visiongraph 1.1.0.1

Visiongraph is a high level computer vision framework.

Visiongraph

Visiongraph is a high level computer vision framework that includes predefined modules to quickly create and run algorithms on images. It is based on opencv and includes other computer vision frameworks like Intel openVINO and Google MediaPipe.

Here an example on how to start a webcam capture and display the image:

from visiongraph import vg

vg.create_graph(vg.VideoCaptureInput()).then(vg.ImagePreview()).open()

Get started with visiongraph by reading the documentation.

Installation

Visiongraph supports Python 3.10 and 3.11. Other versions may also work, but are not officially supported. Usually this is a third-party dependency problem: for example, pyrealsense2 does not have wheel packages for 3.12.

To install visiongraph with all dependencies call pip like this:

pip install "visiongraph[all]"

It is also possible to only install certain packages depending on your needs (recommended):

# example on how to install realsense and openvino support only
pip install "visiongraph[realsense, openvino]"

Please read more about the extra packages in the documentation.

Optional Mediapipe Support

Visiongraph can integrate Google’s MediaPipe for advanced hand, face and object tracking pipelines. Unfortunately, the official PyPI MediaPipe wheels declare a strict dependency on numpy<2.0, which prevents installation alongside NumPy 2.x, even though most functionality works fine with NumPy 2.0 and above. To work around this limitation, we maintain a custom mediapipe-numpy2 build that removes the <2.0 pin.

When you install with the mediapipe extra, pip will automatically fetch the matching patched wheel for your OS and Python version.

Alternative: Use the Official MediaPipe Release

If you’re happy to stick with NumPy <2.0, you can skip our custom package entirely and install the upstream MediaPipe wheel from PyPI:

pip install visiongraph mediapipe

This will install Visiongraph plus the official mediapipe package (which requires numpy<2.0). Just make sure your environment’s NumPy version is below 2.0 when using this route.

Examples

To demonstrate the possibilities of visiongraph there are already implemented examples ready for you to try out. Here is a list of the current examples:

• SimpleVisionGraph - SSD object detection & tracking of live webcam input with 5 lines of code.
• VisionGraphExample - A face detection and tracking example with custom events.
• InputExample - A basic input example that determines the center if possible.
• RealSenseDepthExample - Display the RealSense or Azure Kinect depth map.
• FaceDetectionExample - A face detection pipeline example.
• FindFaceExample - A face recognition example to find a target face.
• CascadeFaceDetectionExample - A face detection pipeline that also predicts other feature points of the face.
• HandDetectionExample - A hand detection pipeline example.
• PoseEstimationExample - A pose estimation pipeline which annotates the generic pose keypoints.
• ProjectedPoseExample - Project the pose estimation into 3d space with the RealSense camera.
• ObjectDetectionExample - An object detection & tracking example.
• InstanceSegmentationExample - Intance Segmentation based on COCO80 dataset.
• InpaintExample - GAN based inpainting example.
• MidasDepthExample - Realtime depth prediction with the midas-small network.
• RGBDSmoother - Smooth RGB-D depth map videos with a one-euro filter per pixel.
• FaceMeshVVADExample.py - Detect voice activation by landmark sequence classification.

There are even more examples where visiongraph is currently in use:

• Spout/Syphon RGB-D Example - Share RGB-D images over spout or syphon.
• WebRTC Input - WebRTC input example for visiongraph

Development

To develop on visiongraph it is recommended to clone this repository and install the dependencies like this. First install the uv package manager.

# in the visiongraph directory install all dependencies
uv sync --all-extras --dev --group docs

Build

To build a new wheel package of visiongraph run the following command in the root directory. Please find the wheel and source distribution in ./dist.

uv run python setup.py generate_init
uv build

Docs

To generate the documentation, use the following commands.

# create documentation into "./docs
uv run python setup.py doc

# launch pdoc webserver
uv run python setup.py doc --launch

Dependencies

Parts of these libraries are directly included and adapted to work with visiongraph.

• motpy - simple multi object tracking library (MIT License)
• motrackers - Multi-object trackers in Python (MIT License)
• OneEuroFilter-Numpy - (MIT License)

Here you can find a list of the dependencies of visiongraph and their licence:

depthai               MIT License
faiss-cpu             MIT License
filterpy              MIT License
mediapipe             Apache License 2.0
moviepy               MIT License
numba                 BSD License
onnxruntime           MIT License
onnxruntime-directml  MIT License
onnxruntime-gpu       MIT License
opencv-python         Apache License 2.0
openvino              Apache License 2.0
pyk4a-bundle          MIT License
pyopengl              BSD License
pyrealsense2          Apache License 2.0
pyrealsense2-macosx   Apache License 2.0
requests              Apache License 2.0
scipy                 MIT License
SpoutGL               BSD License
syphon-python         MIT License
tqdm                  MIT License
vector                BSD License
vidgear               Apache License 2.0
wheel                 MIT License

For more information about the dependencies have a look at the requirements.txt.

Please note that some models (such as Ultralytics YOLOv8 and YOLOv11) have specific licences (AGPLv3). Always check the model licence before using the model.

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Copyright (c) 2025 Florian Bruggisser