| """Load RGB Stokes/S2L2 data and masks from either polarization subset.""" | |
| from pathlib import Path | |
| import argparse | |
| import csv | |
| import numpy as np | |
| import OpenImageIO as oiio | |
| from PIL import Image | |
| SUBSETS = ('full_stokes', 'linear_stokes') | |
| def load_scene(root, scene_id, representation='thetaphi', subset=None): | |
| if representation not in {'thetaphi', 'duff', 's2l2'}: | |
| raise ValueError('representation must be thetaphi, duff, or s2l2') | |
| root = Path(root) | |
| if subset is not None: | |
| if subset not in SUBSETS: | |
| raise ValueError(f'subset must be one of {SUBSETS}') | |
| root = root / subset | |
| if not (root / 'metadata.csv').is_file(): | |
| raise ValueError('Choose a subset with subset="full_stokes" or "linear_stokes", or pass its directory as root.') | |
| with (root / 'metadata.csv').open(newline='', encoding='utf-8') as f: | |
| rows = [r for r in csv.DictReader(f) if int(r['scene_id']) == int(scene_id)] | |
| if len(rows) != 1: | |
| raise KeyError(f'Unknown scene ID: {scene_id}') | |
| metadata = rows[0] | |
| metadata['s3_measured'] = metadata['s3_measured'].lower() == 'true' | |
| if not metadata[representation].strip(): | |
| raise FileNotFoundError(f'Scene {scene_id} has no released data files; its metadata file paths are empty.') | |
| path = root / metadata[representation] | |
| inp = oiio.ImageInput.open(str(path)) | |
| if inp is None: | |
| raise IOError(f'Cannot open {path}') | |
| try: | |
| spec = inp.spec() | |
| names = list(spec.channelnames) | |
| pixels = inp.read_image(format=oiio.FLOAT) | |
| if pixels is None: | |
| raise IOError(f'Cannot decode {path}') | |
| count = 12 if representation == 's2l2' else 4 | |
| parameters = [f'S2L2{i:02d}' for i in range(count)] if representation == 's2l2' else [f'S{i}' for i in range(count)] | |
| data = np.stack([np.stack([pixels[..., names.index(f'{p}.{c}')] for p in parameters], axis=-1) for c in 'RGB'], axis=-2) | |
| finally: | |
| inp.close() | |
| result = {'data': data, 'metadata': metadata, 's3_measured': metadata['s3_measured']} | |
| for name in ['privacy_mask', 'saturation_mask', 'invalid_mask']: | |
| with Image.open(root / metadata[name]) as image: | |
| result[name] = np.asarray(image) != 0 | |
| if result[name].shape != data.shape[:2]: | |
| raise ValueError(f'Mask dimensions do not match EXR: {name}') | |
| return result | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser(description=__doc__) | |
| parser.add_argument('--root', default='.') | |
| parser.add_argument('--subset', choices=SUBSETS) | |
| parser.add_argument('--scene-id', required=True, type=int) | |
| parser.add_argument('--representation', default='thetaphi', choices=['thetaphi', 'duff', 's2l2']) | |
| args = parser.parse_args() | |
| scene = load_scene(args.root, args.scene_id, args.representation, args.subset) | |
| print({'scene_id': args.scene_id, 'shape': scene['data'].shape, 'dtype': str(scene['data'].dtype), 's3_measured': scene['s3_measured'], 'privacy_review_status': scene['metadata']['privacy_review_status']}) | |