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Commit d7962a58 by vincent
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latest dist

parent e0b53014
Showing with 165 additions and 46 deletions
dist/NetInput.js
import { getContext2dOrThrow, getElement, getMediaDimensions } from './utils';
import { createCanvas, getContext2dOrThrow, getElement, getMediaDimensions } from './utils';
var NetInput = /** @class */ (function () {
function NetInput(mediaArg, dims) {
var _this = this;
......@@ -28,9 +28,7 @@ var NetInput = /** @class */ (function () {
}
// if input is batch type, make sure every canvas has the same dimensions
var _a = this.dims || dims || getMediaDimensions(media), width = _a.width, height = _a.height;
var canvas = document.createElement('canvas');
canvas.width = width;
canvas.height = height;
var canvas = createCanvas({ width: width, height: height });
getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
this._canvases.push(canvas);
};
......
dist/NetInput.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/extractFaceTensors.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
import { NetInput } from './NetInput';
import { TNetInput } from './types';
/**
* Extracts the tensors of the image regions containing the detected faces.
* Returned tensors have to be disposed manually once you don't need them anymore!
* Useful if you want to compute the face descriptors for the face
* images. Using this method is faster then extracting a canvas for each face and
* converting them to tensors individually.
*
* @param input The image that face detection has been performed on.
* @param detections The face detection results for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
export declare function extractFaceTensors(image: tf.Tensor | NetInput | TNetInput, detections: FaceDetectionResult[]): tf.Tensor4D[];
dist/extractFaceTensors.js 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from './transformInputs';
/**
* Extracts the tensors of the image regions containing the detected faces.
* Returned tensors have to be disposed manually once you don't need them anymore!
* Useful if you want to compute the face descriptors for the face
* images. Using this method is faster then extracting a canvas for each face and
* converting them to tensors individually.
*
* @param input The image that face detection has been performed on.
* @param detections The face detection results for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
export function extractFaceTensors(image, detections) {
return tf.tidy(function () {
var imgTensor = getImageTensor(image);
// TODO handle batches
var _a = imgTensor.shape, batchSize = _a[0], imgHeight = _a[1], imgWidth = _a[2], numChannels = _a[3];
var faceTensors = detections.map(function (det) {
var _a = det.forSize(imgWidth, imgHeight).box, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return tf.slice(imgTensor, [0, y, x, 0], [1, height, width, numChannels]);
});
return faceTensors;
});
}
//# sourceMappingURL=extractFaceTensors.js.map
\ No newline at end of file
dist/extractFaceTensors.js.map 0 → 100644
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\ No newline at end of file
dist/extractFaces.d.ts 0 → 100644
import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
/**
* Extracts the image regions containing the detected faces.
*
* @param input The image that face detection has been performed on.
* @param detections The face detection results for that image.
* @returns The Canvases of the corresponding image region for each detected face.
*/
export declare function extractFaces(image: HTMLCanvasElement, detections: FaceDetectionResult[]): HTMLCanvasElement[];
dist/extractFaces.js 0 → 100644
import { createCanvas, getContext2dOrThrow } from './utils';
/**
* Extracts the image regions containing the detected faces.
*
* @param input The image that face detection has been performed on.
* @param detections The face detection results for that image.
* @returns The Canvases of the corresponding image region for each detected face.
*/
export function extractFaces(image, detections) {
var ctx = getContext2dOrThrow(image);
return detections.map(function (det) {
var _a = det.forSize(image.width, image.height).box, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var faceImg = createCanvas({ width: width, height: height });
getContext2dOrThrow(faceImg)
.putImageData(ctx.getImageData(x, y, width, height), 0, 0);
return faceImg;
});
}
//# sourceMappingURL=extractFaces.js.map
\ No newline at end of file
dist/extractFaces.js.map 0 → 100644
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dist/face-recognition.min.js
This source diff could not be displayed because it is too large. You can view the blob instead.
dist/faceDetectionNet/FaceDetectionResult.d.ts
import { FaceDetectionNet } from './types';
export declare class FaceDetectionResult {
private score;
private top;
private left;
private bottom;
private right;
constructor(score: number, top: number, left: number, bottom: number, right: number);
private _score;
private _topRelative;
private _leftRelative;
private _bottomRelative;
private _rightRelative;
constructor(score: number, topRelative: number, leftRelative: number, bottomRelative: number, rightRelative: number);
forSize(width: number, height: number): FaceDetectionNet.Detection;
}
dist/faceDetectionNet/FaceDetectionResult.js
var FaceDetectionResult = /** @class */ (function () {
function FaceDetectionResult(score, top, left, bottom, right) {
this.score = score;
this.top = Math.max(0, top),
this.left = Math.max(0, left),
this.bottom = Math.min(1.0, bottom),
this.right = Math.min(1.0, right);
function FaceDetectionResult(score, topRelative, leftRelative, bottomRelative, rightRelative) {
this._score = score;
this._topRelative = Math.max(0, topRelative),
this._leftRelative = Math.max(0, leftRelative),
this._bottomRelative = Math.min(1.0, bottomRelative),
this._rightRelative = Math.min(1.0, rightRelative);
}
FaceDetectionResult.prototype.forSize = function (width, height) {
var x = Math.floor(this._leftRelative * width);
var y = Math.floor(this._topRelative * height);
return {
score: this.score,
score: this._score,
box: {
top: this.top * height,
left: this.left * width,
bottom: this.bottom * height,
right: this.right * width
x: x,
y: y,
width: Math.floor(this._rightRelative * width) - x,
height: Math.floor(this._bottomRelative * height) - y
}
};
};
......
dist/faceDetectionNet/FaceDetectionResult.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/faceDetectionNet/types.d.ts
......@@ -56,10 +56,10 @@ export declare namespace FaceDetectionNet {
type Detection = {
score: number;
box: {
top: number;
left: number;
right: number;
bottom: number;
x: number;
y: number;
width: number;
height: number;
};
};
}
dist/faceRecognitionNet/index.js
......@@ -10,7 +10,13 @@ export function faceRecognitionNet(weights) {
var params = extractParams(weights);
function forward(input) {
return tf.tidy(function () {
var x = normalize(padToSquare(getImageTensor(input)));
// TODO pad on both sides, to keep face centered
var x = padToSquare(getImageTensor(input));
// work with 150 x 150 sized face images
if (x.shape[1] !== 150 || x.shape[2] !== 150) {
x = tf.image.resizeBilinear(x, [150, 150]);
}
x = normalize(x);
var out = convDown(x, params.conv32_down);
out = tf.maxPool(out, 3, 2, 'valid');
out = residual(out, params.conv32_1);
......
dist/faceRecognitionNet/index.js.map
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dist/index.d.ts
......@@ -2,5 +2,8 @@ import { euclideanDistance } from './euclideanDistance';
import { faceDetectionNet } from './faceDetectionNet';
import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput';
export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput };
import * as tf from '@tensorflow/tfjs-core';
export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput, tf };
export * from './extractFaces';
export * from './extractFaceTensors';
export * from './utils';
dist/index.js
......@@ -2,6 +2,9 @@ import { euclideanDistance } from './euclideanDistance';
import { faceDetectionNet } from './faceDetectionNet';
import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput';
export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput };
import * as tf from '@tensorflow/tfjs-core';
export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput, tf };
export * from './extractFaces';
export * from './extractFaceTensors';
export * from './utils';
//# sourceMappingURL=index.js.map
\ No newline at end of file
dist/index.js.map
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dist/types.d.ts
......@@ -10,7 +10,9 @@ export declare type DrawBoxOptions = {
color: string;
};
export declare type DrawTextOptions = {
lineWidth: number;
fontSize: number;
fontStyle: string;
color: string;
};
export declare type DrawOptions = DrawBoxOptions & DrawTextOptions;
dist/utils.d.ts
import { FaceDetectionNet } from './faceDetectionNet/types';
import { DrawBoxOptions, DrawTextOptions } from './types';
import { Dimensions, DrawBoxOptions, DrawOptions, DrawTextOptions } from './types';
export declare function isFloat(num: number): boolean;
export declare function round(num: number): number;
export declare function getElement(arg: string | any): any;
export declare function getContext2dOrThrow(canvas: HTMLCanvasElement): CanvasRenderingContext2D;
export declare function getMediaDimensions(media: HTMLImageElement | HTMLVideoElement): HTMLImageElement | {
export declare function createCanvas({width, height}: Dimensions): HTMLCanvasElement;
export declare function createCanvasWithImageData({width, height}: Dimensions, buf: Uint8ClampedArray): HTMLCanvasElement;
export declare function getMediaDimensions(media: HTMLImageElement | HTMLVideoElement): {
width: number;
height: number;
};
export declare function bufferToImage(buf: Blob): Promise<HTMLImageElement>;
export declare function getDefaultDrawOptions(): DrawOptions;
export declare function drawBox(ctx: CanvasRenderingContext2D, x: number, y: number, w: number, h: number, options: DrawBoxOptions): void;
export declare function drawText(ctx: CanvasRenderingContext2D, x: number, y: number, text: string, options: DrawTextOptions): void;
export declare function drawDetection(canvasArg: string | HTMLCanvasElement, detection: FaceDetectionNet.Detection | FaceDetectionNet.Detection[], options?: DrawBoxOptions & DrawTextOptions & {
......
dist/utils.js
......@@ -17,7 +17,23 @@ export function getContext2dOrThrow(canvas) {
}
return ctx;
}
export function createCanvas(_a) {
var width = _a.width, height = _a.height;
var canvas = document.createElement('canvas');
canvas.width = width;
canvas.height = height;
return canvas;
}
export function createCanvasWithImageData(_a, buf) {
var width = _a.width, height = _a.height;
var canvas = createCanvas({ width: width, height: height });
getContext2dOrThrow(canvas).putImageData(new ImageData(buf, width, height), 0, 0);
return canvas;
}
export function getMediaDimensions(media) {
if (media instanceof HTMLImageElement) {
return { width: media.naturalWidth, height: media.naturalHeight };
}
if (media instanceof HTMLVideoElement) {
return { width: media.videoWidth, height: media.videoHeight };
}
......@@ -39,15 +55,24 @@ export function bufferToImage(buf) {
reader.readAsDataURL(buf);
});
}
export function getDefaultDrawOptions() {
return {
color: 'blue',
lineWidth: 2,
fontSize: 20,
fontStyle: 'Georgia'
};
}
export function drawBox(ctx, x, y, w, h, options) {
ctx.strokeStyle = options.color;
ctx.lineWidth = options.lineWidth;
ctx.strokeRect(x, y, w, h);
}
export function drawText(ctx, x, y, text, options) {
var padText = 2 + options.lineWidth;
ctx.fillStyle = options.color;
ctx.font = options.fontSize + "px " + options.fontStyle;
ctx.fillText(text, x, y);
ctx.fillText(text, x + padText, y + padText + (options.fontSize * 0.6));
}
export function drawDetection(canvasArg, detection, options) {
var canvas = getElement(canvasArg);
......@@ -59,13 +84,13 @@ export function drawDetection(canvasArg, detection, options) {
: [detection];
detectionArray.forEach(function (det) {
var score = det.score, box = det.box;
var left = box.left, right = box.right, top = box.top, bottom = box.bottom;
var _a = (options || {}), _b = _a.color, color = _b === void 0 ? 'blue' : _b, _c = _a.lineWidth, lineWidth = _c === void 0 ? 2 : _c, _d = _a.fontSize, fontSize = _d === void 0 ? 20 : _d, _e = _a.fontStyle, fontStyle = _e === void 0 ? 'Georgia' : _e, _f = _a.withScore, withScore = _f === void 0 ? true : _f;
var padText = 2 + lineWidth;
var x = box.x, y = box.y, width = box.width, height = box.height;
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
var withScore = Object.assign({ withScore: true }, (options || {})).withScore;
var ctx = getContext2dOrThrow(canvas);
drawBox(ctx, left, top, right - left, bottom - top, { lineWidth: lineWidth, color: color });
drawBox(ctx, x, y, width, height, drawOptions);
if (withScore) {
drawText(ctx, left + padText, top + (fontSize * 0.6) + padText, "" + round(score), { fontSize: fontSize, fontStyle: fontStyle, color: color });
drawText(ctx, x, y, "" + round(score), drawOptions);
}
});
}
......
dist/utils.js.map
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