Create extras/thumbnail_s2.py

extras/thumbnail_s2.py

@@ -0,0 +1,68 @@
+"""
+    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 s2l1c_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 s2l1c_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 = s2l1c_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-S2L1C/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 = s2l1c_thumbnail_from_datarow(first_row_group)
+    
+    thumbnail.save('example_thumbnail.png', format = 'PNG')