Align photos before descriptor extraction
Signed-off-by: James Ketrenos <james_gitlab@ketrenos.com>
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James Ketrenos
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4f7b677185
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0799e214f5
@ -43,6 +43,53 @@ let photoDB = null;
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console.log("Loading pictures out of: " + picturesPath);
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function alignFromLandmarks(image, landmarks) {
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const faceMargin = 0.3,
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width = 256, height = 256,
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dY = landmarks._positions[45]._y - landmarks._positions[36]._y,
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dX = landmarks._positions[45]._x - landmarks._positions[36]._x,
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mid = {
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x: landmarks._positions[36]._x + 0.5 * dX,
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y: landmarks._positions[36]._y + 0.5 * dY
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},
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rotation = -Math.atan2(dY, dX),
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cosRotation = Math.cos(rotation),
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sinRotation = Math.sin(rotation),
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eyeDistance = Math.sqrt(dY * dY + dX * dX),
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scale = width * (1.0 - 2. * faceMargin) / eyeDistance,
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canvas = createCanvas(width, height),
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ctx = canvas.getContext("2d");
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const prime = {
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x: mid.x * cosRotation - mid.y * sinRotation,
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y: mid.y * cosRotation + mid.x * sinRotation
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};
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mid.x = prime.x;
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mid.y = prime.y;
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ctx.translate(
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0.5 * width - mid.x * scale,
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0.5 * height - (height * (0.5 - faceMargin)) - mid.y * scale);
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ctx.rotate(rotation);
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ctx.scale(scale, scale);
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ctx.drawImage(image, 0, 0);
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/*
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ctx.strokeStyle = "red";
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ctx.strokeWidth = "1";
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ctx.beginPath();
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landmarks._positions.forEach((point, index) => {
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if (index == 0) {
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ctx.moveTo(point._x, point._y);
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} else {
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ctx.lineTo(point._x, point._y);
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}
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});
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ctx.stroke();
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*/
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return canvas;
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}
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process.stdout.write("Loading DB.");
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require("./db/photos").then(function(db) {
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process.stdout.write("done\n");
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@ -82,13 +129,15 @@ require("./db/photos").then(function(db) {
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/* Remove any existing face data for this photo */
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return photoDB.sequelize.query("SELECT id FROM faces WHERE photoId=:id", {
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replacements: photo,
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type: photoDB.sequelize.QueryTypes.SELECT,
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raw: true
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}).then((faces) => {
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/* For each face-id, remove any face-data files, and then remove all the entries
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* from the DB */
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return Promise.map(faces, (face) => {
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return Promise.map([ "-data.json", "-original.png" ], (suffix) => {
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const id = face.id,
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dataPath = faceData + "/" + (id % 100) + "/" + id + suffix;
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dataPath = faceData + (id % 100) + "/" + id + suffix;
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return exists(dataPath).then((result) => {
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if (result) {
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console.log(`...removing ${dataPath}`);
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@ -108,14 +157,17 @@ require("./db/photos").then(function(db) {
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new faceapi.SsdMobilenetv1Options({
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minConfidence: 0.8
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})
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).withFaceLandmarks().withFaceDescriptors();
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).withFaceLandmarks();
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if (detections.length > 0) {
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console.log(`...${detections.length} faces identified in ${photoPath}.`);
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}
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return Promise.map(detections, (face) => {
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const detection = face.detection;
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return Promise.map(detections, async (face) => {
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const detection = face.detection,
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canvas = alignFromLandmarks(image, face.landmarks);
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face.descriptor = await faceapi.computeFaceDescriptor(canvas);
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const width = detection._box._width,
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height = detection._box._height,
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replacements = {
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@ -137,24 +189,14 @@ require("./db/photos").then(function(db) {
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return mkdir(path).then(() => {
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const dataPath = `${path}/${id}-data.json`, data = [];
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console.log(`...writing descriptor data to ${dataPath}...`);
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/* Confert from sparse object to dense array */
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for (let i = 0; i < 128; i++) {
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data.push(face.descriptor[i]);
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}
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fs.writeFileSync(dataPath, JSON.stringify(data));
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}).then(() => {
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const canvas = createCanvas(200, 200),
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target = `${path}/${id}-original.png`,
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ctx = canvas.getContext('2d'),
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box = face.detection._box,
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aspect = box._width / box._height,
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dx = (aspect > 1.0) ? 200 : (200 * aspect),
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dy = (aspect < 1.0) ? 200 : (200 / aspect);
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ctx.fillStyle = "rgba(0, 0, 0, 0)";
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ctx.fillRect(0, 0, 200, 200);
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ctx.drawImage(image, box._x, box._y, box._width, box._height,
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Math.floor((200 - dx) * 0.5),
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Math.floor((200 - dy) * 0.5), dx, dy);
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console.log(`...writing face crop to ${target}.`);
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const target = `${path}/${id}-original.png`;
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console.log(`...writing aligned face crop to ${target}.`);
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fs.writeFileSync(target, canvas.toBuffer("image/png", {
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quality: 0.95,
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chromaSubsampling: false
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@ -24,6 +24,53 @@ require("./console-line.js"); /* Monkey-patch console.log with line numbers */
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const picturesPath = config.get("picturesPath").replace(/\/$/, "") + "/",
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faceData = picturesPath + "face-data/";
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function alignFromLandmarks(image, landmarks) {
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const faceMargin = 0.3,
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width = 256, height = 256,
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dY = landmarks._positions[45]._y - landmarks._positions[36]._y,
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dX = landmarks._positions[45]._x - landmarks._positions[36]._x,
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mid = {
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x: landmarks._positions[36]._x + 0.5 * dX,
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y: landmarks._positions[36]._y + 0.5 * dY
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},
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rotation = -Math.atan2(dY, dX),
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cosRotation = Math.cos(rotation),
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sinRotation = Math.sin(rotation),
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eyeDistance = Math.sqrt(dY * dY + dX * dX),
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scale = width * (1.0 - 2. * faceMargin) / eyeDistance,
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canvas = createCanvas(width, height),
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ctx = canvas.getContext("2d");
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const prime = {
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x: mid.x * cosRotation - mid.y * sinRotation,
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y: mid.y * cosRotation + mid.x * sinRotation
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};
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mid.x = prime.x;
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mid.y = prime.y;
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ctx.translate(
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0.5 * width - mid.x * scale,
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0.5 * height - (height * (0.5 - faceMargin)) - mid.y * scale);
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ctx.rotate(rotation);
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ctx.scale(scale, scale);
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ctx.drawImage(image, 0, 0);
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/*
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ctx.strokeStyle = "red";
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ctx.strokeWidth = "1";
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ctx.beginPath();
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landmarks._positions.forEach((point, index) => {
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if (index == 0) {
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ctx.moveTo(point._x, point._y);
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} else {
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ctx.lineTo(point._x, point._y);
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}
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});
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ctx.stroke();
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*/
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return canvas;
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}
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process.stdout.write("Loading DB.");
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require("./db/photos").then(function(db) {
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process.stdout.write("done\n");
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@ -65,7 +112,6 @@ require("./db/photos").then(function(db) {
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console.log(`Scanning ${args.length} faces.`);
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return Promise.map(args, (arg) => {
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const file = arg;
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let id = parseInt(arg);
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let loader;
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@ -94,6 +140,20 @@ require("./db/photos").then(function(db) {
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const file = photo.path + photo.filename;
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return canvas.loadImage(picturesPath + file).then(async (image) => {
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const detectors = await faceapi.detectAllFaces(image,
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new faceapi.SsdMobilenetv1Options({
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minConfidence: 0.8
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})
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).withFaceLandmarks();
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detectors.forEach(async (detector) => {
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const canvas = alignFromLandmarks(image, detector.landmarks);
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const descriptor = await faceapi.computeFaceDescriptor(canvas);
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const data = [];
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/* Confert from sparse object to dense array */
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for (let i = 0; i < 128; i++) {
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data.push(descriptor[i]);
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}
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const detectors = [ {
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detection: {
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_box: {
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@ -112,17 +172,21 @@ require("./db/photos").then(function(db) {
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/* This is a file */
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console.log(`Loading ${file}...`);
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id = undefined;
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loader = canvas.loadImage(picturesPath + file).then(async (image) => {
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const detectors = await faceapi.detectAllFaces(image,
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new faceapi.SsdMobilenetv1Options({
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minConfidence: 0.8
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})
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).withFaceLandmarks().withFaceDescriptors();
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detectors.forEach((detector) => {
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).withFaceLandmarks();
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detectors.forEach(async (detector) => {
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const canvas = alignFromLandmarks(image, detector.landmarks);
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const descriptor = await faceapi.computeFaceDescriptor(canvas);
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const data = [];
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/* Confert from sparse object to dense array */
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for (let i = 0; i < 128; i++) {
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data.push(detector.descriptor[i]);
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data.push(descriptor[i]);
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}
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detector.descriptor = data;
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});
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