extras/thumbnail_s2.py

"""
 NOTE: Major TOM standard does not require any specific type of thumbnail to be computed.
 
 Instead these are shared as optional help since this is how the Core dataset thumbnails have been computed.
"""

from rasterio.io import MemoryFile
from PIL import Image
import numpy as np

def s2l2a_thumbnail(B04, B03, B02, gain=1.3, gamma=0.6):
 """
 Takes B04, B03, B02 numpy arrays along with the corresponding NODATA values (default is -32768.0)
 
 Returns a numpy array with the thumbnail
 """
 
 # concatenate
 thumb = np.stack([B04, B03, B02], -1)

 # apply gain & gamma
 thumb = gain*((thumb/10_000)**gamma)
 
 return (thumb.clip(0,1)*255).astype(np.uint8)

def s2l2a_thumbnail_from_datarow(datarow):
 """
 Takes a datarow directly from one of the data parquet files
 
 Returns a PIL Image
 """

 # red
 with MemoryFile(datarow['B04'][0].as_py()) as mem_f:
 with mem_f.open(driver='GTiff') as f:
 B04=f.read().squeeze()
 B04_NODATA = f.nodata

 # green
 with MemoryFile(datarow['B03'][0].as_py()) as mem_f:
 with mem_f.open(driver='GTiff') as f:
 B03=f.read().squeeze()
 B03_NODATA = f.nodata

 # blue
 with MemoryFile(datarow['B02'][0].as_py()) as mem_f:
 with mem_f.open(driver='GTiff') as f:
 B02=f.read().squeeze()
 B02_NODATA = f.nodata

 img = s2l2a_thumbnail(B04,B03,B02)

 return Image.fromarray(img)

if __name__ == '__main__': 
 from fsspec.parquet import open_parquet_file
 import pyarrow.parquet as pq

 print('[example run] reading file from HuggingFace...')
 url = "https://huggingface.co/datasets/Major-TOM/Core-S2L2A/resolve/main/images/part_01000.parquet"
 with open_parquet_file(url, columns = ["B04", "B03", "B02"]) as f:
 with pq.ParquetFile(f) as pf:
 first_row_group = pf.read_row_group(1, columns = ["B04", "B03", "B02"])
 
 print('[example run] computing the thumbnail...') 
 thumbnail = s2l2a_thumbnail_from_datarow(first_row_group)
 
 thumbnail.save('example_thumbnail.png', format = 'PNG')