Build an Image Stitcher using OpenCV and Python

Panoramic images are a fantastic way to capture a wide view of a landscape or to combine multiple images for a unique perspective. In this tutorial, we'll create an image stitcher using OpenCV and Python that can combine multiple images into one seamless panorama.

Prerequisites

To follow this tutorial, you should have a basic understanding of Python programming and some familiarity with OpenCV. You will also need the following Python libraries installed:

• OpenCV
• NumPy

You can install them using pip:

pip install opencv-python numpy

Step 1: Load the Images

First, we'll load the images that we want to stitch together. We'll assume that the images have been taken in order from left to right.

import cv2
import numpy as np

# Load the images
image1 = cv2.imread('image1.jpg')
image2 = cv2.imread('image2.jpg')

Step 2: Find Key Points and Descriptors

Next, we'll find key points and descriptors in both images using the SIFT (Scale-Invariant Feature Transform) algorithm. This will help us match the images later on.

# Create a SIFT object
sift = cv2.xfeatures2d.SIFT_create()

# Find key points and descriptors in both images
keypoints1, descriptors1 = sift.detectAndCompute(image1, None)
keypoints2, descriptors2 = sift.detectAndCompute(image2, None)

Step 3: Match Descriptors

Now we'll match the descriptors from both images using the FLANN (Fast Library for Approximate Nearest Neighbors) matcher.

# Create FLANN matcher parameters
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)

# Create the FLANN matcher
matcher = cv2.FlannBasedMatcher(index_params, search_params)

# Match the descriptors
matches = matcher.knnMatch(descriptors1, descriptors2, k=2)

Step 4: Filter Good Matches

We'll filter the good matches by applying the ratio test, which helps us to keep only the best matches.

# Apply ratio test
good_matches = []
for m, n in matches:
    if m.distance < 0.7 * n.distance:
        good_matches.append(m)

Step 5: Find Homography

We'll now find the homography matrix that maps the first image onto the second image using the RANSAC (Random Sample Consensus) algorithm.

# Find homography matrix using RANSAC
src_pts = np.float32([keypoints1[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)
dst_pts = np.float32([keypoints2[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)

H, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)

Step 6: Stitch the Images

Finally, we'll warp the first image using the homography matrix and stitch it with the second image to create a panoramic image.

# Get the dimensions of the second image
height, width, _ = image2.shape

# Warp the first image using the homography matrix
warped_image1 = cv2.warpPerspective(image1, H, (width, height))

# Stitch the warped first image with the second image
result = cv2.addWeighted(warped_image1, 0.5, image2, 0.5, 0)

Step 7: Display the Result

Now that we have our stitched image, let's display the result.

# Display the stitched image
cv2.imshow('Stitched Image', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

Conclusion

In this tutorial, we built an image stitcher using OpenCV and Python that can combine multiple images into one seamless panorama. You can now use this tool to create stunning panoramic images from your own photos. Feel free to experiment with different images and settings to get the best results.