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Rename distribution retina-sdk -> trio-retina (import stays 'retina')
PyPI blocks 'retina-sdk' as too similar to the existing 'retina' project, so the published package is 'trio-retina' (matches the repo + brand). The import name is unchanged: 'import retina'. Updates pyproject name, all README/docs install commands, and the install hints in error messages / examples. Also lands the DX fixes from the usability test: - non-ML on-ramp at the top of the README - examples/rtsp_to_webhook.py + examples/from_supervision.py (runnable, no model) - honest 'no model/GPU' scoping for the forecast/video demos Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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‎README.md‎

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@@ -24,13 +24,13 @@ Think **OpenTelemetry for perception** — it doesn't build the sensors, it norm
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## 💻 install
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From source — a PyPI `retina-sdk` release is landing shortly:
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From source — a PyPI `trio-retina` release is landing shortly:
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```bash
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pip install "retina-sdk @ git+https://github.com/machinefi/trio-retina" # core: numpy only
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pip install "retina-sdk[yolo] @ git+https://github.com/machinefi/trio-retina" # + Ultralytics YOLO adapter
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pip install "retina-sdk[video] @ git+https://github.com/machinefi/trio-retina" # + OpenCV frame source (files / RTSP / webcam)
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pip install "retina-sdk[all] @ git+https://github.com/machinefi/trio-retina" # everything
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pip install "trio-retina @ git+https://github.com/machinefi/trio-retina" # core: numpy only
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pip install "trio-retina[yolo] @ git+https://github.com/machinefi/trio-retina" # + Ultralytics YOLO adapter
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pip install "trio-retina[video] @ git+https://github.com/machinefi/trio-retina" # + OpenCV frame source (files / RTSP / webcam)
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pip install "trio-retina[all] @ git+https://github.com/machinefi/trio-retina" # everything
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```
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## 🔥 quickstart
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python examples/from_supervision.py # ingest a Roboflow sv.Detections pipeline
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```
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Real-footage / dynamics demos need a clip and the extras — `pip install 'retina-sdk[all]'`:
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Real-footage / dynamics demos need a clip and the extras — `pip install 'trio-retina[all]'`:
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```bash
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python examples/yolo_video.py v.mp4 # YOLO on a video file
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| | DeepStream / Holoscan | **Retina** |
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|---|---|---|
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| Install | CUDA + TensorRT + containers | `pip install retina-sdk` |
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| Install | CUDA + TensorRT + containers | `pip install trio-retina` |
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| Hardware | NVIDIA / Jetson locked | any machine — CPU is fine |
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| Model | tied to the NV stack | **bring any model** (or none) |
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| Shape | a platform you build *inside* | a library you `import` |

‎docs/index.md‎

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@@ -8,13 +8,13 @@ Think **OpenTelemetry for perception** — it doesn't build the sensors, it norm
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## Install
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From source — a PyPI `retina-sdk` release is landing shortly:
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From source — a PyPI `trio-retina` release is landing shortly:
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```bash
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pip install "retina-sdk @ git+https://github.com/machinefi/trio-retina" # core: numpy only
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pip install "retina-sdk[yolo] @ git+https://github.com/machinefi/trio-retina" # + Ultralytics YOLO adapter
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pip install "retina-sdk[video] @ git+https://github.com/machinefi/trio-retina" # + OpenCV frame source (files / RTSP / webcam)
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pip install "retina-sdk[all] @ git+https://github.com/machinefi/trio-retina" # everything
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pip install "trio-retina @ git+https://github.com/machinefi/trio-retina" # core: numpy only
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pip install "trio-retina[yolo] @ git+https://github.com/machinefi/trio-retina" # + Ultralytics YOLO adapter
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pip install "trio-retina[video] @ git+https://github.com/machinefi/trio-retina" # + OpenCV frame source (files / RTSP / webcam)
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pip install "trio-retina[all] @ git+https://github.com/machinefi/trio-retina" # everything
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```
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## Quickstart

‎examples/forecast/forecast_video.py‎

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but is far from perfect — that gap is what a learned, interaction-aware dynamics
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model is for.
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pip install 'retina-sdk[yolo,video]'
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pip install 'trio-retina[yolo,video]'
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python examples/forecast/forecast_video.py /tmp/demo.mp4
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"""
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‎examples/rtsp_to_webhook.py‎

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@@ -43,7 +43,7 @@ def main(src: str | None, webhook_url: str | None) -> None:
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detector = ScriptedDetector()
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frames = [(np.zeros((100, 100, 3), np.uint8), float(i)) for i in range(18)]
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else:
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from retina import YoloDetector # needs retina-sdk[yolo]
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from retina import YoloDetector # needs trio-retina[yolo]
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detector = YoloDetector("yolo11n.pt", classes={"person"})
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frames = video_frames(src, live=True) # RTSP / HLS / webcam, wall-clock t

‎examples/yolo_video.py‎

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"""Real-model demo: YOLO on a video file -> events to JSONL.
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pip install 'retina-sdk[all]'
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pip install 'trio-retina[all]'
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python examples/yolo_video.py path/to/video.mp4
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Same code shape as quickstart — only the detector and source change. That's the

‎pyproject.toml‎

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build-backend = "hatchling.build"
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[project]
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name = "retina-sdk"
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name = "trio-retina"
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version = "0.1.0"
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description = "Model-agnostic computer-vision pipeline: turn any detector (YOLO, VLM, Grounding DINO) into structured tracking events — zone intrusion, line-crossing, dwell, counting — over video or RTSP. The state layer for world models."
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readme = "README.md"

‎retina/detect.py‎

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class YoloDetector(Pipeable):
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"""Optional Ultralytics YOLO adapter. `pip install retina-sdk[yolo]`.
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"""Optional Ultralytics YOLO adapter. `pip install trio-retina[yolo]`.
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Loads any Ultralytics weights — YOLOv5/8/9/10/11/12, YOLO-World, RT-DETR —
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so swapping models is just a different weights string. Not imported unless
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from ultralytics import YOLO
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except ImportError as e: # pragma: no cover - exercised only with extra
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raise ImportError(
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"YoloDetector needs ultralytics. Install with: pip install 'retina-sdk[yolo]'"
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"YoloDetector needs ultralytics. Install with: pip install 'trio-retina[yolo]'"
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) from e
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self._model = YOLO(weights)
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# Open-vocabulary: with a YOLO-World weights file, `vocab` sets the
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class GroundingDinoDetector(Pipeable):
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"""Open-vocabulary detection from a text prompt via Grounding DINO (HF
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transformers). `pip install 'retina-sdk[grounding]'`. Detects any classes you
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transformers). `pip install 'trio-retina[grounding]'`. Detects any classes you
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name — no training. Heavy (torch); not imported unless instantiated."""
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def __init__(
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except ImportError as e: # pragma: no cover - exercised only with extra
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raise ImportError(
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"GroundingDinoDetector needs transformers+torch. "
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"Install with: pip install 'retina-sdk[grounding]'"
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"Install with: pip install 'trio-retina[grounding]'"
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) from e
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self._processor = AutoProcessor.from_pretrained(model_id)
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self._model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id)

‎retina/sources.py‎

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A source is just an iterable of `(frame, timestamp)` pairs, so the core never
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depends on OpenCV — you can feed it a list of numpy arrays in tests. The
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`video_frames` helper is an optional convenience that uses OpenCV (install with
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`pip install 'retina-sdk[video]'`) for files / RTSP / webcam.
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`pip install 'trio-retina[video]'`) for files / RTSP / webcam.
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"""
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from __future__ import annotations
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import cv2
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except ImportError as e: # pragma: no cover - exercised only with extra
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raise ImportError(
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"video_frames needs OpenCV. Install with: pip install 'retina-sdk[video]'"
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"video_frames needs OpenCV. Install with: pip install 'trio-retina[video]'"
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) from e
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cap = cv2.VideoCapture(source)

‎retina/track.py‎

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class NorfairTracker(Pipeable):
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"""Norfair adapter — pure-Python Kalman tracking with re-association, better
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ID stability through occlusion than IoUTracker. `pip install 'retina-sdk[norfair]'`.
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ID stability through occlusion than IoUTracker. `pip install 'trio-retina[norfair]'`.
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Surfaces only tracks detected *this* frame (coasting/occluded ones are kept
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internally for re-association but not returned, so occupancy/dwell stay honest)."""
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from norfair import Tracker as _NfTracker
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except ImportError as e: # pragma: no cover - exercised only with extra
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"NorfairTracker needs norfair. Install with: pip install 'retina-sdk[norfair]'"
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"NorfairTracker needs norfair. Install with: pip install 'trio-retina[norfair]'"
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self._nf = _NfTracker(
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distance_function="euclidean",

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