clmtrackr/src/facedetector/faceDetection.js

Javascript library for precise tracking of facial features via Constrained Local Models

import mosse from 'mosse';
import jsfeat from 'jsfeat';

import findFaceWorker from './faceDetection_worker.js';

// import { drawDetection, drawFacialPoints, drawBoundingBox } from './utils/debugging.js';

var faceDetection = function(pdmModel, params) {

	// processes an image, detects a face and returns the initial face parameters for clmtrackr
	//   calls a callback function when it's done
	//   optionally uses web workers

	if (params === undefined) params = {};
	if (params.workSize === undefined) params.workSize = 200;
	if (params.minScale === undefined) params.minScale = 2;
	if (params.scaleFactor === undefined) params.scaleFactor = 1.15;
	if (params.useCanny === undefined) params.useCanny = false;
	if (params.edgesDensity === undefined) params.edgesDensity = 0.13;
	if (params.equalizeHistogram === undefined) params.equalizeHistogram = false;
	if (params.min_neighbors === undefined) params.min_neighbors = 2;
	if (params.confidenceThreshold === undefined) params.confidenceThreshold = 106.1;
	if (params.useWebWorkers === undefined) params.useWebWorkers = true;

	// disable web workers if not exists
	if (!window.Worker) params.useWebWorkers = false;

	var msxmin, msymin, msymax;
	var msmodelheight;
	var element;

	var model = pdmModel;

	var mosseFilter = mosse.mosseFilter;
	var left_eye_filter = mosse.filters.left_eye_filter;
	var right_eye_filter = mosse.filters.right_eye_filter;
	var nose_filter = mosse.filters.nose_filter;

	var mossef_lefteye, mossef_righteye, mossef_nose;
	var right_eye_position = [0.0,0.0];
	var left_eye_position = [0.0,0.0];
	var nose_position = [0.0,0.0];

	if (model.hints && mosseFilter && left_eye_filter && right_eye_filter && nose_filter) {
		mossef_lefteye = new mosseFilter();
		mossef_lefteye.load(left_eye_filter);
		mossef_righteye = new mosseFilter();
		mossef_righteye.load(right_eye_filter);
		mossef_nose = new mosseFilter();
		mossef_nose.load(nose_filter);
	} else {
		console.log('MOSSE filters not found, using rough approximation for initialization.');
	}

	// load mean shape
	var meanShape = model.shapeModel.meanShape;
	var numPatches = model.patchModel.numPatches;

	// get max and mins, width and height of meanshape
	msymax = 0;
	msxmin = msymin = 1000000;
	for (var i = 0;i < numPatches;i++) {
		if (meanShape[i][0] < msxmin) msxmin = meanShape[i][0];
		if (meanShape[i][1] < msymin) msymin = meanShape[i][1];
		if (meanShape[i][1] > msymax) msymax = meanShape[i][1];
	}
	msmodelheight = msymax-msymin;

	var jf = new jsfeat_face(params);

	this.init = function(video) {
		element = video;

		jf.init(element);
	}

	var getBoundingBox = function(box) {
		return new Promise(function(resolve, reject) {
			if (box) {
				resolve({x : box[0], y : box[1], width : box[2], height : box[3]});
			} else {
				resolve(jf.findFace());
			}
		});
	}

	var getFinegrainedPosition = function(candidate) {
		var translateX, translateY, scaling, rotation;
		var x = candidate.x;
		var y = candidate.y;
		var w = candidate.width;
		var h = candidate.height;

		// var debugCC = document.getElementById('overlay2').getContext('2d')
		if (model.hints && mosseFilter && left_eye_filter && right_eye_filter && nose_filter) {
			var noseFilterWidth = w * 4.5/10;
			var eyeFilterWidth = w * 6/10;

			// detect position of eyes and nose via mosse filter
			var nose_result = mossef_nose.track(element, Math.round(x+(w/2)-(noseFilterWidth/2)), Math.round(y+h*(5/8)-(noseFilterWidth/2)), noseFilterWidth, noseFilterWidth, false);
			var right_result = mossef_righteye.track(element, Math.round(x+(w*3/4)-(eyeFilterWidth/2)), Math.round(y+h*(2/5)-(eyeFilterWidth/2)), eyeFilterWidth, eyeFilterWidth, false);
			var left_result = mossef_lefteye.track(element, Math.round(x+(w/4)-(eyeFilterWidth/2)), Math.round(y+h*(2/5)-(eyeFilterWidth/2)), eyeFilterWidth, eyeFilterWidth, false);
			right_eye_position[0] = Math.round(x+(w*3/4)-(eyeFilterWidth/2))+right_result[0];
			right_eye_position[1] = Math.round(y+h*(2/5)-(eyeFilterWidth/2))+right_result[1];
			left_eye_position[0] = Math.round(x+(w/4)-(eyeFilterWidth/2))+left_result[0];
			left_eye_position[1] = Math.round(y+h*(2/5)-(eyeFilterWidth/2))+left_result[1];
			nose_position[0] = Math.round(x+(w/2)-(noseFilterWidth/2))+nose_result[0];
			nose_position[1] = Math.round(y+h*(5/8)-(noseFilterWidth/2))+nose_result[1];

			// drawDetection(debugCC, candidate, [left_eye_position, right_eye_positions, nose_position]);

			// get eye and nose positions of model
			var lep = model.hints.leftEye;
			var rep = model.hints.rightEye;
			var mep = model.hints.nose;

			// get scaling, rotation, etc. via procrustes analysis
			var procrustes_params = procrustes([left_eye_position, right_eye_position, nose_position], [lep, rep, mep]);
			translateX = procrustes_params[0];
			translateY = procrustes_params[1];
			scaling = procrustes_params[2];
			rotation = procrustes_params[3];

			// drawFacialPoints(debugCC, [lep, rep, mep], procrustes_params);
		} else {
			// drawBoundingBox(debugCC, [x,y,w,h]);
			scaling = w/msmodelheight;
			rotation = 0;
			translateX = x-(msxmin*scaling)+0.1*w;
			translateY = y-(msymin*scaling)+0.25*h;
		}

		return [scaling, rotation, translateX, translateY];
	}

	// get initial starting point for model
	this.getInitialPosition = function(box) {
		return new Promise(function(resolve, reject) {
			getBoundingBox(box)
				.then(getFinegrainedPosition)
				.then(resolve)
				.catch(reject);
		});
	}

	// procrustes analysis
	function procrustes(template, shape) {
		// assume template and shape is a vector of x,y-coordinates
		//i.e. template = [[x1,y1], [x2,y2], [x3,y3]];
		var templateClone = [];
		var shapeClone = [];
		for (var i = 0;i < template.length;i++) {
			templateClone[i] = [template[i][0], template[i][1]];
		}
		for (var i = 0;i < shape.length;i++) {
			shapeClone[i] = [shape[i][0], shape[i][1]];
		}
		shape = shapeClone;
		template = templateClone;

		// calculate translation
		var templateMean = [0.0, 0.0];
		for (var i = 0;i < template.length;i++) {
			templateMean[0] += template[i][0];
			templateMean[1] += template[i][1];
		}
		templateMean[0] /= template.length;
		templateMean[1] /= template.length;

		var shapeMean = [0.0, 0.0];
		for (var i = 0;i < shape.length;i++) {
			shapeMean[0] += shape[i][0];
			shapeMean[1] += shape[i][1];
		}
		shapeMean[0] /= shape.length;
		shapeMean[1] /= shape.length;

		var translationX = templateMean[0] - shapeMean[0];
		var translationY = templateMean[1] - shapeMean[1];

		// centralize
		for (var i = 0;i < shape.length;i++) {
			shape[i][0] -= shapeMean[0];
			shape[i][1] -= shapeMean[1];
		}
		for (var i = 0;i < template.length;i++) {
			template[i][0] -= templateMean[0];
			template[i][1] -= templateMean[1];
		}

		// scaling

		var scaleS = 0.0;
		for (var i = 0;i < shape.length;i++) {
			scaleS += ((shape[i][0])*(shape[i][0]));
			scaleS += ((shape[i][1])*(shape[i][1]));
		}
		scaleS = Math.sqrt(scaleS/shape.length);

		var scaleT = 0.0;
		for (var i = 0;i < template.length;i++) {
			scaleT += ((template[i][0])*(template[i][0]));
			scaleT += ((template[i][1])*(template[i][1]));
		}
		scaleT = Math.sqrt(scaleT/template.length);

		var scaling = scaleT/scaleS;

		for (var i = 0;i < shape.length;i++) {
			shape[i][0] *= scaling;
			shape[i][1] *= scaling;
		}

		// rotation

		var top = 0.0;
		var bottom = 0.0;
		for (var i = 0;i < shape.length;i++) {
			top += (shape[i][0]*template[i][1] - shape[i][1]*template[i][0]);
			bottom += (shape[i][0]*template[i][0] + shape[i][1]*template[i][1]);
		}
		var rotation = Math.atan(top/bottom);

		translationX += (shapeMean[0]-(scaling*Math.cos(-rotation)*shapeMean[0])-(scaling*shapeMean[1]*Math.sin(-rotation)));
		translationY += (shapeMean[1]+(scaling*Math.sin(-rotation)*shapeMean[0])-(scaling*shapeMean[1]*Math.cos(-rotation)));

		return [translationX, translationY, scaling, rotation];
	}
}

// simple wrapper for jsfeat face detector that can run as a webworker
var jsfeat_face = function(parameters) {

	var params = parameters;
	var maxWorkSize = params.workSize;
	var useWebWorkers = params.useWebWorkers;

	var work_canvas = document.createElement('canvas');
	var work_ctx = work_canvas.getContext('2d');

	var videoWidth, videoHeight, scale, video, w, h;
	var img_u8, edg, ii_sum, ii_sqsum, ii_tilted, ii_canny, classifier;
	var worker;

	if (useWebWorkers) {
		// Courtesy of stackoverflow
		Worker.createURL = function(func_or_string) {
			var str = (typeof func_or_string === 'function')?func_or_string.toString():func_or_string;
			var blob = new Blob(['\'use strict\';\nself.onmessage ='+str], { type: 'text/javascript' });
			return window.URL.createObjectURL(blob);
		};

		Worker.create = function(func_or_string) {
			return new Worker(Worker.createURL(func_or_string));
		};

		worker = Worker.create(findFaceWorker);
	}

	this.init = function(element) {
		video = element;
		videoWidth = video.width;
		videoHeight = video.height;

		// scale down canvas we do detection on (to reduce noisy detections)
		scale = Math.min(maxWorkSize/videoWidth, maxWorkSize/videoHeight);
		w = (videoWidth*scale)|0;
		h = (videoHeight*scale)|0;

		work_canvas.height = h;
		work_canvas.width = w;

		if (!useWebWorkers) {
			img_u8 = new jsfeat.matrix_t(w, h, jsfeat.U8_t | jsfeat.C1_t);
			edg = new jsfeat.matrix_t(w, h, jsfeat.U8_t | jsfeat.C1_t);
			ii_sum = new Int32Array((w+1)*(h+1));
			ii_sqsum = new Int32Array((w+1)*(h+1));
			ii_tilted = new Int32Array((w+1)*(h+1));
			ii_canny = new Int32Array((w+1)*(h+1));
			classifier = jsfeat.haar.frontalface;
		}
	}

	this.findFace = function () {
		work_ctx.drawImage(video, 0, 0, work_canvas.width, work_canvas.height);
		var imageData = work_ctx.getImageData(0, 0, work_canvas.width, work_canvas.height);

		return new Promise(function(resolve, reject) {
			if (useWebWorkers) {
				worker.addEventListener('message', function (e) {
					if (e.data.faces.length > 0) {
						resolve(e.data.faces[0]);
					} else {
						reject();
					}
				}.bind(this), false);

				worker.postMessage({
					w: work_canvas.width,
					h: work_canvas.height,
					videoWidth: videoWidth,
					imageData:imageData,
					params: params
				});
			} else {
				jsfeat.imgproc.grayscale(imageData.data, work_canvas.width, work_canvas.height, img_u8);

				// possible params
				if(params.equalizeHistogram) {
					jsfeat.imgproc.equalize_histogram(img_u8, img_u8);
				}
				//jsfeat.imgproc.gaussian_blur(img_u8, img_u8, 3);

				jsfeat.imgproc.compute_integral_image(img_u8, ii_sum, ii_sqsum, classifier.tilted ? ii_tilted : null);

				if(params.useCanny) {
					jsfeat.imgproc.canny(img_u8, edg, 10, 50);
					jsfeat.imgproc.compute_integral_image(edg, ii_canny, null, null);
				}

				jsfeat.haar.edgesDensity = params.edgesDensity;
				var rects = jsfeat.haar.detect_multi_scale(ii_sum, ii_sqsum, ii_tilted, params.useCanny? ii_canny : null, img_u8.cols, img_u8.rows, classifier, params.scaleFactor, params.minScale);
				rects = jsfeat.haar.group_rectangles(rects, params.min_neighbors);

				for (var i = rects.length-1;i >= 0;i--) {
					if (rects[i].confidence < params.confidenceThreshold) {
						rects.splice(i,1);
					}
				}

				var rl = rects.length;
				if (rl == 0) {
					reject();
				} else {
					var best = rects[0];
					for (var i = 1; i < rl; i++) {
						if (rects[i].neighbors > best.neighbors) {
							best = rects[i];
						} else if (rects[i].neighbors == best.neighbors) {
							// if (rects[i].width > best.width) best = rects[i]; // use biggest rect
							if (rects[i].confidence > best.confidence) best = rects[i]; // use most confident rect
						}
					}

					var sc = videoWidth / img_u8.cols;
					best.x = (best.x*sc)|0;
					best.y = (best.y*sc)|0;
					best.width = (best.width*sc)|0;
					best.height = (best.height*sc)|0;

					resolve(best);
				}
			}
		});
	};
};

/**
 * this cascade is derived from https://github.com/mtschirs/js-objectdetect implementation
 * @author Martin Tschirsich / http://www.tu-darmstadt.de/~m_t
 */
jsfeat.haar.frontalface = 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