How to Detect and Draw FAST Feature Points in OpenCV Python?

OpenCV (Open Source Computer Vision) is a popular open-source computer vision and machine learning software library. It is written in C++ and provides programming interfaces in several languages like Python, Java, etc. OpenCV is mainly used for image processing, object detection, and recognition, and more. In this article, we will learn how to detect and draw FAST (Features from Accelerated Segment Test) feature points in OpenCV Python.

FAST Feature Points

FAST algorithm is used to detect feature points in an image. These feature points correspond to corners, edges, or other distinctive points in an image. The FAST algorithm is popular because of its speed and simplicity. FAST algorithm is based on a simple criterion – a pixel is considered a feature point if it is brighter or darker than its surrounding pixels by a certain threshold value.

Installing OpenCV in Python

Before we start working with OpenCV, we need to install it. We can use the following command to install OpenCV in Python:

pip install opencv-python

Importing Libraries

We will need to import the following libraries to work with OpenCV in Python:

import cv2
import numpy as np

Loading and Displaying an Image

Let’s load and display an image using OpenCV in Python:

# Load an image
img = cv2.imread('image.jpg')

# Display the image
cv2.imshow('image', img)

# Wait for a key
cv2.waitKey(0)

# Close all windows
cv2.destroyAllWindows()

Detecting FAST Feature Points

We can detect FAST feature points using the cv2.FastFeatureDetector_create() method in OpenCV. We can set the threshold parameter to control the minimum brightness or darkness difference required for a pixel to be considered a feature point. We can also set the non-maximal suppression parameter to control how close multiple feature points can be.

# Create a FAST object
fast = cv2.FastFeatureDetector_create(threshold=25, nonmaxSuppression=True)

# Detect feature points
keypoints = fast.detect(img, None)

# Draw detected feature points
img2 = cv2.drawKeypoints(img, keypoints, None, color=(0, 255, 0))
cv2.imshow('FAST features', img2)
cv2.waitKey(0)
cv2.destroyAllWindows()

Conclusion

In conclusion, we have learned how to detect and draw FAST feature points in OpenCV Python. The FAST algorithm is a simple and efficient way to detect feature points in an image. We have also learned how to install OpenCV and import the necessary libraries for working with OpenCV in Python. With this knowledge, we can now continue to explore the various other features and capabilities of OpenCV in Python for image processing and computer vision.