Teachable

It is a package to work with google teachable machine in flutter using inappwebview. It helps developers to make posenet classifier

Mandatory Section

HTML for teachable machine

Store this code in html file in your assets directory

<div><canvas id="canvas"
        style="position:fixed;min-height:100%;min-width:100%;height:100%;width:100%;top:0%;left:0%;resize:none;"></canvas>
</div>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.3.1/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@teachablemachine/pose@0.8/dist/teachablemachine-pose.min.js"></script>
<script type="text/javascript">
    const URL = "Your URL comes here";
    let model, webcam, ctx, labelContainer, maxPredictions;

    async function init() {
        const modelURL = URL + "model.json";
        const metadataURL = URL + "metadata.json";
        model = await tmPose.load(modelURL, metadataURL);

        maxPredictions = model.getTotalClasses();

        // Convenience function to setup a webcam
        const size = 600;
        const flip = true; // whether to flip the webcam
        webcam = new tmPose.Webcam(size, size, flip); // width, height, flip
        await webcam.setup(); // request access to the webcam
        await webcam.play();
        window.requestAnimationFrame(loop);

        // append/get elements to the DOM
        const canvas = document.getElementById("canvas");
        canvas.width = size; canvas.height = size;
        ctx = canvas.getContext("2d");
    }

    async function loop(timestamp) {
        webcam.update(); // update the webcam frame
        await predict();
        window.requestAnimationFrame(loop);
    }

    async function predict() {
        // Prediction #1: run input through posenet
        // estimatePose can take in an image, video or canvas html element
        const { pose, posenetOutput } = await model.estimatePose(webcam.canvas);
        // Prediction 2: run input through teachable machine classification model
        const prediction = await model.predict(posenetOutput);

        let ans = 0, score = 0;
        for (let i = 0; i < maxPredictions; i++) {
            if (prediction[i].probability.toFixed(2) > score) {
                score = prediction[i].probability.toFixed(2);
                ans = prediction[i].className;
            }
        }

        // finally draw the poses
        drawPose(pose);

        try {
            // Code to run
            //  updater(ans,score);
            window.flutter_inappwebview.callHandler('updater', prediction);
            //  [break;]
        }

        catch (e) {
            // Code to run if an exception occurs
            //  [break;]
        }
    }

    function drawPose(pose) {
        if (webcam.canvas) {
            ctx.drawImage(webcam.canvas, 0, 0);
            // draw the keypoints and skeleton
            if (pose) {
                const minPartConfidence = 0.5;
                tmPose.drawKeypoints(pose.keypoints, minPartConfidence, ctx);
                tmPose.drawSkeleton(pose.keypoints, minPartConfidence, ctx);
            }
        }
    }
    init();
</script>

</html>

Flutter Section

void main() async {
  WidgetsFlutterBinding.ensureInitialized();
  // Add permissions using permission_handler plugin
  await Permission.camera.request();
  await Permission.microphone.request();
  runApp(MyApp());
}

Permission Handler Plugin

Use of teachable widget

Teachable(
    path: "Path to your html file",
    results: (res) {
        // Recieve JSON data here

        // Convert json to usable format
        var resp = jsonDecode(res); 
        print("The values are $resp");
    },
)

Permissions

Android

<uses-permission android:name="android.permission.INTERNET"/>
<uses-permission android:name="android.permission.CAMERA" />
<uses-permission android:name="android.permission.RECORD_AUDIO" />
<uses-permission android:name="android.permission.MODIFY_AUDIO_SETTINGS" />
<uses-permission android:name="android.permission.VIDEO_CAPTURE" />
<uses-permission android:name="android.permission.AUDIO_CAPTURE" />