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DFC2020.py

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+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