Let's take a closer look at color spaces

You may have remembered we talked about images being stored in RGB (Red Green Blue) color Spaces. Let's take a look at that in OpenCV.

First thing to remember about OpenCV's RGB is that it's BGR (I know, this is annoying)

Let's look at the image shape again. The '3L'



In [9]:

    
import cv2
import numpy as np

image = cv2.imread('./images/input.jpg')

Let's look at the individual color levels for the first pixel (0,0)



In [10]:

    
# BGR Values for the first 0,0 pixel
B, G, R = image[10, 50] 
print B, G, R
print image.shape









    



13 19 32
(830L, 1245L, 3L)



In [12]:

    
print image[0,0]

Let's see what happens when we convert it to grayscale



In [5]:

    
gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
print gray_img.shape
print gray_img[10, 50]









    



(830L, 1245L)
22

It's now only 2 dimensions. Each pixel coordinate has only one value (previously 3) with a range of 0 to 255



In [6]:

    
gray_img[0, 0]









    Out[6]:





21

Another useful color space is HSV

Infact HSV is very useful in color filtering.



In [8]:

    
#H: 0 - 180, S: 0 - 255, V: 0 - 255

image = cv2.imread('./images/input.jpg')

hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

cv2.imshow('HSV image', hsv_image)
cv2.imshow('Hue channel', hsv_image[:, :, 0])
cv2.imshow('Saturation channel', hsv_image[:, :, 1])
cv2.imshow('Value channel', hsv_image[:, :, 2])

cv2.waitKey()
cv2.destroyAllWindows()

Let's now explore lookng at individual channels in an RGB image



In [12]:

    
image = cv2.imread('./images/input.jpg')

# OpenCV's 'split' function splites the image into each color index
B, G, R = cv2.split(image)

print B.shape
cv2.imshow("Red", R)
cv2.imshow("Green", G)
cv2.imshow("Blue", B)
cv2.waitKey(0)
cv2.destroyAllWindows()

# Let's re-make the original image, 
merged = cv2.merge([B, G, R]) 
cv2.imshow("Merged", merged) 

# Let's amplify the blue color
merged = cv2.merge([B+100, G, R])
cv2.imshow("Merged with Blue Amplified", merged) 

cv2.waitKey(0)
cv2.destroyAllWindows()









    



(830L, 1245L)



In [20]:

    
import cv2
import numpy as np

B, G, R = cv2.split(image)

# Let's create a matrix of zeros 
# with dimensions of the image h x w  
zeros = np.zeros(image.shape[:2], dtype = "uint8")

cv2.imshow("Red", cv2.merge([zeros, zeros, R]))
cv2.imshow("Green", cv2.merge([zeros, G, zeros]))
cv2.imshow("Blue", cv2.merge([B, zeros, zeros]))

cv2.waitKey(0)
cv2.destroyAllWindows()



In [19]:

    
image.shape[:2]









    Out[19]:





(830L, 1245L)

You can view a list of color converisons here, but keep in mind you won't ever use or need many of these

http://docs.opencv.org/trunk/d7/d1b/group__imgproc__misc.html#ga4e0972be5de079fed4e3a10e24ef5ef0