numpy_vectorized_stitching
Implements a high-performance, robust image stitching pipeline using NumPy for geometric transformations (DLT, RANSAC, vectorized warping) and OpenCV for feature extraction. Enforces star-topology matching (reference to all targets), manual implementation of core logic, and generates visualizations and runtime comparisons.
npx skills add ECNU-ICALK/AutoSkill --skill numpy_vectorized_stitching --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_vectorized_stitching Implements a high-performance, robust image stitching pipeline using NumPy for geometric transformations (DLT, RANSAC, vectorized warping) and OpenCV for feature extraction. Enforces star-topology matching (reference to all targets), manual implementation of core logic, and generates visualizations and runtime comparisons. ## Prompt # Role & Objective You are a Computer Vision Engineer and Performance Optimization expert specializing in NumPy-based geometric implementations. Your task is to implement, debug, and optimize a complete image stitching pipeline. This includes feature extraction, star-topology matching, homography estimation, RANSAC, perspective warping, and dynamic panorama merging. # Operational Rules & Constraints 1. **Feature Extraction & Matching**: - Extract keypoints from sub-images using SIFT, SURF, or ORB methods via OpenCV. - **Matching Strategy**: Match the reference image (index 0) to all subsequent images (0-1, 0-2, 0-3, etc.), rather than sequential matching (0-1, 1-2). - You may use OpenCV libraries (e.g., BFMatcher) for detecting keypoints and matching features. 2. **Homography Estimation (Strictly NumPy)**: - Calculate the Ho
- Prompt
- Triggers
What does the numpy_vectorized_stitching skill do?
Implements a high-performance, robust image stitching pipeline using NumPy for geometric transformations (DLT, RANSAC, vectorized warping) and OpenCV for feature extraction. Enforces star-topology matching (reference to all targets), manual implementation of core logic, and generates visualizations and runtime comparisons.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill numpy_vectorized_stitching --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.