This intermediate path guides you through building an automated image-stitching workflow with Python and OpenCV. You will prepare and inspect overlapping images, improve contrast, and assess texture, blur, exposure, and other factors that affect stitching quality.
You will detect local features with methods such as SIFT, ORB, and AKAZE, then match descriptors and filter unreliable correspondences. You will use RANSAC to estimate homographies, identify inliers and outliers, diagnose weak alignments, and warp images into a shared frame.
Finally, you will composite aligned images, crop unwanted borders, and package the workflow into reusable functions for stitching image pairs and sequences. This path is suited to learners with Python experience who want practical computer vision skills.
