Photo by Immo Wegmann on Unsplash

If you are stuck home like the rest of world, and sick of counting the number of new corona virus cases like I am. Then it’s time to take a break from the chaos and apocalyptic news on social media. Join me lets build a fun computer vision object tracking application in 5 minutes.

Here is youtube video demonstration of our final project.

If you are fired up like I am then let’s do this!

Requirements

For this project, the core library I am using is openCV. The methods you will see shortly have been implemented in the OpenCV package. In addition, I am running my code on jupyter notebook.

The code is written using python 3. Feel free to use whatever environment is suitable for you, but make sure that, opencv has been installed properly depending on the computer system you are using.

Step 1: Import relevant Libraries

import cv2
import numpy as np

We need access to openCV built-in methods, so we import it as above. Numpy library is always handy for mathematical manipulations.

Step 2: Set Color Thresholds.

#sets lower threshold for blue color
color_lower = np.array([150 , 50 , 0] , dtype = 'uint8');

 #sets upper threshold for blue color
color_upper = np.array([255, 150 , 100] , dtype = 'uint8');

First we need to set the lower and upper threshold of the color of the object we want to track. In this project, I am tracking a blue object, that is why my values for the blue color channel are much higher than the others. Here is a link to help you select color of your own object to track.

Step 3: Tracking the Object.

At the end of this step, I have provided the entire code block. However, to ease understanding, I will explain sections of the code allowing you to have an in-depth feel of what is going on. So lets get to it.

  • Capture Video from our WebCam. If you have a pre-recorded video, you could load it using same function by passing the file name of the video as argument to the function like this cv2.VideoCapture(‘video_filename’)
capture = cv2.VideoCapture(0)
  • It is important to know that a video is simply a sequence of images called frames, displayed at a particular frame rate. So the idea is that, frames are extracted from the video and this is achieved with the code snippet below.
ret, frame = capture.read()
  • Threshold Video Frame based on the lower and upper color threshold limits set above. The cv2.inRange() returns a binary image with pixels that fall with the color range set to white(255) and the rest set to black(0)
color_threshold = cv2.inRange(frame, color_lower, color_upper)
  • Filter thresholded image by applying Gaussian Blur to the binary image to reduce noise resulting from the thresholding operation and improve object tracking.
 = cv2.GaussianBlur(color_threshold, (3,3) , 0)
  • Find Contours of Object using the cv2.findContours() method that returns a list of contours each corresponding to detected object boundaries. Note a copy of the original binary image is made as this function is destructive.
(cnts , _)  = cv2.findContours(blur.copy() , cv2.RETR_EXTERNAL , cv2.CHAIN_APPROX_SIMPLE)
  • Find Minimum Bounding Box around the detected object using the cv2.minAreaRect() function.
bbox = cv2.minAreaRect(cnt)
  • Next we draw Bounding boxes around the detected object using the cv2.drawContours() method back unto the original video frame
cv2.drawContours(frame, [rects] , -1 , (0,255,0) , 2 )
  • Finally display the Tracking of Object
cv2.imshow("Tracking...", frame)

Here is the complete block code for all the steps explained in this section.

capture = cv2.VideoCapture(0)

while True:

    ret, frame = capture.read()

    #threshold based on color
    color_threshold = cv2.inRange(frame , color_lower , color_upper);

    blur = cv2.GaussianBlur(color_threshold , (3, 3) , 0); # to reduce noise and increase detection of the tracking of our object

    #cv2.findContours is used to get the contours corresponding to the object.
    (cnts , _)  = cv2.findContours(blur.copy() , cv2.RETR_EXTERNAL , cv2.CHAIN_APPROX_SIMPLE);


    if len(cnts) > 0:

      # sorts the list of contours from largest to smallest and returns the largest based on area
        cnt = sorted(cnts , key = cv2.contourArea , reverse = True)[0] (cv2.contourArea)

        #cv2.minAreaRect() computes the mininimum bounding box around the contour,
        bbox = cv2.minAreaRect(cnt)

        # BoxPoints()converts these bounding box to list of points #
        rects = np.int0(cv2.boxPoints(bbox))

        # Draw a bounding around object.
        cv2.drawContours(frame , [rects] , -1 , (0, 255, 0) , 2);

    #Tracking object    
    cv2.imshow('Tracking In Progress'  , frame) ;

    #show the thresholded image
    #cv2.imshow('Threshold' , color_threshold);

    #time.sleep(0.0025);

    key = cv2.waitKey(30) & 0xff

    if key == 27:
        break;

capture.release();

cv2.destroyAllWindows()

For the complete code, visit my github page.

The Takeaway

In this post, we have seen how to track an object based on it’s color in a video stream using a combination of some interesting opencv functions. The project will more fun I hope you had fun implementing this cool project. If you have any questions, please, leave a comment below, I will be happy to help. Make sure you subscribe so you can get notified immediately I drop another fun project.

Stay safe.

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