band_array_gist.py

# GEE area gist:
# https://gist.github.com/jdbcode/f4d56d72f7fc5beeaa3859999b1f5c3d

import ee
import numpy as np
import matplotlib.pyplot as plt

ee.Authenticate()
ee.Initialize()
     

# Define an image.
img = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_038029_20180810') \
  .select(['B4', 'B5', 'B6'])

# Define an area of interest.
aoi = ee.Geometry.Polygon(
  [[[-110.8, 44.7],
    [-110.8, 44.6],
    [-110.6, 44.6],
    [-110.6, 44.7]]], None, False)

# Get 2-d pixel array for AOI - returns feature with 2-D pixel array as property per band.
band_arrs = img.sampleRectangle(region=aoi)

# Get individual band arrays.
band_arr_b4 = band_arrs.get('B4')
band_arr_b5 = band_arrs.get('B5')
band_arr_b6 = band_arrs.get('B6')
     


# Transfer the arrays from server to client and cast as np array.
np_arr_b4 = np.array(band_arr_b4.getInfo())
np_arr_b5 = np.array(band_arr_b5.getInfo())
np_arr_b6 = np.array(band_arr_b6.getInfo())
print(np_arr_b4.shape)
print(np_arr_b5.shape)
print(np_arr_b6.shape)

# Expand the dimensions of the images so they can be concatenated into 3-D.
np_arr_b4 = np.expand_dims(np_arr_b4, 2)
np_arr_b5 = np.expand_dims(np_arr_b5, 2)
np_arr_b6 = np.expand_dims(np_arr_b6, 2)
print(np_arr_b4.shape)
print(np_arr_b5.shape)
print(np_arr_b6.shape)

# Stack the individual bands to make a 3-D array.
rgb_img = np.concatenate((np_arr_b6, np_arr_b5, np_arr_b4), 2)
print(rgb_img.shape)

# Scale the data to [0, 255] to show as an RGB image.
rgb_img_test = (255*((rgb_img - 100)/3500)).astype('uint8')
plt.imshow(rgb_img_test)
plt.show()