python-camera-manager-directshow 0.2.0

Python camera manager with DirectShow capability discovery and .NET bridge integration

Python Camera Manager (DirectShow Bridge)

Python-first camera discovery, control, and live preview built on top of a .NET DirectShow wrapper.

This project was created to solve a common limitation in OpenCV camera workflows: while OpenCV can open cameras quickly, it does not provide a reliable, discoverable, device-specific model of control capabilities (valid ranges, step sizes, defaults, and auto/manual support) across many webcams.

Highlights

• Discover connected cameras.
• Query supported formats per device.
• Query control ranges and flags (min/max/step/default/current, supported, auto supported, auto enabled).
• Open camera streams through DirectShow from Python.
• Change auto/manual modes and set property values.
• Preview frames in a PyQt5 GUI.
• Decode MJPG using PyTurboJPEG first (when available), with OpenCV fallback.

Architecture

• .NET layer in runtime/dotnet:
  • DirectShowLib.dll
  • DirectShowLibWrapper.dll
• Python bridge layer:
  • camera/camera_inspector_bridge.py
  • camera/camera_device_bridge.py
• Python facade/API:
  • camera/camera_manager.py
• GUI:
  • GUI/main_GUI.py (PyQt5)

camera/camera_manager.py exposes Python-native objects (NamedTuple, dict, list) so consumers do not need to deal with .NET object APIs directly.

Requirements

• Windows (DirectShow)
• Python 3.10+
• .NET runtime compatible with the shipped wrapper DLLs
• DirectShow-capable camera

Python dependencies:

• pythonnet
• opencv-python
• Pillow
• PyQt5

Optional for faster MJPG decode:

• PyTurboJPEG==2.2.0
• libjpeg-turbo native DLL (turbojpeg.dll or libturbojpeg.dll)

PyTurboJPEG is not required for base functionality and is intentionally not included in default package dependencies.

Setup

py -m venv .venv
.venv\Scripts\activate
python -m pip install -U pip
python -m pip install -e .

If you want accelerated MJPG decode:

python -m pip install PyTurboJPEG==2.2.0

Without PyTurboJPEG, MJPG decoding falls back to OpenCV automatically.

Notes:

• Do not install the unrelated package named turbojpeg (different project, different API).
• If needed, point to your native turbojpeg DLL with TURBOJPEG_LIB_PATH.

Run

python -m app.main

Alternative:

python app/main.py

If installed as a package entry point:

camera-manager

Quick API Example

from camera.camera_manager import Camera

devices = Camera.get_connected_cameras(get_formats=True, get_ranges=True)
if not devices:
    raise RuntimeError("No camera detected")

selected = devices[0]
fmt = selected.formats[0]

cam = Camera(debug_logging=False)
ok = cam.open(selected.path, fmt, request_rgb24_conversion=False)
if not ok:
    raise RuntimeError("Failed to open camera")

try:
    exposure = cam.property_ranges.get("Exposure")
    if exposure and exposure.property_supported:
        cam.set_property_auto_mode("Exposure", False)
        cam.set_property_value("Exposure", int(exposure.default))

    success, frame = cam.get_frame()
finally:
    cam.close()

Decoder Behavior (MJPG)

For MJPG/MJPEG formats, decode priority is:

1. PyTurboJPEG (if import + native DLL initialization succeeds)
2. OpenCV imdecode
3. Unavailable (frame skipped with error log)

For uncompressed formats (RGB24, BGR24, GRAY8, etc.), decoding does not require MJPG decoders.

Troubleshooting

PyQt5 import error

If you see ModuleNotFoundError: No module named 'PyQt5', verify you are using the project venv and install dependencies:

.venv\Scripts\python.exe -m pip install -e .

Pylance false positives with Qt/pythonnet

Some warnings are static-analysis false positives due to dynamic runtime APIs in PyQt/pythonnet. Use workspace-level Pylance settings in .vscode/settings.json to tune diagnostic severity as needed.

TurboJPEG confusion

If the wrong package was installed before, clean and reinstall:

.venv\Scripts\python.exe -m pip uninstall -y turbojpeg
.venv\Scripts\python.exe -m pip install PyTurboJPEG==2.2.0

Maintainer Release Checklist

Before publishing an update:

1. Bump [project].version in pyproject.toml.
2. Keep requirements.txt and pyproject.toml dependencies aligned.
3. Verify project.urls in pyproject.toml point to the correct repository.
4. Confirm runtime DLLs are present under runtime/dotnet.
5. Sanity test end-to-end:
   • camera enumeration
   • open/close
   • frame preview
   • property set/get
6. Update README and changelog notes for behavior/API changes.

Companion .NET Repository

The current Python project repository is:

• https://github.com/LBlokshtein/DirectShowLibWrapper

The .NET wrapper source code is maintained separately; this repository focuses on Python API ergonomics and application usage.

License

MIT. See LICENSE.