Create extras/thumbnail_dem.py

extras/thumbnail_dem.py

@@ -0,0 +1,77 @@
+"""
+    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
+import os
+from pathlib import Path
+import rasterio as rio
+from matplotlib.colors import LightSource
+
+def get_grayscale(x):
+    """
+        Normalized grayscale visualisation
+    """
+    
+    # normalize
+    x_n = x-x.min()
+    x_n = x_n/x_n.max()
+    
+    return np.uint8(x_n*255)
+
+def get_hillshade(x, azdeg=315, altdeg=45,ve=1):
+    """
+        Hillshade visualisation for DEM
+    """
+    ls = LightSource(azdeg=azdeg, altdeg=altdeg)
+    
+    return np.uint8(255*ls.hillshade(x, vert_exag=ve))
+    
+def dem_thumbnail(dem, dem_NODATA = -32768.0, hillshade=True):
+    """
+        Takes vv and vh numpy arrays along with the corresponding NODATA values (default is -32768.0)
+        
+        Returns a numpy array with the thumbnail
+    """
+    if hillshade:
+        return get_hillshade(dem)
+    else:
+        return get_grayscale(dem)
+    
+
+def dem_thumbnail_from_datarow(datarow):
+    """
+        Takes a datarow directly from one of the data parquet files
+        
+        Returns a PIL Image
+    """
+
+    with MemoryFile(datarow['DEM'][0].as_py()) as mem_f:
+        with mem_f.open(driver='GTiff') as f:
+            dem=f.read().squeeze()
+            dem_NODATA = f.nodata
+
+    img = dem_thumbnail(dem, dem_NODATA)
+
+    return Image.fromarray(img,'L')
+
+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-DEM/resolve/main/images/part_01001.parquet"
+    with open_parquet_file(url) as f:
+        with pq.ParquetFile(f) as pf:
+            first_row_group = pf.read_row_group(1)
+    
+    print('[example run] computing the thumbnail...')    
+    thumbnail = dem_thumbnail_from_datarow(first_row_group)
+
+    thumbnail_fname = 'example_thumbnail.png'
+    thumbnail.save(thumbnail_fname, format = 'PNG')
+    print('[example run] saved as "{}"'.format(thumbnail_fname))