object tracking using color model

In this tutorial, we will learn how we can use color models for object tracking. You can use any color model. Here, I have used HSI because it is easier to represent a color using the HSI model (as it separates the color component from greyscale). Let’s see how to do this

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.

TheAILearner

Mastering Artificial Intelligence

Tag Archives: object tracking using color model

Object Tracking Using Color Models OpenCV-Python

Steps

Code