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Commit 68fe7ca5 by vincent
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build dist

parent a644af17
Showing with 545 additions and 164 deletions
dist/NetInput.js
import { createCanvas, getContext2dOrThrow, getElement, getMediaDimensions } from './utils';
import { createCanvasFromMedia, getElement } from './utils';
var NetInput = /** @class */ (function () {
function NetInput(mediaArg, dims) {
var _this = this;
......@@ -27,10 +27,8 @@ var NetInput = /** @class */ (function () {
return;
}
// 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 = createCanvas({ width: width, height: height });
getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
this._canvases.push(canvas);
var canvasDims = this.dims || dims;
this._canvases.push(createCanvasFromMedia(media, canvasDims));
};
Object.defineProperty(NetInput.prototype, "canvases", {
get: function () {
......
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/Point.d.ts 0 → 100644
export declare class Point {
x: number;
y: number;
constructor(x: number, y: number);
}
dist/Point.js 0 → 100644
var Point = /** @class */ (function () {
function Point(x, y) {
this.x = x;
this.y = y;
}
return Point;
}());
export { Point };
//# sourceMappingURL=Point.js.map
\ No newline at end of file
dist/Point.js.map 0 → 100644
{"version":3,"file":"Point.js","sourceRoot":"","sources":["../src/Point.ts"],"names":[],"mappings":"AAAA;IAIE,eAAY,CAAS,EAAE,CAAS;QAC9B,IAAI,CAAC,CAAC,GAAG,CAAC,CAAA;QACV,IAAI,CAAC,CAAC,GAAG,CAAC,CAAA;IACZ,CAAC;IACH,YAAC;AAAD,CAAC,AARD,IAQC"}
\ No newline at end of file
dist/Rect.d.ts 0 → 100644
export declare class Rect {
x: number;
y: number;
width: number;
height: number;
constructor(x: number, y: number, width: number, height: number);
}
dist/Rect.js 0 → 100644
var Rect = /** @class */ (function () {
function Rect(x, y, width, height) {
this.x = x;
this.y = y;
this.width = width;
this.height = height;
}
return Rect;
}());
export { Rect };
//# sourceMappingURL=Rect.js.map
\ No newline at end of file
dist/Rect.js.map 0 → 100644
{"version":3,"file":"Rect.js","sourceRoot":"","sources":["../src/Rect.ts"],"names":[],"mappings":"AAAA;IAME,cAAY,CAAS,EAAE,CAAS,EAAE,KAAa,EAAE,MAAc;QAC7D,IAAI,CAAC,CAAC,GAAG,CAAC,CAAA;QACV,IAAI,CAAC,CAAC,GAAG,CAAC,CAAA;QACV,IAAI,CAAC,KAAK,GAAG,KAAK,CAAA;QAClB,IAAI,CAAC,MAAM,GAAG,MAAM,CAAA;IACtB,CAAC;IACH,WAAC;AAAD,CAAC,AAZD,IAYC"}
\ No newline at end of file
dist/commons/convLayer.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from './types';
export declare function convLayer(x: tf.Tensor4D, params: ConvParams, padding?: 'valid' | 'same', withRelu?: boolean): tf.Tensor4D;
dist/commons/convLayer.js 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
export function convLayer(x, params, padding, withRelu) {
if (padding === void 0) { padding = 'same'; }
if (withRelu === void 0) { withRelu = false; }
return tf.tidy(function () {
var out = tf.add(tf.conv2d(x, params.filters, [1, 1], padding), params.bias);
return withRelu ? tf.relu(out) : out;
});
}
//# sourceMappingURL=convLayer.js.map
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dist/commons/convLayer.js.map 0 → 100644
{"version":3,"file":"convLayer.js","sourceRoot":"","sources":["../../src/commons/convLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,MAAM,oBACJ,CAAc,EACd,MAAkB,EAClB,OAAkC,EAClC,QAAyB;IADzB,wBAAA,EAAA,gBAAkC;IAClC,yBAAA,EAAA,gBAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,IAAM,GAAG,GAAG,EAAE,CAAC,GAAG,CAChB,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,EAC7C,MAAM,CAAC,IAAI,CACG,CAAA;QAEhB,OAAO,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAA;IACtC,CAAC,CAAC,CAAA;AACJ,CAAC"}
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dist/commons/extractConvParamsFactory.d.ts 0 → 100644
import { ConvParams, ExtractWeightsFunction } from './types';
export declare function extractConvParamsFactory(extractWeights: ExtractWeightsFunction): (channelsIn: number, channelsOut: number, filterSize: number) => ConvParams;
dist/commons/extractConvParamsFactory.js 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
export function extractConvParamsFactory(extractWeights) {
return function (channelsIn, channelsOut, filterSize) {
var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut));
return {
filters: filters,
bias: bias
};
};
}
//# sourceMappingURL=extractConvParamsFactory.js.map
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dist/commons/extractConvParamsFactory.js.map 0 → 100644
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dist/commons/extractWeightsFactory.d.ts 0 → 100644
export declare function extractWeightsFactory(weights: Float32Array): {
extractWeights: (numWeights: number) => Float32Array;
getRemainingWeights: () => Float32Array;
};
dist/commons/extractWeightsFactory.js 0 → 100644
export function extractWeightsFactory(weights) {
var remainingWeights = weights;
function extractWeights(numWeights) {
var ret = remainingWeights.slice(0, numWeights);
remainingWeights = remainingWeights.slice(numWeights);
return ret;
}
function getRemainingWeights() {
return remainingWeights;
}
return {
extractWeights: extractWeights,
getRemainingWeights: getRemainingWeights
};
}
//# sourceMappingURL=extractWeightsFactory.js.map
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dist/commons/extractWeightsFactory.js.map 0 → 100644
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\ No newline at end of file
dist/commons/types.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
export declare type ConvParams = {
filters: tf.Tensor4D;
bias: tf.Tensor1D;
};
export declare type ExtractWeightsFunction = (numWeights: number) => Float32Array;
dist/commons/types.js 0 → 100644
//# sourceMappingURL=types.js.map
\ No newline at end of file
dist/commons/types.js.map 0 → 100644
{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/commons/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
dist/extractFaceTensors.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { NetInput } from './NetInput';
import { TNetInput } from './types';
/**
......@@ -13,4 +13,4 @@ import { TNetInput } from './types';
* @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[];
export declare function extractFaceTensors(image: tf.Tensor | NetInput | TNetInput, detections: FaceDetection[]): tf.Tensor4D[];
dist/extractFaceTensors.js
......@@ -17,7 +17,7 @@ export function extractFaceTensors(image, detections) {
// 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;
var _a = det.forSize(imgWidth, imgHeight).getBox(), 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;
......
dist/extractFaceTensors.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/extractFaces.d.ts
import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
/**
* Extracts the image regions containing the detected faces.
*
......@@ -6,4 +6,4 @@ import { FaceDetectionResult } from './faceDetectionNet/FaceDetectionResult';
* @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[];
export declare function extractFaces(image: HTMLCanvasElement, detections: FaceDetection[]): HTMLCanvasElement[];
dist/extractFaces.js
......@@ -9,7 +9,7 @@ import { createCanvas, getContext2dOrThrow } from './utils';
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 _a = det.forSize(image.width, image.height).getBox(), 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);
......
dist/extractFaces.js.map
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dist/face-api.js
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dist/face-api.min.js
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dist/faceDetectionNet/FaceDetection.d.ts 0 → 100644
import { Rect } from '../Rect';
import { Dimensions } from '../types';
export declare class FaceDetection {
private _score;
private _box;
private _imageWidth;
private _imageHeight;
constructor(score: number, relativeBox: Rect, imageDims: Dimensions);
getScore(): number;
getBox(): Rect;
getRelativeBox(): Rect;
forSize(width: number, height: number): FaceDetection;
}
dist/faceDetectionNet/FaceDetection.js 0 → 100644
import { Rect } from '../Rect';
var FaceDetection = /** @class */ (function () {
function FaceDetection(score, relativeBox, imageDims) {
var width = imageDims.width, height = imageDims.height;
this._imageWidth = width;
this._imageHeight = height;
this._score = score;
this._box = new Rect(Math.floor(relativeBox.x * width), Math.floor(relativeBox.y * height), Math.floor(relativeBox.width * width), Math.floor(relativeBox.height * height));
}
FaceDetection.prototype.getScore = function () {
return this._score;
};
FaceDetection.prototype.getBox = function () {
return this._box;
};
FaceDetection.prototype.getRelativeBox = function () {
return new Rect(this._box.x / this._imageWidth, this._box.y / this._imageHeight, this._box.width / this._imageWidth, this._box.height / this._imageHeight);
};
FaceDetection.prototype.forSize = function (width, height) {
return new FaceDetection(this._score, this.getRelativeBox(), { width: width, height: height });
};
return FaceDetection;
}());
export { FaceDetection };
//# sourceMappingURL=FaceDetection.js.map
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dist/faceDetectionNet/FaceDetection.js.map 0 → 100644
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dist/faceDetectionNet/FaceDetectionResult.d.ts deleted 100644 → 0
import { FaceDetectionNet } from './types';
export declare class FaceDetectionResult {
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 deleted 100644 → 0
var FaceDetectionResult = /** @class */ (function () {
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,
box: {
x: x,
y: y,
width: Math.floor(this._rightRelative * width) - x,
height: Math.floor(this._bottomRelative * height) - y
}
};
};
return FaceDetectionResult;
}());
export { FaceDetectionResult };
//# sourceMappingURL=FaceDetectionResult.js.map
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dist/faceDetectionNet/FaceDetectionResult.js.map deleted 100644 → 0
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dist/faceDetectionNet/boxPredictionLayer.js
import * as tf from '@tensorflow/tfjs-core';
function convWithBias(x, params) {
return tf.tidy(function () {
return tf.add(tf.conv2d(x, params.filters, [1, 1], 'same'), params.bias);
});
}
import { convLayer } from '../commons/convLayer';
export function boxPredictionLayer(x, params) {
return tf.tidy(function () {
var batchSize = x.shape[0];
var boxPredictionEncoding = tf.reshape(convWithBias(x, params.box_encoding_predictor_params), [batchSize, -1, 1, 4]);
var classPrediction = tf.reshape(convWithBias(x, params.class_predictor_params), [batchSize, -1, 3]);
var boxPredictionEncoding = tf.reshape(convLayer(x, params.box_encoding_predictor_params), [batchSize, -1, 1, 4]);
var classPrediction = tf.reshape(convLayer(x, params.class_predictor_params), [batchSize, -1, 3]);
return {
boxPredictionEncoding: boxPredictionEncoding,
classPrediction: classPrediction
......
dist/faceDetectionNet/boxPredictionLayer.js.map
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dist/faceDetectionNet/extractParams.js
import * as tf from '@tensorflow/tfjs-core';
import { extractWeightsFactory } from '../commons/extractWeightsFactory';
function extractorsFactory(extractWeights) {
function extractDepthwiseConvParams(numChannels) {
var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
......@@ -14,7 +15,7 @@ function extractorsFactory(extractWeights) {
batch_norm_variance: batch_norm_variance
};
}
function extractConvWithBiasParams(channelsIn, channelsOut, filterSize) {
function extractConvParams(channelsIn, channelsOut, filterSize) {
var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
var bias = tf.tensor1d(extractWeights(channelsOut));
return {
......@@ -23,7 +24,7 @@ function extractorsFactory(extractWeights) {
};
}
function extractPointwiseConvParams(channelsIn, channelsOut, filterSize) {
var _a = extractConvWithBiasParams(channelsIn, channelsOut, filterSize), filters = _a.filters, bias = _a.bias;
var _a = extractConvParams(channelsIn, channelsOut, filterSize), filters = _a.filters, bias = _a.bias;
return {
filters: filters,
batch_norm_offset: bias
......@@ -72,18 +73,18 @@ function extractorsFactory(extractWeights) {
var conv_5_params = extractPointwiseConvParams(128, 256, 3);
var conv_6_params = extractPointwiseConvParams(256, 64, 1);
var conv_7_params = extractPointwiseConvParams(64, 128, 3);
var box_encoding_0_predictor_params = extractConvWithBiasParams(512, 12, 1);
var class_predictor_0_params = extractConvWithBiasParams(512, 9, 1);
var box_encoding_1_predictor_params = extractConvWithBiasParams(1024, 24, 1);
var class_predictor_1_params = extractConvWithBiasParams(1024, 18, 1);
var box_encoding_2_predictor_params = extractConvWithBiasParams(512, 24, 1);
var class_predictor_2_params = extractConvWithBiasParams(512, 18, 1);
var box_encoding_3_predictor_params = extractConvWithBiasParams(256, 24, 1);
var class_predictor_3_params = extractConvWithBiasParams(256, 18, 1);
var box_encoding_4_predictor_params = extractConvWithBiasParams(256, 24, 1);
var class_predictor_4_params = extractConvWithBiasParams(256, 18, 1);
var box_encoding_5_predictor_params = extractConvWithBiasParams(128, 24, 1);
var class_predictor_5_params = extractConvWithBiasParams(128, 18, 1);
var box_encoding_0_predictor_params = extractConvParams(512, 12, 1);
var class_predictor_0_params = extractConvParams(512, 9, 1);
var box_encoding_1_predictor_params = extractConvParams(1024, 24, 1);
var class_predictor_1_params = extractConvParams(1024, 18, 1);
var box_encoding_2_predictor_params = extractConvParams(512, 24, 1);
var class_predictor_2_params = extractConvParams(512, 18, 1);
var box_encoding_3_predictor_params = extractConvParams(256, 24, 1);
var class_predictor_3_params = extractConvParams(256, 18, 1);
var box_encoding_4_predictor_params = extractConvParams(256, 24, 1);
var class_predictor_4_params = extractConvParams(256, 18, 1);
var box_encoding_5_predictor_params = extractConvParams(128, 24, 1);
var class_predictor_5_params = extractConvParams(128, 18, 1);
var box_predictor_0_params = {
box_encoding_predictor_params: box_encoding_0_predictor_params,
class_predictor_params: class_predictor_0_params
......@@ -131,20 +132,16 @@ function extractorsFactory(extractWeights) {
};
}
export function extractParams(weights) {
var extractWeights = function (numWeights) {
var ret = weights.slice(0, numWeights);
weights = weights.slice(numWeights);
return ret;
};
var _a = extractorsFactory(extractWeights), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory(extractWeights), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
var mobilenetv1_params = extractMobilenetV1Params();
var prediction_layer_params = extractPredictionLayerParams();
var extra_dim = tf.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
var output_layer_params = {
extra_dim: extra_dim
};
if (weights.length !== 0) {
throw new Error("weights remaing after extract: " + weights.length);
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
return {
mobilenetv1_params: mobilenetv1_params,
......
dist/faceDetectionNet/extractParams.js.map
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\ No newline at end of file
dist/faceDetectionNet/index.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from '../NetInput';
import { FaceDetectionResult } from './FaceDetectionResult';
import { FaceDetection } from './FaceDetection';
export declare function faceDetectionNet(weights: Float32Array): {
forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => {
forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => {
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
};
locateFaces: (input: tf.Tensor<tf.Rank> | NetInput, minConfidence?: number, maxResults?: number) => Promise<FaceDetectionResult[]>;
locateFaces: (input: NetInput | tf.Tensor<tf.Rank>, minConfidence?: number, maxResults?: number) => Promise<FaceDetection[]>;
};
dist/faceDetectionNet/index.js
......@@ -3,12 +3,13 @@ import * as tf from '@tensorflow/tfjs-core';
import { getImageTensor } from '../getImageTensor';
import { padToSquare } from '../padToSquare';
import { extractParams } from './extractParams';
import { FaceDetectionResult } from './FaceDetectionResult';
import { FaceDetection } from './FaceDetection';
import { mobileNetV1 } from './mobileNetV1';
import { nonMaxSuppression } from './nonMaxSuppression';
import { outputLayer } from './outputLayer';
import { predictionLayer } from './predictionLayer';
import { resizeLayer } from './resizeLayer';
import { Rect } from '../Rect';
export function faceDetectionNet(weights) {
var params = extractParams(weights);
function forwardTensor(imgTensor) {
......@@ -26,7 +27,7 @@ export function faceDetectionNet(weights) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (maxResults === void 0) { maxResults = 100; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var paddedHeightRelative, paddedWidthRelative, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, results;
var paddedHeightRelative, paddedWidthRelative, imageDimensions, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, results;
return tslib_1.__generator(this, function (_d) {
switch (_d.label) {
case 0:
......@@ -34,6 +35,7 @@ export function faceDetectionNet(weights) {
_a = tf.tidy(function () {
var imgTensor = getImageTensor(input);
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
imageDimensions = { width: width, height: height };
imgTensor = padToSquare(imgTensor);
paddedHeightRelative = imgTensor.shape[1] / height;
paddedWidthRelative = imgTensor.shape[2] / width;
......@@ -52,7 +54,17 @@ export function faceDetectionNet(weights) {
iouThreshold = 0.5;
indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
results = indices
.map(function (idx) { return new FaceDetectionResult(scoresData[idx], boxes.get(idx, 0) * paddedHeightRelative, boxes.get(idx, 1) * paddedWidthRelative, boxes.get(idx, 2) * paddedHeightRelative, boxes.get(idx, 3) * paddedWidthRelative); });
.map(function (idx) {
var _a = [
Math.max(0, boxes.get(idx, 0)),
Math.min(1.0, boxes.get(idx, 2))
].map(function (val) { return val * paddedHeightRelative; }), top = _a[0], bottom = _a[1];
var _b = [
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(function (val) { return val * paddedWidthRelative; }), left = _b[0], right = _b[1];
return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), imageDimensions);
});
boxes.dispose();
scores.dispose();
return [2 /*return*/, results];
......
dist/faceDetectionNet/index.js.map
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\ No newline at end of file
dist/faceDetectionNet/types.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
export declare namespace FaceDetectionNet {
type PointwiseConvParams = {
filters: tf.Tensor4D;
......@@ -21,13 +22,9 @@ export declare namespace FaceDetectionNet {
conv_pair_params: ConvPairParams[];
};
}
type ConvWithBiasParams = {
filters: tf.Tensor4D;
bias: tf.Tensor1D;
};
type BoxPredictionParams = {
box_encoding_predictor_params: ConvWithBiasParams;
class_predictor_params: ConvWithBiasParams;
box_encoding_predictor_params: ConvParams;
class_predictor_params: ConvParams;
};
type PredictionLayerParams = {
conv_0_params: PointwiseConvParams;
......@@ -53,13 +50,4 @@ export declare namespace FaceDetectionNet {
prediction_layer_params: PredictionLayerParams;
output_layer_params: OutputLayerParams;
};
type Detection = {
score: number;
box: {
x: number;
y: number;
width: number;
height: number;
};
};
}
dist/faceLandmarkNet/FaceLandmarks.d.ts 0 → 100644
import { Point } from '../Point';
import { Dimensions } from '../types';
export declare class FaceLandmarks {
private _faceLandmarks;
private _imageWidth;
private _imageHeight;
constructor(relativeFaceLandmarkPositions: Point[], imageDims: Dimensions);
getPositions(): Point[];
getRelativePositions(): Point[];
getJawOutline(): Point[];
getLeftEyeBrow(): Point[];
getRightEyeBrow(): Point[];
getNose(): Point[];
getLeftEye(): Point[];
getRightEye(): Point[];
getMouth(): Point[];
forSize(width: number, height: number): FaceLandmarks;
}
dist/faceLandmarkNet/FaceLandmarks.js 0 → 100644
import { Point } from '../Point';
var FaceLandmarks = /** @class */ (function () {
function FaceLandmarks(relativeFaceLandmarkPositions, imageDims) {
var width = imageDims.width, height = imageDims.height;
this._imageWidth = width;
this._imageHeight = height;
this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return new Point(pt.x * width, pt.y * height); });
}
FaceLandmarks.prototype.getPositions = function () {
return this._faceLandmarks;
};
FaceLandmarks.prototype.getRelativePositions = function () {
var _this = this;
return this._faceLandmarks.map(function (pt) { return new Point(pt.x / _this._imageWidth, pt.y / _this._imageHeight); });
};
FaceLandmarks.prototype.getJawOutline = function () {
return this._faceLandmarks.slice(0, 17);
};
FaceLandmarks.prototype.getLeftEyeBrow = function () {
return this._faceLandmarks.slice(17, 22);
};
FaceLandmarks.prototype.getRightEyeBrow = function () {
return this._faceLandmarks.slice(22, 27);
};
FaceLandmarks.prototype.getNose = function () {
return this._faceLandmarks.slice(27, 36);
};
FaceLandmarks.prototype.getLeftEye = function () {
return this._faceLandmarks.slice(36, 42);
};
FaceLandmarks.prototype.getRightEye = function () {
return this._faceLandmarks.slice(42, 48);
};
FaceLandmarks.prototype.getMouth = function () {
return this._faceLandmarks.slice(48, 68);
};
FaceLandmarks.prototype.forSize = function (width, height) {
return new FaceLandmarks(this.getRelativePositions(), { width: width, height: height });
};
return FaceLandmarks;
}());
export { FaceLandmarks };
//# sourceMappingURL=FaceLandmarks.js.map
\ No newline at end of file
dist/faceLandmarkNet/FaceLandmarks.js.map 0 → 100644
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\ No newline at end of file
dist/faceLandmarkNet/extractParams.d.ts 0 → 100644
import { FaceLandmarkNet } from './types';
export declare function extractParams(weights: Float32Array): FaceLandmarkNet.NetParams;
dist/faceLandmarkNet/extractParams.js 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { extractConvParamsFactory } from '../commons/extractConvParamsFactory';
import { extractWeightsFactory } from '../commons/extractWeightsFactory';
export function extractParams(weights) {
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var extractConvParams = extractConvParamsFactory(extractWeights);
function extractFcParams(channelsIn, channelsOut) {
var fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
var fc_bias = tf.tensor1d(extractWeights(channelsOut));
return {
weights: fc_weights,
bias: fc_bias
};
}
var conv0_params = extractConvParams(3, 32, 3);
var conv1_params = extractConvParams(32, 64, 3);
var conv2_params = extractConvParams(64, 64, 3);
var conv3_params = extractConvParams(64, 64, 3);
var conv4_params = extractConvParams(64, 64, 3);
var conv5_params = extractConvParams(64, 128, 3);
var conv6_params = extractConvParams(128, 128, 3);
var conv7_params = extractConvParams(128, 256, 3);
var fc0_params = extractFcParams(6400, 1024);
var fc1_params = extractFcParams(1024, 136);
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
return {
conv0_params: conv0_params,
conv1_params: conv1_params,
conv2_params: conv2_params,
conv3_params: conv3_params,
conv4_params: conv4_params,
conv5_params: conv5_params,
conv6_params: conv6_params,
conv7_params: conv7_params,
fc0_params: fc0_params,
fc1_params: fc1_params
};
}
//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
dist/faceLandmarkNet/extractParams.js.map 0 → 100644
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\ No newline at end of file
dist/faceLandmarkNet/fullyConnectedLayer.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { FaceLandmarkNet } from './types';
export declare function fullyConnectedLayer(x: tf.Tensor2D, params: FaceLandmarkNet.FCParams): tf.Tensor2D;
dist/faceLandmarkNet/fullyConnectedLayer.js 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
export function fullyConnectedLayer(x, params) {
return tf.tidy(function () {
return tf.add(tf.matMul(x, params.weights), params.bias);
});
}
//# sourceMappingURL=fullyConnectedLayer.js.map
\ No newline at end of file
dist/faceLandmarkNet/fullyConnectedLayer.js.map 0 → 100644
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\ No newline at end of file
dist/faceLandmarkNet/index.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from '../NetInput';
import { FaceLandmarks } from './FaceLandmarks';
export declare function faceLandmarkNet(weights: Float32Array): {
detectLandmarks: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => Promise<FaceLandmarks>;
};
dist/faceLandmarkNet/index.js 0 → 100644
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
import { convLayer } from '../commons/convLayer';
import { getImageTensor } from '../getImageTensor';
import { padToSquare } from '../padToSquare';
import { extractParams } from './extractParams';
import { FaceLandmarks } from './FaceLandmarks';
import { fullyConnectedLayer } from './fullyConnectedLayer';
function conv(x, params) {
return convLayer(x, params, 'valid', true);
}
function maxPool(x, strides) {
if (strides === void 0) { strides = [2, 2]; }
return tf.maxPool(x, [2, 2], strides, 'valid');
}
export function faceLandmarkNet(weights) {
var params = extractParams(weights);
function detectLandmarks(input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var adjustRelativeX, adjustRelativeY, imageDimensions, outTensor, faceLandmarksArray, _a, _b, xCoords, yCoords;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
case 0:
adjustRelativeX = 0;
adjustRelativeY = 0;
outTensor = tf.tidy(function () {
var imgTensor = getImageTensor(input);
var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
imageDimensions = { width: width, height: height };
imgTensor = padToSquare(imgTensor, true);
adjustRelativeX = (height > width) ? imgTensor.shape[2] / (2 * width) : 0;
adjustRelativeY = (width > height) ? imgTensor.shape[1] / (2 * height) : 0;
// work with 128 x 128 sized face images
if (imgTensor.shape[1] !== 128 || imgTensor.shape[2] !== 128) {
imgTensor = tf.image.resizeBilinear(imgTensor, [128, 128]);
}
var out = conv(imgTensor, params.conv0_params);
out = maxPool(out);
out = conv(out, params.conv1_params);
out = conv(out, params.conv2_params);
out = maxPool(out);
out = conv(out, params.conv3_params);
out = conv(out, params.conv4_params);
out = maxPool(out);
out = conv(out, params.conv5_params);
out = conv(out, params.conv6_params);
out = maxPool(out, [1, 1]);
out = conv(out, params.conv7_params);
var fc0 = tf.relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params));
var fc1 = fullyConnectedLayer(fc0, params.fc1_params);
return fc1;
});
_b = (_a = Array).from;
return [4 /*yield*/, outTensor.data()];
case 1:
faceLandmarksArray = _b.apply(_a, [_c.sent()]);
xCoords = faceLandmarksArray.filter(function (c, i) { return (i - 1) % 2; }).map(function (x) { return x + adjustRelativeX; });
yCoords = faceLandmarksArray.filter(function (c, i) { return i % 2; }).map(function (y) { return y + adjustRelativeY; });
outTensor.dispose();
return [2 /*return*/, new FaceLandmarks(Array(68).fill(0).map(function (_, i) { return ({ x: xCoords[i], y: yCoords[i] }); }), imageDimensions)];
}
});
});
}
return {
detectLandmarks: detectLandmarks
};
}
//# sourceMappingURL=index.js.map
\ No newline at end of file
dist/faceLandmarkNet/index.js.map 0 → 100644
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\ No newline at end of file
dist/faceLandmarkNet/types.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
export declare namespace FaceLandmarkNet {
type FCParams = {
weights: tf.Tensor2D;
bias: tf.Tensor1D;
};
type NetParams = {
conv0_params: ConvParams;
conv1_params: ConvParams;
conv2_params: ConvParams;
conv3_params: ConvParams;
conv4_params: ConvParams;
conv5_params: ConvParams;
conv6_params: ConvParams;
conv7_params: ConvParams;
fc0_params: FCParams;
fc1_params: FCParams;
};
}
dist/faceLandmarkNet/types.js 0 → 100644
//# sourceMappingURL=types.js.map
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dist/faceLandmarkNet/types.js.map 0 → 100644
{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
dist/faceRecognitionNet/convLayer.js
import * as tf from '@tensorflow/tfjs-core';
import { scale } from './scaleLayer';
function convLayer(x, params, stride, withRelu, padding) {
function convLayer(x, params, strides, withRelu, padding) {
if (padding === void 0) { padding = 'same'; }
var _a = params.conv, filters = _a.filters, biases = _a.biases;
var out = tf.conv2d(x, filters, [stride, stride], padding);
out = tf.add(out, biases);
var _a = params.conv, filters = _a.filters, bias = _a.bias;
var out = tf.conv2d(x, filters, strides, padding);
out = tf.add(out, bias);
out = scale(out, params.scale);
return withRelu ? tf.relu(out) : out;
}
export function conv(x, params) {
return convLayer(x, params, 1, true);
return convLayer(x, params, [1, 1], true);
}
export function convNoRelu(x, params) {
return convLayer(x, params, 1, false);
return convLayer(x, params, [1, 1], false);
}
export function convDown(x, params) {
return convLayer(x, params, 2, true, 'valid');
return convLayer(x, params, [2, 2], true, 'valid');
}
//# sourceMappingURL=convLayer.js.map
\ No newline at end of file
dist/faceRecognitionNet/convLayer.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/faceRecognitionNet/extractParams.js
import * as tf from '@tensorflow/tfjs-core';
import { extractWeightsFactory } from '../commons/extractWeightsFactory';
import { isFloat } from '../utils';
function extractorsFactory(extractWeights) {
function extractFilterValues(numFilterValues, numFilters, filterSize) {
......@@ -19,12 +20,12 @@ function extractorsFactory(extractWeights) {
}
function extractConvLayerParams(numFilterValues, numFilters, filterSize) {
var conv_filters = extractFilterValues(numFilterValues, numFilters, filterSize);
var conv_biases = tf.tensor1d(extractWeights(numFilters));
var conv_bias = tf.tensor1d(extractWeights(numFilters));
var scale = extractScaleLayerParams(numFilters);
return {
conv: {
filters: conv_filters,
biases: conv_biases
bias: conv_bias
},
scale: scale
};
......@@ -44,12 +45,8 @@ function extractorsFactory(extractWeights) {
};
}
export function extractParams(weights) {
var extractWeights = function (numWeights) {
var ret = weights.slice(0, numWeights);
weights = weights.slice(numWeights);
return ret;
};
var _a = extractorsFactory(extractWeights), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var _b = extractorsFactory(extractWeights), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
var conv32_down = extractConvLayerParams(4704, 32, 7);
var conv32_1 = extractResidualLayerParams(9216, 32, 3);
var conv32_2 = extractResidualLayerParams(9216, 32, 3);
......@@ -66,8 +63,8 @@ export function extractParams(weights) {
var conv256_2 = extractResidualLayerParams(589824, 256, 3);
var conv256_down_out = extractResidualLayerParams(589824, 256, 3);
var fc = tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]);
if (weights.length !== 0) {
throw new Error("weights remaing after extract: " + weights.length);
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
return {
conv32_down: conv32_down,
......
dist/faceRecognitionNet/extractParams.js.map
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dist/faceRecognitionNet/index.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from '../NetInput';
export declare function faceRecognitionNet(weights: Float32Array): {
computeFaceDescriptor: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => Promise<Int32Array | Uint8Array | Float32Array>;
computeFaceDescriptorSync: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => Int32Array | Uint8Array | Float32Array;
forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => tf.Tensor<tf.Rank.R2>;
computeFaceDescriptor: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => Promise<Int32Array | Uint8Array | Float32Array>;
computeFaceDescriptorSync: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => Int32Array | Uint8Array | Float32Array;
forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => tf.Tensor<tf.Rank.R2>;
};
dist/faceRecognitionNet/types.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { ConvParams } from '../commons/types';
export declare namespace FaceRecognitionNet {
type ScaleLayerParams = {
weights: tf.Tensor1D;
......@@ -8,10 +9,6 @@ export declare namespace FaceRecognitionNet {
conv1: ConvLayerParams;
conv2: ConvLayerParams;
};
type ConvParams = {
filters: tf.Tensor4D;
biases: tf.Tensor1D;
};
type ConvLayerParams = {
conv: ConvParams;
scale: ScaleLayerParams;
......
dist/index.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance';
import { faceDetectionNet } from './faceDetectionNet';
import { faceLandmarkNet } from './faceLandmarkNet';
import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput';
import { padToSquare } from './padToSquare';
export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput, tf, padToSquare };
export { euclideanDistance, faceDetectionNet, faceLandmarkNet, faceRecognitionNet, NetInput, tf, padToSquare };
export * from './extractFaces';
export * from './extractFaceTensors';
export * from './utils';
dist/index.js
import * as tf from '@tensorflow/tfjs-core';
import { euclideanDistance } from './euclideanDistance';
import { faceDetectionNet } from './faceDetectionNet';
import { faceLandmarkNet } from './faceLandmarkNet';
import { faceRecognitionNet } from './faceRecognitionNet';
import { NetInput } from './NetInput';
import { padToSquare } from './padToSquare';
export { euclideanDistance, faceDetectionNet, faceRecognitionNet, NetInput, tf, padToSquare };
export { euclideanDistance, faceDetectionNet, faceLandmarkNet, faceRecognitionNet, NetInput, tf, padToSquare };
export * from './extractFaces';
export * from './extractFaceTensors';
export * from './utils';
......
dist/index.js.map
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dist/types.d.ts
......@@ -6,13 +6,22 @@ export declare type Dimensions = {
height: number;
};
export declare type DrawBoxOptions = {
lineWidth: number;
color: string;
lineWidth?: number;
color?: string;
};
export declare type DrawTextOptions = {
lineWidth?: number;
fontSize?: number;
fontStyle?: string;
color?: string;
};
export declare type DrawLandmarksOptions = {
lineWidth?: number;
color?: string;
};
export declare type DrawOptions = {
lineWidth: number;
fontSize: number;
fontStyle: string;
color: string;
};
export declare type DrawOptions = DrawBoxOptions & DrawTextOptions;
dist/utils.d.ts
import { FaceDetectionNet } from './faceDetectionNet/types';
import { Dimensions, DrawBoxOptions, DrawOptions, DrawTextOptions } from './types';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
import { Dimensions, DrawBoxOptions, DrawLandmarksOptions, 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 createCanvas({width, height}: Dimensions): HTMLCanvasElement;
export declare function createCanvasWithImageData({width, height}: Dimensions, buf: Uint8ClampedArray): HTMLCanvasElement;
export declare function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement, dims?: Dimensions): HTMLCanvasElement;
export declare function getMediaDimensions(media: HTMLImageElement | HTMLVideoElement): {
width: number;
height: number;
......@@ -14,6 +15,9 @@ 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 & {
export declare function drawDetection(canvasArg: string | HTMLCanvasElement, detection: FaceDetection | FaceDetection[], options?: DrawBoxOptions & DrawTextOptions & {
withScore: boolean;
}): void;
export declare function drawLandmarks(canvasArg: string | HTMLCanvasElement, faceLandmarks: FaceLandmarks, options?: DrawLandmarksOptions & {
drawLines: boolean;
}): void;
dist/utils.js
......@@ -24,10 +24,10 @@ export function createCanvas(_a) {
canvas.height = height;
return canvas;
}
export function createCanvasWithImageData(_a, buf) {
var width = _a.width, height = _a.height;
export function createCanvasFromMedia(media, dims) {
var _a = dims || getMediaDimensions(media), width = _a.width, height = _a.height;
var canvas = createCanvas({ width: width, height: height });
getContext2dOrThrow(canvas).putImageData(new ImageData(buf, width, height), 0, 0);
getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
return canvas;
}
export function getMediaDimensions(media) {
......@@ -64,15 +64,17 @@ export function getDefaultDrawOptions() {
};
}
export function drawBox(ctx, x, y, w, h, options) {
ctx.strokeStyle = options.color;
ctx.lineWidth = options.lineWidth;
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
ctx.strokeStyle = drawOptions.color;
ctx.lineWidth = drawOptions.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 + padText, y + padText + (options.fontSize * 0.6));
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
var padText = 2 + drawOptions.lineWidth;
ctx.fillStyle = drawOptions.color;
ctx.font = drawOptions.fontSize + "px " + drawOptions.fontStyle;
ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6));
}
export function drawDetection(canvasArg, detection, options) {
var canvas = getElement(canvasArg);
......@@ -83,15 +85,60 @@ export function drawDetection(canvasArg, detection, options) {
? detection
: [detection];
detectionArray.forEach(function (det) {
var score = det.score, box = det.box;
var x = box.x, y = box.y, width = box.width, height = box.height;
var _a = det.getBox(), x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
var withScore = Object.assign({ withScore: true }, (options || {})).withScore;
var ctx = getContext2dOrThrow(canvas);
drawBox(ctx, x, y, width, height, drawOptions);
if (withScore) {
drawText(ctx, x, y, "" + round(score), drawOptions);
drawText(ctx, x, y, "" + round(det.getScore()), drawOptions);
}
});
}
function drawContour(ctx, points, isClosed) {
if (isClosed === void 0) { isClosed = false; }
ctx.beginPath();
points.slice(1).forEach(function (_a, prevIdx) {
var x = _a.x, y = _a.y;
var from = points[prevIdx];
ctx.moveTo(from.x, from.y);
ctx.lineTo(x, y);
});
if (isClosed) {
var from = points[points.length - 1];
var to = points[0];
if (!from || !to) {
return;
}
ctx.moveTo(from.x, from.y);
ctx.lineTo(to.x, to.y);
}
ctx.stroke();
}
export function drawLandmarks(canvasArg, faceLandmarks, options) {
var canvas = getElement(canvasArg);
if (!(canvas instanceof HTMLCanvasElement)) {
throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
}
var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
var drawLines = Object.assign({ drawLines: false }, (options || {})).drawLines;
var ctx = getContext2dOrThrow(canvas);
var lineWidth = drawOptions.lineWidth, color = drawOptions.color;
if (drawLines) {
ctx.strokeStyle = color;
ctx.lineWidth = lineWidth;
drawContour(ctx, faceLandmarks.getJawOutline());
drawContour(ctx, faceLandmarks.getLeftEyeBrow());
drawContour(ctx, faceLandmarks.getRightEyeBrow());
drawContour(ctx, faceLandmarks.getNose());
drawContour(ctx, faceLandmarks.getLeftEye(), true);
drawContour(ctx, faceLandmarks.getRightEye(), true);
drawContour(ctx, faceLandmarks.getMouth(), true);
return;
}
// else draw points
var ptOffset = lineWidth / 2;
ctx.fillStyle = color;
faceLandmarks.getPositions().forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
}
//# sourceMappingURL=utils.js.map
\ No newline at end of file
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