#  Visiongraph [](https://pypi.org/project/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](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html) and [Google MediaPipe](https://google.github.io/mediapipe/).
Here an example on how to start a webcam capture and display the image:
```python
import visiongraph as vg
vg.create_graph(vg.VideoCaptureInput()).then(vg.ImagePreview()).open()
```
The main goal is to implement a platform independent and high performance framework for day-to-day computer vision tasks.
## Installation
Visiongraph supports python `3.9` and `3.10`. Other versions might work as well but are not officially supported.
To install visiongraph with all dependencies call [pip](https://pypi.org/project/pip/) like this:
```bash
pip install "visiongraph[all]"
```
🚨 *Please note that visiongraph is in an early alpha phase and the API will still undergo changes.*
It is also possible to only install certain packages depending on your needs:
```bash
pip install "visiongraph[realsense, openvino, mediapipe, onnx, media, azure, numba, opencv-contrib]"
```
### Development
To develop visiograph itself it is recommended to clone this repository and install the dependencies like this:
```bash
# in the visiongraph directory
pip install -e ".[all]"
```
### Build
To build a new wheel package of visiongraph run the following command in the root directory.
```bash
python setup.py bdist_wheel
```
## Examples
To demonstrate the possibilities of visiongraph there are already implemented [examples](examples) ready for you to try out. Here is a list of the current examples:
- [SimpleVisionGraph](examples/SimpleVisionGraph.py) - SSD object detection & tracking of live webcam input with `5` lines of code.
- [VisionGraphExample](examples/VisionGraphExample.py) - A face detection and tracking example with custom events.
- [InputExample](examples/InputExample.py) - A basic input example that determines the center if possible.
- [RealSenseDepthExample](examples/DepthCameraExample.py) - Display the RealSense or Azure Kinect depth map.
- [FaceDetectionExample](examples/FaceDetectionExample.py) - A face detection pipeline example.
- [FindFaceExample](examples/FindFaceExample.py) - A face recognition example to find a target face.
- [CascadeFaceDetectionExample](examples/CascadeFaceDetectionExample.py) - A face detection pipeline that also predicts other feature points of the face.
- [HandDetectionExample](examples/HandDetectionExample.py) - A hand detection pipeline example.
- [PoseEstimationExample](examples/PoseEstimationExample.py) - A pose estimation pipeline which annotates the generic pose keypoints.
- [ProjectedPoseExample](examples/ProjectedPoseExample.py) - Project the pose estimation into 3d space with the RealSense camera.
- [ObjectDetectionExample](examples/ObjectDetectionExample.py) - An object detection & tracking example.
- [InstanceSegmentationExample](examples/InstanceSegmentationExample.py) - Intance Segmentation based on COCO80 dataset.
- [InpaintExample](examples/InpaintExample.py) - GAN based inpainting example.
- [MidasDepthExample](examples/MidasDepthExample.py) - Realtime depth prediction with the [midas-small](https://github.com/isl-org/MiDaS) network.
- [RGBDSmoother](examples/RGBDSmoother.py) - Smooth RGB-D depth map videos with a one-euro filter per pixel.
There are even more examples where visiongraph is currently in use:
- [Spout/Syphon RGB-D Example](https://github.com/cansik/spout-rgbd-example) - Share RGB-D images over spout or syphon.
- [WebRTC Input](https://github.com/cansik/visiongraph-webrtc) - WebRTC input example for visiongraph
## Documentation
This documentation is intended to provide an overview of the framework. A full documentation will be available later.
### Graph
The core component of visiongraph is the [BaseGraph](https://github.com/cansik/visiongraph/blob/main/visiongraph/BaseGraph.py) class. It contains and handles all the nodes of the graph. A BaseGraph can run on the same thread as called or a new thread or process. The nodes in the graph are just a list, the graph itself is created by nesting nodes into each other.
#### Graph Node
A [GraphNode](https://github.com/cansik/visiongraph/blob/main/visiongraph/GraphNode.py) is a single step in the graph. It has a input and output type and processes the data within the `process()` method.
#### Graph Builder
The graph builder helps to create new graphs on a single line in python. It creates a [VisionGraph](https://github.com/cansik/visiongraph/blob/main/visiongraph/VisionGraph.py) object which is a child of the BaseGraph. The following code snippet is an example of the graph builder which creates a smooth pose estimation graph.
```python
import visiongraph as vg
graph = (vg.create_graph(name="Smooth Pose Estimation",
input_node=vg.VideoCaptureInput(0),
handle_signals=True)
.apply(ssd=vg.sequence(vg.OpenPoseEstimator.create(), vg.MotpyTracker(), vg.LandmarkSmoothFilter()),
image=vg.passthrough())
.then(vg.ResultAnnotator(image="image"), vg.ImagePreview())
)
graph.open()
```
### Input
Supported are image, video, webcam, RealSense and Azure Kinect input types.
### Estimator
Usually an estimator is a graph node which takes an image as an input and estimates an information about the content. This could be a pose estimation or a face detection. It is also possible to have a transformation of the image, for example de-blurring it or estimate the depth map.
### Object Detection Tracker
Object detection trackers allow a detected object to be assigned an id that remains the same across successive frames.
### DSP (Digital Signal Processing)
To filter noisy estimations or inputs, the DSP package provides different filters which can be applied directly into a graph.
### Recorder
To record incoming frames or annotated results, multiple frame recorders are provided.
### Assets
Most estimators use big model and weight descriptions for their neural networks. To keep visiongraph small and easy to install, these assets are hosted externally on github. Visiongraph provides a system to directly download and cache these files.
### Argparse
To support rapid prototyping many graph and estimator options are already provided to add to the argparse parser.
### Logging
To enable logging for visiongraph imports please set the following environment variable:
```bash
# zsh / bash
export VISIONGRAPH_LOGLEVEL=INFO
# cmd
set VISIONGRAPH_LOGLEVEL=INFO
# powershell
$env:VISIONGRAPH_LOGLEVEL="INFO"
```
## Roadmap
Next roadmap points:
- Async input and network model (run when ready)
## About
Copyright (c) 2023 Florian Bruggisser
### Included Libraries
Parts of these libraries are directly included and adapted to work with visiongraph.
* [motpy](https://github.com/wmuron/motpy) - simple multi object tracking library (MIT License)
* [motrackers](https://github.com/adipandas/multi-object-tracker) - Multi-object trackers in Python (MIT License)
* [OneEuroFilter-Numpy](https://github.com/HoBeom/OneEuroFilter-Numpy) - (MIT License)
For more information about the dependencies have a look at the [requirements.txt](https://github.com/cansik/visiongraph/blob/main/requirements.txt).
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"description": "#  Visiongraph [](https://pypi.org/project/visiongraph/)\nVisiongraph 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](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html) and [Google MediaPipe](https://google.github.io/mediapipe/).\n\nHere an example on how to start a webcam capture and display the image:\n\n```python\nimport visiongraph as vg\nvg.create_graph(vg.VideoCaptureInput()).then(vg.ImagePreview()).open()\n```\n\nThe main goal is to implement a platform independent and high performance framework for day-to-day computer vision tasks.\n\n## Installation\nVisiongraph supports python `3.9` and `3.10`. Other versions might work as well but are not officially supported.\n\nTo install visiongraph with all dependencies call [pip](https://pypi.org/project/pip/) like this:\n\n```bash\npip install \"visiongraph[all]\"\n```\n\n\ud83d\udea8 *Please note that visiongraph is in an early alpha phase and the API will still undergo changes.*\n\nIt is also possible to only install certain packages depending on your needs:\n\n```bash\npip install \"visiongraph[realsense, openvino, mediapipe, onnx, media, azure, numba, opencv-contrib]\"\n```\n\n### Development\nTo develop visiograph itself it is recommended to clone this repository and install the dependencies like this:\n\n```bash\n# in the visiongraph directory\npip install -e \".[all]\"\n```\n\n### Build\nTo build a new wheel package of visiongraph run the following command in the root directory.\n\n```bash\npython setup.py bdist_wheel\n```\n\n## Examples\nTo demonstrate the possibilities of visiongraph there are already implemented [examples](examples) ready for you to try out. Here is a list of the current examples:\n\n- [SimpleVisionGraph](examples/SimpleVisionGraph.py) - SSD object detection & tracking of live webcam input with `5` lines of code.\n- [VisionGraphExample](examples/VisionGraphExample.py) - A face detection and tracking example with custom events.\n- [InputExample](examples/InputExample.py) - A basic input example that determines the center if possible.\n- [RealSenseDepthExample](examples/DepthCameraExample.py) - Display the RealSense or Azure Kinect depth map.\n- [FaceDetectionExample](examples/FaceDetectionExample.py) - A face detection pipeline example.\n- [FindFaceExample](examples/FindFaceExample.py) - A face recognition example to find a target face.\n- [CascadeFaceDetectionExample](examples/CascadeFaceDetectionExample.py) - A face detection pipeline that also predicts other feature points of the face.\n- [HandDetectionExample](examples/HandDetectionExample.py) - A hand detection pipeline example.\n- [PoseEstimationExample](examples/PoseEstimationExample.py) - A pose estimation pipeline which annotates the generic pose keypoints.\n- [ProjectedPoseExample](examples/ProjectedPoseExample.py) - Project the pose estimation into 3d space with the RealSense camera.\n- [ObjectDetectionExample](examples/ObjectDetectionExample.py) - An object detection & tracking example.\n- [InstanceSegmentationExample](examples/InstanceSegmentationExample.py) - Intance Segmentation based on COCO80 dataset.\n- [InpaintExample](examples/InpaintExample.py) - GAN based inpainting example.\n- [MidasDepthExample](examples/MidasDepthExample.py) - Realtime depth prediction with the [midas-small](https://github.com/isl-org/MiDaS) network.\n- [RGBDSmoother](examples/RGBDSmoother.py) - Smooth RGB-D depth map videos with a one-euro filter per pixel.\n\nThere are even more examples where visiongraph is currently in use:\n\n- [Spout/Syphon RGB-D Example](https://github.com/cansik/spout-rgbd-example) - Share RGB-D images over spout or syphon.\n- [WebRTC Input](https://github.com/cansik/visiongraph-webrtc) - WebRTC input example for visiongraph\n\n## Documentation\nThis documentation is intended to provide an overview of the framework. A full documentation will be available later.\n\n### Graph\nThe core component of visiongraph is the [BaseGraph](https://github.com/cansik/visiongraph/blob/main/visiongraph/BaseGraph.py) class. It contains and handles all the nodes of the graph. A BaseGraph can run on the same thread as called or a new thread or process. The nodes in the graph are just a list, the graph itself is created by nesting nodes into each other.\n\n#### Graph Node\nA [GraphNode](https://github.com/cansik/visiongraph/blob/main/visiongraph/GraphNode.py) is a single step in the graph. It has a input and output type and processes the data within the `process()` method.\n\n#### Graph Builder\nThe graph builder helps to create new graphs on a single line in python. It creates a [VisionGraph](https://github.com/cansik/visiongraph/blob/main/visiongraph/VisionGraph.py) object which is a child of the BaseGraph. The following code snippet is an example of the graph builder which creates a smooth pose estimation graph.\n\n```python\nimport visiongraph as vg\n\ngraph = (vg.create_graph(name=\"Smooth Pose Estimation\",\n input_node=vg.VideoCaptureInput(0),\n handle_signals=True)\n .apply(ssd=vg.sequence(vg.OpenPoseEstimator.create(), vg.MotpyTracker(), vg.LandmarkSmoothFilter()),\n image=vg.passthrough())\n .then(vg.ResultAnnotator(image=\"image\"), vg.ImagePreview())\n )\ngraph.open()\n```\n\n### Input\nSupported are image, video, webcam, RealSense and Azure Kinect input types.\n\n### Estimator\nUsually an estimator is a graph node which takes an image as an input and estimates an information about the content. This could be a pose estimation or a face detection. It is also possible to have a transformation of the image, for example de-blurring it or estimate the depth map.\n\n### Object Detection Tracker\nObject detection trackers allow a detected object to be assigned an id that remains the same across successive frames.\n\n### DSP (Digital Signal Processing)\nTo filter noisy estimations or inputs, the DSP package provides different filters which can be applied directly into a graph.\n\n### Recorder\nTo record incoming frames or annotated results, multiple frame recorders are provided.\n\n### Assets\nMost estimators use big model and weight descriptions for their neural networks. To keep visiongraph small and easy to install, these assets are hosted externally on github. Visiongraph provides a system to directly download and cache these files.\n\n### Argparse\nTo support rapid prototyping many graph and estimator options are already provided to add to the argparse parser.\n\n### Logging\nTo enable logging for visiongraph imports please set the following environment variable:\n\n```bash\n# zsh / bash\nexport VISIONGRAPH_LOGLEVEL=INFO\n\n# cmd\nset VISIONGRAPH_LOGLEVEL=INFO\n\n# powershell\n$env:VISIONGRAPH_LOGLEVEL=\"INFO\"\n```\n\n## Roadmap\nNext roadmap points:\n\n- Async input and network model (run when ready)\n\n## About\nCopyright (c) 2023 Florian Bruggisser\n\n### Included Libraries\n\nParts of these libraries are directly included and adapted to work with visiongraph.\n\n* [motpy](https://github.com/wmuron/motpy) - simple multi object tracking library (MIT License)\n* [motrackers](https://github.com/adipandas/multi-object-tracker) - Multi-object trackers in Python (MIT License)\n* [OneEuroFilter-Numpy](https://github.com/HoBeom/OneEuroFilter-Numpy) - (MIT License)\n\nFor more information about the dependencies have a look at the [requirements.txt](https://github.com/cansik/visiongraph/blob/main/requirements.txt).\n\n\n",
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