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face
Commit aa200d18 by vincent
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build dist

parent 8263bfd2
Showing with 324 additions and 45 deletions
dist/Point.d.ts
......@@ -10,4 +10,7 @@ export declare class Point implements IPoint {
sub(pt: IPoint): Point;
mul(pt: IPoint): Point;
div(pt: IPoint): Point;
abs(): Point;
magnitude(): number;
floor(): Point;
}
dist/Point.js
......@@ -15,6 +15,15 @@ var Point = /** @class */ (function () {
Point.prototype.div = function (pt) {
return new Point(this.x / pt.x, this.y / pt.y);
};
Point.prototype.abs = function () {
return new Point(Math.abs(this.x), Math.abs(this.y));
};
Point.prototype.magnitude = function () {
return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2));
};
Point.prototype.floor = function () {
return new Point(Math.floor(this.x), Math.floor(this.y));
};
return Point;
}());
export { Point };
......
dist/Point.js.map
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\ No newline at end of file
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\ No newline at end of file
dist/alignFace.d.ts 0 → 100644
import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
import { Rect } from './Rect';
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
* after face detection has been performed, before they are passed to the face recognition net.
* This will make the computed face descriptor more accurate.
*
* @param detection (optional) The bounding box of the face or the face detection result. If
* no argument was passed the position of the face landmarks are assumed to be relative to
* it's current shift.
* @returns The bounding box of the aligned face.
*/
export declare function alignFace(faceLandmarks: FaceLandmarks, detection?: Rect): Rect;
dist/alignFace.js 0 → 100644
import { getCenterPoint } from './commons/getCenterPoint';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { Rect } from './Rect';
var relX = 0.5;
var relY = 0.43;
var relScale = 0.45;
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
* after face detection has been performed, before they are passed to the face recognition net.
* This will make the computed face descriptor more accurate.
*
* @param detection (optional) The bounding box of the face or the face detection result. If
* no argument was passed the position of the face landmarks are assumed to be relative to
* it's current shift.
* @returns The bounding box of the aligned face.
*/
export function alignFace(faceLandmarks, detection) {
if (detection) {
var box = detection instanceof FaceDetection
? detection.getBox().floor()
: detection;
return alignFace(faceLandmarks.shift(box.x, box.y));
}
var centers = [
faceLandmarks.getLeftEye(),
faceLandmarks.getRightEye(),
faceLandmarks.getMouth()
].map(getCenterPoint);
var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); };
var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
var size = Math.floor(eyeToMouthDist / relScale);
var refPoint = getCenterPoint(centers);
// TODO: pad in case rectangle is out of image bounds
var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
return new Rect(x, y, size, size);
}
//# sourceMappingURL=alignFace.js.map
\ No newline at end of file
dist/alignFace.js.map 0 → 100644
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\ No newline at end of file
dist/commons/getCenterPoint.d.ts 0 → 100644
import { Point } from '../Point';
export declare function getCenterPoint(pts: Point[]): Point;
dist/commons/getCenterPoint.js 0 → 100644
import { Point } from '../Point';
export function getCenterPoint(pts) {
return pts.reduce(function (sum, pt) { return sum.add(pt); }, new Point(0, 0))
.div(new Point(pts.length, pts.length));
}
//# sourceMappingURL=getCenterPoint.js.map
\ No newline at end of file
dist/commons/getCenterPoint.js.map 0 → 100644
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\ No newline at end of file
dist/extractFaceTensors.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { NetInput } from './NetInput';
import { Rect } from './Rect';
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
* 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.
* @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
export declare function extractFaceTensors(image: tf.Tensor | NetInput | TNetInput, detections: FaceDetection[]): tf.Tensor4D[];
export declare function extractFaceTensors(image: tf.Tensor | NetInput | TNetInput, detections: Array<FaceDetection | Rect>): tf.Tensor4D[];
dist/extractFaceTensors.js
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { getImageTensor } from './getImageTensor';
/**
* 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
* 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.
* @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
export function extractFaceTensors(image, detections) {
......@@ -16,8 +16,11 @@ export function extractFaceTensors(image, detections) {
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).getBox().floor(), x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var boxes = detections.map(function (det) { return det instanceof FaceDetection
? det.forSize(imgWidth, imgHeight).getBox().floor()
: det; });
var faceTensors = boxes.map(function (_a) {
var 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 { FaceDetection } from './faceDetectionNet/FaceDetection';
import { Rect } from './Rect';
/**
* 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.
* @param detections The face detection results or face bounding boxes for that image.
* @returns The Canvases of the corresponding image region for each detected face.
*/
export declare function extractFaces(image: HTMLCanvasElement, detections: FaceDetection[]): HTMLCanvasElement[];
export declare function extractFaces(image: HTMLCanvasElement, detections: Array<FaceDetection | Rect>): HTMLCanvasElement[];
dist/extractFaces.js
import { FaceDetection } from './faceDetectionNet/FaceDetection';
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.
* @param detections The face detection results or face bounding boxes 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).getBox().floor(), x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var boxes = detections.map(function (det) { return det instanceof FaceDetection
? det.forSize(image.width, image.height).getBox().floor()
: det; });
return boxes.map(function (_a) {
var 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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\ No newline at end of file
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\ No newline at end of file
dist/face-api.js
......@@ -351,6 +351,22 @@
reader.readAsDataURL(buf);
});
}
function imageTensorToCanvas(imgTensor, canvas) {
return __awaiter$1(this, void 0, void 0, function () {
var targetCanvas, _a, _, height, width, numChannels;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
targetCanvas = canvas || document.createElement('canvas');
_a = imgTensor.shape, _ = _a[0], height = _a[1], width = _a[2], numChannels = _a[3];
return [4 /*yield*/, toPixels(imgTensor.as3D(height, width, numChannels).toInt(), targetCanvas)];
case 1:
_b.sent();
return [2 /*return*/, targetCanvas];
}
});
});
}
function getDefaultDrawOptions() {
return {
color: 'blue',
......@@ -521,7 +537,7 @@
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
* @param imgTensor The image tensor.
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image.
* @returns The padded tensor with width === height.
*/
function padToSquare(imgTensor, isCenterImage) {
......@@ -1045,6 +1061,15 @@
Point.prototype.div = function (pt) {
return new Point(this.x / pt.x, this.y / pt.y);
};
Point.prototype.abs = function () {
return new Point(Math.abs(this.x), Math.abs(this.y));
};
Point.prototype.magnitude = function () {
return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2));
};
Point.prototype.floor = function () {
return new Point(Math.floor(this.x), Math.floor(this.y));
};
return Point;
}());
......@@ -1097,6 +1122,15 @@
};
}
function getCenterPoint(pts) {
return pts.reduce(function (sum, pt) { return sum.add(pt); }, new Point(0, 0))
.div(new Point(pts.length, pts.length));
}
// face alignment constants
var relX = 0.5;
var relY = 0.43;
var relScale = 0.45;
var FaceLandmarks = /** @class */ (function () {
function FaceLandmarks(relativeFaceLandmarkPositions, imageDims, shift) {
if (shift === void 0) { shift = new Point(0, 0); }
......@@ -1106,6 +1140,15 @@
this._shift = shift;
this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new Point(width, height)).add(shift); });
}
FaceLandmarks.prototype.getShift = function () {
return new Point(this._shift.x, this._shift.y);
};
FaceLandmarks.prototype.getImageWidth = function () {
return this._imageWidth;
};
FaceLandmarks.prototype.getImageHeight = function () {
return this._imageHeight;
};
FaceLandmarks.prototype.getPositions = function () {
return this._faceLandmarks;
};
......@@ -1140,6 +1183,39 @@
FaceLandmarks.prototype.shift = function (x, y) {
return new FaceLandmarks(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point(x, y));
};
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
* after face detection has been performed, before they are passed to the face recognition net.
* This will make the computed face descriptor more accurate.
*
* @param detection (optional) The bounding box of the face or the face detection result. If
* no argument was passed the position of the face landmarks are assumed to be relative to
* it's current shift.
* @returns The bounding box of the aligned face.
*/
FaceLandmarks.prototype.align = function (detection) {
if (detection) {
var box = detection instanceof FaceDetection
? detection.getBox().floor()
: detection;
return this.shift(box.x, box.y).align();
}
var centers = [
this.getLeftEye(),
this.getRightEye(),
this.getMouth()
].map(getCenterPoint);
var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); };
var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
var size = Math.floor(eyeToMouthDist / relScale);
var refPoint = getCenterPoint(centers);
// TODO: pad in case rectangle is out of image bounds
var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
return new Rect(x, y, size, size);
};
return FaceLandmarks;
}());
......@@ -1415,13 +1491,16 @@
* 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.
* @param detections The face detection results or face bounding boxes for that image.
* @returns The Canvases of the corresponding image region for each detected face.
*/
function extractFaces(image, detections) {
var ctx = getContext2dOrThrow(image);
return detections.map(function (det) {
var _a = det.forSize(image.width, image.height).getBox().floor(), x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var boxes = detections.map(function (det) { return det instanceof FaceDetection
? det.forSize(image.width, image.height).getBox().floor()
: det; });
return boxes.map(function (_a) {
var 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);
......@@ -1431,13 +1510,12 @@
/**
* 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
* 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.
* @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
function extractFaceTensors(image$$1, detections) {
......@@ -1445,8 +1523,11 @@
var imgTensor = getImageTensor(image$$1);
// 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).getBox().floor(), x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var boxes = detections.map(function (det) { return det instanceof FaceDetection
? det.forSize(imgWidth, imgHeight).getBox().floor()
: det; });
var faceTensors = boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return slice(imgTensor, [0, y, x, 0], [1, height, width, numChannels]);
});
return faceTensors;
......@@ -1470,6 +1551,7 @@
exports.createCanvasFromMedia = createCanvasFromMedia;
exports.getMediaDimensions = getMediaDimensions;
exports.bufferToImage = bufferToImage;
exports.imageTensorToCanvas = imageTensorToCanvas;
exports.getDefaultDrawOptions = getDefaultDrawOptions;
exports.drawBox = drawBox;
exports.drawText = drawText;
......
dist/face-api.min.js
This source diff could not be displayed because it is too large. You can view the blob instead.
dist/faceDetectionNet/index.d.ts
......@@ -2,9 +2,9 @@ import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from '../NetInput';
import { FaceDetection } from './FaceDetection';
export declare function faceDetectionNet(weights: Float32Array): {
forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | NetInput | tf.Tensor<tf.Rank>) => {
forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => {
boxes: tf.Tensor<tf.Rank.R2>[];
scores: tf.Tensor<tf.Rank.R1>[];
};
locateFaces: (input: NetInput | tf.Tensor<tf.Rank>, minConfidence?: number, maxResults?: number) => Promise<FaceDetection[]>;
locateFaces: (input: tf.Tensor<tf.Rank> | NetInput, minConfidence?: number, maxResults?: number) => Promise<FaceDetection[]>;
};
dist/faceLandmarkNet/FaceLandmarks.d.ts
import { Point } from '../Point';
import { Rect } from '../Rect';
import { Dimensions } from '../types';
export declare class FaceLandmarks {
private _faceLandmarks;
private _imageWidth;
private _imageHeight;
private _shift;
private _faceLandmarks;
constructor(relativeFaceLandmarkPositions: Point[], imageDims: Dimensions, shift?: Point);
getShift(): Point;
getImageWidth(): number;
getImageHeight(): number;
getPositions(): Point[];
getRelativePositions(): Point[];
getJawOutline(): Point[];
......@@ -17,4 +21,16 @@ export declare class FaceLandmarks {
getMouth(): Point[];
forSize(width: number, height: number): FaceLandmarks;
shift(x: number, y: number): FaceLandmarks;
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
* after face detection has been performed, before they are passed to the face recognition net.
* This will make the computed face descriptor more accurate.
*
* @param detection (optional) The bounding box of the face or the face detection result. If
* no argument was passed the position of the face landmarks are assumed to be relative to
* it's current shift.
* @returns The bounding box of the aligned face.
*/
align(detection?: Rect): Rect;
}
dist/faceLandmarkNet/FaceLandmarks.js
import { getCenterPoint } from '../commons/getCenterPoint';
import { FaceDetection } from '../faceDetectionNet/FaceDetection';
import { Point } from '../Point';
import { Rect } from '../Rect';
// face alignment constants
var relX = 0.5;
var relY = 0.43;
var relScale = 0.45;
var FaceLandmarks = /** @class */ (function () {
function FaceLandmarks(relativeFaceLandmarkPositions, imageDims, shift) {
if (shift === void 0) { shift = new Point(0, 0); }
......@@ -8,6 +15,15 @@ var FaceLandmarks = /** @class */ (function () {
this._shift = shift;
this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new Point(width, height)).add(shift); });
}
FaceLandmarks.prototype.getShift = function () {
return new Point(this._shift.x, this._shift.y);
};
FaceLandmarks.prototype.getImageWidth = function () {
return this._imageWidth;
};
FaceLandmarks.prototype.getImageHeight = function () {
return this._imageHeight;
};
FaceLandmarks.prototype.getPositions = function () {
return this._faceLandmarks;
};
......@@ -42,6 +58,39 @@ var FaceLandmarks = /** @class */ (function () {
FaceLandmarks.prototype.shift = function (x, y) {
return new FaceLandmarks(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point(x, y));
};
/**
* Aligns the face landmarks after face detection from the relative positions of the faces
* bounding box, or it's current shift. This function should be used to align the face images
* after face detection has been performed, before they are passed to the face recognition net.
* This will make the computed face descriptor more accurate.
*
* @param detection (optional) The bounding box of the face or the face detection result. If
* no argument was passed the position of the face landmarks are assumed to be relative to
* it's current shift.
* @returns The bounding box of the aligned face.
*/
FaceLandmarks.prototype.align = function (detection) {
if (detection) {
var box = detection instanceof FaceDetection
? detection.getBox().floor()
: detection;
return this.shift(box.x, box.y).align();
}
var centers = [
this.getLeftEye(),
this.getRightEye(),
this.getMouth()
].map(getCenterPoint);
var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); };
var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
var size = Math.floor(eyeToMouthDist / relScale);
var refPoint = getCenterPoint(centers);
// TODO: pad in case rectangle is out of image bounds
var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
return new Rect(x, y, size, size);
};
return FaceLandmarks;
}());
export { FaceLandmarks };
......
dist/faceLandmarkNet/FaceLandmarks.js.map
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dist/faceLandmarkNet/index.d.ts
......@@ -2,5 +2,5 @@ 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>;
detectLandmarks: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => Promise<FaceLandmarks>;
};
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)[] | 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>;
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>;
};
dist/imageTensorToCanvas.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
export declare function imageTensorToCanvas(imgTensor: tf.Tensor4D, canvas?: HTMLCanvasElement): Promise<HTMLCanvasElement>;
dist/imageTensorToCanvas.js 0 → 100644
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
export function imageTensorToCanvas(imgTensor, canvas) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var targetCanvas, _a, _, height, width, numChannels;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
targetCanvas = canvas || document.createElement('canvas');
_a = imgTensor.shape, _ = _a[0], height = _a[1], width = _a[2], numChannels = _a[3];
return [4 /*yield*/, tf.toPixels(imgTensor.as3D(height, width, numChannels).toInt(), targetCanvas)];
case 1:
_b.sent();
return [2 /*return*/, targetCanvas];
}
});
});
}
//# sourceMappingURL=imageTensorToCanvas.js.map
\ No newline at end of file
dist/imageTensorToCanvas.js.map 0 → 100644
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dist/padToSquare.d.ts
......@@ -3,7 +3,7 @@ import * as tf from '@tensorflow/tfjs-core';
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
* @param imgTensor The image tensor.
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image.
* @returns The padded tensor with width === height.
*/
export declare function padToSquare(imgTensor: tf.Tensor4D, isCenterImage?: boolean): tf.Tensor4D;
dist/padToSquare.js
......@@ -3,7 +3,7 @@ import * as tf from '@tensorflow/tfjs-core';
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
* @param imgTensor The image tensor.
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image.
* @returns The padded tensor with width === height.
*/
export function padToSquare(imgTensor, isCenterImage) {
......
dist/utils.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { FaceDetection } from './faceDetectionNet/FaceDetection';
import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
import { Dimensions, DrawBoxOptions, DrawLandmarksOptions, DrawOptions, DrawTextOptions } from './types';
......@@ -12,6 +13,7 @@ export declare function getMediaDimensions(media: HTMLImageElement | HTMLVideoEl
height: number;
};
export declare function bufferToImage(buf: Blob): Promise<HTMLImageElement>;
export declare function imageTensorToCanvas(imgTensor: tf.Tensor4D, canvas?: HTMLCanvasElement): Promise<HTMLCanvasElement>;
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;
......
dist/utils.js
import * as tslib_1 from "tslib";
import * as tf from '@tensorflow/tfjs-core';
export function isFloat(num) {
return num % 1 !== 0;
}
......@@ -55,6 +57,22 @@ export function bufferToImage(buf) {
reader.readAsDataURL(buf);
});
}
export function imageTensorToCanvas(imgTensor, canvas) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var targetCanvas, _a, _, height, width, numChannels;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
targetCanvas = canvas || document.createElement('canvas');
_a = imgTensor.shape, _ = _a[0], height = _a[1], width = _a[2], numChannels = _a[3];
return [4 /*yield*/, tf.toPixels(imgTensor.as3D(height, width, numChannels).toInt(), targetCanvas)];
case 1:
_b.sent();
return [2 /*return*/, targetCanvas];
}
});
});
}
export function getDefaultDrawOptions() {
return {
color: 'blue',
......
dist/utils.js.map
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