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name: image-utilities description: "Guides agents through keras-vis image utilities for data formats, overlays, labels, optional image dependencies, and diagnostics." disable-model-invocation: true metadata: disco-role: operating license: MIT

keras-vis image utilities

Use this sub-skill when a task needs keras-vis utility behavior rather than visualization semantics: image/data-format shape handling, channel-order helpers, synthetic arrays, overlays, label lookup, text drawing, image loading, model-layer lookup, model graph refresh after activation edits, or optional Pillow/imageio diagnostics.

Route first

• For activation-maximization recipes, loss weighting, seed images, or softmax-to-linear visualization advice, use the activation-maximization sub-skill instead.
• For saliency, guided saliency, Grad-CAM, heatmap interpretation, or map-overlay workflow semantics, use the saliency-and-cam sub-skill instead; return here only for low-level array overlays and shape checks.
• For custom losses, regularizers, optimizer callbacks, gradient modifiers, input modifiers, or backprop modifier internals, use the optimization-building-blocks sub-skill instead.

Runtime workflow

1. Decide the array contract: Keras backend image_data_format, tensor rank, and whether the array is channels-first tensor data or channels-last display image data. Use data-and-format-guide.md.

2. Pick the helper and exact import path from api-reference.md. Most helpers live under vis.utils.utils; overlay lives under vis.visualization.

3. For environment or compatibility questions, run the bundled deterministic diagnostic script from this sub-skill tree:

   python scripts/check_image_utilities.py
   

   The script uses only synthetic arrays and reports optional Pillow/imageio availability without requiring original images.

4. If a failure involves missing optional image packages, package data, array shape mismatch, graph refresh, or legacy Keras/TensorFlow behavior, use troubleshooting.md.

Keep outputs self-contained

When giving future instructions, copy or generate any tiny image data needed for demonstrations. Do not depend on repository-local sample assets or external data to explain these utilities.