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Datasets:
GFM-Bench
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MARIDA

Tasks:
Image Segmentation
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MARIDA
File size: 5,092 Bytes 
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import os
import json
import shutil
import datasets
import tifffile

import pandas as pd
import numpy as np

S2_MEAN = [0.05197577, 0.04783991, 0.04056812, 0.03163572, 0.02972606, 0.03457443, 0.03875053, 0.03436435, 0.0392113,  0.02358126, 0.01588816]

S2_STD = [0.04725893, 0.04743808, 0.04699043, 0.04967381, 0.04946782, 0.06458357, 0.07594915, 0.07120246, 0.08251058, 0.05111466, 0.03524419]

class MARIDADataset(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")
    
    DATA_URL = "https://huggingface.co/datasets/GFM-Bench/MARIDA/resolve/main/MARIDA.zip"

    metadata = {
        "s2c": {
            "bands": ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B11", "B12"],
            "channel_wv": [442.7, 492.4, 559.8, 664.6, 704.1, 740.5, 782.8, 832.8, 864.7, 1613.7, 2202.4],
            "mean": S2_MEAN,
            "std": S2_STD,
        },
        "s1": {
            "bands": None,
            "channel_wv": None,
            "mean": None,
            "std": None   
        }
    }

    SIZE = HEIGHT = WIDTH = 96

    spatial_resolution = 10 

    NUM_CLASSES = 11

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        mean = np.array(S2_MEAN).astype(np.float32)
        self.impute_nan = np.tile(mean, (self.SIZE, self.SIZE, 1))

    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=(11, self.HEIGHT, self.WIDTH), dtype="float32"),
                "label": datasets.Array2D(shape=(self.HEIGHT, self.WIDTH), dtype="int32"),
                "optical_channel_wv": datasets.Sequence(datasets.Value("float32")),
                "spatial_resolution": datasets.Value("int32"),
            }),
        )

    def _split_generators(self, dl_manager):
        if isinstance(self.DATA_URL, list):
            downloaded_files = dl_manager.download(self.DATA_URL)
            combined_file = os.path.join(dl_manager.download_config.cache_dir, "combined.tar.gz")        
            with open(combined_file, 'wb') as outfile:
                for part_file in downloaded_files:
                    with open(part_file, 'rb') as infile:
                        shutil.copyfileobj(infile, outfile)
            data_dir = dl_manager.extract(combined_file)
            os.remove(combined_file)
        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 = self.metadata["s2c"]["channel_wv"]
        spatial_resolution = self.spatial_resolution

        data_dir = os.path.join(data_dir, "MARIDA")
        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) # CxHxW
            optical = np.transpose(optical, (1, 2, 0))
            nan_mask = np.isnan(optical)
            optical[nan_mask] = self.impute_nan[nan_mask]
            optical = np.transpose(optical, (2, 0, 1))

            label_path = os.path.join(data_dir, row.label_path)
            label = self._read_image(label_path).astype(np.int32)
            label[label==15] = 7
            label[label==14] = 7
            label[label==13] = 7
            label[label==12] = 7
            label -= 1
            label[label==-1] = 255

            sample = {
                "optical": optical,
                "optical_channel_wv": optical_channel_wv,
                "label": label,
                "spatial_resolution": spatial_resolution,
            }

            yield f"{index}", sample
    
    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)
        if len(image.shape) == 3:
            image = np.transpose(image, (2, 0, 1))

        return image