detstream 0.1.1

Modular object detection for live video feeds

detstream

Modular object detection framework for live video feeds.

Pipeline

1. Source: Where frames come from. youtube, stream (RTSP/HLS/HTTP), file (a video file or webcam device)
2. Detector: What to look for. yolo-world, an open-vocabulary model prompted with a word or phrase (person, forklift, bird); roboflow, a Workflow you built on Roboflow, run through its hosted inference API; rf-detr, RF-DETR run locally, COCO-pretrained or pointed at a fine-tuned checkpoint. Bring your own by registering one (see Plugins).
3. Tracker: When a detection counts as a sighting. Hysteresis and cooldown give you one alert per sighting instead of one per frame, which is what keeps alerts from becoming spam.
4. Sinks: Where alerts go. console, supabase (rows + thumbnails for a website), discord (rich embeds), dataset (raw peak frames to disk, for building a training set).

Install

pip install detstream                          # core only
pip install "detstream[yolo,youtube,supabase]" # for example, everything otterwatch uses

The core install pulls what every feed needs: numpy, opencv-python-headless (decoding and annotation), pydantic (config), pyyaml, and httpx.

Extra	Enables	Pulls in
yolo	the yolo-world detector	ultralytics>=8.3.0, torch>=2.2.0. ultralytics fetches CLIP on first use of the detector
youtube	the youtube source	yt-dlp>=2026.03.17 and deno>=2.8.0, which ships the deno binary yt-dlp runs to solve YouTube's stream challenge
roboflow	the roboflow detector	inference-sdk>=1.3.0
rf-detr	the rf-detr detector	rfdetr>=1.8.0, which declares its own torch
supabase	the supabase sink	supabase>=2.4.0

Run

detstream --config examples/otters.yaml

A config lists feeds and shared sink settings:

feeds:
  - id: monterey-otters
    name: Monterey Sea Otters
    source:   { type: youtube, url: "https://www.youtube.com/watch?v=abbR-Ttd-cA" }
    detector: { type: yolo-world, prompt: otter swimming, confidence_threshold: 0.4 }
    debounce: { enter_frames: 3, exit_frames: 5, cooldown_s: 120, sample_interval_s: 2 }
    sinks: [console, supabase]
sinks:
  supabase: { bucket: thumbnails, detector_label: yolo-world, retention_hours: 3, thumbnail_width: 960 }

Credentials and webhook URLs are configured in .env: DETSTREAM_SUPABASE_URL, DETSTREAM_SUPABASE_KEY, and DETSTREAM_DISCORD_WEBHOOK_URL.

The dataset sink writes the raw peak frame of each sighting to {dir}/{feed_id}/ as JPEG, no box drawn, for building a training set. It needs no extra: dataset: { dir: ./frames, quality: 95 }.

The roboflow detector runs a Workflow you built on Roboflow through its hosted inference API. Install the extra (pip install "detstream[roboflow]"), then give it your workspace and workflow ID (both shown in the Workflow's deploy snippet):

detector:
  type: roboflow
  workspace: cats-workspace-zqd47
  workflow_id: find-otter
  output_key: predictions    # name of the workflow's detection output block
  class_name: otter          # optional: only count this class, omit to accept any
  confidence_threshold: 0.3

The API key comes from ROBOFLOW_API_KEY in .env, or an explicit api_key: in the block. The Workflow must contain an object-detection block whose output is named by output_key. confidence_threshold is sent to the Workflow as its confidence input and used as detstream's sighting cutoff, so the server filters at the same level. Set other Workflow inputs (iou_threshold, max_detections) with an optional parameters: block.

The rf-detr detector runs RF-DETR locally. Install the extra (pip install "detstream[rf-detr]"), which pulls rfdetr and torch. The model uses the GPU when one is visible and falls back to CPU.

detector:
  type: rf-detr
  model: medium              # nano, small, medium (default), or large
  weights: ./otter.pth       # local path or http(s) URL; omit for the COCO-pretrained model
  weights_sha256: ""         # optional: pin a URL download to this digest
  class_name: otter          # optional: only count this class, omit to accept any
  confidence_threshold: 0.4

Pretrained weights are COCO, so the built-in classes are COCO's 80 (no otter). Point weights at a fine-tuned checkpoint to detect your own classes. A URL is downloaded once and cached under ~/.cache/detstream/rfdetr/. For a fine-tuned model whose labels differ from COCO, list them in order with class_names: [otter, ...] so class_name resolves.

A checkpoint is loaded with torch, which unpickles it, so a malicious weights file runs arbitrary code on load. Point weights only at files you trust. For a URL, set weights_sha256 to pin the artifact: a download that does not match the digest is rejected before it is loaded.

Plugins

Built-in components register themselves on import. To add your own detector (an HF model, a cloud API, a fine-tuned ONNX, etc.), register a factory and declare an entry point:

# mypkg/detector.py
from detstream.detectors import detectors, Detection

class MyDetector:
    def detect(self, frame) -> Detection: ...

@detectors.register("my-model")
def _build(config: dict) -> MyDetector:
    return MyDetector(**config)

# mypkg/pyproject.toml
[project.entry-points."detstream.detectors"]
my-model = "mypkg.detector"

After pip install, reference it in config as detector: { type: my-model, ... }. detstream discovers it through the entry point with no change to detstream itself. The same pattern works for detstream.sources and detstream.sinks.

Layout

detstream/
  registry.py     register + create + entry-point discovery
  config.py       FeedConfig / AppConfig, loads YAML
  runner.py       per-feed asyncio loop
  state.py        SightingTracker: hysteresis + cooldown, no I/O
  events.py       SightingStarted / SightingEnded
  sources/        youtube, stream, file_device (+ shared reconnect base)
  detectors/      yolo_world, roboflow, rf_detr
  sinks/          console, supabase, discord, dataset
examples/         otters.yaml, eagles.yaml
tests/            config, registry, sources, state, sinks, detectors

License

MIT. See LICENSE.