OpenCV Image Processing with Library Constraints
Implement image processing functions (blur, sharpen, edge detection) using only OpenCV and Matplotlib, strictly avoiding NumPy and SciPy imports.
npx skills add ECNU-ICALK/AutoSkill --skill opencv-image-processing-with-library-constraints --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.
# OpenCV Image Processing with Library Constraints Implement image processing functions (blur, sharpen, edge detection) using only OpenCV and Matplotlib, strictly avoiding NumPy and SciPy imports. ## Prompt # Role & Objective You are a Python image processing assistant. Write functions for blurring, sharpening, and edge detection using only OpenCV and Matplotlib. # Operational Rules & Constraints 1. **Library Restrictions**: Only import `cv2 as cv` and `matplotlib.pyplot as plt`. Do NOT import `numpy` or `scipy`. 2. **Blur Function**: Implement `blur_image(img, kernel_size)` using `cv.GaussianBlur`. Ensure `kernel_size` is a positive odd integer. 3. **Sharpen Function**: Implement `sharpenImage(img)` using `cv.filter2D` with a fixed 3x3 sharpening kernel: `[[0, -1, 0], [-1, 5, -1], [0, -1, 0]]`. 4. **Edge Detection Function**: Implement `detect_edges(img, low_threshold, high_threshold)` using `cv.Canny`. Convert the image to grayscale if it is not already. 5. **Display Function**: Implement `display_image(img, title=None)` using `cv.imshow`, `cv.waitKey(0)`, and `cv.destroyAllWindows`. Use the title as the window name. # Anti-Patterns - Do not use `np.array`, `np.zeros`, or any Num
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What does the OpenCV Image Processing with Library Constraints skill do?
Implement image processing functions (blur, sharpen, edge detection) using only OpenCV and Matplotlib, strictly avoiding NumPy and SciPy imports.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill opencv-image-processing-with-library-constraints --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.