Read colormap and colorinterp from GeoTIFF as an attribute by weiji14 · Pull Request #414 · corteva/rioxarray

weiji14 · 2021-10-02T00:21:24Z

Preliminary support to read in and write color interpretation information from GeoTIFF files. Partially based on @alando46's code at pydata/xarray#3136. Rasterio reference at https://rasterio.readthedocs.io/en/latest/topics/color.html

Addresses Add ability to describe color interpretation #191
Tests added
Fully documented, including docs/history.rst for all changes and docs/rioxarray.rst for new API

Cherry-picked from pydata/xarray#3136 Co-Authored-By: Alando Ballantyne <alando@radiant.earth>

Crossref https://rasterio.readthedocs.io/en/latest/topics/color.html.

snowman2 · 2021-10-04T13:56:30Z

test/integration/test_integration__io.py

        )
        assert attrs == {
            "add_offset": 0.0,
+            "colorinterp": (rasterio.enums.ColorInterp.gray,),


This will need to be converted to either a string representation or the integer representation. The reason for this is to enable calling to_netcdf without serialization issues.

Here is an example converting to the integer enumeration value:

>>> import rasterio >>> rasterio.enums.ColorInterp.gray <ColorInterp.gray: 1> >>> rasterio.enums.ColorInterp.gray.value 1

An alternative would be to read/write the photometric creation option of the profile into/from the attributes. This is always in the string format and shouldn't have any serialization issues.

Actually, the colorinterp enum can be serialized since it has an integer representation. It's the colormap dict which I'm having trouble with. The colormap dict looks something like this:

{0: (248, 224, 168, 255), 1: (240, 224, 152, 255), 2: (104, 104, 96, 255), ... 255: (248, 248, 248, 255)}

Do you think we should keep this colormap as an xarray attr, or should it be loaded into the data_var as 4 bands (i.e. RGBA)?

Since it is a dict, you could use json.dumps & json.loads to convert to/from a string.

Ok, the json serialization has been implemented in 54f9560. Had to do some extra magic because the dict keys have to be int, but JSON only allows str keys. I did try an alternative method using yaml in 5b07d7f as suggested by https://stackoverflow.com/questions/17099556/why-do-int-keys-of-a-python-dict-turn-into-strings-when-using-json-dumps#comment24737635_17099556, but maybe will stick with JSON.

FYI, this is how the xarray.Dataset looks like:

<xarray.DataArray (band: 1, y: 180, x: 360)> array([[[251, 251, ..., 251, 251], [251, 252, ..., 251, 252], ..., [ 68, 16, ..., 68, 217], [149, 149, ..., 149, 16]]], dtype=uint8) Coordinates: * band (band) int64 1 * x (x) float64 -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5 * y (y) float64 89.5 88.5 87.5 86.5 ... -86.5 -87.5 -88.5 -89.5 spatial_ref int64 0 Attributes: scale_factor: 1.0 add_offset: 0.0 colormap: {"0": [248, 224, 168, 255], "1": [240, 224, 152, 255], "2"... colorinterp: (<ColorInterp.palette: 2>,)

Do you think we should keep this colormap as an xarray attr, or should it be loaded into the data_var as 4 bands (i.e. RGBA)?

Just to follow up on the data_var loading, I tried converting the earth_day_01d_p.tif GeoTiff to NetCDF directly using gdal_translate -of NetCDF -expand rgba earth_day_01d_p.tif earth_day_01d_p.nc.

This is the output of gdalinfo earth_day_01d_p.nc

Driver: netCDF/Network Common Data Format Files: earth_day_01d_p.nc Size is 512, 512 Metadata: NC_GLOBAL#Conventions=CF-1.5 NC_GLOBAL#GDAL=GDAL 3.3.2, released 2021/09/01 NC_GLOBAL#GDAL_AREA_OR_POINT=Area NC_GLOBAL#history=Sat Nov 06 23:41:47 2021: GDAL CreateCopy( earth_day_01d_p.nc, ... ) Subdatasets: SUBDATASET_1_NAME=NETCDF:"earth_day_01d_p.nc":Band1 SUBDATASET_1_DESC=[180x360] Band1 (8-bit integer) SUBDATASET_2_NAME=NETCDF:"earth_day_01d_p.nc":Band2 SUBDATASET_2_DESC=[180x360] Band2 (8-bit integer) SUBDATASET_3_NAME=NETCDF:"earth_day_01d_p.nc":Band3 SUBDATASET_3_DESC=[180x360] Band3 (8-bit integer) SUBDATASET_4_NAME=NETCDF:"earth_day_01d_p.nc":Band4 SUBDATASET_4_DESC=[180x360] Band4 (8-bit integer) Corner Coordinates: Upper Left ( 0.0, 0.0) Lower Left ( 0.0, 512.0) Upper Right ( 512.0, 0.0) Lower Right ( 512.0, 512.0) Center ( 256.0, 256.0)

So the bands have been expanded into NetCDF subdatasets. I then loaded earth_day_01d_p.nc using rioxarray.open_rasterio, and it produces the following:

<xarray.Dataset> Dimensions: (y: 180, x: 360, band: 1) Coordinates: * y (y) float64 89.5 88.5 87.5 86.5 85.5 ... -86.5 -87.5 -88.5 -89.5 * x (x) float64 -179.5 -178.5 -177.5 -176.5 ... 176.5 177.5 178.5 179.5 * band (band) int64 1 crs int64 0 Data variables: Band1 (band, y, x) uint8 ... Band2 (band, y, x) uint8 ... Band3 (band, y, x) uint8 ... Band4 (band, y, x) uint8 ... Attributes: Conventions: CF-1.5 GDAL: GDAL 3.3.2, released 2021/09/01 GDAL_AREA_OR_POINT: Area history: Sat Nov 06 23:41:47 2021: GDAL CreateCopy( earth_day...

Notice the Band1-Band4 data variables. So I'm thinking there should be a way to mimic this structure. Need to find a way to use the colormap dict currently in the attr, and get these Bands 1/2/3/4 information out as data variables.

Mind doing ncdump -h earth_day_01d_p.nc and pasting the output here?

snowman2 · 2021-10-04T14:04:31Z

rioxarray/_io.py

+    if hasattr(riods, "colormap"):
+        # A dict containing the colormap for a band
+        try:
+            attrs["colormap"] = riods.colormap(1)


This only gets the colormap for the first band. I imagine it would be good to handle this for all of the bands. docs

Yep, probably need to loop through all the bands, but need to think about https://github.com/corteva/rioxarray/pull/414/files#r722041137 first 🙂

snowman2 · 2021-11-08T15:29:00Z

#429 should help.

codecov · 2021-11-08T18:35:34Z

Codecov Report

Merging #414 (edc9972) into master (1755f90) will increase coverage by 0.04%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master     #414      +/-   ##
==========================================
+ Coverage   96.07%   96.11%   +0.04%     
==========================================
  Files          13       13              
  Lines        1530     1546      +16     
==========================================
+ Hits         1470     1486      +16     
  Misses         60       60

Impacted Files	Coverage Δ
rioxarray/_io.py	`95.21% <100.00%> (+0.09%)`	⬆️
rioxarray/raster_writer.py	`91.50% <100.00%> (+0.69%)`	⬆️

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snowman2 · 2021-11-09T15:52:26Z

rioxarray/raster_writer.py

+    # write colorinterp and colormap
+    # see https://rasterio.readthedocs.io/en/latest/topics/color.html
+    try:
+        raster_handle.colorinterp = tags["colorinterp"]


Mind using separate try/except blocks for colorinterp and colormap?

snowman2 · 2023-04-18T17:33:54Z

Closing because this PR has become stale. Feel free to re-open or submit a new PR if you would like to contribute this feature.

weiji14 and others added 3 commits October 2, 2021 09:49

Load colormap and colorinterp info from GeoTIFF

aab671b

Cherry-picked from pydata/xarray#3136 Co-Authored-By: Alando Ballantyne <alando@radiant.earth>

Handle NULL color tables and fix tests to include colorinterp attribute

899d03d

Add test for loading colormap and colorinterp attr from geotiff

62671e3

weiji14 changed the title ~~Read colortable and colorinterp from GeoTIFF as an attribute~~ Read colormap and colorinterp from GeoTIFF as an attribute Oct 2, 2021

weiji14 added 3 commits October 2, 2021 13:54

Handle writing of colorinterp and colormap attributes to GeoTIFF

3d9fac2

Crossref https://rasterio.readthedocs.io/en/latest/topics/color.html.

Convert xarray.Dataset to xarray.DataArray so that rio.to_raster works

118fa5d

Fix pylint error

9bf48ac

snowman2 reviewed Oct 4, 2021

View reviewed changes

weiji14 added 3 commits November 6, 2021 20:02

Merge branch 'master' into geotiff-color-interp

4ebf216

Serialize color table dict as yaml

5b07d7f

Serialize colortable dict as json

54f9560

weiji14 mentioned this pull request Nov 7, 2021

Integrate with rioxarray and implement GMT_IMAGE to read RGB GeoTIFFs GenericMappingTools/pygmt#1555

Open

5 tasks

Merge branch 'corteva:master' into geotiff-color-interp

edc9972

snowman2 reviewed Nov 9, 2021

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weiji14 mentioned this pull request Apr 27, 2022

RasterDataset with RBG Tif torchgeo/torchgeo#517

Closed

weiji14 mentioned this pull request Dec 7, 2022

Function to load NASA Blue and Black marble mosaic images GenericMappingTools/pygmt#1442

Closed

weiji14 mentioned this pull request Feb 26, 2023

Add function to load raster tile maps using contextily GenericMappingTools/pygmt#2125

Merged

6 tasks

snowman2 closed this Apr 18, 2023

snowman2 mentioned this pull request Oct 31, 2023

Adding rasterio's colourmap functionality #706

Open

weiji14 mentioned this pull request Jul 8, 2024

**BREAKING** pygmt.grdcut: Refactor to store output in virtualfiles for grids GenericMappingTools/pygmt#3115

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