dust3r/utils/eval_metadata.py

import os
import glob
from tqdm import tqdm

# Define the merged dataset metadata dictionary
dataset_metadata = {
    'davis': {
        'img_path': "./data/davis/DAVIS/JPEGImages/480p",
        'mask_path': "./data/davis/DAVIS/Annotations/480p",
        'dir_path_func': lambda img_path, seq: os.path.join(img_path, seq),
        'gt_traj_func': lambda img_path, anno_path, seq: None,
        'traj_format': None,
        'seq_list': None,
        'full_seq': True,
        'mask_path_seq_func': lambda mask_path, seq: os.path.join(mask_path, seq),
        'skip_condition': None,
        'process_func': None,  # Not used in mono depth estimation
    },
    'test': {
        'img_path': "./data/test_set",
        'mask_path': None,
        'dir_path_func': lambda img_path, seq: os.path.join(img_path, seq),
        'gt_traj_func': lambda img_path, anno_path, seq: None,
        'traj_format': None,
        'seq_list': None,
        'full_seq': True,
        'mask_path_seq_func': lambda mask_path, seq: None,
        'skip_condition': None,
        'process_func': None,  # Not used in mono depth estimation
    },
    'bonn': {
        'img_path': "./data/bonn/rgbd_bonn_dataset",
        'mask_path': None,
        'dir_path_func': lambda img_path, seq: os.path.join(img_path, f'rgbd_bonn_{seq}', 'rgb_110'),
        'gt_traj_func': lambda img_path, anno_path, seq: os.path.join(img_path, f'rgbd_bonn_{seq}', 'groundtruth_110.txt'),
        'traj_format': 'tum',
        'seq_list': ["balloon2", "crowd2", "crowd3", "person_tracking2", "synchronous"],
        'full_seq': False,
        'mask_path_seq_func': lambda mask_path, seq: None,
        'skip_condition': None,
        'process_func': lambda args, img_path: process_bonn(args, img_path),
    },
    'tum': {
        'img_path': "./data/tum",
        'mask_path': None,
        'dir_path_func': lambda img_path, seq: os.path.join(img_path, seq, 'rgb_50'),
        'gt_traj_func': lambda img_path, anno_path, seq: os.path.join(img_path, seq, 'groundtruth_50.txt'),
        'traj_format': 'tum',
        'seq_list': None,
        'full_seq': True,
        'mask_path_seq_func': lambda mask_path, seq: None,
        'skip_condition': None,
        'process_func': None,
    },
    'sintel': {
        'img_path': "./data/MPI-Sintel/MPI-Sintel-training_images/training/final",
        'anno_path': "./data/MPI-Sintel/MPI-Sintel-depth-training/training/camdata_left",
        'mask_path': "./data/MPI-Sintel/MPI-Sintel-depth-training/training/dynamic_label_perfect/",
        'dir_path_func': lambda img_path, seq: os.path.join(img_path, seq),
        'gt_traj_func': lambda img_path, anno_path, seq: os.path.join(anno_path, seq),
        'traj_format': None,
        'seq_list': ["alley_2", "ambush_4", "ambush_5", "ambush_6", "cave_2", "cave_4", "market_2",
                     "market_5", "market_6", "shaman_3", "sleeping_1", "sleeping_2", "temple_2", "temple_3"],
        'full_seq': False,
        'mask_path_seq_func': lambda mask_path, seq: None,
        'skip_condition': None,
        'process_func': lambda args, img_path: process_sintel(args, img_path),
    },
}

# Define processing functions for each dataset
def process_kitti(args, img_path):
    for dir in tqdm(sorted(glob.glob(f'{img_path}/*'))):
        filelist = sorted(glob.glob(f'{dir}/*.png'))
        save_dir = f'{args.output_dir}/{os.path.basename(dir)}'
        yield filelist, save_dir

def process_bonn(args, img_path):
    if args.full_seq:
        for dir in tqdm(sorted(glob.glob(f'{img_path}/*/'))):
            filelist = sorted(glob.glob(f'{dir}/rgb/*.png'))
            save_dir = f'{args.output_dir}/{os.path.basename(os.path.dirname(dir))}'
            yield filelist, save_dir
    else:
        seq_list = ["balloon2", "crowd2", "crowd3", "person_tracking2", "synchronous"] if args.seq_list is None else args.seq_list
        for seq in tqdm(seq_list):
            filelist = sorted(glob.glob(f'{img_path}/rgbd_bonn_{seq}/rgb_110/*.png'))
            save_dir = f'{args.output_dir}/{seq}'
            yield filelist, save_dir

def process_nyu(args, img_path):
    filelist = sorted(glob.glob(f'{img_path}/*.png'))
    save_dir = f'{args.output_dir}'
    yield filelist, save_dir

def process_scannet(args, img_path):
    seq_list = sorted(glob.glob(f'{img_path}/*'))
    for seq in tqdm(seq_list):
        filelist = sorted(glob.glob(f'{seq}/color_90/*.jpg'))
        save_dir = f'{args.output_dir}/{os.path.basename(seq)}'
        yield filelist, save_dir

def process_sintel(args, img_path):
    if args.full_seq:
        for dir in tqdm(sorted(glob.glob(f'{img_path}/*/'))):
            filelist = sorted(glob.glob(f'{dir}/*.png'))
            save_dir = f'{args.output_dir}/{os.path.basename(os.path.dirname(dir))}'
            yield filelist, save_dir
    else:
        seq_list = ["alley_2", "ambush_4", "ambush_5", "ambush_6", "cave_2", "cave_4", "market_2",
                    "market_5", "market_6", "shaman_3", "sleeping_1", "sleeping_2", "temple_2", "temple_3"]
        for seq in tqdm(seq_list):
            filelist = sorted(glob.glob(f'{img_path}/{seq}/*.png'))
            save_dir = f'{args.output_dir}/{seq}'
            yield filelist, save_dir