libreyolo 3dmood

Run 3dmood 3D detection from the command line.

Synopsis

bash

libreyolo 3dmood source=IMAGE intrinsics=camera.npy text='["CLASS"]' [OPTIONS]

Arguments

Argument	Default	Meaning
`source`	`required`	Image path or directory
`model`	`upstream default checkpoint`	Local checkpoint path
`size`	`None`	Model size: t or b; inferred from official filenames
`intrinsics`	`required`	Original-image 3x3 calibration .npy file
`text`	`required`	JSON category list, e.g. ["car","person"]
`device`	`auto`	auto, cpu, mps, or a CUDA device
`runtime-path`	`None`	Optional upstream 3D-MOOD checkout
`runtime-python`	`None`	Python interpreter for the upstream runtime
`conf`	`None`	Detection confidence threshold
`iou`	`None`	Class-agnostic NMS IoU threshold
`max-det`	`None`	Maximum detections per image
`save`	`False`	Save projected cuboids
`output-path`	`None`	Single-image output filename
`json`	`false`	JSON output to stdout
`quiet`	`False`	Suppress stderr
`help-json`	`False`	Dump command schema as JSON

Examples

Inspect the installed command schema:

bash

libreyolo 3dmood --help-json

Predict on the shipped sample image. For commands requiring calibration, camera.npy must contain its measured original-image 3x3 intrinsic matrix. Install the model's runtime before prediction.

bash

libreyolo 3dmood source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg intrinsics=camera.npy text='["person"]'

Request machine-readable results:

bash

libreyolo 3dmood source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg intrinsics=camera.npy text='["person"]' --json --quiet

Notes

Configuration, runtime-loading and I/O errors exit through the shared CLI error handler. --json writes structured results to stdout; diagnostics use stderr. --quiet suppresses stderr. See 3d-mood for installation, calibration and model constraints.