---

name: feature-detection description: "Guides SimpleCV blob, line, corner, template, Haar, keypoint, barcode, OCR, and feature-set workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Feature Detection

Use this sub-skill when a task needs SimpleCV to find, measure, sort, filter, draw, or interpret features in an image.

Read first

Read references/workflows.md for detector recipes and feature object handling. Read references/troubleshooting.md for empty detections, threshold tuning, optional detectors, and OpenCV feature availability. Read the root ../../references/api-reference.md for verified method signatures. Run scripts/feature_recipe.py --help for finite sample-image detection recipes.

Use this for

• FeatureSet, Feature, Blob, Corner, Line, TemplateMatch, Circle, KeyPoint, HaarFeature, Barcode, Chessboard, and ROI objects.
• Image.findBlobs, findLines, findCorners, findTemplate, findTemplateOnce, findCircle, findKeypoints, findKeypointMatch, findHaarFeatures, findBarcode, and readText.
• Blob measurements such as area, radius, contour, hull, min rectangle, shape tests, and masks.
• Template matching and keypoint matching decisions.
• Optional barcode/OCR/Haar/keypoint dependencies and limitations.

Route elsewhere

• Static transforms before or after detection → ../image-processing-basics/SKILL.md.
• Live camera/display loops that supply frames → ../acquisition-display-shell/SKILL.md.
• Stateful segmentation masks and trackers → ../segmentation-tracking/SKILL.md.
• Classifier training from extracted features → ../machine-learning-legacy/SKILL.md.

Core workflow

1. Load or create a non-empty Image.
2. Choose the detector method and make thresholds explicit.
3. Handle None or empty FeatureSet before indexing or drawing.
4. Draw or measure features, then call applyLayers() before saving an annotated image.
5. If the detector relies on optional dependencies or nonfree OpenCV features, prove those before treating an empty result as an algorithm problem.

Example pattern:

from SimpleCV import Image, Color
img = Image('coins.jpg', sample=True)
features = img.invert().findBlobs(minsize=200)
if features:
    biggest = features[-1]
    img.drawText('radius=%s' % biggest.radius(), biggest.x, biggest.y, color=Color.RED)
    img.applyLayers().save('coins_annotated.png')

Important decisions

• findBlobs threshold/minsize defaults are convenient, not universal.
• Template matching methods have different score semantics; document the chosen method.
• findKeypoints defaults to SURF, which may be unavailable in many OpenCV builds.
• Barcode and OCR are optional integrations; missing ZXing or tesseract is not a core SimpleCV failure.
• Feature coordinates are image coordinates; verify image resize/scale before reusing a bounding box.

Bundled helper

python sub-skills/feature-detection/scripts/feature_recipe.py --recipe blobs --output-dir /tmp/simplecv-features
python sub-skills/feature-detection/scripts/feature_recipe.py --recipe template --output-dir /tmp/simplecv-features

The helper adapts safe parts of source examples and writes finite outputs instead of opening windows.

Verification hooks

Good final native candidates include test_detection_findCorners, test_detection_blobs, test_template_match_once, test_detection_lines, test_findKeypoints_all, test_keypoint_match, and test_findHaarFeatures. Run them only after integration and environment readiness.