Merge pull request #52 from nasa/GITC-7383

GITC-7383: Fixed JPEG driver issue, updated maintainer information

Author: jackiryan

CHANGELOG.md

@@ -4,6 +4,13 @@ HyBIG follows semantic versioning. All notable changes to this project will be
 documented in this file. The format is based on [Keep a
 Changelog](http://keepachangelog.com/en/1.0.0/).
 
+## [2.4.1] - 2025-06-23
+
+### Changed
+
+* Fix bug with JPEG driver on single-banded input granules. Since we are now palettizing where possible since 2.4.0, this creates an issue when trying to output in JPEG since color palettes (and transparency) are not supported.
+
+
 ## [v2.4.0] - 2025-04-28
 
 ### Changed

README.md

@@ -478,6 +478,5 @@ Guide](https://github.com/nasa/harmony/blob/main/docs/guides/managing-existing-s
 
 You can reach out to the maintainers of this repository via email:
 
-* [email protected]
-* [email protected]
-* [email protected]
+* [email protected]
+* [email protected]

docker/service_version.txt

@@ -1 +1 @@
-2.4.0
+2.4.1

hybig/browse.py

@@ -171,9 +171,17 @@ def create_browse_imagery(
                 color_palette = get_color_palette(
                     in_dataset, source, item_color_palette
                 )
-                raster, color_map = convert_singleband_to_raster(
-                    rio_in_array, color_palette
-                )
+                if output_driver == 'JPEG':
+                    # For JPEG output, convert to RGB
+                    # color_map will be None
+                    raster, color_map = convert_singleband_to_rgb(
+                        rio_in_array, color_palette
+                    )
+                else:
+                    # For PNG output, use palettized approach
+                    raster, color_map = convert_singleband_to_raster(
+                        rio_in_array, color_palette
+                    )
             elif rio_in_array.rio.count in (3, 4):
                 raster = convert_mulitband_to_raster(rio_in_array)
                 color_map = None
@@ -310,6 +318,78 @@ def scale_grey_1band(data_array: DataArray) -> tuple[ndarray, ColorMap]:
     return np.array(raster_data, dtype='uint8'), grey_colormap
 
 
+def convert_singleband_to_rgb(
+    data_array: DataArray,
+    color_palette: ColorPalette | None = None,
+) -> tuple[ndarray, None]:
+    """Convert input 1-band dataset to RGB image for JPEG output.
+
+    Uses a palette if provided, otherwise returns a greyscale RGB image.
+    Returns a 3-band RGB array and None for colormap (since RGB doesn't need colormap).
+    """
+    if color_palette is None:
+        return scale_grey_1band_to_rgb(data_array)
+    return scale_paletted_1band_to_rgb(data_array, color_palette)
+
+
+def scale_grey_1band_to_rgb(data_array: DataArray) -> tuple[ndarray, None]:
+    """Normalize input array and return as 3-band RGB grayscale image."""
+    band = data_array[0, :, :]
+    norm = Normalize(vmin=np.nanmin(band), vmax=np.nanmax(band))
+
+    # Scale input data from 0 to 254 (palettized data is 254-level quantized)
+    normalized_data = norm(band) * 254.0
+
+    # Set any missing to 0 (black), no transparency
+    normalized_data[np.isnan(normalized_data)] = 0
+
+    grey_data = np.round(normalized_data).astype('uint8')
+    rgb_data = np.stack([grey_data, grey_data, grey_data], axis=0)
+
+    return rgb_data, None
+
+
+def scale_paletted_1band_to_rgb(
+    data_array: DataArray, palette: ColorPalette
+) -> tuple[ndarray, None]:
+    """Scale a 1-band image with palette into RGB image for JPEG output."""
+    band = data_array[0, :, :]
+    levels = list(palette.pal.keys())
+    colors = [
+        palette.color_to_color_entry(value, with_alpha=True)
+        for value in palette.pal.values()
+    ]
+    norm = matplotlib.colors.BoundaryNorm(levels, len(levels) - 1)
+
+    # handle palette no data value
+    nodata_color = (0, 0, 0, 0)
+    if palette.ndv is not None:
+        nodata_color = palette.color_to_color_entry(palette.ndv, with_alpha=True)
+
+    # Apply normalization to get palette indices
+    indexed_band = norm(band)
+
+    # Create RGB output array
+    height, width = band.shape
+    rgb_array = np.zeros((3, height, width), dtype='uint8')
+
+    # Apply colors based on palette indices
+    for i, color in enumerate(colors):
+        mask = indexed_band == i
+        rgb_array[0, mask] = color[0]  # Red
+        rgb_array[1, mask] = color[1]  # Green
+        rgb_array[2, mask] = color[2]  # Blue
+
+    # Handle NaN/nodata values
+    nan_mask = np.isnan(band)
+    if nan_mask.any():
+        rgb_array[0, nan_mask] = nodata_color[0]
+        rgb_array[1, nan_mask] = nodata_color[1]
+        rgb_array[2, nan_mask] = nodata_color[2]
+
+    return rgb_array, None
+
+
 def scale_paletted_1band(
     data_array: DataArray, palette: ColorPalette
 ) -> tuple[ndarray, ColorMap]:

pyproject.toml

@@ -5,11 +5,13 @@ dynamic = ["dependencies", "version"]
 authors = [
   {name="Matt Savoie", email="[email protected]"},
   {name="Owen Littlejohns", email="[email protected]"},
+  {name="Jacqueline Ryan", email="[email protected]"},
+  {name="Mauricio Hess-Flores", email="[email protected]"},
 ]
 
 maintainers = [
-  {name="Matt Savoie", email="[email protected]"},
-  {name="Owen Littlejohns", email="owen.m.littlejohns@nasa.gov"},
+  {name="Jacqueline Ryan", email="[email protected]"},
+  {name="Mauricio Hess-Flores", email="Mauricio.A.Hess.Flores@jpl.nasa.gov"},
 ]
 
 description = "Python package designed to produce browse imagery compatible with NASA's Global Image Browse Services (GIBS)."

tests/unit/test_browse.py

@@ -209,9 +209,10 @@ def test_create_browse_imagery_with_mocks(
         target_transform = Affine(90.0, 0.0, -180.0, 0.0, -45.0, 90.0)
         dest = np.zeros((da_mock.rio.height, da_mock.rio.width), dtype='uint8')
 
-        # since we are no longer de-palettizing, we only have to reproject a single band
-        self.assertEqual(reproject_mock.call_count, 1)
+        # For JPEG output with 1-band input, we convert to RGB, so we reproject 3 bands
+        self.assertEqual(reproject_mock.call_count, 3)
 
+        # For RGB output, we expect 3 calls (one per band) with TRANSPARENT nodata
         expected_calls = [
             call(
                 source=expected_raster[0, :, :],
@@ -220,7 +221,27 @@ def test_create_browse_imagery_with_mocks(
                 src_crs=da_mock.rio.crs,
                 dst_transform=target_transform,
                 dst_crs=CRS.from_string('EPSG:4326'),
-                dst_nodata=255,  # NODATA_IDX
+                dst_nodata=0,  # TRANSPARENT for RGB data
+                resampling=Resampling.nearest,
+            ),
+            call(
+                source=expected_raster[0, :, :],
+                destination=dest,
+                src_transform=file_transform,
+                src_crs=da_mock.rio.crs,
+                dst_transform=target_transform,
+                dst_crs=CRS.from_string('EPSG:4326'),
+                dst_nodata=0,  # TRANSPARENT for RGB data
+                resampling=Resampling.nearest,
+            ),
+            call(
+                source=expected_raster[0, :, :],
+                destination=dest,
+                src_transform=file_transform,
+                src_crs=da_mock.rio.crs,
+                dst_transform=target_transform,
+                dst_crs=CRS.from_string('EPSG:4326'),
+                dst_nodata=0,  # TRANSPARENT for RGB data
                 resampling=Resampling.nearest,
             ),
         ]