top of page

The EyesOnIt Large Vision Model REST API




As covered in previous posts, the mission of EyesOnIt is to bring the power of Large Vision Models to users who may not have the data science or computer programming skills to work with Artificial Intelligence at a deep level. The EyesOnIt Docker image includes a web UI that allows users to apply our Large Vision Model to images and video just by using text prompts. The goal of this web UI is to provide a starting point so that all users can see results quickly. To keep the web UI simple and intuitive, it is intentionally limited. It lacks support for customizations that many users would need.


To support more advanced customization, EyesOnIt offers a REST API that allows programmers with limited experience to customize EyesOnIt for their own use cases. In fact, the Web UI mentioned above uses the EyesOnIt REST API to configure and control EyesOnIt. Through the REST API, programmers can develop extensive customizations and integrations which can:


  • Dynamically update object descriptions based on conditions such as time, weather, lighting, or location (for mobile cameras)

  • Programmatically update object descriptions in response to sensors including motion, proximity, access control or other sensors

  • Integrate EyesOnIt with Video Management Systems, databases, alerting systems, retail checkout or other systems

  • Implement custom logic for multi-layered detections, complex multi-sensor fusion, or alerting conditions with several criteria


The possibilities for what can be accomplished with EyesOnIt through custom code are quite extensive!


The REST API

The EyesOnIt REST API provides access to all the capabilities of the EyesOnIt Large Vision Model. The REST API also supports use of features layered on top of the Large Vision Model, including tile masking and SMS alerting. Since REST calls are supported by most programming languages, software developers can use the language of their choice to customize their use of EyesOnIt.


The endpoints supported by the REST API are outlined below, including the path, REST method, inputs, and outputs for each method call.


add_stream Method


POST /add_stream – add a stream that you would like to monitor to the EyesOnIt stream list


Inputs:

  • The RTSP URL of the stream to add

  • A friendly name for the stream

  • The frame rate for processing (default = 5)

  • The text descriptions of objects to look for in each frame and the alerting threshold for each object description

  • The tiling configuration, including the number of rows and columns, and which tiles should be processed

  • The alerting configuration, including the positive alert time, negative reset time, phone number and image alerting options


Outputs:

  • Success / failure (with an error message if unsuccessful)

monitor_stream Method


POST /monitor_stream – start monitoring a specified stream


Inputs:

  • The RTSP URL of the stream to monitor

  • The duration to monitor the stream (default = infinite)


Outputs:

  • Success / failure (with an error message if unsuccessful)

stop_monitoring Method


POST /stop_monitoring – stop monitoring a specified stream


Inputs:

  • The RTSP URL of the stream to stop monitor


Outputs:

  • Success / failure (with an error message if unsuccessful)

remove_stream Method


POST /remove_stream – remove a specified stream from the EyesOnIt stream list


Inputs:

  • The RTSP URL of the stream to remove


Outputs:

  • Success / failure (with an error message if unsuccessful)

get_video_frame Method


POST /get_video_frame – gets the latest video frame from a stream that is being monitored


Inputs:

  • The RTSP URL of the stream from which to get a frame


Outputs:

  • Success: the video frame is returned as a base64 encoded image

  • Failure: an error message is returned

get_preview_video_frame Method


POST /get_preview_video_frame – gets a video frame from a stream that is not in the EyesOnIt stream list


Inputs:

  • The RTSP URL of the stream from which to get a frame


Outputs:

  • Success: the video frame is returned as a base64 encoded image

  • Failure: an error message is returned



get_last_detection_info Method


POST /get_last_detection_info – gets data including the video frame for the last object detection


Inputs:

  • The RTSP URL of the stream for which to get the last detection information


Outputs:

  • Success: data about the last detection including:

  • The video frame when the detection occurred

  • The object description that triggered the detection

  • The confidence levels of all object descriptions when the detection occurred

  • The time of the detection

  • Failure: an error message is returned



get_streams_info Method


GET /get_streams_info – gets data about the streams in the EyesOnIt stream list


Inputs:

  • None – this method returns data for all streams in the EyesOnIt stream list


Outputs:

  • A JSON structure including one set of data for each known stream. The data for each stream includes:

  • The RTSP URL of the stream

  • The friendly name of the stream

  • The frame rate for processing

  • The object descriptions with the alerting threshold for each

  • The tiling configuration, including the number of rows and columns, and which tiles will be processed The alerting configuration, including the positive alert time, negative reset time, phone number and image alerting options Data about the last detection including the object description that triggered the detection, the confidence levels of all object descriptions and the time of the detection



process_image Method


POST /process_image – process a single image with EyesOnIt


Inputs:

  • The text descriptions of objects to look for in the image

  • The tiling configuration, including the number of rows and columns, and which tiles should be processed

  • The image to process specified as a base-64 encoded image string


Outputs:

 

To get the full details for each of the above methods including the JSON format of inputs and outputs, run the EyesOnIt Docker image according to the instructions in the User Guide, and navigate to http://localhost:8000/docs in a browser (IP address and port may be different depending on how you ran the Docker image). The /docs page will provide all the details you need to start using the REST API.


Language-Specific Libraries

To simplify the use of the REST API, the EyesOnIt team is creating open-source language-specific libraries with methods that mirror the REST API methods and data structures that match the inputs and outputs for each method. The team is nearly done with the libraries for JavaScript, TypeScript, and C#. A Python library will be coming soon. Future blog posts will provide more details about how to use these libraries.

 

Conclusion

Large Vision Models are the future of computer vision. EyesOnIt makes Large Vision Models available to all users to process images and videos. While the EyesOnIt web UI provides basic capabilities, the EyesOnIt REST API allows programmers to customize and integrate EyesOnIt in extremely powerful ways. Please contact us and let us know how you are using the EyesOnIt REST API.


112 views

Comments


bottom of page