Methods of Feature Matching in OpenCV
1. ORB (Oriented FAST and Rotated BRIEF)
ORB is a powerful tool in computer vision applications because it brings together the FAST keypoint detector and the BRIEF descriptor. There are several reasons why ORB is a preferred choice. First and foremost, it’s lightning fast, which is crucial for real-time applications. It can quickly process images without any lag. Another advantage is its scale invariance. This means that it can detect features at different sizes within an image, making it perfect for handling objects or scenes of varying sizes. Moreover, ORB is rotation invariant, which means it can detect and match features regardless of their orientation in the image. This makes it really robust when it comes to changes in viewpoint. Unlike SIFT and SURF, ORB doesn’t require any licensing fees, so it’s a cost-effective solution for commercial use.
Code Implementation of using ORB for Feature matching in OpenCV
- This code s demonstrates how to use OpenCV to detect and display keypoints in two images using the ORB (Oriented FAST and Rotated BRIEF) feature detection algorithm. Initially, the code reads two grayscale images,
img1andimg2. The ORB detector is then created and used to detect keypoints and compute their descriptors for both images. Keypoints are the distinctive, repeatable patterns in an image that ORB identifies. - The detected keypoints are then drawn on the original images in green color. Finally, the images with the highlighted keypoints are displayed using the
cv2_imshowfunction, allowing for a visual examination of the detected features in both images. This process is essential for applications like object recognition, image stitching, and motion tracking, where identifying consistent points across images is crucial.
import cv2
from google.colab.patches import cv2_imshow
# Load the images
img1 = cv2.imread('image1.png', 0)
img2 = cv2.imread('image2.png', 0)
# Detect keypoints and descriptors using ORB
orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(img1, None)
kp2, des2 = orb.detectAndCompute(img2, None)
# Draw keypoints
img1_kp = cv2.drawKeypoints(img1, kp1, None, color=(0, 255, 0))
img2_kp = cv2.drawKeypoints(img2, kp2, None, color=(0, 255, 0))
# Display keypoints
cv2_imshow(img1_kp)
cv2_imshow(img2_kp)
cv2.waitKey(0)
cv2.destroyAllWindows()
Output:
image1 -output
image2-output
To know more about how to use ORB for Feature matching in OpenCV : Click here
2. BFMatcher (Brute-Force Matcher)
Using ORB with BFMatcher has some cool advantages. First off, it’s super fast and efficient, which makes it perfect for real-time apps and embedded systems. ORB is great at quickly detecting and describing keypoints, while BFMatcher is a simple yet effective way to match binary descriptors using Hamming distance. The best part is that this combo is really easy to implement and doesn’t require a ton of tweaking. Plus, it can handle image rotations like a champ.
Code Implementation of Using BFMatcher for Feature Matching in OpenCV
- This code demonstrates how to use OpenCV to detect and match keypoints between two images using the ORB (Oriented FAST and Rotated BRIEF) feature detection algorithm and the BFMatcher (Brute Force Matcher) with Hamming distance. Initially, two grayscale images, img1 and img2, are loaded. ORB is then employed to detect keypoints and compute their descriptors for both images.
- The BFMatcher is initialized with Hamming distance, suitable for binary descriptors like those produced by ORB. Descriptors from both images are matched, and the matches are sorted based on distance, ensuring the best matches come first. The top 10 matches are drawn with a dark green color on a combined image of img1 and img2, visually illustrating the correspondence between keypoints in both images.
- Finally, the image displaying these matches is shown using cv2_imshow, providing a clear visual representation of the matched features, which is critical for applications like object recognition, image stitching, and 3D reconstruction.
import cv2
import numpy as np
from google.colab.patches import cv2_imshow
# Load the images
img1 = cv2.imread('image1.png', 0)
img2 = cv2.imread('image2.png', 0)
# Detect keypoints and descriptors using ORB
orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(img1, None)
kp2, des2 = orb.detectAndCompute(img2, None)
# Initialize BFMatcher with Hamming distance
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
# Match descriptors
matches = bf.match(des1, des2)
# Sort matches by distance (best matches first)
matches = sorted(matches, key=lambda x: x.distance)
# Draw matches with dark green color (BGR: (0, 100, 0))
img_matches = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None, matchColor=(0, 100, 0), flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
# Display the matches
cv2_imshow(img_matches)
cv2.waitKey(0)
cv2.destroyAllWindows()
Output:
output
To know more about using BFMatcher (Brute-Force Matcher) for feature matching in OpenCV: Click here.
3. FLANN-based Matcher (Fast Library for Approximate Nearest Neighbors)
When it comes to fast and efficient keypoint detection and description for real-time applications, using ORB with the FLANN-based Matcher and KNN Matching is a great choice. The FLANN matcher, which is configured for binary descriptors using the LSH algorithm, offers efficient and scalable matching. On top of that, KNN Matching enhances robustness by finding multiple potential matches and using the ratio test to filter out poor matches. This combination is highly effective when dealing with large datasets and ensures high matching accuracy.
Code Implementation of Using FLANN for Feature Matching in OpenCV
- This code demonstrates how to detect and match keypoints between two images using OpenCV’s ORB (Oriented FAST and Rotated BRIEF) feature detection algorithm and the FLANN-based matcher. The process begins by loading two grayscale images, followed by detecting keypoints and computing their descriptors using ORB.
- The FLANN-based matcher is configured with LSH parameters for binary descriptors and performs KNN matching to find the two closest matches for each descriptor. A ratio test is applied to filter out false matches, retaining only those where the distance of the closest match is less than 75% of the second closest match.
- The good matches are then drawn on a combined image of the two input images, highlighted in dark green, and displayed using
cv2_imshowfor visual inspection. This technique is vital for applications like object recognition, image stitching, and 3D reconstruction, where identifying consistent points across images is essential.
import cv2
import numpy as np
from google.colab.patches import cv2_imshow
# Load the images
img1 = cv2.imread('image1.png', 0) # Query image
img2 = cv2.imread('image2.png', 0) # Train image
# Detect keypoints and descriptors using ORB
orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(img1, None)
kp2, des2 = orb.detectAndCompute(img2, None)
# FLANN parameters for ORB (LSH algorithm)
FLANN_INDEX_LSH = 6
index_params = dict(algorithm=FLANN_INDEX_LSH,
table_number=6, # 12
key_size=12, # 20
multi_probe_level=1) # 2
search_params = dict(checks=50) # or pass an empty dictionary
# Initialize FLANN-based matcher
flann = cv2.FlannBasedMatcher(index_params, search_params)
# Perform KNN matching
matches = flann.knnMatch(des1, des2, k=2)
# Apply ratio test to filter matches
good_matches = []
for m_n in matches:
if len(m_n) == 2:
m, n = m_n
if m.distance < 0.75 * n.distance:
good_matches.append(m)
# Draw matches with dark green color (BGR: (0, 100, 0))
img_matches = cv2.drawMatches(img1, kp1, img2, kp2, good_matches, None, matchColor=(0, 100, 0), flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
# Display the matches
cv2_imshow(img_matches)
cv2.waitKey(0)
cv2.destroyAllWindows()
Output:
output
To know more about using FLANN for Feature Matching in OpenCV: Click here.
Feature Matching in OpenCV
OpenCV feature matching is a super cool technology in computer vision that’s changing how machines understand the visual world. It’s super important in things like image search, object recognition, image stitching, and making pictures look better. If you want to level up your image analysis, classification, and autonomous navigation skills, mastering OpenCV feature matching is a must. In this article, we will discuss the various techniques required for feature matching in Open CV.