Overlay VLM bounding box annotations from JSON onto images using Python and Pillow.
npx skills add https://github.com/ghjghjghkimo/skills --skill vlm-visualizer
Please help me install this Agent Skill. Skill: vlm-visualizer Source: https://github.com/ghjghjghkimo/skills/tree/main/.gemini/skills/vlm-visualizer Command: npx skills add https://github.com/ghjghjghkimo/skills --skill vlm-visualizer
Generates visual verification by overlaying VLM bounding box JSON data onto the original image, helping validate segmentation results and produce clear visual reports.
Run the visualization script to overlay VLM bounding boxes on an image using your JSON data.
High-intent search queries and answers about installing and using this skill.
To overlay bounding box JSON data, you need an image paired with a JSON file containing a panels array of objects with label and box_2d coordinates, which are converted to pixel coordinates to generate a visually annotated image using Python and Pillow.
The JSON structure required for visualizing VLM segmentation results must contain a panels array where each item provides a label and box_2d coordinates, allowing the script to read the file and map normalized coordinates onto the original image.
Yes, this visualization script works with Pillow as its core dependency to process image annotation, reading the JSON file and outputting the final visually annotated image for quick QA checks.
Yes, you can use normalized coordinates from a VLM because the script reads box_2d coordinates from the JSON file and converts them into pixel coordinates to accurately draw bounding boxes on the original images.
The best way to create visual reports from VLM bounding box annotations is to run a Python script that overlays the box_2d data onto the source image, producing clear annotated visuals for segmentation verification in CV pipelines.
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