# Image Color Space Conversion using Python and OpenCV

Published
Mar 18, 2018
Tags
python
code
image-processing
🗓 March 18th, 2018

While I’m in my last semester at Telkom University, I attended the Research Group in Multimedia Laboratory. The research we’ve been doing is about Digital Image Processing and Computer Vision. Almost every week, we met and learn about DIP and did some assignments. The second assignment was about image conversion. But before we jump to the idea about image conversion, why we should learn about it?

## Importance of learning color conversion

Well, after doing some research on the internet and digital image processing books, I have some reasons.
First, sometimes processing images on color images (usually on RGB) may take a long time. That’s why, before applying on color images, some algorithms tested or applied on grayscale images or binary images. Or we can say that some algorithms is having big complexity when processed on color images rather than grayscale or binary images.
Second, each color model has its own purposes and advantages. For example, there are several kinds of color models, like CMYK that is used for printing, Lab which is designed to approximate human vision, color image with transparent RGBA used for graphic design, and so on.
There are so many reasons to learn about color conversion, I think there are many sources that discuss more further about it. The fact that different color spaces are better for different applications is interesting to consider.

## Color Conversion

When we talk about conversing an image from a color space to another color space, we absolutely talked about mathematics equations.
In this article, I will discuss 3 color-conversions methods from RGB images. But before we jump to the code, please make sure you have Python 2.7 and OpenCV for Python installed on your machine. I’ll provide codes with two different approaches, using the OpenCV library and without the OpenCV library.

### RGB to Grayscale

We can convert RGB images using this equation
How it works: The big difference between RGB image and Grayscale image is their color channel. RGB images have 3 channels, while a Grayscale image only has 1 channel. We take the i-th pixel of channels Red, Green, and Blue then use the equation above to calculate the value at i-th pixel at the Grayscale image. Here’s the implementation in Python.
"""
@file: RGBtoGraysacle.py
@author : Ibe Dwi - @dskusuma
Notes   :
There are two kind of solutions. The first is solution using the Open-CV's
library. The second is without the library.
"""

import numpy as np
import cv2

# Get the image's height, width, and channels
height, width, channel = img.shape

# Create blank grayscale image
img_grayscale = np.zeros((height,width,1))

# ======================================================
# IMPLEMENTATION USING OPENCV LIBRARY
# ======================================================
#  img_grayscale = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# ======================================================
# IMPLEMENTATION WITHOUT OPENCV LIBRARY
# ======================================================

# CALCULATE
for i in np.arange(height):
for j in np.arange(width):
r = img.item(i,j,0)
g = img.item(i,j,1)
b = img.item(i,j,2)

# RGB to Grayscale
y = 0.299*r + 0.587*g + 0.144*b

img_grayscale.itemset((i,j,0),int(y))

# Write image
cv2.imwrite('image_grayscale.jpg',img_grayscale)
# View image
cv2.imshow('image',img_grayscale)
cv2.waitKey(0)
cv2.destroyAllWindows()

### RGB to Binary

To convert RGB image to Binary image, we have to the RGB image into a Grayscale image first. Then we set the threshold value. Then we use this equation:
How it works: We iterate on every pixel in our RGB image. If the pixel’s value is below the threshold value, we set it as 0, otherwise, we set it as 1. Here’s the implementation in Python.
"""
@file: RGBtoBinary.py
@author : Ibe Dwi - @dskusuma
Notes   :
There are two kind of solutions. The first is solution using the Open-CV's
library. The second is without the library.
"""

import numpy as np
import cv2

# Get the image's height, width, and channels
height, width, channels = img.shape

# Create blank Binary Image
img_binary = np.zeros((height,width,1))

# Create grayscale image
img_grayscale = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# print img_grayscale.shape
# ======================================================
# IMPLEMENTATION USING OPENCV LIBRARY
# ======================================================
(thresh, img_binary) = cv2.threshold(img_grayscale, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

# ======================================================
# IMPLEMENTATION WITHOUT OPENCV LIBRARY
# ======================================================
# Set Threshold
# thresh = 120
# CALCULATE
# for i in np.arange(height):
#     for j in np.arange(width):
#         x = img_grayscale.item(i,j)
#         if x >= thresh:
#             y = 1
#         else :
#             y = 0

#         img_binary.itemset((i,j,0),int(y))

# Write image
cv2.imwrite('image_binary.jpg',img_binary)
# View image
cv2.imshow('image',img_binary)
cv2.waitKey(0)
cv2.destroyAllWindows()

### RGB to HSV

The first step to convert RGB to HSV is to divide each channel with 255 to change the range of color from 0..255 to 0..1
Then we calculate the value of Cmax, Cmin, and Delta:
After that, we are ready to calculate the Hue, Saturation, and Value for HSV color space using these equations:
How it works: After we calculate the HSV for the i-th pixel of the RGB image, we take the H, S, and V to form a 3-channel image. Here’s the implementation using Python:
"""
@file: RGBtoHSV.py
@author : Ibe Dwi - @dskusuma
Notes   :
There are two kind of solutions. The first is solution using the Open-CV's
library. The second is without the library.
"""

import numpy as np
import cv2

# Get the image's height, width, and channels
height,width,channel = img.shape

# Create balnk HSV image
img_hsv = np.zeros((height,width,3))

# ======================================================
# IMPLEMENTATION USING OPENCV LIBRARY
# ======================================================
# img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# ======================================================
# IMPLEMENTATION WITHOUT OPENCV LIBRARY
# ======================================================

# CALCULATE
for i in np.arange(height):
for j in np.arange(width):
r = img.item(i,j,0)
g = img.item(i,j,1)
b = img.item(i,j,2)

r_ = r/255.
g_ = g/255.
b_ = b/255.
Cmax = max(r_,g_,b_)
Cmin = min(r_,g_,b_)
delta = Cmax-Cmin

# Hue Calculation
if delta == 0:
H = 0
elif Cmax == r_ :
H = 60 * (((g_ - b_)/delta) % 6)
elif Cmax == g_:
H = 60 * (((b_ - r_)/delta) + 2)
elif Cmax == b_:
H = 60 * (((r_ - g_)/delta) + 4)

# Saturation Calculation
if Cmax == 0:
S = 0
else :
S = delta / Cmax

# Value Calculation
V = Cmax

# Set H,S,and V to image
img_hsv.itemset((i,j,0),int(H))
img_hsv.itemset((i,j,1),int(S))
img_hsv.itemset((i,j,2),int(V))

# Write image
cv2.imwrite('image_hsv.jpg', img_hsv)

# View image
cv2.imshow('image', img_hsv)
cv2.waitKey(0)
cv2.destroyAllWindows()

The conclusion is learning about image conversion and color space is really important to help us understand the concept of digital image processing. I really wish this article will help you. Happy code!