How to Implement FLANN Based Feature Matching in OpenCV Python

Introduction

OpenCV is a widely used computer vision library that provides various algorithms for image recognition and processing. One of the most important tasks in computer vision is feature matching, which involves finding corresponding features in two or more images.

In OpenCV, feature matching is commonly implemented using the FLANN (Fast Library for Approximate Nearest Neighbors) algorithm. FLANN is a popular algorithm for finding the nearest neighbors in high-dimensional spaces, which is well-suited for feature matching in computer vision applications.

In this article, we will explore how to implement FLANN-based feature matching in OpenCV using the Python programming language. We will provide step-by-step instructions and sample code to help you get started with feature matching in OpenCV.

Getting Started

Before we dive into feature matching, let’s first set up our development environment. We assume that you have already installed OpenCV and Python on your system.

First, let’s import the necessary libraries and modules in our Python script:

import cv2
import numpy as np

Next, let’s load two images that we want to match:

img1 = cv2.imread('image1.jpg')
img2 = cv2.imread('image2.jpg')

We will use these two images to demonstrate feature matching in OpenCV.

Feature Extraction

The first step in feature matching is to extract features from the images. In OpenCV, we can extract features using various algorithms, such as the Scale-Invariant Feature Transform (SIFT) or the Speeded Up Robust Feature (SURF) algorithm.

For this example, we will use the SIFT algorithm to extract features from the images. Here’s how we can extract SIFT features from the two images:

sift = cv2.xfeatures2d.SIFT_create()

kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

In the above code, we first create a SIFT object using the cv2.xfeatures2d.SIFT_create() function. Next, we use the detectAndCompute() function to detect and compute the SIFT features for both images.

The detectAndCompute() function returns two values: kp (keypoints) and des (descriptors). The keypoints represent the locations of the detected features, while the descriptors represent the feature vectors for each keypoint.

Feature Matching

Now that we have extracted the SIFT features from the images, we can use the FLANN algorithm to match the corresponding features between the images.

Here’s how we can use the FLANN algorithm to match the SIFT features:

FLANN_INDEX_KDTREE = 1
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)

In the above code, we first define the FLANN algorithm parameters (index_params and search_params). We then create a cv2.FlannBasedMatcher object and use it to match the SIFT features of the two images.

The knnMatch() function returns a list of the top k matches for each descriptor in the first image (des1). We set k=2 to get the two best matches for each keypoint.

We can further filter these matches by using the ratio test. The ratio test helps us filter out the false matches by checking the ratio of the distances between the two best matches. Here’s how we can apply the ratio test:

good_matches = []
for m, n in matches:
    if m.distance < 0.75*n.distance:
        good_matches.append(m)

In the above code, we loop through each match returned by the knnMatch() function. We then check if the distance between the first and second best matches (m.distance and n.distance, respectively) is less than 0.75 times the distance between the first and third best matches. If the ratio is less than the threshold (0.75), we consider the match to be a good match and add it to the good_matches list.

Visualizing the Matches

Finally, we can visualize the matches between the two images. Here’s how we can do it:

img_matches = cv2.drawMatches(img1, kp1, img2, kp2, good_matches, None)
cv2.imshow('Matches', img_matches)
cv2.waitKey(0)

In the above code, we use the cv2.drawMatches() function to create animage that shows the matching keypoints between the two images. We pass the two images (img1 and img2), the keypoints (kp1 and kp2), and the good matches (good_matches) to the function. We then display the resulting image using the cv2.imshow() function.

Putting it All Together

Now that we have gone through each step separately, we can put everything together into a single Python script:

import cv2
import numpy as np

img1 = cv2.imread('image1.jpg')
img2 = cv2.imread('image2.jpg')

sift = cv2.xfeatures2d.SIFT_create()

kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

FLANN_INDEX_KDTREE = 1
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)

good_matches = []
for m, n in matches:
    if m.distance < 0.75*n.distance:
        good_matches.append(m)

img_matches = cv2.drawMatches(img1, kp1, img2, kp2, good_matches, None)
cv2.imshow('Matches', img_matches)
cv2.waitKey(0)

Conclusion

In this article, we have demonstrated how to implement FLANN-based feature matching in OpenCV using Python. We have shown how to extract SIFT features from two images, match the corresponding features using the FLANN algorithm, and visualize the resulting keypoints.

Feature matching is an essential task in computer vision, and OpenCV provides a reliable and efficient way to implement it. By following the steps outlined in this article, you can start working with feature matching using OpenCV and Python today.