

In [56]:

    
%matplotlib inline

import cv2
import matplotlib.pyplot as plt



In [57]:

    
bgr = cv2.imread("image077.png")
rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
plt.imshow(rgb)









    Out[57]:





<matplotlib.image.AxesImage at 0x7fbb4f701f90>



In [58]:

    
r = rgb[:, :, 0]
g = rgb[:, :, 1]
b = rgb[:, :, 2]



In [59]:

    
field = (g > 50) & (r < 50) & (b < 100)
plt.imshow(field, cmap='gray')









    Out[59]:





<matplotlib.image.AxesImage at 0x7fbb4f701510>

convert BGR image to HSV

In HSV, it is more easier to represent a color than RGB color-space.

For HSV, Hue range is [0,179], Saturation range is [0,255] and Value range is [0,255].



In [60]:

    
hsv = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV)



In [62]:

    
# define range of blue color in HSV
lower_green = np.array([50,50,50])
upper_green = np.array([100,255,255])



In [63]:

    
# Threshold the HSV image to get only green colors
mask = cv2.inRange(hsv, lower_green, upper_green)



In [64]:

    
# Bitwise-AND mask and original image
res = cv2.bitwise_and(rgb, rgb, mask=mask)



In [65]:

    
plt.imshow(res)









    Out[65]:





<matplotlib.image.AxesImage at 0x7fbb4f573bd0>



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