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Mikkeli 55b7a6f6d9 Broaden scope: the Orin Nano is the target, the Pi 5 was the test rig

Written as a Pi 5 project because that is where the first camera went. That was
never the destination — the camera is meant for an Orin Nano, and the Pi was
convenient and available first.

⚠ This is not a wording change. The two boards do not share a camera stack:
libcamera/picamera2 on the Pi, V4L2/GStreamer with Argus for CSI Bayer sensors
on the Jetson. Code written directly against picamera2 does not run on the Orin
at all. So capture goes behind an interface with a backend per platform, chosen
at runtime from what the hardware reports, and everything above it depends only
on "a source of frames". Recorded as the main design constraint rather than
left to be discovered when the code moves.

The same split decides where face recognition can live: TensorRT on GPU/DLA on
the Orin, CPU-only on the Pi. The Pi proves the pipeline, never the
performance, and AGENTS.md now requires a measurement to say which board it
came from.

Also generalises the camera-not-detected section to cover both boards, and
keeps the cable notes that cost a module: the Pi 5's 22-pin FPC versus 15-pin
Pi 4-era cables, and that Jetson carriers use their own pinout, so a cable
fitting a Pi does not necessarily carry the same signals.

Status corrected to "no camera connected anywhere" — the first module's cable
is confirmed faulty and the module may be damaged; a second is being tried on
an Orin.

2026-08-05 23:20:55 +09:00

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camera-webui

A camera service for single-board computers: a live video feed served through a small web UI, with face recognition as a later stage.

Target hardware is a Jetson Orin Nano. A Raspberry Pi 5 is the development and test platform — convenient, and available first — but it is not where this is meant to end up. That distinction is load-bearing: the two boards do not share a camera stack, and only one of them can realistically run face recognition on a live stream.

Platforms

	Jetson Orin Nano	Raspberry Pi 5
Role	target	test / development
Arch	aarch64	aarch64
Stack	L4T / JetPack, CUDA, TensorRT	Raspberry Pi OS (Debian 12)
Camera path	V4L2 / GStreamer (Argus for Bayer CSI sensors)	libcamera / `rpicam` / `picamera2`
Inference	GPU + DLA	CPU only

Recorded for mikkeli-orin-nano-2 (192.168.2.209): L4T 36.4.4, CUDA 12.6, TensorRT 10.7. Confirm against whichever unit is actually used — there is more than one Orin here, and the camera is going to whichever one gets it wired first.

⚠ The camera stacks are not the same, and that is the main design constraint

This is the thing to get right early, because retrofitting it is expensive:

Pi 5 uses libcamera. picamera2 is the idiomatic Python entry point.
Orin Nano uses V4L2 and GStreamer. CSI Bayer sensors go through NVIDIA's Argus stack (nvarguscamerasrc); USB/UVC cameras are plain V4L2.
Code written directly against picamera2 will not run on the Orin at all.

So: put capture behind an interface with one backend per platform, and let everything above it — streaming, the web UI, recognition — depend only on "a source of frames". Pick the backend at runtime from what the machine actually has, not from a build flag.

The same applies to inference. On the Orin, face recognition should go through TensorRT and can use the GPU or DLA. On the Pi 5 it is CPU-only and will not keep up with a live stream at full resolution. Treat the Pi as proof the pipeline works, never as evidence the performance works.

⚠ Current state: no camera is connected anywhere

Nothing is built yet, and no working camera has been attached to either board.

The first module, on the Pi 5, was not detected at all:

$ rpicam-hello --list-cameras
No cameras available!

Diagnosed to hardware, not software. The imaging pipeline was up (pisp_be loaded, /dev/media0-2 present), but /sys/bus/i2c/devices/ held only i2c-13 and i2c-14 — no camera i2c bus was instantiated and no CFE bound, and dmesg had no sensor probe lines. camera_auto_detect=1 loads a sensor overlay when it finds something, so an absent bus means the firmware found nothing to probe. Unchanged across a reboot.

Confirmed a cable fault; the module itself may also be damaged. A second module is being tried on an Orin Nano.

⚠ Do not treat /dev/video* as evidence of a camera. Those nodes exist on both boards with nothing attached — on the Pi 5 they are the codec and ISP blocks.

Checking a connection

# Pi 5
rpicam-hello --list-cameras
dmesg | grep -iE 'imx|ov5647|cfe'
ls /sys/bus/i2c/devices/          # a camera bus should appear

# Orin Nano
v4l2-ctl --list-devices
dmesg | grep -iE 'imx|camera|argus|vi:'

Cable notes worth keeping, since they cost a module here:

The Pi 5 uses the narrow 22-pin FPC; Pi 4-era modules ship with a 15-pin cable and need the adapter. Both Pi 5 connectors (CAM/DISP 0 and 1) are dual-purpose, so either accepts a camera.
Jetson carrier boards use their own pinout — a cable that fits a Pi does not necessarily carry the same signals. Match the cable to the carrier, not to the sensor.
Ribbon orientation differs at each end. Always power off first.

Planned stages

Capture — get a sensor detected on the target, grab a still, establish resolution and format.
Capture abstraction — one interface, a backend per platform, chosen at runtime.
Live feed — MJPEG first, because it works in any browser with no negotiation. WebRTC later only if latency demands it.
Web UI — one page: live feed and basic controls.
Face recognition — detection before recognition, on downscaled frames, off the capture thread. TensorRT on the Orin.

Each stage should be usable on its own before the next begins.

Development

Codex runs on the boards themselves, so development happens on the target rather than cross-compiled or deployed. The model endpoint and MCP gateway live on halogen and are reachable from both boards by name. See AGENTS.md.

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