【Python OPENCV】 Color spaces

 

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'

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import cv2

import numpy as np

 

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

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

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# BGR Values for the first 0,0 pixel

B, G, R = image[10, 50] 

print(B, G, R)

print(image.shape)

12 19 34
(415, 622, 3)

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

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gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

print(gray_img.shape)

print(gray_img[10, 50]) 

(415, 622)
23

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

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gray_img[0, 0] 

21

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Another useful color space is HSV

Infact HSV is very useful in color filtering.

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#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()

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

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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()

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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()

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image.shape[:2]

(415, 622)

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