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DroneAid for TensorFlow.js

This is a TensorFlow.js port of the DroneAid pre-trained model. The DroneAid model was trained to identify specific emergency status symbols in times of natural disasters.

Build

  1. Clone this repository

    git clone https://github.ibm.com/callforcode/DroneAid.git

  2. Change to droneaid-tfjs directory

    cd DroneAid/droneaid-tfjs

  3. Install dependencies & build package

    npm install
npm run build

A dist/ directory is created and populated with different JavaScript versions of the library (e.g., droneaid-tfjs.js)

Install

Browser

<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
<script src="dist/droneaid-tfjs.js"></script>

Use

The /examples directory contains sample code.

Note: When loaded in a browser, the global variable droneaid will be available to access the API.

let image = document.getElementById("my-image");

droneaid.predict(image).then((predictions) => {
  console.log(predictions);
});

API

  • loadModel(init)

    Loads the model files.

    init - if true, a prediction will be triggered using an all zero Tensor to warm up the model (helps increase speed of subsequent predictions when running in a browser). Default is true.

    Returns the TensorFlow.js model.

  • processInput(image)

    Processes the input image to the shape and format expected by the model. The image is resized and converted to a 4D Tensor.

    image - an instance of HTMLImageElement, HTMLCanvasElement, or HTMLVideoElement.

    Returns a 4D Tensor that can be passed to the model.

  • runInference(inputTensor)

    Runs inference on the input Tensor passed. The output is an array with 2 Tensors the bounding box and corresponding scores of identified symbols.

    inputTensor - a 4D Tensor representing an ImageData

    Returns the inference results.

  • processOutput(inferenceResults)

    Processes the inference output replacing the output Tensor with an array objects. Each object represents an detected symbol

    inferenceResults - the model output from running inference.

    Returns an array of objects containing

    • class: a number representing the symbol id
    • score: a number for the confidence score
    • bbbox: the bounding box (i.e., [x0, y0, x1, y1]) for the symbol where x and y are the corners of the bounding box (as percentages of the input image's width and height)
    • label: the symbol name

  • predict(image)

    Loads the model (if not loaded), processes the input image, runs inference, processes the inference output, and returns a prediction object. This is a convenience function to avoid having to call each of the functions (loadModel, processInput, runInference, processOutput) individually.

    image - an instance of HTMLImageElement, HTMLCanvasElement, or HTMLVideoElement.

    Returns an array of objects containing

    • class: a number representing the symbol id
    • score: a number for the confidence score
    • bbbox: the bounding box (i.e., [x0, y0, x1, y1]) for the symbol where x and y are the corners of the bounding box (as percentages of the input image's width and height)
    • label: the symbol name

  • labels()

    An array of labels (i.e., symbol names) where the label's index corresponds to its id.

  • version

    Returns the version

Model

The model assets produced by converting the pre-trained model to the TensorFlow.js format can be found in the /model_web directory.

Links

License

Apache-2.0