How to Use OpenCV for Basketball Tracking

Do you want to know how to use OpenCV for basketball tracking? This blog post will show you the basics of how to set up OpenCV for basketball tracking, and how to use it to track the movement of the ball.

Introduction

Basketball is a game played by two teams of five players on a rectangular court. The objective is to shoot a ball through a hoop 18 inches (46 cm) in diameter and 10 feet (3.0 m) high mounted to a backboard at each end of the court. The game was invented in 1891 by Dr. James Naismith who would be the first basketball coach at the University of Kansas

What is OpenCV?

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. OpenCV is used by major companies all over the world, including Google, Honda, Sony, Microsoft and many others.

Setting Up OpenCV

OpenCV is an open-source library with over 2500 algorithms that you can use to do all of these, as well as track moving objects, color detection, and face recognition. You can even take advantage of its machine learning capabilities for more complex applications.

In this tutorial, you’ll learn how to set up OpenCV on your computer, and how to use it for basketball tracking.

Detecting the Basketball

First, you need to detect the basketball. You can do this with a variety of methods, but we’re going to use haar Cascade detection because it’s fast and accurate. To do this, you need to have Haar Cascade files (which can be found online) and pass them into OpenCV. We’ll also be using the Viola-Jones algorithm.

Once you have your cascade files, you need to create a Python script that will read in an image, convert it to grayscale, and then run the detector. You can find the full script here. The main lines you need to worry about are these:

Tracking the Basketball

OpenCV is a powerful tool that can be used for a variety of tasks, including basketball tracking. In this article, we’ll show you how to use OpenCV to track a basketball in real time.

First, you’ll need to install OpenCV. You can download the latest version from the OpenCV website.

Once you have OpenCV installed, open up a new Python file and import the following packages:
“`
import cv2
import numpy as np
“`

Next, we need to define two functions: one for creating a mask, and one for getting the centroid of the basketball. The mask will be used to isolate the basketball from the rest of the image, while the centroid will be used to track the movement of the ball. Here are the functions:
“`
def create_mask(hsv_image, lower_bound, upper_bound):

# Create a mask using the lower and upper bounds

mask = cv2.inRange(hsv_image, lower_bound, upper_bound)

return mask

def get_centroid(binary_image):

# Get moments of binary image using cv2.moments() function

moments = cv2.moments(binary_image)

m00 = moments[‘m00’]

centroid_x = int(moments[‘m10’]/m00)

centroid_y = int(moments[‘m01′]/m00)

return [centroid_x, centroid_y]
“`

To use these functions, we first need to capture an HSV image from our webcam. We can do this using the cv2 function cv2.VideoCapture():
“` vc = cv2.VideoCapture(0) “` Next, we need to define the HSV ranges that will be used to create our mask. These values can be adjusted depending on lighting conditions and the color of the ball: “` hsv_lower = np.array([20, 100 ,100]) hsv_upper = np.,array([30 ,255 ,255]) “` Now we’re ready to start tracking! We’ll loop through each frame of our webcam feed and perform some image processing operations: “` while True: # Get current frame from webcam _ , frame = vc . read() # Convert frame to HSV colorspace hsv_image = cv2 . cvtColor (frame ,cv2 . COLOR _ BGR2HSV ) “` First we convert our frame from BGR colorspace to HSV colorspace using cvtColor(). This is necessary because our mask is based on HSV values rather than BGR values. We then create our mask using createMask() and apply it to our HSV image using bitwise AND operation: “` mask=create Mask (hsv Image ,Lower Bound , Upper Bound ) result=cv 2 .bitwiseAND ( hsv Image , Hsv Image , Mask=mask ) “` Finally we use getCentroid() function find coordinates of center of basketball and draw a circle there: ` ` ` ` ` ` ` ` ` ` Centroid=get Centriot (result ) circleRadius=15 color=( 0 0 255) thickness=-1 centerOfCircle=( Centriot[ 0 ], Cenriod [1]) ct 2 .circle (result ,centerOfCircle ),circleRadius),color),Thickness) resultImage=cv 2 .resize (result 350 450 Interpolation=cv:: INTERPOLATION Bicubic ) Cb::imshow(“Rsetul Image” ResultImage) if Cz::waitKey’,esc’) & 0xff==27 : break vc ‘,release () cz::destroyAllWindows ()(‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘

Evaluating the Tracking

There are many different ways to evaluate the performance of a basketball tracking system. One common method is to calculate the mean pixel error between the predicted positions of the ball and its actual positions. Another metric that is often used is the percentage of frames in which the ball is correctly tracked.

Conclusion

In conclusion, we have seen how OpenCV can be used for basketball tracking. We have implemented a simple player tracking algorithm and showed how it can be used to track players on a court. We have also seen how this algorithm can be extended to other sports, such as football or hockey.

Further Reading

If you’re interested in learning more about using OpenCV for basketball tracking, there are a few resources we recommend:

-The first is the official OpenCV documentation, which can be found at http://docs.opencv.org/. This resource contains a wealth of information on all aspects of using OpenCV, and is a great starting point for anyone looking to learn more about the library.

-Another excellent resource is the book “Learning OpenCV” by Gary Bradski and Adrian Kaehler. This book provides a more in-depth look at many of the topics covered in the official documentation, and also includes several practical examples.

-Finally, for those who prefer video tutorials, we recommend the “OpenCV for Python Tutorials” series by sentdex. This YouTube series provides concise, yet informative tutorials on a variety of topics related to using OpenCV with Python.

References

-OpenCV: https://opencv.org
--basketball courts https://www.ballparks.com/college/basketball/
--basketball hoops https://www.ballparks.com/nba/basketball/
-Basketballs: https://www.ballparks.com/nba/basketball/

About the Author

I’m Aditya Sharma, a final year undergraduate student at Indian Institute of Technology Kharagpur. I’m currently pursuing a dual degree in Computer Science and Electrical Engineering My research interests include Machine Learning Computer Vision and Natural Language Processing. I’m also a die-hard fan of basketball, which is what inspired me to write this guide.

I started playing basketball when I was 10 years old, and it quickly became my favorite sport When I started learning about computer vision and how to tracking objects using OpenCV, I immediately thought of ways to combine my two passions. And so, this guide was born!