vision-camera-realtime-object-detection

React Native
Realtime Object Detection

📷 VisionCamera Frame Processor Plugin for object detection using TensorFlow Lite Task Vision.

With this library, you can use the benefits of Machine Learning in your React Native app without a single line of native code. Create your own model or find and use one commonly available on TFHub. Implement the solution in a few simple steps:

Minimum requirements​

• react-native >= 0.71.3
• react-native-reanimated >= 2.14.4
• react-native-vision-camera >= 2.15.4

You can find the model structure requirements here

Installation

Install the required packages in your React Native project:

npm install --save vision-camera-realtime-object-detection  
# or yarn 
yarn add vision-camera-realtime-object-detection

If you're on a Mac and developing for iOS, you need to install the pods (via Cocoapods) to complete the linking.

npx pod-install

Add this to your babel.config.js

[
  'react-native-reanimated/plugin',
  {
    globals: ['__detectObjects'],
  },
]

---

‼️ Make sure you correctly setup react-native-reanimated and insert as a first line of your index.tsx

import 'react-native-reanimated'

Usage

Step 1

To add your custom TensorFlow Lite model to your app, copy your *.tflite file to your asset/model directory

...
|-- assets
    |-- images
    |-- fonts
    |-- model
        |-- your_custom_model.tflite
|-- src
    |-- App.tsx
...

Step 2

Add to your react-native.config.js

...
 "assets": [
    "./assets/model/",
  ]

and run command:

npx react-native-asset

Step 3

🎉 Use Realtime Object Deteciton in your own component!

import { DetectedObject, detectObjects, FrameProcessorConfig } from 'vision-camera-realtime-object-detection';

// ...

const frameProcessorConfig: FrameProcessorConfig = {
    modelFile: 'your_custom_model.tflite', // <!-- name and extension of your model
    scoreThreshold: 0.5,
};

const frameProcessor = useFrameProcessor((frame) => {
  'worklet';

  const detectedObjects: DetectedObject[] = detectObjects(frame, frameProcessorConfig);
}, []);

return (
  <Camera
    device={device}
    isActive={true}
    frameProcessorFps={5}
    frameProcessor={frameProcessor}
  />);

Types

FrameProcessorConfig

Use the configuration interface to customize the library on your own. In it you can find the following properties:

Prop	Type	Mandatory	Default	Note
modelFile	string	✔	-	The name and extension of your custom TensorFlow Lite model (f.e. model.tflite)
scoreThreshold	number	-	0.3	(between 0 and 1) Cut-off threshold below which you will discard detection result
maxResults	number	-	1	Maximum number of top-scored detection results to return.
numThreads	number	-	1	the number of threads to be used for TFLite ops that support multi-threading when running inference with CPU.

---

DetectedObject

detectObjects method returns a list of detected objects in the lens in the following form

Prop	Type	Note
labels	ObjectLabel[]	An array of labels to match the detected object
top	number	(percentage: between 0 and 1) absolute position of the detected object's top edge relative to the frame
left	number	(percentage: between 0 and 1) absolute position of the detected object's left edge relative to the frame
width	number	(percentage: between 0 and 1) width of the detected object relative to the frame
height	number	(percentage: between 0 and 1) height of the detected object's top edge relative to the frame

ObjectLabel

Prop	Type	Note
label	string	label matching the detected object
confidence	number	a number between 0 and 1 that indicates confidence that the object of above type was genuinely detected

Before the release of version 1.0.0

List of tasks to be implemented:

• [ ] Adjusting to VisionCamera V3 (the future version intends to rewrite frame processors and introduces exciting new features, like: drawing on frame in a Frame Processor using RN Skia)
• [ ] CPU and NNAPI delegates for Android
• [ ] GPU and Core ML delegates for IOS
• [ ] Clean up native code

Contributing

See the contributing guide to learn how to contribute to the repository and the development workflow.

License

MIT

---

Made with create-react-native-library