| | import os |
| | import json |
| | import shutil |
| | import string |
| | import tarfile |
| | import tifffile |
| | import datasets |
| |
|
| | import numpy as np |
| | import pandas as pd |
| |
|
| | from tqdm import tqdm |
| |
|
| | class_sets = { |
| | 19: [ |
| | 'Urban fabric', |
| | 'Industrial or commercial units', |
| | 'Arable land', |
| | 'Permanent crops', |
| | 'Pastures', |
| | 'Complex cultivation patterns', |
| | 'Land principally occupied by agriculture, with significant areas of natural vegetation', |
| | 'Agro-forestry areas', |
| | 'Broad-leaved forest', |
| | 'Coniferous forest', |
| | 'Mixed forest', |
| | 'Natural grassland and sparsely vegetated areas', |
| | 'Moors, heathland and sclerophyllous vegetation', |
| | 'Transitional woodland, shrub', |
| | 'Beaches, dunes, sands', |
| | 'Inland wetlands', |
| | 'Coastal wetlands', |
| | 'Inland waters', |
| | 'Marine waters', |
| | ], |
| | 43: [ |
| | 'Continuous urban fabric', |
| | 'Discontinuous urban fabric', |
| | 'Industrial or commercial units', |
| | 'Road and rail networks and associated land', |
| | 'Port areas', |
| | 'Airports', |
| | 'Mineral extraction sites', |
| | 'Dump sites', |
| | 'Construction sites', |
| | 'Green urban areas', |
| | 'Sport and leisure facilities', |
| | 'Non-irrigated arable land', |
| | 'Permanently irrigated land', |
| | 'Rice fields', |
| | 'Vineyards', |
| | 'Fruit trees and berry plantations', |
| | 'Olive groves', |
| | 'Pastures', |
| | 'Annual crops associated with permanent crops', |
| | 'Complex cultivation patterns', |
| | 'Land principally occupied by agriculture, with significant areas of natural vegetation', |
| | 'Agro-forestry areas', |
| | 'Broad-leaved forest', |
| | 'Coniferous forest', |
| | 'Mixed forest', |
| | 'Natural grassland', |
| | 'Moors and heathland', |
| | 'Sclerophyllous vegetation', |
| | 'Transitional woodland/shrub', |
| | 'Beaches, dunes, sands', |
| | 'Bare rock', |
| | 'Sparsely vegetated areas', |
| | 'Burnt areas', |
| | 'Inland marshes', |
| | 'Peatbogs', |
| | 'Salt marshes', |
| | 'Salines', |
| | 'Intertidal flats', |
| | 'Water courses', |
| | 'Water bodies', |
| | 'Coastal lagoons', |
| | 'Estuaries', |
| | 'Sea and ocean', |
| | ], |
| | } |
| |
|
| | label_converter = { |
| | 0: 0, |
| | 1: 0, |
| | 2: 1, |
| | 11: 2, |
| | 12: 2, |
| | 13: 2, |
| | 14: 3, |
| | 15: 3, |
| | 16: 3, |
| | 18: 3, |
| | 17: 4, |
| | 19: 5, |
| | 20: 6, |
| | 21: 7, |
| | 22: 8, |
| | 23: 9, |
| | 24: 10, |
| | 25: 11, |
| | 31: 11, |
| | 26: 12, |
| | 27: 12, |
| | 28: 13, |
| | 29: 14, |
| | 33: 15, |
| | 34: 15, |
| | 35: 16, |
| | 36: 16, |
| | 38: 17, |
| | 39: 17, |
| | 40: 18, |
| | 41: 18, |
| | 42: 18, |
| | } |
| |
|
| | S2_MEAN = [752.40087073, 884.29673756, 1144.16202635, 1297.47289228, 1624.90992062, 2194.6423161, 2422.21248945, 2517.76053101, 2581.64687018, 2645.51888987, 2368.51236873, 1805.06846033] |
| | S2_STD = [1108.02887453, 1155.15170768, 1183.6292542, 1368.11351514, 1370.265037, 1355.55390699, 1416.51487101, 1474.78900051, 1439.3086061, 1582.28010962, 1455.52084939, 1343.48379601] |
| |
|
| | S1_MEAN = [-12.54847273, -20.19237134] |
| | S1_STD = [5.25697717, 5.91150917] |
| |
|
| | parts = [f"a{letter}" for letter in string.ascii_lowercase] |
| | parts.extend([f"b{letter}" for letter in string.ascii_lowercase[:8]]) |
| |
|
| | class BigEarthNetDataset(datasets.GeneratorBasedBuilder): |
| | VERSION = datasets.Version("1.0.0") |
| |
|
| | DATA_URL = [ |
| | f"https://huggingface.co/datasets/GFM-Bench/BigEarthNet/resolve/main/data/bigearthnet_part_{part}" |
| | for part in parts |
| | ] |
| |
|
| | metadata = { |
| | "s2c": { |
| | "bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B11", "B12"], |
| | "channel_wv": [442.7, 492.4, 559.8, 664.6, 704.1, 740.5, 782.8, 832.8, 864.7, 945.1, 1613.7, 2202.4], |
| | "mean": S2_MEAN, |
| | "std": S2_STD |
| | }, |
| | "s1": { |
| | "bands": ["VV", "VH"], |
| | "channel_wv": [5500, 5700], |
| | "mean": S1_MEAN, |
| | "std": S1_STD |
| | } |
| | } |
| |
|
| | SIZE = HEIGHT = WIDTH = 120 |
| |
|
| | NUM_CLASSES = 19 |
| |
|
| | spatial_resolution = 10 |
| |
|
| | def __init__(self, *args, **kwargs): |
| | self.class2idx = {c: i for i, c in enumerate(class_sets[43])} |
| |
|
| | super().__init__(*args, **kwargs) |
| |
|
| | def _info(self): |
| | metadata = self.metadata |
| | metadata['size'] = self.SIZE |
| | metadata['num_classes'] = self.NUM_CLASSES |
| | metadata['spatial_resolution'] = self.spatial_resolution |
| | return datasets.DatasetInfo( |
| | description=json.dumps(metadata), |
| | features=datasets.Features({ |
| | "optical": datasets.Array3D(shape=(12, self.HEIGHT, self.WIDTH), dtype="float32"), |
| | "radar": datasets.Array3D(shape=(2, self.HEIGHT, self.WIDTH), dtype="float32"), |
| | "optical_channel_wv": datasets.Sequence(datasets.Value("float32")), |
| | "radar_channel_wv": datasets.Sequence(datasets.Value("float32")), |
| | "label": datasets.Sequence(datasets.Value("float32"), length=self.NUM_CLASSES), |
| | "spatial_resolution": datasets.Value("int32"), |
| | }), |
| | ) |
| |
|
| | def _split_generators(self, dl_manager): |
| | print(dl_manager.download_config.cache_dir) |
| | |
| | if dl_manager.download_config.cache_dir is None: |
| | return [] |
| | if isinstance(self.DATA_URL, list): |
| | try: |
| | downloaded_files = dl_manager.download(self.DATA_URL) |
| | print(f"downloaded files: {downloaded_files}") |
| | combined_file = os.path.join(dl_manager.download_config.cache_dir, "combined.tar.gz") |
| | print(f"copying files to {combined_file}") |
| | target_dir = os.path.dirname(combined_file) |
| | os.makedirs(target_dir, exist_ok=True) |
| | with open(combined_file, 'wb') as outfile: |
| | counter = 0 |
| | for part_file in tqdm(downloaded_files, desc="Copying files", unit="file"): |
| | |
| | with open(part_file, 'rb') as infile: |
| | shutil.copyfileobj(infile, outfile) |
| | counter += 1 |
| | print(f"extacting from {combined_file}") |
| | |
| | data_dir = os.path.join(dl_manager.download_config.cache_dir, "extracted") |
| | os.makedirs(data_dir, exist_ok=True) |
| | with tarfile.open(combined_file, "r:gz") as tar: |
| | tar.extractall(path=data_dir) |
| | os.remove(combined_file) |
| | print(f"data_dir: {data_dir}") |
| | except Exception as e: |
| | print(f"exception: {e}, so setting data_dir to None") |
| | data_dir = None |
| | else: |
| | data_dir = dl_manager.download_and_extract(self.DATA_URL) |
| |
|
| | return [ |
| | datasets.SplitGenerator( |
| | name="train", |
| | gen_kwargs={ |
| | "split": 'train', |
| | "data_dir": data_dir, |
| | }, |
| | ), |
| | datasets.SplitGenerator( |
| | name="val", |
| | gen_kwargs={ |
| | "split": 'val', |
| | "data_dir": data_dir, |
| | }, |
| | ), |
| | datasets.SplitGenerator( |
| | name="test", |
| | gen_kwargs={ |
| | "split": 'test', |
| | "data_dir": data_dir, |
| | }, |
| | ) |
| | ] |
| |
|
| | def _generate_examples(self, split, data_dir): |
| | optical_channel_wv = np.array(self.metadata["s2c"]["channel_wv"]) |
| | radar_channel_wv = np.array(self.metadata["s1"]["channel_wv"]) |
| | spatial_resolution = self.spatial_resolution |
| |
|
| | data_dir = os.path.join(data_dir, "BigEarthNet") |
| | metadata = pd.read_csv(os.path.join(data_dir, "metadata.csv")) |
| | metadata = metadata[metadata["split"] == split].reset_index(drop=True) |
| |
|
| | for index, row in metadata.iterrows(): |
| | optical_path = os.path.join(data_dir, row.optical_path) |
| | optical = self._read_image(optical_path).astype(np.float32) |
| |
|
| | radar_path = os.path.join(data_dir, row.radar_path) |
| | radar = self._read_image(radar_path).astype(np.float32) |
| |
|
| | label_path = os.path.join(data_dir, row.label_path) |
| | label = self._load_label(label_path) |
| |
|
| | sample = { |
| | "optical": optical, |
| | "radar": radar, |
| | "optical_channel_wv": optical_channel_wv, |
| | "radar_channel_wv": radar_channel_wv, |
| | "label": label, |
| | "spatial_resolution": spatial_resolution, |
| | } |
| |
|
| | yield f"{index}", sample |
| | |
| | def _load_label(self, label_path): |
| | with open(label_path) as f: |
| | labels = json.load(f)['labels'] |
| | indices =[self.class2idx[label] for label in labels] |
| | indices_optional = [label_converter.get(idx) for idx in indices] |
| | indices = [idx for idx in indices_optional if idx is not None] |
| | label = np.zeros(19, dtype=np.int64) |
| | label[indices] = 1 |
| | return label |
| | |
| | def _read_image(self, image_path): |
| | """Read tiff image from image_path |
| | Args: |
| | image_path: |
| | Image path to read from |
| | |
| | Return: |
| | image: |
| | C, H, W numpy array image |
| | """ |
| | image = tifffile.imread(image_path) |
| | image = np.transpose(image, (2, 0, 1)) |
| |
|
| | return image |
