---

name: image-processing-basics description: "Guides SimpleCV Image, ImageSet, color, drawing, transform, DFT, and scanline workflows on static images." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Image Processing Basics

Use this sub-skill when the task is about static SimpleCV images rather than live cameras, detectors, trackers, or classifiers.

Read first

Read references/workflows.md for detailed image recipes, parameter notes, and source-example replacements. Read references/troubleshooting.md when loading, saving, showing, sample-image, color-space, or filter steps fail. Read the root ../../references/api-reference.md for verified constructor and method signatures. Run scripts/image_recipe.py --help when you need a finite sample-image smoke helper.

Use this for

• Image(...) construction from sample names, files, numpy arrays, or existing matrices.
• ImageSet(...) directory/sample loading and list-style processing.
• Cropping, resizing, scaling, rotating, shearing, warping, flipping, blitting, side-by-side composition, and drawing.
• Thresholding, binarization, masks, morphology, gradients, edges, histograms, palettes, and color models.
• DFT filters, line scans, pixel access, and arithmetic/logical image operations.
• Headless conversions where Image.save(...) is safer than Image.show(...).

Route elsewhere

• Camera, Display, shell, stream, calibration, or physical device questions → ../acquisition-display-shell/SKILL.md.
• Blobs, lines, corners, templates, Haar, keypoints, barcode, or OCR → ../feature-detection/SKILL.md.
• Segmentation masks over frames or trackers → ../segmentation-tracking/SKILL.md.
• Classifier training/testing wrappers → ../machine-learning-legacy/SKILL.md.

Core workflow

1. Confirm the package imports with the root ../../scripts/check_env.py if the environment is unknown.
2. Load sample images with package data instead of source-checkout paths:

   from SimpleCV import Image
   img = Image('simplecv')
   coins = Image('coins.jpg', sample=True)
   

3. Use finite operations and save outputs:

   crop = img.crop(0, 0, 100, 100).scale(64, 64)
   crop.save('crop.png')
   

4. Only use show() when a display is available; otherwise use save() and applyLayers().
5. Validate non-empty images and dimensions before chaining expensive or feature-dependent operations.

Important decisions

• If an image path is user-supplied, check file existence and whether SimpleCV is interpreting it as a package sample.
• If the task needs color thresholds, clarify whether colors are RGB tuples in SimpleCV's public API or BGR/OpenCV internals.
• If a transform changes image size, choose whether fixed-size output or full extents are needed.
• If drawing layers are used, call applyLayers() or save() through the rendered image when comparing outputs.
• If a filter depends on old OpenCV symbols, fix the root OpenCV compatibility issue before changing the algorithm.

Bundled helper

Run a finite image smoke recipe:

python sub-skills/image-processing-basics/scripts/image_recipe.py --recipe all --output-dir /tmp/simplecv-image

Use --repo-root only when SimpleCV is not installed and you intentionally want to inspect a separate checkout.

Verification hooks

Good native-backed checks for this sub-skill include test_image_loadsave, test_image_numpy_constructor, test_color_meancolor, test_image_scale, DFT tests, and line-scan tests from SimpleCV's native suite. Use them only after the full skill has been integrated and the runtime environment is ready.