opencv-bioimage-analysis
Computer vision for bio-image preprocessing, feature detection, real-time microscopy. Color conversion, morphology, contour/blob detection, template matching, optical flow on fluorescence/brightfield. 10-100× faster than pure Python via C++. Use scikit-image for scientific morphometry/regionprops; OpenCV for real-time, video, classical feature extraction.
npx skills add BioTender-max/awesome-bio-agent-skills --skill opencv-bioimage-analysis --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 — Bio-image Computer Vision ## Overview OpenCV (cv2) provides optimized C++-backed image processing routines for preprocessing, segmentation, feature extraction, and video analysis of biological images. In life sciences, OpenCV is used for fluorescence image enhancement (background subtraction, CLAHE), morphological segmentation (watershed, contour detection), brightfield cell detection, and real-time microscopy stream processing. Unlike scikit-image (which emphasizes scientific measurement), OpenCV prioritizes computational speed and video support — making it ideal for preprocessing pipelines and real-time imaging applications. ## When to Use - Preprocessing fluorescence or brightfield images: background subtraction, CLAHE, Gaussian/median blur - Detecting cell contours, blobs, or edges without deep learning (classical methods) - Processing video streams from live-cell imaging microscopes in real-time - Template matching for finding repeated structures (organelles, crystals, patterns) - Applying morphological operations (erosion, dilation, opening, closing) for mask refinement - Computing optical flow between video frames for cell tracking - Use **scikit-image** instead f
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Module 1: Image I/O and Color Space Conversion
- Module 2: Filtering and Enhancement
- Module 3: Thresholding and Binary Segmentation
- Module 4: Contour Detection and Measurement
- Module 5: Morphological Operations for Mask Refinement
- Module 6: Video Processing for Live-Cell Imaging
- Key Parameters
- Common Workflows
- Workflow 1: Fluorescence Nucleus Detection Pipeline
Install OpenCV pip install opencv-python Install with extra contributed modules (SIFT, SURF, etc.) pip install opencv-contrib-python Verify python -c "import cv2; print(cv2.__version__)"
What does the opencv-bioimage-analysis skill do?
Computer vision for bio-image preprocessing, feature detection, real-time microscopy. Color conversion, morphology, contour/blob detection, template matching, optical flow on fluorescence/brightfield. 10-100× faster than pure Python via C++. Use scikit-image for scientific morphometry/regionprops; OpenCV for real-time, video, classical feature extraction.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill opencv-bioimage-analysis --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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.