[web][photos] solve TODOs
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laurenspriem
parent
41b22abc66
commit
ae5496f306
2 changed files with 110 additions and 115 deletions
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@ -27,8 +27,13 @@ import {
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pixelRGBBilinear,
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warpAffineFloat32List,
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} from "./image";
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import { transformFaceDetections } from "./transform-box";
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import {
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Matrix as transformMatrix,
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applyToPoint,
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compose,
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scale,
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translate,
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} from "transformation-matrix";
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/**
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* Index faces in the given file.
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*
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@ -138,7 +143,7 @@ const indexFaces_ = async (enteFile: EnteFile, imageBitmap: ImageBitmap) => {
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/**
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* Detect faces in the given {@link imageBitmap}.
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*
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* The model used is YOLO, running in an ONNX runtime.
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* The model used is YOLOv5Face, running in an ONNX runtime.
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*/
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const detectFaces = async (
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imageBitmap: ImageBitmap,
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@ -149,16 +154,14 @@ const detectFaces = async (
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const { yoloInput, yoloSize } =
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convertToYOLOInputFloat32ChannelsFirst(imageBitmap);
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const yoloOutput = await workerBridge.detectFaces(yoloInput);
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const faces = faceDetectionsFromYOLOOutput(yoloOutput);
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const faces = filterExtractDetectionsFromYOLOOutput(yoloOutput);
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const faceDetections = transformFaceDetections(
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faces,
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rect(yoloSize),
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rect(imageBitmap),
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);
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const maxFaceDistancePercent = Math.sqrt(2) / 100;
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const maxFaceDistance = imageBitmap.width * maxFaceDistancePercent;
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return removeDuplicateDetections(faceDetections, maxFaceDistance);
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return naiveNonMaxSuppression(faceDetections, 0.4);
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};
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/**
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@ -214,14 +217,16 @@ const convertToYOLOInputFloat32ChannelsFirst = (imageBitmap: ImageBitmap) => {
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};
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/**
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* Extract detected faces from the YOLO's output.
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* Extract detected faces from the YOLOv5Face's output.
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*
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* Only detections that exceed a minimum score are returned.
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*
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* @param rows A Float32Array of shape [25200, 16], where each row
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* represents a bounding box.
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*/
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const faceDetectionsFromYOLOOutput = (rows: Float32Array): FaceDetection[] => {
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const filterExtractDetectionsFromYOLOOutput = (
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rows: Float32Array,
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): FaceDetection[] => {
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const faces: FaceDetection[] = [];
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// Iterate over each row.
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for (let i = 0; i < rows.length; i += 16) {
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@ -266,61 +271,111 @@ const faceDetectionsFromYOLOOutput = (rows: Float32Array): FaceDetection[] => {
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};
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/**
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* Removes duplicate face detections from an array of detections.
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*
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* This function sorts the detections by their probability in descending order,
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* then iterates over them.
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*
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* For each detection, it calculates the Euclidean distance to all other
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* detections.
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*
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* If the distance is less than or equal to the specified threshold
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* (`withinDistance`), the other detection is considered a duplicate and is
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* removed.
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*
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* @param detections - An array of face detections to remove duplicates from.
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*
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* @param withinDistance - The maximum Euclidean distance between two detections
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* for them to be considered duplicates.
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*
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* @returns An array of face detections with duplicates removed.
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* Transform the given {@link faceDetections} from their coordinate system in
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* which they were detected ({@link inBox}) back to the coordinate system of the
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* original image ({@link toBox}).
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*/
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const removeDuplicateDetections = (
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const transformFaceDetections = (
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faceDetections: FaceDetection[],
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inBox: Box,
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toBox: Box,
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): FaceDetection[] => {
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const transform = boxTransformationMatrix(inBox, toBox);
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return faceDetections.map((f) => ({
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box: transformBox(f.box, transform),
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landmarks: f.landmarks.map((p) => transformPoint(p, transform)),
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probability: f.probability,
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}));
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};
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const boxTransformationMatrix = (inBox: Box, toBox: Box): transformMatrix =>
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compose(
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translate(toBox.x, toBox.y),
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scale(toBox.width / inBox.width, toBox.height / inBox.height),
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);
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const transformPoint = (point: Point, transform: transformMatrix) => {
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const txdPoint = applyToPoint(transform, point);
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return new Point(txdPoint.x, txdPoint.y);
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};
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const transformBox = (box: Box, transform: transformMatrix) => {
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const topLeft = transformPoint(new Point(box.x, box.y), transform);
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const bottomRight = transformPoint(
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new Point(box.x + box.width, box.y + box.height),
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transform,
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);
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return new Box({
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x: topLeft.x,
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y: topLeft.y,
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width: bottomRight.x - topLeft.x,
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height: bottomRight.y - topLeft.y,
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});
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};
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/**
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* Remove overlapping faces from an array of face detections through non-maximum suppression algorithm.
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*
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* This function sorts the detections by their probability in descending order, then iterates over them.
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*
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* For each detection, it calculates the Intersection over Union (IoU) with all other detections.
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*
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* If the IoU is greater than or equal to the specified threshold (`iouThreshold`), the other detection is considered overlapping and is removed.
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*
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* @param detections - An array of face detections to remove overlapping faces from.
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*
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* @param iouThreshold - The minimum IoU between two detections for them to be considered overlapping.
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*
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* @returns An array of face detections with overlapping faces removed
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*/
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const naiveNonMaxSuppression = (
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detections: FaceDetection[],
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withinDistance: number,
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) => {
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iouThreshold: number,
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): FaceDetection[] => {
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// Sort the detections by score, the highest first
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detections.sort((a, b) => b.probability - a.probability);
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const dupIndices = new Set<number>();
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for (let i = 0; i < detections.length; i++) {
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if (dupIndices.has(i)) continue;
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// Loop through the detections and calculate the IOU
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for (let i = 0; i < detections.length - 1; i++) {
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for (let j = i + 1; j < detections.length; j++) {
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if (dupIndices.has(j)) continue;
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const centeri = faceDetectionCenter(detections[i]);
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const centerj = faceDetectionCenter(detections[j]);
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const dist = euclidean(
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[centeri.x, centeri.y],
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[centerj.x, centerj.y],
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);
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if (dist <= withinDistance) dupIndices.add(j);
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const iou = calculateIOU(detections[i], detections[j]);
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if (iou >= iouThreshold) {
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detections.splice(j, 1);
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j--;
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}
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}
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}
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return detections.filter((_, i) => !dupIndices.has(i));
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return detections;
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};
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const faceDetectionCenter = (detection: FaceDetection) => {
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const center = new Point(0, 0);
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// TODO-ML(LAURENS): first 4 landmarks is applicable to blazeface only this
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// needs to consider eyes, nose and mouth landmarks to take center
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detection.landmarks?.slice(0, 4).forEach((p) => {
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center.x += p.x;
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center.y += p.y;
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});
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return new Point(center.x / 4, center.y / 4);
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const calculateIOU = (a: FaceDetection, b: FaceDetection): number => {
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const intersectionMinX = Math.max(a.box.x, b.box.x);
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const intersectionMinY = Math.max(a.box.y, b.box.y);
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const intersectionMaxX = Math.min(
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a.box.x + a.box.width,
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b.box.x + b.box.width,
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);
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const intersectionMaxY = Math.min(
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a.box.y + a.box.height,
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b.box.y + b.box.height,
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);
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const intersectionWidth = intersectionMaxX - intersectionMinX;
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const intersectionHeight = intersectionMaxY - intersectionMinY;
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if (intersectionWidth < 0 || intersectionHeight < 0) {
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return 0.0; // If boxes do not overlap, IoU is 0
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}
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const areaA = a.box.width * a.box.height;
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const areaB = b.box.width * b.box.height;
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const intersectionArea = intersectionWidth * intersectionHeight;
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const unionArea = areaA + areaB - intersectionArea;
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return intersectionArea / unionArea;
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};
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const makeFaceID = (
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@ -689,14 +744,12 @@ const extractFaceCrop = (
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const scaleForPadding = 1 + padding * 2;
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const paddedBox = roundBox(enlargeBox(alignmentBox, scaleForPadding));
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// TODO-ML(LAURENS): The rotation doesn't seem to be used? it's set to 0.
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return cropWithRotation(imageBitmap, paddedBox, 0, 256);
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return cropWithRotation(imageBitmap, paddedBox, 256);
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};
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const cropWithRotation = (
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imageBitmap: ImageBitmap,
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cropBox: Box,
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rotation: number,
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maxDimension: number,
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) => {
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const box = roundBox(cropBox);
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@ -714,7 +767,6 @@ const cropWithRotation = (
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offscreenCtx.imageSmoothingQuality = "high";
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offscreenCtx.translate(outputSize.width / 2, outputSize.height / 2);
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rotation && offscreenCtx.rotate(rotation);
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const outputBox = new Box({
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x: -outputSize.width / 2,
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@ -1,57 +0,0 @@
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import { Box, Point } from "services/face/geom";
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import type { FaceDetection } from "services/face/types";
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// TODO-ML(LAURENS): Do we need two separate Matrix libraries?
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//
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// Keeping this in a separate file so that we can audit this. If these can be
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// expressed using ml-matrix, then we can move this code to f-index.ts
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import {
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Matrix,
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applyToPoint,
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compose,
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scale,
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translate,
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} from "transformation-matrix";
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/**
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* Transform the given {@link faceDetections} from their coordinate system in
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* which they were detected ({@link inBox}) back to the coordinate system of the
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* original image ({@link toBox}).
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*/
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export const transformFaceDetections = (
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faceDetections: FaceDetection[],
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inBox: Box,
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toBox: Box,
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): FaceDetection[] => {
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const transform = boxTransformationMatrix(inBox, toBox);
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return faceDetections.map((f) => ({
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box: transformBox(f.box, transform),
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landmarks: f.landmarks.map((p) => transformPoint(p, transform)),
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probability: f.probability,
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}));
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};
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const boxTransformationMatrix = (inBox: Box, toBox: Box): Matrix =>
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compose(
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translate(toBox.x, toBox.y),
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scale(toBox.width / inBox.width, toBox.height / inBox.height),
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);
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const transformPoint = (point: Point, transform: Matrix) => {
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const txdPoint = applyToPoint(transform, point);
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return new Point(txdPoint.x, txdPoint.y);
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};
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const transformBox = (box: Box, transform: Matrix) => {
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const topLeft = transformPoint(new Point(box.x, box.y), transform);
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const bottomRight = transformPoint(
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new Point(box.x + box.width, box.y + box.height),
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transform,
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);
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return new Box({
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x: topLeft.x,
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y: topLeft.y,
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width: bottomRight.x - topLeft.x,
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height: bottomRight.y - topLeft.y,
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});
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};
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