100 numpy to cog

Create a fresh conda env to run this example if needed.

conda create -n cog python=3.9
conda install mamba -c conda-forge
mamba install geemap rio-cogeo -c conda-forge

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# !pip install geemap rio-cogeo

# !pip install geemap rio-cogeo

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import geemap

import geemap

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url = "https://github.com/giswqs/leafmap/raw/master/examples/data/cog.tif"
in_cog = "cog.tif"
out_cog = "ndvi.tif"

url = "https://github.com/giswqs/leafmap/raw/master/examples/data/cog.tif" in_cog = "cog.tif" out_cog = "ndvi.tif"

Download a sample dataset.

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geemap.download_from_url(url, in_cog)

geemap.download_from_url(url, in_cog)

Convert image to numpy array.

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arr = geemap.image_to_numpy(in_cog)

arr = geemap.image_to_numpy(in_cog)

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arr.shape

arr.shape

Computer NDVI.

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ndvi = (arr[3] - arr[0]) / (arr[3] + arr[0])

ndvi = (arr[3] - arr[0]) / (arr[3] + arr[0])

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ndvi.shape

ndvi.shape

Convert numpy array to COG.

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geemap.numpy_to_cog(ndvi, out_cog, profile=in_cog)

geemap.numpy_to_cog(ndvi, out_cog, profile=in_cog)

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m = geemap.Map()
m.add_raster(in_cog, indexes=[4, 1, 2], layer_name="Color infrared")
m.add_raster(out_cog, colormap="Greens", layer_name="NDVI")
m

m = geemap.Map() m.add_raster(in_cog, indexes=[4, 1, 2], layer_name="Color infrared") m.add_raster(out_cog, colormap="Greens", layer_name="NDVI") m