Computer Vision Setup

1. Choose a Capture Tool

Tool	Best fit
DS Capture	General DirectShow devices
KS Capture	Compatible Windows Kernel Streaming devices
MF Capture	Media Foundation devices, including common Elgato hardware
Magewell Capture	Magewell Pro capture cards
OpenCV Capture	Simple DirectShow/OpenCV compatibility path
OBS Capture	An OBS scene or source through the Helios OBS integration

Select the device, frame rate, resolution, and format shown by that tool. Start it and confirm Write FPS remains stable. If a requested combination fails, choose a format/resolution the device actually exposes.

2. Configure Compute and Python

For CV Python, choose a valid Python 3.10+ environment under Preferences → Python.

For detection, pose, segmentation, OCR, or TensorRT engine building, choose Preferences → Hardware → Compute GPU. DirectML and TensorRT availability depends on the detected GPU and installed environment.

3. Start a CV Script

CV Python: select a .py script. The panel must show a valid Python environment and active video input.
CV C++: select a supported CV DLL. The panel must show active video input.

Use Restart after changing a script or when you need to reset its state.

4. Inspect the Result

Open OpenGL Display for the hardware-accelerated preview or Video Display for the CPU-rendered preview. Lower the display rate if previewing consumes resources needed by capture or inference.

Use Fuser output when the script draws to the Fuser target and you want those overlays on a selected display.

5. Connect Controller Output

Computer vision does not require controller output unless the script also publishes data or affects an output workflow. When it does:

Step	Action
1	Start the physical input tool if the script reads controller state.
2	Start the compatible processing or output tool that uses the script's result.
3	Start the selected hardware, virtual, or Remote Play destination.
4	Verify the final result in Device Monitor.

Dataset Collection

Use Tools → Dataset Capture to save raw or cropped frames. Time and button triggers work with raw video; class-ID triggers require running inference.

Performance Checklist

Keep capture resolution and FPS no higher than the workflow needs.
Use a tight inference or vision ROI when possible.
Reduce display preview rate before reducing capture rate.
Avoid running multiple capture tools for the same source.
Build TensorRT engines on the GPU and environment where they will be used.