FLANN Feature Matching Example with OpenCV

       In computer vision, feature matching is a fundamental task used in applications like image recognition, object tracking, and image stitching. FLANN, which stands for Fast Library for Approximate Nearest Neighbors, is a powerful tool that can be used for this purpose. In this blog post, we'll delve into the FLANN feature matching technique and demonstrate how to use it with OpenCV. The tutorial covers:

1. Understanding FLANN feature matching
   
2. Explanation of cv2.FlannBasedMatcher()
3. Feature matching with FLANN
   
4. Conclusion
   

     Let's get started.

 

Understanding FLANN feature matching

      FLANN (Fast Library for Approximate Nearest Neighbors) is a technique used for efficient and approximate nearest neighbor search in high-dimensional spaces. In the context of computer vision and OpenCV, FLANN is often employed for feature matching, where it helps find corresponding features between two or more images. 

    In OpenCV, FLANN is often used in combination with various feature detectors and descriptors. It provides a flexible and fast way to find correspondences between keypoints in images, making it a fundamental component of many computer vision algorithms. While FLANN provides approximate matches, it's usually accurate enough for practical applications and significantly speeds up the matching process compared to brute-force methods.

Explanation of cv2.FlannBasedMatcher()

 

   The cv2.FlannBasedMatcher() function in OpenCV is used to create a matcher object for feature matching, particularly designed to work with large datasets using the FLANN algorithm. This function takes care of finding the best matches between feature descriptors extracted from two sets of images.     

    To implement FLANN technique we create FLANN matcher object. This function takes care of finding the best matches between feature descriptors extracted from two sets of images.

 

 

# Create a FLANN matcher object

flann = cv2.FlannBasedMatcher(index_params, search_params)

• index_params: This dictionary parameter specifies the algorithm and other related parameters for creating the index used in FLANN. 
• search_params: This dictionary parameter controls the search process in FLANN. It includes parameters like the number of checks to perform during the search.

    We use the flann.knnMatch() method of the flann object to find matches between features in two sets of data, like finding similar points or objects in different images.

 

 

# Match descriptors between the two images

matches = flann.knnMatch(descriptors1, descriptors2, k=2)

 

 

 

Feature matching with FLANN

    Now, let's take a look at an example of step-by-step image feature matching  using cv2.FlannBasedMatcher(). We'll start loading the target images and convert them into grayscale.  We initialize the SIFT detector to find keypoints and descriptors in both images. We set FLANN parameters. You can experiment with different parameters for better results. We create a FLANN-based matcher object using cv2.FlannBasedMatcher() and match descriptors with knnMatch() method. Then, apply Lowe's ratio test to get good matches. Finally, we draw the matches and display the result. 

  

 

import cv2

from matplotlib import pyplot as plt

 

# Load the two images you want to match

image1 = cv2.imread('items.jpg', cv2.IMREAD_GRAYSCALE)

image2 = cv2.imread('rotated_items1.jpg', cv2.IMREAD_GRAYSCALE)

# Initialize the SIFT detector

sift = cv2.SIFT_create()

# Find keypoints and descriptors in both images

keypoints1, descriptors1 = sift.detectAndCompute(image1, None)

keypoints2, descriptors2 = sift.detectAndCompute(image2, None)

# Create FLANN parameters (you can experiment with these)

index_params = dict(algorithm=0, trees=5) # FLANN_INDEX_KDTREE

search_params = dict(checks=50) # Higher values are more accurate but slower

# Create a FLANN Matcher

flann = cv2.FlannBasedMatcher(index_params, search_params)

# Match descriptors between the two images

matches = flann.knnMatch(descriptors1, descriptors2, k=2)

# Apply Lowe's ratio test to get good matches

good_matches = []

for m, n in matches:

if m.distance < 0.3 * n.distance:

good_matches.append(m)

# Draw the matches

matched_image = cv2.drawMatches(image1, keypoints1, image2, 

        keypoints2, good_matches, None, 

        flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)

# Display the result

plt.figure(figsize = (14, 10))

plt.imshow(matched_image)

plt.title('FLANN feature matching')

plt.show()

 

 

 

 Conclusion

  

    In this tutorial, we've briefly explored FLANN feature matching and learned how to match images with this method in OpenCV. 

    Feature matching is at the core of many computer vision applications, and FLANN offers a remarkable solution to accelerate this process. Its efficiency, speed, and ability to handle large datasets make it a go-to choice for many computer vision practitioners. By using OpenCV built-in functions you can easily apply the method and speed up your the matching tasks.

 

 

References:

1. OpenCV image processing