[1042] Stitch multiple images based on overlay areas in Python

To stitch multiple images into one big image based on their overlay areas in Python, you can use the OpenCV library. Here’s a step-by-step guide:

1. Install Required Libraries:

   pip install opencv-python numpy

2. Stitch Images:

   import cv2
   import numpy as np
    
   # Load the images
   img1 = cv2.imread('image1.jpg')
   img2 = cv2.imread('image2.jpg')
    
   # Create a stitcher object
   stitcher = cv2.Stitcher_create()
    
   # Perform the stitching process
   (status, stitched) = stitcher.stitch([img1, img2])
    
   if status == cv2.Stitcher_OK:
       print("Stitching successful!")
       cv2.imwrite('stitched_image.jpg', stitched)
   else:
       print("Stitching failed:", status)

In this example:

• cv2.imread loads the images.
• cv2.Stitcher_create creates a stitcher object.
• stitcher.stitch performs the stitching process.

This code will stitch the images together based on their overlapping areas. You can add more images to the list passed to the stitch method if needed.

For more complex stitching tasks, you might need to detect features and match them manually. Here’s a more detailed approach:

1. Detect Features and Match Them:

   import cv2
   import numpy as np
    
   # Load the images
   img1 = cv2.imread('image1.jpg')
   img2 = cv2.imread('image2.jpg')
    
   # Convert images to grayscale
   gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
   gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
    
   # Detect ORB features and compute descriptors
   orb = cv2.ORB_create()
   keypoints1, descriptors1 = orb.detectAndCompute(gray1, None)
   keypoints2, descriptors2 = orb.detectAndCompute(gray2, None)
    
   # Match features
   bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
   matches = bf.match(descriptors1, descriptors2)
   matches = sorted(matches, key=lambda x: x.distance)
    
   # Draw matches (optional)
   img_matches = cv2.drawMatches(img1, keypoints1, img2, keypoints2, matches[:10], None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
   cv2.imwrite('matches.jpg', img_matches)
    
   # Extract location of good matches
   points1 = np.zeros((len(matches), 2), dtype=np.float32)
   points2 = np.zeros((len(matches), 2), dtype=np.float32)
    
   for i, match in enumerate(matches):
       points1[i, :] = keypoints1[match.queryIdx].pt
       points2[i, :] = keypoints2[match.trainIdx].pt
    
   # Find homography
   h, mask = cv2.findHomography(points2, points1, cv2.RANSAC)
    
   # Use homography to warp images
   height, width, channels = img1.shape
   img2_warped = cv2.warpPerspective(img2, h, (width, height))
    
   # Combine images
   stitched = np.maximum(img1, img2_warped)
   cv2.imwrite('stitched_image.jpg', stitched)

This approach involves:

• Detecting features using ORB.
• Matching features between images.
• Finding the homography matrix.
• Warping one image to align with the other.
• Combining the images.

This method provides more control and can handle more complex stitching scenarios¹².

Are you working on a specific project that requires image stitching? Let me know if you need more detailed steps or have any questions!

¹: PyImageSearch ²: GeeksforGeeks