merge from main

CHANGES.md

@@ -67,6 +67,14 @@
             >> ("b1", "array")
         ```
 
+# 7.2.2 (2024-11-18)
+
+* Catch and expand error message when GDAL cannot encode data using specified image driver (https://github.com/cogeotiff/rio-tiler/pull/767)
+
+# 7.2.1 (2024-11-14)
+
+* add official support for floating point values in ColorMap
+* cast data to `uint8` datatype when applying linear colormap
 
 # 7.2.0 (2024-11-05)
 

pyproject.toml

@@ -158,7 +158,7 @@ ignore = [
 max-complexity = 14
 
 [tool.bumpversion]
-current_version = "7.2.0"
+current_version = "7.2.2"
 search = "{current_version}"
 replace = "{new_version}"
 regex = false

rio_tiler/__init__.py

@@ -1,6 +1,6 @@
 """rio-tiler."""
 
-__version__ = "7.2.0"
+__version__ = "7.2.2"
 
 from . import (  # noqa
     colormap,

rio_tiler/colormap.py

@@ -4,6 +4,7 @@
 import os
 import pathlib
 import re
+import warnings
 from typing import Dict, List, Sequence, Tuple, Union
 
 import attr
@@ -18,6 +19,7 @@
 from rio_tiler.types import (
     ColorMapType,
     DataMaskType,
+    DiscreteColorMapType,
     GDALColorMapType,
     IntervalColorMapType,
 )
@@ -116,28 +118,38 @@ def apply_cmap(data: numpy.ndarray, colormap: ColorMapType) -> DataMaskType:
     # rio_tiler.colormap.make_lut, because we don't want to create a `lookup table`
     # with more than 256 entries (256 x 4) array. In this case we use `apply_discrete_cmap`
     # which can work with arbitrary colormap dict.
-    if len(colormap) != 256 or max(colormap) >= 256 or min(colormap) < 0:
+    if (
+        len(colormap) != 256
+        or max(colormap) >= 256
+        or min(colormap) < 0
+        or any(isinstance(k, float) for k in colormap)
+    ):
         return apply_discrete_cmap(data, colormap)
 
-    lookup_table = make_lut(colormap)
+    # For now we assume ColorMap are in uint8
+    if data.dtype != numpy.uint8:
+        warnings.warn(
+            f"Input array is of type {data.dtype} and `will be converted to Int in order to apply the ColorMap.",
+            UserWarning,
+        )
+        data = data.astype(numpy.uint8)
+
+    lookup_table = make_lut(colormap)  # type: ignore
     data = lookup_table[data[0], :]
 
     data = numpy.transpose(data, [2, 0, 1])
 
-    # If the colormap has values between 0-255
-    # we cast the output array to Uint8.
-    if data.min() >= 0 and data.max() <= 255:
-        data = data.astype("uint8")
-
     return data[:-1], data[-1]
 
 
-def apply_discrete_cmap(data: numpy.ndarray, colormap: GDALColorMapType) -> DataMaskType:
+def apply_discrete_cmap(
+    data: numpy.ndarray, colormap: Union[GDALColorMapType, DiscreteColorMapType]
+) -> DataMaskType:
     """Apply discrete colormap.
 
     Args:
         data (numpy.ndarray): 1D image array to translate to RGB.
-        colormap (GDALColorMapType): Discrete ColorMap dictionary.
+        colormap (GDALColorMapType or DiscreteColorMapType): Discrete ColorMap dictionary.
 
     Returns:
         tuple: Data (numpy.ndarray) and Alpha band (numpy.ndarray).

rio_tiler/types.py

@@ -18,11 +18,15 @@
 # ColorMap Dict: {1: (0, 0, 0, 255), ...}
 GDALColorMapType = Dict[int, ColorTuple]
 
+# Discrete Colormap, like GDALColorMapType but accept Float: {0.1: (0, 0, 0, 255), ...}
+DiscreteColorMapType = Dict[NumType, ColorTuple]
+
 # Intervals ColorMap: [((0, 1), (0, 0, 0, 0)), ...]
 IntervalColorMapType = Sequence[Tuple[IntervalTuple, ColorTuple]]
 
 ColorMapType = Union[
     GDALColorMapType,
+    DiscreteColorMapType,
     IntervalColorMapType,
 ]
 

rio_tiler/utils.py

@@ -33,7 +33,7 @@
 
 from rio_tiler.colormap import apply_cmap
 from rio_tiler.constants import WEB_MERCATOR_CRS, WGS84_CRS
-from rio_tiler.errors import RioTilerError
+from rio_tiler.errors import InvalidFormat, RioTilerError
 from rio_tiler.types import BBox, ColorMapType, IntervalTuple, RIOResampling
 
 
@@ -588,24 +588,30 @@ def render(
     }
     output_profile.update(creation_options)
 
-    with warnings.catch_warnings():
-        warnings.filterwarnings(
-            "ignore",
-            category=NotGeoreferencedWarning,
-            module="rasterio",
-        )
-        with MemoryFile() as memfile:
-            with memfile.open(**output_profile) as dst:
-                dst.write(data, indexes=list(range(1, count + 1)))
+    try:
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                category=NotGeoreferencedWarning,
+                module="rasterio",
+            )
+            with MemoryFile() as memfile:
+                with memfile.open(**output_profile) as dst:
+                    dst.write(data, indexes=list(range(1, count + 1)))
+
+                    # Use Mask as an alpha band
+                    if mask is not None:
+                        if ColorInterp.alpha not in dst.colorinterp:
+                            dst.colorinterp = *dst.colorinterp[:-1], ColorInterp.alpha
 
-                # Use Mask as an alpha band
-                if mask is not None:
-                    if ColorInterp.alpha not in dst.colorinterp:
-                        dst.colorinterp = *dst.colorinterp[:-1], ColorInterp.alpha
+                        dst.write(mask.astype(data.dtype), indexes=count + 1)
 
-                    dst.write(mask.astype(data.dtype), indexes=count + 1)
+                return memfile.read()
 
-            return memfile.read()
+    except Exception as e:
+        raise InvalidFormat(
+            f"Could not encode array of shape ({count},{height},{width}) and of datatype `{data.dtype}` using {img_format} driver"
+        ) from e
 
 
 def mapzen_elevation_rgb(data: numpy.ndarray) -> numpy.ndarray:

tests/test_cmap.py

@@ -301,6 +301,8 @@ def test_parse_color_bad():
 
 def test_discrete_float():
     """test for titiler issue 738."""
+
+    # make sure we apply discrete colormap when we have less than 256 cmap entries
     cm = {
         0: (0, 255, 255, 255),
         1: (83, 151, 145, 255),
@@ -325,3 +327,20 @@ def test_discrete_float():
     dd, mm = colormap.apply_discrete_cmap(data.copy(), cm)
     assert d.dtype == numpy.uint8
     assert m.dtype == numpy.uint8
+    numpy.testing.assert_array_equal(d, dd)
+    numpy.testing.assert_array_equal(m, mm)
+
+    # make we allow float keys in discrete colormap
+    cm = {
+        0.5: (0, 255, 255, 255),
+        1.5: (83, 151, 145, 255),
+        2.5: (87, 194, 23, 255),
+    }
+
+    data = numpy.random.choice([0.5, 2.5], 256 * 256).reshape(1, 256, 256)
+    d, m = colormap.apply_cmap(data.copy(), cm)
+    dd, mm = colormap.apply_discrete_cmap(data.copy(), cm)
+    assert d.dtype == numpy.uint8
+    assert m.dtype == numpy.uint8
+    numpy.testing.assert_array_equal(d, dd)
+    numpy.testing.assert_array_equal(m, mm)

tests/test_io_rasterio.py

@@ -17,6 +17,7 @@
 from rasterio.vrt import WarpedVRT
 from rasterio.warp import transform_bounds
 
+from rio_tiler.colormap import cmap
 from rio_tiler.constants import WEB_MERCATOR_TMS, WGS84_CRS
 from rio_tiler.errors import (
     ExpressionMixingWarning,
@@ -1120,3 +1121,27 @@ def test_inverted_latitude():
     with pytest.warns(UserWarning):
         with Reader(COG_INVERTED) as src:
             _ = src.tile(0, 0, 0)
+
+
+def test_int16_colormap():
+    """Should raise a warning about invalid data type for applying colormap.
+
+    ref: https://github.com/developmentseed/titiler/issues/1023
+    """
+    data = os.path.join(PREFIX, "cog_int16.tif")
+    color_map = cmap.get("viridis")
+
+    with Reader(data) as src:
+        info = src.info()
+        assert info.dtype == "int16"
+        assert info.nodata_type == "Nodata"
+        assert info.nodata_value == -32768
+
+        img = src.preview()
+        assert img.mask.any()
+
+        with pytest.warns(UserWarning):
+            im = img.apply_colormap(color_map)
+
+            # make sure we keep the nodata part masked
+            assert not im.mask.all()

tests/test_models.py

@@ -10,7 +10,11 @@
 from rasterio.errors import NotGeoreferencedWarning
 from rasterio.io import MemoryFile
 
-from rio_tiler.errors import InvalidDatatypeWarning, InvalidPointDataError
+from rio_tiler.errors import (
+    InvalidDatatypeWarning,
+    InvalidFormat,
+    InvalidPointDataError,
+)
 from rio_tiler.models import ImageData, PointData
 
 
@@ -456,3 +460,9 @@ def test_imagedata_coverage():
 
     coverage = im.get_coverage_array(poly, cover_scale=1000)
     assert numpy.round(numpy.unique(coverage), decimals=3).tolist() == [0, 0.125, 0.25]
+
+
+def test_image_encoding_error():
+    """Test ImageData error when using bad data array shape."""
+    with pytest.raises(InvalidFormat):
+        ImageData(numpy.zeros((5, 256, 256), dtype="uint8")).render(img_format="PNG")