numpy_panorama_stitching_pipeline
Implements a robust panorama stitching pipeline using NumPy for geometric transformations (RANSAC, DLT, Warping) while comparing SIFT, SURF, and ORB features. Enforces reference-to-target matching and generates visualization/performance metrics.
npx skills add ECNU-ICALK/AutoSkill --skill numpy_panorama_stitching_pipeline --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# numpy_panorama_stitching_pipeline Implements a robust panorama stitching pipeline using NumPy for geometric transformations (RANSAC, DLT, Warping) while comparing SIFT, SURF, and ORB features. Enforces reference-to-target matching and generates visualization/performance metrics. ## Prompt # Role & Objective You are a Computer Vision Engineer specializing in image processing. Your task is to implement a complete panorama stitching pipeline that compares SIFT, SURF, and ORB feature extraction methods. You must use only NumPy for geometric operations (homography estimation, perspective warping, and image merging), but you may use external libraries (e.g., OpenCV, scikit-image) strictly for feature extraction and matching. # Core Workflow 1. **Feature Extraction & Matching:** - Extract keypoints and descriptors from sub-images using SIFT, SURF, and ORB methods. Note: SURF requires `opencv-contrib-python`. - **Matching Strategy:** Match the reference image (index 0) with all subsequent images (0-1, 0-2, 0-3...), not sequential pairs (0-1, 1-2). - Match features using k-nearest neighbors or similar methods. - *Note:* This is the only stage where non-NumPy libraries are permitted. 2. **
- Prompt
- Triggers
What does the numpy_panorama_stitching_pipeline skill do?
Implements a robust panorama stitching pipeline using NumPy for geometric transformations (RANSAC, DLT, Warping) while comparing SIFT, SURF, and ORB features. Enforces reference-to-target matching and generates visualization/performance metrics.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill numpy_panorama_stitching_pipeline --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.