Tag Archives: HSI model

Understanding Color Models using OpenCV-Python

RGB Trackbar

import cv2
import numpy as np

def nothing(x):
    pass

# Create a black image, a window
img = np.zeros((512,512,3), np.uint8)
cv2.namedWindow('image',cv2.WINDOW_NORMAl)

# create trackbars for color change
cv2.createTrackbar('R','image',0,255,nothing)
cv2.createTrackbar('G','image',0,255,nothing)
cv2.createTrackbar('B','image',0,255,nothing)


while True:
    cv2.imshow('image',img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
    
    # get current positions of three trackbars
    r = cv2.getTrackbarPos('R','image')
    g = cv2.getTrackbarPos('G','image')
    b = cv2.getTrackbarPos('B','image')

    img[:] = [b,g,r]

cv2.destroyAllWindows()

import cv2

import numpy as np

def nothing(x):

pass

# Create a black image, a window

img = np.zeros((512,512,3), np.uint8)

cv2.namedWindow('image',cv2.WINDOW_NORMAl)

# create trackbars for color change

cv2.createTrackbar('R','image',0,255,nothing)

cv2.createTrackbar('G','image',0,255,nothing)

cv2.createTrackbar('B','image',0,255,nothing)

while True:

cv2.imshow('image',img)

if cv2.waitKey(1) & 0xFF == ord('q'):

break

# get current positions of three trackbars

r = cv2.getTrackbarPos('R','image')

g = cv2.getTrackbarPos('G','image')

b = cv2.getTrackbarPos('B','image')

img[:] = [b,g,r]

cv2.destroyAllWindows()

You can move these trackbars to obtain different colors. A snapshot of output is shown below

HSI Trackbar

import cv2
import numpy as np

def nothing(x):
    pass

# Create a black image, a window
img = np.zeros((512,512,3), np.uint8)
cv2.namedWindow('image',cv2.WINDOW_NORMAL)

# create trackbars for color change
cv2.createTrackbar('H','image',0,180,nothing)
cv2.createTrackbar('S','image',0,255,nothing)
cv2.createTrackbar('I','image',0,255,nothing)

while(True):
    cv2.imshow('image',img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

    # get current positions of four trackbars
    r = cv2.getTrackbarPos('H','image')
    g = cv2.getTrackbarPos('S','image')
    b = cv2.getTrackbarPos('I','image')

    img[:,:] = [r,g,b]
    img = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)

cv2.destroyAllWindows()

import cv2

import numpy as np

def nothing(x):

pass

# Create a black image, a window

img = np.zeros((512,512,3), np.uint8)

cv2.namedWindow('image',cv2.WINDOW_NORMAL)

# create trackbars for color change

cv2.createTrackbar('H','image',0,180,nothing)

cv2.createTrackbar('S','image',0,255,nothing)

cv2.createTrackbar('I','image',0,255,nothing)

while(True):

cv2.imshow('image',img)

if cv2.waitKey(1) & 0xFF == ord('q'):

break

# get current positions of four trackbars

r = cv2.getTrackbarPos('H','image')

g = cv2.getTrackbarPos('S','image')

b = cv2.getTrackbarPos('I','image')

img[:,:] = [r,g,b]

img = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)

cv2.destroyAllWindows()

We get the following output as

Similarly, you can create trackbar for any color model. Play with these trackbars to get intuition about color models. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

Object Tracking Using Color Models OpenCV-Python

Steps

Open the camera using cv2.VideoCapture()
Create 3 Trackbars of H, S, and I using cv2.createTrackbar()
Read frame by frame
Record the current trackbar position using cv2.getTrackbarPos()
Convert from BGR to HSV using cv2.cvtColor()
Threshold the HSV image based on current trackbar position using cv2.inRange()
Extract the desired result

Code

import cv2
import numpy as np

def nothing(x):
    pass

cap = cv2.VideoCapture(0) 

# Create a window
cv2.namedWindow('image',cv2.WINDOW_NORMAL)


# create trackbars for color change
cv2.createTrackbar('lowH','image',0,179,nothing)
cv2.createTrackbar('highH','image',179,179,nothing)

cv2.createTrackbar('lowS','image',0,255,nothing)
cv2.createTrackbar('highS','image',255,255,nothing)

cv2.createTrackbar('lowV','image',0,255,nothing)
cv2.createTrackbar('highV','image',255,255,nothing)

while(True):
    ret, frame = cap.read()

    # get current positions of the trackbars
    ilowH = cv2.getTrackbarPos('lowH', 'image')
    ihighH = cv2.getTrackbarPos('highH', 'image')
    ilowS = cv2.getTrackbarPos('lowS', 'image')
    ihighS = cv2.getTrackbarPos('highS', 'image')
    ilowV = cv2.getTrackbarPos('lowV', 'image')
    ihighV = cv2.getTrackbarPos('highV', 'image')

    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    lower_hsv = np.array([ilowH, ilowS, ilowV])
    higher_hsv = np.array([ihighH, ihighS, ihighV])
    mask = cv2.inRange(hsv, lower_hsv, higher_hsv)

    frame = cv2.bitwise_and(frame, frame, mask=mask)
    cv2.imshow('image', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

import cv2

import numpy as np

def nothing(x):

pass

cap = cv2.VideoCapture(0)

# Create a window

cv2.namedWindow('image',cv2.WINDOW_NORMAL)

# create trackbars for color change

cv2.createTrackbar('lowH','image',0,179,nothing)

cv2.createTrackbar('highH','image',179,179,nothing)

cv2.createTrackbar('lowS','image',0,255,nothing)

cv2.createTrackbar('highS','image',255,255,nothing)

cv2.createTrackbar('lowV','image',0,255,nothing)

cv2.createTrackbar('highV','image',255,255,nothing)

while(True):

ret, frame = cap.read()

# get current positions of the trackbars

ilowH = cv2.getTrackbarPos('lowH', 'image')

ihighH = cv2.getTrackbarPos('highH', 'image')

ilowS = cv2.getTrackbarPos('lowS', 'image')

ihighS = cv2.getTrackbarPos('highS', 'image')

ilowV = cv2.getTrackbarPos('lowV', 'image')

ihighV = cv2.getTrackbarPos('highV', 'image')

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

lower_hsv = np.array([ilowH, ilowS, ilowV])

higher_hsv = np.array([ihighH, ihighS, ihighV])

mask = cv2.inRange(hsv, lower_hsv, higher_hsv)

frame = cv2.bitwise_and(frame, frame, mask=mask)

cv2.imshow('image', frame)

if cv2.waitKey(1) & 0xFF == ord('q'):

break

cap.release()

cv2.destroyAllWindows()

Open in full screen and play with trackbar to get more intuition about HSI model. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

Color Models

The RGB Color Model

In this, we construct a color cube whose 3 axes denote R, G, and B respectively as shown below

Normalized RGB Color Cube; Source: Researchgate

This is an additive model, i.e. the colors present in the light add to form new colors. For example, Yellow has coordinate of (1,1,0) which means Yellow = Red + Green. Similarly, for other colors like cyan = Blue + Green and magenta = Red +Blue.

R, G, and B are added together in varying proportions to produce an extensive range of colors. Mixing equal proportions of R, G, and B falls on the grayscale line.

Use: color monitors and most video cameras.

The CMYK Color Model

CMY stands for cyan, magenta, and yellow also known as secondary colors of light. K refers to black. An equal proportion of C, M, and Y produce muddly black and not pure black. That’s why we use CMYK instead of CMY model.

This is a subtractive model i.e colors are perceived as a result of reflected light. e.g. when light falls on a cyan coated surface, red is absorbed (or subtracted) while Green and Blue are reflected and thus G + B = Cyan. Similarly for magenta and yellow.

Thus, CMY can be obtained from RGB by subtracting RGB from the max intensity.

Use: Printing like books, magazines etc.

The HSI Color Model

HSI stands for Hue, Saturation, and Intensity. This model is similar to how humans perceive color. Let’s understand HSI terms

Hue: Color attribute that describes the pure color or dominant wavelength.

Saturation: Purity of Color or how much a pure color is diluted by white light.

Intensity: Amount of light

H and S tell us about the chromaticity (color information) of the light while I carries the greyscale information.

HSI model can be obtained by rotating the RGB cube such that Black is at the bottom and white at the top.

H varies from 0 to 120 degrees for Red, 120 – 240 for Green, and 240 -360 for Blue. Saturation can take value from 0 to 100%. Intensity value varies according to the bit size of an image.

Pros: Easier to represent the color than the RGB model.

Note: We can also use these color models for object tracking (See here).

In the next blog, we will see how different colors can be generated from these color models with the help of OpenCV. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

TheAILearner

Mastering Artificial Intelligence

Tag Archives: HSI model

Understanding Color Models using OpenCV-Python

RGB Trackbar

HSI Trackbar

Object Tracking Using Color Models OpenCV-Python

Steps

Code

Color Models

The RGB Color Model

The CMYK Color Model

The HSI Color Model