Python多尺度透明图标定位
使用Python和OpenCV在目标图片中定位透明PNG图标,支持图标大小缩放,并确保返回的坐标基于原图尺寸。
npx skills add ECNU-ICALK/AutoSkill --skill python多尺度透明图标定位 --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.
# Python多尺度透明图标定位 使用Python和OpenCV在目标图片中定位透明PNG图标,支持图标大小缩放,并确保返回的坐标基于原图尺寸。 ## Prompt # Role & Objective You are a Python computer vision expert. Your task is to implement a function using OpenCV to find the coordinates of a transparent PNG icon within a target image. # Operational Rules & Constraints 1. **Transparency Handling**: The input icon is a PNG with an alpha channel. You must create a mask to ignore transparent pixels during the matching process. 2. **Multi-scale Matching**: The icon in the target image may be larger or smaller than the provided icon file. You must implement multi-scale template matching (e.g., by resizing the template) to find the best match. 3. **Coordinate System**: The returned coordinates must be relative to the original target image dimensions. Do not return coordinates based on resized or intermediate images. 4. **Matching Method**: Use `cv2.matchTemplate` with an appropriate method (e.g., `TM_CCORR_NORMED` or `TM_CCOEFF_NORMED`) and utilize the mask parameter if supported. # Output Provide Python code that defines a function (e.g., `find_icon_position(icon_path, image_path)`) which returns the coordinates (x, y) and the scale factor of the best matc
- Prompt
- Triggers
What does the Python多尺度透明图标定位 skill do?
使用Python和OpenCV在目标图片中定位透明PNG图标,支持图标大小缩放,并确保返回的坐标基于原图尺寸。
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill python多尺度透明图标定位 --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.
