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