47 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 leafmap rio-cogeo -c conda-forge

In [1]:

# !pip install leafmap

# !pip install leafmap

In [2]:

# !pip install rio-cogeo

# !pip install rio-cogeo

In [3]:

import leafmap

import leafmap

In [4]:

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.

In [5]:

leafmap.download_from_url(url, in_cog)

leafmap.download_from_url(url, in_cog)

Downloading https://github.com/giswqs/leafmap/raw/master/examples/data/cog.tif ...
Data downloaded to: /home/runner/work/leafmap/leafmap/docs/notebooks/cog.tif

Convert image to numpy array.

In [6]:

arr = leafmap.image_to_numpy(in_cog)

arr = leafmap.image_to_numpy(in_cog)

In [7]:

arr.shape

arr.shape

Out[7]:

(4, 206, 343)

Computer NDVI.

In [8]:

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

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

In [9]:

ndvi.shape

ndvi.shape

Out[9]:

(206, 343)

Convert numpy array to COG.

In [10]:

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

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

In [11]:

m = leafmap.Map()
m.add_raster(in_cog, band=[4, 1, 2], layer_name="Color infrared")
m.add_raster(out_cog, palette="Greens", layer_name="NDVI")
m

m = leafmap.Map() m.add_raster(in_cog, band=[4, 1, 2], layer_name="Color infrared") m.add_raster(out_cog, palette="Greens", layer_name="NDVI") m

Out[11]:

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Last update: 2023-03-04
Created: 2021-06-10