sentinel_funcs.py

# Import important packages
import numpy as np
import matplotlib.pyplot as plt

def calculate_NDVI(image):
    """ This function calculates the Normalised Differential Vegetation Index for
    the 4-band data provided in this exercise. A 2D array is returned"""
    r = image[:,:,2]
    ir = image[:,:,3]
    return (ir-r)/(ir+r)

def image_histogram_equalization(image):
    """ Image histogram equlisation"""
    # from http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html

    # get image histogram
    image_histogram, bins = np.histogram(image.flatten(), 256, normed=True)
    cdf = image_histogram.cumsum() # cumulative distribution function
    cdf = (255-1) * cdf / cdf[-1] # normalize

    # use linear interpolation of cdf to find new pixel values
    image_equalized = np.interp(image.flatten(), bins[:-1], cdf)
    
    if len(image.shape) == 3:
        return image_equalized.reshape(image.shape).astype('uint8')[:,:,::-1]
    else:
        return image_equalized.reshape(image.shape).astype('uint8')