"""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']})