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Manav Rathi 2024-04-11 12:06:50 +05:30
parent 79a975a281
commit 38e9e901f6
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2 changed files with 62 additions and 64 deletions
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63
web/apps/photos/src/services/machineLearning/yoloFaceDetectionService.ts
View file

@ -12,7 +12,6 @@ import {
normalizePixelBetween0And1,
} from "utils/image";
import { newBox } from "utils/machineLearning";
import { removeDuplicateDetections } from "utils/machineLearning/faceDetection";
import {
computeTransformToBox,
transformBox,
@ -324,3 +323,65 @@ class YoloFaceDetectionService implements FaceDetectionService {
}
export default new YoloFaceDetectionService();
import { euclidean } from "hdbscan";
/**
* Removes duplicate face detections from an array of detections.
*
* This function sorts the detections by their probability in descending order, then iterates over them.
* For each detection, it calculates the Euclidean distance to all other detections.
* If the distance is less than or equal to the specified threshold (`withinDistance`), the other detection is considered a duplicate and is removed.
*
* @param detections - An array of face detections to remove duplicates from.
* @param withinDistance - The maximum Euclidean distance between two detections for them to be considered duplicates.
*
* @returns An array of face detections with duplicates removed.
*/
function removeDuplicateDetections(
detections: Array<FaceDetection>,
withinDistance: number,
) {
// console.time('removeDuplicates');
detections.sort((a, b) => b.probability - a.probability);
const isSelected = new Map<number, boolean>();
for (let i = 0; i < detections.length; i++) {
if (isSelected.get(i) === false) {
continue;
}
isSelected.set(i, true);
for (let j = i + 1; j < detections.length; j++) {
if (isSelected.get(j) === false) {
continue;
}
const centeri = getDetectionCenter(detections[i]);
const centerj = getDetectionCenter(detections[j]);
const dist = euclidean(
[centeri.x, centeri.y],
[centerj.x, centerj.y],
);
if (dist <= withinDistance) {
isSelected.set(j, false);
}
}
}
const uniques: Array<FaceDetection> = [];
for (let i = 0; i < detections.length; i++) {
isSelected.get(i) && uniques.push(detections[i]);
}
// console.timeEnd('removeDuplicates');
return uniques;
}
function getDetectionCenter(detection: FaceDetection) {
const center = new Point(0, 0);
// TODO: first 4 landmarks is applicable to blazeface only
// this needs to consider eyes, nose and mouth landmarks to take center
detection.landmarks?.slice(0, 4).forEach((p) => {
center.x += p.x;
center.y += p.y;
});
return center.div({ x: 4, y: 4 });
}

63
web/apps/photos/src/utils/machineLearning/faceDetection.ts
View file

@ -1,63 +0,0 @@
import { euclidean } from "hdbscan";
import { FaceDetection } from "types/machineLearning";
import { Point } from "../../../thirdparty/face-api/classes";
export function getDetectionCenter(detection: FaceDetection) {
const center = new Point(0, 0);
// TODO: first 4 landmarks is applicable to blazeface only
// this needs to consider eyes, nose and mouth landmarks to take center
detection.landmarks?.slice(0, 4).forEach((p) => {
center.x += p.x;
center.y += p.y;
});
return center.div({ x: 4, y: 4 });
}
/**
* Removes duplicate face detections from an array of detections.
*
* This function sorts the detections by their probability in descending order, then iterates over them.
* For each detection, it calculates the Euclidean distance to all other detections.
* If the distance is less than or equal to the specified threshold (`withinDistance`), the other detection is considered a duplicate and is removed.
*
* @param detections - An array of face detections to remove duplicates from.
* @param withinDistance - The maximum Euclidean distance between two detections for them to be considered duplicates.
*
* @returns An array of face detections with duplicates removed.
*/
export function removeDuplicateDetections(
detections: Array<FaceDetection>,
withinDistance: number,
) {
// console.time('removeDuplicates');
detections.sort((a, b) => b.probability - a.probability);
const isSelected = new Map<number, boolean>();
for (let i = 0; i < detections.length; i++) {
if (isSelected.get(i) === false) {
continue;
}
isSelected.set(i, true);
for (let j = i + 1; j < detections.length; j++) {
if (isSelected.get(j) === false) {
continue;
}
const centeri = getDetectionCenter(detections[i]);
const centerj = getDetectionCenter(detections[j]);
const dist = euclidean(
[centeri.x, centeri.y],
[centerj.x, centerj.y],
);
if (dist <= withinDistance) {
isSelected.set(j, false);
}
}
}
const uniques: Array<FaceDetection> = [];
for (let i = 0; i < detections.length; i++) {
isSelected.get(i) && uniques.push(detections[i]);
}
// console.timeEnd('removeDuplicates');
return uniques;
}