Uncaught (in promise) TypeError: faceapi.allFacesMtcnn(...).map is not a function

[chtsngn]

I'm using VueJS. How can I fix these errors?

Code (VueJS)

onPlay: async function(videoEl) {
                if(videoEl.paused || videoEl.ended || !modelLoaded)
                    return false;
                let minFaceSize = 200;
                const { width, height } = faceapi.getMediaDimensions(videoEl);
                const canvas = document.getElementById('inputVideo');
                canvas.width = width;
                canvas.height = height;
                const mtcnnParams = {
                    minFaceSize
                };
                const fullFaceDescriptions = (faceapi.allFacesMtcnn(videoEl, mtcnnParams)).map(fd => fd.forSize(width, height));
                fullFaceDescriptions.forEach(({ detection, landmarks, descriptor }) => {
                    faceapi.drawDetection('overlay', [detection], { withScore: false });
                    faceapi.drawLandmarks('overlay', landmarks.forSize(width, height), { lineWidth: 4, color: 'red' });
                    const bestMatch = getBestMatch(trainDescriptorsByClass, descriptor);
                    const text = `${bestMatch.distance < maxDistance ? bestMatch.className : 'unkown'} (${bestMatch.distance})`;
                    const { x, y, height: boxHeight } = detection.getBox();
                    faceapi.drawText(
                    canvas.getContext('2d'),
                    x,
                    y + boxHeight,
                    text,
                    Object.assign(faceapi.getDefaultDrawOptions(), { color: 'red', fontSize: 16 })
                    )
                });
                setTimeout(() => onPlay(videoEl), 150);
            },
            run: async function() {
                await faceapi.loadMtcnnModel("/img/face-api/weights");
                await faceapi.loadFaceRecognitionModel("/img/face-api/weights/");
                trainDescriptorsByClass = initTrainDescriptorsByClass(faceapi.recognitionNet);
                modelLoaded = true;
                const videoEl = document.getElementById('inputVideo');
                navigator.getUserMedia(
                    { video: {} },
                    stream => videoEl.srcObject = stream,
                    err => console.error(err)
                );
                this.onPlay(videoEl);
            }

Code (HTML)

<div style="position: relative" class="margin">
        <video ref="inputVideo" onload="onPlay(this)" id="inputVideo" autoplay="true" muted></video>
        <canvas id="overlay" />
</div>

[justadudewhohacks]

Hi,

faceapi.allFacesMtcnn returns a Promise. You forgot the await keyword: (await faceapi.allFacesMtcnn(videoEl, mtcnnParams)).map(...).

[chtsngn]

@justadudewhohacks thanks, it's working. But there is another problem.

Uncaught (in promise)

Event {isTrusted: true, type: "error", target: null, currentTarget: null, eventPhase: 0, …}

I guess, I forgot one more place.

[justadudewhohacks]

This could be caused by anything. Could you show me the line, where this error is thrown.

[chtsngn]

I solved this. The path to the Images folder isn't correct.

Last question; How can i train recognition? I took 10 images (from web camera). But, distance value changes between 0.6 - 0.7.

[justadudewhohacks]

You could use multiple images to compute reference descriptors and then compute the average of the euclidean distances between all reference descriptors and a input descriptor.

Usually, you should already get quite good results using a single image only. Can you share the image/s you are using as reference and the images of the faces, you are trying to recognize.

[chtsngn]

Ok. Thanks for help.

[thirukumars]

script2.js:17 Uncaught (in promise) TypeError: fd.forSize is not a function
at script2.js:17
at Array.map ()
at onPlay (script2.js:17)

[thirukumars]

this is html code

[thirukumars]

and the script is

$(document).ready(function() {
run()
})

async function onPlay(videoEl) {
if(videoEl.paused || videoEl.ended || !modelLoaded)
return false;
let minFaceSize = 200;
const { width, height } = faceapi.getMediaDimensions(videoEl);
const canvas = document.getElementById('inputVideo');
canvas.width = width;
canvas.height = height;
const mtcnnParams = {
minFaceSize
};
const fullFaceDescriptions = (await faceapi.allFacesMtcnn(videoEl, mtcnnParams)).map(fd => fd.forSize(width, height));
fullFaceDescriptions.forEach(({ detection, landmarks, descriptor }) => {
faceapi.drawDetection('overlay', [detection], { withScore: false });
faceapi.drawLandmarks('overlay', landmarks.forSize(width, height), { lineWidth: 4, color: 'red' });
// const bestMatch = getBestMatch(trainDescriptorsByClass, descriptor);
// const text = ${bestMatch.distance < maxDistance ? bestMatch.className : 'unkown'} (${bestMatch.distance});
// const { x, y, height: boxHeight } = detection.getBox();
faceapi.drawText(
canvas.getContext('2d'),
x,
y + boxHeight,
text,
Object.assign(faceapi.getDefaultDrawOptions(), { color: 'red', fontSize: 16 })
)
});
setTimeout(() => onPlay(videoEl), 150);
};
async function run() {
await faceapi.loadMtcnnModel("/models");
await faceapi.loadFaceRecognitionModel("/models");
// trainDescriptorsByClass = initTrainDescriptorsByClass(faceapi.recognitionNet);
modelLoaded = true;
const videoEl = document.getElementById('inputVideo');
navigator.getUserMedia(
{ video: {} },
stream => videoEl.srcObject = stream,
err => console.error(err)
);
this.onPlay(videoEl);
}