Use reflectance data from datasets to generate simulated true colour plots.
This script needs emsarray
, matplotlib
and scipy
installed.
Last active
January 17, 2024 04:22
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Simulated true colour plots
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import emsarray | |
import emsarray.plot | |
import matplotlib.pyplot as plt | |
import numpy | |
from scipy.interpolate import interp1d | |
def reflectance_to_rgb( | |
r_645: numpy.ndarray, | |
r_555: numpy.ndarray, | |
r_470: numpy.ndarray, | |
*, | |
bright_factor: float = 12.5, | |
desaturate_factor: float = 0.6, | |
) -> numpy.ndarray: | |
""" | |
Take an array of radiance values and transform it in to true colour. | |
The colours are ramped a bit, desatureated, etc. | |
Parameters | |
---------- | |
r_645, r_555, r_470 : numpy.ndarray | |
Arrays representing the surface reflectance (radiance?) | |
in wavelengths of 645 nm, 555 nm, and 470 nm respectively. | |
The arrays must be the same shape, | |
but can have any number of axes. | |
bright_factor : float | |
A multiplier that increases the brightness of the image. | |
Higher numbers are brighter. | |
desaturate_factor : float | |
How much to desaturate the image. | |
Valid range between 0 (grey scale) to 1 (full saturation) | |
Returns | |
------- | |
rgb: numpy.ndarray | |
An array the same shape as the input arrays | |
except for one extra axis of length 3. | |
The final axis represents RGB values suitable for plotting. | |
The values will be between 0 and 1. | |
""" | |
# setup our rgb array using the 3 vars | |
rgb = numpy.stack([r_645, r_555, r_470], axis=-1) | |
rgb = numpy.nan_to_num(rgb, nan=1) | |
# Some colour ramps | |
in_scale = numpy.array([0, 30, 60, 120, 190, 255], dtype=numpy.float64) | |
out_scale = numpy.array([0, 130, 160, 210, 240, 255], dtype=numpy.float64) | |
# Pull the green channel down a little more than the others, | |
# otherwise it can look more saturated than expected. | |
g_scale = out_scale - 50 | |
g_scale[0] = 0 | |
g_scale[5] = 255 | |
in_scale = in_scale / 255. | |
out_scale = out_scale / 255. | |
g_scale = g_scale / 255. | |
# apply color channel enhancement | |
rgb[..., 0] = interp1d(in_scale, out_scale)(rgb[..., 0]) | |
rgb[..., 1] = interp1d(in_scale, g_scale)(rgb[..., 1]) | |
rgb[..., 2] = interp1d(in_scale, out_scale)(rgb[..., 2]) | |
# turn brightness back up a bit | |
brighter = rgb * bright_factor | |
# Desaturate the image a bit by blending with a greyscale copy. | |
# The particulars here are how Pillows ImageEnhance.Color class operates. | |
# | |
# Convert to greyscale using the ITU-R 601-2 luma transform. | |
greyscale_value = ( | |
brighter[..., 0] * 299/1000 | |
+ brighter[..., 1] * 587/1000 | |
+ brighter[..., 2] * 114/1000 | |
) | |
greyscale = numpy.stack([greyscale_value] * 3, axis=-1) | |
# Blend with the original to get a desaturated image | |
rgb = brighter * desaturate_factor + greyscale * (1 - desaturate_factor) | |
# Clip the values to the [0..1] interval | |
rgb = numpy.clip(rgb, 0, 1) | |
# Done! | |
return rgb | |
# Open the dataset | |
dataset_path = 'https://dapds00.nci.org.au/thredds/dodsC/fx3/gbr4_bgc_GBR4_H2p0_B3p1_Cfur_Dnrt/gbr4_bgc_simple_2023-10-19.nc' | |
dataset = emsarray.open_dataset(dataset_path) | |
# Arbitrarily select the first time step | |
dataset = dataset.isel(time=0) | |
# Red, green, blue channels | |
reflectance_data_arrays = [ | |
dataset['R_645'], | |
dataset['R_555'], | |
dataset['R_470'], | |
] | |
# Calculate the true colours from the reflectances | |
rgb = reflectance_to_rgb( | |
# Flatten the reflectance data arrays as we pass them in | |
*(dataset.ems.ravel(da).values for da in reflectance_data_arrays), | |
bright_factor=10, desaturate_factor=0.6) | |
# Make a figure | |
figure = plt.figure(figsize=(6, 10), layout='constrained') | |
axes = figure.add_subplot(projection=dataset.ems.data_crs) | |
axes.set_aspect(aspect='equal', adjustable='datalim') | |
# Add the simulated true colour data | |
axes.add_collection(dataset.ems.make_poly_collection( | |
color=rgb[dataset.ems.mask], edgecolor='face')) | |
# Finish styling the plot | |
axes.set_title("True colour") | |
emsarray.plot.add_coast(axes, facecolor='green') | |
axes.autoscale() | |
# Save the plot then show it | |
plt.savefig('./true_colour.png') | |
plt.show() |
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