Camera ML Example

This template serves as a starting point for implementing a Camera and connecting it to a Native SDK, sending the results back to Flutter.

Getting Started

This project is associated with the article available at: Implementing Face Liveness Detection in Flutter with High Performance

Here's the VDO from the demo https://www.youtube.com/watch?v=kfFJI_ipdso

To use this template, follow these steps:

iOS

1. Change FaceLivenessDelegate.swift: Implement your own callback or delegate from the SDK. Adjust the interface based on your SDK.
2. FaceLivenessDetection.swift: Add the initial setup for your ML to replace our initial SDK.
3. FaceLivenessDetection.swift: Add your ML processing here and replace it with our feeding logic.
4. You can change the camera resolution with this line: captureSession.sessionPreset = .photo. Currently, it's set to the maximum.

Android

1. Change FaceLivenessListener.kt and replace YourSDKDelegate with your callback.
2. Change FaceLivenessDetection.kt: Add the initial setup for your ML and your ML processing.
3. You can change the camera resolution at ResolutionStrategy on line 45. I added a parameter from Dart to adjust the resolution based on the controller from Flutter.

Please note that this template is not a one-size-fits-all solution, but it provides a solid starting point (around 70-80% completion). You will need to implement the remaining functionality and customize it to meet your specific requirements.

Enjoy coding!