Hey Folks! In this tutorial, we are going to understand how to recognize key points in an image using the OpenCV Library in the Python programming language.
OpenCV keypoints are utilized in a variety of computer vision applications, including human posture detection, human face identification, hand gesture detection, and so on.
Why is it necessary to identify Keypoints in an Image?
While performing image processing, a computer should be able to recognize comparable qualities in a given image regardless of the transformations and rotations that it goes through.
The computer should also be able to detect similarities between photographs from the same category. This may be accomplished by observing the important points in a given image.
The main points on a human face, for example, are the two eye corners, two mouth corners, the chin, and the tip of the nose.
The essential notion is that, regardless of how much a picture changes, the computer should discover the same important features in the new image.
When the photos are updated, the computer examines the pixel values surrounding a certain key point and recognizes it.
The essential notion underlying all computer vision applications is key point determination. In this section, we will plot key points on a given picture.
For this, we will employ the ORB algorithm. First, we’ll include the cv2 library and the cv2 imshow() method.
from google.colab.patches import cv2_imshow
Now we’ll use the imread() method to read the picture. The image we’re going to utilize is colored. As a result, we’ll change it to black and white by setting the flag value to zero.
img = cv2.imread('sample.jpg',0)
We will now utilize the cv2.ORB create() method. We’ll pass 200 as the desired amount of points.
orb = cv2.ORB_create(200)
Now we’ll use orb.detectAndCompute() to find key points and calculate descriptors. Finally, the picture will be passed as an argument.
It gives back two values: key points and descriptions.
We will plot all of the key points using the drawKeypoints() method. The picture, key points, and flag value will then be sent as inputs.
keypoint, des = orb.detectAndCompute(img, None)
img_final = cv2.drawKeypoints(img, keypoint, None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
Finally, we will use cv2_imshow to plot all of the key points in our picture ().
Congratulations! You just learned how to find key points in an image using the OpenCV library in Python.
Hope you enjoyed it! 😇
Liked the tutorial? In any case, I would recommend you to have a look at the tutorials mentioned below:
- Visualizing Colors In Images Using Histograms – Python OpenCV
- Draw Shapes Using OpenCV – A Complete How-To Guide
- Credit Card Reader in Python using OpenCV
- Python OpenCV filter2D() function – A Complete Guide
Thank you for taking your time out! Hope you learned something new!! 😄