QVideo 3.4.1

PyQt-based framework for integrating video cameras into research applications

QVideo: PyQt support for video cameras

QVideo is a framework for integrating video cameras into PyQt projects for scientific research. It provides a unified, registration-based property system so that every camera backend — USB webcams, GenICam devices, FLIR cameras, Raspberry Pi cameras — is controlled through the same API. Property trees, display widgets, and a digital video recorder are built on top of that abstraction and require no camera-specific code.

Features

• Unified camera API — QCamera subclasses expose adjustable parameters via registerProperty / registerMethod; UI and recording layers consume them without knowing the underlying hardware.
• Auto-built property trees — QCameraTree reads the registered property map and builds a pyqtgraph parameter tree widget automatically.
• Threaded video source — QVideoSource wraps any camera in a QThread and emits newFrame(ndarray) at acquisition rate.
• Composable filter pipeline — VideoFilter / QFilterBank sit between source and display; filters include blur, edge detection, RGB channel selection, sample-and-hold, and statistical median variants.
• Digital video recorder — lossless HDF5 (with timestamps) and OpenCV video formats; QDVRWidget is the composite UI widget.
• Live display — QVideoScreen supports mouse-aware graphical overlays for annotations, regions of interest, and user interaction.

Installation

pip install QVideo

Optional hardware backends

Backend	Extra	Notes
GenICam cameras (Vimba, etc.)	pip install QVideo[genicam]	Requires a vendor-supplied .cti producer file
Raspberry Pi camera	pip install QVideo[picamera]	Requires picamera2

Quick start

import pyqtgraph as pg
from QVideo.cameras.Noise import QNoiseSource
from QVideo.lib import QVideoScreen

pg.mkApp()

source = QNoiseSource()          # synthetic noise — no hardware needed
screen = QVideoScreen()
source.newFrame.connect(screen.setImage)

screen.show()
source.start()
pg.exec()

Replace QNoiseSource with QOpenCVSource, QGenicamSource, etc. to switch hardware — the rest of the code is identical.

Camera backends

Backend	Class	Hardware
cameras/Noise	QNoiseCamera	Synthetic — no hardware required
cameras/OpenCV	QOpenCVCamera	USB webcams via OpenCV
cameras/Genicam	QGenicamCamera	Abstract base for all GenICam/GigE Vision cameras
cameras/Flir	QFlirCamera	FLIR cameras via GenICam (Spinnaker GenTL producer)
cameras/Basler	QBaslerCamera	Basler cameras via GenICam (pylon GenTL producer)
cameras/IDS	QIDSCamera	IDS Imaging cameras via GenICam
cameras/MV	QMVCamera	Any GenICam camera via MATRIX VISION mvGenTLProducer
cameras/Vimbax	QVimbaXCamera	Allied Vision cameras via VimbaX GenTL producer
cameras/Picamera	QPicamera	Raspberry Pi camera module

Writing a new camera backend

Subclass QCamera and implement three methods:

from QVideo.lib import QCamera

class MyCamera(QCamera):

    def _initialize(self) -> bool:
        self.device = open_my_hardware()
        if not self.device:
            return False
        self.registerProperty('exposure',
                              getter=lambda: self.device.get_exposure(),
                              setter=lambda v: self.device.set_exposure(v),
                              ptype=float)
        return True

    def _deinitialize(self) -> None:
        self.device.close()

    def read(self):
        ok, frame = self.device.read_frame()
        return ok, frame

QCameraTree and QVideoSource work with MyCamera immediately — no additional code needed.

Acknowledgements

Work on this project at New York University is supported by the National Science Foundation of the United States under award number DMR-2428983 and by an award from the TAC Program of New York University.