OpenCV Python – Implementing feature matching between two images using SIFT

In computer vision, image recognition and matching is one of the popular fields in recent years. The goal is to perform recognition tasks by finding unique features or objects in a set of images. Feature matching is a technique used to identify similar features between two images. In this article, we will discuss how we can use OpenCV Python library to implement feature matching between two images using SIFT (Scale-Invariant Feature Transform).

What is SIFT?

SIFT is an algorithm that aims to detect and describe local features in images. These features are used to match different images of the same object or scene. It is a highly effective method and has been widely used for image recognition tasks.

Getting Started

Before we begin with implementing the feature matching algorithm, make sure that you have OpenCV library and Python installed on your system.

# import libraries
import cv2
import numpy as np

Loading images

We will start by loading the two images we want to match. For simplicity, we will use two images of the same object with slight differences. You can use any images you want.

# load images
img1 = cv2.imread("image1.jpg")
img2 = cv2.imread("image2.jpg")

SIFT feature detection

Next, we need to use the cv2.xfeatures2d.SIFT_create() method to initialize the SIFT detector. We will use this detector to detect the keypoints and compute their descriptors for both images.

# detect SIFT keypoints and compute descriptors
sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

Here, kp1 and kp2 are the keypoints detected in the two images. des1 and des2 are the descriptors computed for these keypoints.

Feature matching using FLANN

Now that we have the keypoints and descriptors for both images, we need to match these features. We will use the Fast Library for Approximate Nearest Neighbors (FLANN) algorithm to do this.

# match keypoints using FLANN
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)

flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)

We first define the index and search parameters for FLANN. Then, we create a FlannBasedMatcher object which is used to match the descriptors. The knnMatch() method returns k nearest matches for each descriptor from both images.

Applying ratio test

The knnMatch() method may sometimes give us false matches. To eliminate these false matches, we need to apply a ratio test. For each match, we select the one with the minimum distance and check if its distance is smaller than a threshold. If it is, we keep that match, otherwise, we discard it.

# apply ratio test
good_matches = []
for i, (m1, m2) in enumerate(matches):
  if m1.distance < 0.7 * m2.distance:
    good_matches.append(m1)

Drawing keypoints and matches

Finally, we can draw the detected keypoints and their matches using cv2.drawMatches() method.

# draw keypoints and matches
img_matches = cv2.drawMatches(img1, kp1, img2, kp2, good_matches, None)

# display result
cv2.imshow("matches", img_matches)
cv2.waitKey()

Summary

In this article, we discussed how we can use OpenCV Python library to implement feature matching between two images using SIFT algorithm. We loaded the images, detected the keypoints and their descriptors using SIFT, matched the keypoints using FLANN, applied a ratio test to eliminate false matches, and finally drew the keypoints and matches.

Implementing feature matching between two images using SIFT can be useful in various applications such as object recognition, image stitching, and 3D modeling. You can modify the code to perform feature matching on your own set of images.

Conclusion

Feature matching is a crucial step in image recognition and computer vision. SIFT is a widely used algorithm for detecting and describing local features in images. In this article, we learned how to implement feature matching between two images using SIFT in Python using OpenCV library. We hope this article has been helpful and has provided you with a good starting point for your next project.