算法艺术创作
algorithmic-art
通过代码和可调参数创作生成艺术与交互式视觉效果。
utilities【设计与创意】Guides Autodistill image loading, visualization, model comparison, video splitting, and Roboflow utility boundaries.
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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.
load_image accepted inputs/outputs and headless plotting guidance.load_image, split_data, split_video_frames, sync_with_roboflow, plot, and compare.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.
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.
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.