OpenCV Python – How to detect and draw keypoints in an image using SIFT?
Image processing is an important aspect of computer vision and one of the most widely-used libraries is OpenCV. In this article, we will explore how to use SIFT (Scale-Invariant Feature Transform) to detect and draw keypoints in an image using OpenCV Python.
What is SIFT?
SIFT is an algorithm used to detect and describe local features in an image. These local features, also known as keypoints, are points in an image that are unique and stable, meaning they can be used to identify the same point in different images. The SIFT algorithm is scale-invariant, meaning that it can identify keypoints regardless of their size or orientation.
Installing OpenCV
Before we get started, we need to install OpenCV and its Python bindings. OpenCV can be installed using pip, which is a package installer for Python. Here is the command to install OpenCV:
pip install opencv-python
Importing the libraries
Now that we have OpenCV installed, we can import the necessary libraries:
import cv2
import numpy as np
Reading the image
We can read an image using the cv2.imread()
function. This function takes the path to the image as an argument and returns a NumPy array representing the image.
img = cv2.imread('image.jpg')
Detecting keypoints using SIFT
To detect keypoints using SIFT, we first need to create an instance of the cv2.xfeatures2d.SIFT_create()
class. This class takes a few optional parameters, such as the number of keypoints to detect and the number of octaves to use.
sift = cv2.xfeatures2d.SIFT_create()
Next, we need to use the detect()
method to detect keypoints in the image. This method takes the image as an argument and returns a list of cv2.KeyPoint
objects.
keypoints = sift.detect(img, None)
Drawing keypoints on the image
Now that we have detected the keypoints, we can draw them on the image using the cv2.drawKeypoints()
function. This function takes the following arguments:
img
: The image on which to draw the keypoints.keypoints
: The list of keypoints to draw.outImage
: An optional output image.color
: The color of the keypoints.flags
: Various drawing flags.
img_keypoints = cv2.drawKeypoints(img, keypoints, None, color=(0,255,0), flags=0)
Displaying the image
Finally, we can display the original image and the image with keypoints using the cv2.imshow()
function. This function takes the following arguments:
winname
: The name of the window to display the image in.mat
: The image to display.
cv2.imshow('Image', img)
cv2.imshow('Image with Keypoints', img_keypoints)
cv2.waitKey(0)
cv2.destroyAllWindows()
Here’s the full code:
import cv2
import numpy as np
# Read the image
img = cv2.imread('image.jpg')
# Create SIFT object
sift = cv2.xfeatures2d.SIFT_create()
# Detect keypoints
keypoints = sift.detect(img, None)
# Draw keypoints on the image
img_keypoints = cv2.drawKeypoints(img, keypoints, None, color=(0,255,0), flags=0)
# Display the image
cv2.imshow('Image', img)
cv2.imshow('Keypoints', img_keypoints)
cv2.waitKey(0)
cv2.destroyAllWindows()
Conclusion
In this article, we have learned how to use SIFT to detect and draw keypoints in an image using OpenCV Python. We have also seen how to display the original image and the image with keypoints using OpenCV. With this knowledge, you can now start detecting keypoints in your own images and using them for various applications in computer vision.