DFC2020.py

import os
import json
import shutil
import tifffile
import datasets

import pandas as pd
import numpy as np

S2_MEAN = [1370.19151926, 1184.3824625, 1120.77120066, 1136.26026392, 1263.73947144, 1645.40315151, 1846.87040806, 1762.59530783, 1972.62420416,  582.72633433, 14.77112979, 1732.16362238, 1247.91870117]

S2_STD = [633.15169573, 650.2842772, 712.12507725, 965.23119807, 948.9819932, 1108.06650639, 1258.36394548, 1233.1492281, 1364.38688993, 472.37967789, 14.3114637, 1310.36996126, 1087.6020813]

S1_MEAN = [-12.54847273, -20.19237134]

S1_STD = [5.25697717, 5.91150917]

class DFC2020Dataset(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")
    
    DATA_URL = "https://huggingface.co/datasets/GFM-Bench/DFC2020/resolve/main/data/DFC2020.zip"

    metadata = {
        "s2c": {
            "bands": ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B10", "B11", "B12"],
            "channel_wv": [442.7, 492.4, 559.8, 664.6, 704.1, 740.5, 782.8, 832.8, 864.7, 945.1, 1373.5, 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 = 96

    spatial_resolution = 10 

    DFC2020_CLASSES = [
        255,  # class 0 unused in both schemes
        0, 0, 0, 0, 0,
        1, 1,
        255,  # --> will be masked if no_savanna == True
        255,  # --> will be masked if no_savanna == True
        2,
        3,
        4,  # 12 --> 6
        5,  # 13 --> 7
        4,  # 14 --> 6
        255,
        6,
        7
    ]

    NUM_CLASSES = 8

    def __init__(self, *args, **kwargs):
        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=(13, self.HEIGHT, self.WIDTH), dtype="float32"),
                "radar": datasets.Array3D(shape=(2, self.HEIGHT, self.WIDTH), dtype="float32"),
                "label": datasets.Array2D(shape=(self.HEIGHT, self.WIDTH), dtype="int32"),
                "optical_channel_wv": datasets.Sequence(datasets.Value("float32")),
                "radar_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"]
        radar_channel_wv = self.metadata["s1"]["channel_wv"]
        spatial_resolution = self.spatial_resolution

        data_dir = os.path.join(data_dir, "DFC2020")
        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

            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._read_image(label_path)[0, :, :]
            label = np.take(self.DFC2020_CLASSES, label.astype(np.int64)) 
            label = label.astype(np.int32)

            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 _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