARkitscense_url}/raw/{split}/{video_id}" + "/{}"
+                file_names = raw_files(video_id, raw_dataset_assets, metadata)
+        elif dataset == "3dod":
+            url_prefix = f"{ARkitscense_url}/threedod/{split}" + "/{}"
+            file_names = [
+                f"{video_id}.zip",
+            ]
+        elif dataset == "upsampling":
+            url_prefix = f"{ARkitscense_url}/upsampling/{split}" + "/{}"
+            file_names = [
+                f"{video_id}.zip",
+            ]
+        else:
+            raise Exception(f"No such dataset = {dataset}")
+
+        if should_download_laser_scanner_point_cloud and dataset == "raw":
+            # Point clouds only available for the raw dataset
+            download_laser_scanner_point_clouds_for_video(video_id, metadata, download_dir)
+
+        for file_name in file_names:
+            dst_path = os.path.join(dst_dir, file_name)
+            url = url_prefix.format(file_name)
+
+            if not file_name.endswith(".zip") or not os.path.isdir(dst_path[: -len(".zip")]):
+                download_file(url, dst_path, dst_dir)
+            else:
+                print(f"WARNING: skipping download of existing zip file: {dst_path}")
+            if file_name.endswith(".zip") and os.path.isfile(dst_path):
+                unzip_file(file_name, dst_dir, keep_zip)
+
+
+def ensure_recording_downloaded(video_id: str) -> Path:
+    """Only downloads from validation set."""
+    data_path = DATASET_DIR / "raw" / "Validation" / video_id
+    download_data(
+        dataset="raw",
+        video_ids=[video_id],
+        dataset_splits=[VALIDATION],
+        download_dir=DATASET_DIR,
+        keep_zip=False,
+        raw_dataset_assets=[
+            "lowres_wide",
+            "lowres_depth",
+            "lowres_wide_intrinsics",
+            "lowres_wide.traj",
+            "wide",
+            "wide_intrinsics",
+            "highres_depth",
+            "annotation",
+            "mesh",
+        ],
+        should_download_laser_scanner_point_cloud=False,
+    )
+    return data_path
+
+
+def ensure_recording_available(video_id: str) -> Path:
+    """
+    Returns the path to the recording for a given video_id.
+
+    Args:
+        video_id (str): Identifier for the recording.
+
+    Returns
+    -------
+        Path: Path object representing the path to the recording.
+
+    Raises
+    ------
+        AssertionError: If the recording path does not exist.
+    """
+    recording_path = ensure_recording_downloaded(video_id)
+    assert recording_path.exists(), f"Recording path {recording_path} does not exist."
+    return recording_path  # Return the path to the recording