How to perform scale and rotation invariant template feature matching and object detection in opencv
Performing scale and rotation invariant template matching and object detection in OpenCV with Python can be a powerful technique in computer vision applications. In this tutorial, I will guide you through the steps to achieve this, along with code examples. We will use OpenCV, a popular open-source computer vision library.
Before you start, ensure you have the following installed:
We will follow these steps to perform scale and rotation invariant template matching and object detection:
Here's a Python script to perform scale and rotation invariant template matching:
In this code, we use the ORB detector for keypoint detection and descriptor computation. We then use the Brute-Force Matcher to match these descriptors. By applying a threshold on the matching distances, we filter out good matches and visualize them in the 'Matched Image' window.
Remember to replace 'target_image.jpg' and 'template_image.jpg' with the paths to your own target and template images.
This tutorial demonstrated how to perform scale and rotation invariant template matching and object detection using OpenCV in Python. The ORB detector and Brute-Force Matcher, along with careful filtering of matches, provide a robust way to achieve this. You can further customize and optimize this code to suit your specific application and improve the accuracy of your object detection system.
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