Changing Colorspaces

Goal

• In this tutorial, you will learn how to convert images from one color-space to another, like BGR Gray, BGR HSV etc.
• In addition to that, we will create an application which extracts a colored object in a video
• You will learn following functions : cv2.cvtColor(), cv2.inRange() etc.

Changing Color-space

There are more than 150 color-space conversion methods available in OpenCV. But we will look into only two which are most widely used ones, BGR Gray and BGR HSV.

For color conversion, we use the function cv2.cvtColor(input_image, flag) where flag determines the type of conversion.

For BGR Gray conversion we use the flags cv2.COLOR_BGR2GRAY. Similarly for BGR HSV, we use the flag cv2.COLOR_BGR2HSV. To get other flags, just run following commands in your Python terminal :

>>> import cv2
>>> flags = [i for i in dir(cv2) if i.startswith('COLOR_')]
>>> print flags

Note

For HSV, Hue range is [0,179], Saturation range is [0,255] and Value range is [0,255]. Different softwares use different scales. So if you are comparing OpenCV values with them, you need to normalize these ranges.

Object Tracking

Now we know how to convert BGR image to HSV, we can use this to extract a colored object. In HSV, it is more easier to represent a color than RGB color-space. In our application, we will try to extract a blue colored object. So here is the method:

• Take each frame of the video
• Convert from BGR to HSV color-space
• We threshold the HSV image for a range of blue color
• Now extract the blue object alone, we can do whatever on that image we want.

Below is the code which are commented in detail :

import cv2
import numpy as np

cap = cv2.VideoCapture(0)

while(1):

    # Take each frame
    _, frame = cap.read()

    # Convert BGR to HSV
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # define range of blue color in HSV
    lower_blue = np.array([110,50,50])
    upper_blue = np.array([130,255,255])

    # Threshold the HSV image to get only blue colors
    mask = cv2.inRange(hsv, lower_blue, upper_blue)

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

    cv2.imshow('frame',frame)
    cv2.imshow('mask',mask)
    cv2.imshow('res',res)
    k = cv2.waitKey(5) & 0xFF
    if k == 27:
        break

cv2.destroyAllWindows()