fixed landmark postprocessing + resize input canvases instead of tensors to net…

examples/views/detectAndDrawFaces.html

@@ -84,12 +84,9 @@
       const detections = await faceapi.locateFaces(input, minConfidence)
       faceapi.drawDetection('overlay', detections.map(det => det.forSize(width, height)))
 
-      const faceImages = await faceapi.extractFaces(input.inputs[0], detections)
+      const faceImages = await faceapi.extractFaces(inputImgEl, detections)
       $('#facesContainer').empty()
       faceImages.forEach(canvas => $('#facesContainer').append(canvas))
-
-      // free memory for input tensors
-      input.dispose()
     }
 
     async function onSelectionChanged(uri) {

examples/views/detectAndDrawLandmarks.html

@@ -89,11 +89,8 @@
       const input = await faceapi.toNetInput(inputImgEl)
       const locations = await faceapi.locateFaces(input, minConfidence)
 
-      const faceTensors = (await faceapi.extractFaceTensors(input, locations))
-      let landmarksByFace = await Promise.all(faceTensors.map(t => faceapi.detectLandmarks(t)))
-
-      // free memory for face image tensors after we computed their descriptors
-      faceTensors.forEach(t => t.dispose())
+      const faces = await faceapi.extractFaces(input, locations)
+      let landmarksByFace = await Promise.all(faces.map(face => faceapi.detectLandmarks(face)))
 
       // shift and scale the face landmarks to the face image position in the canvas
       landmarksByFace = landmarksByFace.map((landmarks, i) => {
@@ -103,9 +100,6 @@
 
       faceapi.drawLandmarks(canvas, landmarksByFace, { lineWidth: drawLines ? 2 : 4, drawLines, color: 'red' })
       faceapi.drawDetection('overlay', locations.map(det => det.forSize(width, height)))
-
-      // free memory for input tensors
-      input.dispose()
     }
 
     async function run() {

examples/views/faceAlignment.html

@@ -86,26 +86,17 @@
     }
 
     async function locateAndAlignFacesWithMtcnn(inputImgEl) {
-      const input = await faceapi.toNetInput(
-        inputImgEl,
-        // dispose input manually
-        false,
-        // keep canvases (required for mtcnn)
-        true
-      )
+      const input = await faceapi.toNetInput(inputImgEl)
 
       const results = await faceapi.mtcnn(input, { minFaceSize: 100 })
 
-      const unalignedFaceImages = await faceapi.extractFaces(input.inputs[0], results.map(res => res.faceDetection))
+      const unalignedFaceImages = await faceapi.extractFaces(input.getInput(0), results.map(res => res.faceDetection))
 
       const alignedFaceBoxes = results
         .filter(res => res.faceDetection.score > minConfidence)
         .map(res => res.faceLandmarks.align())
 
-      const alignedFaceImages = await faceapi.extractFaces(input.inputs[0], alignedFaceBoxes)
-
-      // free memory for input tensors
-      input.dispose()
+      const alignedFaceImages = await faceapi.extractFaces(input.getInput(0), alignedFaceBoxes)
 
       return {
         unalignedFaceImages,
@@ -118,7 +109,7 @@
 
       const locations = await faceapi.locateFaces(input, minConfidence)
 
-      const unalignedFaceImages = await faceapi.extractFaces(input.inputs[0], locations)
+      const unalignedFaceImages = await faceapi.extractFaces(input.getInput(0), locations)
 
       // detect landmarks and get the aligned face image bounding boxes
       const alignedFaceBoxes = await Promise.all(unalignedFaceImages.map(
@@ -127,10 +118,7 @@
           return faceLandmarks.align(locations[i])
         }
       ))
-      const alignedFaceImages = await faceapi.extractFaces(input.inputs[0], alignedFaceBoxes)
-
-      // free memory for input tensors
-      input.dispose()
+      const alignedFaceImages = await faceapi.extractFaces(input.getInput(0), alignedFaceBoxes)
 
       return {
         unalignedFaceImages,

package-lock.json

@@ -5838,18 +5838,20 @@
       "dev": true
     },
     "tfjs-image-recognition-base": {
-      "version": "git+https://github.com/justadudewhohacks/tfjs-image-recognition-base.git#2f2072f883dd098bc539e2e89a61878720e400a1",
+      "version": "0.1.0",
+      "resolved": "https://registry.npmjs.org/tfjs-image-recognition-base/-/tfjs-image-recognition-base-0.1.0.tgz",
+      "integrity": "sha512-rgbDz+96qwDaH3dUbyMAfCc+ptlTH9jV0M8ucuCfSmxTKwvQ7alAOI5EblubSaKq0y+ioNRoK2eBkkfIenPOSQ==",
       "requires": {
         "@tensorflow/tfjs-core": "0.12.14"
       }
     },
     "tfjs-tiny-yolov2": {
-      "version": "0.0.2",
-      "resolved": "https://registry.npmjs.org/tfjs-tiny-yolov2/-/tfjs-tiny-yolov2-0.0.2.tgz",
-      "integrity": "sha512-NtiPErN2tIP9EkZA7rGjW5wF7iN4JAjI0LwoC1HvMfd4oPpmqwFSbXxwDvFbZY3a5mPUyW6E4/AqoXjSg4w3yA==",
+      "version": "0.1.0",
+      "resolved": "https://registry.npmjs.org/tfjs-tiny-yolov2/-/tfjs-tiny-yolov2-0.1.0.tgz",
+      "integrity": "sha512-UZyvukF61XExoGAGvvCTYKALe6mz0ZNUczgfW4P+qE6mVbPzIFHiY1yweSwc9u4LZm6FstQf5EgiBFgXoFoxgw==",
       "requires": {
         "@tensorflow/tfjs-core": "0.12.14",
-        "tfjs-image-recognition-base": "git+https://github.com/justadudewhohacks/tfjs-image-recognition-base.git#2f2072f883dd098bc539e2e89a61878720e400a1"
+        "tfjs-image-recognition-base": "0.1.0"
       }
     },
     "through2": {
@@ -5983,8 +5985,7 @@
     "tslib": {
       "version": "1.9.3",
       "resolved": "https://registry.npmjs.org/tslib/-/tslib-1.9.3.tgz",
-      "integrity": "sha512-4krF8scpejhaOgqzBEcGM7yDIEfi0/8+8zDRZhNZZ2kjmHJ4hv3zCbQWxoJGz1iw5U0Jl0nma13xzHXcncMavQ==",
-      "dev": true
+      "integrity": "sha512-4krF8scpejhaOgqzBEcGM7yDIEfi0/8+8zDRZhNZZ2kjmHJ4hv3zCbQWxoJGz1iw5U0Jl0nma13xzHXcncMavQ=="
     },
     "tty-browserify": {
       "version": "0.0.0",

package.json

@@ -22,8 +22,8 @@
   "license": "MIT",
   "dependencies": {
     "@tensorflow/tfjs-core": "^0.12.14",
-    "tfjs-image-recognition-base": "^0.0.0",
-    "tfjs-tiny-yolov2": "0.0.2",
+    "tfjs-image-recognition-base": "0.1.0",
+    "tfjs-tiny-yolov2": "0.1.0",
     "tslib": "^1.9.3"
   },
   "devDependencies": {

src/allFacesFactory.ts

@@ -5,9 +5,9 @@ import { TinyYolov2 } from '.';
 import { FaceDetection } from './classes/FaceDetection';
 import { FaceLandmarks68 } from './classes/FaceLandmarks68';
 import { FullFaceDescription } from './classes/FullFaceDescription';
-import { extractFaceTensors } from './dom';
+import { extractFaces } from './dom';
 import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
-import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
+import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net';
 import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
 import { Mtcnn } from './mtcnn/Mtcnn';
 import { MtcnnForwardParams } from './mtcnn/types';
@@ -16,23 +16,21 @@ function computeDescriptorsFactory(
   recognitionNet: FaceRecognitionNet
 ) {
   return async function(input: TNetInput, alignedFaceBoxes: Rect[], useBatchProcessing: boolean) {
-    const alignedFaceTensors = await extractFaceTensors(input, alignedFaceBoxes)
+    const alignedFaceCanvases = await extractFaces(input, alignedFaceBoxes)
 
     const descriptors = useBatchProcessing
-      ? await recognitionNet.computeFaceDescriptor(alignedFaceTensors) as Float32Array[]
-      : await Promise.all(alignedFaceTensors.map(
-        faceTensor => recognitionNet.computeFaceDescriptor(faceTensor)
+      ? await recognitionNet.computeFaceDescriptor(alignedFaceCanvases) as Float32Array[]
+      : await Promise.all(alignedFaceCanvases.map(
+        canvas => recognitionNet.computeFaceDescriptor(canvas)
       )) as Float32Array[]
 
-    alignedFaceTensors.forEach(t => t.dispose())
-
     return descriptors
   }
 }
 
 function allFacesFactory(
   detectFaces: (input: TNetInput) => Promise<FaceDetection[]>,
-  landmarkNet: FaceLandmarkNet,
+  landmarkNet: FaceLandmark68Net,
   recognitionNet: FaceRecognitionNet
 ) {
   const computeDescriptors = computeDescriptorsFactory(recognitionNet)
@@ -43,15 +41,14 @@ function allFacesFactory(
   ): Promise<FullFaceDescription[]> {
 
     const detections = await detectFaces(input)
-    const faceTensors = await extractFaceTensors(input, detections)
+    const faceCanvases = await extractFaces(input, detections)
 
     const faceLandmarksByFace = useBatchProcessing
-      ? await landmarkNet.detectLandmarks(faceTensors) as FaceLandmarks68[]
-      : await Promise.all(faceTensors.map(
-        faceTensor => landmarkNet.detectLandmarks(faceTensor)
+      ? await landmarkNet.detectLandmarks(faceCanvases) as FaceLandmarks68[]
+      : await Promise.all(faceCanvases.map(
+        canvas => landmarkNet.detectLandmarks(canvas)
       )) as FaceLandmarks68[]
 
-    faceTensors.forEach(t => t.dispose())
 
     const alignedFaceBoxes = faceLandmarksByFace.map(
       (landmarks, i) => landmarks.align(detections[i].getBox())
@@ -74,7 +71,7 @@ function allFacesFactory(
 
 export function allFacesSsdMobilenetv1Factory(
   ssdMobilenetv1: FaceDetectionNet,
-  landmarkNet: FaceLandmarkNet,
+  landmarkNet: FaceLandmark68Net,
   recognitionNet: FaceRecognitionNet
 ) {
   return async function(
@@ -90,7 +87,7 @@ export function allFacesSsdMobilenetv1Factory(
 
 export function allFacesTinyYolov2Factory(
   tinyYolov2: TinyYolov2,
-  landmarkNet: FaceLandmarkNet,
+  landmarkNet: FaceLandmark68Net,
   recognitionNet: FaceRecognitionNet
 ) {
   return async function(

src/dom/extractFaceTensors.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { Rect, TNetInput, toNetInput } from 'tfjs-image-recognition-base';
+import { isTensor4D, Rect } from 'tfjs-image-recognition-base';
 
 import { FaceDetection } from '../classes/FaceDetection';
 
@@ -9,28 +9,21 @@ import { FaceDetection } from '../classes/FaceDetection';
  * 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 imageTensor The image tensor that face detection has been performed on.
  * @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 async function extractFaceTensors(
-  input: TNetInput,
+  imageTensor: tf.Tensor3D | tf.Tensor4D,
   detections: Array<FaceDetection | Rect>
-): Promise<tf.Tensor4D[]> {
+): Promise<tf.Tensor3D[]> {
 
-  const netInput = await toNetInput(input, true)
-
-  if (netInput.batchSize > 1) {
-    if (netInput.isManaged) {
-      netInput.dispose()
-    }
+  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
     throw new Error('extractFaceTensors - batchSize > 1 not supported')
   }
 
   return tf.tidy(() => {
-    const imgTensor = netInput.inputs[0].expandDims().toFloat() as tf.Tensor4D
-
-    const [imgHeight, imgWidth, numChannels] = imgTensor.shape.slice(1)
+    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)
 
     const boxes = detections.map(
       det => det instanceof FaceDetection
@@ -40,12 +33,9 @@ export async function extractFaceTensors(
       .map(box => box.clipAtImageBorders(imgWidth, imgHeight))
 
     const faceTensors = boxes.map(({ x, y, width, height }) =>
-      tf.slice4d(imgTensor, [0, y, x, 0], [1, height, width, numChannels])
+      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])
     )
 
-    if (netInput.isManaged) {
-      netInput.dispose()
-    }
     return faceTensors
   })
 }
\ No newline at end of file

src/dom/extractFaces.ts

@@ -24,24 +24,19 @@ export async function extractFaces(
   let canvas = input as HTMLCanvasElement
 
   if (!(input instanceof HTMLCanvasElement)) {
-    const netInput = await toNetInput(input, true)
+    const netInput = await toNetInput(input)
 
     if (netInput.batchSize > 1) {
-      if (netInput.isManaged) {
-        netInput.dispose()
-      }
       throw new Error('extractFaces - batchSize > 1 not supported')
     }
 
-    canvas = await imageTensorToCanvas(netInput.inputs[0])
-
-    if (netInput.isManaged) {
-      netInput.dispose()
-    }
+    const tensorOrCanvas = netInput.getInput(0)
+    canvas = tensorOrCanvas instanceof HTMLCanvasElement
+      ? tensorOrCanvas
+      : await imageTensorToCanvas(tensorOrCanvas)
   }
 
   const ctx = getContext2dOrThrow(canvas)
-
   const boxes = detections.map(
     det => det instanceof FaceDetection
       ? det.forSize(canvas.width, canvas.height).getBox().floor()

src/faceDetectionNet/FaceDetectionNet.ts

@@ -25,7 +25,7 @@ export class FaceDetectionNet extends NeuralNetwork<NetParams> {
     }
 
     return tf.tidy(() => {
-      const batchTensor = input.toBatchTensor(512, false)
+      const batchTensor = input.toBatchTensor(512, false).toFloat()
 
       const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D
       const features = mobileNetV1(x, params.mobilenetv1)
@@ -40,7 +40,7 @@ export class FaceDetectionNet extends NeuralNetwork<NetParams> {
   }
 
   public async forward(input: TNetInput) {
-    return this.forwardInput(await toNetInput(input, true))
+    return this.forwardInput(await toNetInput(input))
   }
 
   public async locateFaces(
@@ -49,7 +49,7 @@ export class FaceDetectionNet extends NeuralNetwork<NetParams> {
     maxResults: number = 100
   ): Promise<FaceDetection[]> {
 
-    const netInput = await toNetInput(input, true)
+    const netInput = await toNetInput(input)
 
     const {
       boxes: _boxes,
@@ -77,18 +77,21 @@ export class FaceDetectionNet extends NeuralNetwork<NetParams> {
       minConfidence
     )
 
-    const paddings = netInput.getRelativePaddings(0)
+    const reshapedDims = netInput.getReshapedInputDimensions(0)
+    const inputSize = netInput.inputSize as number
+    const padX = inputSize / reshapedDims.width
+    const padY = inputSize / reshapedDims.height
 
     const results = indices
       .map(idx => {
         const [top, bottom] = [
           Math.max(0, boxes.get(idx, 0)),
           Math.min(1.0, boxes.get(idx, 2))
-        ].map(val => val * paddings.y)
+        ].map(val => val * padY)
         const [left, right] = [
           Math.max(0, boxes.get(idx, 1)),
           Math.min(1.0, boxes.get(idx, 3))
-        ].map(val => val * paddings.x)
+        ].map(val => val * padX)
         return new FaceDetection(
           scoresData[idx],
           new Rect(

src/faceLandmarkNet/FaceLandmarkNet.ts → src/faceLandmarkNet/FaceLandmark68Net.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { isEven, NetInput, NeuralNetwork, Point, TNetInput, toNetInput } from 'tfjs-image-recognition-base';
+import { NetInput } from 'tfjs-image-recognition-base';
 import { convLayer, ConvParams } from 'tfjs-tiny-yolov2';
 
-import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
 import { extractParams } from './extractParams';
+import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
 import { fullyConnectedLayer } from './fullyConnectedLayer';
 import { loadQuantizedParams } from './loadQuantizedParams';
 import { NetParams } from './types';
@@ -16,22 +16,22 @@ function maxPool(x: tf.Tensor4D, strides: [number, number] = [2, 2]): tf.Tensor4
   return tf.maxPool(x, [2, 2], strides, 'valid')
 }
 
-export class FaceLandmarkNet extends NeuralNetwork<NetParams> {
+export class FaceLandmark68Net extends FaceLandmark68NetBase<NetParams> {
 
   constructor() {
-    super('FaceLandmarkNet')
+    super('FaceLandmark68Net')
   }
 
-  public forwardInput(input: NetInput): tf.Tensor2D {
+  public runNet(input: NetInput): tf.Tensor2D {
 
     const { params } = this
 
     if (!params) {
-      throw new Error('FaceLandmarkNet - load model before inference')
+      throw new Error('FaceLandmark68Net - load model before inference')
     }
 
     return tf.tidy(() => {
-      const batchTensor = input.toBatchTensor(128, true)
+      const batchTensor = input.toBatchTensor(128, true).toFloat() as tf.Tensor4D
 
       let out = conv(batchTensor, params.conv0)
       out = maxPool(out)
@@ -46,77 +46,11 @@ export class FaceLandmarkNet extends NeuralNetwork<NetParams> {
       out = maxPool(out, [1, 1])
       out = conv(out, params.conv7)
       const fc0 = tf.relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0))
-      const fc1 = fullyConnectedLayer(fc0, params.fc1)
 
-      const createInterleavedTensor = (fillX: number, fillY: number) =>
-        tf.stack([
-          tf.fill([68], fillX),
-          tf.fill([68], fillY)
-        ], 1).as2D(1, 136).as1D()
-
-      /* shift coordinates back, to undo centered padding
-        x = ((x * widthAfterPadding) - shiftX) / width
-        y = ((y * heightAfterPadding) - shiftY) / height
-      */
-
-      const landmarkTensors = fc1
-        .mul(tf.stack(Array.from(Array(input.batchSize), (_, batchIdx) =>
-          createInterleavedTensor(
-            input.getPaddings(batchIdx).x + input.getInputWidth(batchIdx),
-            input.getPaddings(batchIdx).y + input.getInputHeight(batchIdx)
-          )
-        )))
-        .sub(tf.stack(Array.from(Array(input.batchSize), (_, batchIdx) =>
-          createInterleavedTensor(
-            Math.floor(input.getPaddings(batchIdx).x / 2),
-            Math.floor(input.getPaddings(batchIdx).y / 2)
-          )
-        )))
-        .div(tf.stack(Array.from(Array(input.batchSize), (_, batchIdx) =>
-          createInterleavedTensor(
-            input.getInputWidth(batchIdx),
-            input.getInputHeight(batchIdx)
-          )
-        )))
-
-      return landmarkTensors as tf.Tensor2D
+      return fullyConnectedLayer(fc0, params.fc1)
     })
   }
 
-  public async forward(input: TNetInput): Promise<tf.Tensor2D> {
-    return this.forwardInput(await toNetInput(input, true))
-  }
-
-  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {
-    const netInput = await toNetInput(input, true)
-
-    const landmarkTensors = tf.tidy(
-      () => tf.unstack(this.forwardInput(netInput))
-    )
-
-    const landmarksForBatch = await Promise.all(landmarkTensors.map(
-      async (landmarkTensor, batchIdx) => {
-        const landmarksArray = Array.from(await landmarkTensor.data())
-        const xCoords = landmarksArray.filter((_, i) => isEven(i))
-        const yCoords = landmarksArray.filter((_, i) => !isEven(i))
-
-        return new FaceLandmarks68(
-          Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])),
-          {
-            height: netInput.getInputHeight(batchIdx),
-            width : netInput.getInputWidth(batchIdx),
-          }
-        )
-      }
-    ))
-
-    landmarkTensors.forEach(t => t.dispose())
-
-    return netInput.isBatchInput
-      ? landmarksForBatch
-      : landmarksForBatch[0]
-  }
-
   protected loadQuantizedParams(uri: string | undefined) {
     return loadQuantizedParams(uri)
   }

src/faceLandmarkNet/FaceLandmark68NetBase.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { isEven, NetInput, NeuralNetwork, Point, TNetInput, toNetInput, Dimensions } from 'tfjs-image-recognition-base';
+
+import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
+
+export class FaceLandmark68NetBase<NetParams> extends NeuralNetwork<NetParams> {
+
+  // TODO: make super.name protected
+  private __name: string
+
+  constructor(_name: string) {
+    super(_name)
+    this.__name = _name
+  }
+
+  public runNet(_: NetInput): tf.Tensor2D {
+    throw new Error(`${this.__name} - runNet not implemented`)
+  }
+
+  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: Dimensions[]): tf.Tensor2D {
+
+    const inputDimensions = originalDimensions.map(({ width, height }) => {
+      const scale = inputSize / Math.max(height, width)
+      return {
+        width: width * scale,
+        height: height * scale
+      }
+    })
+
+    const batchSize = inputDimensions.length
+
+    return tf.tidy(() => {
+      const createInterleavedTensor = (fillX: number, fillY: number) =>
+        tf.stack([
+          tf.fill([68], fillX),
+          tf.fill([68], fillY)
+        ], 1).as2D(1, 136).as1D()
+
+      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {
+        const { width, height } = inputDimensions[batchIdx]
+        return cond(width, height) ? Math.abs(width - height) / 2 : 0
+      }
+      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)
+      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)
+
+      const landmarkTensors = output
+        .mul(tf.fill([batchSize, 136], inputSize))
+        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>
+          createInterleavedTensor(
+            getPaddingX(batchIdx),
+            getPaddingY(batchIdx)
+          )
+        )))
+        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>
+          createInterleavedTensor(
+            inputDimensions[batchIdx].width,
+            inputDimensions[batchIdx].height
+          )
+        )))
+
+      return landmarkTensors as tf.Tensor2D
+    })
+  }
+
+  public forwardInput(input: NetInput): tf.Tensor2D {
+    return tf.tidy(() => {
+      const out = this.runNet(input)
+      return this.postProcess(
+        out,
+        input.inputSize as number,
+        input.inputDimensions.map(([height, width]) => ({ height, width }))
+      )
+    })
+  }
+
+  public async forward(input: TNetInput): Promise<tf.Tensor2D> {
+    return this.forwardInput(await toNetInput(input))
+  }
+
+  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {
+    const netInput = await toNetInput(input)
+    const landmarkTensors = tf.tidy(
+      () => tf.unstack(this.forwardInput(netInput))
+    )
+
+    const landmarksForBatch = await Promise.all(landmarkTensors.map(
+      async (landmarkTensor, batchIdx) => {
+        const landmarksArray = Array.from(await landmarkTensor.data())
+        const xCoords = landmarksArray.filter((_, i) => isEven(i))
+        const yCoords = landmarksArray.filter((_, i) => !isEven(i))
+
+        return new FaceLandmarks68(
+          Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])),
+          {
+            height: netInput.getInputHeight(batchIdx),
+            width : netInput.getInputWidth(batchIdx),
+          }
+        )
+      }
+    ))
+
+    landmarkTensors.forEach(t => t.dispose())
+
+    return netInput.isBatchInput
+      ? landmarksForBatch
+      : landmarksForBatch[0]
+  }
+}
\ No newline at end of file

src/faceLandmarkNet/index.ts

-import { FaceLandmarkNet } from './FaceLandmarkNet';
+import { FaceLandmark68Net } from './FaceLandmark68Net';
 
-export * from './FaceLandmarkNet';
+export * from './FaceLandmark68Net';
+
+export class FaceLandmarkNet extends FaceLandmark68Net {}
 
 export function createFaceLandmarkNet(weights: Float32Array) {
   const net = new FaceLandmarkNet()

src/faceRecognitionNet/FaceRecognitionNet.ts

@@ -23,7 +23,7 @@ export class FaceRecognitionNet extends NeuralNetwork<NetParams> {
     }
 
     return tf.tidy(() => {
-      const batchTensor = input.toBatchTensor(150, true)
+      const batchTensor = input.toBatchTensor(150, true).toFloat()
 
       const meanRgb = [122.782, 117.001, 104.298]
       const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D
@@ -57,11 +57,11 @@ export class FaceRecognitionNet extends NeuralNetwork<NetParams> {
   }
 
   public async forward(input: TNetInput): Promise<tf.Tensor2D> {
-    return this.forwardInput(await toNetInput(input, true))
+    return this.forwardInput(await toNetInput(input))
   }
 
   public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {
-    const netInput = await toNetInput(input, true)
+    const netInput = await toNetInput(input)
 
     const faceDescriptorTensors = tf.tidy(
       () => tf.unstack(this.forwardInput(netInput))

src/globalApi.ts

@@ -7,14 +7,14 @@ import { FaceDetection } from './classes/FaceDetection';
 import { FaceLandmarks68 } from './classes/FaceLandmarks68';
 import { FullFaceDescription } from './classes/FullFaceDescription';
 import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
-import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
+import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net';
 import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
 import { Mtcnn } from './mtcnn/Mtcnn';
 import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
 import { TinyYolov2 } from './tinyYolov2/TinyYolov2';
 
 export const detectionNet = new FaceDetectionNet()
-export const landmarkNet = new FaceLandmarkNet()
+export const landmarkNet = new FaceLandmark68Net()
 export const recognitionNet = new FaceRecognitionNet()
 
 // nets need more specific names, to avoid ambiguity in future

src/mtcnn/Mtcnn.ts

@@ -32,7 +32,6 @@ export class Mtcnn extends NeuralNetwork<NetParams> {
       throw new Error('Mtcnn - load model before inference')
     }
 
-    const inputTensor = input.inputs[0]
     const inputCanvas = input.canvases[0]
 
     if (!inputCanvas) {
@@ -45,14 +44,13 @@ export class Mtcnn extends NeuralNetwork<NetParams> {
 
     const imgTensor = tf.tidy(() =>
       bgrToRgbTensor(
-        tf.expandDims(inputTensor).toFloat() as tf.Tensor4D
+        tf.expandDims(tf.fromPixels(inputCanvas)).toFloat() as tf.Tensor4D
       )
     )
 
     const onReturn = (results: any) => {
       // dispose tensors on return
       imgTensor.dispose()
-      input.dispose()
       stats.total = Date.now() - tsTotal
       return results
     }
@@ -131,7 +129,7 @@ export class Mtcnn extends NeuralNetwork<NetParams> {
   ): Promise<MtcnnResult[]> {
     return (
       await this.forwardInput(
-        await toNetInput(input, true, true),
+        await toNetInput(input),
         forwardParams
       )
     ).results
@@ -142,7 +140,7 @@ export class Mtcnn extends NeuralNetwork<NetParams> {
     forwardParams: MtcnnForwardParams = {}
   ): Promise<{ results: MtcnnResult[], stats: any }> {
     return this.forwardInput(
-      await toNetInput(input, true, true),
+      await toNetInput(input),
       forwardParams
     )
   }

src/mtcnn/MtcnnBox.ts

+import { Box } from 'tfjs-image-recognition-base';
+
+export class MtcnnBox extends Box<MtcnnBox> {
+  constructor(left: number, top: number, right: number, bottom: number) {
+    super({ left, top, right, bottom }, true)
+  }
+}
\ No newline at end of file

src/mtcnn/extractImagePatches.ts

 import * as tf from '@tensorflow/tfjs-core';
+import { Box, createCanvas, Dimensions, getContext2dOrThrow } from 'tfjs-image-recognition-base';
 
 import { normalize } from './normalize';
-import { BoundingBox, Dimensions, getContext2dOrThrow, createCanvas } from 'tfjs-image-recognition-base';
 
 export async function extractImagePatches(
   img: HTMLCanvasElement,
-  boxes: BoundingBox[],
+  boxes: Box[],
   { width, height }: Dimensions
 ): Promise<tf.Tensor4D[]> {
 

src/mtcnn/stage1.ts

@@ -3,6 +3,7 @@ import { BoundingBox, nonMaxSuppression, Point } from 'tfjs-image-recognition-ba
 
 import { CELL_SIZE, CELL_STRIDE } from './config';
 import { getSizesForScale } from './getSizesForScale';
+import { MtcnnBox } from './MtcnnBox';
 import { normalize } from './normalize';
 import { PNet } from './PNet';
 import { PNetParams } from './types';
@@ -45,7 +46,7 @@ function extractBoundingBoxes(
 
     const score = scoresTensor.get(idx.y, idx.x)
 
-    const region = new BoundingBox(
+    const region = new MtcnnBox(
       regionsTensor.get(idx.y, idx.x, 0),
       regionsTensor.get(idx.y, idx.x, 1),
       regionsTensor.get(idx.y, idx.x, 2),

src/mtcnn/stage2.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { BoundingBox, nonMaxSuppression } from 'tfjs-image-recognition-base';
+import { Box, nonMaxSuppression } from 'tfjs-image-recognition-base';
 
 import { extractImagePatches } from './extractImagePatches';
+import { MtcnnBox } from './MtcnnBox';
 import { RNet } from './RNet';
 import { RNetParams } from './types';
 
 export async function stage2(
   img: HTMLCanvasElement,
-  inputBoxes: BoundingBox[],
+  inputBoxes: Box[],
   scoreThreshold: number,
   params: RNetParams,
   stats: any
@@ -41,7 +42,7 @@ export async function stage2(
   const filteredBoxes = indices.map(idx => inputBoxes[idx])
   const filteredScores = indices.map(idx => scores[idx])
 
-  let finalBoxes: BoundingBox[] = []
+  let finalBoxes: Box[] = []
   let finalScores: number[] = []
 
   if (filteredBoxes.length > 0) {
@@ -54,7 +55,7 @@ export async function stage2(
     stats.stage2_nms = Date.now() - ts
 
     const regions = indicesNms.map(idx =>
-      new BoundingBox(
+      new MtcnnBox(
         rnetOuts[indices[idx]].regions.get(0, 0),
         rnetOuts[indices[idx]].regions.get(0, 1),
         rnetOuts[indices[idx]].regions.get(0, 2),

src/mtcnn/stage3.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { BoundingBox, nonMaxSuppression, Point } from 'tfjs-image-recognition-base';
+import { BoundingBox, Box, nonMaxSuppression, Point } from 'tfjs-image-recognition-base';
 
 import { extractImagePatches } from './extractImagePatches';
+import { MtcnnBox } from './MtcnnBox';
 import { ONet } from './ONet';
 import { ONetParams } from './types';
 
@@ -38,7 +39,7 @@ export async function stage3(
     .filter(c => c.score > scoreThreshold)
     .map(({ idx }) => idx)
 
-  const filteredRegions = indices.map(idx => new BoundingBox(
+  const filteredRegions = indices.map(idx => new MtcnnBox(
     onetOuts[idx].regions.get(0, 0),
     onetOuts[idx].regions.get(0, 1),
     onetOuts[idx].regions.get(0, 2),
@@ -48,7 +49,7 @@ export async function stage3(
     .map((idx, i) => inputBoxes[idx].calibrate(filteredRegions[i]))
   const filteredScores = indices.map(idx => scores[idx])
 
-  let finalBoxes: BoundingBox[] = []
+  let finalBoxes: Box[] = []
   let finalScores: number[] = []
   let points: Point[][] = []
 

test/data/facesFaceDescriptors.json

+[[-0.11301642656326294, 0.05311831831932068, 0.11299516260623932, 0.06697624176740646, -0.21439769864082336, 0.018440978601574898, -0.02246944047510624, -0.07858379185199738, 0.1299576610326767, -0.07627607882022858, 0.13028405606746674, -0.07381529361009598, -0.2666480243206024, 0.1386503130197525, -0.0318572111427784, 0.19745664298534393, -0.06815779209136963, -0.10036905854940414, -0.14179939031600952, -0.08129233121871948, 0.022137288004159927, 0.040264301002025604, 0.040526989847421646, 0.13235996663570404, -0.18518206477165222, -0.32066434621810913, -0.02661932446062565, -0.05834241956472397, 0.03743532672524452, -0.00874195247888565, -0.07306552678346634, 0.08665299415588379, -0.16994774341583252, -0.013914648443460464, 0.045591093599796295, 0.011259820312261581, -0.015375563874840736, -0.02478434517979622, 0.22394348680973053, 0.019354572519659996, -0.29177573323249817, 0.007424432784318924, 0.09004595875740051, 0.196985125541687, 0.17434047162532806, 0.07935666292905807, 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\ No newline at end of file

test/data/facesFaceDescriptorsMtcnn.json

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\ No newline at end of file

test/data/facesFaceDescriptorsSsd.json

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test/data/facesFaceLandmarkPositions.json

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\ No newline at end of file

test/data/mtcnnFaceLandmarkPositions.json

+[[{"x":117.85171800851822,"y":58.91067498922348},{"x":157.70139408111572,"y":64.48519098758698},{"x":142.3133249282837,"y":88.54253697395325},{"x":110.16610914468765,"y":99.86233913898468},{"x":149.25052666664124,"y":106.37608766555786}], [{"x":260.46802616119385,"y":82.86598587036133},{"x":305.55760955810547,"y":83.54110813140869},{"x":281.4357223510742,"y":113.98349380493164},{"x":257.06039476394653,"y":125.50608730316164},{"x":306.0191822052002,"y":127.20984458923341}], [{"x":82.91613873839378,"y":292.6100924015045},{"x":133.91112035512924,"y":304.814593821764},{"x":104.43486452102661,"y":330.3951778411865},{"x":72.6984107196331,"y":342.633121073246},{"x":120.51901644468307,"y":354.2677878141403}], [{"x":278.20400857925415,"y":273.83238887786865},{"x":318.7582621574402,"y":273.39686036109924},{"x":295.54277753829956,"y":300.43398427963257},{"x":279.5109224319458,"y":311.497838973999},{"x":317.0187101364136,"y":313.05305886268616}], [{"x":489.58824399113655,"y":224.56882098317146},{"x":534.514480471611,"y":223.28146517276764},{"x":507.2082565128803,"y":250.17186474800113},{"x":493.0139665305615,"y":271.0716395378113},{"x":530.7517347931862,"y":270.4143014550209}], [{"x":606.397784024477,"y":105.43332603573799},{"x":645.2468676567078,"y":111.50095802545547},{"x":625.1735819578171,"y":133.40740483999252},{"x":598.8033188581467,"y":141.26283955574036},{"x":637.2144679427147,"y":147.32198816537857}]]
\ No newline at end of file

test/tests/dom/extractFaceTensors.test.ts

-import { bufferToImage, extractFaceTensors, Rect } from '../../../src';
+import { bufferToImage, extractFaceTensors, Rect, tf } from '../../../src';
 
 describe('extractFaceTensors', () => {
 
-  let imgEl: HTMLImageElement
+  let imgTensor: tf.Tensor3D
 
   beforeAll(async () => {
     const img = await (await fetch('base/test/images/face1.png')).blob()
-    imgEl = await bufferToImage(img)
+    imgTensor = tf.fromPixels(await bufferToImage(img))
   })
 
   describe('extracts tensors', () => {
 
     it('single box', async () => {
       const rect = new Rect(0, 0, 50, 60)
-      const tensors = await extractFaceTensors(imgEl, [rect])
+      const tensors = await extractFaceTensors(imgTensor, [rect])
 
       expect(tensors.length).toEqual(1)
-      expect(tensors[0].shape).toEqual([1, 60, 50, 3])
+      expect(tensors[0].shape).toEqual([60, 50, 3])
       tensors[0].dispose()
     })
 
@@ -25,11 +25,11 @@ describe('extractFaceTensors', () => {
         new Rect(0, 0, 50, 60),
         new Rect(50, 50, 70, 80),
       ]
-      const tensors = await extractFaceTensors(imgEl, rects)
+      const tensors = await extractFaceTensors(imgTensor, rects)
 
       expect(tensors.length).toEqual(2)
-      expect(tensors[0].shape).toEqual([1, 60, 50, 3])
-      expect(tensors[1].shape).toEqual([1, 80, 70, 3])
+      expect(tensors[0].shape).toEqual([60, 50, 3])
+      expect(tensors[1].shape).toEqual([80, 70, 3])
       tensors[0].dispose()
       tensors[1].dispose()
     })
@@ -40,25 +40,25 @@ describe('extractFaceTensors', () => {
 
     it('clips upper left corner', async () => {
       const rect = new Rect(-10, -10, 110, 110)
-      const tensors = await extractFaceTensors(imgEl, [rect])
+      const tensors = await extractFaceTensors(imgTensor, [rect])
 
-      expect(tensors[0].shape).toEqual([1, 100, 100, 3])
+      expect(tensors[0].shape).toEqual([100, 100, 3])
       tensors[0].dispose()
     })
 
     it('clips bottom right corner', async () => {
-      const rect = new Rect(imgEl.width - 100, imgEl.height - 100, 110, 110)
-      const tensors = await extractFaceTensors(imgEl, [rect])
+      const rect = new Rect(imgTensor.shape[1] - 100, imgTensor.shape[0] - 100, 110, 110)
+      const tensors = await extractFaceTensors(imgTensor, [rect])
 
-      expect(tensors[0].shape).toEqual([1, 100, 100, 3])
+      expect(tensors[0].shape).toEqual([100, 100, 3])
       tensors[0].dispose()
     })
 
     it('clips upper left and bottom right corners', async () => {
-      const rect = new Rect(-10, -10, imgEl.width + 20, imgEl.height + 20)
-      const tensors = await extractFaceTensors(imgEl, [rect])
+      const rect = new Rect(-10, -10, imgTensor.shape[1] + 20, imgTensor.shape[0] + 20)
+      const tensors = await extractFaceTensors(imgTensor, [rect])
 
-      expect(tensors[0].shape).toEqual([1, imgEl.height, imgEl.width, 3])
+      expect(tensors[0].shape).toEqual([imgTensor.shape[1], imgTensor.shape[0], 3])
       tensors[0].dispose()
     })
 

test/tests/e2e/allFacesMtcnn.test.ts

 import { bufferToImage } from 'tfjs-image-recognition-base';
 
-import { FaceLandmarks5 } from '../../../src';
-import { describeWithNets, expectAllTensorsReleased } from '../../utils';
-import { expectMtcnnResults } from './expectedResults';
+import {
+  assembleExpectedFullFaceDescriptions,
+  describeWithNets,
+  expectAllTensorsReleased,
+  ExpectedFullFaceDescription,
+} from '../../utils';
+import { expectAllFacesResults, expectedMtcnnBoxes } from './expectedResults';
 
 describe('allFacesMtcnn', () => {
 
   let imgEl: HTMLImageElement
-  let facesFaceDescriptors: number[][]
+  let expectedFullFaceDescriptions: ExpectedFullFaceDescription[]
 
   beforeAll(async () => {
     const img = await (await fetch('base/test/images/faces.jpg')).blob()
     imgEl = await bufferToImage(img)
-    facesFaceDescriptors = await (await fetch('base/test/data/facesFaceDescriptorsMtcnn.json')).json()
+    expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedMtcnnBoxes, 'mtcnnFaceLandmarkPositions.json')
   })
 
   describeWithNets('computes full face descriptions', { withAllFacesMtcnn: true }, ({ allFacesMtcnn }) => {
@@ -25,14 +29,13 @@ describe('allFacesMtcnn', () => {
       const results = await allFacesMtcnn(imgEl, forwardParams)
       expect(results.length).toEqual(6)
 
-      const mtcnnResult = results.map(res => ({
-        faceDetection: res.detection,
-        faceLandmarks: res.landmarks as FaceLandmarks5
-      }))
-      expectMtcnnResults(mtcnnResult, [0, 1, 2, 3, 4, 5], 1, 1)
-      results.forEach(({ descriptor }, i) => {
-        expect(descriptor).toEqual(new Float32Array(facesFaceDescriptors[i]))
-      })
+      const expectedScores = [1, 1, 1, 1, 0.99, 0.99]
+      const deltas = {
+        maxBoxDelta: 2,
+        maxLandmarksDelta: 1,
+        maxDescriptorDelta: 0.4
+      }
+      expectAllFacesResults(results, expectedFullFaceDescriptions, expectedScores, deltas)
     })
 
   })

test/tests/e2e/allFacesSsdMobilenetv1.test.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-import { bufferToImage, NetInput, Point, toNetInput } from '../../../src';
-import { describeWithNets, expectAllTensorsReleased, expectPointClose, expectRectClose } from '../../utils';
-import { expectedSsdBoxes } from './expectedResults';
+import { bufferToImage } from '../../../src';
+import {
+  assembleExpectedFullFaceDescriptions,
+  describeWithNets,
+  expectAllTensorsReleased,
+  ExpectedFullFaceDescription,
+} from '../../utils';
+import { expectAllFacesResults, expectedSsdBoxes } from './expectedResults';
 
 
 describe('allFacesSsdMobilenetv1', () => {
 
   let imgEl: HTMLImageElement
-  let facesFaceLandmarkPositions: Point[][]
-  let facesFaceDescriptors: number[][]
+  let expectedFullFaceDescriptions: ExpectedFullFaceDescription[]
 
   beforeAll(async () => {
     const img = await (await fetch('base/test/images/faces.jpg')).blob()
     imgEl = await bufferToImage(img)
-    facesFaceLandmarkPositions = await (await fetch('base/test/data/facesFaceLandmarkPositions.json')).json()
-    facesFaceDescriptors = await (await fetch('base/test/data/facesFaceDescriptorsSsd.json')).json()
+    expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedSsdBoxes)
   })
 
   describeWithNets('computes full face descriptions', { withAllFacesSsdMobilenetv1: true }, ({ allFacesSsdMobilenetv1 }) => {
 
-    const expectedScores = [0.97, 0.88, 0.83, 0.82, 0.59, 0.52]
-    const maxBoxDelta = 5
-    const maxLandmarkPointsDelta = 1
-
     it('scores > 0.8', async () => {
       const results = await allFacesSsdMobilenetv1(imgEl, 0.8)
 
       expect(results.length).toEqual(4)
-      results.forEach(({ detection, landmarks, descriptor }, i) => {
-        expect(detection.getImageWidth()).toEqual(imgEl.width)
-        expect(detection.getImageHeight()).toEqual(imgEl.height)
-        expect(detection.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(detection.getBox(), expectedSsdBoxes[i], maxBoxDelta)
-        landmarks.getPositions().forEach((pt, j) => expectPointClose(pt, facesFaceLandmarkPositions[i][j], maxLandmarkPointsDelta))
-        expect(descriptor).toEqual(new Float32Array(facesFaceDescriptors[i]))
-      })
+
+      const expectedScores = [-1, 0.81, 0.97, 0.88, 0.84, -1]
+      const deltas = {
+        maxBoxDelta: 5,
+        maxLandmarksDelta: 4,
+        maxDescriptorDelta: 0.01
+      }
+      expectAllFacesResults(results, expectedFullFaceDescriptions, expectedScores, deltas)
     })
 
     it('scores > 0.5', async () => {
       const results = await allFacesSsdMobilenetv1(imgEl, 0.5)
 
       expect(results.length).toEqual(6)
-      results.forEach(({ detection, landmarks, descriptor }, i) => {
-        expect(detection.getImageWidth()).toEqual(imgEl.width)
-        expect(detection.getImageHeight()).toEqual(imgEl.height)
-        expect(detection.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(detection.getBox(), expectedSsdBoxes[i], maxBoxDelta)
-        landmarks.getPositions().forEach((pt, j) => expectPointClose(pt, facesFaceLandmarkPositions[i][j], maxLandmarkPointsDelta))
-        expect(descriptor).toEqual(new Float32Array(facesFaceDescriptors[i]))
-      })
+
+      const expectedScores = [0.54, 0.81, 0.97, 0.88, 0.84, 0.61]
+      const deltas = {
+        maxBoxDelta: 5,
+        maxLandmarksDelta: 4,
+        maxDescriptorDelta: 0.01
+      }
+      expectAllFacesResults(results, expectedFullFaceDescriptions, expectedScores, deltas)
     })
 
   })
@@ -66,8 +65,7 @@ describe('allFacesSsdMobilenetv1', () => {
       const tensor = tf.fromPixels(imgEl)
 
       await expectAllTensorsReleased(async () => {
-        const netInput = (new NetInput([tensor])).managed()
-        await allFacesSsdMobilenetv1(netInput)
+        await allFacesSsdMobilenetv1(tensor)
       })
 
       tensor.dispose()
@@ -77,7 +75,7 @@ describe('allFacesSsdMobilenetv1', () => {
       const tensor = tf.tidy(() => tf.fromPixels(imgEl).expandDims()) as tf.Tensor4D
 
       await expectAllTensorsReleased(async () => {
-        await allFacesSsdMobilenetv1(await toNetInput(tensor, true))
+        await allFacesSsdMobilenetv1(tensor)
       })
 
       tensor.dispose()

test/tests/e2e/allFacesTinyYolov2.test.ts

 import * as tf from '@tensorflow/tfjs-core';
 import { TinyYolov2Types } from 'tfjs-tiny-yolov2';
 
-import { bufferToImage, NetInput, Point, toNetInput } from '../../../src';
-import { describeWithNets, expectAllTensorsReleased, expectMaxDelta, expectPointClose, expectRectClose } from '../../utils';
-import { expectedTinyYolov2SeparableConvBoxes } from './expectedResults';
+import { bufferToImage } from '../../../src';
+import {
+  assembleExpectedFullFaceDescriptions,
+  describeWithNets,
+  expectAllTensorsReleased,
+  ExpectedFullFaceDescription,
+} from '../../utils';
+import { expectAllFacesResults, expectedTinyYolov2Boxes } from './expectedResults';
 
 describe('allFacesTinyYolov2', () => {
 
   let imgEl: HTMLImageElement
-  let facesFaceLandmarkPositions: Point[][]
-  let facesFaceDescriptors: number[][]
+  let expectedFullFaceDescriptions: ExpectedFullFaceDescription[]
 
   beforeAll(async () => {
     const img = await (await fetch('base/test/images/faces.jpg')).blob()
     imgEl = await bufferToImage(img)
-    facesFaceLandmarkPositions = await (await fetch('base/test/data/facesFaceLandmarkPositions.json')).json()
-    facesFaceDescriptors = await (await fetch('base/test/data/facesFaceDescriptorsSsd.json')).json()
+    expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedTinyYolov2Boxes)
   })
 
   describeWithNets('computes full face descriptions', { withAllFacesTinyYolov2: true }, ({ allFacesTinyYolov2 }) => {
 
 
     it('TinyYolov2Types.SizeType.LG', async () => {
-      const expectedScores = [0.9, 0.9, 0.89, 0.85, 0.85, 0.85]
-      const maxBoxDelta = 5
-      const maxLandmarkPointsDelta = 10
-      const maxDescriptorDelta = 0.06
 
       const results = await allFacesTinyYolov2(imgEl, { inputSize: TinyYolov2Types.SizeType.LG })
-      const detectionOrder = [0, 2, 3, 4, 1, 5]
 
       expect(results.length).toEqual(6)
-      results.forEach(({ detection, landmarks, descriptor }, i) => {
-        expect(detection.getImageWidth()).toEqual(imgEl.width)
-        expect(detection.getImageHeight()).toEqual(imgEl.height)
-        expect(detection.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(detection.getBox(), expectedTinyYolov2SeparableConvBoxes[i], maxBoxDelta)
-        landmarks.getPositions().forEach((pt, j) => expectPointClose(pt, facesFaceLandmarkPositions[detectionOrder[i]][j], maxLandmarkPointsDelta))
-        descriptor.forEach((val, j) => expectMaxDelta(val, facesFaceDescriptors[detectionOrder[i]][j], maxDescriptorDelta))
-      })
+
+      const expectedScores = [0.85, 0.88, 0.9, 0.86, 0.9, 0.85]
+      const deltas = {
+        maxBoxDelta: 25,
+        maxLandmarksDelta: 10,
+        maxDescriptorDelta: 0.24
+      }
+      expectAllFacesResults(results, expectedFullFaceDescriptions, expectedScores, deltas)
+
     })
 
     it('TinyYolov2Types.SizeType.MD', async () => {
-      const expectedScores = [0.85, 0.85, 0.84, 0.83, 0.8, 0.8]
-      const maxBoxDelta = 17
-      const maxLandmarkPointsDelta = 16
-      const maxDescriptorDelta = 0.05
-
       const results = await allFacesTinyYolov2(imgEl, { inputSize: TinyYolov2Types.SizeType.MD })
-      const boxOrder = [5, 1, 4, 3, 2, 0]
-      const detectionOrder = [5, 2, 1, 4, 3, 0]
 
       expect(results.length).toEqual(6)
-      results.forEach(({ detection, landmarks, descriptor }, i) => {
-        expect(detection.getImageWidth()).toEqual(imgEl.width)
-        expect(detection.getImageHeight()).toEqual(imgEl.height)
-        expect(detection.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(detection.getBox(), expectedTinyYolov2SeparableConvBoxes[boxOrder[i]], maxBoxDelta)
-        landmarks.getPositions().forEach((pt, j) => expectPointClose(pt, facesFaceLandmarkPositions[detectionOrder[i]][j], maxLandmarkPointsDelta))
-        descriptor.forEach((val, j) => expectMaxDelta(val, facesFaceDescriptors[detectionOrder[i]][j], maxDescriptorDelta))
-      })
+
+      const expectedScores = [0.85, 0.8, 0.8, 0.85, 0.85, 0.82]
+      const deltas = {
+        maxBoxDelta: 34,
+        maxLandmarksDelta: 18,
+        maxDescriptorDelta: 0.2
+      }
+      expectAllFacesResults(results, expectedFullFaceDescriptions, expectedScores, deltas)
     })
 
   })
@@ -76,8 +68,7 @@ describe('allFacesTinyYolov2', () => {
       const tensor = tf.fromPixels(imgEl)
 
       await expectAllTensorsReleased(async () => {
-        const netInput = (new NetInput([tensor])).managed()
-        await allFacesTinyYolov2(netInput)
+        await allFacesTinyYolov2(tensor)
       })
 
       tensor.dispose()
@@ -87,7 +78,7 @@ describe('allFacesTinyYolov2', () => {
       const tensor = tf.tidy(() => tf.fromPixels(imgEl).expandDims()) as tf.Tensor4D
 
       await expectAllTensorsReleased(async () => {
-        await allFacesTinyYolov2(await toNetInput(tensor, true))
+        await allFacesTinyYolov2(tensor)
       })
 
       tensor.dispose()

test/tests/e2e/expectedResults.ts

 import * as faceapi from '../../../src';
-import { expectMaxDelta, expectPointClose, expectRectClose } from '../../utils';
-import { Point } from '../../../src';
+import { FullFaceDescription } from '../../../src/classes/FullFaceDescription';
+import { euclideanDistance } from '../../../src/euclideanDistance';
+import {
+  ExpectedFullFaceDescription,
+  expectMaxDelta,
+  expectPointClose,
+  expectRectClose,
+  sortBoxes,
+  sortByDistanceToOrigin,
+  sortFullFaceDescriptions,
+} from '../../utils';
+import { IPoint, IRect } from '../../../src';
+import { FaceDetection } from '../../../src/classes/FaceDetection';
+import { sortFaceDetections } from '../../utils';
 
-export const expectedSsdBoxes = [
+export type BoxAndLandmarksDeltas = {
+  maxBoxDelta: number
+  maxLandmarksDelta: number
+}
+
+export type AllFacesDeltas = BoxAndLandmarksDeltas & {
+  maxDescriptorDelta: number
+}
+
+export const expectedSsdBoxes = sortBoxes([
   { x: 48, y: 253, width: 104, height: 129 },
   { x: 260, y: 227, width: 76, height: 117 },
   { x: 466, y: 165, width: 88, height: 130 },
   { x: 234, y: 36, width: 84, height: 119 },
   { x: 577, y: 65, width: 84, height: 105 },
   { x: 84, y: 14, width: 79, height: 132 }
-]
+])
 
-export const expectedMtcnnBoxes = [
-  { x: 70, y: 21, width: 112, height: 112 },
-  { x: 36, y: 250, width: 133, height: 132 },
-  { x: 221, y: 43, width: 112, height: 111 },
-  { x: 247, y: 231, width: 106, height: 107 },
-  { x: 566, y: 67, width: 104, height: 104 },
-  { x: 451, y: 176, width: 122, height: 122 }
-]
-
-export const expectedTinyYolov2Boxes = [
+export const expectedTinyYolov2Boxes = sortBoxes([
   { x: 52, y: 263, width: 106, height: 102 },
   { x: 455, y: 191, width: 103, height: 97 },
   { x: 236, y: 57, width: 90, height: 85 },
   { x: 257, y: 243, width: 86, height: 95 },
   { x: 578, y: 76, width: 86, height: 91 },
   { x: 87, y: 30, width: 92, height: 93 }
-]
+])
 
-export const expectedTinyYolov2SeparableConvBoxes = [
+export const expectedTinyYolov2SeparableConvBoxes = sortBoxes([
   { x: 42, y: 257, width: 111, height: 121 },
   { x: 454, y: 175, width: 104, height: 121 },
   { x: 230, y: 45, width: 94, height: 104 },
   { x: 574, y: 62, width: 88, height: 113 },
   { x: 260, y: 233, width: 82, height: 104 },
   { x: 83, y: 24, width: 85, height: 111 }
-]
+])
 
-export const expectedMtcnnFaceLandmarks = [
-  [new Point(117, 58), new Point(156, 63), new Point(141, 86), new Point(109, 98), new Point(147, 104)],
-  [new Point(82, 292), new Point(134, 304), new Point(104, 330), new Point(72, 342), new Point(120, 353)],
-  [new Point(261, 82), new Point(306, 83), new Point(282, 113), new Point(257, 124), new Point(306, 126)],
-  [new Point(277, 273), new Point(318, 273), new Point(295, 300), new Point(279, 311), new Point(316, 313)],
-  [new Point(607, 110), new Point(645, 115), new Point(626, 138), new Point(601, 144), new Point(639, 150)],
-  [new Point(489, 224), new Point(534, 223), new Point(507, 250), new Point(493, 271), new Point(530, 270)]
-]
+export const expectedMtcnnBoxes = sortBoxes([
+  { x: 70, y: 21, width: 112, height: 112 },
+  { x: 36, y: 250, width: 133, height: 132 },
+  { x: 221, y: 43, width: 112, height: 111 },
+  { x: 247, y: 231, width: 106, height: 107 },
+  { x: 566, y: 67, width: 104, height: 104 },
+  { x: 451, y: 176, width: 122, height: 122 }
+])
 
 export function expectMtcnnResults(
   results: { faceDetection: faceapi.FaceDetection, faceLandmarks: faceapi.FaceLandmarks5 }[],
-  boxOrder: number[],
-  maxBoxDelta: number,
-  maxLandmarkPointsDelta: number
+  expectedMtcnnFaceLandmarks: IPoint[][],
+  deltas: BoxAndLandmarksDeltas
 ) {
-  results.forEach((result, i) => {
+  sortByDistanceToOrigin(results, res => res.faceDetection.box).forEach((result, i) => {
     const { faceDetection, faceLandmarks } = result
     expect(faceDetection instanceof faceapi.FaceDetection).toBe(true)
     expect(faceLandmarks instanceof faceapi.FaceLandmarks5).toBe(true)
-    expectRectClose(faceDetection.getBox(), expectedMtcnnBoxes[boxOrder[i]], maxBoxDelta)
-    faceLandmarks.getPositions().forEach((pt, j) => expectPointClose(pt, expectedMtcnnFaceLandmarks[boxOrder[i]][j], maxLandmarkPointsDelta))
+    expectRectClose(faceDetection.getBox(), expectedMtcnnBoxes[i], deltas.maxBoxDelta)
+    faceLandmarks.getPositions().forEach((pt, j) => expectPointClose(pt, expectedMtcnnFaceLandmarks[i][j], deltas.maxLandmarksDelta))
     expectMaxDelta(faceDetection.getScore(), 0.99, 0.01)
   })
+}
+
+export function expectDetectionResults(results: FaceDetection[], allExpectedFaceDetections: IRect[], expectedScores: number[], maxBoxDelta: number) {
+
+  const expectedDetections = expectedScores
+    .map((score, i) => ({
+      score,
+      ...allExpectedFaceDetections[i]
+    }))
+    .filter(expected => expected.score !== -1)
+
+  const sortedResults = sortFaceDetections(results)
+
+  expectedDetections.forEach((expectedDetection, i) => {
+    const det = sortedResults[i]
+    expect(det.score).toBeCloseTo(expectedDetection.score, 2)
+    expectRectClose(det.box, expectedDetection, maxBoxDelta)
+  })
+}
+
+export function expectAllFacesResults(results: FullFaceDescription[], allExpectedFullFaceDescriptions: ExpectedFullFaceDescription[], expectedScores: number[], deltas: AllFacesDeltas) {
+
+  const expectedFullFaceDescriptions = expectedScores
+    .map((score, i) => ({
+      score,
+      ...allExpectedFullFaceDescriptions[i]
+    }))
+    .filter(expected => expected.score !== -1)
+
+  const sortedResults = sortFullFaceDescriptions(results)
+
+  expectedFullFaceDescriptions.forEach((expected, i) => {
+    const { detection, landmarks, descriptor } = sortedResults[i]
+    expect(detection.score).toBeCloseTo(expected.score, 2)
+    expectRectClose(detection.box, expected.detection, deltas.maxBoxDelta)
+    landmarks.getPositions().forEach((pt, j) => expectPointClose(pt, expected.landmarks[j], deltas.maxLandmarksDelta))
+    expect(euclideanDistance(descriptor, expected.descriptor)).toBeLessThan(deltas.maxDescriptorDelta)
+  })
 }
\ No newline at end of file

test/tests/e2e/faceDetectionNet.test.ts

 import * as faceapi from '../../../src';
 import { describeWithNets, expectAllTensorsReleased, expectRectClose } from '../../utils';
-import { expectedSsdBoxes } from './expectedResults';
+import { expectedSsdBoxes, expectDetectionResults } from './expectedResults';
 
 describe('faceDetectionNet', () => {
 
@@ -13,62 +13,52 @@ describe('faceDetectionNet', () => {
 
   describeWithNets('uncompressed weights', { withFaceDetectionNet: { quantized: false } }, ({ faceDetectionNet }) => {
 
-    const expectedScores = [0.98, 0.89, 0.82, 0.75, 0.58, 0.55]
-    const maxBoxDelta = 1
-
     it('scores > 0.8', async () => {
       const detections = await faceDetectionNet.locateFaces(imgEl) as faceapi.FaceDetection[]
 
       expect(detections.length).toEqual(3)
-      detections.forEach((det, i) => {
-        expect(det.getImageWidth()).toEqual(imgEl.width)
-        expect(det.getImageHeight()).toEqual(imgEl.height)
-        expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(det.getBox(), expectedSsdBoxes[i], maxBoxDelta)
-      })
+
+      const expectedScores = [-1, -1, 0.98, 0.88, 0.81, -1]
+      const maxBoxDelta = 3
+
+      expectDetectionResults(detections, expectedSsdBoxes, expectedScores, maxBoxDelta)
     })
 
     it('scores > 0.5', async () => {
       const detections = await faceDetectionNet.locateFaces(imgEl, 0.5) as faceapi.FaceDetection[]
 
       expect(detections.length).toEqual(6)
-      detections.forEach((det, i) => {
-        expect(det.getImageWidth()).toEqual(imgEl.width)
-        expect(det.getImageHeight()).toEqual(imgEl.height)
-        expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(det.getBox(), expectedSsdBoxes[i], maxBoxDelta)
-      })
+
+      const expectedScores = [0.57, 0.74, 0.98, 0.88, 0.81, 0.58]
+      const maxBoxDelta = 3
+
+      expectDetectionResults(detections, expectedSsdBoxes, expectedScores, maxBoxDelta)
     })
 
   })
 
   describeWithNets('quantized weights', { withFaceDetectionNet: { quantized: true } }, ({ faceDetectionNet }) => {
 
-    const expectedScores = [0.97, 0.88, 0.83, 0.82, 0.59, 0.52]
-    const maxBoxDelta = 5
-
     it('scores > 0.8', async () => {
       const detections = await faceDetectionNet.locateFaces(imgEl) as faceapi.FaceDetection[]
 
       expect(detections.length).toEqual(4)
-      detections.forEach((det, i) => {
-        expect(det.getImageWidth()).toEqual(imgEl.width)
-        expect(det.getImageHeight()).toEqual(imgEl.height)
-        expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(det.getBox(), expectedSsdBoxes[i], maxBoxDelta)
-      })
+
+      const expectedScores = [-1, 0.81, 0.97, 0.88, 0.84, -1]
+      const maxBoxDelta = 4
+
+      expectDetectionResults(detections, expectedSsdBoxes, expectedScores, maxBoxDelta)
     })
 
     it('scores > 0.5', async () => {
       const detections = await faceDetectionNet.locateFaces(imgEl, 0.5) as faceapi.FaceDetection[]
 
       expect(detections.length).toEqual(6)
-      detections.forEach((det, i) => {
-        expect(det.getImageWidth()).toEqual(imgEl.width)
-        expect(det.getImageHeight()).toEqual(imgEl.height)
-        expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(det.getBox(), expectedSsdBoxes[i], maxBoxDelta)
-      })
+
+      const expectedScores = [0.54, 0.81, 0.97, 0.88, 0.84, 0.61]
+      const maxBoxDelta = 5
+
+      expectDetectionResults(detections, expectedSsdBoxes, expectedScores, maxBoxDelta)
     })
 
   })

test/tests/e2e/faceLandmarkNet.test.ts

@@ -11,7 +11,7 @@ import {
   toNetInput,
 } from '../../../src';
 import { FaceLandmarks68 } from '../../../src/classes/FaceLandmarks68';
-import { FaceLandmarkNet } from '../../../src/faceLandmarkNet/FaceLandmarkNet';
+import { FaceLandmark68Net } from '../../../src/faceLandmarkNet/FaceLandmark68Net';
 import { describeWithNets, expectAllTensorsReleased, expectMaxDelta } from '../../utils';
 
 function getInputDims (input: tf.Tensor | TMediaElement): Dimensions {
@@ -54,8 +54,8 @@ describe('faceLandmarkNet', () => {
       expect(result.getShift().x).toEqual(0)
       expect(result.getShift().y).toEqual(0)
       result.getPositions().forEach(({ x, y }, i) => {
-        expectMaxDelta(x, faceLandmarkPositions1[i].x, 0.1)
-        expectMaxDelta(y, faceLandmarkPositions1[i].y, 0.1)
+        expectMaxDelta(x, faceLandmarkPositions1[i].x, 1)
+        expectMaxDelta(y, faceLandmarkPositions1[i].y, 1)
       })
     })
 
@@ -68,8 +68,8 @@ describe('faceLandmarkNet', () => {
       expect(result.getShift().x).toEqual(0)
       expect(result.getShift().y).toEqual(0)
       result.getPositions().forEach(({ x, y }, i) => {
-        expectMaxDelta(x, faceLandmarkPositionsRect[i].x, 0.1)
-        expectMaxDelta(y, faceLandmarkPositionsRect[i].y, 0.1)
+        expectMaxDelta(x, faceLandmarkPositionsRect[i].x, 2)
+        expectMaxDelta(y, faceLandmarkPositionsRect[i].y, 2)
       })
     })
 
@@ -128,8 +128,8 @@ describe('faceLandmarkNet', () => {
         expect(result.getShift().x).toEqual(0)
         expect(result.getShift().y).toEqual(0)
         result.getPositions().forEach(({ x, y }, i) => {
-          expectMaxDelta(x, faceLandmarkPositions[batchIdx][i].x, 0.1)
-          expectMaxDelta(y, faceLandmarkPositions[batchIdx][i].y, 0.1)
+          expectMaxDelta(x, faceLandmarkPositions[batchIdx][i].x, 2)
+          expectMaxDelta(y, faceLandmarkPositions[batchIdx][i].y, 2)
         })
       })
     })
@@ -153,33 +153,8 @@ describe('faceLandmarkNet', () => {
         expect(result.getShift().x).toEqual(0)
         expect(result.getShift().y).toEqual(0)
         result.getPositions().forEach(({ x, y }, i) => {
-          expectMaxDelta(x, faceLandmarkPositions[batchIdx][i].x, 0.1)
-          expectMaxDelta(y, faceLandmarkPositions[batchIdx][i].y, 0.1)
-        })
-      })
-    })
-
-    it('computes face landmarks for tf.Tensor4D', async () => {
-      const inputs = [imgEl1, imgEl2].map(el => tf.fromPixels(el))
-
-      const faceLandmarkPositions = [
-        faceLandmarkPositions1,
-        faceLandmarkPositions2,
-        faceLandmarkPositionsRect
-      ]
-
-      const results = await faceLandmarkNet.detectLandmarks(tf.stack(inputs) as tf.Tensor4D) as FaceLandmarks68[]
-      expect(Array.isArray(results)).toBe(true)
-      expect(results.length).toEqual(2)
-      results.forEach((result, batchIdx) => {
-        const { width, height } = getInputDims(inputs[batchIdx])
-        expect(result.getImageWidth()).toEqual(width)
-        expect(result.getImageHeight()).toEqual(height)
-        expect(result.getShift().x).toEqual(0)
-        expect(result.getShift().y).toEqual(0)
-        result.getPositions().forEach(({ x, y }, i) => {
-          expectMaxDelta(x, faceLandmarkPositions[batchIdx][i].x, 0.1)
-          expectMaxDelta(y, faceLandmarkPositions[batchIdx][i].y, 0.1)
+          expectMaxDelta(x, faceLandmarkPositions[batchIdx][i].x, 1)
+          expectMaxDelta(y, faceLandmarkPositions[batchIdx][i].y, 1)
         })
       })
     })
@@ -203,8 +178,8 @@ describe('faceLandmarkNet', () => {
         expect(result.getShift().x).toEqual(0)
         expect(result.getShift().y).toEqual(0)
         result.getPositions().forEach(({ x, y }, i) => {
-          expectMaxDelta(x, faceLandmarkPositions[batchIdx][i].x, 0.1)
-          expectMaxDelta(y, faceLandmarkPositions[batchIdx][i].y, 0.1)
+          expectMaxDelta(x, faceLandmarkPositions[batchIdx][i].x, 1)
+          expectMaxDelta(y, faceLandmarkPositions[batchIdx][i].y, 1)
         })
       })
     })
@@ -230,7 +205,7 @@ describe('faceLandmarkNet', () => {
 
       it('disposes all param tensors', async () => {
         await expectAllTensorsReleased(async () => {
-          const net = new FaceLandmarkNet()
+          const net = new FaceLandmark68Net()
           await net.load('base/weights')
           net.dispose()
         })
@@ -242,7 +217,7 @@ describe('faceLandmarkNet', () => {
 
       it('single image element', async () => {
         await expectAllTensorsReleased(async () => {
-          const netInput = (new NetInput([imgEl1])).managed()
+          const netInput = new NetInput([imgEl1])
           const outTensor = await faceLandmarkNet.forwardInput(netInput)
           outTensor.dispose()
         })
@@ -250,7 +225,7 @@ describe('faceLandmarkNet', () => {
 
       it('multiple image elements', async () => {
         await expectAllTensorsReleased(async () => {
-          const netInput = (new NetInput([imgEl1, imgEl1, imgEl1])).managed()
+          const netInput = new NetInput([imgEl1, imgEl1, imgEl1])
           const outTensor = await faceLandmarkNet.forwardInput(netInput)
           outTensor.dispose()
         })
@@ -260,7 +235,7 @@ describe('faceLandmarkNet', () => {
         const tensor = tf.fromPixels(imgEl1)
 
         await expectAllTensorsReleased(async () => {
-          const netInput = (new NetInput([tensor])).managed()
+          const netInput = new NetInput([tensor])
           const outTensor = await faceLandmarkNet.forwardInput(netInput)
           outTensor.dispose()
         })
@@ -272,7 +247,7 @@ describe('faceLandmarkNet', () => {
         const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(el))
 
         await expectAllTensorsReleased(async () => {
-          const netInput = (new NetInput(tensors)).managed()
+          const netInput = new NetInput(tensors)
           const outTensor = await faceLandmarkNet.forwardInput(netInput)
           outTensor.dispose()
         })
@@ -284,7 +259,7 @@ describe('faceLandmarkNet', () => {
         const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
 
         await expectAllTensorsReleased(async () => {
-          const outTensor = await faceLandmarkNet.forwardInput(await toNetInput(tensor, true))
+          const outTensor = await faceLandmarkNet.forwardInput(await toNetInput(tensor))
           outTensor.dispose()
         })
 
@@ -296,7 +271,7 @@ describe('faceLandmarkNet', () => {
           .map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[]
 
         await expectAllTensorsReleased(async () => {
-          const outTensor = await faceLandmarkNet.forwardInput(await toNetInput(tensors, true))
+          const outTensor = await faceLandmarkNet.forwardInput(await toNetInput(tensors))
           outTensor.dispose()
         })
 

test/tests/e2e/faceRecognitionNet.test.ts

 import * as tf from '@tensorflow/tfjs-core';
 
 import { bufferToImage, FaceRecognitionNet, NetInput, toNetInput } from '../../../src';
+import { euclideanDistance } from '../../../src/euclideanDistance';
 import { createFaceRecognitionNet } from '../../../src/faceRecognitionNet';
 import { describeWithNets, expectAllTensorsReleased } from '../../utils';
 
@@ -30,13 +31,13 @@ describe('faceRecognitionNet', () => {
     it('computes face descriptor for squared input', async () => {
       const result = await faceRecognitionNet.computeFaceDescriptor(imgEl1) as Float32Array
       expect(result.length).toEqual(128)
-      expect(result).toEqual(new Float32Array(faceDescriptor1))
+      expect(euclideanDistance(result, faceDescriptor1)).toBeLessThan(0.1)
     })
 
     it('computes face descriptor for rectangular input', async () => {
       const result = await faceRecognitionNet.computeFaceDescriptor(imgElRect) as Float32Array
       expect(result.length).toEqual(128)
-      expect(result).toEqual(new Float32Array(faceDescriptorRect))
+      expect(euclideanDistance(result, faceDescriptorRect)).toBeLessThan(0.1)
     })
 
   })
@@ -75,11 +76,11 @@ describe('faceRecognitionNet', () => {
       expect(Array.isArray(results)).toBe(true)
       expect(results.length).toEqual(3)
       results.forEach((result, batchIdx) => {
-        expect(result).toEqual(new Float32Array(faceDescriptors[batchIdx]))
+        expect(euclideanDistance(result, faceDescriptors[batchIdx])).toBeLessThan(0.1)
       })
     })
 
-    it('computes face landmarks for batch of tf.Tensor3D', async () => {
+    it('computes face descriptors for batch of tf.Tensor3D', async () => {
       const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.fromPixels(el))
 
       const faceDescriptors = [
@@ -92,28 +93,11 @@ describe('faceRecognitionNet', () => {
       expect(Array.isArray(results)).toBe(true)
       expect(results.length).toEqual(3)
       results.forEach((result, batchIdx) => {
-        expect(result).toEqual(new Float32Array(faceDescriptors[batchIdx]))
-      })
-    })
-
-    it('computes face landmarks for tf.Tensor4D', async () => {
-      const inputs = [imgEl1, imgEl2].map(el => tf.fromPixels(el))
-
-      const faceDescriptors = [
-        faceDescriptor1,
-        faceDescriptor2,
-        faceDescriptorRect
-      ]
-
-      const results = await faceRecognitionNet.computeFaceDescriptor(tf.stack(inputs) as tf.Tensor4D) as Float32Array[]
-      expect(Array.isArray(results)).toBe(true)
-      expect(results.length).toEqual(2)
-      results.forEach((result, batchIdx) => {
-        expect(result).toEqual(new Float32Array(faceDescriptors[batchIdx]))
+        expect(euclideanDistance(result, faceDescriptors[batchIdx])).toBeLessThan(0.1)
       })
     })
 
-    it('computes face landmarks for batch of mixed inputs', async () => {
+    it('computes face descriptors for batch of mixed inputs', async () => {
       const inputs = [imgEl1, tf.fromPixels(imgEl2), tf.fromPixels(imgElRect)]
 
       const faceDescriptors = [
@@ -126,7 +110,7 @@ describe('faceRecognitionNet', () => {
       expect(Array.isArray(results)).toBe(true)
       expect(results.length).toEqual(3)
       results.forEach((result, batchIdx) => {
-        expect(result).toEqual(new Float32Array(faceDescriptors[batchIdx]))
+        expect(euclideanDistance(result, faceDescriptors[batchIdx])).toBeLessThan(0.1)
       })
     })
 
@@ -163,7 +147,7 @@ describe('faceRecognitionNet', () => {
 
       it('single image element', async () => {
         await expectAllTensorsReleased(async () => {
-          const netInput = (new NetInput([imgEl1])).managed()
+          const netInput = new NetInput([imgEl1])
           const outTensor = await faceRecognitionNet.forwardInput(netInput)
           outTensor.dispose()
         })
@@ -171,7 +155,7 @@ describe('faceRecognitionNet', () => {
 
       it('multiple image elements', async () => {
         await expectAllTensorsReleased(async () => {
-          const netInput = (new NetInput([imgEl1, imgEl1, imgEl1])).managed()
+          const netInput = new NetInput([imgEl1, imgEl1, imgEl1])
           const outTensor = await faceRecognitionNet.forwardInput(netInput)
           outTensor.dispose()
         })
@@ -181,7 +165,7 @@ describe('faceRecognitionNet', () => {
         const tensor = tf.fromPixels(imgEl1)
 
         await expectAllTensorsReleased(async () => {
-          const netInput = (new NetInput([tensor])).managed()
+          const netInput = new NetInput([tensor])
           const outTensor = await faceRecognitionNet.forwardInput(netInput)
           outTensor.dispose()
         })
@@ -193,7 +177,7 @@ describe('faceRecognitionNet', () => {
         const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(el))
 
         await expectAllTensorsReleased(async () => {
-          const netInput = (new NetInput(tensors)).managed()
+          const netInput = new NetInput(tensors)
           const outTensor = await faceRecognitionNet.forwardInput(netInput)
           outTensor.dispose()
         })
@@ -205,7 +189,7 @@ describe('faceRecognitionNet', () => {
         const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
 
         await expectAllTensorsReleased(async () => {
-          const outTensor = await faceRecognitionNet.forwardInput(await toNetInput(tensor, true))
+          const outTensor = await faceRecognitionNet.forwardInput(await toNetInput(tensor))
           outTensor.dispose()
         })
 
@@ -217,7 +201,7 @@ describe('faceRecognitionNet', () => {
           .map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[]
 
         await expectAllTensorsReleased(async () => {
-          const outTensor = await faceRecognitionNet.forwardInput(await toNetInput(tensors, true))
+          const outTensor = await faceRecognitionNet.forwardInput(await toNetInput(tensors))
           outTensor.dispose()
         })
 

test/tests/e2e/mtcnn.test.ts

 import * as faceapi from '../../../src';
-import { describeWithNets, expectAllTensorsReleased } from '../../utils';
+import { describeWithNets, expectAllTensorsReleased, sortByDistanceToOrigin } from '../../utils';
 import { expectMtcnnResults } from './expectedResults';
+import { IPoint } from '../../../src';
 
 
 describe('mtcnn', () => {
 
   let imgEl: HTMLImageElement
+  let expectedMtcnnLandmarks: IPoint[][]
 
   beforeAll(async () => {
     const img = await (await fetch('base/test/images/faces.jpg')).blob()
     imgEl = await faceapi.bufferToImage(img)
+    expectedMtcnnLandmarks = await (await fetch('base/test/data/mtcnnFaceLandmarkPositions.json')).json()
   })
 
   describeWithNets('uncompressed weights', { withMtcnn: { quantized: false } }, ({ mtcnn }) => {
@@ -22,7 +25,12 @@ describe('mtcnn', () => {
 
       const results = await mtcnn.forward(imgEl, forwardParams)
       expect(results.length).toEqual(6)
-      expectMtcnnResults(results, [0, 1, 2, 3, 4, 5], 1, 1)
+
+      const deltas = {
+        maxBoxDelta: 2,
+        maxLandmarksDelta: 5
+      }
+      expectMtcnnResults(results, expectedMtcnnLandmarks, deltas)
     })
 
     it('minFaceSize = 80, finds all faces', async () => {
@@ -33,7 +41,11 @@ describe('mtcnn', () => {
       const results = await mtcnn.forward(imgEl, forwardParams)
 
       expect(results.length).toEqual(6)
-      expectMtcnnResults(results, [0, 5, 3, 1, 2, 4], 12, 12)
+      const deltas = {
+        maxBoxDelta: 15,
+        maxLandmarksDelta: 13
+      }
+      expectMtcnnResults(results, expectedMtcnnLandmarks, deltas)
     })
 
     it('all optional params passed, finds all faces', async () => {
@@ -46,7 +58,12 @@ describe('mtcnn', () => {
 
       const results = await mtcnn.forward(imgEl, forwardParams)
       expect(results.length).toEqual(6)
-      expectMtcnnResults(results, [5, 1, 4, 2, 3, 0], 6, 10)
+
+      const deltas = {
+        maxBoxDelta: 8,
+        maxLandmarksDelta: 7
+      }
+      expectMtcnnResults(results, expectedMtcnnLandmarks, deltas)
     })
 
     it('scale steps passed, finds all faces', async () => {
@@ -56,7 +73,12 @@ describe('mtcnn', () => {
 
       const results = await mtcnn.forward(imgEl, forwardParams)
       expect(results.length).toEqual(6)
-      expectMtcnnResults(results, [5, 1, 3, 0, 2, 4], 7, 15)
+
+      const deltas = {
+        maxBoxDelta: 8,
+        maxLandmarksDelta: 10
+      }
+      expectMtcnnResults(results, expectedMtcnnLandmarks, deltas)
     })
 
   })

test/tests/e2e/tinyYolov2.test.ts

 import { TinyYolov2Types } from 'tfjs-tiny-yolov2';
 
 import { bufferToImage, createTinyYolov2, TinyYolov2 } from '../../../src';
-import { describeWithNets, expectAllTensorsReleased, expectRectClose } from '../../utils';
-import { expectedTinyYolov2Boxes } from './expectedResults';
+import { describeWithNets, expectAllTensorsReleased } from '../../utils';
+import { expectDetectionResults, expectedTinyYolov2Boxes } from './expectedResults';
 
 describe('tinyYolov2', () => {
 
@@ -18,43 +18,31 @@ describe('tinyYolov2', () => {
     it('inputSize lg, finds all faces', async () => {
       const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: TinyYolov2Types.SizeType.LG })
 
-      const expectedScores = [0.86, 0.86, 0.85, 0.83, 0.81, 0.81]
-      const maxBoxDelta = 3
-      const boxOrder = [0, 1, 2, 3, 4, 5]
+      const expectedScores = [0.8, 0.85, 0.86, 0.83, 0.86, 0.81]
+      const maxBoxDelta = 4
 
       expect(detections.length).toEqual(6)
-      detections.forEach((det, i) => {
-        expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
-      })
+      expectDetectionResults(detections, expectedTinyYolov2Boxes, expectedScores, maxBoxDelta)
     })
 
     it('inputSize md, finds all faces', async () => {
       const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: TinyYolov2Types.SizeType.MD })
 
-      const expectedScores = [0.89, 0.87, 0.83, 0.82, 0.81, 0.72]
-      const maxBoxDelta = 16
-      const boxOrder = [5, 4, 0, 2, 1, 3]
+      const expectedScores = [0.89, 0.81, 0.82, 0.72, 0.81, 0.86]
+      const maxBoxDelta = 27
 
       expect(detections.length).toEqual(6)
-      detections.forEach((det, i) => {
-        expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
-      })
+      expectDetectionResults(detections, expectedTinyYolov2Boxes, expectedScores, maxBoxDelta)
     })
 
     it('inputSize custom, finds all faces', async () => {
       const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: 416 })
 
-      const expectedScores = [0.89, 0.87, 0.83, 0.82, 0.81, 0.72]
-      const maxBoxDelta = 16
-      const boxOrder = [5, 4, 0, 2, 1, 3]
+      const expectedScores = [0.89, 0.81, 0.82, 0.72, 0.81, 0.86]
+      const maxBoxDelta = 27
 
       expect(detections.length).toEqual(6)
-      detections.forEach((det, i) => {
-        expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
-      })
+      expectDetectionResults(detections, expectedTinyYolov2Boxes, expectedScores, maxBoxDelta)
     })
 
   })
@@ -64,43 +52,31 @@ describe('tinyYolov2', () => {
     it('inputSize lg, finds all faces', async () => {
       const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: TinyYolov2Types.SizeType.LG })
 
-      const expectedScores = [0.86, 0.86, 0.85, 0.83, 0.81, 0.81]
+      const expectedScores = [0.81, 0.85, 0.86, 0.83, 0.86, 0.81]
       const maxBoxDelta = 1
-      const boxOrder = [0, 1, 2, 3, 4, 5]
 
       expect(detections.length).toEqual(6)
-      detections.forEach((det, i) => {
-        expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
-      })
+      expectDetectionResults(detections, expectedTinyYolov2Boxes, expectedScores, maxBoxDelta)
     })
 
     it('inputSize md, finds all faces', async () => {
       const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: TinyYolov2Types.SizeType.MD })
 
-      const expectedScores = [0.89, 0.87, 0.83, 0.83, 0.81, 0.73]
-      const maxBoxDelta = 14
-      const boxOrder = [5, 4, 2, 0, 1, 3]
+      const expectedScores = [0.89, 0.82, 0.82, 0.72, 0.81, 0.86]
+      const maxBoxDelta = 24
 
       expect(detections.length).toEqual(6)
-      detections.forEach((det, i) => {
-        expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
-      })
+      expectDetectionResults(detections, expectedTinyYolov2Boxes, expectedScores, maxBoxDelta)
     })
 
     it('inputSize custom, finds all faces', async () => {
       const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: 416 })
 
-      const expectedScores = [0.89, 0.87, 0.83, 0.83, 0.81, 0.73]
-      const maxBoxDelta = 14
-      const boxOrder = [5, 4, 2, 0, 1, 3]
+      const expectedScores = [0.89, 0.82, 0.82, 0.72, 0.81, 0.86]
+      const maxBoxDelta = 24
 
       expect(detections.length).toEqual(6)
-      detections.forEach((det, i) => {
-        expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(det.getBox(), expectedTinyYolov2Boxes[boxOrder[i]], maxBoxDelta)
-      })
+      expectDetectionResults(detections, expectedTinyYolov2Boxes, expectedScores, maxBoxDelta)
     })
 
   })

test/tests/e2e/tinyYolov2SeparableConv.test.ts

@@ -2,7 +2,7 @@ import { TinyYolov2Types } from 'tfjs-tiny-yolov2';
 
 import { bufferToImage, createTinyYolov2, TinyYolov2 } from '../../../src';
 import { describeWithNets, expectAllTensorsReleased, expectRectClose } from '../../utils';
-import { expectedTinyYolov2SeparableConvBoxes } from './expectedResults';
+import { expectedTinyYolov2SeparableConvBoxes, expectDetectionResults, expectedTinyYolov2Boxes } from './expectedResults';
 
 describe('tinyYolov2, with separable convolutions', () => {
 
@@ -18,43 +18,31 @@ describe('tinyYolov2, with separable convolutions', () => {
     it('inputSize lg, finds all faces', async () => {
       const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: TinyYolov2Types.SizeType.LG })
 
-      const expectedScores = [0.9, 0.9, 0.89, 0.85, 0.85, 0.85]
-      const maxBoxDelta = 1
-      const boxOrder = [0, 1, 2, 3, 4, 5]
+      const expectedScores = [0.85, 0.88, 0.9, 0.85, 0.9, 0.85]
+      const maxBoxDelta = 25
 
       expect(detections.length).toEqual(6)
-      detections.forEach((det, i) => {
-        expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(det.getBox(), expectedTinyYolov2SeparableConvBoxes[boxOrder[i]], maxBoxDelta)
-      })
+      expectDetectionResults(detections, expectedTinyYolov2Boxes, expectedScores, maxBoxDelta)
     })
 
     it('inputSize md, finds all faces', async () => {
       const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: TinyYolov2Types.SizeType.MD })
 
-      const expectedScores = [0.85, 0.85, 0.84, 0.83, 0.8, 0.8]
-      const maxBoxDelta = 17
-      const boxOrder = [5, 1, 4, 3, 2, 0]
+      const expectedScores = [0.85, 0.8, 0.8, 0.85, 0.85, 0.83]
+      const maxBoxDelta = 34
 
       expect(detections.length).toEqual(6)
-      detections.forEach((det, i) => {
-        expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(det.getBox(), expectedTinyYolov2SeparableConvBoxes[boxOrder[i]], maxBoxDelta)
-      })
+      expectDetectionResults(detections, expectedTinyYolov2Boxes, expectedScores, maxBoxDelta)
     })
 
     it('inputSize custom, finds all faces', async () => {
       const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: 416 })
 
-      const expectedScores = [0.85, 0.85, 0.84, 0.83, 0.8, 0.8]
-      const maxBoxDelta = 17
-      const boxOrder = [5, 1, 4, 3, 2, 0]
+      const expectedScores = [0.85, 0.8, 0.8, 0.85, 0.85, 0.83]
+      const maxBoxDelta = 34
 
       expect(detections.length).toEqual(6)
-      detections.forEach((det, i) => {
-        expect(det.getScore()).toBeCloseTo(expectedScores[i], 2)
-        expectRectClose(det.getBox(), expectedTinyYolov2SeparableConvBoxes[boxOrder[i]], maxBoxDelta)
-      })
+      expectDetectionResults(detections, expectedTinyYolov2Boxes, expectedScores, maxBoxDelta)
     })
 
   })

test/tests/faceLandmarkNet/FaceLandmark68NetBase.test.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+import { FaceLandmark68NetBase } from '../../../src/faceLandmarkNet/FaceLandmark68NetBase';
+
+describe('FaceLandmark68NetBase', () => {
+
+  describe('postProcess', () => {
+
+    const net = new FaceLandmark68NetBase('')
+
+    describe('single batch', () => {
+
+      it('transform x coordinates for width < height', () => {
+
+        const landmarksData = Array(136).fill(0)
+        landmarksData[0] = 0.4
+        landmarksData[1] = 0.55
+        landmarksData[2] = 0.2
+        landmarksData[3] = 0.55
+        landmarksData[4] = 0.1
+        landmarksData[5] = 0.55
+
+        const out = net.postProcess(
+          tf.tensor2d(landmarksData, [1, 136]),
+          128,
+          [{ width: 200, height: 300 }]
+        ).dataSync()
+
+        expect(out[0]).toBeCloseTo(0.35, 2)
+        expect(out[1]).toBeCloseTo(0.55, 2)
+        expect(out[2]).toBeCloseTo(0.05, 2)
+        expect(out[3]).toBeCloseTo(0.55, 2)
+        expect(out[4]).toBeCloseTo(-0.1, 2)
+        expect(out[5]).toBeCloseTo(0.55, 2)
+      })
+
+      it('transform y coordinates for height < width', () => {
+
+        const landmarksData = Array(136).fill(0)
+        landmarksData[0] = 0.55
+        landmarksData[1] = 0.4
+        landmarksData[2] = 0.55
+        landmarksData[3] = 0.2
+        landmarksData[4] = 0.55
+        landmarksData[5] = 0.1
+
+        const out = net.postProcess(
+          tf.tensor2d(landmarksData, [1, 136]),
+          128,
+          [{ width: 300, height: 200 }]
+        ).dataSync()
+
+        expect(out[0]).toBeCloseTo(0.55, 2)
+        expect(out[1]).toBeCloseTo(0.35, 2)
+        expect(out[2]).toBeCloseTo(0.55, 2)
+        expect(out[3]).toBeCloseTo(0.05, 2)
+        expect(out[4]).toBeCloseTo(0.55, 2)
+        expect(out[5]).toBeCloseTo(-0.1, 2)
+      })
+
+      it('no transformation for height === width', () => {
+
+        const landmarksData = Array(136).fill(0)
+        landmarksData[0] = 0.55
+        landmarksData[1] = 0.4
+        landmarksData[2] = 0.55
+        landmarksData[3] = 0.2
+        landmarksData[4] = 0.55
+        landmarksData[5] = 0.1
+
+        const out = net.postProcess(
+          tf.tensor2d(landmarksData, [1, 136]),
+          128,
+          [{ width: 300, height: 300 }]
+        ).dataSync()
+
+        expect(out[0]).toBeCloseTo(0.55, 2)
+        expect(out[1]).toBeCloseTo(0.4, 2)
+        expect(out[2]).toBeCloseTo(0.55, 2)
+        expect(out[3]).toBeCloseTo(0.2, 2)
+        expect(out[4]).toBeCloseTo(0.55, 2)
+        expect(out[5]).toBeCloseTo(0.1, 2)
+      })
+
+    })
+
+    describe('multiple batches', () => {
+
+      it('transform coordinates correctly for multiple batches', () => {
+
+        const landmarksData1 = Array(136).fill(0)
+        landmarksData1[0] = 0.4
+        landmarksData1[1] = 0.55
+        landmarksData1[2] = 0.2
+        landmarksData1[3] = 0.55
+        landmarksData1[4] = 0.1
+        landmarksData1[5] = 0.55
+        const landmarksData2 = Array(136).fill(0)
+        landmarksData2[0] = 0.55
+        landmarksData2[1] = 0.4
+        landmarksData2[2] = 0.55
+        landmarksData2[3] = 0.2
+        landmarksData2[4] = 0.55
+        landmarksData2[5] = 0.1
+
+        const out = net.postProcess(
+          tf.tensor2d(landmarksData1.concat(landmarksData2).concat(landmarksData1), [3, 136]),
+          128,
+          [{ width: 200, height: 300 }, { width: 300, height: 200 }, { width: 300, height: 300 }]
+        ).dataSync()
+
+        expect(out[0]).toBeCloseTo(0.35, 2)
+        expect(out[1]).toBeCloseTo(0.55, 2)
+        expect(out[2]).toBeCloseTo(0.05, 2)
+        expect(out[3]).toBeCloseTo(0.55, 2)
+        expect(out[4]).toBeCloseTo(-0.1, 2)
+        expect(out[5]).toBeCloseTo(0.55, 2)
+        expect(out[0 + 136]).toBeCloseTo(0.55, 2)
+        expect(out[1 + 136]).toBeCloseTo(0.35, 2)
+        expect(out[2 + 136]).toBeCloseTo(0.55, 2)
+        expect(out[3 + 136]).toBeCloseTo(0.05, 2)
+        expect(out[4 + 136]).toBeCloseTo(0.55, 2)
+        expect(out[5 + 136]).toBeCloseTo(-0.1, 2)
+        expect(out[0 + (136 * 2)]).toBeCloseTo(0.4, 2)
+        expect(out[1 + (136 * 2)]).toBeCloseTo(0.55, 2)
+        expect(out[2 + (136 * 2)]).toBeCloseTo(0.2, 2)
+        expect(out[3 + (136 * 2)]).toBeCloseTo(0.55, 2)
+        expect(out[4 + (136 * 2)]).toBeCloseTo(0.1, 2)
+        expect(out[5 + (136 * 2)]).toBeCloseTo(0.55, 2)
+      })
+
+    })
+
+  })
+
+})

test/utils.ts

@@ -11,6 +11,9 @@ import {
   TinyYolov2,
 } from '../src/';
 import { allFacesMtcnnFactory, allFacesSsdMobilenetv1Factory, allFacesTinyYolov2Factory } from '../src/allFacesFactory';
+import { FaceDetection } from '../src/classes/FaceDetection';
+import { FaceLandmarks } from '../src/classes/FaceLandmarks';
+import { FullFaceDescription } from '../src/classes/FullFaceDescription';
 import { allFacesMtcnnFunction, allFacesSsdMobilenetv1Function, allFacesTinyYolov2Function } from '../src/globalApi';
 
 jasmine.DEFAULT_TIMEOUT_INTERVAL = 60000
@@ -25,14 +28,16 @@ export async function expectAllTensorsReleased(fn: () => any) {
   expect(tf.memory().numTensors - numTensorsBefore).toEqual(0)
 }
 
+export function pointDistance(pt1: IPoint, pt2: IPoint) {
+  return Math.sqrt(Math.pow(pt1.x - pt2.x, 2) + Math.pow(pt1.y - pt2.y, 2))
+}
+
 export function expectPointClose(
   result: IPoint,
   expectedPoint: IPoint,
   maxDelta: number
 ) {
-  const { x, y } = result
-  expectMaxDelta(x, expectedPoint.x, maxDelta)
-  expectMaxDelta(y, expectedPoint.y, maxDelta)
+  expect(pointDistance(result, expectedPoint)).toBeLessThan(maxDelta)
 }
 
 export function expectRectClose(
@@ -40,10 +45,52 @@ export function expectRectClose(
   expectedBox: IRect,
   maxDelta: number
 ) {
-  const { width, height } = result
   expectPointClose(result, expectedBox, maxDelta)
-  expectMaxDelta(width, expectedBox.width, maxDelta)
-  expectMaxDelta(height, expectedBox.height, maxDelta)
+  expectPointClose({ x: result.width, y: result.height }, { x:expectedBox.width, y: expectedBox.height }, maxDelta)
+}
+
+export function sortByDistanceToOrigin<T>(objs: T[], originGetter: (obj: T) => IPoint) {
+  const origin = { x: 0, y: 0 }
+  return objs.sort((obj1, obj2) =>
+    pointDistance(originGetter(obj1), origin)
+      - pointDistance(originGetter(obj2), origin)
+  )
+}
+
+export function sortBoxes(boxes: IRect[]) {
+  return sortByDistanceToOrigin(boxes, rect => rect)
+}
+
+export function sortFaceDetections(boxes: FaceDetection[]) {
+  return sortByDistanceToOrigin(boxes, det => det.box)
+}
+
+export function sortLandmarks(landmarks: FaceLandmarks[]) {
+  return sortByDistanceToOrigin(landmarks, l => l.getPositions()[0])
+}
+
+export function sortFullFaceDescriptions(descs: FullFaceDescription[]) {
+  return sortByDistanceToOrigin(descs, d => d.detection.box)
+}
+
+export type ExpectedFullFaceDescription = {
+  detection: IRect
+  landmarks: IPoint[]
+  descriptor: Float32Array
+}
+
+export async function assembleExpectedFullFaceDescriptions(
+  detections: IRect[],
+  landmarksFile: string = 'facesFaceLandmarkPositions.json'
+): Promise<ExpectedFullFaceDescription[]> {
+  const landmarks = await (await fetch(`base/test/data/${landmarksFile}`)).json()
+  const descriptors = await (await fetch('base/test/data/facesFaceDescriptors.json')).json()
+
+  return detections.map((detection, i) => ({
+    detection,
+    landmarks: landmarks[i],
+    descriptor: descriptors[i]
+  }))
 }
 
 export type WithNetOptions = {