Image Stitching Example with OpenCV

      Image stitching is a technique that provides the seamless combination of multiple images into a panorama. It plays a crucial role in photography and computer vision, allowing the capture of landscapes and the creation of virtual tours.

    In this tutorial, we will delve into the image stitching with OpenCV and learn how to create panorama image through the stitching. The tutorial covers:

  1. Understanding the image stitching
  2. Explanation of cv2.Stitcher class
  3. Creating panorama image
  4. Conclusion

     Let's get started.

Understanding the image stitching

    Image stitching involves merging multiple overlapping images to create a single, wide-angle image. The goal is to create a smooth transition between adjacent images, making it appear as if they were captured in one shot.

    Before diving into stitching, you need to prepare your images. Make sure significant overlap, common features, as well as consistent lighting and exposure settings when capturing your images.

    Keep in mind that the success of image stitching depends on the quality of the images and their overlapping regions. If the images do not have sufficient overlap or if they have significant distortions, the stitching process might not give accurate results.  


Explanation of cv2.Stitcher class
   The cv2.Stitcher class in OpenCV provides a high-level interface for image stitching, allowing us to automatically stitch multiple images together to create panoramas. It encapsulates the underlying stitching algorithms and provides a simplified way to perform image stitching without needing to manage the details of feature detection, matching, and blending manually.

Here's how we can use the cv2.Stitcher class:
    1. To begin, we create an instance of the cv2.Stitcher class using the cv2.Stitcher_create() function.
# Create a Stitcher object
stitcher = cv2.Stitcher_create()
    2. We use the stitch() method of the Stitcher object to stitch a list of images together. The method takes a list of images as an argument and returns a status code and the resulting stitched image.
# Stitch images
status, stitched_image = stitcher.stitch(images)

The status variable will hold one of the following values:
  • cv2.Stitcher_OK: Stitching was successful.
  • cv2.Stitcher_ERR_NEED_MORE_IMGS: Not enough images were provided for stitching.
  • cv2.Stitcher_ERR_HOMOGRAPHY_EST_FAIL: Homography estimation failed.
  • cv2.Stitcher_ERR_CAMERA_PARAMS_ADJUST_FAIL: Camera parameter adjustment failed.
    3. If the stitching is successful (status is cv2.Stitcher_OK), we can use the panorama image for further processing, display, or saving.
Creating panorama image with cv2.Stitcher

    Now, let's take a look at an example of step-by-step image stitching using 'cv2.Stitcher'. We'll start loading the target images (you can use multiple image) and display them to check the input images. Then we create a Stitcher object. We use stitch() method to stitch input images. If the status is 'cv2.Stitcher_OK' we'll display the stitched_image otherwise print the error cases.
import cv2
from matplotlib import pyplot as plt

# Load images for stitching
image1 = cv2.imread('part01.jpg')
image2 = cv2.imread('part02.jpg')

fig, ax = plt.subplots(1,2, figsize=(14,10))

# Create a Stitcher object
stitcher = cv2.Stitcher_create()

# Stitch images
status, stitched_image = stitcher.stitch((image1, image2))

if status == cv2.Stitcher_OK:
# Display the stitched image
plt.figure(figsize = (14, 10))
plt.title('Stitched image')
elif status == cv2.Stitcher_ERR_NEED_MORE_IMGS:
print('Not enough images for stitching.')
elif status == cv2.Stitcher_ERR_HOMOGRAPHY_EST_FAIL:
print('Homography estimation failed.')
print('Image stitching failed!')

    Image stitching is a technique that combines the art of photography with the science of computer vision. OpenCV serves as a valuable tool to master this technique and create stunning panoramas.
    In this tutorial, we've briefly explored image stitching and creating panorama images by using OpenCV functions. 


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