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Author: MuonRay

ndvi_denoise_batch.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Mon Sep 20 10:37:45 2021
+
+@author: cosmi
+"""
+
+"""
+Denoised Vegetation Index Mapping program using DJI Mavic 2 Pro
+JPEG 16-bit combo images taken using InfraBlue Filter
+%(c)-J. Campbell MuonRay Enterprises 2021
+% creative commons For non-profit use only
+This Python script was created using the Spyder Editor
+"""
+
+import warnings
+warnings.filterwarnings('ignore')
+
+from PIL import Image
+import pylab
+import rof
+
+from scipy import misc
+import imageio
+import numpy as np
+from matplotlib import pyplot as plt  # For image viewing
+
+#!/usr/bin/python
+import os
+import getopt
+import sys
+
+import matplotlib.pyplot as plt
+from matplotlib import colors
+from matplotlib import ticker
+from matplotlib.colors import LinearSegmentedColormap
+
+
+
+#a nice selection of grayscale colour palettes
+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:] == "jpg" or infile[-3:] == "JPG" :
+       # print "is tif or DNG (RAW)"
+       outfile = infile[:-3] + "jpg"
+       rgb = misc.imread(infile)
+       
+       
+       print( "new filename : " + outfile)
+       # Extract Red, Green and Blue channels and save as separate files
+       
+
+       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')
+       
+       #denoise
+       
+       denoised_ir_channel = ir
+       
+       
+       U,T = rof.denoise(denoised_ir_channel,denoised_ir_channel)
+       
+       #pylab.figure()
+       #pylab.gray()
+       #pylab.imshow(U)
+       #pylab.axis('equal')
+       #pylab.axis('off')
+       #pylab.show()
+
+
+# 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(U, r), np.add(U, 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()
+
+        # plt.show()

uv_photo_denoise.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Mon Sep 20 10:37:45 2021
+
+@author: cosmi
+"""
+
+
+"""
+Experimental UV Absorption Index program using UV-Pass Filter on DJI Mavic 2 Pro 
+JPEG 16-bit combo images taken using Ultraviolet-Only Pass Filter 
+Useful for Batch Processing Multiple Images
+
+%(c)-J. Campbell MuonRay Enterprises 2020 
+This Python script was created using the Spyder Editor
+"""
+from scipy import misc
+
+import warnings
+warnings.filterwarnings('ignore')
+
+from PIL import Image
+import pylab
+import rof
+
+import imageio
+import numpy as np
+from matplotlib import pyplot as plt  # For image viewing
+
+#!/usr/bin/python
+import getopt
+import sys
+
+import matplotlib.pyplot as plt
+from matplotlib import colors
+from matplotlib import ticker
+from matplotlib.colors import LinearSegmentedColormap
+
+#dng reading requires libraw to work
+
+# Open an image
+image = misc.imread('DJI_0212.JPG')
+
+# Get the red band from the rgb image, and open it as a numpy matrix
+#NIR = image[:, :, 0]
+         
+#ir = np.asarray(NIR, float)
+
+
+ir = (image[:,:,0]).astype('float')
+
+
+# Get one of the IR image bands (all bands should be same)
+#blue = image[:, :, 2]
+
+#r = np.asarray(blue, float)
+
+r = (image[:,:,2]).astype('float')
+
+
+g = (image[:,:,1]).astype('float')
+
+
+ir = (image[:,:,0]).astype('float')
+
+denoised_g_channel = g
+
+
+""" negating noise in the output green channel using
+  an implementation of the Rudin-Osher-Fatemi (ROF) denoising model
+    using the numerical procedure presented in eq (11) A. Chambolle (2005).
+     
+The ROF model has the interesting property that it finds a smoother version 
+of the image while preserving edges and structures.
+
+
+    Input: noisy input image (grayscale), initial guess for U, weight of
+    the TV-regularizing term, steplength, tolerance for stop criterion.
+
+    Output: denoised and detextured image, texture residual. """
+
+U,T = rof.denoise(denoised_g_channel,denoised_g_channel)
+
+pylab.figure()
+pylab.gray()
+pylab.imshow(U)
+pylab.axis('equal')
+pylab.axis('off')
+pylab.show()
+
+
+#(NIR + Green)
+irg = np.add(ir, U)
+       
+       
+L=0.5;
+       
+rplusb = np.add(ir, r)
+rplusbplusg = np.add(ir, r, U)
+rminusb = np.subtract(ir, r)
+oneplusL = np.add(1, L)
+# Create a numpy matrix of zeros to hold the calculated UVRI values for each pixel
+uvri = np.zeros(r.size)  # The UVRI image will be the same size as the input image
+
+# Calculate UV Reflectance Index
+
+uvri = np.true_divide(np.subtract(U, rminusb), np.add(U, rplusb))
+
+
+# Display the results
+output_name = 'UVReflectanceIndex4.jpg'
+
+#a nice selection of grayscale colour palettes
+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=cols4)
+
+#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("UVRI", fontsize=10, x=0.5, y=0.5, labelpad=-25)
+
+fig, ax = plt.subplots()
+image = ax.imshow(uvri, cmap=create_colormap(colors))
+plt.axis('off')
+
+create_colorbar(fig, image)
+
+extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
+fig.savefig(output_name, dpi=600, transparent=True, bbox_inches=extent, pad_inches=0)
+        # plt.show()