assets/pointcloud_eval/preprocess_tof_360.py

"""
Preprocessing Script for ToF-360

Author: Mahdi Chamseddine (mahdi.chamseddine@dfki.de)
Please cite our work if the code is helpful to you.
"""
from pathlib import Path

import cv2
import numpy as np
import open3d as o3d


def map_label(label: int) -> int:

    match label:
        case 0 | 33 | 34:  # <UNK>
            return -1
        case 2 | 20 | 42:  # ceiling
            return 0
        case 3 | 18:  # floor
            return 1
        case 1 | 40:  # wall
            return 2
        # case 17:  # beam
        #     return 3
        case 14:  # column
            return 4
        case 8:  # window
            return 5
        case 7:  # door
            return 6
        case 12:  # table
            return 7
        case 5:  # chair
            return 8
        case 4:  # sofa
            return 9
        case 31:  # bookcase
            return 10
        # case 26:  # board
        #     return 11
        case _:  # clutter
            return 12


def downsample(xyz: np.ndarray, voxel_size: float = 0.01) -> np.ndarray:
    min_vals = np.min(xyz, axis=0)
    max_vals = np.max(xyz, axis=0)
    point_cloud = o3d.geometry.PointCloud()
    point_cloud.points = o3d.utility.Vector3dVector(xyz)
    _, _, indices = point_cloud.voxel_down_sample_and_trace(
        voxel_size, min_vals, max_vals
    )
    indices = [np.random.choice(idx) for idx in indices]
    return np.asarray(indices)


def preprocess_scans(area_path: Path) -> None:
    xyz_dir = area_path / "XYZ"
    for scan_path in xyz_dir.glob("*.npy*"):
        scan_name = scan_path.stem[: -len("_xxx")]
        parse_scan(scan_name, area_path)


def parse_scan(scan_name: str, area_path: Path, debug: bool = False):
    output_name = area_path.stem + "_" + scan_name
    print(f"Parsing scan: {output_name}", flush=True)
    processed_path = (
        area_path.parent.parent / "preprocessed" / area_path.parent.stem / output_name
    )
    # if processed_path.exists():
    #     return
    processed_path.mkdir(parents=True, exist_ok=True)

    print(f"--- [{output_name}] reading point cloud", flush=True)
    xyz_path = Path(area_path / "XYZ", scan_name + "_XYZ.npy")
    temp = np.load(xyz_path)

    temp = temp.reshape(-1, 3) / 1000  # mm to m
    coord = temp.copy()
    coord[:, 1] = temp[:, 2]
    coord[:, 2] = -temp[:, 1]
    png_path = Path(area_path / "RGB", scan_name + "_rgb.png")
    color = cv2.imread(png_path.resolve())
    color = cv2.cvtColor(color, cv2.COLOR_BGR2RGB).reshape(-1, 3) / 255

    print(f"--- [{output_name}] loading labels", flush=True)
    semantic_path = Path(area_path / "semantics", scan_name + "_semantic.npy")
    segment = np.load(semantic_path).reshape(-1)
    segment = np.vectorize(map_label)(segment)

    normal_path = Path(area_path / "normal", scan_name + "_normal.png")
    temp = cv2.imread(normal_path.resolve())
    temp = cv2.cvtColor(temp, cv2.COLOR_BGR2RGB).reshape(-1, 3) * 2 / 255
    temp = temp - 1
    normal = temp.copy()
    normal[:, 1] = temp[:, 2]
    normal[:, 2] = -temp[:, 1]

    print(f"--- [{output_name}] down sampling", flush=True)
    idx = downsample(coord)

    print(f"--- [{output_name}] saving", flush=True)
    coord = np.ascontiguousarray(coord[idx, :], dtype=np.float32)
    np.save(Path(processed_path, "coord.npy"), coord)
    color = np.ascontiguousarray(color[idx, :], dtype=np.float32)
    np.save(Path(processed_path, "color.npy"), color)
    normal = np.ascontiguousarray(normal[idx, :], dtype=np.float32)
    np.save(Path(processed_path, "normal.npy"), normal)
    segment = np.ascontiguousarray(segment[idx], dtype=np.int32)
    np.save(Path(processed_path, "segment.npy"), segment)


def main():
    # splits = ["test", "train", "val"]
    # splits = ["train"]
    splits = [""]
    dataset_directory = "path/to/ToF-360/"

    areas = ["Hospital", "Office_Room_1", "Office_Room_2", "Parking_Lot"]

    for split in splits:
        split_directory = dataset_directory + split
        split_path = Path(split_directory)
        # Check if the parent directory exists
        if not split_path.is_dir():
            print(
                f"Error: '{split_path.resolve()}' is not a valid directory.",
                flush=True,
            )
            return
        for area_path in split_path.iterdir():
            if area_path.is_dir() and area_path.stem in areas:
                preprocess_scans(area_path)


if __name__ == "__main__":
    main()