data_preprocess/stru3d/generate_coco_stru3d.py

import argparse
import json
import os
import sys

from stru3d_utils import generate_coco_dict, generate_density, normalize_annotations, parse_floor_plan_polys
from tqdm import tqdm

sys.path.append("../.")
from common_utils import export_density, read_scene_pc

### Note: Some scenes have missing/wrong annotations. These are the indices that you should additionally exclude
### to be consistent with MonteFloor and HEAT:
invalid_scenes_ids = [
    76,
    183,
    335,
    491,
    663,
    681,
    703,
    728,
    865,
    936,
    985,
    986,
    1009,
    1104,
    1155,
    1221,
    1282,
    1365,
    1378,
    1635,
    1745,
    1772,
    1774,
    1816,
    1866,
    2037,
    2076,
    2274,
    2334,
    2357,
    2580,
    2665,
    2706,
    2713,
    2771,
    2868,
    3156,
    3192,
    3198,
    3261,
    3271,
    3276,
    3296,
    3342,
    3387,
    3398,
    3466,
    3496,
]

type2id = {
    "living room": 0,
    "kitchen": 1,
    "bedroom": 2,
    "bathroom": 3,
    "balcony": 4,
    "corridor": 5,
    "dining room": 6,
    "study": 7,
    "studio": 8,
    "store room": 9,
    "garden": 10,
    "laundry room": 11,
    "office": 12,
    "basement": 13,
    "garage": 14,
    "undefined": 15,
    "door": 16,
    "window": 17,
}


def config():
    a = argparse.ArgumentParser(description="Generate coco format data for Structured3D")
    a.add_argument(
        "--data_root", default="Structured3D_panorama", type=str, help="path to raw Structured3D_panorama folder"
    )
    a.add_argument("--output", default="coco_stru3d", type=str, help="path to output folder")

    args = a.parse_args()
    return args


def main(args):
    data_root = args.data_root
    data_parts = os.listdir(data_root)

    ### prepare
    outFolder = args.output
    if not os.path.exists(outFolder):
        os.mkdir(outFolder)

    annotation_outFolder = os.path.join(outFolder, "annotations")
    if not os.path.exists(annotation_outFolder):
        os.mkdir(annotation_outFolder)

    train_img_folder = os.path.join(outFolder, "train")
    val_img_folder = os.path.join(outFolder, "val")
    test_img_folder = os.path.join(outFolder, "test")

    for img_folder in [train_img_folder, val_img_folder, test_img_folder]:
        if not os.path.exists(img_folder):
            os.mkdir(img_folder)

    coco_train_json_path = os.path.join(annotation_outFolder, "train.json")
    coco_val_json_path = os.path.join(annotation_outFolder, "val.json")
    coco_test_json_path = os.path.join(annotation_outFolder, "test.json")

    coco_train_dict = {"images": [], "annotations": [], "categories": []}
    coco_val_dict = {"images": [], "annotations": [], "categories": []}
    coco_test_dict = {"images": [], "annotations": [], "categories": []}

    for key, value in type2id.items():
        type_dict = {"supercategory": "room", "id": value, "name": key}
        coco_train_dict["categories"].append(type_dict)
        coco_val_dict["categories"].append(type_dict)
        coco_test_dict["categories"].append(type_dict)

    ### begin processing
    instance_id = 0
    for part in tqdm(data_parts):
        scenes = os.listdir(os.path.join(data_root, part, "Structured3D"))
        for scene in tqdm(scenes):
            scene_path = os.path.join(data_root, part, "Structured3D", scene)
            scene_id = scene.split("_")[-1]

            if int(scene_id) in invalid_scenes_ids:
                print("skip {}".format(scene))
                continue

            # load pre-generated point cloud
            ply_path = os.path.join(scene_path, "point_cloud.ply")
            points = read_scene_pc(ply_path)
            xyz = points[:, :3]

            ### project point cloud to density map
            density, normalization_dict = generate_density(xyz, width=256, height=256)

            ### rescale raw annotations
            normalized_annos = normalize_annotations(scene_path, normalization_dict)

            ### prepare coco dict
            img_id = int(scene_id)
            img_dict = {}
            img_dict["file_name"] = scene_id + ".png"
            img_dict["id"] = img_id
            img_dict["width"] = 256
            img_dict["height"] = 256

            ### parse annotations
            polys = parse_floor_plan_polys(normalized_annos)
            polygons_list = generate_coco_dict(normalized_annos, polys, instance_id, img_id, ignore_types=["outwall"])

            instance_id += len(polygons_list)

            ### train
            if int(scene_id) < 3000:
                coco_train_dict["images"].append(img_dict)
                coco_train_dict["annotations"] += polygons_list
                export_density(density, train_img_folder, scene_id)

            ### val
            elif int(scene_id) >= 3000 and int(scene_id) < 3250:
                coco_val_dict["images"].append(img_dict)
                coco_val_dict["annotations"] += polygons_list
                export_density(density, val_img_folder, scene_id)

            ### test
            else:
                coco_test_dict["images"].append(img_dict)
                coco_test_dict["annotations"] += polygons_list
                export_density(density, test_img_folder, scene_id)

            print(scene_id)

    with open(coco_train_json_path, "w") as f:
        json.dump(coco_train_dict, f)
    with open(coco_val_json_path, "w") as f:
        json.dump(coco_val_dict, f)
    with open(coco_test_json_path, "w") as f:
        json.dump(coco_test_dict, f)


if __name__ == "__main__":
    main(config())