vd3 0.4.1

DVC-backed media database for computer vision and ML developers

VD3Storage

A DVC-backed media database for computer vision and ML developers. Tracks video and imageset assets, annotations, and worksets as MP4/JSON media with CSV-based metadata, so datasets stay versioned and reproducible across local disks and remote storage backends.

Installation

uv sync

To use as a dependency:

# pyproject.toml
[project]
dependencies = ["vd3"]

Quick Start

# Initialize a content database in the current directory
vd3 db init

# ...or in a specific directory
vd3 db init /path/to/mydb

# Add a video under a datasource
vd3 datasource add-video my-datasource clip.mp4 -p /path/to/mydb

# Add multiple videos with a glob (quote to prevent shell expansion)
vd3 datasource add-video my-datasource '*.mp4' -p /path/to/mydb

# List assets in a datasource
vd3 datasource assets my-datasource -p /path/to/mydb

# Show media availability
vd3 media status -p /path/to/mydb

Every command reads vd3 <noun> <verb> [args]. The nouns are db, datasource, workset, asset, layer, and media. Containers (datasource, workset) own ingest verbs and contained-asset listings; the asset noun is reserved for operations on a known asset.

All commands that accept --path also honor the VD3_DB environment variable, so you can point at a database once and drop the -p flag from individual commands. On startup vd3 automatically loads a .env file from the current directory or the nearest ancestor, so a project-level .env containing:

VD3_DB=/path/to/mydb

is enough — no export or shell sourcing required:

vd3 asset list
vd3 media status

Precedence: explicit --path > shell-exported VD3_DB > .env VD3_DB > current directory. Shell-exported values win over .env so you can do one-off overrides without editing the file.

--path on a specific command still overrides the env var.

Core Concepts

• Datasource — groups assets by origin (e.g. dashcam-2024, test-data). Required when importing.
• Asset — a single video (MP4 + JSON metadata) or imageset (directory of images).
• Workset — a named subset of assets, optionally organized into packages (folders). Independent of storage layout.
• Annotation layer — detections or tracks attached to an asset, with a key (e.g. gt, det/yolo-v8) and a human or machine source. Layers are git-backed by default (small, always present after clone); large layers can be DVC-backed (fetched on demand like media) via add_annotation_layer(..., dvc=True) / vd3 layer add-vd3 --dvc, or converted later with vd3 layer migrate-to-dvc.
• Per-image metadata — arbitrary non-bbox metadata attached per image (labels, attributes, …), stored as a DVC-backable image_metadata layer via add_image_metadata / read_image_metadata. Keeps bulky per-image metadata out of the git-tracked imageset.json manifest; vd3 layer migrate-imageset-extra moves existing inline extra into one.

Adding Assets

Videos

# Single file
vd3 datasource add-video dashcam clip.mp4

# Glob (recursive)
vd3 datasource add-video dashcam 'rawdata/**/*.mp4'

# Force re-import of a duplicate (matched by SHA-256)
vd3 datasource add-video dashcam clip.mp4 --force

# Add and assign to a workset/package
vd3 datasource add-video dashcam clip.mp4 -w my-workset -k batch1

Imagesets

# Directory of images
vd3 datasource add-imageset my-datasource /path/to/images

# Tar archive
vd3 datasource add-imageset my-datasource images.tar

Annotation layers

VD3 JSON detections/tracks into an existing asset. The second positional is a layer-name prefix prepended to every layer key in the file (e.g. importing a file with det/yolo under run-1 produces run-1/det/yolo):

vd3 layer add-vd3 clip run-1 results.json -p /path/to/mydb

COCO

Import COCO annotations into an existing imageset:

vd3 layer add-coco my-imageset gt annotations.json \
    --source human --reviewed-all

Import a full COCO dataset (creates the imageset and imports annotations in one step):

vd3 datasource add-imageset-from-coco my-datasource gt annotations.json \
    --image-root /path/to/images

Worksets

# Create
vd3 workset create "My Experiment"

# Add assets by name or ID
vd3 workset add my-experiment clip-001 clip-002

# ...or by media-path glob (run from the database root; files must be on disk)
cd /path/to/mydb
vd3 workset add my-experiment 'db/media/videos/fc/*.mp4'

# Inspect
vd3 workset list
vd3 workset show my-experiment      # metadata + layers + packages
vd3 workset assets my-experiment    # assets in the workset

# Remove an asset / delete the workset
vd3 workset remove my-experiment clip-001
vd3 workset delete my-experiment

Remote Storage

Media files are tracked by DVC. A content database has a single configured remote.

# Set the remote (replaces any existing one)
vd3 media remote set gs://my-bucket/vd3-data
vd3 media remote show

# Sync (push and pull both accept --workset/-w, --asset/-a, --datasource/-d, --all)
vd3 media push --all
vd3 media pull --workset my-experiment
vd3 media status

Supported backends:

Backend	URL form	Notes
Google Cloud Storage	gs://bucket/path	gcloud auth application-default login
Amazon S3	s3://bucket/path	Standard AWS credential chain
Azure Blob Storage	azure://container/path
Google Drive	gdrive://folder-id	via dvc-gdrive
Local / NAS	/mnt/nas/vd3-backup

Listing & Inspection

vd3 asset list                       # all assets (cross-container)
vd3 datasource list                  # all datasources
vd3 datasource assets dashcam        # assets in a datasource
vd3 datasource assets dashcam --paths      # one media path per line
vd3 datasource assets dashcam --filenames  # one filename per line
vd3 datasource layers dashcam        # annotation layers across the datasource
vd3 workset assets my-experiment     # assets in a workset
vd3 workset layers my-experiment     # annotation layers across the workset
vd3 asset layers clip                # annotation layers on an asset
vd3 asset show clip                  # asset details
vd3 db info                          # database overview
vd3 db query "SELECT ..."            # raw DuckDB SQL against the CSV tables

Exporting

# Extract frames from a video or images from an imageset
vd3 asset export-frames clip -o ./out

Library API

The CLI is a thin wrapper around VD3Storage, which is also usable directly.

from vd3storage import VD3Storage, Asset, Workset  # Tag, WorksetAsset also exported

# Open an existing database (or use VD3Storage.init(path) to create one)
storage = VD3Storage("/path/to/mydb")

# Browse assets
for a in storage.list_assets(datasource="dashcam"):
    print(f"{a.name} ({a.asset_type}): {a.frame_count} frames @ {a.nominal_fps} fps")

# Look up by (datasource, name) or by ID
clip = storage.get_asset("dashcam", "clip-001")
clip = storage.get_asset_by_id("3f1a...")

# Import a video
asset = storage.import_video("clip.mp4", datasource="dashcam")

# Resolve where the media file lives on disk
storage.resolve_media_path(clip)

# Annotation layers
storage.list_annotation_layers(clip.asset_id)
storage.read_annotation_layer(clip.asset_id, "gt")

# Worksets
ws = storage.create_workset("My Experiment")
storage.add_asset_to_workset(ws.workset_id, clip.asset_id, package="batch1")
storage.list_workset_assets(ws.workset_id)

# Raw DuckDB SQL against the underlying CSV tables
rows = storage.execute_sql("SELECT name, frame_count FROM assets WHERE asset_type = 'video'")

Other useful methods: import_imageset, import_coco, import_coco_dataset, import_result, export_coco, open_video, open_imageset, get_frame_image, add_tag, is_media_available, pull, push. Inspect help(VD3Storage) for the full surface.

Versioning & stability

The package follows Semantic Versioning. All notable changes are recorded in CHANGELOG.md, and every release is tagged vX.Y.Z in git.

While the package is in 0.x, minor bumps (0.2 → 0.3) may contain breaking changes; patch bumps (0.2.1 → 0.2.2) are backwards compatible. Every breaking change is called out under a ### Breaking heading in the CHANGELOG entry for that release. Once the package reaches 1.0 it will follow strict SemVer (MAJOR = breaking, MINOR = additive, PATCH = fix).

Public API

A change is "breaking" only if it alters one of the following:

1. Names re-exported from the top-level vd3storage package (i.e. listed in vd3storage.__all__):
   • VD3Storage and its documented methods
   • AlreadyInitializedError
   • The model classes Asset, Tag, Workset, WorksetAsset (including their field names and types)
   • __version__
2. The vd3 CLI — command names, option names, exit codes, and the documented input file formats (VD3 JSON, COCO).
3. The on-disk layout of a content database — directory structure under db/, CSV table schemas (tracked by SCHEMA_VERSION in db/tables/), the shape of video.json / imageset.json / annotation JSON files, and the structure of generated pyproject.toml / .gitignore.

Everything else is internal and may change without a major-version bump even if it is reachable via an import path. That includes the vd3storage.orm, vd3storage.dvc, vd3storage.media, vd3storage.importers, vd3storage.exporters, and vd3storage.cli submodules; helper functions in vd3storage.storage that start with _; and the on-disk format of files written into .dvc/ (those belong to DVC).

Deprecations

When a public API needs to change incompatibly, the old form keeps working and emits DeprecationWarning for at least one minor release before being removed. Current deprecations are listed in the CHANGELOG under each release's ### Deprecated heading.

To surface them in your own code:

python -W "default::DeprecationWarning:vd3storage" your_script.py

CLI Reference

vd3 --help                       Top-level help
vd3 <noun> --help                Help for a noun
vd3 <noun> <verb> --help         Help for a specific command

Every command reads vd3 <noun> <verb> [args]. Positionals carry identity (target → composite parts → payload); flags carry modifiers (--paths, --filenames, --source human, ...).

Command	Description
db init	Initialize a content database (defaults to cwd)
db info	Show database overview
db query	Run raw DuckDB SQL against the CSV tables
datasource list	List datasources
datasource show	Show datasource stats
datasource assets	List assets in a datasource
datasource layers	List annotation layers across a datasource (per-layer coverage)
datasource add-video	Import video files into a datasource
datasource add-imageset	Import an imageset (directory or tar) into a datasource
datasource add-imageset-from-coco	Import a COCO dataset as imageset + layer
workset create	Create a workset
workset list	List worksets
workset show	Show workset metadata + packages
workset assets	List assets in a workset
workset layers	List annotation layers across a workset (per-layer coverage)
workset add	Add assets to a workset
workset remove	Remove an asset from a workset
workset delete	Delete a workset (assets are kept)
asset list	List every asset (cross-container)
asset layers	List annotation layers on an asset
asset show	Show asset details
asset remove	Delete an asset
asset export-frames	Extract frames from a video or imageset
layer add-coco	Import COCO annotations into an existing imageset
layer add-vd3	Import VD3 JSON detections/tracks under a layer-name prefix (--dvc stores them DVC-backed)
layer migrate-to-dvc	Convert existing git-backed annotation layers to DVC-backed (--all, --min-size, --dry-run)
media status	Show media availability
media push	Push media to remote storage
media pull	Pull media from remote storage
media remote set	Set the remote storage URL
media remote show	Show the configured remote

Development

Tests

uv sync --all-groups        # install dev dependencies including pytest-cov
uv run pytest tests -q      # run the suite

Coverage

The repo ships with line + branch coverage measurement via pytest-cov. The quickest entry points:

./scripts/coverage.sh              # term-missing + HTML (htmlcov/) + XML (coverage.xml)
./scripts/coverage.sh --quick      # term-missing only, no HTML/XML write
./scripts/coverage.sh --check      # CI mode — fails if below the fail_under floor

The configured floor lives in [tool.coverage.report] of pyproject.toml and is a ratchet: it tracks just below the current overall percentage so that breaking the gate is always an actual regression. Bumping the floor up is the responsibility of whoever raises coverage.

Branch coverage is enabled from day one. Files explicitly excluded from measurement (constants, release metadata) live in [tool.coverage.run].omit.

CI

The test GitHub Actions workflow runs the same ./scripts/coverage.sh --check on every push to main and every pull request, on ubuntu-latest / Python 3.12. The generated coverage.xml is uploaded as a 14-day workflow artifact so diff-cover (or a human) can inspect per-file deltas after the fact.

Linting / Type-checking

The monorepo uses prek (pre-commit runner) configured at the repo root in .pre-commit-config.yaml. Run prek --all-files from the repo root to apply ruff (lint + format) and ty (type-check) against vd3storage/. The same hooks run as a pre-commit step on staged files.

Publishing

See .claude/skills/publish/SKILL.md (invoked as /publish from Claude Code) for the release flow. It walks through version selection, CHANGELOG maintenance, the quality gates (prek, pytest, coverage, build), and the final upload + tag with confirmation prompts at every destructive step.

For a fully manual release, scripts/publish.sh is the underlying script; --yes skips the confirmation prompt, --test targets TestPyPI.