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デザイン · SKILL.md

utilities

utilities

Guides Autodistill image loading, visualization, model comparison, video splitting, and Roboflow utility boundaries.

Independent GitHub snapshot · Automated classification · Original documentation governs

スキルの説明

GitHub

name: utilities description: "Guides Autodistill image loading, visualization, model comparison, video splitting, and Roboflow utility boundaries." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Autodistill Utilities

Use this sub-skill when a task asks about image loading/conversion, plotting detections, comparing model predictions, splitting video frames, understanding split_data side effects, or Roboflow synchronization helper boundaries.

For main auto-labeling workflows and dataset layout, use dataset labeling. For CLI model selection, use CLI and model registry. For custom model interface design, use ontologies and model interfaces.

Quick Route

  • Image input conversion: read image loading and visualization for load_image accepted inputs/outputs and headless plotting guidance.
  • Utility function signatures and side effects: read utility reference for load_image, split_data, split_video_frames, sync_with_roboflow, plot, and compare.
  • Utility failures: read troubleshooting for invalid paths, unsupported return formats, URL/network failures, OpenCV/Pillow issues, headless display, Roboflow credentials, and directory mutation.
  • Safe local image-loading check: run scripts/check_image_loading.py. It creates its own local fixture by default and does not need network.

Common Pattern

from autodistill.helpers import load_image

cv2_image = load_image("image.jpg", return_format="cv2")
pil_image = load_image(cv2_image, return_format="PIL")
np_image = load_image(pil_image, return_format="numpy")

Use load_image when a plugin expects a specific image object format but your workflow has a path, PIL image, or NumPy/OpenCV array.

Headless Visualization Pattern

from autodistill.utils import plot

annotated = plot(image=image, detections=detections, classes=classes, raw=True)
# Save or inspect annotated instead of relying on an interactive display.

plot(..., raw=False) uses supervision.plot_image, which may not be suitable for headless runs.

Safety Notes

  • load_image can fetch HTTP URLs; avoid that branch unless network use is approved.
  • sync_with_roboflow logs into Roboflow, downloads images, labels them, and uploads annotations; treat it as credentialed and externally mutating.
  • split_data mutates a dataset output directory by moving files and converting .png/.jpeg to .jpg inside that output layout.
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