# Visiongraph NDI [](https://pypi.org/project/visiongraph-ndi/)
Visiongraph NDI is an extension of the [visiongraph](https://github.com/cansik/visiongraph) library, adding support for real-time video streaming over the network via NewTek's NDI® protocol. This library provides both sending and receiving capabilities, making it easy to integrate NDI into computer vision workflows.
## Installation
Install visiongraph NDI with pip:
```bash
pip install visiongraph-ndi
```
### Requirements
Ensure you have the latest version of visiongraph installed.
## Usage Examples
### Sending Video Streams
The `NDIVideoOutput` class allows you to broadcast images in real-time. This example shows how to generate a simple time-based pattern and send it as a video stream.
```python
from visiongraph_ndi.NDIVideoOutput import NDIVideoOutput
# Set up NDI output with a stream name
with NDIVideoOutput("Demo") as ndi:
image_bgr = ... # load or get an image in BGR format
ndi.send(image_bgr) # Send the generated image
```
The `NDIVideoOutput` class initializes an NDI output stream with the given name. Use `ndi.send(frame)` to transmit frames, which can be in `BGR` or `BGRA` format.
### Receiving Video Streams
The `NDIVideoInput` class supports finding available NDI sources and streaming from them in real-time. Below are examples of how to find NDI sources and receive streams.
#### Finding Sources
Use `NDIVideoInput.find_sources()` to list all available NDI sources on the network. It is possible to set the timeout to find sources (default: `1.0` seconds).
```python
from visiongraph_ndi.NDIVideoInput import NDIVideoInput
# Discover NDI sources for 5 seconds
for source in NDIVideoInput.find_sources(timeout=5.0):
print(source.name)
```
#### Receiving Video Streams
The following example demonstrates how to receive a video stream from a the first NDI source available and display it using OpenCV.
```python
import cv2
from visiongraph_ndi.NDIVideoInput import NDIVideoInput
# Connect to an NDI source
with NDIVideoInput(stream_name="Demo") as ndi:
while ndi.is_connected:
ts, frame = ndi.read() # Receive timestamped frame
if frame is None:
continue # Skip if no frame is received
# Display the frame
cv2.imshow(f"NDI {ndi.source.stream_name}", frame)
cv2.waitKey(1)
```
The `NDIVideoInput` class automatically connects to the first available NDI source or allows for specific source selection if desired (using the `stream_name` and/or `host_name` parameter in the constructor). By calling `ndi.read()`, you retrieve frames as `numpy` arrays, ready for processing or display.
### About
Visiongraph NDI is powered by the NDI Python wrapper [cyndilib](https://github.com/nocarryr/cyndilib) (`MIT license`). Special thanks to nocarryr for making this integration possible.
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"description": "# Visiongraph NDI [](https://pypi.org/project/visiongraph-ndi/)\n\nVisiongraph NDI is an extension of the [visiongraph](https://github.com/cansik/visiongraph) library, adding support for real-time video streaming over the network via NewTek's NDI\u00ae protocol. This library provides both sending and receiving capabilities, making it easy to integrate NDI into computer vision workflows.\n\n## Installation\n\nInstall visiongraph NDI with pip:\n\n```bash\npip install visiongraph-ndi\n```\n\n### Requirements\n\nEnsure you have the latest version of visiongraph installed.\n\n## Usage Examples\n\n### Sending Video Streams\n\nThe `NDIVideoOutput` class allows you to broadcast images in real-time. This example shows how to generate a simple time-based pattern and send it as a video stream.\n\n```python\nfrom visiongraph_ndi.NDIVideoOutput import NDIVideoOutput\n\n# Set up NDI output with a stream name\nwith NDIVideoOutput(\"Demo\") as ndi:\n image_bgr = ... # load or get an image in BGR format\n ndi.send(image_bgr) # Send the generated image\n```\n\nThe `NDIVideoOutput` class initializes an NDI output stream with the given name. Use `ndi.send(frame)` to transmit frames, which can be in `BGR` or `BGRA` format.\n\n### Receiving Video Streams\n\nThe `NDIVideoInput` class supports finding available NDI sources and streaming from them in real-time. Below are examples of how to find NDI sources and receive streams.\n\n#### Finding Sources\n\nUse `NDIVideoInput.find_sources()` to list all available NDI sources on the network. It is possible to set the timeout to find sources (default: `1.0` seconds).\n\n```python\nfrom visiongraph_ndi.NDIVideoInput import NDIVideoInput\n\n# Discover NDI sources for 5 seconds\nfor source in NDIVideoInput.find_sources(timeout=5.0):\n print(source.name)\n```\n\n#### Receiving Video Streams\n\nThe following example demonstrates how to receive a video stream from a the first NDI source available and display it using OpenCV.\n\n```python\nimport cv2\nfrom visiongraph_ndi.NDIVideoInput import NDIVideoInput\n\n# Connect to an NDI source\nwith NDIVideoInput(stream_name=\"Demo\") as ndi:\n while ndi.is_connected:\n ts, frame = ndi.read() # Receive timestamped frame\n\n if frame is None:\n continue # Skip if no frame is received\n\n # Display the frame\n cv2.imshow(f\"NDI {ndi.source.stream_name}\", frame)\n cv2.waitKey(1)\n```\n\nThe `NDIVideoInput` class automatically connects to the first available NDI source or allows for specific source selection if desired (using the `stream_name` and/or `host_name` parameter in the constructor). By calling `ndi.read()`, you retrieve frames as `numpy` arrays, ready for processing or display.\n\n### About\n\nVisiongraph NDI is powered by the NDI Python wrapper [cyndilib](https://github.com/nocarryr/cyndilib) (`MIT license`). Special thanks to nocarryr for making this integration possible.\n",
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