added mtcnn e2e tests + check in final model weights

src/mtcnn/Mtcnn.ts

@@ -72,7 +72,7 @@ export class Mtcnn extends NeuralNetwork<NetParams> {
       scaleSteps
     } = Object.assign({}, getDefaultMtcnnForwardParams(), forwardParams)
 
-    const scales = scaleSteps || pyramidDown(minFaceSize, scaleFactor, [height, width])
+    const scales = (scaleSteps || pyramidDown(minFaceSize, scaleFactor, [height, width]))
       .filter(scale => {
         const sizes = getSizesForScale(scale, [height, width])
         return Math.min(sizes.width, sizes.height) > CELL_SIZE

test/tests/NetInput.test.ts

-import * as tf from '@tensorflow/tfjs-core';
-
 import { NetInput } from '../../src/NetInput';
-import { bufferToImage, createCanvasFromMedia } from '../../src/utils';
+import { bufferToImage } from '../../src/utils';
 import { expectAllTensorsReleased, tensor3D } from '../utils';
 
-
 describe('NetInput', () => {
 
   let imgEl: HTMLImageElement

test/tests/e2e/faceDetectionNet.test.ts

 import * as faceapi from '../../../src';
-import { FaceDetection } from '../../../src/faceDetectionNet/FaceDetection';
-import { IRect } from '../../../src/Rect';
-import { describeWithNets, expectAllTensorsReleased, expectMaxDelta } from '../../utils';
-
-function expectRectClose(
-  result: IRect,
-  expectedBox: IRect,
-  maxDelta: number
-) {
-  const { x, y, width, height } = result
-  expectMaxDelta(x, expectedBox.x, maxDelta)
-  expectMaxDelta(y, expectedBox.y, maxDelta)
-  expectMaxDelta(width, expectedBox.width, maxDelta)
-  expectMaxDelta(height, expectedBox.height, maxDelta)
-}
+import { describeWithNets, expectAllTensorsReleased, expectRectClose } from '../../utils';
 
 const expectedBoxes = [
   { x: 48, y: 253, width: 104, height: 129 },
@@ -39,7 +25,7 @@ describe('faceDetectionNet', () => {
     const maxBoxDelta = 1
 
     it('scores > 0.8', async () => {
-      const detections = await faceDetectionNet.locateFaces(imgEl) as FaceDetection[]
+      const detections = await faceDetectionNet.locateFaces(imgEl) as faceapi.FaceDetection[]
 
       expect(detections.length).toEqual(3)
       detections.forEach((det, i) => {
@@ -51,7 +37,7 @@ describe('faceDetectionNet', () => {
     })
 
     it('scores > 0.5', async () => {
-      const detections = await faceDetectionNet.locateFaces(imgEl, 0.5) as FaceDetection[]
+      const detections = await faceDetectionNet.locateFaces(imgEl, 0.5) as faceapi.FaceDetection[]
 
       expect(detections.length).toEqual(6)
       detections.forEach((det, i) => {
@@ -70,7 +56,7 @@ describe('faceDetectionNet', () => {
     const maxBoxDelta = 5
 
     it('scores > 0.8', async () => {
-      const detections = await faceDetectionNet.locateFaces(imgEl) as FaceDetection[]
+      const detections = await faceDetectionNet.locateFaces(imgEl) as faceapi.FaceDetection[]
 
       expect(detections.length).toEqual(4)
       detections.forEach((det, i) => {
@@ -82,7 +68,7 @@ describe('faceDetectionNet', () => {
     })
 
     it('scores > 0.5', async () => {
-      const detections = await faceDetectionNet.locateFaces(imgEl, 0.5) as FaceDetection[]
+      const detections = await faceDetectionNet.locateFaces(imgEl, 0.5) as faceapi.FaceDetection[]
 
       expect(detections.length).toEqual(6)
       detections.forEach((det, i) => {
@@ -103,7 +89,7 @@ describe('faceDetectionNet', () => {
         await expectAllTensorsReleased(async () => {
           const res = await fetch('base/weights/uncompressed/face_detection_model.weights')
           const weights = new Float32Array(await res.arrayBuffer())
-          const net = faceapi.faceDetectionNet(weights)
+          const net = faceapi.createFaceDetectionNet(weights)
           net.dispose()
         })
       })

test/tests/e2e/faceLandmarkNet.test.ts

@@ -2,7 +2,6 @@ import * as tf from '@tensorflow/tfjs-core';
 
 import * as faceapi from '../../../src';
 import { isTensor3D } from '../../../src/commons/isTensor';
-import { FaceLandmarks } from '../../../src/faceLandmarkNet/FaceLandmarks';
 import { Point } from '../../../src/Point';
 import { Dimensions, TMediaElement } from '../../../src/types';
 import { expectMaxDelta, expectAllTensorsReleased, tensor3D, describeWithNets } from '../../utils';
@@ -43,7 +42,7 @@ describe('faceLandmarkNet', () => {
     it('computes face landmarks for squared input', async () => {
       const { width, height } = imgEl1
 
-      const result = await faceLandmarkNet.detectLandmarks(imgEl1) as FaceLandmarks
+      const result = await faceLandmarkNet.detectLandmarks(imgEl1) as faceapi.FaceLandmarks68
       expect(result.getImageWidth()).toEqual(width)
       expect(result.getImageHeight()).toEqual(height)
       expect(result.getShift().x).toEqual(0)
@@ -57,7 +56,7 @@ describe('faceLandmarkNet', () => {
     it('computes face landmarks for rectangular input', async () => {
       const { width, height } = imgElRect
 
-      const result = await faceLandmarkNet.detectLandmarks(imgElRect) as FaceLandmarks
+      const result = await faceLandmarkNet.detectLandmarks(imgElRect) as faceapi.FaceLandmarks68
       expect(result.getImageWidth()).toEqual(width)
       expect(result.getImageHeight()).toEqual(height)
       expect(result.getShift().x).toEqual(0)
@@ -75,7 +74,7 @@ describe('faceLandmarkNet', () => {
     it('computes face landmarks for squared input', async () => {
       const { width, height } = imgEl1
 
-      const result = await faceLandmarkNet.detectLandmarks(imgEl1) as FaceLandmarks
+      const result = await faceLandmarkNet.detectLandmarks(imgEl1) as faceapi.FaceLandmarks68
       expect(result.getImageWidth()).toEqual(width)
       expect(result.getImageHeight()).toEqual(height)
       expect(result.getShift().x).toEqual(0)
@@ -89,7 +88,7 @@ describe('faceLandmarkNet', () => {
     it('computes face landmarks for rectangular input', async () => {
       const { width, height } = imgElRect
 
-      const result = await faceLandmarkNet.detectLandmarks(imgElRect) as FaceLandmarks
+      const result = await faceLandmarkNet.detectLandmarks(imgElRect) as faceapi.FaceLandmarks68
       expect(result.getImageWidth()).toEqual(width)
       expect(result.getImageHeight()).toEqual(height)
       expect(result.getShift().x).toEqual(0)
@@ -113,7 +112,7 @@ describe('faceLandmarkNet', () => {
         faceLandmarkPositionsRect
       ]
 
-      const results = await faceLandmarkNet.detectLandmarks(inputs) as FaceLandmarks[]
+      const results = await faceLandmarkNet.detectLandmarks(inputs) as faceapi.FaceLandmarks68[]
       expect(Array.isArray(results)).toBe(true)
       expect(results.length).toEqual(3)
       results.forEach((result, batchIdx) => {
@@ -138,7 +137,7 @@ describe('faceLandmarkNet', () => {
         faceLandmarkPositionsRect
       ]
 
-      const results = await faceLandmarkNet.detectLandmarks(inputs) as FaceLandmarks[]
+      const results = await faceLandmarkNet.detectLandmarks(inputs) as faceapi.FaceLandmarks68[]
       expect(Array.isArray(results)).toBe(true)
       expect(results.length).toEqual(3)
       results.forEach((result, batchIdx) => {
@@ -163,7 +162,7 @@ describe('faceLandmarkNet', () => {
         faceLandmarkPositionsRect
       ]
 
-      const results = await faceLandmarkNet.detectLandmarks(tf.stack(inputs) as tf.Tensor4D) as FaceLandmarks[]
+      const results = await faceLandmarkNet.detectLandmarks(tf.stack(inputs) as tf.Tensor4D) as faceapi.FaceLandmarks68[]
       expect(Array.isArray(results)).toBe(true)
       expect(results.length).toEqual(2)
       results.forEach((result, batchIdx) => {
@@ -188,7 +187,7 @@ describe('faceLandmarkNet', () => {
         faceLandmarkPositionsRect
       ]
 
-      const results = await faceLandmarkNet.detectLandmarks(inputs) as FaceLandmarks[]
+      const results = await faceLandmarkNet.detectLandmarks(inputs) as faceapi.FaceLandmarks68[]
       expect(Array.isArray(results)).toBe(true)
       expect(results.length).toEqual(3)
       results.forEach((result, batchIdx) => {
@@ -214,7 +213,7 @@ describe('faceLandmarkNet', () => {
         await expectAllTensorsReleased(async () => {
           const res = await fetch('base/weights/uncompressed/face_landmark_68_model.weights')
           const weights = new Float32Array(await res.arrayBuffer())
-          const net = faceapi.faceLandmarkNet(weights)
+          const net = faceapi.createFaceLandmarkNet(weights)
           net.dispose()
         })
       })

test/tests/e2e/faceRecognitionNet.test.ts

@@ -141,7 +141,7 @@ describe('faceRecognitionNet', () => {
         await expectAllTensorsReleased(async () => {
           const res = await fetch('base/weights/uncompressed/face_recognition_model.weights')
           const weights = new Float32Array(await res.arrayBuffer())
-          const net = faceapi.faceRecognitionNet(weights)
+          const net = faceapi.createFaceRecognitionNet(weights)
           net.dispose()
         })
       })

test/tests/e2e/mtcnn.test.ts

+import * as faceapi from '../../../src';
+import { MtcnnResult } from '../../../src/mtcnn/types';
+import { Point } from '../../../src/Point';
+import { describeWithNets, expectAllTensorsReleased, expectPointClose, expectRectClose, expectMaxDelta } from '../../utils';
+
+const expectedBoxes = [
+  { 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 }
+]
+
+const expectedFaceLandmarks = [
+  [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)]
+]
+
+describe('mtcnn', () => {
+
+  let imgEl: HTMLImageElement
+
+  beforeAll(async () => {
+    const img = await (await fetch('base/test/images/faces.jpg')).blob()
+    imgEl = await faceapi.bufferToImage(img)
+  })
+
+  describeWithNets('uncompressed weights', { withMtcnn: { quantized: false } }, ({ mtcnn }) => {
+
+    function expectResults(
+      results: MtcnnResult[],
+      boxOrder: number[],
+      maxBoxDelta: number,
+      maxLandmarkPointsDelta: number
+    ) {
+      results.forEach((result, i) => {
+        const { faceDetection, faceLandmarks } = result
+        expect(faceDetection instanceof faceapi.FaceDetection).toBe(true)
+        expect(faceLandmarks instanceof faceapi.FaceLandmarks5).toBe(true)
+        expectRectClose(faceDetection.getBox(), expectedBoxes[boxOrder[i]], maxBoxDelta)
+        faceLandmarks.getPositions().forEach((pt, j) => expectPointClose(pt, expectedFaceLandmarks[boxOrder[i]][j], maxLandmarkPointsDelta))
+        expectMaxDelta(faceDetection.getScore(), 0.99, 0.01)
+      })
+    }
+
+    it('minFaceSize = 20, finds all faces', async () => {
+      const forwardParams = {
+        minFaceSize: 20
+      }
+
+      const results = await mtcnn.forward(imgEl, forwardParams)
+      expect(results.length).toEqual(6)
+      expectResults(results, [0, 1, 2, 3, 4, 5], 1, 1)
+    })
+
+    it('minFaceSize = 80, finds all faces', async () => {
+      const forwardParams = {
+        minFaceSize: 80
+      }
+
+      const results = await mtcnn.forward(imgEl, forwardParams)
+
+      expect(results.length).toEqual(6)
+      expectResults(results, [0, 5, 3, 1, 2, 4], 12, 12)
+    })
+
+    it('all optional params passed, finds all faces', async () => {
+      const forwardParams = {
+        maxNumScales: 10,
+        scaleFactor: 0.8,
+        scoreThresholds: [0.8, 0.8, 0.9],
+        minFaceSize: 20
+      }
+
+      const results = await mtcnn.forward(imgEl, forwardParams)
+      expect(results.length).toEqual(6)
+      expectResults(results, [5, 1, 4, 3, 2, 0], 6, 10)
+    })
+
+    it('scale steps passed, finds all faces', async () => {
+      const forwardParams = {
+        scaleSteps: [0.6, 0.4, 0.2, 0.15, 0.1, 0.08, 0.02]
+      }
+
+      const results = await mtcnn.forward(imgEl, forwardParams)
+      expect(results.length).toEqual(6)
+      expectResults(results, [5, 1, 3, 0, 2, 4], 7, 15)
+    })
+
+  })
+
+  describe('no memory leaks', () => {
+
+    describe('NeuralNetwork, uncompressed model', () => {
+
+      it('disposes all param tensors', async () => {
+        await expectAllTensorsReleased(async () => {
+          const res = await fetch('base/weights/uncompressed/mtcnn_model.weights')
+          const weights = new Float32Array(await res.arrayBuffer())
+          const net = faceapi.createMtcnn(weights)
+          net.dispose()
+        })
+      })
+
+    })
+
+    describe('NeuralNetwork, quantized model', () => {
+
+      it('disposes all param tensors', async () => {
+        await expectAllTensorsReleased(async () => {
+          const net = new faceapi.Mtcnn()
+          await net.load('base/weights')
+          net.dispose()
+        })
+      })
+
+    })
+
+  })
+
+})
\ No newline at end of file

test/tests/toNetInput.test.ts

-import { tf } from '../../src';
+import * as tf from '@tensorflow/tfjs-core';
+
 import { NetInput } from '../../src/NetInput';
 import { toNetInput } from '../../src/toNetInput';
 import { bufferToImage, createCanvasFromMedia } from '../../src/utils';

test/utils.ts

 import * as tf from '@tensorflow/tfjs-core';
 
+import { IRect } from '../build/Rect';
 import * as faceapi from '../src/';
 import { NeuralNetwork } from '../src/commons/NeuralNetwork';
+import { IPoint } from '../src/';
 
 export function zeros(length: number): Float32Array {
   return new Float32Array(length)
@@ -25,6 +27,27 @@ export function tensor3D() {
   return tf.tensor3d([[[0]]])
 }
 
+export function expectPointClose(
+  result: IPoint,
+  expectedPoint: IPoint,
+  maxDelta: number
+) {
+  const { x, y } = result
+  expectMaxDelta(x, expectedPoint.x, maxDelta)
+  expectMaxDelta(y, expectedPoint.y, maxDelta)
+}
+
+export function expectRectClose(
+  result: IRect,
+  expectedBox: IRect,
+  maxDelta: number
+) {
+  const { width, height } = result
+  expectPointClose(result, expectedBox, maxDelta)
+  expectMaxDelta(width, expectedBox.width, maxDelta)
+  expectMaxDelta(height, expectedBox.height, maxDelta)
+}
+
 export type WithNetOptions = {
   quantized?: boolean
 }
@@ -33,12 +56,14 @@ export type InjectNetArgs = {
   faceDetectionNet: faceapi.FaceDetectionNet
   faceLandmarkNet: faceapi.FaceLandmarkNet
   faceRecognitionNet: faceapi.FaceRecognitionNet
+  mtcnn: faceapi.Mtcnn
 }
 
 export type DescribeWithNetsOptions = {
   withFaceDetectionNet?: WithNetOptions
   withFaceLandmarkNet?: WithNetOptions
   withFaceRecognitionNet?: WithNetOptions
+  withMtcnn?: WithNetOptions
 }
 
 async function loadNetWeights(uri: string): Promise<Float32Array> {
@@ -63,16 +88,17 @@ export function describeWithNets(
 ) {
 
   describe(description, () => {
-
     let faceDetectionNet: faceapi.FaceDetectionNet = new faceapi.FaceDetectionNet()
     let faceLandmarkNet: faceapi.FaceLandmarkNet = new faceapi.FaceLandmarkNet()
     let faceRecognitionNet: faceapi.FaceRecognitionNet = new faceapi.FaceRecognitionNet()
+    let mtcnn: faceapi.Mtcnn = new faceapi.Mtcnn()
 
     beforeAll(async () => {
       const {
         withFaceDetectionNet,
         withFaceLandmarkNet,
-        withFaceRecognitionNet
+        withFaceRecognitionNet,
+        withMtcnn
       } = options
 
       if (withFaceDetectionNet) {
@@ -93,15 +119,22 @@ export function describeWithNets(
           !withFaceRecognitionNet.quantized && 'face_recognition_model.weights'
         )
       }
+      if (withMtcnn) {
+        await initNet<faceapi.Mtcnn>(
+          mtcnn,
+          !withMtcnn.quantized && 'mtcnn_model.weights'
+        )
+      }
     })
 
     afterAll(() => {
       faceDetectionNet && faceDetectionNet.dispose()
       faceLandmarkNet && faceLandmarkNet.dispose()
       faceRecognitionNet && faceRecognitionNet.dispose()
+      mtcnn && mtcnn.dispose()
     })
 
-    specDefinitions({ faceDetectionNet, faceLandmarkNet, faceRecognitionNet })
+    specDefinitions({ faceDetectionNet, faceLandmarkNet, faceRecognitionNet, mtcnn })
   })
 }
 

weights/mtcnn_model-weights_manifest.json

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+[{"paths":["mtcnn_model-shard1"],"weights":[{"dtype":"float32","name":"pnet/conv1/weights","shape":[3,3,3,10]},{"dtype":"float32","name":"pnet/conv1/bias","shape":[10]},{"dtype":"float32","name":"pnet/prelu1_alpha","shape":[10]},{"dtype":"float32","name":"pnet/conv2/weights","shape":[3,3,10,16]},{"dtype":"float32","name":"pnet/conv2/bias","shape":[16]},{"dtype":"float32","name":"pnet/prelu2_alpha","shape":[16]},{"dtype":"float32","name":"pnet/conv3/weights","shape":[3,3,16,32]},{"dtype":"float32","name":"pnet/conv3/bias","shape":[32]},{"dtype":"float32","name":"pnet/prelu3_alpha","shape":[32]},{"dtype":"float32","name":"pnet/conv4_1/weights","shape":[1,1,32,2]},{"dtype":"float32","name":"pnet/conv4_1/bias","shape":[2]},{"dtype":"float32","name":"pnet/conv4_2/weights","shape":[1,1,32,4]},{"dtype":"float32","name":"pnet/conv4_2/bias","shape":[4]},{"dtype":"float32","name":"rnet/conv1/weights","shape":[3,3,3,28]},{"dtype":"float32","name":"rnet/conv1/bias","shape":[28]},{"dtype":"float32","name":"rnet/prelu1_alpha","shape":[28]},{"dtype":"float32","name":"rnet/conv2/weights","shape":[3,3,28,48]},{"dtype":"float32","name":"rnet/conv2/bias","shape":[48]},{"dtype":"float32","name":"rnet/prelu2_alpha","shape":[48]},{"dtype":"float32","name":"rnet/conv3/weights","shape":[2,2,48,64]},{"dtype":"float32","name":"rnet/conv3/bias","shape":[64]},{"dtype":"float32","name":"rnet/prelu3_alpha","shape":[64]},{"dtype":"float32","name":"rnet/fc1/weights","shape":[576,128]},{"dtype":"float32","name":"rnet/fc1/bias","shape":[128]},{"dtype":"float32","name":"rnet/prelu4_alpha","shape":[128]},{"dtype":"float32","name":"rnet/fc2_1/weights","shape":[128,2]},{"dtype":"float32","name":"rnet/fc2_1/bias","shape":[2]},{"dtype":"float32","name":"rnet/fc2_2/weights","shape":[128,4]},{"dtype":"float32","name":"rnet/fc2_2/bias","shape":[4]},{"dtype":"float32","name":"onet/conv1/weights","shape":[3,3,3,32]},{"dtype":"float32","name":"onet/conv1/bias","shape":[32]},{"dtype":"float32","name":"onet/prelu1_alpha","shape":[32]},{"dtype":"float32","name":"onet/conv2/weights","shape":[3,3,32,64]},{"dtype":"float32","name":"onet/conv2/bias","shape":[64]},{"dtype":"float32","name":"onet/prelu2_alpha","shape":[64]},{"dtype":"float32","name":"onet/conv3/weights","shape":[3,3,64,64]},{"dtype":"float32","name":"onet/conv3/bias","shape":[64]},{"dtype":"float32","name":"onet/prelu3_alpha","shape":[64]},{"dtype":"float32","name":"onet/conv4/weights","shape":[2,2,64,128]},{"dtype":"float32","name":"onet/conv4/bias","shape":[128]},{"dtype":"float32","name":"onet/prelu4_alpha","shape":[128]},{"dtype":"float32","name":"onet/fc1/weights","shape":[1152,256]},{"dtype":"float32","name":"onet/fc1/bias","shape":[256]},{"dtype":"float32","name":"onet/prelu5_alpha","shape":[256]},{"dtype":"float32","name":"onet/fc2_1/weights","shape":[256,2]},{"dtype":"float32","name":"onet/fc2_1/bias","shape":[2]},{"dtype":"float32","name":"onet/fc2_2/weights","shape":[256,4]},{"dtype":"float32","name":"onet/fc2_2/bias","shape":[4]},{"dtype":"float32","name":"onet/fc2_3/weights","shape":[256,10]},{"dtype":"float32","name":"onet/fc2_3/bias","shape":[10]}]}]
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