---

name: segmentation-tracking description: "Guides SimpleCV color, difference, running, MOG segmentation, motion, and tracking workflows across images or frames." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Segmentation and Tracking

Use this sub-skill when the task needs foreground/background masks, segmentation state, frame differencing, or object tracking across frames.

Read first

Read references/workflows.md for segmentation model setup, track routing, and finite static-image recipes. Read references/troubleshooting.md for empty masks, unstable models, tracker availability, and hardware/display traps. Read the root ../../references/api-reference.md for verified constructor and method signatures. Run scripts/segmentation_recipe.py --help for a finite static-image segmentation helper.

Use this for

• ColorSegmentation, DiffSegmentation, RunningSegmentation, and MOGSegmentation.
• Image.findMotion(...) and Image.track(...).
• Track, TrackSet, CAMShiftTrack, LKTrack, SURFTrack, and MFTrack concepts.
• Turning segmented masks into blobs and trackable regions.
• Explaining live camera tracking workflows without accidentally running infinite loops.

Route elsewhere

• Image preprocessing, masks as static transforms, or DFT/filter operations → ../image-processing-basics/SKILL.md.
• One-shot object detection with blobs/templates/lines/keypoints → ../feature-detection/SKILL.md.
• Camera/display setup required to acquire frames → ../acquisition-display-shell/SKILL.md.
• Classifier training/testing → ../machine-learning-legacy/SKILL.md.

Core segmentation workflow

1. Start with two or more non-empty Image frames or a static image plus a color model.
2. Choose the state model:
   • ColorSegmentation for learned foreground/background colors.
   • DiffSegmentation for frame-to-frame differences.
   • RunningSegmentation for running-average background subtraction.
   • MOGSegmentation for mixture-of-Gaussians background modeling.
3. Call addImage(...) for each frame.
4. Check isReady() and isError() before reading results.
5. Use getSegmentedImage(...), getRawImage(), or getSegmentedBlobs() depending on whether the user needs a mask, raw diff, or blob objects.

Core tracking workflow

Use tracking only after the input source and bounding box are clear:

track_set = img.track(method='CAMShift', ts=track_set, img=previous_img, bb=bounding_box)

The old source examples often require a live camera and display loop. For automation, convert them into finite frame sequences or use VirtualCamera from ../acquisition-display-shell/.

Important decisions

• Tracking asks usually need frame sequences; do not solve them with a single static detector unless the user asked for a one-shot detection.
• SURF/LK/MF trackers rely on old OpenCV feature or optical-flow APIs; verify the OpenCV build before promising a specific tracker.
• Segmentation thresholds are image- and lighting-dependent; output masks must be inspected before turning them into blob claims.
• If the request needs camera frames, decide hardware availability before using Camera.

Bundled helper

python sub-skills/segmentation-tracking/scripts/segmentation_recipe.py --recipe diff --output-dir /tmp/simplecv-segmentation
python sub-skills/segmentation-tracking/scripts/segmentation_recipe.py --recipe color --output-dir /tmp/simplecv-segmentation

Verification hooks

Good final candidates include test_segmentation_diff, test_segmentation_running, test_segmentation_color, movement-related native tests, and static image-pair segmentation checks. Live tracking demos remain optional/hardware-gated unless explicitly requested.