[PublicLab] Used the PiNoIR camera with blue filter for my experiment in order to obtain the values of NIR and Red region, now i wanted to know what out put we are getting is right or wrong? (#22327)
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Public Lab contributor harshitha just posted a new research note entitled 'Used the PiNoIR camera with blue filter for my experiment in order to obtain the values of NIR and Red region, now i wanted to know what out put we are getting is right or wrong?':
Read and respond to the post here: https://publiclab.org/notes/harshitha/01-16-2020/used-the-pinoir-camera-with-blue-filter-for-my-experiment-in-order-to-obtain-the-values-of-nir-and-red-region-now-i-wanted-to-know-what-out-put-we-are-getting-is-right-or-wrong
Hello... I am an engineering student working on a project based on NDVI calculation to monitor crop health. I used the PiNoIR camera with a blue filter for my experiment in order to obtain the values of the NIR and the Red region. I used the following code to extract the required values and to calculate the NDVI. But in the output image, the empty regions (the area where no leaves are present as shown in the below figure) and ground have higher NDVI values. The shadowed regions are shown in the range from 0.5 to 0.6. I wanted to know whether the output is correct and what corrections can be done in the -code in order to correct the error. The code is given below.
from PIL import Image
import numpy as np
import cv2
from cv2 import imread
from matplotlib import cm
rgb_matrix =cv2.imread('inputimg.jpg')
w=rgb_matrix.shape[1] #columns
h=rgb_matrix.shape[0] #rows
print(w)
print(h)
Compute ndvi values for each pixel
NDVI=(NIR-R)/(NIR+R)
res=[]
for i in range(h):
row=[]
for j in range(w):
val=rgb_matrix[i][j]
n=val[2]
r=val[1]
num=((int(n)-int(r)))
den=((int(n)+int(r)))
if(den == 0):
r=0.0
else:
r=np.divide(num,den)
row.append(r)
res.append(row)
print('Done')
based on NDVI values, give different
colors for easier identification
for i in range(h):
for j in range(w):
if(res[i][j] >=-1 and res[i][j] <0):
rgb_matrix[i][j]=[128,128,128]
grey
elif(res[i][j]>=0 and res[i][j]<0.2):
rgb_matrix[i][j]=[64,255,0]
parrot green
elif(res[i][j]>=0.2 and res[i][j]<0.3):
rgb_matrix[i][j]=[125,255,255]
yellow
elif(res[i][j]>=0.3 and res[i][j]<0.4):
rgb_matrix[i][j]=[0,128,128]
dark green
elif(res[i][j]>=0.4 and res[i][j]<0.5):
rgb_matrix[i][j]=[255,255,0]
sky blue
elif(res[i][j]>=0.5 and res[i][j]<0.6):
rgb_matrix[i][j]=[255,51,153]
purple
elif(res[i][j]>=0.6 and res[i][j]<0.7):
rgb_matrix[i][j]=[0,128,255]
orange
elif(res[i][j]>=0.7 and res[i][j]<0.8):
rgb_matrix[i][j]=[255,43,255]
pink
elif(res[i][j]>=0.8 and res[i][j]<0.9):
rgb_matrix[i][j]=[40,40,255]
red
else:
rgb_matrix[i][j]=[255,0,0]
dark blue
cv2.imwrite('outputimg.jpg',rgb_matrix)
print("Completed!!")
(Ignore the indentation errors)
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