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https://github.com/luxonis/depthai

DepthAI Python API utilities, examples, and tutorials.
https://github.com/luxonis/depthai

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DepthAI Python API utilities, examples, and tutorials.

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README 

        
# DepthAI API Demo Program



[![Discord](https://img.shields.io/discord/790680891252932659?label=Discord)](https://discord.gg/luxonis)

[![Forum](https://img.shields.io/badge/Forum-discuss-orange)](https://discuss.luxonis.com/)

[![Docs](https://img.shields.io/badge/Docs-DepthAI-yellow)](https://docs.luxonis.com)

[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)



This repo contains demo application, which can load different networks, create pipelines, record video, etc.



_Click on the GIF below to see a full example run_



[![depthai demo](https://user-images.githubusercontent.com/5244214/142426845-82f5f8fd-ad1a-4873-97a5-2b3fcdb0ca2e.gif)](https://www.youtube.com/watch?v=sCZpsFQBffk)



__Documentation is available at [https://docs.luxonis.com/en/latest/pages/tutorials/first_steps/](https://docs.luxonis.com/en/latest/pages/tutorials/first_steps/#first-steps-with-depthai).__



## Installation

First you need to clone this repository with

```

git clone --recursive https://github.com/luxonis/depthai.git

```

In case you have repository already cloned, you can update your submodules with:

```

git pull --recurse-submodules 

```

There are two installation steps that need to be performed to make sure the demo works:

- **One-time installation** that will add all necessary packages to your OS.

  ```

  $ sudo curl -fL https://docs.luxonis.com/install_dependencies.sh | bash

  ```

  Please follow [this installation page](https://docs.luxonis.com/projects/api/en/latest/install/) to see instructions for other platforms



- **Python dependencies installation** that makes sure your Python interpreter has all required packages installed.

  This script is safe to be run multiple times and should be ran after every demo update

  ```

  $ python3 install_requirements.py

  ```



## Docker



One may start any DepthAI programs also through Docker:

(Allowing X11 access from container might be required: `xhost local:root`)



DepthAI Demo

```

docker run --privileged -v /dev/bus/usb:/dev/bus/usb --device-cgroup-rule='c 189:* rmw' -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix --network host --rm -i -t luxonis/depthai:latest python3 /depthai/depthai_demo.py

```



Calibration

```

docker run --privileged -v /dev/bus/usb:/dev/bus/usb --device-cgroup-rule='c 189:* rmw' -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix --network host --rm -i -t luxonis/depthai:latest python3 /depthai/calibrate.py [parameters]

```



## Usage



This repository and the demo script itself can be used in various independent cases:

- As a tool to try out different DepthAI features, explorable either with command line arguments (with `--guiType cv`) or in clickable QT interface (with `--guiType qt`)

- As a quick prototyping backbone, either utilising [callbacks mechanism](https://docs.luxonis.com/en/latest/pages/tutorials/first_steps/#callbacks-file) or by [extending the `Demo` class itself](https://docs.luxonis.com/en/latest/pages/tutorials/first_steps/#importing-demo-as-a-class)



### QT GUI usage



See instuctions [here](https://docs.luxonis.com/en/latest/pages/tutorials/first_steps/#default-run)



![qt demo](https://user-images.githubusercontent.com/5244214/142722740-47e545b7-c7fe-4132-9704-ae3b47d60957.png)



### Command line usage



**Examples**



`python3 depthai_demo.py -gt cv` - RGB & CNN inference example



`python3 depthai_demo.py -gt cv -vid ` - CNN inference on video example



`python3 depthai_demo.py -gt cv -cnn person-detection-retail-0013` - Runs `person-detection-retail-0013` model from `resources/nn` directory



`python3 depthai_demo.py -gt cv -cnn tiny-yolo-v3 -sh 8` - Run `tiny-yolo-v3` model from `resources/nn` directory and compile for 8 shaves



**Demo**



![cv demo](https://user-images.githubusercontent.com/18037362/177605180-8f327513-131d-4c31-9829-3579bd717c6a.jpg)



For the full reference, run ``$ depthai_demo.py --help``.



## DepthAI Apps



We currently have 2 apps, ``uvc`` and ``record``.



### UVC app



This app will upload an UVC pipeline to the connected OAK camera, so you will be able to use an [OAK as a webcam](https://docs.luxonis.com/en/latest/pages/oak_webcam/).



### Record App



Record app lets you record encoded and synced video streams (MJPEG/H265) across all available devices into .mp4, [Foxglove's .MCAP](https://mcap.dev/), or [ROS' .bag ](http://wiki.ros.org/rosbag). Since mono streams are synced, you will be able to reconstruct the whole stereo depth perception.



Run using ``$ depthai_demo.py -app record [-p SAVE_PATH] [-q QUALITY] [--save STREAMS] [-fc] [-tl]``. More information about the arguments and replaying can be [found here](https://github.com/luxonis/depthai-experiments/tree/master/gen2-record-replay).



## Supported models



We have added support for a number of different AI models that work (decoding and visualization) out-of-the-box with the demo. You can specify which model to run with `-cnn` argument, as shown above. Currently supported models:



```

- deeplabv3p_person

- face-detection-adas-0001

- face-detection-retail-0004

- human-pose-estimation-0001

- mobilenet-ssd

- openpose2

- pedestrian-detection-adas-0002

- person-detection-retail-0013

- person-vehicle-bike-detection-crossroad-1016

- road-segmentation-adas-0001

- tiny-yolo-v3

- vehicle-detection-adas-0002

- vehicle-license-plate-detection-barrier-0106

- yolo-v3

```



If you would like to use a custom AI model, see [documentation here](https://docs.luxonis.com/en/latest/pages/tutorials/first_steps/#using-custom-models).



## Usage statistics



By default, the demo script will collect anonymous usage statistics during runtime. These include:

- Device-specific information (like mxid, connected cameras, device state and connection type)

- Environment-specific information (like OS type, python version, package versions)



We gather this data to better understand what environemnts are our users using, as well as assist better in support questions.



**All of the data we collect is anonymous and you can disable it at any time**. To do so, click on the "Misc" tab and disable sending the statistics or create a `.consent` file in repository root with the following content



```

{"statistics": false}

```



## Reporting issues



We are actively developing the DepthAI framework, and it's crucial for us to know what kind of problems you are facing.

If you run into a problem, please follow the steps below and email [email protected]:



1. Run `log_system_information.sh` and share the output from (`log_system_information.txt`).

2. Take a photo of a device you are using (or provide us a device model)

3. Describe the expected results;

4. Describe the actual running results (what you see after started your script with DepthAI)

5. How you are using the DepthAI python API (code snippet, for example)

6. Console output