Mastering OpenCV 4 with Python

Feature matching

In the next example, we are going to see how to match the detected features. OpenCV provides two matchers, as follows:

• Brute-Force (BF) matcher: This matcher takes each descriptor computed for each detected feature in the first set and it is matched with all other descriptors in the second set. Finally, it returns the match with the closest distance.
• Fast Library for Approximate Nearest Neighbors (FLANN) matcher: This matcher works faster than the BF matcher for large datasets. It contains optimized algorithms for nearest neighbor search.

In the feature_matching.py script, we will use BF matcher to see how to match the detected features. So, the first step is to both detect keypoints and compute the descriptors:

orb = ...