OpenCV FLANN Matcher: Matching Feature Points in Multiple Images

Using FLANN Matcher to Match Feature Points in Multiple Images

This guide explains how to match feature points across four different images using the FLANN Matcher in OpenCV.

Steps Involved

1. Feature Detection and Descriptor Extraction: Detect keypoints in each image and extract their descriptors using the ORB algorithm.
2. FLANN Matcher Initialization: Create a FLANN Matcher object with appropriate index parameters.
3. Matching Feature Points: Match the descriptors of image pairs (e.g., image 1 and image 2, image 2 and image 3, etc.) using the FLANN Matcher.
4. Filtering Matches: Apply a threshold to the matching results to filter out less confident matches.
5. Visualizing Results: Display the matched images with highlighted feature point connections.

Code Example

import cv2

# Read images
img1 = cv2.imread('1.png')
img2 = cv2.imread('2.png')
img3 = cv2.imread('3.png')
img4 = cv2.imread('4.png')

# Create ORB object
orb = cv2.ORB_create()

# Detect keypoints and extract descriptors
keypoints1, descriptors1 = orb.detectAndCompute(img1, None)
keypoints2, descriptors2 = orb.detectAndCompute(img2, None)
keypoints3, descriptors3 = orb.detectAndCompute(img3, None)
keypoints4, descriptors4 = orb.detectAndCompute(img4, None)

# Create FLANN Matcher object
FLANN_INDEX_LSH = 6
index_params = dict(algorithm=FLANN_INDEX_LSH, table_number=6, key_size=12, multi_probe_level=1)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)

# Match feature points (image 1 and 2)
matches12 = flann.knnMatch(descriptors1, descriptors2, k=2)
good_matches12 = []
for m, n in matches12:
    if m.distance < 0.7 * n.distance:
        good_matches12.append(m)

# Match feature points (image 2 and 3)
matches23 = flann.knnMatch(descriptors2, descriptors3, k=2)
good_matches23 = []
for m, n in matches23:
    if m.distance < 0.7 * n.distance:
        good_matches23.append(m)

# Match feature points (image 3 and 4)
matches34 = flann.knnMatch(descriptors3, descriptors4, k=2)
good_matches34 = []
for m, n in matches34:
    if m.distance < 0.7 * n.distance:
        good_matches34.append(m)

# Visualize matching results
img_matches12 = cv2.drawMatches(img1, keypoints1, img2, keypoints2, good_matches12, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
img_matches23 = cv2.drawMatches(img2, keypoints2, img3, keypoints3, good_matches23, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
img_matches34 = cv2.drawMatches(img3, keypoints3, img4, keypoints4, good_matches34, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
cv2.imshow('Matches12', img_matches12)
cv2.imshow('Matches23', img_matches23)
cv2.imshow('Matches34', img_matches34)
cv2.waitKey(0)

This code provides a practical example of how to use the FLANN Matcher for matching feature points between multiple images. You can adapt this code to your specific image analysis tasks by adjusting the image paths, feature detectors, and matching parameters. Remember to replace '1.png', '2.png', '3.png', and '4.png' with the actual paths to your image files.

Note:

• The good_matches threshold value of 0.7 in the code can be adjusted depending on the quality of your images and desired match accuracy.
• This code demonstrates matching between pairs of images. For more complex scenarios involving multiple images, consider extending this approach to create a graph of image connections based on the matching results.