Feature matching OpenCV

Feature matching is a fundamental technique in computer vision used to find corresponding points between two images. It allows us to identify similar objects or scenes in different images and is widely used in various applications, such as image stitchingImage stitching is the process of combining multiple photographic images with overlapping fields of view to produce a high-resolution panorama or a high-resolution image., object recognition, and augmented reality.

In this Answer, we will explore how to perform feature matching using OpenCV, a popular library for computer vision tasks.

Understanding feature matching

Feature matching is a fundamental technique in computer vision that allows us to find corresponding points between two images. It works by detecting distinctive key points in each image and comparing their descriptors, which represent the unique characteristics of these key points. By matching the descriptors in both images, we can establish correspondences and identify similar objects or scenes in different images.

Prerequisites

Before we dive into feature matching, make sure to have OpenCV installed. We can install it using pip:

Step 1: Import necessary libraries

Let's start by importing the necessary libraries: OpenCV and NumPy.

Step 2: Load the images

In this step, we will load two images that we want to match features between. We must ensure the images are in the same directory as your Python script or provide the appropriate path.

Here, we read the images in grayscale mode using cv2.IMREAD_GRAYSCALE. Grayscale images are sufficient for feature matching and can lead to faster computations.

These are the images we will be using in this tutorial:

As we can see, these are two different views of the Eiffel Tower.

Step 3: Detect key points and descriptors

Now, we will use a keypoint detector (e.g., SIFT, SURF, ORB) to find key points in the images and calculate their descriptors. In this example, we will use the SIFT (scale-invariant feature transform) algorithm.

Note: To learn about the SIFT algorithm in more detail, refer to this Answer.

Step 4: Match features

After obtaining the key points and descriptors, we can use a feature matcher (e.g., Brute-Force matcher, FLANN matcher) to find matches between the images' key points. Here we are using the Brute-Force matcher.

Step 5: Apply the ratio test

The ratio test helps us filter out ambiguous matches. It ensures that the nearest neighbor's distance is significantly smaller than the second nearest neighbor's distance.

Step 6: Draw the matches

Now, let's draw the matches on a new image to visualize the corresponding points between the two images.

Step 7: Display the result

Finally, let's display the matching result using OpenCV.

Complete code

Here's the complete executable code implementing the above steps:

Code explanation

Here’s an explanation of the complete code step by step:

Lines 1–2: Import necessary libraries

We start by importing the required libraries for our feature matching task. OpenCV (cv2) is the primary library we’ll be using for image processing, and NumPy (np) is used for numerical computations.

Lines 5–6: Load the images

We load two images that we want to match features between. The images are loaded in grayscale mode (cv2.IMREAD_GRAYSCALE) to simplify the feature matching process.

Line 9: Initialize SIFT detector

In this step, we initialize the SIFT (Scale-Invariant Feature Transform) detector, which is a keypoint detector and a descriptor extractor.

Lines 12–13: Detect key points and descriptors

We use the SIFT algorithm to find distinctive key points in both images and compute their descriptors, which represent the local image content around those key points.

Line 16: Initialize the Brute-Force matcher

The Brute-Force matcher is a simple feature matching algorithm that computes all pairwise distances between the descriptors of two sets of key points. It is a straightforward matching method but might be computationally expensive for large sets of descriptors.

Line 19: Match descriptors

We use the Brute-Force matcher to find the best matching descriptors between the two images. The knnMatch function returns a list of k best matches for each descriptor in the first set (image1) from the second set (image2).

Lines 22–25: Apply ratio test

To filter out ambiguous matches, we apply a ratio test. For each pair of descriptors in the matches, we check if the distance to the best match (m) is significantly smaller than the distance to the second-best match (n). If it satisfies the condition, we consider it a good match and add it to the good_matches list.

Line 28: Draw the matches

We draw the good matches on a new image called matching_result. The drawMatches function takes the two input images, the key points and descriptors of both images, and the list of good matches. It returns an image with the matches drawn as lines between the corresponding key points.

Lines 31–33: Display the result

Finally, we display the matching result using OpenCV’s imshow function. The matched points between the two images are shown as lines connecting the corresponding key points. The waitKey(0) function waits for a keyboard event, and destroyAllWindows() closes all open windows when a key is pressed.

Output

This is the output of the above implementation:

The matching result shows lines connecting corresponding key points between the two images. These lines represent the matches found by the feature matching algorithm, indicating similar regions or objects present in both images. The output allows us to visualize the areas of similarity and correspondence between the two input images.

Conclusion

In this Answer, we have learned how to perform feature matching in OpenCV using the SIFT algorithm. By detecting key points, computing descriptors, and matching features, we can find corresponding points between two images. Feature matching is a powerful technique used in various computer vision applications and can be further extended with other keypoint detectors and descriptors available in OpenCV.