Python OpenCV Image Stitching
Budget: $10 – $30 USD
I need a solid, production-ready image-stitching pipeline written in Python with OpenCV. The input will be a mix of landscape and portrait photos taken from the same viewpoint; the goal is to merge them into a single, seamless panoramic image.
What matters most is reliable feature detection and robust alignment. I am comfortable with classic approaches such as SIFT and SURF, but ORB, AKAZE, or other methods are fine if they achieve crisp, ghost-free results. A Laplacian pyramid or multi-band blend is preferred for smooth exposure transitions, and the final output should preserve full resolution without noticeable seams or vignetting.
Deliverables
• A well-commented Python script or small module that accepts an arbitrary number of images from the command line and returns the stitched panorama.
• Clear, step-by-step instructions (readme or docstring) so I can run the code in a clean virtual environment.
• Example before/after images demonstrating that the algorithm delivers a tight alignment on a mixed-orientation set.
Acceptance criteria
– The script handles at least five mixed-orientation photos without manual tweaking.
– No black borders or misaligned overlaps in the result.
– Runs with the latest stable OpenCV build on Python 3.10+.
If you have prior work or a quick demo, feel free to point me to it; otherwise, tell me how you plan to tackle feature matching, homography estimation, warping, and blending. I will share sample images as soon as we agree on the approach so you can start coding right away.
What matters most is reliable feature detection and robust alignment. I am comfortable with classic approaches such as SIFT and SURF, but ORB, AKAZE, or other methods are fine if they achieve crisp, ghost-free results. A Laplacian pyramid or multi-band blend is preferred for smooth exposure transitions, and the final output should preserve full resolution without noticeable seams or vignetting.
Deliverables
• A well-commented Python script or small module that accepts an arbitrary number of images from the command line and returns the stitched panorama.
• Clear, step-by-step instructions (readme or docstring) so I can run the code in a clean virtual environment.
• Example before/after images demonstrating that the algorithm delivers a tight alignment on a mixed-orientation set.
Acceptance criteria
– The script handles at least five mixed-orientation photos without manual tweaking.
– No black borders or misaligned overlaps in the result.
– Runs with the latest stable OpenCV build on Python 3.10+.
If you have prior work or a quick demo, feel free to point me to it; otherwise, tell me how you plan to tackle feature matching, homography estimation, warping, and blending. I will share sample images as soon as we agree on the approach so you can start coding right away.
Related categories:
JavaScript
Python
Software Architecture
Photo Editing
Machine Learning (ML)
Image Processing
OpenCV
Computer Vision