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import os
import json
import shutil
import datasets
import tifffile
import pandas as pd
import numpy as np
import geopandas as gpd
from datetime import datetime
from GFMBench.datasets.base_dataset import GFMBenchDataset
S2_MEAN = [1180.2278549 , 1387.76882557, 1436.67627781, 1773.66437066, 2735.86417202, 3080.12530686, 3223.60015887, 3338.35639825, 2418.01390106, 1630.11250759]
S2_STD = [1976.91493068, 1917.02121286, 1996.45123112, 1903.34296117, 1785.08356262, 1796.4477813 , 1811.90019014, 1793.47036145, 1474.46979658, 1309.88416505]
S1A_MEAN = [-10.91848081, -17.34320436]
S1A_STD = [3.26830557, 3.19895575]
S1D_MEAN = [-11.07395082, -17.45261358]
S1D_STD = [3.33774017, 3.15584225]
S1_MEAN = [-10.996215815 -17.39790897]
S1_STD = [3.30411987, 3.177943]
s1_metadata = {
'radar': {
'mean': S1_MEAN,
'std': S1_STD,
},
'radar_a': {
'mean': S1A_MEAN,
'std': S1A_STD,
},
'radar_d': {
'mean': S1D_MEAN,
'std': S1D_STD,
},
}
s1_num_seq = {
'radar': 142,
'radar_a': 71,
'radar_d': 71,
}
sats = {
"radar": ["S2", "S1A", "S1D"],
"radar_a": ["S2", "S1A"],
"radar_d": ["S2", "S1D"],
}
class PASTISDataset(GFMBenchDataset):
VERSION = datasets.Version("1.0.0")
DATA_URL = "https://huggingface.co/datasets/GFM-Bench/PASTIS/resolve/main/PASTIS.tar.xz"
metadata = {
"s2c": {
"bands": ["B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B11", "B12"],
"channel_wv": [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": ["VV", "VH"],
"channel_wv": [5500, 5700],
}
}
SIZE = HEIGHT = WIDTH = 128
spatial_resolution = 10
NUM_CLASSES = 20 # 0 is background class, and 19 is the void label
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="default"),
*[datasets.BuilderConfig(name=name) for name in ['radar', 'radar_a', 'radar_d']]
]
DEFAULT_CONFIG_NAME = "radar"
def __init__(self, reference_date="2018-09-10", **kwargs):
name = kwargs.get('config_name', None)
print(f"config_name: {name}")
self.reference_date = datetime(*map(int, reference_date.split("-")))
print(f"reference_date: {reference_date} -> {self.reference_date}")
config = "radar" if name == "default" or name is None else name
self.NUM_RADAR_SEQ = s1_num_seq[config]
self.sats = sats[config]
self.metadata["s1"].update(s1_metadata[config])
self.sats_name = config
super().__init__( **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.Array4D(shape=(61, 10, self.HEIGHT, self.WIDTH), dtype="float32"),
"radar": datasets.Array4D(shape=(self.NUM_RADAR_SEQ, 2, self.HEIGHT, self.WIDTH), dtype="float32"),
"label": datasets.Array2D(shape=(self.HEIGHT, self.WIDTH), dtype="int32"),
"optical_dates": datasets.Sequence(datasets.Value("int32")),
"radar_dates": datasets.Sequence(datasets.Value("int32")),
"optical_sequence_len": datasets.Value("int32"),
"radar_sequence_len": datasets.Value("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, "PASTIS")
metadata = pd.read_csv(os.path.join(data_dir, "metadata.csv"))
metadata = metadata[metadata["split"] == split].reset_index(drop=True)
self._prepare_meta_patch(data_dir)
self._prepare_date_tables()
for index, row in metadata.iterrows():
id_patch = row.optical_path.replace("DATA_S2/S2_", "").replace(".tif", "")
optical_path = os.path.join(data_dir, row.optical_path)
optical = self._read_image(optical_path).astype(np.float32) # TxCxHxW
optical_sequence_len = optical.shape[0]
optical = self._pad_sequence(optical, sat="S2") # 61xCxHxW
optical_dates = self._get_dates(id_patch=id_patch, sat="S2")
radar_sequence_len = 0
if self.sats_name in ["radar", "radar_a"]:
radar_a_path = os.path.join(data_dir, row.radar_a_path)
radar_a = self._read_image(radar_a_path).astype(np.float32)[:, :2, :, :] # T, 2, 128, 128
radar_a_dates = self._get_dates(id_patch=id_patch, sat="S1A")
radar_sequence_len += radar_a.shape[0]
if self.sats_name == "radar_a":
radar = self._pad_sequence(radar_a, "S1A") # 71, 2, 128, 128
radar_dates = radar_a_dates
if self.sats_name in ["radar", "radar_d"]:
radar_d_path = os.path.join(data_dir, row.radar_d_path)
radar_d = self._read_image(radar_d_path).astype(np.float32)[:, :2, :, :]
radar_d_dates = self._get_dates(id_patch=id_patch, sat="S1D")
radar_sequence_len += radar_d.shape[0]
if self.sats_name == "radar_d":
radar = self._pad_sequence(radar_d, sat="S1D") # 71, 2, 128, 128
radar_dates = radar_d_dates
if self.sats_name == "radar":
assert radar_a is not None and radar_d is not None
radar, radar_dates = self._merge_sort_dates(radar_a_dates, radar_d_dates, radar_a, radar_d)
radar = self._pad_sequence(radar, sat="S1_both") # 142, 2, 128, 128
label_path = os.path.join(data_dir, row.label_path) # 3xHxW
label = tifffile.imread(label_path)[0] # HxW
sample = {
"optical": optical,
"optical_channel_wv": optical_channel_wv,
"optical_dates": optical_dates,
"optical_sequence_len": optical_sequence_len,
"radar": radar,
"radar_channel_wv": radar_channel_wv,
"radar_dates": radar_dates,
"radar_sequence_len": radar_sequence_len,
"label": label,
"spatial_resolution": spatial_resolution,
}
yield f"{index}", sample
# util functions
def _prepare_meta_patch(self, data_dir):
self.meta_patch = gpd.read_file(os.path.join(data_dir, "metadata.geojson"))
self.meta_patch.index = self.meta_patch["ID_PATCH"].astype(int)
self.meta_patch.sort_index(inplace=True)
def _prepare_date_tables(self):
self.date_tables = {sat: None for sat in self.sats}
self.date_range = np.array(range(-200, 600))
for s in self.sats:
dates = self.meta_patch["dates-{}".format(s)]
date_table = pd.DataFrame(
index=self.meta_patch.index, columns=self.date_range, dtype=int
)
for pid, date_seq in dates.items():
if type(date_seq) == str:
date_seq = json.loads(date_seq)
d = pd.DataFrame().from_dict(date_seq, orient="index")
d = d[0].apply(
lambda x: (
datetime(int(str(x)[:4]), int(str(x)[4:6]), int(str(x)[6:]))
- self.reference_date
).days
)
date_table.loc[pid, d.values] = 1
date_table = date_table.fillna(0)
self.date_tables[s] = {
index: np.array(list(d.values()))
for index, d in date_table.to_dict(orient="index").items()
}
def _get_dates(self, id_patch, sat="S2"):
id_patch = int(id_patch)
return self.date_range[np.where(self.date_tables[sat][id_patch] == 1)[0]]
def _merge_sort_dates(self, radar_a_dates, radar_d_dates, radar_a, radar_d):
merged_dates = np.concatenate((radar_a_dates, radar_d_dates))
sorted_indices = np.argsort(merged_dates)
sorted_images = np.concatenate((radar_a, radar_d), axis=0)[sorted_indices]
sorted_dates = merged_dates[sorted_indices]
return sorted_images, sorted_dates
def _pad_sequence(self, image, sat="S2"):
assert sat in ["S2", "S1A", "S1D", "S1_both"]
sizes = {"S2": 61, "S1A": 71, "S1D": 71, "S1_both": 142}
assert image.shape[0] <= sizes[sat]
padding_size = sizes[sat] - image.shape[0]
if padding_size == 0:
return image
pad = np.zeros((padding_size, *image.shape[1:]))
padded_image = np.concatenate((image, pad), axis=0)
return padded_image |