ndvibatch.py

# -*- coding: utf-8 -*-
"""
Created on Sat Oct 12 03:38:28 2019

@author: cosmi

rawpy is an easy-to-use Python wrapper for the LibRaw library. 
rawpy works natively with numpy arrays and supports a lot of options, 
including direct access to the unprocessed Bayer data
It also contains some extra functionality for finding and repairing hot/dead pixels.
import rawpy.enhance for this

First, install the LibRaw library on your system.

"""

"""
Experimental Vegetation Index Mapping program using DJI Mavic 2 Pro 
JPEG 16-bit combo images taken using InfraBlue Filter 
Useful for Batch Processing Multiple Images

%(c)-J. Campbell MuonRay Enterprises 2019 
This Python script was created using the Spyder Editor

"""

import warnings
warnings.filterwarnings('ignore')
#import imageio
import numpy as np
from matplotlib import pyplot as plt  # For image viewing

from matplotlib import colors
from matplotlib import ticker
from matplotlib.colors import LinearSegmentedColormap

#dng reading requires libraw to work

import os
import rawpy
import imageio


cols1 = ['blue', 'green', 'yellow', 'red']
cols2 =  ['gray', 'gray', 'red', 'yellow', 'green']
cols3 = ['gray', 'blue', 'green', 'yellow', 'red']
cols4 = ['black', 'gray', 'blue', 'green', 'yellow', 'red']

def create_colormap(args):
    return LinearSegmentedColormap.from_list(name='custom1', colors=cols3)

#colour bar to match grayscale units
def create_colorbar(fig, image):
        position = fig.add_axes([0.125, 0.19, 0.2, 0.05])
        norm = colors.Normalize(vmin=-1., vmax=1.)
        cbar = plt.colorbar(image,
                            cax=position,
                            orientation='horizontal',
                            norm=norm)
        cbar.ax.tick_params(labelsize=6)
        tick_locator = ticker.MaxNLocator(nbins=3)
        cbar.locator = tick_locator
        cbar.update_ticks()
        cbar.set_label("NDVI", fontsize=10, x=0.5, y=0.5, labelpad=-25)


for infile in os.listdir("./"):
    print( "file : " + infile)
    if infile[-3:] == "tif" or infile[-3:] == "DNG" :
       # print "is tif or DNG (RAW)"
       outfile = infile[:-3] + "jpg"
       raw = rawpy.imread(infile)
       
       
       print( "new filename : " + outfile)
       # Extract Red, Green and Blue channels and save as separate files
       
       rgb = raw.postprocess(gamma=(1,1), no_auto_bright=True, output_bps=16)

       R = rgb[:,:,0]
       G = rgb[:,:,1]
       B = rgb[:,:,2]
       
              # Get the red band from the rgb image, and open it as a numpy matrix
#NIR = image[:, :, 0]
#ir = np.asarray(NIR, float)
              
       ir = (R).astype('float')
       
# Get one of the IR image bands (all bands should be same)
#blue = image[:, :, 2]

#r = np.asarray(blue, float)
       
       r = (B).astype('float')
       
       
# Create a numpy matrix of zeros to hold the calculated NDVI values for each pixel
  # The NDVI image will be the same size as the input image

       
       ndvi = np.zeros(r.size)       
       
# Calculate NDVI
       
       
       ndvi = np.true_divide(np.subtract(ir, r), np.add(ir, r))
       fig, ax = plt.subplots()

       image = ax.imshow(ndvi, cmap=create_colormap(colors))
       plt.axis('off')
       #Lock or Unlock Key Bar Here for Mapping/Sampling/Showcasing:
       #create_colorbar(fig, image)
       extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
       #imageio.imsave(outfile, ndvi)
       fig.savefig(outfile, dpi=600, transparent=True, bbox_inches=extent, pad_inches=0)

        # plt.show()
       
       
#       rgb = raw.postprocess()