QVideo 3.8.0

PyQt-based framework for integrating video cameras into research applications

QVideo: Qt framework for video cameras

QVideo is a framework for integrating video cameras into Qt projects (PyQt5, PyQt6, or PySide) 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, binary threshold, and blob coloring.
• Graphical overlays — QTrackpyWidget for live particle tracking and QYoloWidget for real-time object detection render markers directly on QVideoScreen; composite mode lets the DVR record the annotated scene.
• 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.mkQApp()

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.

Filters

Filter	Class	Description
Gaussian blur	QBlurFilter	Smoothing with adjustable kernel radius
Canny edge detection	QEdgeFilter	Edge map with configurable thresholds
RGB channel selection	QRGBFilter	Pass one or more color channels
Sample and hold	QSampleHold	Background normalization via a sampled median estimate
Binary threshold	QThresholdFilter	Convert to binary mask at a configurable level
Blob coloring	QBlobFilter	Color connected foreground regions with distinct hues
YOLO annotation	QYOLOFilter	Annotate frames with YOLO bounding boxes (requires ultralytics)

Overlays

Overlays render analysis results directly on the live QVideoScreen and require pip install QVideo[overlays].

Overlay	Class	Description
Particle tracking	QTrackpyWidget	Live particle detection and tracking using trackpy
Object detection	QYoloWidget	Real-time bounding-box detection using YOLO (ultralytics)

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.