200 lines
9.1 KiB
Markdown
200 lines
9.1 KiB
Markdown
# camera-webui
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A camera service for single-board computers: a live video feed served through a small web UI, with
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face recognition as a later stage.
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**Target hardware is a Jetson Orin Nano.** A Raspberry Pi 5 is the development and test platform —
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convenient, and available first — but it is not where this is meant to end up. That distinction is
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load-bearing: the two boards do not share a camera stack, and only one of them can realistically run
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face recognition on a live stream.
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## Platforms
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| | Jetson Orin Nano | Raspberry Pi 5 |
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| Role | **target** | test / development |
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| Arch | aarch64 | aarch64 |
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| Stack | L4T / JetPack, CUDA, TensorRT | Raspberry Pi OS (Debian 12) |
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| Camera path | GStreamer / Argus (`nvarguscamerasrc`) for CSI Bayer sensors, V4L2 for USB | libcamera / `rpicam` / `picamera2` |
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| Inference | GPU + DLA | CPU only |
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Recorded for `mikkeli-orin-nano-2` (`192.168.2.209`): L4T 36.4.4, CUDA 12.6, TensorRT 10.7. **Confirm
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against whichever unit is actually used** — there is more than one Orin here, and the camera is going
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to whichever one gets it wired first.
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## ⚠ The camera stacks are not the same, and that is the main design constraint
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This is the thing to get right early, because retrofitting it is expensive:
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- **Pi 5** uses libcamera. `picamera2` is the idiomatic Python entry point.
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- **Orin Nano** uses GStreamer with NVIDIA's Argus stack (`nvarguscamerasrc`) for CSI Bayer
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sensors; plain V4L2 for USB/UVC cameras.
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- Code written directly against `picamera2` **will not run on the Orin at all.**
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So: **put capture behind an interface** with one backend per platform, and let everything above it —
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streaming, the web UI, recognition — depend only on "a source of frames". Pick the backend at
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runtime from what the machine actually has, not from a build flag.
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The same applies to inference. On the Orin, face recognition should go through TensorRT and can use
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the GPU or DLA. On the Pi 5 it is CPU-only and will not keep up with a live stream at full
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resolution. Treat the Pi as proof the *pipeline* works, never as evidence the *performance* works.
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## Current state
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**Stages 1-4 are complete.** The service is running as a systemd unit on the Orin and serving a live
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camera feed through a web browser at `http://<orin-ip>:5000/`.
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### Camera: working
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An IMX219 on CAM0 is live. The backend uses GStreamer (`nvarguscamerasrc` → `nvvidconv` → NV12 →
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appsink) and converts frames to RGB24 in Python via numpy. Resolution is 1920×1080 at 30fps.
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### ⚠ The Orin needed a device-tree overlay — it does NOT auto-detect
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This cost a whole debugging session, because the symptom is identical to a dead camera: no sensor
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lines in `dmesg`, nothing on i2c.
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**The Pi auto-detects cameras. The Jetson does not.** `camera_auto_detect=1` has no equivalent — the
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sensor's overlay must be selected explicitly, and until it is, a perfectly good camera is invisible:
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```bash
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sudo /opt/nvidia/jetson-io/config-by-hardware.py -l # list modules per header
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sudo /opt/nvidia/jetson-io/config-by-hardware.py -n 2="Camera IMX219-A" # header 2 = 24-pin CSI
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sudo reboot # required
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```
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⚠ **`-n` needs the header number** (`2=` for the CSI connector). Without it the tool defaults to the
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40-pin header and fails with `No configuration found for Camera IMX219-A on Jetson 40pin Header!`,
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which reads like the module is unsupported.
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Naming: **`-A` is CAM0, `-C` is CAM1.** `extlinux.conf` is backed up before the change, and the
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result is one `OVERLAYS` line — remove it and reboot to undo.
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⚠ **`v4l2-ctl` is not installed by default** (`sudo apt install v4l-utils`), and its absence reports
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as `command not found`, which is easy to misread as "no camera".
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### The Pi 5 camera is still dead
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Separate fault, genuinely hardware: not detected, traced to a bad cable with the module possibly
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damaged too. **Do not generalise the Orin's fix to it** — the Pi's auto-detect had nothing to
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configure, so an overlay is not the answer there.
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Evidence, for contrast with the Orin's *configuration* fault above:
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```console
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$ rpicam-hello --list-cameras
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No cameras available!
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```
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The imaging pipeline was up (`pisp_be` loaded, `/dev/media0-2` present), but
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`/sys/bus/i2c/devices/` held only `i2c-13` and `i2c-14` — **no camera i2c bus and no CFE bound** —
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and `dmesg` had no sensor probe lines. On the Pi, `camera_auto_detect=1` loads an overlay when it
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finds something, so an absent bus means the firmware found nothing to probe. Unchanged across a
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reboot, with the module's IR LEDs lit.
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⚠ **Lit IR LEDs prove power, not a working sensor.** The illuminator is wired to the 3.3V rail
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independently of the i2c and CSI lanes, so it lights whenever the ribbon is seated well enough to
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carry power — while a creased or partly-seated cable can still have broken exactly the i2c traces
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detection depends on. Which is what happened.
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⚠ **Do not treat `/dev/video*` as evidence of a camera.** Those nodes exist on both boards with
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nothing attached — on the Pi 5 they are the codec and ISP blocks, on the Orin
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`tegra-camrtc-ca`/`/dev/media0` is the VI platform block.
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### Checking a connection
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```bash
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# Pi 5
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rpicam-hello --list-cameras
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dmesg | grep -iE 'imx|ov5647|cfe'
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ls /sys/bus/i2c/devices/ # a camera bus should appear
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# Orin Nano
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grep -i OVERLAYS /boot/extlinux/extlinux.conf # ⚠ CHECK THIS FIRST — no line, no camera
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v4l2-ctl --list-devices
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dmesg | grep -iE 'imx|camera|argus|vi:'
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```
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On the Orin, check the overlay **before** suspecting hardware. An unconfigured Jetson and a dead
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camera look exactly alike.
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Cable notes worth keeping, since they cost a module here:
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- The **Pi 5 uses the narrow 22-pin FPC**; Pi 4-era modules ship with a **15-pin** cable and need the
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adapter. Both Pi 5 connectors (`CAM/DISP 0` and `1`) are dual-purpose, so either accepts a camera.
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- **Jetson carrier boards use their own pinout** — a cable that fits a Pi does not necessarily carry
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the same signals. Match the cable to the carrier, not to the sensor.
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- Ribbon orientation differs at each end. Always power off first.
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## Application structure
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```
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src/camera_webui/
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├── app.py # Flask app, backend auto-detect, routes
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├── stream.py # MJPEG streaming (multipart/x-mixed-replace)
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├── templates/index.html # Web UI (dark, full-viewport video feed)
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└── camera/
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├── __init__.py
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├── interface.py # Abstract Camera base class + FrameMeta
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├── gstreamer_backend.py # Jetson Orin — nvarguscamerasrc via GStreamer
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├── v4l2_backend.py # USB cameras / generic V4L2 devices
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├── picamera2_backend.py # Raspberry Pi — picamera2 (for when camera is replaced)
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├── null_backend.py # Blank frames for testing without hardware
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└── utils.py # JPEG encoding helpers (numpy + Pillow)
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```
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### Backend auto-detection
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On startup the app tries backends in order:
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1. **picamera2** (Pi 5) — fails silently if library absent
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2. **GStreamer / Argus** (Orin) — fails silently if GStreamer unavailable
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3. **V4L2** (USB / generic) — fails silently if no device supports it
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4. **NullCamera** — produces blank frames as a safe fallback
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Each failure is logged at INFO level, so you can see which path was taken.
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### Systemd service
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The service is installed at `/etc/systemd/system/camera-webui.service`:
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```bash
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sudo systemctl start camera-webui # Start now
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sudo systemctl stop camera-webui # Stop
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sudo systemctl restart camera-webui # Restart (e.g. after code changes)
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sudo systemctl status camera-webui # Check status
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sudo journalctl -u camera-webui -f # Live logs
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```
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The service depends on `nvargus-daemon.service` and restarts automatically on failure.
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## Planned stages
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1. ~~**Capture** — get a sensor detected on the target, grab a still, establish resolution and~~
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format.~~ **Done on the Orin** (IMX219, GStreamer/Argus, 1920×1080@30fps).
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2. ~~**Capture abstraction** — one interface, a backend per platform, chosen at runtime.~~ **Done**.
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3. ~~**Live feed** — MJPEG first, because it works in any browser with no negotiation. WebRTC later**
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only if latency demands it.~~ **Done** (MJPEG at `/feed`).
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4. ~~**Web UI** — one page: live feed and basic controls.~~ **Done**.
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5. **Face recognition** — detection before recognition, on downscaled frames, off the capture thread.
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TensorRT on the Orin.
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Each stage should be usable on its own before the next begins.
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## Development
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Codex runs on the boards themselves, so development happens on the target rather than cross-compiled
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or deployed. The model endpoint and MCP gateway live on `halogen` and are reachable from both boards
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by name. See [AGENTS.md](AGENTS.md).
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## Dependencies
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Installed via `uv sync` in the project virtual environment:
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- **flask** — web server
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- **numpy** — array manipulation (NV12→RGB conversion)
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- **pillow** — JPEG encoding
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GStreamer is provided by the system (Ubuntu 22.04 on L4T) — `libgstreamer1.0-0` and the NVIDIA
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GStreamer plugins (`gstreamer1.0-plugins-bad`, `gstreamer1.0-nvvconv-plugin`, etc.). No pip
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packages for GStreamer.
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