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‎NDVIStandAlone.py

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# -*- coding: utf-8 -*-
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"""
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Experimental Vegetation Index Mapping program using DJI Mavic Pro DNG (raw image) +
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JPEG 16-bit combo images taken using InfraBlue Filter
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This Version uses a special Broad Dual Bandpass NIR and Blue filter with a Narrow Bandpass in the Green
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to Create a combo NIR Blue + Green Image Allowing for Standalone JPG to NDVI
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%(c)-J. Campbell MuonRay Enterprises 2019
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This Python script was created using the Spyder Editor
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"""
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import warnings
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warnings.filterwarnings('ignore')
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from scipy import misc
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import imageio
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import numpy as np
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from matplotlib import pyplot as plt # For image viewing
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#!/usr/bin/python
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import getopt
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import sys
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import matplotlib.pyplot as plt
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from matplotlib import colors
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from matplotlib import ticker
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from matplotlib.colors import LinearSegmentedColormap
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class NDVI(object):
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def __init__(self, file_path, output_file=False, colors=False):
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self.image = plt.imread(file_path)
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self.output_name = 'NDVI.jpg'
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self.colors = ['gray', 'gray', 'red', 'yellow', 'green']
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def create_colormap(self, *args):
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return LinearSegmentedColormap.from_list(name='custom1', colors=args)
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def create_colorbar(self, fig, image):
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position = fig.add_axes([0.125, 0.19, 0.2, 0.05])
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norm = colors.Normalize(vmin=-1., vmax=1.)
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cbar = plt.colorbar(image,
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cax=position,
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orientation='horizontal',
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norm=norm)
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cbar.ax.tick_params(labelsize=6)
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tick_locator = ticker.MaxNLocator(nbins=3)
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cbar.locator = tick_locator
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cbar.update_ticks()
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cbar.set_label("NDVI", fontsize=10, x=0.5, y=0.5, labelpad=-25)
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def convert(self):
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"""
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This function performs the NDVI calculation and returns an GrayScaled frame with mapped colors)
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"""
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NIR = (self.image[:, :, 0]).astype('float')
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blue = (self.image[:, :, 2]).astype('float')
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green = (self.image[:, :, 1]).astype('float')
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bottom = (blue - green) ** 2
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bottom[bottom == 0] = 1 # remove 0 from nd.array
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VIS = (blue + green) ** 2 / bottom
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NDVI = (NIR - VIS) / (NIR + VIS)
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fig, ax = plt.subplots()
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image = ax.imshow(NDVI, cmap=self.create_colormap(*self.colors))
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plt.axis('off')
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self.create_colorbar(fig, image)
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extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
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fig.savefig(self.output_name, dpi=600, transparent=True, bbox_inches=extent, pad_inches=0)
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# plt.show()
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def main(argv):
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input_file = 'DJI_0069.JPG'
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output_file = False
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colors = False
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blue_ndvi = NDVI(input_file, output_file=output_file or False, colors=colors or False)
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blue_ndvi.convert()
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if __name__ == "__main__":
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main(sys.argv[1:])

‎ndviandendviandsavibatchversion.py

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# -*- coding: utf-8 -*-
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"""
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Created on Sat Oct 12 03:38:28 2019
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@author: cosmi
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rawpy is an easy-to-use Python wrapper for the LibRaw library.
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rawpy works natively with numpy arrays and supports a lot of options,
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including direct access to the unprocessed Bayer data
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It also contains some extra functionality for finding and repairing hot/dead pixels.
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import rawpy.enhance for this
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First, install the LibRaw library on your system.
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pip install libraw.py
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then install rawpy
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pip install rawpy
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"""
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"""
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Experimental Vegetation Index Mapping program using DJI Mavic 2 Pro
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JPEG 16-bit combo images taken using InfraBlue Filter
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Useful for Batch Processing Multiple Images
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%(c)-J. Campbell MuonRay Enterprises 2019
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This Python script was created using the Spyder Editor
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"""
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import warnings
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warnings.filterwarnings('ignore')
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#import imageio
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import numpy as np
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from matplotlib import pyplot as plt # For image viewing
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from matplotlib import colors
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from matplotlib import ticker
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from matplotlib.colors import LinearSegmentedColormap
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#dng reading requires libraw to work
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import os
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import rawpy
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cols1 = ['blue', 'green', 'yellow', 'red']
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cols2 = ['gray', 'gray', 'red', 'yellow', 'green']
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cols3 = ['gray', 'blue', 'green', 'yellow', 'red']
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cols4 = ['black', 'gray', 'blue', 'green', 'yellow', 'red']
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def create_colormap(args):
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return LinearSegmentedColormap.from_list(name='custom1', colors=cols3)
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#colour bar to match grayscale units
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def create_colorbar(fig, image):
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position = fig.add_axes([0.125, 0.19, 0.2, 0.05])
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norm = colors.Normalize(vmin=-1., vmax=1.)
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cbar = plt.colorbar(image,
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cax=position,
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orientation='horizontal',
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norm=norm)
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cbar.ax.tick_params(labelsize=6)
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tick_locator = ticker.MaxNLocator(nbins=3)
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cbar.locator = tick_locator
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cbar.update_ticks()
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cbar.set_label("NDVI", fontsize=10, x=0.5, y=0.5, labelpad=-25)
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for infile in os.listdir("./"):
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print( "file : " + infile)
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if infile[-3:] == "tif" or infile[-3:] == "DNG" :
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# print "is tif or DNG (RAW)"
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outfile = infile[:-3] + "jpeg"
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raw = rawpy.imread(infile)
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print( "new filename : " + outfile)
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# Extract Red, Green and Blue channels and save as separate files
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rgb = raw.postprocess(gamma=(1,1), no_auto_bright=True, output_bps=16)
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R = rgb[:,:,0]
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G = rgb[:,:,1]
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B = rgb[:,:,2]
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# Get the red band from the rgb image, and open it as a numpy matrix
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#NIR = image[:, :, 0]
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#ir = np.asarray(NIR, float)
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ir = (R).astype('float')
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# Get one of the IR image bands (all bands should be same)
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#blue = image[:, :, 2]
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#r = np.asarray(blue, float)
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r = (B).astype('float')
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"For ENDVI"
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#g = np.asarray(G, float)
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g = (G).astype('float')
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#(NIR + Green)
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irg = np.add(ir, g)
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"For SAVVI"
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L=0.5;
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rplusb = np.add(ir, r)
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rminusb = np.subtract(ir, r)
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oneplusL = np.add(1, L)
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# Create a numpy matrix of zeros to hold the calculated NDVI values for each pixel
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# The NDVI image will be the same size as the input image
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ndvi = np.zeros(r.size)
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ndvi = np.true_divide(np.subtract(irg, 2*r), np.add(irg, 2*r))
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#ndvi = np.true_divide(np.subtract(ir, r), np.add(ir, r))
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#savi = np.multiply(oneplusL , (np.true_divide((rminusb), np.add(rplusb, L))))
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#endvi = np.true_divide(np.subtract(irg, 2*r), np.add(irg, 2*r))
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fig, ax = plt.subplots()
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image = ax.imshow(ndvi, cmap=create_colormap(colors))
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plt.axis('off')
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#Lock or Unlock Key Bar Here for Mapping/Sampling/Showcasing:
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#create_colorbar(fig, image)
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extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
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#imageio.imsave(outfile, ndvi)
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fig.savefig(outfile, dpi=600, transparent=True, bbox_inches=extent, pad_inches=0)
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# plt.show()
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# rgb = raw.postprocess()
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‎ndviandendviandsavibatchversionwithgreyscale.py

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# -*- coding: utf-8 -*-
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"""
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Created on Sat Oct 12 03:38:28 2019
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@author: cosmi
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rawpy is an easy-to-use Python wrapper for the LibRaw library.
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rawpy works natively with numpy arrays and supports a lot of options,
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including direct access to the unprocessed Bayer data
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It also contains some extra functionality for finding and repairing hot/dead pixels.
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import rawpy.enhance for this
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First, install the LibRaw library on your system.
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pip install libraw.py
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then install rawpy
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pip install rawpy
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"""
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"""
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Experimental Vegetation Index Mapping program using DJI Mavic 2 Pro
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JPEG 16-bit combo images taken using InfraBlue Filter
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Useful for Batch Processing Multiple Images
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%(c)-J. Campbell MuonRay Enterprises 2019
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This Python script was created using the Spyder Editor
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"""
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import warnings
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warnings.filterwarnings('ignore')
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#import imageio
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import numpy as np
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from matplotlib import pyplot as plt # For image viewing
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from matplotlib import colors
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from matplotlib import ticker
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from matplotlib.colors import LinearSegmentedColormap
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#dng reading requires libraw to work
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import os
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import rawpy
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cols1 = ['blue', 'green', 'yellow', 'red']
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cols2 = ['gray', 'gray', 'red', 'yellow', 'green']
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cols3 = ['gray', 'blue', 'green', 'yellow', 'red']
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cols4 = ['black', 'gray', 'blue', 'green', 'yellow', 'red']
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def create_colormap(args):
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return LinearSegmentedColormap.from_list(name='custom1', colors=cols3)
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#colour bar to match grayscale units
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def create_colorbar(fig, image):
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position = fig.add_axes([0.125, 0.19, 0.2, 0.05])
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norm = colors.Normalize(vmin=-1., vmax=1.)
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cbar = plt.colorbar(image,
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cax=position,
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orientation='horizontal',
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norm=norm)
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cbar.ax.tick_params(labelsize=6)
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tick_locator = ticker.MaxNLocator(nbins=3)
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cbar.locator = tick_locator
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cbar.update_ticks()
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cbar.set_label("NDVI", fontsize=10, x=0.5, y=0.5, labelpad=-25)
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for infile in os.listdir("./"):
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print( "file : " + infile)
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if infile[-3:] == "tif" or infile[-3:] == "DNG" :
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# print "is tif or DNG (RAW)"
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outfile = infile[:-3] + "jpeg"
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raw = rawpy.imread(infile)
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print( "new filename : " + outfile)
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# Extract Red, Green and Blue channels and save as separate files
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rgb = raw.postprocess(gamma=(1,1), no_auto_bright=True, output_bps=16)
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R = rgb[:,:,0]
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G = rgb[:,:,1]
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B = rgb[:,:,2]
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# Get the red band from the rgb image, and open it as a numpy matrix
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#NIR = image[:, :, 0]
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#ir = np.asarray(NIR, float)
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ir = (R).astype('float')
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# Get one of the IR image bands (all bands should be same)
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#blue = image[:, :, 2]
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#r = np.asarray(blue, float)
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r = (B).astype('float')
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"For ENDVI"
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#g = np.asarray(G, float)
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g = (G).astype('float')
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#(NIR + Green)
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irg = np.add(ir, g)
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"For SAVI"
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L=0.5;
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rplusb = np.add(ir, r)
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rminusb = np.subtract(ir, r)
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oneplusL = np.add(1, L)
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# Create a numpy matrix of zeros to hold the calculated NDVI values for each pixel
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# The NDVI image will be the same size as the input image
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ndvi = np.zeros(r.size)
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ndvi = np.true_divide(np.subtract(irg, 2*r), np.add(irg, 2*r))
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#ndvi = np.true_divide(np.subtract(ir, r), np.add(ir, r))
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#savi = np.true_divide(np.multiply(rminusb, 1+L), np.add(rplusb, L))
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#endvi = np.true_divide(np.subtract(irg, 2*r), np.add(irg, 2*r))
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fig, ax = plt.subplots()
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image = ax.imshow(ndvi, cmap='Greys')
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plt.axis('off')
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#Lock or Unlock Key Bar Here for Mapping/Sampling/Showcasing:
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#create_colorbar(fig, image)
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extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
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#imageio.imsave(outfile, ndvi)
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fig.savefig(outfile, dpi=600, transparent=True, bbox_inches=extent, pad_inches=0)
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# plt.show()
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# rgb = raw.postprocess()
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