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Mediapipe facemesh eye blink for face liveness detection example
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| <html> | |
| <head> | |
| <script src="//code.jquery.com/jquery-3.3.1.min.js"></script> | |
| <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css"> | |
| <!-- Require the peer dependencies of facemesh. --> | |
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core@2.6.0/dist/tf-core.min.js"></script> | |
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-converter@2.6.0/dist/tf-converter.min.js"></script> | |
| <!-- You must explicitly require a TF.js backend if you're not using the tfs union bundle. --> | |
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@2.6.0/dist/tf-backend-wasm.min.js"></script> | |
| <!-- Pretrained facemesh model. --> | |
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/face-landmarks-detection@0.0.2/dist/face-landmarks-detection.min.js"></script> | |
| <!-- https://medium.com/swlh/how-to-access-webcam-and-take-picture-with-javascript-b9116a983d78 --> | |
| <script type="text/javascript" src="https://unpkg.com/webcam-easy/dist/webcam-easy.min.js"></script> | |
| </head> | |
| <body> | |
| <div id="webcam-control"> | |
| <input type="checkbox" id="webcam-switch"> | |
| <span id="webcam-caption">Click to Start Camera</span> | |
| </div> | |
| <div id="video-container" class="container" style="display: none;"> | |
| <video id="webcam" autoplay playsinline width="640" height="480"></video> | |
| <div> | |
| <button id="start-capture" class="btn"><i class="fa fa-camera fa-3x fa-fw"></i></button> | |
| <button id="capture-running" class="btn" style="display: none;"><i class="fa fa-refresh fa-spin fa-3x fa-fw"></i></button> | |
| <span id="info-text" style="display: none;">Aguarde</span> | |
| </div> | |
| <canvas id="canvas" class="d-none"></canvas> | |
| </div> | |
| <script> | |
| const webcamElement = document.getElementById('webcam'); | |
| const canvasElement = document.getElementById('canvas'); | |
| const webcam = new Webcam(webcamElement, 'user', canvasElement); | |
| let model = null; | |
| let cameraFrame = null; | |
| let running = false | |
| let timeout = null; | |
| $("#start-capture").click(function () { | |
| startCaptura(); | |
| }); | |
| $("#webcam-switch").change(function () { | |
| if(this.checked){ | |
| webcam.start() | |
| .then(result =>{ | |
| $('#webcam-caption').text('Click to Stop Camera'); | |
| $("#video-container").show(); | |
| console.log("webcam started"); | |
| }) | |
| .catch(err => { | |
| console.log(err); | |
| }); | |
| } | |
| else { | |
| stopCaptura(); | |
| webcam.stop(); | |
| $('#webcam-caption').text('Click to Start Camera'); | |
| $("#video-container").hide(); | |
| console.log("webcam stopped"); | |
| } | |
| }); | |
| async function stopCaptura() { | |
| if (running) { | |
| $("#capture-running").hide(); | |
| $("#start-capture").show(); | |
| $("#info-text").hide(); | |
| running = false; | |
| if(cameraFrame!= null){ | |
| cancelAnimationFrame(cameraFrame); | |
| } | |
| } | |
| } | |
| async function startCaptura() { | |
| $("#start-capture").hide(); | |
| $('#info-text').text('Aguarde.'); | |
| $("#info-text").show(); | |
| // Load the MediaPipe Facemesh package. | |
| faceLandmarksDetection.load( | |
| faceLandmarksDetection.SupportedPackages.mediapipeFacemesh, | |
| {maxFaces: 1} | |
| ).then(mdl => { | |
| $("#capture-running").show(); | |
| $('#info-text').text('Pisque para a camera.'); | |
| model = mdl; | |
| cameraFrame = detectKeyPoints(); | |
| timeout = setTimeout(() => { | |
| stopCaptura(); | |
| }, 5000); | |
| running = true; | |
| }).catch(err => { | |
| console.log(err); | |
| stopCaptura(); | |
| }); | |
| } | |
| async function main() { | |
| await setupFaceLandmarkDetection() | |
| } | |
| async function setupFaceLandmarkDetection() { | |
| // Setup TF Backend type | |
| await tf.setBackend('wasm'); | |
| } | |
| async function detectaPiscada(keypoints) { | |
| return true; | |
| } | |
| async function detectKeyPoints() { | |
| // Pass in a video stream (or an image, canvas, or 3D tensor) to obtain an | |
| // array of detected faces from the MediaPipe graph. If passing in a video | |
| // stream, a single prediction per frame will be returned. | |
| const predictions = await model.estimateFaces({ | |
| input: document.querySelector("video"), | |
| returnTensors: false, | |
| flipHorizontal: true, | |
| predictIrises: true | |
| }); | |
| if (predictions.length > 0) { | |
| /* | |
| `predictions` is an array of objects describing each detected face, for example: | |
| [ | |
| { | |
| faceInViewConfidence: 1, // The probability of a face being present. | |
| boundingBox: { // The bounding box surrounding the face. | |
| topLeft: [232.28, 145.26], | |
| bottomRight: [449.75, 308.36], | |
| }, | |
| mesh: [ // The 3D coordinates of each facial landmark. | |
| [92.07, 119.49, -17.54], | |
| [91.97, 102.52, -30.54], | |
| ... | |
| ], | |
| scaledMesh: [ // The 3D coordinates of each facial landmark, normalized. | |
| [322.32, 297.58, -17.54], | |
| [322.18, 263.95, -30.54] | |
| ], | |
| annotations: { // Semantic groupings of the `scaledMesh` coordinates. | |
| silhouette: [ | |
| [326.19, 124.72, -3.82], | |
| [351.06, 126.30, -3.00], | |
| ... | |
| ], | |
| ... | |
| } | |
| } | |
| ] | |
| */ | |
| // if (detectPiscada && verificando) { | |
| // snapshot = webcam.snapShot(); | |
| // $("#snapShot").setImage(snapShot); | |
| // verificando = false; | |
| // } | |
| // for (let i = 0; i < predictions.length; i++) { | |
| // const keypoints = predictions[i].scaledMesh; | |
| // // Log facial keypoints. | |
| // for (let i = 0; i < keypoints.length; i++) { | |
| // const [x, y, z] = keypoints[i]; | |
| // console.log(`Keypoint ${i}: [${x}, ${y}, ${z}]`); | |
| // } | |
| // } | |
| // Somente 1 face | |
| const keypoints = predictions[0].scaledMesh; | |
| if (detectarPiscada(keypoints)) { | |
| console.log('-----> Piscou'); | |
| clearTimeout(timeout); | |
| stopCaptura(); | |
| let picture = webcam.snap(); | |
| document.querySelector('#download-photo').href = picture; | |
| return null; | |
| } | |
| } | |
| cameraFrame = requestAnimationFrame(detectKeyPoints); | |
| } | |
| function detectarPiscada(keypoints) { | |
| leftEye_l = 263 | |
| leftEye_r = 362 | |
| leftEye_t = 386 | |
| leftEye_b = 374 | |
| rightEye_l = 133 | |
| rightEye_r = 33 | |
| rightEye_t = 159 | |
| rightEye_b = 145 | |
| aL = euclidean_dist(keypoints[leftEye_t][0], keypoints[leftEye_t][1], keypoints[leftEye_b][0], keypoints[leftEye_b][1]); | |
| bL = euclidean_dist(keypoints[leftEye_l][0], keypoints[leftEye_l][1], keypoints[leftEye_r][0], keypoints[leftEye_r][1]); | |
| earLeft = aL / (2 * bL); | |
| aR = euclidean_dist(keypoints[rightEye_t][0], keypoints[rightEye_t][1], keypoints[rightEye_b][0], keypoints[rightEye_b][1]); | |
| bR = euclidean_dist(keypoints[rightEye_l][0], keypoints[rightEye_l][1], keypoints[rightEye_r][0], keypoints[rightEye_r][1]); | |
| earRight = aR / (2 * bR); | |
| console.log('-----> ' + earLeft + '\t' + earRight); | |
| if ((earLeft < 0.1) || (earRight < 0.1)) { | |
| return true; | |
| } else { | |
| return false; | |
| } | |
| } | |
| function euclidean_dist (x1, y1, x2, y2) { | |
| return Math.sqrt( Math.pow((x1-x2), 2) + Math.pow((y1-y2), 2) ); | |
| }; | |
| main(); | |
| </script> | |
| </body> | |
| </html> |
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