Merge pull request #132 from justadudewhohacks/nodejs

.gitignore

@@ -6,3 +6,4 @@ proto
 weights_uncompressed
 weights_unused
 docs
+out
\ No newline at end of file

.travis.yml

@@ -4,16 +4,21 @@ node_js:
   - "node"
   - "10"
   - "8"
-  - "6"
+  # node 6 is not compatible with tfjs-node
+  # - "6"
 env:
+  global:
     - BACKEND_CPU=true EXCLUDE_UNCOMPRESSED=true
+  matrix:
+    - ENV=browser
+    - ENV=node
 addons:
   chrome: stable
 install: npm install
 before_install:
  - export DISPLAY=:99.0
  - sh -e /etc/init.d/xvfb start
- - sleep 3 # give xvfb some time to start
 script:
-  - npm run test-travis
+  - if [ $ENV == 'browser' ]; then npm run test-browser; fi
+  - if [ $ENV == 'node' ]; then npm run test-node; fi
   - npm run build
\ No newline at end of file

README.md

@@ -16,6 +16,9 @@ Table of Contents:
   * **[Face Detection Models](#models-face-detection)**
   * **[68 Point Face Landmark Detection Models](#models-face-landmark-detection)**
   * **[Face Recognition Model](#models-face-recognition)**
+* **[Getting Started](#getting-started)**
+  * **[face-api.js for the Browser](#getting-started-browser)**
+  * **[face-api.js for Nodejs](#getting-started-nodejs)**
 * **[Usage](#usage)**
   * **[Loading the Models](#usage-loading-models)**
   * **[High Level API](#usage-high-level-api)**
@@ -75,15 +78,42 @@ Check out my face-api.js tutorials:
 
 ## Running the Examples
 
+Clone the repository:
+
 ``` bash
 git clone https://github.com/justadudewhohacks/face-api.js.git
-cd face-api.js/examples
+```
+
+### Running the Browser Examples
+
+``` bash
+cd face-api.js/examples/examples-browser
 npm i
 npm start
 ```
 
 Browse to http://localhost:3000/.
 
+### Running the Nodejs Examples
+
+``` bash
+cd face-api.js/examples/examples-nodejs
+npm i
+```
+
+Now run one of the examples using ts-node:
+
+``` bash
+ts-node faceDetection.ts
+```
+
+Or simply compile and run them with node:
+
+``` bash
+tsc faceDetection.ts
+node faceDetection.js
+```
+
 <a name="models"></a>
 
 # Available Models
@@ -130,6 +160,55 @@ The neural net is equivalent to the **FaceRecognizerNet** used in [face-recognit
 
 The size of the quantized model is roughly 6.2 MB (**face_recognition_model**).
 
+<a name="getting-started"></a>
+
+# Getting Started
+
+<a name="getting-started-browser"></a>
+
+## face-api.js for the Browser
+
+Simply include the latest script from [dist/face-api.js](https://github.com/justadudewhohacks/face-api.js/tree/master/dist).
+
+Or install it via npm:
+
+``` bash
+npm i face-api.js
+```
+
+<a name="getting-started-nodejs"></a>
+
+## face-api.js for Nodejs
+
+We can use the equivalent API in a nodejs environment by polyfilling some browser specifics, such as HTMLImageElement, HTMLCanvasElement and ImageData. The easiest way to do so is by installing the node-canvas package.
+
+Alternatively you can simply construct your own tensors from image data and pass tensors as inputs to the API.
+
+Furthermore you want to install @tensorflow/tfjs-node (not required, but highly recommended), which speeds things up drastically by compiling and binding to the native Tensorflow C++ library:
+
+``` bash
+npm i face-api.js canvas @tensorflow/tfjs-node
+```
+
+Now we simply monkey patch the environment to use the polyfills:
+
+``` javascript
+// import nodejs bindings to native tensorflow,
+// not required, but will speed up things drastically (python required)
+import '@tensorflow/tfjs-node';
+
+// implements nodejs wrappers for HTMLCanvasElement, HTMLImageElement, ImageData
+import * as canvas from 'canvas';
+
+import * as faceapi from 'face-api.js';
+
+// patch nodejs environment, we need to provide an implementation of
+// HTMLCanvasElement and HTMLImageElement, additionally an implementation
+// of ImageData is required, in case you want to use the MTCNN
+const { Canvas, Image, ImageData } = canvas
+faceapi.env.monkeyPatch({ Canvas, Image, ImageData })
+```
+
 # Usage
 
 <a name="usage-loading-models"></a>
@@ -150,14 +229,38 @@ await faceapi.loadSsdMobilenetv1Model('/models')
 // await faceapi.loadFaceRecognitionModel('/models')
 ```
 
-Alternatively, you can also create instance of the neural nets:
+All global neural network instances are exported via faceapi.nets:
+
+``` javascript
+console.log(faceapi.nets)
+```
+
+The following is equivalent to `await faceapi.loadSsdMobilenetv1Model('/models')`:
+
+``` javascript
+await faceapi.nets.ssdMobilenetv1.loadFromUri('/models')
+```
+
+In a nodejs environment you can furthermore load the models directly from disk:
+
+``` javascript
+await faceapi.nets.ssdMobilenetv1.loadFromDisk('./models')
+```
+
+You can also load the model from a tf.NamedTensorMap:
+
+``` javascript
+await faceapi.nets.ssdMobilenetv1.loadFromWeightMap(weightMap)
+```
+
+Alternatively, you can also create own instances of the neural nets:
 
 ``` javascript
 const net = new faceapi.SsdMobilenetv1()
 await net.load('/models')
 ```
 
-Using instances, you can also load the weights as a Float32Array (in case you want to use the uncompressed models):
+You can also load the weights as a Float32Array (in case you want to use the uncompressed models):
 
 ``` javascript
 // using fetch
@@ -205,7 +308,7 @@ By default **detectAllFaces** and **detectSingleFace** utilize the SSD Mobilenet
 
 ``` javascript
 const detections1 = await faceapi.detectAllFaces(input, new faceapi.SsdMobilenetv1Options())
-const detections2 = await faceapi.detectAllFaces(input, new faceapi.inyFaceDetectorOptions())
+const detections2 = await faceapi.detectAllFaces(input, new faceapi.TinyFaceDetectorOptions())
 const detections3 = await faceapi.detectAllFaces(input, new faceapi.MtcnnOptions())
 ```
 
@@ -513,12 +616,6 @@ const landmarks2 = await faceapi.detectFaceLandmarksTiny(faceImage)
 const descriptor = await faceapi.computeFaceDescriptor(alignedFaceImage)
 ```
 
-All global neural network instances are exported via faceapi.nets:
-
-``` javascript
-console.log(faceapi.nets)
-```
-
 ### Extracting a Canvas for an Image Region
 
 ``` javascript

examples/public/js/bbt.js → examples/examples-browser/public/js/bbt.js

 const classes = ['amy', 'bernadette', 'howard', 'leonard', 'penny', 'raj', 'sheldon', 'stuart']
 
 function getFaceImageUri(className, idx) {
-  return `images/${className}/${className}${idx}.png`
+  return `${className}/${className}${idx}.png`
 }
 
 function renderFaceImageSelectList(selectListId, onChange, initialValue) {

examples/public/js/commons.js → examples/examples-browser/public/js/commons.js

-function getImageUri(imageName) {
-  return `images/${imageName}`
-}
-
 async function requestExternalImage(imageUrl) {
   const res = await fetch('fetch_external_image', {
     method: 'post',

examples/public/js/imageSelectionControls.js → examples/examples-browser/public/js/imageSelectionControls.js

@@ -17,7 +17,7 @@ function renderImageSelectList(selectListId, onChange, initialValue) {
       renderOption(
         select,
         imageName,
-        getImageUri(imageName)
+        imageName
       )
     )
   }
@@ -25,7 +25,7 @@ function renderImageSelectList(selectListId, onChange, initialValue) {
   renderSelectList(
     selectListId,
     onChange,
-    getImageUri(initialValue),
+    initialValue,
     renderChildren
   )
 }

examples/server.js → examples/examples-browser/server.js

@@ -10,10 +10,10 @@ app.use(express.urlencoded({ extended: true }))
 const viewsDir = path.join(__dirname, 'views')
 app.use(express.static(viewsDir))
 app.use(express.static(path.join(__dirname, './public')))
-app.use(express.static(path.join(__dirname, '../weights')))
-app.use(express.static(path.join(__dirname, '../weights_uncompressed')))
-app.use(express.static(path.join(__dirname, '../dist')))
-app.use(express.static(path.join(__dirname, './node_modules/axios/dist')))
+app.use(express.static(path.join(__dirname, '../images')))
+app.use(express.static(path.join(__dirname, '../media')))
+app.use(express.static(path.join(__dirname, '../../weights')))
+app.use(express.static(path.join(__dirname, '../../dist')))
 
 app.get('/', (req, res) => res.redirect('/face_and_landmark_detection'))
 app.get('/face_and_landmark_detection', (req, res) => res.sendFile(path.join(viewsDir, 'faceAndLandmarkDetection.html')))

examples/views/videoFaceTracking.html → examples/examples-browser/views/videoFaceTracking.html

@@ -18,7 +18,7 @@
       <div class="indeterminate"></div>
     </div>
     <div style="position: relative" class="margin">
-      <video src="media/bbt.mp4" id="inputVideo" autoplay muted loop></video>
+      <video src="bbt.mp4" id="inputVideo" autoplay muted loop></video>
       <canvas id="overlay" />
     </div>
 

examples/examples-nodejs/commons/env.ts

+// import nodejs bindings to native tensorflow,
+// not required, but will speed up things drastically (python required)
+import '@tensorflow/tfjs-node';
+
+// implements nodejs wrappers for HTMLCanvasElement, HTMLImageElement, ImageData
+const canvas = require('canvas')
+
+import * as faceapi from '../../../src';
+
+// patch nodejs environment, we need to provide an implementation of
+// HTMLCanvasElement and HTMLImageElement, additionally an implementation
+// of ImageData is required, in case you want to use the MTCNN
+const { Canvas, Image, ImageData } = canvas
+faceapi.env.monkeyPatch({ Canvas, Image, ImageData })
+
+export { canvas, faceapi }
\ No newline at end of file

examples/examples-nodejs/commons/faceDetection.ts

+import { NeuralNetwork } from 'tfjs-image-recognition-base';
+
+import { faceapi } from './env';
+
+export const faceDetectionNet = faceapi.nets.ssdMobilenetv1
+// export const faceDetectionNet = tinyFaceDetector
+// export const faceDetectionNet = mtcnn
+
+// SsdMobilenetv1Options
+const minConfidence = 0.5
+
+// TinyFaceDetectorOptions
+const inputSize = 408
+const scoreThreshold = 0.5
+
+// MtcnnOptions
+const minFaceSize = 50
+const scaleFactor = 0.8
+
+function getFaceDetectorOptions(net: NeuralNetwork<any>) {
+  return net === faceapi.nets.ssdMobilenetv1
+    ? new faceapi.SsdMobilenetv1Options({ minConfidence })
+    : (net === faceapi.nets.tinyFaceDetector
+      ? new faceapi.TinyFaceDetectorOptions({ inputSize, scoreThreshold })
+      : new faceapi.MtcnnOptions({ minFaceSize, scaleFactor })
+    )
+}
+
+export const faceDetectionOptions = getFaceDetectorOptions(faceDetectionNet)
\ No newline at end of file

examples/examples-nodejs/commons/index.ts

+export { canvas, faceapi } from './env';
+export { faceDetectionNet, faceDetectionOptions } from './faceDetection';
+export { saveFile } from './saveFile';
\ No newline at end of file

examples/examples-nodejs/commons/saveFile.ts

+import * as fs from 'fs';
+import * as path from 'path';
+
+const baseDir = path.resolve(__dirname, '../out')
+
+export function saveFile(fileName: string, buf: Buffer) {
+  if (!fs.existsSync(baseDir)) {
+    fs.mkdirSync(baseDir)
+  }
+
+  fs.writeFileSync(path.resolve(baseDir, fileName), buf)
+}
\ No newline at end of file

examples/examples-nodejs/faceDetection.ts

+import { canvas, faceapi, faceDetectionNet, faceDetectionOptions, saveFile } from './commons';
+
+async function run() {
+
+  await faceDetectionNet.loadFromDisk('../../weights')
+
+  const img = await canvas.loadImage('../images/bbt1.jpg')
+  const detections = await faceapi.detectAllFaces(img, faceDetectionOptions)
+
+  const out = faceapi.createCanvasFromMedia(img) as any
+  faceapi.drawDetection(out, detections)
+
+  saveFile('faceDetection.jpg', out.toBuffer('image/jpeg'))
+}
+
+run()
\ No newline at end of file

examples/examples-nodejs/faceLandmarkDetection.ts

+import { canvas, faceapi, faceDetectionNet, faceDetectionOptions, saveFile } from './commons';
+
+async function run() {
+
+  await faceDetectionNet.loadFromDisk('../../weights')
+  await faceapi.nets.faceLandmark68Net.loadFromDisk('../../weights')
+
+  const img = await canvas.loadImage('../images/bbt1.jpg')
+  const results = await faceapi.detectAllFaces(img, faceDetectionOptions)
+    .withFaceLandmarks()
+
+  const out = faceapi.createCanvasFromMedia(img) as any
+  faceapi.drawDetection(out, results.map(res => res.detection))
+  faceapi.drawLandmarks(out, results.map(res => res.faceLandmarks), { drawLines: true, color: 'red' })
+
+  saveFile('faceLandmarkDetection.jpg', out.toBuffer('image/jpeg'))
+}
+
+run()
\ No newline at end of file

examples/examples-nodejs/faceRecognition.ts

+import { canvas, faceapi, faceDetectionNet, faceDetectionOptions, saveFile } from './commons';
+
+const REFERENCE_IMAGE = '../images/bbt1.jpg'
+const QUERY_IMAGE = '../images/bbt4.jpg'
+
+async function run() {
+
+  await faceDetectionNet.loadFromDisk('../../weights')
+  await faceapi.nets.faceLandmark68Net.loadFromDisk('../../weights')
+  await faceapi.nets.faceRecognitionNet.loadFromDisk('../../weights')
+
+  const referenceImage = await canvas.loadImage(REFERENCE_IMAGE)
+  const queryImage = await canvas.loadImage(QUERY_IMAGE)
+
+  const resultsRef = await faceapi.detectAllFaces(referenceImage, faceDetectionOptions)
+    .withFaceLandmarks()
+    .withFaceDescriptors()
+
+  const resultsQuery = await faceapi.detectAllFaces(queryImage, faceDetectionOptions)
+    .withFaceLandmarks()
+    .withFaceDescriptors()
+
+  const faceMatcher = new faceapi.FaceMatcher(resultsRef)
+
+  const labels = faceMatcher.labeledDescriptors
+    .map(ld => ld.label)
+  const refBoxesWithText = resultsRef
+    .map(res => res.detection.box)
+    .map((box, i) => new faceapi.BoxWithText(box, labels[i]))
+  const outRef = faceapi.createCanvasFromMedia(referenceImage) as any
+  faceapi.drawDetection(outRef, refBoxesWithText)
+  saveFile('referenceImage.jpg', outRef.toBuffer('image/jpeg'))
+
+  const queryBoxesWithText = resultsQuery.map(res => {
+    const bestMatch = faceMatcher.findBestMatch(res.descriptor)
+    return new faceapi.BoxWithText(res.detection.box, bestMatch.toString())
+  })
+  const outQuery = faceapi.createCanvasFromMedia(queryImage) as any
+  faceapi.drawDetection(outQuery, queryBoxesWithText)
+  saveFile('queryImage.jpg', outQuery.toBuffer('image/jpeg'))
+}
+
+run()
\ No newline at end of file

examples/examples-nodejs/package-lock.json

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+        "util-deprecate": "1.0.2"
+      }
+    },
+    "rimraf": {
+      "version": "2.6.2",
+      "resolved": "https://registry.npmjs.org/rimraf/-/rimraf-2.6.2.tgz",
+      "integrity": "sha512-lreewLK/BlghmxtfH36YYVg1i8IAce4TI7oao75I1g245+6BctqTVQiBP3YUJ9C6DQOXJmkYR9X9fCLtCOJc5w==",
+      "requires": {
+        "glob": "7.1.3"
+      }
+    },
+    "safe-buffer": {
+      "version": "5.1.2",
+      "resolved": "https://registry.npmjs.org/safe-buffer/-/safe-buffer-5.1.2.tgz",
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+    },
+    "safer-buffer": {
+      "version": "2.1.2",
+      "resolved": "https://registry.npmjs.org/safer-buffer/-/safer-buffer-2.1.2.tgz",
+      "integrity": "sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg=="
+    },
+    "sax": {
+      "version": "1.2.4",
+      "resolved": "https://registry.npmjs.org/sax/-/sax-1.2.4.tgz",
+      "integrity": "sha512-NqVDv9TpANUjFm0N8uM5GxL36UgKi9/atZw+x7YFnQ8ckwFGKrl4xX4yWtrey3UJm5nP1kUbnYgLopqWNSRhWw=="
+    },
+    "seedrandom": {
+      "version": "2.4.3",
+      "resolved": "https://registry.npmjs.org/seedrandom/-/seedrandom-2.4.3.tgz",
+      "integrity": "sha1-JDhQTa0zkXMUv/GKxNeU8W1qrsw="
+    },
+    "semver": {
+      "version": "5.6.0",
+      "resolved": "https://registry.npmjs.org/semver/-/semver-5.6.0.tgz",
+      "integrity": "sha512-RS9R6R35NYgQn++fkDWaOmqGoj4Ek9gGs+DPxNUZKuwE183xjJroKvyo1IzVFeXvUrvmALy6FWD5xrdJT25gMg=="
+    },
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+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/set-blocking/-/set-blocking-2.0.0.tgz",
+      "integrity": "sha1-BF+XgtARrppoA93TgrJDkrPYkPc="
+    },
+    "signal-exit": {
+      "version": "3.0.2",
+      "resolved": "https://registry.npmjs.org/signal-exit/-/signal-exit-3.0.2.tgz",
+      "integrity": "sha1-tf3AjxKH6hF4Yo5BXiUTK3NkbG0="
+    },
+    "string-width": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/string-width/-/string-width-1.0.2.tgz",
+      "integrity": "sha1-EYvfW4zcUaKn5w0hHgfisLmxB9M=",
+      "requires": {
+        "code-point-at": "1.1.0",
+        "is-fullwidth-code-point": "1.0.0",
+        "strip-ansi": "3.0.1"
+      }
+    },
+    "string_decoder": {
+      "version": "1.1.1",
+      "resolved": "https://registry.npmjs.org/string_decoder/-/string_decoder-1.1.1.tgz",
+      "integrity": "sha512-n/ShnvDi6FHbbVfviro+WojiFzv+s8MPMHBczVePfUpDJLwoLT0ht1l4YwBCbi8pJAveEEdnkHyPyTP/mzRfwg==",
+      "requires": {
+        "safe-buffer": "5.1.2"
+      }
+    },
+    "strip-ansi": {
+      "version": "3.0.1",
+      "resolved": "http://registry.npmjs.org/strip-ansi/-/strip-ansi-3.0.1.tgz",
+      "integrity": "sha1-ajhfuIU9lS1f8F0Oiq+UJ43GPc8=",
+      "requires": {
+        "ansi-regex": "2.1.1"
+      }
+    },
+    "strip-json-comments": {
+      "version": "2.0.1",
+      "resolved": "https://registry.npmjs.org/strip-json-comments/-/strip-json-comments-2.0.1.tgz",
+      "integrity": "sha1-PFMZQukIwml8DsNEhYwobHygpgo="
+    },
+    "tar": {
+      "version": "4.4.6",
+      "resolved": "https://registry.npmjs.org/tar/-/tar-4.4.6.tgz",
+      "integrity": "sha512-tMkTnh9EdzxyfW+6GK6fCahagXsnYk6kE6S9Gr9pjVdys769+laCTbodXDhPAjzVtEBazRgP0gYqOjnk9dQzLg==",
+      "requires": {
+        "chownr": "1.1.1",
+        "fs-minipass": "1.2.5",
+        "minipass": "2.3.5",
+        "minizlib": "1.1.1",
+        "mkdirp": "0.5.1",
+        "safe-buffer": "5.1.2",
+        "yallist": "3.0.2"
+      }
+    },
+    "util-deprecate": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz",
+      "integrity": "sha1-RQ1Nyfpw3nMnYvvS1KKJgUGaDM8="
+    },
+    "wide-align": {
+      "version": "1.1.3",
+      "resolved": "https://registry.npmjs.org/wide-align/-/wide-align-1.1.3.tgz",
+      "integrity": "sha512-QGkOQc8XL6Bt5PwnsExKBPuMKBxnGxWWW3fU55Xt4feHozMUhdUMaBCk290qpm/wG5u/RSKzwdAC4i51YigihA==",
+      "requires": {
+        "string-width": "1.0.2"
+      }
+    },
+    "wrappy": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
+      "integrity": "sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8="
+    },
+    "yallist": {
+      "version": "3.0.2",
+      "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.0.2.tgz",
+      "integrity": "sha1-hFK0u36Dx8GI2AQcGoN8dz1ti7k="
+    }
+  }
+}

examples/examples-nodejs/package.json

+{
+  "author": "justadudewhohacks",
+  "license": "MIT",
+  "dependencies": {
+    "@tensorflow/tfjs-node": "^0.1.19",
+    "canvas": "^2.0.1"
+  }
+}

jasmine-node.js

+
+let spec_files = ['**/*.test.ts'].concat(
+  process.env.EXCLUDE_UNCOMPRESSED
+    ? ['!**/*.uncompressed.test.ts']
+    : []
+)
+
+// exclude browser tests
+spec_files = spec_files.concat(['!**/*.browser.test.ts'])
+
+module.exports = {
+  spec_dir: 'test',
+  spec_files,
+  random: false
+}
\ No newline at end of file

karma.conf.js

@@ -22,10 +22,9 @@ let exclude = (
         'faceRecognitionNet',
         'ssdMobilenetv1',
         'tinyFaceDetector',
-        'mtcnn',
-        'tinyYolov2'
+        'mtcnn'
       ]
-    : ['tinyYolov2']
+    : []
   )
     .filter(ex => ex !== process.env.UUT)
     .map(ex => `test/tests/${ex}/*.ts`)
@@ -37,6 +36,10 @@ exclude = exclude.concat(
     : []
 )
 
+// exclude nodejs tests
+exclude = exclude.concat(['**/*.node.test.ts'])
+
+
 module.exports = function(config) {
   const args = []
   if (process.env.BACKEND_CPU) {

package-lock.json

@@ -5,77 +5,139 @@
   "requires": true,
   "dependencies": {
     "@babel/code-frame": {
-      "version": "7.0.0-rc.3",
-      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.0.0-rc.3.tgz",
-      "integrity": "sha512-vH+ONMtvkQpjvKAXl5shNFyIpBwmkgKjo+buySLpQsMNDlqbJcFIMiYhwDrK4isZsae+QeHJYbqUJ0BYwyKNZw==",
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.0.0.tgz",
+      "integrity": "sha512-OfC2uemaknXr87bdLUkWog7nYuliM9Ij5HUcajsVcMCpQrcLmtxRbVFTIqmcSkSeYRBFBRxs2FiUqFJDLdiebA==",
       "dev": true,
       "requires": {
-        "@babel/highlight": "7.0.0-rc.3"
+        "@babel/highlight": "7.0.0"
       }
     },
     "@babel/highlight": {
-      "version": "7.0.0-rc.3",
-      "resolved": "https://registry.npmjs.org/@babel/highlight/-/highlight-7.0.0-rc.3.tgz",
-      "integrity": "sha512-BwDi54dopTDbp0nIXZ1Ln7dGaZx8Vxqi8IKVFxtHHFhlTMxwVnYZlRFamRy7yOG4KEemRYhENq5ea/cNa9Jvjw==",
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/@babel/highlight/-/highlight-7.0.0.tgz",
+      "integrity": "sha512-UFMC4ZeFC48Tpvj7C8UgLvtkaUuovQX+5xNWrsIoMG8o2z+XFKjKaN9iVmS84dPwVN00W4wPmqvYoZF3EGAsfw==",
       "dev": true,
       "requires": {
         "chalk": "2.4.1",
         "esutils": "2.0.2",
         "js-tokens": "4.0.0"
-      },
-      "dependencies": {
-        "ansi-styles": {
-          "version": "3.2.1",
-          "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-3.2.1.tgz",
-          "integrity": "sha512-VT0ZI6kZRdTh8YyJw3SMbYm/u+NqfsAxEpWO0Pf9sq8/e94WxxOpPKx9FR1FlyCtOVDNOQ+8ntlqFxiRc+r5qA==",
-          "dev": true,
-          "requires": {
-            "color-convert": "1.9.2"
       }
     },
-        "chalk": {
-          "version": "2.4.1",
-          "resolved": "https://registry.npmjs.org/chalk/-/chalk-2.4.1.tgz",
-          "integrity": "sha512-ObN6h1v2fTJSmUXoS3nMQ92LbDK9be4TV+6G+omQlGJFdcUX5heKi1LZ1YnRMIgwTLEj3E24bT6tYni50rlCfQ==",
+    "@protobufjs/aspromise": {
+      "version": "1.1.2",
+      "resolved": "https://registry.npmjs.org/@protobufjs/aspromise/-/aspromise-1.1.2.tgz",
+      "integrity": "sha1-m4sMxmPWaafY9vXQiToU00jzD78=",
+      "dev": true
+    },
+    "@protobufjs/base64": {
+      "version": "1.1.2",
+      "resolved": "https://registry.npmjs.org/@protobufjs/base64/-/base64-1.1.2.tgz",
+      "integrity": "sha512-AZkcAA5vnN/v4PDqKyMR5lx7hZttPDgClv83E//FMNhR2TMcLUhfRUBHCmSl0oi9zMgDDqRUJkSxO3wm85+XLg==",
+      "dev": true
+    },
+    "@protobufjs/codegen": {
+      "version": "2.0.4",
+      "resolved": "https://registry.npmjs.org/@protobufjs/codegen/-/codegen-2.0.4.tgz",
+      "integrity": "sha512-YyFaikqM5sH0ziFZCN3xDC7zeGaB/d0IUb9CATugHWbd1FRFwWwt4ld4OYMPWu5a3Xe01mGAULCdqhMlPl29Jg==",
+      "dev": true
+    },
+    "@protobufjs/eventemitter": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmjs.org/@protobufjs/eventemitter/-/eventemitter-1.1.0.tgz",
+      "integrity": "sha1-NVy8mLr61ZePntCV85diHx0Ga3A=",
+      "dev": true
+    },
+    "@protobufjs/fetch": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmjs.org/@protobufjs/fetch/-/fetch-1.1.0.tgz",
+      "integrity": "sha1-upn7WYYUr2VwDBYZ/wbUVLDYTEU=",
       "dev": true,
       "requires": {
-            "ansi-styles": "3.2.1",
-            "escape-string-regexp": "1.0.5",
-            "supports-color": "5.5.0"
+        "@protobufjs/aspromise": "1.1.2",
+        "@protobufjs/inquire": "1.1.0"
       }
     },
-        "has-flag": {
-          "version": "3.0.0",
-          "resolved": "https://registry.npmjs.org/has-flag/-/has-flag-3.0.0.tgz",
-          "integrity": "sha1-tdRU3CGZriJWmfNGfloH87lVuv0=",
+    "@protobufjs/float": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/@protobufjs/float/-/float-1.0.2.tgz",
+      "integrity": "sha1-Xp4avctz/Ap8uLKR33jIy9l7h9E=",
       "dev": true
     },
-        "js-tokens": {
-          "version": "4.0.0",
-          "resolved": "https://registry.npmjs.org/js-tokens/-/js-tokens-4.0.0.tgz",
-          "integrity": "sha512-RdJUflcE3cUzKiMqQgsCu06FPu9UdIJO0beYbPhHN4k6apgJtifcoCtT9bcxOpYBtpD2kCM6Sbzg4CausW/PKQ==",
+    "@protobufjs/inquire": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmjs.org/@protobufjs/inquire/-/inquire-1.1.0.tgz",
+      "integrity": "sha1-/yAOPnzyQp4tyvwRQIKOjMY48Ik=",
       "dev": true
     },
-        "supports-color": {
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-          "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-5.5.0.tgz",
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+    "@protobufjs/path": {
+      "version": "1.1.2",
+      "resolved": "https://registry.npmjs.org/@protobufjs/path/-/path-1.1.2.tgz",
+      "integrity": "sha1-bMKyDFya1q0NzP0hynZz2Nf79o0=",
+      "dev": true
+    },
+    "@protobufjs/pool": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmjs.org/@protobufjs/pool/-/pool-1.1.0.tgz",
+      "integrity": "sha1-Cf0V8tbTq/qbZbw2ZQbWrXhG/1Q=",
+      "dev": true
+    },
+    "@protobufjs/utf8": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmjs.org/@protobufjs/utf8/-/utf8-1.1.0.tgz",
+      "integrity": "sha1-p3c2C1s5oaLlEG+OhY8v0tBgxXA=",
+      "dev": true
+    },
+    "@tensorflow/tfjs": {
+      "version": "0.13.3",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs/-/tfjs-0.13.3.tgz",
+      "integrity": "sha512-7WXS6wfCnX/5s2CmAYelRRiRvRkpDbdoL/U+qZ+08lS2NWFWT2y+ga/nWq6brNHUEShLpXNdZ8S++UcG2q2t0g==",
       "dev": true,
       "requires": {
-            "has-flag": "3.0.0"
-          }
+        "@tensorflow/tfjs-converter": "0.6.5",
+        "@tensorflow/tfjs-core": "0.13.8",
+        "@tensorflow/tfjs-layers": "0.8.3"
       }
+    },
+    "@tensorflow/tfjs-converter": {
+      "version": "0.6.5",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-converter/-/tfjs-converter-0.6.5.tgz",
+      "integrity": "sha512-jbmxtRtRV/7JZNmhkjZsPJ651ntrWlxPfzTYF10vPWnS2Tz+u0RjJj9pXRYoO4jfsvFz//kNtXKMhb/Icbp5BA==",
+      "dev": true,
+      "requires": {
+        "@types/long": "3.0.32",
+        "protobufjs": "6.8.8"
       }
     },
     "@tensorflow/tfjs-core": {
-      "version": "0.13.2",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-core/-/tfjs-core-0.13.2.tgz",
-      "integrity": "sha512-0+/Lfjmv+eMibVaYCe2trSqps5+K+b5BZbzJTwbHM2fpV1l1XJpp6PLyZ05A4mhQcAiuIgNgim48H97Yl+yAMw==",
+      "version": "0.13.8",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-core/-/tfjs-core-0.13.8.tgz",
+      "integrity": "sha512-Vp0dU6wRisXZRUOEb7w5lPybJkJw2iR32sTlv1ZY42fReMgP7nYKPVoNaByjJ7VFmJe6fK0yURklI+vc+u8SEQ==",
       "requires": {
         "@types/seedrandom": "2.4.27",
-        "@types/webgl-ext": "0.0.29",
+        "@types/webgl-ext": "0.0.30",
         "@types/webgl2": "0.0.4",
-        "seedrandom": "2.4.4"
+        "seedrandom": "2.4.3"
+      }
+    },
+    "@tensorflow/tfjs-layers": {
+      "version": "0.8.3",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-layers/-/tfjs-layers-0.8.3.tgz",
+      "integrity": "sha512-RHnTtXx7bE6d+x0byLi90nejy44caBKhYqBR0zmCVzxagxDSln9wd28ojHEh1/6gQyBejhMEQ3gUK4SsKTpjIw==",
+      "dev": true
+    },
+    "@tensorflow/tfjs-node": {
+      "version": "0.1.19",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-node/-/tfjs-node-0.1.19.tgz",
+      "integrity": "sha512-iQwUu52H1fZjlwNHB3C0nqIhhfGoHz9o7pu9PTxkcFSAuKRqI4quvt96uT2GS089w+FLFyGwABi2hBjXDsYMJw==",
+      "dev": true,
+      "requires": {
+        "@tensorflow/tfjs": "0.13.3",
+        "adm-zip": "0.4.11",
+        "bindings": "1.3.0",
+        "progress": "2.0.1",
+        "rimraf": "2.6.2",
+        "tar": "4.4.6"
       }
     },
     "@types/estree": {
@@ -85,15 +147,21 @@
       "dev": true
     },
     "@types/jasmine": {
-      "version": "2.8.8",
-      "resolved": "https://registry.npmjs.org/@types/jasmine/-/jasmine-2.8.8.tgz",
-      "integrity": "sha512-OJSUxLaxXsjjhob2DBzqzgrkLmukM3+JMpRp0r0E4HTdT1nwDCWhaswjYxazPij6uOdzHCJfNbDjmQ1/rnNbCg==",
+      "version": "2.8.9",
+      "resolved": "https://registry.npmjs.org/@types/jasmine/-/jasmine-2.8.9.tgz",
+      "integrity": "sha512-8dPZwjosElZOGGYw1nwTvOEMof4gjwAWNFS93nBI091BoEfd5drnHOLRMiRF/LOPuMTn5LgEdv0bTUO8QFVuHQ==",
+      "dev": true
+    },
+    "@types/long": {
+      "version": "3.0.32",
+      "resolved": "https://registry.npmjs.org/@types/long/-/long-3.0.32.tgz",
+      "integrity": "sha512-ZXyOOm83p7X8p3s0IYM3VeueNmHpkk/yMlP8CLeOnEcu6hIwPH7YjZBvhQkR0ZFS2DqZAxKtJ/M5fcuv3OU5BA==",
       "dev": true
     },
     "@types/node": {
-      "version": "10.9.2",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-10.9.2.tgz",
-      "integrity": "sha512-pwZnkVyCGJ3LsQ0/3flQK5lCFao4esIzwUVzzk5NvL9vnkEyDhNf4fhHzUMHvyr56gNZywWTS2MR0euabMSz4A==",
+      "version": "10.12.2",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-10.12.2.tgz",
+      "integrity": "sha512-53ElVDSnZeFUUFIYzI8WLQ25IhWzb6vbddNp8UHlXQyU0ET2RhV5zg0NfubzU7iNMh5bBXb0htCzfvrSVNgzaQ==",
       "dev": true
     },
     "@types/seedrandom": {
@@ -102,9 +170,9 @@
       "integrity": "sha1-nbVjk33YaRX2kJK8QyWdL0hXjkE="
     },
     "@types/webgl-ext": {
-      "version": "0.0.29",
-      "resolved": "https://registry.npmjs.org/@types/webgl-ext/-/webgl-ext-0.0.29.tgz",
-      "integrity": "sha512-ZlVjDQU5Vlc9hF4LGdDldujZUf0amwlwGv1RI2bfvdrEHIl6X/7MZVpemJUjS7NxD9XaKfE8SlFrxsfXpUkt/A=="
+      "version": "0.0.30",
+      "resolved": "https://registry.npmjs.org/@types/webgl-ext/-/webgl-ext-0.0.30.tgz",
+      "integrity": "sha512-LKVgNmBxN0BbljJrVUwkxwRYqzsAEPcZOe6S2T6ZaBDIrFp0qu4FNlpc5sM1tGbXUYFgdVQIoeLk1Y1UoblyEg=="
     },
     "@types/webgl2": {
       "version": "0.0.4",
@@ -133,6 +201,12 @@
       "integrity": "sha1-EFSVrlNh1pe9GVyCUZLhrX8lN4c=",
       "dev": true
     },
+    "adm-zip": {
+      "version": "0.4.11",
+      "resolved": "https://registry.npmjs.org/adm-zip/-/adm-zip-0.4.11.tgz",
+      "integrity": "sha512-L8vcjDTCOIJk7wFvmlEUN7AsSb8T+2JrdP7KINBjzr24TJ5Mwj590sLu3BC7zNZowvJWa/JtPmD8eJCzdtDWjA==",
+      "dev": true
+    },
     "after": {
       "version": "0.8.2",
       "resolved": "https://registry.npmjs.org/after/-/after-0.8.2.tgz",
@@ -185,6 +259,21 @@
         "ansi-wrap": "0.1.0"
       }
     },
+    "ansi-regex": {
+      "version": "2.1.1",
+      "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-2.1.1.tgz",
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+      "version": "4.1.2",
+      "resolved": "https://registry.npmjs.org/npmlog/-/npmlog-4.1.2.tgz",
+      "integrity": "sha512-2uUqazuKlTaSI/dC8AzicUck7+IrEaOnN/e0jd3Xtt1KcGpwx30v50mL7oPyr/h9bL3E4aZccVwpwP+5W9Vjkg==",
+      "dev": true,
+      "requires": {
+        "are-we-there-yet": "1.1.5",
+        "console-control-strings": "1.1.0",
+        "gauge": "2.7.4",
+        "set-blocking": "2.0.0"
+      }
+    },
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       "version": "1.0.0",
       "resolved": "https://registry.npmjs.org/null-check/-/null-check-1.0.0.tgz",
@@ -3010,12 +3375,28 @@
       "integrity": "sha1-hUNzx/XCMVkU/Jv8a9gjj92h7Cc=",
       "dev": true
     },
+    "os-homedir": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/os-homedir/-/os-homedir-1.0.2.tgz",
+      "integrity": "sha1-/7xJiDNuDoM94MFox+8VISGqf7M=",
+      "dev": true
+    },
     "os-tmpdir": {
       "version": "1.0.2",
       "resolved": "https://registry.npmjs.org/os-tmpdir/-/os-tmpdir-1.0.2.tgz",
       "integrity": "sha1-u+Z0BseaqFxc/sdm/lc0VV36EnQ=",
       "dev": true
     },
+    "osenv": {
+      "version": "0.1.5",
+      "resolved": "https://registry.npmjs.org/osenv/-/osenv-0.1.5.tgz",
+      "integrity": "sha512-0CWcCECdMVc2Rw3U5w9ZjqX6ga6ubk1xDVKxtBQPK7wis/0F2r9T6k4ydGYhecl7YUBxBVxhL5oisPsNxAPe2g==",
+      "dev": true,
+      "requires": {
+        "os-homedir": "1.0.2",
+        "os-tmpdir": "1.0.2"
+      }
+    },
     "pad": {
       "version": "2.1.0",
       "resolved": "https://registry.npmjs.org/pad/-/pad-2.1.0.tgz",
@@ -3266,6 +3647,41 @@
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       "dev": true
     },
+    "progress": {
+      "version": "2.0.1",
+      "resolved": "https://registry.npmjs.org/progress/-/progress-2.0.1.tgz",
+      "integrity": "sha512-OE+a6vzqazc+K6LxJrX5UPyKFvGnL5CYmq2jFGNIBWHpc4QyE49/YOumcrpQFJpfejmvRtbJzgO1zPmMCqlbBg==",
+      "dev": true
+    },
+    "protobufjs": {
+      "version": "6.8.8",
+      "resolved": "https://registry.npmjs.org/protobufjs/-/protobufjs-6.8.8.tgz",
+      "integrity": "sha512-AAmHtD5pXgZfi7GMpllpO3q1Xw1OYldr+dMUlAnffGTAhqkg72WdmSY71uKBF/JuyiKs8psYbtKrhi0ASCD8qw==",
+      "dev": true,
+      "requires": {
+        "@protobufjs/aspromise": "1.1.2",
+        "@protobufjs/base64": "1.1.2",
+        "@protobufjs/codegen": "2.0.4",
+        "@protobufjs/eventemitter": "1.1.0",
+        "@protobufjs/fetch": "1.1.0",
+        "@protobufjs/float": "1.0.2",
+        "@protobufjs/inquire": "1.1.0",
+        "@protobufjs/path": "1.1.2",
+        "@protobufjs/pool": "1.1.0",
+        "@protobufjs/utf8": "1.1.0",
+        "@types/long": "4.0.0",
+        "@types/node": "10.12.2",
+        "long": "4.0.0"
+      },
+      "dependencies": {
+        "@types/long": {
+          "version": "4.0.0",
+          "resolved": "https://registry.npmjs.org/@types/long/-/long-4.0.0.tgz",
+          "integrity": "sha512-1w52Nyx4Gq47uuu0EVcsHBxZFJgurQ+rTKS3qMHxR1GY2T8c2AJYd6vZoZ9q1rupaDjU0yT+Jc2XTyXkjeMA+Q==",
+          "dev": true
+        }
+      }
+    },
     "public-encrypt": {
       "version": "4.0.2",
       "resolved": "https://registry.npmjs.org/public-encrypt/-/public-encrypt-4.0.2.tgz",
@@ -3365,6 +3781,26 @@
         "unpipe": "1.0.0"
       }
     },
+    "rc": {
+      "version": "1.2.8",
+      "resolved": "https://registry.npmjs.org/rc/-/rc-1.2.8.tgz",
+      "integrity": "sha512-y3bGgqKj3QBdxLbLkomlohkvsA8gdAiUQlSBJnBhfn+BPxg4bc62d8TcBW15wavDfgexCgccckhcZvywyQYPOw==",
+      "dev": true,
+      "requires": {
+        "deep-extend": "0.6.0",
+        "ini": "1.3.5",
+        "minimist": "1.2.0",
+        "strip-json-comments": "2.0.1"
+      },
+      "dependencies": {
+        "minimist": {
+          "version": "1.2.0",
+          "resolved": "http://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
+          "integrity": "sha1-o1AIsg9BOD7sH7kU9M1d95omQoQ=",
+          "dev": true
+        }
+      }
+    },
     "read-pkg": {
       "version": "1.1.0",
       "resolved": "https://registry.npmjs.org/read-pkg/-/read-pkg-1.1.0.tgz",
@@ -3552,7 +3988,7 @@
       "dev": true,
       "requires": {
         "@types/estree": "0.0.39",
-        "@types/node": "10.9.2"
+        "@types/node": "10.12.2"
       }
     },
     "rollup-plugin-commonjs": {
@@ -3754,7 +4190,7 @@
       "integrity": "sha512-f6W31EQLzxSEYfN3x6/lyljHqXSoCjXKcTsnwz3evQvHgU1+qTzU2SE0SIG7tbAvaCewp2UaZ5x3k6nYsxOP9A==",
       "dev": true,
       "requires": {
-        "@babel/code-frame": "7.0.0-rc.3",
+        "@babel/code-frame": "7.0.0",
         "uglify-js": "3.4.8"
       }
     },
@@ -3920,10 +4356,16 @@
       "integrity": "sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg==",
       "dev": true
     },
+    "sax": {
+      "version": "1.2.4",
+      "resolved": "https://registry.npmjs.org/sax/-/sax-1.2.4.tgz",
+      "integrity": "sha512-NqVDv9TpANUjFm0N8uM5GxL36UgKi9/atZw+x7YFnQ8ckwFGKrl4xX4yWtrey3UJm5nP1kUbnYgLopqWNSRhWw==",
+      "dev": true
+    },
     "seedrandom": {
-      "version": "2.4.4",
-      "resolved": "https://registry.npmjs.org/seedrandom/-/seedrandom-2.4.4.tgz",
-      "integrity": "sha512-9A+PDmgm+2du77B5i0Ip2cxOqqHjgNxnBgglxLcX78A2D6c2rTo61z4jnVABpF4cKeDMDG+cmXXvdnqse2VqMA=="
+      "version": "2.4.3",
+      "resolved": "https://registry.npmjs.org/seedrandom/-/seedrandom-2.4.3.tgz",
+      "integrity": "sha1-JDhQTa0zkXMUv/GKxNeU8W1qrsw="
     },
     "semver": {
       "version": "5.5.0",
@@ -3931,6 +4373,12 @@
       "integrity": "sha512-4SJ3dm0WAwWy/NVeioZh5AntkdJoWKxHxcmyP622fOkgHa4z3R0TdBJICINyaSDE6uNwVc8gZr+ZinwZAH4xIA==",
       "dev": true
     },
+    "set-blocking": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/set-blocking/-/set-blocking-2.0.0.tgz",
+      "integrity": "sha1-BF+XgtARrppoA93TgrJDkrPYkPc=",
+      "dev": true
+    },
     "set-immediate-shim": {
       "version": "1.0.1",
       "resolved": "https://registry.npmjs.org/set-immediate-shim/-/set-immediate-shim-1.0.1.tgz",
@@ -4212,6 +4660,16 @@
         "urix": "0.1.0"
       }
     },
+    "source-map-support": {
+      "version": "0.5.9",
+      "resolved": "https://registry.npmjs.org/source-map-support/-/source-map-support-0.5.9.tgz",
+      "integrity": "sha512-gR6Rw4MvUlYy83vP0vxoVNzM6t8MUXqNuRsuBmBHQDu1Fh6X015FrLdgoDKcNdkwGubozq0P4N0Q37UyFVr1EA==",
+      "dev": true,
+      "requires": {
+        "buffer-from": "1.1.1",
+        "source-map": "0.6.1"
+      }
+    },
     "source-map-url": {
       "version": "0.4.0",
       "resolved": "https://registry.npmjs.org/source-map-url/-/source-map-url-0.4.0.tgz",
@@ -4338,6 +4796,17 @@
         }
       }
     },
+    "string-width": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/string-width/-/string-width-1.0.2.tgz",
+      "integrity": "sha1-EYvfW4zcUaKn5w0hHgfisLmxB9M=",
+      "dev": true,
+      "requires": {
+        "code-point-at": "1.1.0",
+        "is-fullwidth-code-point": "1.0.0",
+        "strip-ansi": "3.0.1"
+      }
+    },
     "string_decoder": {
       "version": "1.1.1",
       "resolved": "https://registry.npmjs.org/string_decoder/-/string_decoder-1.1.1.tgz",
@@ -4347,6 +4816,15 @@
         "safe-buffer": "5.1.2"
       }
     },
+    "strip-ansi": {
+      "version": "3.0.1",
+      "resolved": "http://registry.npmjs.org/strip-ansi/-/strip-ansi-3.0.1.tgz",
+      "integrity": "sha1-ajhfuIU9lS1f8F0Oiq+UJ43GPc8=",
+      "dev": true,
+      "requires": {
+        "ansi-regex": "2.1.1"
+      }
+    },
     "strip-bom": {
       "version": "2.0.0",
       "resolved": "https://registry.npmjs.org/strip-bom/-/strip-bom-2.0.0.tgz",
@@ -4365,22 +4843,60 @@
         "get-stdin": "4.0.1"
       }
     },
+    "strip-json-comments": {
+      "version": "2.0.1",
+      "resolved": "https://registry.npmjs.org/strip-json-comments/-/strip-json-comments-2.0.1.tgz",
+      "integrity": "sha1-PFMZQukIwml8DsNEhYwobHygpgo=",
+      "dev": true
+    },
+    "supports-color": {
+      "version": "5.5.0",
+      "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-5.5.0.tgz",
+      "integrity": "sha512-QjVjwdXIt408MIiAqCX4oUKsgU2EqAGzs2Ppkm4aQYbjm+ZEWEcW4SfFNTr4uMNZma0ey4f5lgLrkB0aX0QMow==",
+      "dev": true,
+      "requires": {
+        "has-flag": "3.0.0"
+      },
+      "dependencies": {
+        "has-flag": {
+          "version": "3.0.0",
+          "resolved": "https://registry.npmjs.org/has-flag/-/has-flag-3.0.0.tgz",
+          "integrity": "sha1-tdRU3CGZriJWmfNGfloH87lVuv0=",
+          "dev": true
+        }
+      }
+    },
+    "tar": {
+      "version": "4.4.6",
+      "resolved": "https://registry.npmjs.org/tar/-/tar-4.4.6.tgz",
+      "integrity": "sha512-tMkTnh9EdzxyfW+6GK6fCahagXsnYk6kE6S9Gr9pjVdys769+laCTbodXDhPAjzVtEBazRgP0gYqOjnk9dQzLg==",
+      "dev": true,
+      "requires": {
+        "chownr": "1.1.1",
+        "fs-minipass": "1.2.5",
+        "minipass": "2.3.5",
+        "minizlib": "1.1.1",
+        "mkdirp": "0.5.1",
+        "safe-buffer": "5.1.2",
+        "yallist": "3.0.2"
+      }
+    },
     "tfjs-image-recognition-base": {
-      "version": "0.1.3",
-      "resolved": "https://registry.npmjs.org/tfjs-image-recognition-base/-/tfjs-image-recognition-base-0.1.3.tgz",
-      "integrity": "sha512-Vo1arsSkOxtlBedWxw7w2V/mbpp70izAJPu0Cl6WE62ZJ0kLL6TmFphGAr3zKaqrZ0VOyADVedDqFic3aH84RQ==",
+      "version": "0.2.0",
+      "resolved": "https://registry.npmjs.org/tfjs-image-recognition-base/-/tfjs-image-recognition-base-0.2.0.tgz",
+      "integrity": "sha512-i5IIuYXEpAsZ+jkKttDAkhF0Qd5JbbmmTKLzmnd2ynQ6L+lcr2o4bX5Tl/Rqh0OKu603HrCaOiVDutO5EX899Q==",
       "requires": {
-        "@tensorflow/tfjs-core": "0.13.2",
+        "@tensorflow/tfjs-core": "0.13.8",
         "tslib": "1.9.3"
       }
     },
     "tfjs-tiny-yolov2": {
-      "version": "0.2.1",
-      "resolved": "https://registry.npmjs.org/tfjs-tiny-yolov2/-/tfjs-tiny-yolov2-0.2.1.tgz",
-      "integrity": "sha512-HSdBu6dMyQdtueY32wSO+5IajXDrvu7MufvUBaLD0CubKbfJWM1JogsONUkvp3N948UAI2/K35p9+eEP2woXpw==",
+      "version": "0.3.0",
+      "resolved": "https://registry.npmjs.org/tfjs-tiny-yolov2/-/tfjs-tiny-yolov2-0.3.0.tgz",
+      "integrity": "sha512-z2xidL8L+HgcNewH1yxL+W32Lul5hoibYkBS4EwxeU0CtWkW8vEffCFub7wd5sggBvr+I2z/LYf+NYH4QXXOfA==",
       "requires": {
-        "@tensorflow/tfjs-core": "0.13.2",
-        "tfjs-image-recognition-base": "0.1.3",
+        "@tensorflow/tfjs-core": "0.13.8",
+        "tfjs-image-recognition-base": "0.2.0",
         "tslib": "1.9.3"
       }
     },
@@ -4500,6 +5016,30 @@
       "integrity": "sha1-WIeWa7WCpFA6QetST301ARgVphM=",
       "dev": true
     },
+    "ts-node": {
+      "version": "7.0.1",
+      "resolved": "https://registry.npmjs.org/ts-node/-/ts-node-7.0.1.tgz",
+      "integrity": "sha512-BVwVbPJRspzNh2yfslyT1PSbl5uIk03EZlb493RKHN4qej/D06n1cEhjlOJG69oFsE7OT8XjpTUcYf6pKTLMhw==",
+      "dev": true,
+      "requires": {
+        "arrify": "1.0.1",
+        "buffer-from": "1.1.1",
+        "diff": "3.5.0",
+        "make-error": "1.3.5",
+        "minimist": "1.2.0",
+        "mkdirp": "0.5.1",
+        "source-map-support": "0.5.9",
+        "yn": "2.0.0"
+      },
+      "dependencies": {
+        "minimist": {
+          "version": "1.2.0",
+          "resolved": "http://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
+          "integrity": "sha1-o1AIsg9BOD7sH7kU9M1d95omQoQ=",
+          "dev": true
+        }
+      }
+    },
     "tslib": {
       "version": "1.9.3",
       "resolved": "https://registry.npmjs.org/tslib/-/tslib-1.9.3.tgz",
@@ -4762,6 +5302,15 @@
         "isexe": "2.0.0"
       }
     },
+    "wide-align": {
+      "version": "1.1.3",
+      "resolved": "https://registry.npmjs.org/wide-align/-/wide-align-1.1.3.tgz",
+      "integrity": "sha512-QGkOQc8XL6Bt5PwnsExKBPuMKBxnGxWWW3fU55Xt4feHozMUhdUMaBCk290qpm/wG5u/RSKzwdAC4i51YigihA==",
+      "dev": true,
+      "requires": {
+        "string-width": "1.0.2"
+      }
+    },
     "window-size": {
       "version": "0.1.0",
       "resolved": "https://registry.npmjs.org/window-size/-/window-size-0.1.0.tgz",
@@ -4804,6 +5353,12 @@
       "integrity": "sha1-pcbVMr5lbiPbgg77lDofBJmNY68=",
       "dev": true
     },
+    "yallist": {
+      "version": "3.0.2",
+      "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.0.2.tgz",
+      "integrity": "sha1-hFK0u36Dx8GI2AQcGoN8dz1ti7k=",
+      "dev": true
+    },
     "yargs": {
       "version": "3.10.0",
       "resolved": "https://registry.npmjs.org/yargs/-/yargs-3.10.0.tgz",
@@ -4822,6 +5377,12 @@
       "resolved": "https://registry.npmjs.org/yeast/-/yeast-0.1.2.tgz",
       "integrity": "sha1-AI4G2AlDIMNy28L47XagymyKxBk=",
       "dev": true
+    },
+    "yn": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/yn/-/yn-2.0.0.tgz",
+      "integrity": "sha1-5a2ryKz0CPY4X8dklWhMiOavaJo=",
+      "dev": true
     }
   }
 }

package.json

@@ -12,15 +12,17 @@
     "tsc-es6": "tsc --p tsconfig.es6.json",
     "build": "rm -rf ./build && rm -rf ./dist && npm run rollup && npm run rollup-min && npm run tsc && npm run tsc-es6",
     "test": "karma start",
+    "test-browser": "karma start --single-run",
+    "test-node": "ts-node node_modules/jasmine/bin/jasmine --config=jasmine-node.js",
+    "test-all": "npm run test-browser && npm run test-node",
     "test-facelandmarknets": "set UUT=faceLandmarkNet&& karma start",
     "test-facerecognitionnet": "set UUT=faceRecognitionNet&& karma start",
     "test-ssdmobilenetv1": "set UUT=ssdMobilenetv1&& karma start",
     "test-tinyfacedetector": "set UUT=tinyFaceDetector&& karma start",
     "test-mtcnn": "set UUT=mtcnn&& karma start",
-    "test-tinyyolov2": "set UUT=tinyYolov2&& karma start",
     "test-cpu": "set BACKEND_CPU=true&& karma start",
     "test-exclude-uncompressed": "set EXCLUDE_UNCOMPRESSED=true&& karma start",
-    "test-travis": "karma start --single-run",
+    "test-node-exclude-uncompressed": "set EXCLUDE_UNCOMPRESSED=true&& ts-node node_modules/jasmine/bin/jasmine --config=jasmine-node.js",
     "docs": "typedoc --options ./typedoc.config.js ./src"
   },
   "keywords": [
@@ -33,14 +35,17 @@
   "author": "justadudewhohacks",
   "license": "MIT",
   "dependencies": {
-    "@tensorflow/tfjs-core": "^0.13.2",
-    "tfjs-image-recognition-base": "^0.1.3",
-    "tfjs-tiny-yolov2": "^0.2.1",
+    "@tensorflow/tfjs-core": "0.13.8",
+    "tfjs-image-recognition-base": "0.2.0",
+    "tfjs-tiny-yolov2": "0.3.0",
     "tslib": "^1.9.3"
   },
   "devDependencies": {
+    "@tensorflow/tfjs-node": "^0.1.19",
     "@types/jasmine": "^2.8.8",
     "@types/node": "^10.9.2",
+    "canvas": "^2.0.1",
+    "jasmine": "^3.3.0",
     "jasmine-core": "^3.2.1",
     "karma": "^3.0.0",
     "karma-chrome-launcher": "^2.2.0",
@@ -51,6 +56,7 @@
     "rollup-plugin-node-resolve": "^3.3.0",
     "rollup-plugin-typescript2": "^0.16.1",
     "rollup-plugin-uglify": "^4.0.0",
+    "ts-node": "^7.0.1",
     "typescript": "2.8.4"
   }
 }

src/dom/drawLandmarks.ts

-import { getContext2dOrThrow, getDefaultDrawOptions, resolveInput } from 'tfjs-image-recognition-base';
+import { env, getContext2dOrThrow, getDefaultDrawOptions, resolveInput } from 'tfjs-image-recognition-base';
 
 import { FaceLandmarks } from '../classes/FaceLandmarks';
 import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
@@ -11,7 +11,7 @@ export function drawLandmarks(
   options?: DrawLandmarksOptions
 ) {
   const canvas = resolveInput(canvasArg)
-  if (!(canvas instanceof HTMLCanvasElement)) {
+  if (!(canvas instanceof env.getEnv().Canvas)) {
     throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement')
   }
 

src/dom/extractFaceTensors.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { isTensor4D, Rect } from 'tfjs-image-recognition-base';
+import { isTensor4D, Rect, isTensor3D } from 'tfjs-image-recognition-base';
 
 import { FaceDetection } from '../classes/FaceDetection';
 
@@ -18,6 +18,10 @@ export async function extractFaceTensors(
   detections: Array<FaceDetection | Rect>
 ): Promise<tf.Tensor3D[]> {
 
+  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {
+    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')
+  }
+
   if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
     throw new Error('extractFaceTensors - batchSize > 1 not supported')
   }

src/dom/extractFaces.ts

 import {
   createCanvas,
+  env,
   getContext2dOrThrow,
   imageTensorToCanvas,
   Rect,
@@ -21,9 +22,11 @@ export async function extractFaces(
   detections: Array<FaceDetection | Rect>
 ): Promise<HTMLCanvasElement[]> {
 
+  const { Canvas } = env.getEnv()
+
   let canvas = input as HTMLCanvasElement
 
-  if (!(input instanceof HTMLCanvasElement)) {
+  if (!(input instanceof Canvas)) {
     const netInput = await toNetInput(input)
 
     if (netInput.batchSize > 1) {
@@ -31,7 +34,7 @@ export async function extractFaces(
     }
 
     const tensorOrCanvas = netInput.getInput(0)
-    canvas = tensorOrCanvas instanceof HTMLCanvasElement
+    canvas = tensorOrCanvas instanceof Canvas
       ? tensorOrCanvas
       : await imageTensorToCanvas(tensorOrCanvas)
   }

src/faceLandmarkNet/FaceLandmark68Net.ts

@@ -4,9 +4,9 @@ import { ConvParams, SeparableConvParams } from 'tfjs-tiny-yolov2';
 
 import { depthwiseSeparableConv } from './depthwiseSeparableConv';
 import { extractParams } from './extractParams';
+import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
 import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
 import { fullyConnectedLayer } from './fullyConnectedLayer';
-import { loadQuantizedParams } from './loadQuantizedParams';
 import { DenseBlock4Params, NetParams } from './types';
 
 function denseBlock(
@@ -64,10 +64,13 @@ export class FaceLandmark68Net extends FaceLandmark68NetBase<NetParams> {
     })
   }
 
-  protected loadQuantizedParams(uri: string | undefined) {
-    return loadQuantizedParams(uri)
+  protected getDefaultModelName(): string {
+    return 'face_landmark_68_model'
   }
 
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {
+    return extractParamsFromWeigthMap(weightMap)
+  }
 
   protected extractParams(weights: Float32Array) {
     return extractParams(weights)

src/faceLandmarkNet/FaceLandmark68NetBase.ts

@@ -3,7 +3,7 @@ import { IDimensions, isEven, NetInput, NeuralNetwork, Point, TNetInput, toNetIn
 
 import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
 
-export class FaceLandmark68NetBase<NetParams> extends NeuralNetwork<NetParams> {
+export abstract class FaceLandmark68NetBase<NetParams> extends NeuralNetwork<NetParams> {
 
   // TODO: make super.name protected
   private __name: string
@@ -13,9 +13,7 @@ export class FaceLandmark68NetBase<NetParams> extends NeuralNetwork<NetParams> {
     this.__name = _name
   }
 
-  public runNet(_: NetInput): tf.Tensor2D {
-    throw new Error(`${this.__name} - runNet not implemented`)
-  }
+  public abstract runNet(netInput: NetInput): tf.Tensor2D
 
   public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {
 

src/faceLandmarkNet/FaceLandmark68TinyNet.ts

@@ -6,7 +6,7 @@ import { depthwiseSeparableConv } from './depthwiseSeparableConv';
 import { extractParamsTiny } from './extractParamsTiny';
 import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
 import { fullyConnectedLayer } from './fullyConnectedLayer';
-import { loadQuantizedParamsTiny } from './loadQuantizedParamsTiny';
+import { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';
 import { DenseBlock3Params, TinyNetParams } from './types';
 
 function denseBlock(
@@ -60,8 +60,12 @@ export class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyNetParams>
     })
   }
 
-  protected loadQuantizedParams(uri: string | undefined) {
-    return loadQuantizedParamsTiny(uri)
+  protected getDefaultModelName(): string {
+    return 'face_landmark_68_tiny_model'
+  }
+
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {
+    return extractParamsFromWeigthMapTiny(weightMap)
   }
 
   protected extractParams(weights: Float32Array) {

src/faceLandmarkNet/loadQuantizedParams.ts → src/faceLandmarkNet/extractParamsFromWeigthMap.ts

-import { disposeUnusedWeightTensors, loadWeightMap, ParamMapping } from 'tfjs-image-recognition-base';
+import * as tf from '@tensorflow/tfjs-core';
+import { disposeUnusedWeightTensors, ParamMapping } from 'tfjs-image-recognition-base';
 
 import { loadParamsFactory } from './loadParamsFactory';
 import { NetParams } from './types';
 
-const DEFAULT_MODEL_NAME = 'face_landmark_68_model'
+export function extractParamsFromWeigthMap(
+  weightMap: tf.NamedTensorMap
+): { params: NetParams, paramMappings: ParamMapping[] } {
 
-export async function loadQuantizedParams(
-  uri: string | undefined
-): Promise<{ params: NetParams, paramMappings: ParamMapping[] }> {
-
-  const weightMap = await loadWeightMap(uri, DEFAULT_MODEL_NAME)
   const paramMappings: ParamMapping[] = []
 
   const {

src/faceLandmarkNet/loadQuantizedParamsTiny.ts → src/faceLandmarkNet/extractParamsFromWeigthMapTiny.ts

-import { disposeUnusedWeightTensors, loadWeightMap, ParamMapping } from 'tfjs-image-recognition-base';
+import * as tf from '@tensorflow/tfjs-core';
+import { disposeUnusedWeightTensors, ParamMapping } from 'tfjs-image-recognition-base';
 
 import { loadParamsFactory } from './loadParamsFactory';
 import { TinyNetParams } from './types';
 
-const DEFAULT_MODEL_NAME = 'face_landmark_68_tiny_model'
+export function extractParamsFromWeigthMapTiny(
+  weightMap: tf.NamedTensorMap
+): { params: TinyNetParams, paramMappings: ParamMapping[] } {
 
-export async function loadQuantizedParamsTiny(
-  uri: string | undefined
-): Promise<{ params: TinyNetParams, paramMappings: ParamMapping[] }> {
-
-  const weightMap = await loadWeightMap(uri, DEFAULT_MODEL_NAME)
   const paramMappings: ParamMapping[] = []
 
   const {

src/faceRecognitionNet/FaceRecognitionNet.ts

@@ -3,7 +3,7 @@ import { NetInput, NeuralNetwork, normalize, TNetInput, toNetInput } from 'tfjs-
 
 import { convDown } from './convLayer';
 import { extractParams } from './extractParams';
-import { loadQuantizedParams } from './loadQuantizedParams';
+import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
 import { residual, residualDown } from './residualLayer';
 import { NetParams } from './types';
 
@@ -78,8 +78,12 @@ export class FaceRecognitionNet extends NeuralNetwork<NetParams> {
       : faceDescriptorsForBatch[0]
   }
 
-  protected loadQuantizedParams(uri: string | undefined) {
-    return loadQuantizedParams(uri)
+  protected getDefaultModelName(): string {
+    return 'face_recognition_model'
+  }
+
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {
+    return extractParamsFromWeigthMap(weightMap)
   }
 
   protected extractParams(weights: Float32Array) {

src/faceRecognitionNet/loadQuantizedParams.ts → src/faceRecognitionNet/extractParamsFromWeigthMap.ts

@@ -9,8 +9,6 @@ import {
 
 import { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';
 
-const DEFAULT_MODEL_NAME = 'face_recognition_model'
-
 function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
 
   const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)
@@ -46,11 +44,10 @@ function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
 
 }
 
-export async function loadQuantizedParams(
-  uri: string | undefined
-): Promise<{ params: NetParams, paramMappings: ParamMapping[] }> {
+export function extractParamsFromWeigthMap(
+  weightMap: tf.NamedTensorMap
+): { params: NetParams, paramMappings: ParamMapping[] } {
 
-  const weightMap = await loadWeightMap(uri, DEFAULT_MODEL_NAME)
   const paramMappings: ParamMapping[] = []
 
   const {

src/globalApi/ComputeFaceDescriptorsTasks.ts

+import * as tf from '@tensorflow/tfjs-core';
 import { TNetInput } from 'tfjs-image-recognition-base';
 
 import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
 import { FullFaceDescription } from '../classes/FullFaceDescription';
-import { extractFaces } from '../dom';
+import { extractFaces, extractFaceTensors } from '../dom';
 import { ComposableTask } from './ComposableTask';
 import { nets } from './nets';
 
@@ -20,15 +21,20 @@ export class ComputeAllFaceDescriptorsTask extends ComputeFaceDescriptorsTaskBas
   public async run(): Promise<FullFaceDescription[]> {
 
     const facesWithLandmarks = await this.detectFaceLandmarksTask
-    const alignedFaceCanvases = await extractFaces(
-      this.input,
-      facesWithLandmarks.map(({ landmarks }) => landmarks.align())
-    )
 
-    return await Promise.all(facesWithLandmarks.map(async ({ detection, landmarks }, i) => {
-      const descriptor = await nets.faceRecognitionNet.computeFaceDescriptor(alignedFaceCanvases[i]) as Float32Array
+    const alignedRects = facesWithLandmarks.map(({ alignedRect }) => alignedRect)
+    const alignedFaces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor
+      ? await extractFaceTensors(this.input, alignedRects)
+      : await extractFaces(this.input, alignedRects)
+
+    const fullFaceDescriptions = await Promise.all(facesWithLandmarks.map(async ({ detection, landmarks }, i) => {
+      const descriptor = await nets.faceRecognitionNet.computeFaceDescriptor(alignedFaces[i]) as Float32Array
       return new FullFaceDescription(detection, landmarks, descriptor)
     }))
+
+    alignedFaces.forEach(f => f instanceof tf.Tensor && f.dispose())
+
+    return fullFaceDescriptions
   }
 }
 
@@ -42,8 +48,12 @@ export class ComputeSingleFaceDescriptorTask extends ComputeFaceDescriptorsTaskB
     }
 
     const { detection, landmarks, alignedRect } = detectionWithLandmarks
-    const alignedFaceCanvas = (await extractFaces(this.input, [alignedRect]))[0]
-    const descriptor = await nets.faceRecognitionNet.computeFaceDescriptor(alignedFaceCanvas) as Float32Array
+    const alignedFaces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor
+      ? await extractFaceTensors(this.input, [alignedRect])
+      : await extractFaces(this.input, [alignedRect])
+    const descriptor = await nets.faceRecognitionNet.computeFaceDescriptor(alignedFaces[0]) as Float32Array
+
+    alignedFaces.forEach(f => f instanceof tf.Tensor && f.dispose())
 
     return new FullFaceDescription(detection, landmarks, descriptor)
   }

src/globalApi/DetectFaceLandmarksTasks.ts

+import * as tf from '@tensorflow/tfjs-core';
 import { TNetInput } from 'tfjs-image-recognition-base';
 
 import { FaceDetection } from '../classes/FaceDetection';
 import { FaceDetectionWithLandmarks } from '../classes/FaceDetectionWithLandmarks';
 import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
-import { extractFaces } from '../dom';
+import { extractFaces, extractFaceTensors } from '../dom';
 import { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';
 import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';
 import { ComposableTask } from './ComposableTask';
@@ -31,12 +32,17 @@ export class DetectAllFaceLandmarksTask extends DetectFaceLandmarksTaskBase<Face
   public async run(): Promise<FaceDetectionWithLandmarks[]> {
 
     const detections = await this.detectFacesTask
-    const faceCanvases = await extractFaces(this.input, detections)
 
-    const faceLandmarksByFace = await Promise.all(faceCanvases.map(
-      canvas => this.landmarkNet.detectLandmarks(canvas)
+    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor
+      ? await extractFaceTensors(this.input, detections)
+      : await extractFaces(this.input, detections)
+
+    const faceLandmarksByFace = await Promise.all(faces.map(
+      face => this.landmarkNet.detectLandmarks(face)
     )) as FaceLandmarks68[]
 
+    faces.forEach(f => f instanceof tf.Tensor && f.dispose())
+
     return detections.map((detection, i) =>
       new FaceDetectionWithLandmarks(detection, faceLandmarksByFace[i])
     )
@@ -56,10 +62,18 @@ export class DetectSingleFaceLandmarksTask extends DetectFaceLandmarksTaskBase<F
       return
     }
 
-    const faceCanvas = (await extractFaces(this.input, [detection]))[0]
+    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor
+      ? await extractFaceTensors(this.input, [detection])
+      : await extractFaces(this.input, [detection])
+
+
+    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68
+
+    faces.forEach(f => f instanceof tf.Tensor && f.dispose())
+
     return new FaceDetectionWithLandmarks(
       detection,
-      await this.landmarkNet.detectLandmarks(faceCanvas) as FaceLandmarks68
+      landmarks
     )
   }
 

src/mtcnn/Mtcnn.ts

@@ -7,8 +7,8 @@ import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
 import { bgrToRgbTensor } from './bgrToRgbTensor';
 import { CELL_SIZE } from './config';
 import { extractParams } from './extractParams';
+import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
 import { getSizesForScale } from './getSizesForScale';
-import { loadQuantizedParams } from './loadQuantizedParams';
 import { IMtcnnOptions, MtcnnOptions } from './MtcnnOptions';
 import { pyramidDown } from './pyramidDown';
 import { stage1 } from './stage1';
@@ -146,9 +146,12 @@ export class Mtcnn extends NeuralNetwork<NetParams> {
     )
   }
 
-  // none of the param tensors are quantized yet
-  protected loadQuantizedParams(uri: string | undefined) {
-    return loadQuantizedParams(uri)
+  protected getDefaultModelName(): string {
+    return 'mtcnn_model'
+  }
+
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {
+    return extractParamsFromWeigthMap(weightMap)
   }
 
   protected extractParams(weights: Float32Array) {

src/mtcnn/extractImagePatches.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { Box, createCanvas, getContext2dOrThrow, IDimensions } from 'tfjs-image-recognition-base';
+import {
+  Box,
+  createCanvas,
+  createCanvasFromMedia,
+  env,
+  getContext2dOrThrow,
+  IDimensions,
+} from 'tfjs-image-recognition-base';
 
 import { normalize } from './normalize';
 
@@ -20,7 +27,7 @@ export async function extractImagePatches(
     const fromY = y - 1
     const imgData = imgCtx.getImageData(fromX, fromY, (ex - fromX), (ey - fromY))
 
-    return createImageBitmap(imgData)
+    return env.isNodejs() ? createCanvasFromMedia(imgData) : createImageBitmap(imgData)
   }))
 
   const imagePatchesDatas: number[][] = []

src/mtcnn/loadQuantizedParams.ts → src/mtcnn/extractParamsFromWeigthMap.ts

 import * as tf from '@tensorflow/tfjs-core';
-import {
-  disposeUnusedWeightTensors,
-  extractWeightEntryFactory,
-  loadWeightMap,
-  ParamMapping,
-} from 'tfjs-image-recognition-base';
+import { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from 'tfjs-image-recognition-base';
 import { ConvParams, FCParams } from 'tfjs-tiny-yolov2';
 
 import { NetParams, ONetParams, PNetParams, RNetParams, SharedParams } from './types';
 
-const DEFAULT_MODEL_NAME = 'mtcnn_model'
-
 function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
 
   const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)
@@ -87,11 +80,10 @@ function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
 
 }
 
-export async function loadQuantizedParams(
-  uri: string | undefined
-): Promise<{ params: NetParams, paramMappings: ParamMapping[] }> {
+export function extractParamsFromWeigthMap(
+  weightMap: tf.NamedTensorMap
+): { params: NetParams, paramMappings: ParamMapping[] } {
 
-  const weightMap = await loadWeightMap(uri, DEFAULT_MODEL_NAME)
   const paramMappings: ParamMapping[] = []
 
   const {

src/ssdMobilenetv1/SsdMobilenetv1.ts

@@ -3,7 +3,7 @@ import { NetInput, NeuralNetwork, Rect, TNetInput, toNetInput } from 'tfjs-image
 
 import { FaceDetection } from '../classes/FaceDetection';
 import { extractParams } from './extractParams';
-import { loadQuantizedParams } from './loadQuantizedParams';
+import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
 import { mobileNetV1 } from './mobileNetV1';
 import { nonMaxSuppression } from './nonMaxSuppression';
 import { outputLayer } from './outputLayer';
@@ -116,8 +116,12 @@ export class SsdMobilenetv1 extends NeuralNetwork<NetParams> {
     return results
   }
 
-  protected loadQuantizedParams(uri: string | undefined) {
-    return loadQuantizedParams(uri)
+  protected getDefaultModelName(): string {
+    return 'ssd_mobilenetv1_model'
+  }
+
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {
+    return extractParamsFromWeigthMap(weightMap)
   }
 
   protected extractParams(weights: Float32Array) {

src/ssdMobilenetv1/loadQuantizedParams.ts → src/ssdMobilenetv1/extractParamsFromWeigthMap.ts

@@ -3,15 +3,12 @@ import {
   disposeUnusedWeightTensors,
   extractWeightEntryFactory,
   isTensor3D,
-  loadWeightMap,
   ParamMapping,
 } from 'tfjs-image-recognition-base';
 import { ConvParams } from 'tfjs-tiny-yolov2';
 
 import { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';
 
-const DEFAULT_MODEL_NAME = 'ssd_mobilenetv1_model'
-
 function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
 
   const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)
@@ -114,11 +111,10 @@ function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
   }
 }
 
-export async function loadQuantizedParams(
-  uri: string | undefined
-): Promise<{ params: NetParams, paramMappings: ParamMapping[] }> {
+export function extractParamsFromWeigthMap(
+  weightMap: tf.NamedTensorMap
+): { params: NetParams, paramMappings: ParamMapping[] } {
 
-  const weightMap = await loadWeightMap(uri, DEFAULT_MODEL_NAME)
   const paramMappings: ParamMapping[] = []
 
   const {

src/tinyFaceDetector/TinyFaceDetector.ts

-import { Point, TNetInput } from 'tfjs-image-recognition-base';
-import { TinyYolov2 as TinyYolov2Base, ITinyYolov2Options } from 'tfjs-tiny-yolov2';
+import * as tf from '@tensorflow/tfjs-core';
+import { ParamMapping, Point, TNetInput } from 'tfjs-image-recognition-base';
+import { ITinyYolov2Options, TinyYolov2 as TinyYolov2Base } from 'tfjs-tiny-yolov2';
+import { TinyYolov2NetParams } from 'tfjs-tiny-yolov2/build/commonjs/tinyYolov2/types';
 
 import { FaceDetection } from '../classes';
-import { BOX_ANCHORS, DEFAULT_MODEL_NAME, IOU_THRESHOLD, MEAN_RGB } from './const';
+import { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';
 
 export class TinyFaceDetector extends TinyYolov2Base {
 
@@ -29,8 +31,11 @@ export class TinyFaceDetector extends TinyYolov2Base {
     return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))
   }
 
-  protected loadQuantizedParams(modelUri: string | undefined) {
-    const defaultModelName = DEFAULT_MODEL_NAME
-    return super.loadQuantizedParams(modelUri, defaultModelName) as any
+  protected getDefaultModelName(): string {
+    return 'tiny_face_detector_model'
+  }
+
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {
+    return super.extractParamsFromWeigthMap(weightMap)
   }
 }
\ No newline at end of file

src/tinyFaceDetector/const.ts

@@ -11,5 +11,3 @@ export const BOX_ANCHORS = [
 ]
 
 export const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]
-
-export const DEFAULT_MODEL_NAME = 'tiny_face_detector_model'
\ No newline at end of file

src/tinyYolov2/TinyYolov2.ts

-import { Point, TNetInput } from 'tfjs-image-recognition-base';
-import { ITinyYolov2Options, TinyYolov2 as TinyYolov2Base } from 'tfjs-tiny-yolov2';
+import * as tf from '@tensorflow/tfjs-core';
+import { ParamMapping, Point, TNetInput } from 'tfjs-image-recognition-base';
+import { ITinyYolov2Options, TinyYolov2 as TinyYolov2Base, TinyYolov2NetParams } from 'tfjs-tiny-yolov2';
 
 import { FaceDetection } from '../classes';
 import {
@@ -46,8 +47,11 @@ export class TinyYolov2 extends TinyYolov2Base {
     return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))
   }
 
-  protected loadQuantizedParams(modelUri: string | undefined) {
-    const defaultModelName = this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME
-    return super.loadQuantizedParams(modelUri, defaultModelName) as any
+  protected getDefaultModelName(): string {
+    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME
+  }
+
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {
+    return super.extractParamsFromWeigthMap(weightMap)
   }
 }
\ No newline at end of file

test/env.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { fetchNetWeights, NeuralNetwork } from 'tfjs-image-recognition-base';
+
+import { env, fetchImage, fetchJson } from '../src';
+
+export let fs: any, path: any, canvas: any
+
+jasmine.DEFAULT_TIMEOUT_INTERVAL = 60000
+
+if (env.isNodejs()) {
+  require('@tensorflow/tfjs-node')
+  fs = require('fs')
+  path = require('path')
+  canvas = require('canvas')
+
+  const { Canvas, Image, ImageData } = canvas
+  env.monkeyPatch({ Canvas, Image, ImageData })
+} else {
+  if ((window['__karma__'].config.jasmine.args as string[]).some(arg => arg === 'backend_cpu')) {
+    tf.setBackend('cpu')
+  }
+}
+
+export async function initNet<TNet extends NeuralNetwork<any>>(
+  net: TNet,
+  uncompressedFilename: string | boolean,
+  isUnusedModel: boolean = false
+) {
+  if (env.isNodejs()) {
+    await net.loadFromDisk(path.resolve(__dirname, '../weights'))
+  } else {
+    const url = uncompressedFilename
+      ? await fetchNetWeights(`base/weights_uncompressed/${uncompressedFilename}`)
+      : (isUnusedModel ? 'base/weights_unused' : 'base/weights')
+    await net.load(url)
+  }
+}
+
+export async function loadImage(uri: string): Promise<HTMLImageElement> {
+  if (env.isNodejs()) {
+    return canvas.loadImage(path.resolve(__dirname, '../', uri))
+  }
+  return fetchImage(`base${uri.startsWith('/') ? '' : '/'}${uri}`)
+}
+
+export async function loadJson<T>(uri: string): Promise<T> {
+  if (env.isNodejs()) {
+    return JSON.parse(fs.readFileSync(path.resolve(__dirname, '../', uri)).toString())
+  }
+  return fetchJson<T>(`base${uri.startsWith('/') ? '' : '/'}${uri}`)
+}
+

test/tests/dom/extractFaceTensors.test.ts

-import { bufferToImage, extractFaceTensors, Rect, tf } from '../../../src';
+import { createCanvasFromMedia, extractFaceTensors, Rect, tf } from '../../../src';
+import { loadImage } from '../../env';
 
 describe('extractFaceTensors', () => {
 
   let imgTensor: tf.Tensor3D
 
   beforeAll(async () => {
-    const img = await (await fetch('base/test/images/face1.png')).blob()
-    imgTensor = tf.fromPixels(await bufferToImage(img))
+    imgTensor = tf.fromPixels(createCanvasFromMedia(await loadImage('test/images/face1.png')))
   })
 
   describe('extracts tensors', () => {

test/tests/dom/extractFaces.test.ts

-import { bufferToImage, createCanvasFromMedia, extractFaces, Rect } from '../../../src';
+import { createCanvasFromMedia, env, extractFaces, Rect } from '../../../src';
+import { loadImage } from '../../env';
 
 describe('extractFaces', () => {
 
-  let imgEl: HTMLImageElement, canvasEl: HTMLCanvasElement
+  let imgEl: HTMLImageElement, canvasEl: HTMLCanvasElement, Canvas: typeof HTMLCanvasElement
 
   beforeAll(async () => {
-    const img = await (await fetch('base/test/images/face1.png')).blob()
-    imgEl = await bufferToImage(img)
+    imgEl = await loadImage('test/images/face1.png')
     canvasEl = createCanvasFromMedia(imgEl)
+    Canvas = env.getEnv().Canvas
   })
 
   describe('extracts canvases', () => {
@@ -17,7 +18,7 @@ describe('extractFaces', () => {
       const canvases = await extractFaces(imgEl, [rect])
 
       expect(canvases.length).toEqual(1)
-      expect(canvases[0] instanceof HTMLCanvasElement).toBe(true)
+      expect(canvases[0] instanceof Canvas).toBe(true)
       expect(canvases[0].width).toEqual(50)
       expect(canvases[0].height).toEqual(60)
     })
@@ -30,10 +31,10 @@ describe('extractFaces', () => {
       const canvases = await extractFaces(imgEl, rects)
 
       expect(canvases.length).toEqual(2)
-      expect(canvases[0] instanceof HTMLCanvasElement).toBe(true)
+      expect(canvases[0] instanceof Canvas).toBe(true)
       expect(canvases[0].width).toEqual(50)
       expect(canvases[0].height).toEqual(60)
-      expect(canvases[1] instanceof HTMLCanvasElement).toBe(true)
+      expect(canvases[1] instanceof Canvas).toBe(true)
       expect(canvases[1].width).toEqual(70)
       expect(canvases[1].height).toEqual(80)
     })
@@ -43,7 +44,7 @@ describe('extractFaces', () => {
       const canvases = await extractFaces(canvasEl, [rect])
 
       expect(canvases.length).toEqual(1)
-      expect(canvases[0] instanceof HTMLCanvasElement).toBe(true)
+      expect(canvases[0] instanceof Canvas).toBe(true)
       expect(canvases[0].width).toEqual(50)
       expect(canvases[0].height).toEqual(60)
     })
@@ -56,10 +57,10 @@ describe('extractFaces', () => {
       const canvases = await extractFaces(canvasEl, rects)
 
       expect(canvases.length).toEqual(2)
-      expect(canvases[0] instanceof HTMLCanvasElement).toBe(true)
+      expect(canvases[0] instanceof Canvas).toBe(true)
       expect(canvases[0].width).toEqual(50)
       expect(canvases[0].height).toEqual(60)
-      expect(canvases[1] instanceof HTMLCanvasElement).toBe(true)
+      expect(canvases[1] instanceof Canvas).toBe(true)
       expect(canvases[1].width).toEqual(70)
       expect(canvases[1].height).toEqual(80)
     })

test/tests/faceLandmarkNet/FaceLandmark68NetBase.test.ts

@@ -2,11 +2,29 @@ import * as tf from '@tensorflow/tfjs-core';
 
 import { FaceLandmark68NetBase } from '../../../src/faceLandmarkNet/FaceLandmark68NetBase';
 
+class FakeFaceLandmark68NetBase extends FaceLandmark68NetBase<any> {
+  protected getDefaultModelName(): string {
+    throw new Error('FakeFaceLandmark68NetBase - getDefaultModelName not implemented')
+  }
+
+  protected extractParams(_: any): any {
+    throw new Error('FakeFaceLandmark68NetBase - extractParams not implemented')
+  }
+
+  protected extractParamsFromWeigthMap(_: any): any {
+    throw new Error('FakeFaceLandmark68NetBase - extractParamsFromWeigthMap not implemented')
+  }
+
+  public runNet(): any {
+    throw new Error('FakeFaceLandmark68NetBase - extractParamsFromWeigthMap not implemented')
+  }
+} 
+
 describe('FaceLandmark68NetBase', () => {
 
   describe('postProcess', () => {
 
-    const net = new FaceLandmark68NetBase('')
+    const net = new FakeFaceLandmark68NetBase('')
 
     describe('single batch', () => {
 

test/tests/faceLandmarkNet/faceLandmark68Net.test.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-import { fetchImage, fetchJson, IDimensions, isTensor3D, NetInput, Point, TMediaElement, toNetInput } from '../../../src';
+import { createCanvasFromMedia, IDimensions, isTensor3D, NetInput, Point, TMediaElement, toNetInput } from '../../../src';
 import { FaceLandmarks68 } from '../../../src/classes/FaceLandmarks68';
-import { createFaceLandmarkNet } from '../../../src/faceLandmarkNet';
 import { FaceLandmark68Net } from '../../../src/faceLandmarkNet/FaceLandmark68Net';
+import { loadImage, loadJson } from '../../env';
 import { describeWithNets, expectAllTensorsReleased, expectMaxDelta, expectPointClose } from '../../utils';
 
 function getInputDims (input: tf.Tensor | TMediaElement): IDimensions {
@@ -24,12 +24,12 @@ describe('faceLandmark68Net', () => {
   let faceLandmarkPositionsRect: Point[]
 
   beforeAll(async () => {
-    imgEl1 = await fetchImage('base/test/images/face1.png')
-    imgEl2 = await fetchImage('base/test/images/face2.png')
-    imgElRect = await fetchImage('base/test/images/face_rectangular.png')
-    faceLandmarkPositions1 = await fetchJson<Point[]>('base/test/data/faceLandmarkPositions1.json')
-    faceLandmarkPositions2 = await fetchJson<Point[]>('base/test/data/faceLandmarkPositions2.json')
-    faceLandmarkPositionsRect = await fetchJson<Point[]>('base/test/data/faceLandmarkPositionsRect.json')
+    imgEl1 = await loadImage('test/images/face1.png')
+    imgEl2 = await loadImage('test/images/face2.png')
+    imgElRect = await loadImage('test/images/face_rectangular.png')
+    faceLandmarkPositions1 = await loadJson<Point[]>('test/data/faceLandmarkPositions1.json')
+    faceLandmarkPositions2 = await loadJson<Point[]>('test/data/faceLandmarkPositions2.json')
+    faceLandmarkPositionsRect = await loadJson<Point[]>('test/data/faceLandmarkPositionsRect.json')
   })
 
   describeWithNets('quantized weights', { withFaceLandmark68Net: { quantized: true } }, ({ faceLandmark68Net }) => {
@@ -85,14 +85,14 @@ describe('faceLandmark68Net', () => {
         expect(result.shift.x).toEqual(0)
         expect(result.shift.y).toEqual(0)
         result.positions.forEach(({ x, y }, i) => {
-          expectMaxDelta(x, faceLandmarkPositions[batchIdx][i].x, 2)
-          expectMaxDelta(y, faceLandmarkPositions[batchIdx][i].y, 2)
+          expectMaxDelta(x, faceLandmarkPositions[batchIdx][i].x, 5)
+          expectMaxDelta(y, faceLandmarkPositions[batchIdx][i].y, 5)
         })
       })
     })
 
     it('computes face landmarks for batch of tf.Tensor3D', async () => {
-      const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.fromPixels(el))
+      const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.fromPixels(createCanvasFromMedia(el)))
 
       const faceLandmarkPositions = [
         faceLandmarkPositions1,
@@ -117,7 +117,7 @@ describe('faceLandmark68Net', () => {
     })
 
     it('computes face landmarks for batch of mixed inputs', async () => {
-      const inputs = [imgEl1, tf.fromPixels(imgEl2), tf.fromPixels(imgElRect)]
+      const inputs = [imgEl1, tf.fromPixels(createCanvasFromMedia(imgEl2)), tf.fromPixels(createCanvasFromMedia(imgElRect))]
 
       const faceLandmarkPositions = [
         faceLandmarkPositions1,
@@ -145,18 +145,6 @@ describe('faceLandmark68Net', () => {
 
   describeWithNets('no memory leaks', { withFaceLandmark68Net: { quantized: true } }, ({ faceLandmark68Net }) => {
 
-    describe('NeuralNetwork, quantized model', () => {
-
-      it('disposes all param tensors', async () => {
-        await expectAllTensorsReleased(async () => {
-          const net = new FaceLandmark68Net()
-          await net.load('base/weights')
-          net.dispose()
-        })
-      })
-
-    })
-
     describe('forwardInput', () => {
 
       it('single image element', async () => {
@@ -176,7 +164,7 @@ describe('faceLandmark68Net', () => {
       })
 
       it('single tf.Tensor3D', async () => {
-        const tensor = tf.fromPixels(imgEl1)
+        const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1))
 
         await expectAllTensorsReleased(async () => {
           const netInput = new NetInput([tensor])
@@ -188,7 +176,7 @@ describe('faceLandmark68Net', () => {
       })
 
       it('multiple tf.Tensor3Ds', async () => {
-        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(el))
+        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el)))
 
         await expectAllTensorsReleased(async () => {
           const netInput = new NetInput(tensors)
@@ -200,7 +188,7 @@ describe('faceLandmark68Net', () => {
       })
 
       it('single batch size 1 tf.Tensor4Ds', async () => {
-        const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
+        const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
 
         await expectAllTensorsReleased(async () => {
           const outTensor = await faceLandmark68Net.forwardInput(await toNetInput(tensor))
@@ -212,7 +200,7 @@ describe('faceLandmark68Net', () => {
 
       it('multiple batch size 1 tf.Tensor4Ds', async () => {
         const tensors = [imgEl1, imgEl1, imgEl1]
-          .map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[]
+          .map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
 
         await expectAllTensorsReleased(async () => {
           const outTensor = await faceLandmark68Net.forwardInput(await toNetInput(tensors))
@@ -239,7 +227,7 @@ describe('faceLandmark68Net', () => {
       })
 
       it('single tf.Tensor3D', async () => {
-        const tensor = tf.fromPixels(imgEl1)
+        const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1))
 
         await expectAllTensorsReleased(async () => {
           await faceLandmark68Net.detectLandmarks(tensor)
@@ -249,7 +237,7 @@ describe('faceLandmark68Net', () => {
       })
 
       it('multiple tf.Tensor3Ds', async () => {
-        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(el))
+        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el)))
 
 
         await expectAllTensorsReleased(async () => {
@@ -260,7 +248,7 @@ describe('faceLandmark68Net', () => {
       })
 
       it('single batch size 1 tf.Tensor4Ds', async () => {
-        const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
+        const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
 
         await expectAllTensorsReleased(async () => {
           await faceLandmark68Net.detectLandmarks(tensor)
@@ -271,7 +259,7 @@ describe('faceLandmark68Net', () => {
 
       it('multiple batch size 1 tf.Tensor4Ds', async () => {
         const tensors = [imgEl1, imgEl1, imgEl1]
-          .map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[]
+          .map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
 
         await expectAllTensorsReleased(async () => {
           await faceLandmark68Net.detectLandmarks(tensors)

test/tests/faceLandmarkNet/faceLandmark68Net.uncompressed.test.ts

-import { fetchImage, fetchJson, Point } from '../../../src';
+import { Point } from '../../../src';
 import { FaceLandmarks68 } from '../../../src/classes/FaceLandmarks68';
-import { createFaceLandmarkNet } from '../../../src/faceLandmarkNet';
-import { describeWithNets, expectAllTensorsReleased, expectPointClose } from '../../utils';
+import { loadImage, loadJson } from '../../env';
+import { describeWithNets, expectPointClose } from '../../utils';
 
 describe('faceLandmark68Net, uncompressed', () => {
 
@@ -11,10 +11,10 @@ describe('faceLandmark68Net, uncompressed', () => {
   let faceLandmarkPositionsRect: Point[]
 
   beforeAll(async () => {
-    imgEl1 = await fetchImage('base/test/images/face1.png')
-    imgElRect = await fetchImage('base/test/images/face_rectangular.png')
-    faceLandmarkPositions1 = await fetchJson<Point[]>('base/test/data/faceLandmarkPositions1.json')
-    faceLandmarkPositionsRect = await fetchJson<Point[]>('base/test/data/faceLandmarkPositionsRect.json')
+    imgEl1 = await loadImage('test/images/face1.png')
+    imgElRect = await loadImage('test/images/face_rectangular.png')
+    faceLandmarkPositions1 = await loadJson<Point[]>('test/data/faceLandmarkPositions1.json')
+    faceLandmarkPositionsRect = await loadJson<Point[]>('test/data/faceLandmarkPositionsRect.json')
   })
 
   describeWithNets('uncompressed weights', { withFaceLandmark68Net: { quantized: false } }, ({ faceLandmark68Net }) => {
@@ -43,16 +43,7 @@ describe('faceLandmark68Net, uncompressed', () => {
       expect(result.shift.y).toEqual(0)
       result.positions.forEach((pt, i) => {
         const { x, y } = faceLandmarkPositionsRect[i]
-        expectPointClose(pt, { x, y }, 2)
-      })
-    })
-
-    it('no memory leaks', async () => {
-      await expectAllTensorsReleased(async () => {
-        const res = await fetch('base/weights_uncompressed/face_landmark_68_model.weights')
-        const weights = new Float32Array(await res.arrayBuffer())
-        const net = createFaceLandmarkNet(weights)
-        net.dispose()
+        expectPointClose(pt, { x, y }, 5)
       })
     })
 

test/tests/faceLandmarkNet/faceLandmark68TinyNet.test.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-import { fetchImage, fetchJson, IDimensions, isTensor3D, NetInput, Point, TMediaElement, toNetInput } from '../../../src';
+import { createCanvasFromMedia, IDimensions, isTensor3D, NetInput, Point, TMediaElement, toNetInput } from '../../../src';
 import { FaceLandmarks68 } from '../../../src/classes/FaceLandmarks68';
-import { createFaceLandmarkNet } from '../../../src/faceLandmarkNet';
-import { FaceLandmark68TinyNet } from '../../../src/faceLandmarkNet/FaceLandmark68TinyNet';
+import { loadImage, loadJson } from '../../env';
 import { describeWithNets, expectAllTensorsReleased, expectPointClose } from '../../utils';
 
 function getInputDims (input: tf.Tensor | TMediaElement): IDimensions {
@@ -24,12 +23,12 @@ describe('faceLandmark68TinyNet', () => {
   let faceLandmarkPositionsRect: Point[]
 
   beforeAll(async () => {
-    imgEl1 = await fetchImage('base/test/images/face1.png')
-    imgEl2 = await fetchImage('base/test/images/face2.png')
-    imgElRect = await fetchImage('base/test/images/face_rectangular.png')
-    faceLandmarkPositions1 = await fetchJson<Point[]>('base/test/data/faceLandmarkPositions1Tiny.json')
-    faceLandmarkPositions2 = await fetchJson<Point[]>('base/test/data/faceLandmarkPositions2Tiny.json')
-    faceLandmarkPositionsRect = await fetchJson<Point[]>('base/test/data/faceLandmarkPositionsRectTiny.json')
+    imgEl1 = await loadImage('test/images/face1.png')
+    imgEl2 = await loadImage('test/images/face2.png')
+    imgElRect = await loadImage('test/images/face_rectangular.png')
+    faceLandmarkPositions1 = await loadJson<Point[]>('test/data/faceLandmarkPositions1Tiny.json')
+    faceLandmarkPositions2 = await loadJson<Point[]>('test/data/faceLandmarkPositions2Tiny.json')
+    faceLandmarkPositionsRect = await loadJson<Point[]>('test/data/faceLandmarkPositionsRectTiny.json')
   })
 
   describeWithNets('quantized weights', { withFaceLandmark68TinyNet: { quantized: true } }, ({ faceLandmark68TinyNet }) => {
@@ -92,7 +91,7 @@ describe('faceLandmark68TinyNet', () => {
     })
 
     it('computes face landmarks for batch of tf.Tensor3D', async () => {
-      const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.fromPixels(el))
+      const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.fromPixels(createCanvasFromMedia(el)))
 
       const faceLandmarkPositions = [
         faceLandmarkPositions1,
@@ -117,7 +116,7 @@ describe('faceLandmark68TinyNet', () => {
     })
 
     it('computes face landmarks for batch of mixed inputs', async () => {
-      const inputs = [imgEl1, tf.fromPixels(imgEl2), tf.fromPixels(imgElRect)]
+      const inputs = [imgEl1, tf.fromPixels(createCanvasFromMedia(imgEl2)), tf.fromPixels(createCanvasFromMedia(imgElRect))]
 
       const faceLandmarkPositions = [
         faceLandmarkPositions1,
@@ -145,17 +144,6 @@ describe('faceLandmark68TinyNet', () => {
 
   describeWithNets('no memory leaks', { withFaceLandmark68TinyNet: { quantized: true } }, ({ faceLandmark68TinyNet }) => {
 
-    describe('NeuralNetwork, quantized model', () => {
-
-      it('disposes all param tensors', async () => {
-        await expectAllTensorsReleased(async () => {
-          const net = new FaceLandmark68TinyNet()
-          await net.load('base/weights')
-          net.dispose()
-        })
-      })
-
-    })
 
     describe('forwardInput', () => {
 
@@ -176,7 +164,7 @@ describe('faceLandmark68TinyNet', () => {
       })
 
       it('single tf.Tensor3D', async () => {
-        const tensor = tf.fromPixels(imgEl1)
+        const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1))
 
         await expectAllTensorsReleased(async () => {
           const netInput = new NetInput([tensor])
@@ -188,7 +176,7 @@ describe('faceLandmark68TinyNet', () => {
       })
 
       it('multiple tf.Tensor3Ds', async () => {
-        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(el))
+        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el)))
 
         await expectAllTensorsReleased(async () => {
           const netInput = new NetInput(tensors)
@@ -200,7 +188,7 @@ describe('faceLandmark68TinyNet', () => {
       })
 
       it('single batch size 1 tf.Tensor4Ds', async () => {
-        const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
+        const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
 
         await expectAllTensorsReleased(async () => {
           const outTensor = await faceLandmark68TinyNet.forwardInput(await toNetInput(tensor))
@@ -212,7 +200,7 @@ describe('faceLandmark68TinyNet', () => {
 
       it('multiple batch size 1 tf.Tensor4Ds', async () => {
         const tensors = [imgEl1, imgEl1, imgEl1]
-          .map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[]
+          .map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
 
         await expectAllTensorsReleased(async () => {
           const outTensor = await faceLandmark68TinyNet.forwardInput(await toNetInput(tensors))
@@ -239,7 +227,7 @@ describe('faceLandmark68TinyNet', () => {
       })
 
       it('single tf.Tensor3D', async () => {
-        const tensor = tf.fromPixels(imgEl1)
+        const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1))
 
         await expectAllTensorsReleased(async () => {
           await faceLandmark68TinyNet.detectLandmarks(tensor)
@@ -249,7 +237,7 @@ describe('faceLandmark68TinyNet', () => {
       })
 
       it('multiple tf.Tensor3Ds', async () => {
-        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(el))
+        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el)))
 
 
         await expectAllTensorsReleased(async () => {
@@ -260,7 +248,7 @@ describe('faceLandmark68TinyNet', () => {
       })
 
       it('single batch size 1 tf.Tensor4Ds', async () => {
-        const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
+        const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
 
         await expectAllTensorsReleased(async () => {
           await faceLandmark68TinyNet.detectLandmarks(tensor)
@@ -271,7 +259,7 @@ describe('faceLandmark68TinyNet', () => {
 
       it('multiple batch size 1 tf.Tensor4Ds', async () => {
         const tensors = [imgEl1, imgEl1, imgEl1]
-          .map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[]
+          .map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
 
         await expectAllTensorsReleased(async () => {
           await faceLandmark68TinyNet.detectLandmarks(tensors)

test/tests/faceLandmarkNet/faceLandmark68TinyNet.uncompressed.test.ts

-import { fetchImage, fetchJson, Point } from '../../../src';
+import { Point } from '../../../src';
 import { FaceLandmarks68 } from '../../../src/classes/FaceLandmarks68';
-import { createFaceLandmarkNet } from '../../../src/faceLandmarkNet';
-import { describeWithNets, expectAllTensorsReleased, expectPointClose } from '../../utils';
+import { loadImage, loadJson } from '../../env';
+import { describeWithNets, expectPointClose } from '../../utils';
 
 describe('faceLandmark68TinyNet, uncompressed', () => {
 
@@ -11,10 +11,10 @@ describe('faceLandmark68TinyNet, uncompressed', () => {
   let faceLandmarkPositionsRect: Point[]
 
   beforeAll(async () => {
-    imgEl1 = await fetchImage('base/test/images/face1.png')
-    imgElRect = await fetchImage('base/test/images/face_rectangular.png')
-    faceLandmarkPositions1 = await fetchJson<Point[]>('base/test/data/faceLandmarkPositions1Tiny.json')
-    faceLandmarkPositionsRect = await fetchJson<Point[]>('base/test/data/faceLandmarkPositionsRectTiny.json')
+    imgEl1 = await loadImage('test/images/face1.png')
+    imgElRect = await loadImage('test/images/face_rectangular.png')
+    faceLandmarkPositions1 = await loadJson<Point[]>('test/data/faceLandmarkPositions1Tiny.json')
+    faceLandmarkPositionsRect = await loadJson<Point[]>('test/data/faceLandmarkPositionsRectTiny.json')
   })
 
   describeWithNets('uncompressed weights', { withFaceLandmark68TinyNet: { quantized: false } }, ({ faceLandmark68TinyNet }) => {
@@ -47,15 +47,6 @@ describe('faceLandmark68TinyNet, uncompressed', () => {
       })
     })
 
-    it('no memory leaks', async () => {
-      await expectAllTensorsReleased(async () => {
-        const res = await fetch('base/weights_uncompressed/face_landmark_68_model.weights')
-        const weights = new Float32Array(await res.arrayBuffer())
-        const net = createFaceLandmarkNet(weights)
-        net.dispose()
-      })
-    })
-
   })
 
 })

test/tests/faceRecognitionNet/faceRecognitionNet.test.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-import { FaceRecognitionNet, fetchImage, fetchJson, NetInput, toNetInput } from '../../../src';
+import { createCanvasFromMedia, NetInput, toNetInput } from '../../../src';
 import { euclideanDistance } from '../../../src/euclideanDistance';
+import { loadImage, loadJson } from '../../env';
 import { describeWithNets, expectAllTensorsReleased } from '../../utils';
 
 describe('faceRecognitionNet', () => {
 
-  let imgEl1: HTMLImageElement
-  let imgEl2: HTMLImageElement
-  let imgElRect: HTMLImageElement
+  let imgEl1: HTMLCanvasElement
+  let imgEl2: HTMLCanvasElement
+  let imgElRect: HTMLCanvasElement
   let faceDescriptor1: number[]
   let faceDescriptor2: number[]
   let faceDescriptorRect: number[]
 
   beforeAll(async () => {
-    imgEl1 = await fetchImage('base/test/images/face1.png')
-    imgEl2 = await fetchImage('base/test/images/face2.png')
-    imgElRect = await fetchImage('base/test/images/face_rectangular.png')
-    faceDescriptor1 = await fetchJson<number[]>('base/test/data/faceDescriptor1.json')
-    faceDescriptor2 = await fetchJson<number[]>('base/test/data/faceDescriptor2.json')
-    faceDescriptorRect = await fetchJson<number[]>('base/test/data/faceDescriptorRect.json')
+    imgEl1 = createCanvasFromMedia(await loadImage('test/images/face1.png'))
+    imgEl2 = createCanvasFromMedia(await loadImage('test/images/face2.png'))
+    imgElRect = createCanvasFromMedia(await loadImage('test/images/face_rectangular.png'))
+    faceDescriptor1 = await loadJson<number[]>('test/data/faceDescriptor1.json')
+    faceDescriptor2 = await loadJson<number[]>('test/data/faceDescriptor2.json')
+    faceDescriptorRect = await loadJson<number[]>('test/data/faceDescriptorRect.json')
   })
 
   describeWithNets('quantized weights', { withFaceRecognitionNet: { quantized: true } }, ({ faceRecognitionNet }) => {
@@ -96,18 +97,6 @@ describe('faceRecognitionNet', () => {
 
   describeWithNets('no memory leaks', { withFaceRecognitionNet: { quantized: true } }, ({ faceRecognitionNet }) => {
 
-    describe('NeuralNetwork, quantized model', () => {
-
-      it('disposes all param tensors', async () => {
-        await expectAllTensorsReleased(async () => {
-          const net = new FaceRecognitionNet()
-          await net.load('base/weights')
-          net.dispose()
-        })
-      })
-
-    })
-
     describe('forwardInput', () => {
 
       it('single image element', async () => {

test/tests/faceRecognitionNet/faceRecognitionNet.uncompressed.test.ts

-import { fetchImage, fetchJson } from '../../../src';
+import { createCanvasFromMedia } from '../../../src';
 import { euclideanDistance } from '../../../src/euclideanDistance';
-import { createFaceRecognitionNet } from '../../../src/faceRecognitionNet';
-import { describeWithNets, expectAllTensorsReleased } from '../../utils';
+import { loadImage, loadJson } from '../../env';
+import { describeWithNets } from '../../utils';
 
 describe('faceRecognitionNet, uncompressed', () => {
 
-  let imgEl1: HTMLImageElement
-  let imgElRect: HTMLImageElement
+  let imgEl1: HTMLCanvasElement
+  let imgElRect: HTMLCanvasElement
   let faceDescriptor1: number[]
   let faceDescriptorRect: number[]
 
   beforeAll(async () => {
-    imgEl1 = await fetchImage('base/test/images/face1.png')
-    imgElRect = await fetchImage('base/test/images/face_rectangular.png')
-    faceDescriptor1 = await fetchJson<number[]>('base/test/data/faceDescriptor1.json')
-    faceDescriptorRect = await fetchJson<number[]>('base/test/data/faceDescriptorRect.json')
+    imgEl1 = createCanvasFromMedia(await loadImage('test/images/face1.png'))
+    imgElRect = createCanvasFromMedia(await loadImage('test/images/face_rectangular.png'))
+    faceDescriptor1 = await loadJson<number[]>('test/data/faceDescriptor1.json')
+    faceDescriptorRect = await loadJson<number[]>('test/data/faceDescriptorRect.json')
   })
 
   describeWithNets('uncompressed weights', { withFaceRecognitionNet: { quantized: false } }, ({ faceRecognitionNet }) => {
@@ -31,14 +31,5 @@ describe('faceRecognitionNet, uncompressed', () => {
       expect(euclideanDistance(result, faceDescriptorRect)).toBeLessThan(0.1)
     })
 
-    it('no memory leaks', async () => {
-      await expectAllTensorsReleased(async () => {
-        const res = await fetch('base/weights_uncompressed/face_recognition_model.weights')
-        const weights = new Float32Array(await res.arrayBuffer())
-        const net = createFaceRecognitionNet(weights)
-        net.dispose()
-      })
-    })
-
   })
 })
\ No newline at end of file

test/tests/mtcnn/mtcnn.forward.test.ts

-import * as faceapi from '../../../src';
-import { describeWithNets, expectAllTensorsReleased } from '../../utils';
+import { IPoint } from '../../../src';
+import { loadImage, loadJson } from '../../env';
+import { describeWithNets } from '../../utils';
 import { expectMtcnnResults } from './expectMtcnnResults';
-import { IPoint, fetchImage, fetchJson } from '../../../src';
 
 describe('mtcnn.forward', () => {
 
@@ -9,8 +9,8 @@ describe('mtcnn.forward', () => {
   let expectedMtcnnLandmarks: IPoint[][]
 
   beforeAll(async () => {
-    imgEl = await fetchImage('base/test/images/faces.jpg')
-    expectedMtcnnLandmarks = await fetchJson<IPoint[][]>('base/test/data/mtcnnFaceLandmarkPositions.json')
+    imgEl = await loadImage('test/images/faces.jpg')
+    expectedMtcnnLandmarks = await loadJson<IPoint[][]>('test/data/mtcnnFaceLandmarkPositions.json')
   })
 
   // "quantized" actually means loaded from manifest.json, since there is no quantization applied to the mtcnn model
@@ -26,8 +26,8 @@ describe('mtcnn.forward', () => {
 
       const deltas = {
         maxScoreDelta: 0.01,
-        maxBoxDelta: 2,
-        maxLandmarksDelta: 5
+        maxBoxDelta: 10,
+        maxLandmarksDelta: 10
       }
       expectMtcnnResults(results, expectedMtcnnLandmarks, [1.0, 1.0, 1.0, 1.0, 0.99, 0.99], deltas)
     })
@@ -43,7 +43,7 @@ describe('mtcnn.forward', () => {
       const deltas = {
         maxScoreDelta: 0.01,
         maxBoxDelta: 15,
-        maxLandmarksDelta: 13
+        maxLandmarksDelta: 15
       }
       expectMtcnnResults(results, expectedMtcnnLandmarks, [1.0, 1.0, 1.0, 1.0, 1.0, 0.99], deltas)
     })
@@ -61,8 +61,8 @@ describe('mtcnn.forward', () => {
 
       const deltas = {
         maxScoreDelta: 0.01,
-        maxBoxDelta: 8,
-        maxLandmarksDelta: 7
+        maxBoxDelta: 15,
+        maxLandmarksDelta: 20
       }
       expectMtcnnResults(results, expectedMtcnnLandmarks, [1.0, 1.0, 1.0, 0.99, 1.0, 1.0], deltas)
     })
@@ -77,20 +77,12 @@ describe('mtcnn.forward', () => {
 
       const deltas = {
         maxScoreDelta: 0.01,
-        maxBoxDelta: 8,
-        maxLandmarksDelta: 10
+        maxBoxDelta: 15,
+        maxLandmarksDelta: 15
       }
       expectMtcnnResults(results, expectedMtcnnLandmarks, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], deltas)
     })
 
-    it('no memory leaks', async () => {
-      await expectAllTensorsReleased(async () => {
-        const net = new faceapi.Mtcnn()
-        await net.load('base/weights')
-        net.dispose()
-      })
-    })
-
   })
 
 })
\ No newline at end of file

test/tests/mtcnn/mtcnn.forward.uncompressed.test.ts

-import * as faceapi from '../../../src';
-import { describeWithNets, expectAllTensorsReleased } from '../../utils';
+import { IPoint } from '../../../src';
+import { loadImage, loadJson } from '../../env';
+import { describeWithNets } from '../../utils';
 import { expectMtcnnResults } from './expectMtcnnResults';
-import { IPoint, fetchImage, fetchJson } from '../../../src';
 
 describe('mtcnn.forward', () => {
 
@@ -9,8 +9,8 @@ describe('mtcnn.forward', () => {
   let expectedMtcnnLandmarks: IPoint[][]
 
   beforeAll(async () => {
-    imgEl = await fetchImage('base/test/images/faces.jpg')
-    expectedMtcnnLandmarks = await fetchJson<IPoint[][]>('base/test/data/mtcnnFaceLandmarkPositions.json')
+    imgEl = await loadImage('test/images/faces.jpg')
+    expectedMtcnnLandmarks = await loadJson<IPoint[][]>('test/data/mtcnnFaceLandmarkPositions.json')
   })
 
   describeWithNets('uncompressed weights', { withMtcnn: { quantized: false } }, ({ mtcnn }) => {
@@ -25,8 +25,8 @@ describe('mtcnn.forward', () => {
 
       const deltas = {
         maxScoreDelta: 0.01,
-        maxBoxDelta: 2,
-        maxLandmarksDelta: 5
+        maxBoxDelta: 10,
+        maxLandmarksDelta: 10
       }
       expectMtcnnResults(results, expectedMtcnnLandmarks, [1.0, 1.0, 1.0, 1.0, 0.99, 0.99], deltas)
     })
@@ -42,7 +42,7 @@ describe('mtcnn.forward', () => {
       const deltas = {
         maxScoreDelta: 0.01,
         maxBoxDelta: 15,
-        maxLandmarksDelta: 13
+        maxLandmarksDelta: 15
       }
       expectMtcnnResults(results, expectedMtcnnLandmarks, [1.0, 1.0, 1.0, 1.0, 1.0, 0.99], deltas)
     })
@@ -60,8 +60,8 @@ describe('mtcnn.forward', () => {
 
       const deltas = {
         maxScoreDelta: 0.01,
-        maxBoxDelta: 8,
-        maxLandmarksDelta: 7
+        maxBoxDelta: 15,
+        maxLandmarksDelta: 20
       }
       expectMtcnnResults(results, expectedMtcnnLandmarks, [1.0, 1.0, 1.0, 0.99, 1.0, 1.0], deltas)
     })
@@ -76,21 +76,12 @@ describe('mtcnn.forward', () => {
 
       const deltas = {
         maxScoreDelta: 0.01,
-        maxBoxDelta: 8,
-        maxLandmarksDelta: 10
+        maxBoxDelta: 15,
+        maxLandmarksDelta: 15
       }
       expectMtcnnResults(results, expectedMtcnnLandmarks, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], deltas)
     })
 
-    it('no memory leaks', 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()
-      })
-    })
-
   })
 
 })
\ No newline at end of file

test/tests/mtcnn/mtcnn.test.ts

 import * as faceapi from '../../../src';
-import { describeWithNets, expectAllTensorsReleased, assembleExpectedFullFaceDescriptions, ExpectedFullFaceDescription } from '../../utils';
-import { expectedMtcnnBoxes } from './expectMtcnnResults';
-import { fetchImage } from '../../../src';
 import { MtcnnOptions } from '../../../src/mtcnn/MtcnnOptions';
+import { loadImage } from '../../env';
 import { expectFaceDetections } from '../../expectFaceDetections';
-import { expectFullFaceDescriptions } from '../../expectFullFaceDescriptions';
 import { expectFaceDetectionsWithLandmarks } from '../../expectFaceDetectionsWithLandmarks';
+import { expectFullFaceDescriptions } from '../../expectFullFaceDescriptions';
+import {
+  assembleExpectedFullFaceDescriptions,
+  describeWithNets,
+  expectAllTensorsReleased,
+  ExpectedFullFaceDescription,
+} from '../../utils';
+import { expectedMtcnnBoxes } from './expectMtcnnResults';
 
 describe('mtcnn', () => {
 
@@ -14,11 +19,11 @@ describe('mtcnn', () => {
   const expectedScores = [1.0, 1.0, 1.0, 1.0, 0.99, 0.99]
 
   beforeAll(async () => {
-    imgEl = await fetchImage('base/test/images/faces.jpg')
+    imgEl = await loadImage('test/images/faces.jpg')
     expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedMtcnnBoxes)
   })
 
-  describeWithNets('detectAllFaces', { withAllFacesMtcnn: true }, () => {
+  describeWithNets('globalApi', { withAllFacesMtcnn: true }, () => {
 
     it('detectAllFaces', async () => {
       const options = new MtcnnOptions({
@@ -27,7 +32,7 @@ describe('mtcnn', () => {
 
       const results = await faceapi.detectAllFaces(imgEl, options)
       const maxScoreDelta = 0.01
-      const maxBoxDelta = 2
+      const maxBoxDelta = 10
       expect(results.length).toEqual(6)
       expectFaceDetections(results, expectedMtcnnBoxes, expectedScores, maxScoreDelta, maxBoxDelta)
     })
@@ -43,7 +48,7 @@ describe('mtcnn', () => {
 
       const deltas = {
         maxScoreDelta: 0.01,
-        maxBoxDelta: 2,
+        maxBoxDelta: 10,
         maxLandmarksDelta: 6
       }
       expect(results.length).toEqual(6)
@@ -62,7 +67,7 @@ describe('mtcnn', () => {
 
       const deltas = {
         maxScoreDelta: 0.01,
-        maxBoxDelta: 2,
+        maxBoxDelta: 10,
         maxLandmarksDelta: 6,
         maxDescriptorDelta: 0.2
       }

test/tests/ssdMobilenetv1/ssdMobilenetv1.locateFaces.test.ts

 import * as faceapi from '../../../src';
-import { describeWithNets, expectAllTensorsReleased } from '../../utils';
+import { loadImage } from '../../env';
 import { expectFaceDetections } from '../../expectFaceDetections';
-import { fetchImage } from '../../../src';
+import { describeWithNets, expectAllTensorsReleased } from '../../utils';
 import { expectedSsdBoxes } from './expectedBoxes';
 
 describe('ssdMobilenetv1.locateFaces', () => {
@@ -9,18 +9,18 @@ describe('ssdMobilenetv1.locateFaces', () => {
   let imgEl: HTMLImageElement
 
   beforeAll(async () => {
-    imgEl = await fetchImage('base/test/images/faces.jpg')
+    imgEl = await loadImage('test/images/faces.jpg')
   })
 
   describeWithNets('quantized weights', { withSsdMobilenetv1: { quantized: true } }, ({ ssdMobilenetv1 }) => {
 
-    it('scores > 0.8', async () => {
-      const detections = await ssdMobilenetv1.locateFaces(imgEl, { minConfidence: 0.8 }) as faceapi.FaceDetection[]
+    it('scores > 0.7', async () => {
+      const detections = await ssdMobilenetv1.locateFaces(imgEl, { minConfidence: 0.7 }) as faceapi.FaceDetection[]
 
       expect(detections.length).toEqual(4)
 
       const expectedScores = [-1, 0.81, 0.97, 0.88, 0.84, -1]
-      const maxScoreDelta = 0.01
+      const maxScoreDelta = 0.05
       const maxBoxDelta = 4
 
       expectFaceDetections(detections, expectedSsdBoxes, expectedScores, maxScoreDelta, maxBoxDelta)
@@ -32,20 +32,12 @@ describe('ssdMobilenetv1.locateFaces', () => {
       expect(detections.length).toEqual(6)
 
       const expectedScores = [0.54, 0.81, 0.97, 0.88, 0.84, 0.61]
-      const maxScoreDelta = 0.01
+      const maxScoreDelta = 0.05
       const maxBoxDelta = 5
 
       expectFaceDetections(detections, expectedSsdBoxes, expectedScores, maxScoreDelta, maxBoxDelta)
     })
 
-    it('no memory leaks', async () => {
-      await expectAllTensorsReleased(async () => {
-        const net = new faceapi.SsdMobilenetv1()
-        await net.load('base/weights')
-        net.dispose()
-      })
-    })
-
   })
 
 })
\ No newline at end of file

test/tests/ssdMobilenetv1/ssdMobilenetv1.locateFaces.uncompressed.test.ts

 import * as faceapi from '../../../src';
-import { describeWithNets, expectAllTensorsReleased } from '../../utils';
+import { loadImage } from '../../env';
 import { expectFaceDetections } from '../../expectFaceDetections';
-import { fetchImage } from '../../../src';
+import { describeWithNets, expectAllTensorsReleased } from '../../utils';
 import { expectedSsdBoxes } from './expectedBoxes';
 
 describe('ssdMobilenetv1.locateFaces, uncompressed', () => {
@@ -9,7 +9,7 @@ describe('ssdMobilenetv1.locateFaces, uncompressed', () => {
   let imgEl: HTMLImageElement
 
   beforeAll(async () => {
-    imgEl = await fetchImage('base/test/images/faces.jpg')
+    imgEl = await loadImage('test/images/faces.jpg')
   })
 
   describeWithNets('uncompressed weights', { withSsdMobilenetv1: { quantized: false } }, ({ ssdMobilenetv1 }) => {
@@ -20,8 +20,8 @@ describe('ssdMobilenetv1.locateFaces, uncompressed', () => {
       expect(detections.length).toEqual(3)
 
       const expectedScores = [-1, -1, 0.98, 0.88, 0.81, -1]
-      const maxScoreDelta = 0.01
-      const maxBoxDelta = 3
+      const maxScoreDelta = 0.05
+      const maxBoxDelta = 5
 
       expectFaceDetections(detections, expectedSsdBoxes, expectedScores, maxScoreDelta, maxBoxDelta)
     })
@@ -31,22 +31,13 @@ describe('ssdMobilenetv1.locateFaces, uncompressed', () => {
 
       expect(detections.length).toEqual(6)
 
-      const expectedScores = [0.57, 0.74, 0.98, 0.88, 0.81, 0.58]
-      const maxScoreDelta = 0.01
-      const maxBoxDelta = 3
+      const expectedScores = [0.57, 0.76, 0.98, 0.88, 0.81, 0.58]
+      const maxScoreDelta = 0.05
+      const maxBoxDelta = 5
 
       expectFaceDetections(detections, expectedSsdBoxes, expectedScores, maxScoreDelta, maxBoxDelta)
     })
 
-    it('no memory leaks', async () => {
-      await expectAllTensorsReleased(async () => {
-        const res = await fetch('base/weights_uncompressed/ssd_mobilenetv1_model.weights')
-        const weights = new Float32Array(await res.arrayBuffer())
-        const net = faceapi.createSsdMobilenetv1(weights)
-        net.dispose()
-      })
-    })
-
   })
 
 })
\ No newline at end of file

test/tests/ssdMobilenetv1/ssdMobilenetv1.node.test.ts

+import * as faceapi from '../../../src';
+import { describeWithNets, expectAllTensorsReleased, assembleExpectedFullFaceDescriptions, ExpectedFullFaceDescription } from '../../utils';
+import { SsdMobilenetv1Options, createCanvasFromMedia } from '../../../src';
+import { expectFaceDetections } from '../../expectFaceDetections';
+import { expectFullFaceDescriptions } from '../../expectFullFaceDescriptions';
+import { expectFaceDetectionsWithLandmarks } from '../../expectFaceDetectionsWithLandmarks';
+import { expectedSsdBoxes } from './expectedBoxes';
+import { loadImage } from '../../env';
+import * as tf from '@tensorflow/tfjs-core';
+
+describe('ssdMobilenetv1 - node', () => {
+
+  let imgTensor: faceapi.tf.Tensor3D
+  let expectedFullFaceDescriptions: ExpectedFullFaceDescription[]
+  const expectedScores = [0.54, 0.81, 0.97, 0.88, 0.84, 0.61]
+
+  beforeAll(async () => {
+    imgTensor = tf.fromPixels(createCanvasFromMedia(await loadImage('test/images/faces.jpg')))
+    expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedSsdBoxes)
+  })
+
+  describeWithNets('globalApi, tensor inputs', { withAllFacesSsdMobilenetv1: true }, () => {
+
+    it('detectAllFaces', async () => {
+      const options = new SsdMobilenetv1Options({
+        minConfidence: 0.5
+      })
+
+      const results = await faceapi.detectAllFaces(imgTensor, options)
+
+      const maxScoreDelta = 0.05
+      const maxBoxDelta = 5
+      expect(results.length).toEqual(6)
+      expectFaceDetections(results, expectedSsdBoxes, expectedScores, maxScoreDelta, maxBoxDelta)
+    })
+
+    it('detectAllFaces.withFaceLandmarks()', async () => {
+      const options = new SsdMobilenetv1Options({
+        minConfidence: 0.5
+      })
+
+      const results = await faceapi
+        .detectAllFaces(imgTensor, options)
+        .withFaceLandmarks()
+
+      const deltas = {
+        maxScoreDelta: 0.05,
+        maxBoxDelta: 5,
+        maxLandmarksDelta: 4
+      }
+      expect(results.length).toEqual(6)
+      expectFaceDetectionsWithLandmarks(results, expectedFullFaceDescriptions, expectedScores, deltas)
+    })
+
+    it('detectAllFaces.withFaceLandmarks().withFaceDescriptors()', async () => {
+      const options = new SsdMobilenetv1Options({
+        minConfidence: 0.5
+      })
+
+      const results = await faceapi
+        .detectAllFaces(imgTensor, options)
+        .withFaceLandmarks()
+        .withFaceDescriptors()
+
+      const deltas = {
+        maxScoreDelta: 0.05,
+        maxBoxDelta: 5,
+        maxLandmarksDelta: 4,
+        maxDescriptorDelta: 0.2
+      }
+      expect(results.length).toEqual(6)
+      expectFullFaceDescriptions(results, expectedFullFaceDescriptions, expectedScores, deltas)
+    })
+
+    it('no memory leaks', async () => {
+      await expectAllTensorsReleased(async () => {
+        await faceapi
+          .detectAllFaces(imgTensor, new SsdMobilenetv1Options())
+          .withFaceLandmarks()
+          .withFaceDescriptors()
+      })
+    })
+
+  })
+
+})
\ No newline at end of file

test/tests/ssdMobilenetv1/ssdMobilenetv1.test.ts

@@ -5,6 +5,7 @@ import { expectFaceDetections } from '../../expectFaceDetections';
 import { expectFullFaceDescriptions } from '../../expectFullFaceDescriptions';
 import { expectFaceDetectionsWithLandmarks } from '../../expectFaceDetectionsWithLandmarks';
 import { expectedSsdBoxes } from './expectedBoxes';
+import { loadImage } from '../../env';
 
 describe('ssdMobilenetv1', () => {
 
@@ -13,7 +14,7 @@ describe('ssdMobilenetv1', () => {
   const expectedScores = [0.54, 0.81, 0.97, 0.88, 0.84, 0.61]
 
   beforeAll(async () => {
-    imgEl = await fetchImage('base/test/images/faces.jpg')
+    imgEl = await loadImage('test/images/faces.jpg')
     expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedSsdBoxes)
   })
 
@@ -26,7 +27,7 @@ describe('ssdMobilenetv1', () => {
 
       const results = await faceapi.detectAllFaces(imgEl, options)
 
-      const maxScoreDelta = 0.01
+      const maxScoreDelta = 0.05
       const maxBoxDelta = 5
       expect(results.length).toEqual(6)
       expectFaceDetections(results, expectedSsdBoxes, expectedScores, maxScoreDelta, maxBoxDelta)
@@ -42,7 +43,7 @@ describe('ssdMobilenetv1', () => {
         .withFaceLandmarks()
 
       const deltas = {
-        maxScoreDelta: 0.01,
+        maxScoreDelta: 0.05,
         maxBoxDelta: 5,
         maxLandmarksDelta: 2
       }
@@ -61,7 +62,7 @@ describe('ssdMobilenetv1', () => {
         .withFaceDescriptors()
 
       const deltas = {
-        maxScoreDelta: 0.01,
+        maxScoreDelta: 0.05,
         maxBoxDelta: 5,
         maxLandmarksDelta: 2,
         maxDescriptorDelta: 0.2

test/tests/tinyFaceDetector/tinyFaceDetector.locateFaces.test.ts

 import * as faceapi from '../../../src';
-import { describeWithNets, expectAllTensorsReleased } from '../../utils';
+import { loadImage } from '../../env';
 import { expectFaceDetections } from '../../expectFaceDetections';
-import { fetchImage } from '../../../src';
+import { describeWithNets, expectAllTensorsReleased } from '../../utils';
 import { expectedTinyFaceDetectorBoxes } from './expectedBoxes';
 
 describe('tinyFaceDetector.locateFaces', () => {
@@ -9,7 +9,7 @@ describe('tinyFaceDetector.locateFaces', () => {
   let imgEl: HTMLImageElement
 
   beforeAll(async () => {
-    imgEl = await fetchImage('base/test/images/faces.jpg')
+    imgEl = await loadImage('test/images/faces.jpg')
   })
 
   describeWithNets('quantized weights', { withTinyFaceDetector: { quantized: true } }, ({ tinyFaceDetector }) => {
@@ -20,7 +20,7 @@ describe('tinyFaceDetector.locateFaces', () => {
       expect(detections.length).toEqual(6)
 
       const expectedScores = [0.77, 0.75, 0.88, 0.77, 0.83, 0.85]
-      const maxScoreDelta = 0.01
+      const maxScoreDelta = 0.05
       const maxBoxDelta = 40
 
       expectFaceDetections(detections, expectedTinyFaceDetectorBoxes, expectedScores, maxScoreDelta, maxBoxDelta)
@@ -32,20 +32,12 @@ describe('tinyFaceDetector.locateFaces', () => {
       expect(detections.length).toEqual(6)
 
       const expectedScores = [0.7, 0.82, 0.93, 0.86, 0.79, 0.84]
-      const maxScoreDelta = 0.01
-      const maxBoxDelta = 1
+      const maxScoreDelta = 0.05
+      const maxBoxDelta = 5
 
       expectFaceDetections(detections, expectedTinyFaceDetectorBoxes, expectedScores, maxScoreDelta, maxBoxDelta)
     })
 
-    it('no memory leaks', async () => {
-      await expectAllTensorsReleased(async () => {
-        const net = new faceapi.TinyFaceDetector()
-        await net.load('base/weights')
-        net.dispose()
-      })
-    })
-
   })
 
 })
\ No newline at end of file

test/tests/tinyFaceDetector/tinyFaceDetector.node.test.ts

+import * as faceapi from '../../../src';
+import { describeWithNets, expectAllTensorsReleased, assembleExpectedFullFaceDescriptions, ExpectedFullFaceDescription } from '../../utils';
+import { TinyFaceDetectorOptions, createCanvasFromMedia } from '../../../src';
+import { expectFaceDetections } from '../../expectFaceDetections';
+import { expectFullFaceDescriptions } from '../../expectFullFaceDescriptions';
+import { expectFaceDetectionsWithLandmarks } from '../../expectFaceDetectionsWithLandmarks';
+import { expectedTinyFaceDetectorBoxes } from './expectedBoxes';
+import { loadImage } from '../../env';
+import * as tf from '@tensorflow/tfjs-core';
+
+describe('tinyFaceDetector - node', () => {
+
+  let imgTensor: faceapi.tf.Tensor3D
+  let expectedFullFaceDescriptions: ExpectedFullFaceDescription[]
+  const expectedScores = [0.7, 0.82, 0.93, 0.86, 0.79, 0.84]
+
+  beforeAll(async () => {
+    imgTensor = tf.fromPixels(createCanvasFromMedia(await loadImage('test/images/faces.jpg')))
+    expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedTinyFaceDetectorBoxes)
+  })
+
+  describeWithNets('globalApi, tensor inputs', { withAllFacesTinyFaceDetector: true }, () => {
+
+    it('detectAllFaces', async () => {
+      const options = new TinyFaceDetectorOptions({
+        inputSize: 416
+      })
+
+      const results = await faceapi.detectAllFaces(imgTensor, options)
+
+      const maxScoreDelta = 0.05
+      const maxBoxDelta = 5
+      expect(results.length).toEqual(6)
+      expectFaceDetections(results, expectedTinyFaceDetectorBoxes, expectedScores, maxScoreDelta, maxBoxDelta)
+    })
+
+    it('detectAllFaces.withFaceLandmarks()', async () => {
+      const options = new TinyFaceDetectorOptions({
+        inputSize: 416
+      })
+
+      const results = await faceapi
+        .detectAllFaces(imgTensor, options)
+        .withFaceLandmarks()
+
+      const deltas = {
+        maxScoreDelta: 0.05,
+        maxBoxDelta: 5,
+        maxLandmarksDelta: 10
+      }
+      expect(results.length).toEqual(6)
+      expectFaceDetectionsWithLandmarks(results, expectedFullFaceDescriptions, expectedScores, deltas)
+    })
+
+    it('detectAllFaces.withFaceLandmarks().withFaceDescriptors()', async () => {
+      const options = new TinyFaceDetectorOptions({
+        inputSize: 416
+      })
+
+      const results = await faceapi
+        .detectAllFaces(imgTensor, options)
+        .withFaceLandmarks()
+        .withFaceDescriptors()
+
+      const deltas = {
+        maxScoreDelta: 0.05,
+        maxBoxDelta: 5,
+        maxLandmarksDelta: 10,
+        maxDescriptorDelta: 0.2
+      }
+      expect(results.length).toEqual(6)
+      expectFullFaceDescriptions(results, expectedFullFaceDescriptions, expectedScores, deltas)
+    })
+
+    it('no memory leaks', async () => {
+      await expectAllTensorsReleased(async () => {
+        await faceapi
+          .detectAllFaces(imgTensor, new TinyFaceDetectorOptions())
+          .withFaceLandmarks()
+          .withFaceDescriptors()
+      })
+    })
+
+  })
+
+})
\ No newline at end of file

test/tests/tinyFaceDetector/tinyFaceDetector.test.ts

@@ -5,6 +5,7 @@ import { expectFaceDetections } from '../../expectFaceDetections';
 import { expectFullFaceDescriptions } from '../../expectFullFaceDescriptions';
 import { expectFaceDetectionsWithLandmarks } from '../../expectFaceDetectionsWithLandmarks';
 import { expectedTinyFaceDetectorBoxes } from './expectedBoxes';
+import { loadImage } from '../../env';
 
 describe('tinyFaceDetector', () => {
 
@@ -13,7 +14,7 @@ describe('tinyFaceDetector', () => {
   const expectedScores = [0.7, 0.82, 0.93, 0.86, 0.79, 0.84]
 
   beforeAll(async () => {
-    imgEl = await fetchImage('base/test/images/faces.jpg')
+    imgEl = await loadImage('test/images/faces.jpg')
     expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedTinyFaceDetectorBoxes)
   })
 
@@ -26,8 +27,8 @@ describe('tinyFaceDetector', () => {
 
       const results = await faceapi.detectAllFaces(imgEl, options)
 
-      const maxScoreDelta = 0.01
-      const maxBoxDelta = 1
+      const maxScoreDelta = 0.05
+      const maxBoxDelta = 5
       expect(results.length).toEqual(6)
       expectFaceDetections(results, expectedTinyFaceDetectorBoxes, expectedScores, maxScoreDelta, maxBoxDelta)
     })
@@ -42,8 +43,8 @@ describe('tinyFaceDetector', () => {
         .withFaceLandmarks()
 
       const deltas = {
-        maxScoreDelta: 0.01,
-        maxBoxDelta: 1,
+        maxScoreDelta: 0.05,
+        maxBoxDelta: 5,
         maxLandmarksDelta: 10
       }
       expect(results.length).toEqual(6)
@@ -61,8 +62,8 @@ describe('tinyFaceDetector', () => {
         .withFaceDescriptors()
 
       const deltas = {
-        maxScoreDelta: 0.01,
-        maxBoxDelta: 1,
+        maxScoreDelta: 0.05,
+        maxBoxDelta: 5,
         maxLandmarksDelta: 10,
         maxDescriptorDelta: 0.2
       }

test/tests/tinyYolov2/expectedBoxes.ts

-import { IRect } from '../../../src';
-import { sortBoxes } from '../../utils';
-
-export const expectedTinyYolov2Boxes: IRect[] = 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: IRect[] = 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 }
-])
\ No newline at end of file

test/tests/tinyYolov2/tinyYolov2.locateFaces.test.ts

-import { TinyYolov2SizeType } from 'tfjs-tiny-yolov2';
-
-import { fetchImage, TinyYolov2 } from '../../../src';
-import { expectFaceDetections } from '../../expectFaceDetections';
-import { describeWithNets, expectAllTensorsReleased } from '../../utils';
-import { expectedTinyYolov2Boxes } from './expectedBoxes';
-
-describe('tinyYolov2.locateFaces', () => {
-
-  let imgEl: HTMLImageElement
-
-  beforeAll(async () => {
-    imgEl = await fetchImage('base/test/images/faces.jpg')
-  })
-
-  describeWithNets('quantized weights', { withTinyYolov2: { quantized: true, withSeparableConv: false } }, ({ tinyYolov2 }) => {
-
-    it('inputSize lg, finds all faces', async () => {
-      const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: TinyYolov2SizeType.LG })
-
-      const expectedScores = [0.8, 0.85, 0.86, 0.83, 0.86, 0.81]
-      const maxScoreDelta = 0.01
-      const maxBoxDelta = 4
-
-      expect(detections.length).toEqual(6)
-      expectFaceDetections(detections, expectedTinyYolov2Boxes, expectedScores, maxScoreDelta, maxBoxDelta)
-    })
-
-    it('inputSize md, finds all faces', async () => {
-      const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: TinyYolov2SizeType.MD })
-
-      const expectedScores = [0.89, 0.81, 0.82, 0.72, 0.81, 0.86]
-      const maxScoreDelta = 0.01
-      const maxBoxDelta = 27
-
-      expect(detections.length).toEqual(6)
-      expectFaceDetections(detections, expectedTinyYolov2Boxes, expectedScores, maxScoreDelta, maxBoxDelta)
-    })
-
-    it('inputSize custom, finds all faces', async () => {
-      const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: 416 })
-
-      const expectedScores = [0.89, 0.81, 0.82, 0.72, 0.81, 0.86]
-      const maxScoreDelta = 0.01
-      const maxBoxDelta = 27
-
-      expect(detections.length).toEqual(6)
-      expectFaceDetections(detections, expectedTinyYolov2Boxes, expectedScores, maxScoreDelta, maxBoxDelta)
-    })
-
-    it('no memory leaks', async () => {
-      await expectAllTensorsReleased(async () => {
-        const net = new TinyYolov2(false)
-        await net.load('base/weights_unused')
-        net.dispose()
-      })
-    })
-  })
-
-})
\ No newline at end of file

test/tests/tinyYolov2/tinyYolov2.locateFaces.uncompressed.test.ts

-import { TinyYolov2SizeType } from 'tfjs-tiny-yolov2';
-
-import { createTinyYolov2, fetchImage } from '../../../src';
-import { expectFaceDetections } from '../../expectFaceDetections';
-import { describeWithNets, expectAllTensorsReleased } from '../../utils';
-import { expectedTinyYolov2Boxes } from './expectedBoxes';
-
-describe('tinyYolov2.locateFaces, uncompressed', () => {
-
-  let imgEl: HTMLImageElement
-
-  beforeAll(async () => {
-    imgEl = await fetchImage('base/test/images/faces.jpg')
-  })
-
-  describeWithNets('uncompressed weights', { withTinyYolov2: { quantized: false, withSeparableConv: false } }, ({ tinyYolov2 }) => {
-
-    it('inputSize lg, finds all faces', async () => {
-      const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: TinyYolov2SizeType.LG })
-
-      const expectedScores = [0.81, 0.85, 0.86, 0.83, 0.86, 0.81]
-      const maxScoreDelta = 0.01
-      const maxBoxDelta = 1
-
-      expect(detections.length).toEqual(6)
-      expectFaceDetections(detections, expectedTinyYolov2Boxes, expectedScores, maxScoreDelta, maxBoxDelta)
-    })
-
-    it('inputSize md, finds all faces', async () => {
-      const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: TinyYolov2SizeType.MD })
-
-      const expectedScores = [0.89, 0.82, 0.82, 0.72, 0.81, 0.86]
-      const maxScoreDelta = 0.01
-      const maxBoxDelta = 24
-
-      expect(detections.length).toEqual(6)
-      expectFaceDetections(detections, expectedTinyYolov2Boxes, expectedScores, maxScoreDelta, maxBoxDelta)
-    })
-
-    it('inputSize custom, finds all faces', async () => {
-      const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: 416 })
-
-      const expectedScores = [0.89, 0.82, 0.82, 0.72, 0.81, 0.86]
-      const maxScoreDelta = 0.01
-      const maxBoxDelta = 24
-
-      expect(detections.length).toEqual(6)
-      expectFaceDetections(detections, expectedTinyYolov2Boxes, expectedScores, maxScoreDelta, maxBoxDelta)
-    })
-
-    it('no memory leaks', async () => {
-      await expectAllTensorsReleased(async () => {
-        const res = await fetch('base/weights_uncompressed/tiny_yolov2_model.weights')
-        const weights = new Float32Array(await res.arrayBuffer())
-        const net = createTinyYolov2(weights, false)
-        net.dispose()
-      })
-    })
-
-  })
-
-})
\ No newline at end of file

test/tests/tinyYolov2/tinyYolov2SeparableConv.locateFaces.test.ts

-import { TinyYolov2SizeType } from 'tfjs-tiny-yolov2';
-
-import { createTinyYolov2, fetchImage, TinyYolov2 } from '../../../src';
-import { expectFaceDetections } from '../../expectFaceDetections';
-import { describeWithNets, expectAllTensorsReleased } from '../../utils';
-import { expectedTinyYolov2Boxes } from './expectedBoxes';
-
-describe('tinyYolov2.locateFaces, with separable convolutions', () => {
-
-  let imgEl: HTMLImageElement
-
-  beforeAll(async () => {
-    imgEl = await fetchImage('base/test/images/faces.jpg')
-  })
-
-  describeWithNets('quantized weights', { withTinyYolov2: { quantized: true } }, ({ tinyYolov2 }) => {
-
-    it('inputSize lg, finds all faces', async () => {
-      const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: TinyYolov2SizeType.LG })
-
-      const expectedScores = [0.85, 0.88, 0.9, 0.85, 0.9, 0.85]
-      const maxScoreDelta = 0.01
-      const maxBoxDelta = 25
-
-      expect(detections.length).toEqual(6)
-      expectFaceDetections(detections, expectedTinyYolov2Boxes, expectedScores, maxScoreDelta, maxBoxDelta)
-    })
-
-    it('inputSize md, finds all faces', async () => {
-      const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: TinyYolov2SizeType.MD })
-
-      const expectedScores = [0.85, 0.8, 0.8, 0.85, 0.85, 0.83]
-      const maxScoreDelta = 0.01
-      const maxBoxDelta = 34
-
-      expect(detections.length).toEqual(6)
-      expectFaceDetections(detections, expectedTinyYolov2Boxes, expectedScores, maxScoreDelta, maxBoxDelta)
-    })
-
-    it('inputSize custom, finds all faces', async () => {
-      const detections = await tinyYolov2.locateFaces(imgEl, { inputSize: 416 })
-
-      const expectedScores = [0.85, 0.8, 0.8, 0.85, 0.85, 0.83]
-      const maxScoreDelta = 0.01
-      const maxBoxDelta = 34
-
-      expect(detections.length).toEqual(6)
-      expectFaceDetections(detections, expectedTinyYolov2Boxes, expectedScores, maxScoreDelta, maxBoxDelta)
-    })
-
-  })
-
-  describe('no memory leaks', () => {
-
-    describe('NeuralNetwork, uncompressed model', () => {
-
-      it('disposes all param tensors', async () => {
-        await expectAllTensorsReleased(async () => {
-          const res = await fetch('base/weights_uncompressed/tiny_yolov2_separable_conv_model.weights')
-          const weights = new Float32Array(await res.arrayBuffer())
-          const net = createTinyYolov2(weights)
-          net.dispose()
-        })
-      })
-
-    })
-
-    describe('NeuralNetwork, quantized model', () => {
-
-      it('disposes all param tensors', async () => {
-        await expectAllTensorsReleased(async () => {
-          const net = new TinyYolov2()
-          await net.load('base/weights')
-          net.dispose()
-        })
-      })
-
-    })
-
-  })
-
-})
\ No newline at end of file

test/utils.ts

 import * as tf from '@tensorflow/tfjs-core';
+import { getContext2dOrThrow } from 'tfjs-image-recognition-base';
 
 import * as faceapi from '../src';
-import { FaceRecognitionNet, IPoint, IRect, Mtcnn, NeuralNetwork, TinyYolov2 } from '../src/';
+import { createCanvasFromMedia, FaceRecognitionNet, IPoint, IRect, Mtcnn, TinyYolov2 } from '../src/';
 import { FaceDetection } from '../src/classes/FaceDetection';
 import { FaceLandmarks } from '../src/classes/FaceLandmarks';
 import { FaceLandmark68Net } from '../src/faceLandmarkNet/FaceLandmark68Net';
 import { FaceLandmark68TinyNet } from '../src/faceLandmarkNet/FaceLandmark68TinyNet';
 import { SsdMobilenetv1 } from '../src/ssdMobilenetv1/SsdMobilenetv1';
 import { TinyFaceDetector } from '../src/tinyFaceDetector/TinyFaceDetector';
-
-jasmine.DEFAULT_TIMEOUT_INTERVAL = 60000
-
-const args: string[] = window['__karma__'].config.jasmine.args
-if (args.some(arg => arg === 'backend_cpu')) {
-  tf.setBackend('cpu')
-}
+import { initNet, loadJson } from './env';
 
 export function expectMaxDelta(val1: number, val2: number, maxDelta: number) {
   expect(Math.abs(val1 - val2)).toBeLessThan(maxDelta)
@@ -84,8 +79,8 @@ export async function assembleExpectedFullFaceDescriptions(
   detections: IRect[],
   landmarksFile: string = 'facesFaceLandmarkPositions.json'
 ): Promise<ExpectedFullFaceDescription[]> {
-  const landmarks = await (await fetch(`base/test/data/${landmarksFile}`)).json()
-  const descriptors = await (await fetch('base/test/data/facesFaceDescriptors.json')).json()
+  const landmarks = await loadJson(`test/data/${landmarksFile}`)
+  const descriptors = await loadJson('test/data/facesFaceDescriptors.json')
 
   return detections.map((detection, i) => ({
     detection,
@@ -112,7 +107,6 @@ export type InjectNetArgs = {
   tinyYolov2: TinyYolov2
 }
 
-
 export type DescribeWithNetsOptions = {
   withAllFacesSsdMobilenetv1?: boolean
   withAllFacesTinyFaceDetector?: boolean
@@ -127,21 +121,6 @@ export type DescribeWithNetsOptions = {
   withTinyYolov2?: WithTinyYolov2Options
 }
 
-async function loadNetWeights(uri: string): Promise<Float32Array> {
-  return new Float32Array(await (await fetch(uri)).arrayBuffer())
-}
-
-async function initNet<TNet extends NeuralNetwork<any>>(
-  net: TNet,
-  uncompressedFilename: string | boolean,
-  isUnusedModel: boolean = false
-) {
-  const url = uncompressedFilename
-    ? await loadNetWeights(`base/weights_uncompressed/${uncompressedFilename}`)
-    : (isUnusedModel ? 'base/weights_unused' : 'base/weights')
-  await net.load(url)
-}
-
 export function describeWithNets(
   description: string,
   options: DescribeWithNetsOptions,