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from datasets import load_dataset as _load_dataset
from os import environ
from PIL import Image
import numpy as np
import json
from pyarrow.parquet import ParquetFile
from pyarrow import Table as pa_Table
from datasets import Dataset
DATASET = "satellogic/EarthView"
sets = {
"satellogic": {
"shards" : 3676,
},
"sentinel_1": {
"shards" : 1763,
},
"neon": {
"config" : "default",
"shards" : 607,
"path" : "data",
}
}
def get_subsets():
return sets.keys()
def get_nshards(subset):
return sets[subset]["shards"]
def get_path(subset):
return sets[subset].get("path", subset)
def get_config(subset):
return sets[subset].get("config", subset)
def load_dataset(subset, dataset="satellogic/EarthView", split="train", shards = None, streaming=True, **kwargs):
config = get_config(subset)
nshards = get_nshards(subset)
path = get_path(subset)
if shards is None:
data_files = None
else:
data_files = [f"{path}/{split}-{shard:05d}-of-{nshards:05d}.parquet" for shard in shards]
data_files = {split: data_files}
ds = _load_dataset(
path=dataset,
name=config,
save_infos=True,
split=split,
data_files=data_files,
streaming=streaming,
token=environ.get("HF_TOKEN", None),
**kwargs)
return ds
def load_parquet(subset_or_filename, batch_size=100):
if subset_or_filename in get_subsets():
pqfile = ParquetFile(f"dataset/{subset_or_filename}/sample.parquet")
else:
pqfile = subset_or_filename
batch = pqfile.iter_batches(batch_size=batch_size)
return Dataset(pa_Table.from_batches(batch))
def item_to_images(subset, item):
"""
Converts the images within an item (arrays), as retrieved from the dataset to proper PIL.Image
subset: The name of the Subset, one of "satellogic", "default", "sentinel-1"
item: The item as retrieved from the subset
returns the item, with arrays converted to PIL.Image
"""
metadata = item["metadata"]
if type(metadata) == str:
metadata = json.loads(metadata)
item = {
k: np.asarray(v).astype("uint8")
for k,v in item.items()
if k != "metadata"
}
item["metadata"] = metadata
if subset == "satellogic":
# item["rgb"] = [
# Image.fromarray(np.average(image.transpose(1,2,0), 2).astype("uint8"))
# for image in item["rgb"]
# ]
rgbs = []
for rgb in item["rgb"]:
rgbs.append(Image.fromarray(rgb.transpose(1,2,0)))
# rgbs.append(Image.fromarray(rgb[0,:,:])) # Red
# rgbs.append(Image.fromarray(rgb[1,:,:])) # Green
# rgbs.append(Image.fromarray(rgb[2,:,:])) # Blue
item["rgb"] = rgbs
item["1m"] = [
Image.fromarray(image[0,:,:])
for image in item["1m"]
]
count = len(item["1m"])
elif subset == "sentinel_1":
# Mapping of V and H to RGB. May not be correct
# https://gis.stackexchange.com/questions/400726/creating-composite-rgb-images-from-sentinel-1-channels
i10m = item["10m"]
i10m = np.concatenate(
( i10m,
np.expand_dims(
i10m[:,0,:,:]/(i10m[:,1,:,:]+0.01)*256,
1
).astype("uint8")
),
1
)
item["10m"] = [
Image.fromarray(image.transpose(1,2,0))
for image in i10m
]
count = len(item["10m"])
elif subset == "neon":
item["rgb"] = [
Image.fromarray(image.transpose(1,2,0))
for image in item["rgb"]
]
item["chm"] = [
Image.fromarray(image[0])
for image in item["chm"]
]
# The next is a very arbitrary conversion from the 369 hyperspectral data to RGB
# It just averages each 1/3 of the bads and assigns it to a channel
item["1m"] = [
Image.fromarray(
np.concatenate((
np.expand_dims(np.average(image[:124],0),2),
np.expand_dims(np.average(image[124:247],0),2),
np.expand_dims(np.average(image[247:],0),2))
,2).astype("uint8"))
for image in item["1m"]
]
count = len(item["rgb"])
item["metadata"]["count"] = count
return item
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