Or... +2. Open this [p5 web editor sketch with ml5.js added](https://editor.p5js.org/ml5/sketches/oYweRi2H7). +3. You can also copy and paste the cdn link to the ml5 library here: + + ``` + + ``` + +*** +#### Quickstart: Plain JavaScript + +Reference the [latest version](https://unpkg.com/ml5@0.4.0/dist/ml5.min.js) of ml5.js using a script tag in an HTML file as below: + + +In an **index.html** file, copy and paste the following and open up that file in your web browser. + +```HTML + + + +
+ + + +## Join Our Community {docsify-ignore} + +Coming soon + + +## Contribute to ml5.js {docsify-ignore} + +ml5 is an open source project that values all contributions. ml5 contributions often take the shape of workshops, design contributions, helping to answer people's questions on Github, flagging bugs in code, fixing bugs, adding new features, and more. + +If you'd like to contribute, you're welcome to browse through the issues in our [Github](https://github.com/ml5js/ml5-library/issues) or create a new issue. If you're still unsure of where to start, feel free to ping us at [@ml5js on twitter](https://twitter.com/ml5js), hello@ml5js.org + +## Support {docsify-ignore} + +ml5 is always on the look out for grants and funding to support the maintenance and development of the ml5 project (including our educational and community based initiatives). If you are an educational institution, grant funding organization, or otherwise interested in funding the ml5 community of students, researchers, artists, educators, designers, and developers, we'd love to hear from you. + +Feel free to reach out at hello@ml5js.org. + +## Acknowledgements {docsify-ignore} + +ml5.js is supported by the time and dedication of open source developers from all over the world. Funding and support is generously provided by a [Google Education grant](https://edu.google.com/why-google/our-commitment/?modal_active=none%2F) at [NYU's ITP/IMA program](https://itp.nyu.edu/). + +Many thanks [BrowserStack](https://www.browserstack.com/) for providing testing support. + + diff --git a/docs/_media/GitHub-Mark-120px-plus.png b/docs/_media/GitHub-Mark-120px-plus.png new file mode 100644 index 000000000..ea6ff545a Binary files /dev/null and b/docs/_media/GitHub-Mark-120px-plus.png differ diff --git a/docs/_media/reference__header-bodypix.png b/docs/_media/reference__header-bodypix.png new file mode 100644 index 000000000..2b9235d8f Binary files /dev/null and b/docs/_media/reference__header-bodypix.png differ diff --git a/docs/_media/reference__header-charrnn.png b/docs/_media/reference__header-charrnn.png new file mode 100644 index 000000000..a0b32703e Binary files /dev/null and b/docs/_media/reference__header-charrnn.png differ diff --git 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[StyleTransfer](/reference/style-transfer.md) + * [pix2pix](/reference/pix2pix.md) + * [CVAE](/reference/cvae.md) + * [DCGAN](/reference/dcgan.md) + * [SketchRNN](/reference/sketchrnn.md) + * [YOLO](/reference/yolo.md) + * **Sound** π» + * [SoundClassification](/reference/sound-classifier.md) + * [PitchDetection](/reference/pitch-detection.md) + * **Text** π + * [CharRNN](/reference/charrnn.md) + * [Sentiment](/reference/sentiment.md) + * [Word2Vec](/reference/word2vec.md) + * [SketchRNN](/reference/sketchrnn.md) + +* **Contributing** π + + + * [Contributor Notes](/styleguide/contributor-notes.md) + * [Maintenance Notes](/styleguide/maintenance-notes.md) + * Style Guide + * [Reference guidelines](/styleguide/reference-guidelines.md) + * [Development guidelines](/styleguide/development-guidelines.md) + * [Design guidelines](/styleguide/design-guidelines.md) \ No newline at end of file diff --git a/docs/faq.md b/docs/faq.md new file mode 100644 index 000000000..6b609099d --- /dev/null +++ b/docs/faq.md @@ -0,0 +1,31 @@ +# Frequently Asked Questions (FAQ) + +### Can I use ml5.js in the [p5 web editor](editor.p5js.org)? + +Mostly. + +A number of the ml5 sketches don't currently work in the p5 web editor due to some of the ways that the editor handles data files and some of the network communication regarding making requests to external data (e.g. the big model files that allow ml5.js to run things like image detection, etc). + +There are lots of developments in the p5 web editor as well as in ml5 to make sure these environments all play nicely together. If something doesn't work in the web editor, the best thing to do is to try and run things locally if possible. See [running a local web server tutorial](/docs/tutorials/local-web-server.md). + +Thanks! + +### Can I use ml5.js with node.js? + +No. Not at the moment. + +ml5.js uses TensorFlow.js which uses the browser's GPU to run all the calculations. As a result, all of the ml5.js functionality is based around using the browser GPU. We hope to have ml5.js run in node-js sometime in the near future (especially now that [node support for TensorFlow is a thing](https://www.tensorflow.org/js/guide/nodejs) but the current ml5 setup does not support node.js. We hope to support this in the future. + + +[For more discussion about node and ml5.js, visit this issue thread.](https://github.com/ml5js/ml5-library/issues/377) + + +### Can I contribute? + +Yes! Absolutely. We welcome all forms of socially and technically driven contributions. No contribution is too small. + +Please contact us at [@ml5js on twitter](https://twitter.com/ml5js), hello@ml5js.org, or [Github](https://github.com/ml5js/ml5-library/issues). + +### How can I contribute? + +Please refer to the contributor documentation [on Github](https://github.com/ml5js/ml5-library/blob/development/CONTRIBUTING.md). \ No newline at end of file diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 000000000..dfc973051 --- /dev/null +++ b/docs/index.html @@ -0,0 +1,144 @@ + + + + +
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++ +*"Autoencoders are neural networks that are capable of creating sparse representations of the input data and can therefore be used for image compression. There are denoising autoencoders that after learning these sparse representations, can be presented with noisy images. What is even better is a variant that is called the variational autoencoder that not only learns these sparse representations, but can also draw new images as well. We can, for instance, ask it to create new handwritten digits and we can actually expect the results to make sense!"* + + +## Quickstart + +```js +const cvae = ml5.CVAE('model/quick_draw/manifest.json', modelReady); + +function modelReady() { + // generate an image of an airplane + cvae.generate('airplane', gotImage); +} + +function gotImage(error, result) { + if (error) { + console.log(error); + return; + } + // log the result + console.log(result); +} +``` + + +## Usage + +### Initialize + +```js +const magic = ml5.CVAE(?model, ?callback) +``` + +#### Parameters +* **model**: REQUIRED. The url path to your model. Can be an absolute or relative path. +* **callback**: REQUIRED. A function to run once the model has been loaded. + + +### Properties + +*** +#### .ready +> *BOOLEAN*. Boolean value that specifies if the model has loaded. +*** + + +### Methods + + +*** +#### .generate(label, callback); +> Given a label, will generate an image. + +```js +cvae.generate(label, callback); +``` + +π₯ **Inputs** + +* **label**: REQUIRED. String. A label of the feature your want to generate. +* **callback**: REQUIRED. Function. A function to handle the results of ".generate()". Likely a function to do something with the generated image data. + +π€ **Outputs** + +* **Object**: Returns "raw", "blob", and "tensor". If p5.js is available, a "p5Image" will be returned as well. + +*** + + + + + + +## Examples + +**p5.js** +* [CVAE_QuickDraw](https://github.com/ml5js/ml5-examples/tree/development/p5js/CVAE/CVAE_QuickDraw) + +**p5 web editor** +* [CVAE_QuickDraw]() + +**plain javascript** +* [CVAE_QuickDraw](https://github.com/ml5js/ml5-examples/tree/development/javascript/CVAE/CVAE_QuickDraw) + + +## Demo + +No demos yet - contribute one today! + +## Tutorials + +No tutorials yet - contribute one today! + +## Acknowledgements + +**Contributors**: + * Wenhe Li & Dingsu (Derek) Wang + +**Credits**: + * Paper Reference | Website URL | Github Repo | Book reference | etc + + +## Source Code + +[/src/CVAE/](https://github.com/ml5js/ml5-library/tree/development/src/CVAE) diff --git a/docs/reference/dcgan.md b/docs/reference/dcgan.md new file mode 100644 index 000000000..033edfa80 --- /dev/null +++ b/docs/reference/dcgan.md @@ -0,0 +1,143 @@ +# DCGAN: Deep Convolutional Generative Adversarial Networks + + +
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++ +ml5.js is being developed to make machine learning more accessible to a wider audience. Along with supporting education and critical engagement with machine learning, the ml5 team is working actively to wrap exciting machine learning functionality in friendlier and easier-to-use way. The following example introduces you ml5.js through a classic application of machine learning: **image classification**. + +
+ +This example showcases how you can use a [pre-trained model](https://youtu.be/yNkAuWz5lnY?t=33) called [MobileNet](https://github.com/tensorflow/tfjs-models/tree/master/mobilenet) -- a machine learning model trained to recognize the content of certain images -- in ml5.js. The example aims to highlight a general pattern for how ml5.js projects are setup. + +
+ +ml5.js is growing every day, so be sure to see some of the other applications of ml5 in the [reference](/reference) section and their accompanying examples for the latest offerings. + +## Setup + +If you've arrived here, we assume you've checked out our [quickstart](/getting-started) page to get a simple ml5.js project set up. To get this to run, you'll need: + +> + π A text editor (e.g. [Atom](https://atom.io/), [VSCode](https://code.visualstudio.com/), [Sublimetext](https://www.sublimetext.com/)) +> + π» Your web browser: Chrome & Firefox preferred +> + πΌ An image to run your classification on + + +Your project directory should look something like this: + +``` +|_ /hello-ml5 + |_ π/images + |_ πΌ bird.png + |_ πindex.html + |_ πsketch.js +``` + +**Where**: + +* π**/hello-ml5**: is the root project folder + * π**/images**: is a folder that contains your image + * πΌ **bird.png**: is a .png image of a bird (it can also be something else!) + * π**index.html**: is an .html file that has your html markup and library references + * π**sketch.js**: is where you'll be writing your javascript + +## Demo + +This example is built with p5.js. You can also find the same example without p5.js [here](https://github.com/ml5js/ml5-examples/tree/release/javascript/ImageClassification). + +
+ + + + + + +## Code + +### Your index.html + +Here you can see that we read in the javascript libraries. This includes our ml5.js version as well as p5.js. You can copy and paste this into your **index.html** file or for good practice you can type it all out. Make sure to save the file and refresh your browser after saving. + +```html + + + + +
Image classification using MobileNet and p5.js
+ + + + +``` + + +### Your sketch.js + +Inside your **sketch.js** file you can type out (or copy and paste) the following code. Notice in this example we have a reference to "images/bird.png". You'll replace this with the name of your image. + +```javascript +// Initialize the Image Classifier method with MobileNet. A callback needs to be passed. +let classifier; + +// A variable to hold the image we want to classify +let img; + +function preload() { + classifier = ml5.imageClassifier('MobileNet'); + img = loadImage('images/bird.png'); +} + +function setup() { + createCanvas(400, 400); + classifier.classify(img, gotResult); + image(img, 0, 0); +} + +// A function to run when we get any errors and the results +function gotResult(error, results) { + // Display error in the console + if (error) { + console.error(error); + } else { + // The results are in an array ordered by confidence. + console.log(results); + createDiv('Label: ' + results[0].label); + createDiv('Confidence: ' + nf(results[0].confidence, 0, 2)); + } +} +``` + +## Our sketch.js explained in 4 steps + +### Step 1: Define your variables + +Here we define our variables that we will assign our classifier and image to. + +```javascript +// Initialize the Image Classifier method with MobileNet. A callback needs to be passed. +let classifier; + +// A variable to hold the image we want to classify +let img; +``` + +### Step 2: Load your imageClassifier and image + +Use p5's **preload()** function to load our imageClassifier model and our bird image before running the rest of our code. Since machine learning models can be large, it can take time to load. We use **preload()** in this case to make sure our imageClassifier and image are ready to go before we can apply the image classification in the next step. + +```javascript +function preload() { + classifier = ml5.imageClassifier('MobileNet'); + img = loadImage('images/bird.png'); +} +``` + +### Step 3: Setup, classify, and display + +In p5.js we use the **setup()** function for everything in our program that just runs once. In our program, we use the **setup()** function to: +1. create a canvas to render our image +2. call .classify() on our classifier to classify our image +3. render the image to the canvas + +You will notice that the **.classify()** function takes two parameters: 1. the image you want to classify, and 2. a callback function called **gotResult**. Let's look at what **gotResult** does. + +```javascript +function setup() { + createCanvas(400, 400); + classifier.classify(img, gotResult); + image(img, 0, 0); +} +``` + +### Step 4: Define the gotResult() callback function + +The **gotResult()** function takes two parameters: 1. error, and 2. results. These get passed along to **gotResult()** when the **.classify()** function finishes classifying the image. If there is an error, then an **error** will be logged. If our classifier manages to recognize the content of the image, then a **result** will be returned. + ++ +In the case of our program, we create a **div** that displays the **label** and the **confidence** of the content of the image that has been classified. The **[nf()](https://p5js.org/reference/#/p5/nf)** function is a p5 function that formats our number to a nicer string. + +```javascript +// A function to run when we get any errors and the results +function gotResult(error, results) { + // Display error in the console + if (error) { + console.error(error); + } else { + // The results are in an array ordered by confidence. + console.log(results); + createDiv('Label: ' + results[0].label); + createDiv('Confidence: ' + nf(results[0].confidence, 0, 2)); + } +} +``` + +## And voila! + +You've just made a simple machine learning powered program that: +1. takes an image, +2. classifies the content of that image, and +3. displays the results all in your web browser! + +Not all of our examples are structured exactly like this, but this provides a taste into how ml5.js is trying to make machine learning more approachable. Try using different images and seeing what kinds of things get returned. + +
+ +Some guiding questions you might start to think about are: + +1. When classifying an image with MobileNet, does the computer see people? If not, why do you think that is? +2. Do you notice that MobileNet is better at classifying some animals over others? Why do you think that is? + +## [Source](https://github.com/ml5js/ml5-examples/tree/master/p5js/ImageClassification/ImageClassification) + +- [ml5.js image classification on Github](https://github.com/ml5js/ml5-examples/tree/master/p5js/ImageClassification/ImageClassification) +- [ml5.js image classification on p5 web editor](https://editor.p5js.org/ml5/sketches/ImageClassification) \ No newline at end of file diff --git a/docs/tutorials/local-web-server.md b/docs/tutorials/local-web-server.md new file mode 100644 index 000000000..1c4c33732 --- /dev/null +++ b/docs/tutorials/local-web-server.md @@ -0,0 +1,41 @@ +# Running a local web server + +If you've looked at our [quickstart](/getting-started/) and [introduction to ml5](/getting-started/hello-ml5), you will have dipped your toes into the ml5 universe. + +
+ +A big part of the ml5 project is to create lots of examples as launching points for all your creative project ideas. You can find all of the ml5.js examples at out Github page: [ml5-examples](https://github.com/ml5js/ml5-examples). If you start to explore these examples you can see how the different ml5 functions are used to accomplish different outcomes. We try our best to keep the examples as simple as possible so you can easily start to build your ideas on top of them. + +You can check out all the examples running: +> - [on this massive list](https://github.com/ml5js/ml5-examples#examples-index) +> - [on the p5 web editor](https://editor.p5js.org/ml5/sketches) + +If you'd like to download the examples and run them locally, download the [ml5-examples github repo](https://github.com/ml5js/ml5-examples) and run a local web server at the root directory (if this sentence sounds foreign to you, see the How-To section below). The fastest way to do this is: + +
+ +with **python3** installed on your computer: +```bash +cd /path/to/ml5-examples +python -m http.server 8081 +``` + +
+ +with **python2.7** installed on your computer: + +```bash +cd /path/to/ml5-examples +python -m SimpleHTTPServer 8081 +``` + +Then open up your web browser to the url: **localhost:8081** + +
+ +Here we set the port number to **8081**, but you can change the port number to something else like **3000** (then: **localhost:3030**), **3001** (then: **localhost:3031**) for example. + + +## How to: Run examples and develop "locally" + +Coming soon! In the meantime, you can watch [CodingTrain - getting set up](https://www.youtube.com/watch?v=UCHzlUiDD10) for a nice intro on getting set up with writing code for the web. diff --git a/docs/tutorials/promises-and-callbacks.md b/docs/tutorials/promises-and-callbacks.md new file mode 100644 index 000000000..d7ef7567b --- /dev/null +++ b/docs/tutorials/promises-and-callbacks.md @@ -0,0 +1,53 @@ +# Promises and Callback support in ml5 + +ml5.js is heavily inspired by the syntax, patterns and style of the [p5.js](https://p5js.org/) library. However, there are several differences in how asynchronous operations are handled by ml5.js. ml5.js supports both error-first callbacks and Promises in all methods. + +## Using Callbacks + +In [p5.js](https://p5js.org/), [callbacks](https://developer.mozilla.org/en-US/docs/Glossary/Callback_function) are passed as arguments to functions that often perform some asynchronous operation. For example, [p5.js](https://p5js.org/) defines the [**loadJSON()**](https://p5js.org/reference/#/p5/loadJSON) function as the following: + +```javascript +loadJSON("http//example.com/data.json", function(results) { + // Do something with the results +}); +``` + +Notice that the results from the callback in [p5.js](https://p5js.org/) are given as the only argument to the function. There is no error argument in the callback. + +ml5.js, on the other hand, uses a pattern referred to as an error-first callback: + +> With this pattern, a callback function is passed to the method as an argument. When the operation either completes or an error is raised, the callback function is called with the Error object (if any) passed as the first argument. If no error was raised, the first argument will be passed as null. [Taken from the Node.js documentation](https://nodejs.org/api/errors.html#errors_error_first_callbacks) + +For example if you are using the **imageClassifier()** method, you will need to construct it in the following way: + +```javascript +// Pass a callback function to constructor +const classifier = ml5.imageClassifier('MobileNet', function(err, model) { + console.log('Model Loaded!'); +} + +// Make a prediction with the selected image and pass a callback function with two arguments +classifier.predict(image, function(err, results) { + // Check for errors. If no errors, then do something with the results +}); +``` + +Error first callbacks is a convention common to many JavaScript libraries that we have chosen to adopt. The language JavaScript itself does not enforce this pattern. Keep in mind that most ml5.js methods and functions are asynchronous (machine learning models can take significant amounts of time to process inputs and generate outputs!). You will need to use the error-first callback pattern if you want to use callbacks. + +##Β Using Promises + +ml5.js also supports [Promises](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Promise). If no callback is provided to any asynchronous function then a Promise is returned. + +With Promises, the image classification example can be used in the following way: + +```javascript +// No callback needs to be passed to use Promises. +ml5 + .imageClassifier("MobileNet") + .then(classifier => classifier.predict(image)) + .then(results => { + // Do something with the results + }); +``` + +For some video tutorials about Promises, you can find this [Coding Train playlist](https://www.youtube.com/playlist?list=PLRqwX-V7Uu6bKLPQvPRNNE65kBL62mVfx). There is also a [video tutorial about the ES6 arrow notation (**=>**)](https://youtu.be/mrYMzpbFz18). diff --git a/karma.conf.js b/karma.conf.js index ad4123f58..e7e918f95 100644 --- a/karma.conf.js +++ b/karma.conf.js @@ -1,9 +1,13 @@ + module.exports = (config) => { config.set({ + client: { + model: config.model + }, frameworks: ['jasmine'], files: [ 'src/index.js', - 'src/**/*_test.js', + `src/${config.model ? config.model : '**'}/*_test.js`, ], preprocessors: { 'src/index.js': ['webpack'], diff --git a/package-lock.json b/package-lock.json index 13c1cffd1..04628e48a 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1,9 +1,187 @@ { "name": "ml5", - "version": "0.3.0", + "version": "0.3.1", "lockfileVersion": 1, "requires": true, "dependencies": { + "@babel/code-frame": { + "version": "7.5.5", + "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.5.5.tgz", + "integrity": 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"version": "5.3.1", + "resolved": "https://registry.npmjs.org/camelcase/-/camelcase-5.3.1.tgz", + "integrity": "sha512-L28STB170nwWS63UjtlEOE3dldQApaJXZkOI1uMFfzf3rRuPegHaHesyee+YxQ+W6SvRDQV6UrdOdRiR153wJg==", + "dev": true + } } }, "yeast": { diff --git a/package.json b/package.json index 5bb304f4c..4cf791a3f 100644 --- a/package.json +++ b/package.json @@ -1,6 +1,6 @@ { "name": "ml5", - "version": "0.3.1", + "version": "0.4.0", "description": "A friendly machine learning library for the web.", "main": "dist/ml5.min.js", "directories": { @@ -13,9 +13,20 @@ "start": "webpack-dev-server --open --config webpack.dev.babel.js", "manual-test": "webpack-dev-server --open --config webpack.test.babel.js", "build": "webpack --config webpack.prod.babel.js", - "test": "./node_modules/karma/bin/karma start karma.conf.js", + "test": "./node_modules/karma/bin/karma start karma.conf.js ", "test:single": "./node_modules/karma/bin/karma start karma.conf.js --single-run", - "test-travis": "./scripts/test-travis.sh" + "test-travis": "./scripts/test-travis.sh", + "dev:docs": "docsify serve docs", + "update:packageVersion": "node ./scripts/updatePackageVersion.js", + "checkout:latest": "git checkout -b v$npm_package_version", + "release:prep": "npm-run-all --sequential update:packageVersion checkout:latest", + "update:readme": "node ./scripts/updateReadme.js", + "release:build": "npm install && npm run build", + "release:commitAndPush": "git add . && git commit -m 'bumps version and adds latest build' && git push origin v$npm_package_version", + "release:tag": "git tag v$npm_package_version && git push --tags", + "development:sync": "git checkout development && git fetch && git pull", + "release:sync": "git checkout release && git fetch && git pull", + "publish:npm": "npm publish" }, "repository": { "type": "git", @@ -39,8 +50,9 @@ "babel-polyfill": "6.26.0", "babel-preset-env": "1.6.1", "babel-register": "6.26.0", - "commitizen": "2.9.6", + "commitizen": "4.0.3", "cz-conventional-changelog": "2.1.0", + "docsify-cli": "^4.3.0", "eslint": "4.18.2", "eslint-config-airbnb-base": "12.1.0", "eslint-config-prettier": "^4.1.0", @@ -51,20 +63,20 @@ "html-webpack-plugin": "^3.0.7", "jasmine-core": "3.1.0", "karma": "2.0.0", - "karma-browserstack-launcher": "~1.3.0", + "karma-browserstack-launcher": "^1.5.1", "karma-chrome-launcher": "2.2.0", "karma-jasmine": "1.1.1", "karma-mocha-reporter": "^2.2.5", "karma-safari-launcher": "1.0.0", "karma-webpack": "3.0.0", - "npm-run-all": "4.1.2", + "npm-run-all": "^4.1.2", "regenerator-runtime": "0.11.1", "rimraf": "2.6.2", - "semantic-release": "15.0.2", + "semantic-release": "^15.13.26", "uglifyjs-webpack-plugin": "^1.2.5", "webpack": "4.1.1", "webpack-cli": "2.0.10", - "webpack-dev-server": "3.1.0", + "webpack-dev-server": "3.1.11", "webpack-merge": "^4.1.2" }, "config": { @@ -89,12 +101,14 @@ }, "dependencies": { "@magenta/sketch": "0.2.0", - "@tensorflow-models/knn-classifier": "1.0.0", - "@tensorflow-models/mobilenet": "1.0.0", - "@tensorflow-models/posenet": "1.0.0", - "@tensorflow-models/speech-commands": "0.3.8", - "@tensorflow-models/body-pix": "1.0.1", - "@tensorflow/tfjs": "1.1.2", - "events": "^3.0.0" + "@tensorflow-models/body-pix": "1.1.2", + "@tensorflow-models/knn-classifier": "1.2.1", + "@tensorflow-models/mobilenet": "2.0.3", + "@tensorflow-models/posenet": "2.1.3", + "@tensorflow-models/speech-commands": "0.3.9", + "@tensorflow/tfjs": "~1.2.1", + "@tensorflow/tfjs-vis": "^1.1.0", + "events": "^3.0.0", + "face-api.js": "0.20.1" } } diff --git a/scripts/updatePackageVersion.js b/scripts/updatePackageVersion.js new file mode 100644 index 000000000..d57151775 --- /dev/null +++ b/scripts/updatePackageVersion.js @@ -0,0 +1,23 @@ +const fs = require('fs'); +const newVersionNumber = process.env.newversion; + +function checkVersionGiven(){ + if(newVersionNumber === undefined){ + console.log('π₯π₯π₯submit the new version number π₯π₯π₯'); + process.exit(22); + } +} + +function updatePackageVersion(fpath){ + checkVersionGiven(); + let packageJson = fs.readFileSync(fpath); + packageJson = JSON.parse(packageJson); + packageJson.version = newVersionNumber; + + + fs.writeFileSync(fpath, JSON.stringify(packageJson, null, 2)); + +} +updatePackageVersion('./package.json') + +// module.exports = updatePackageVersion; \ No newline at end of file diff --git a/scripts/updateReadme.js b/scripts/updateReadme.js new file mode 100644 index 000000000..ec7f00634 --- /dev/null +++ b/scripts/updateReadme.js @@ -0,0 +1,48 @@ +const fs = require('fs'); + +function getReadme(fpath){ + + const readme = fs.readFileSync(fpath, 'utf8'); + + return readme; +} + +function readPackageJson(fpath){ + let output = fs.readFileSync(fpath); + output = JSON.parse(output); + return output; +} + +function makeNewVersionString(newVersionNumber){ + + const newVersionString = ` +
+ +* You can use the latest version (${newVersionNumber}) by adding it to the head section of your HTML document: + +**v${newVersionNumber}** + + + +
+ ` + + return newVersionString; +} + + +function make(){ + + const packageJson = readPackageJson('./package.json'); + const newVersionNumber = packageJson.version; + const newVersionString = makeNewVersionString(newVersionNumber); + + console.log(`---- updating README to version ${newVersionNumber} ----`) + + const readme = getReadme('./README.md'); + const newReadme = readme.replace(/([\s\S]*)<\/p data-id="latest-version">/g, newVersionString); + + fs.writeFileSync('./README.md', newReadme); + +} +make(); \ No newline at end of file diff --git a/src/BodyPix/index.js b/src/BodyPix/index.js index 62dd1665f..c231cfd7a 100644 --- a/src/BodyPix/index.js +++ b/src/BodyPix/index.js @@ -14,7 +14,7 @@ import * as tf from '@tensorflow/tfjs'; import * as bp from '@tensorflow-models/body-pix'; import callCallback from '../utils/callcallback'; -import * as p5Utils from '../utils/p5Utils'; +import p5Utils from '../utils/p5Utils'; const DEFAULTS = { "multiplier": 0.75, @@ -255,25 +255,19 @@ class BodyPix { imgToSegment = this.video; callback = optionsOrCallback; // clean the following conditional statement up! - } else if (optionsOrCallback instanceof HTMLImageElement) { - imgToSegment = optionsOrCallback; - } else if ( - typeof optionsOrCallback === 'object' && - optionsOrCallback.elt instanceof HTMLImageElement - ) { + } else if (optionsOrCallback instanceof HTMLImageElement + || optionsOrCallback instanceof HTMLCanvasElement + || optionsOrCallback instanceof HTMLVideoElement + || optionsOrCallback instanceof ImageData) { + imgToSegment = optionsOrCallback; + } else if (typeof optionsOrCallback === 'object' && (optionsOrCallback.elt instanceof HTMLImageElement + || optionsOrCallback.elt instanceof HTMLCanvasElement + || optionsOrCallback.elt instanceof ImageData)){ imgToSegment = optionsOrCallback.elt; // Handle p5.js image - } else if (optionsOrCallback instanceof HTMLCanvasElement) { - imgToSegment = optionsOrCallback; - } else if ( - typeof optionsOrCallback === 'object' && - optionsOrCallback.elt instanceof HTMLCanvasElement - ) { - imgToSegment = optionsOrCallback.elt; // Handle p5.js image - } else if ( - typeof optionsOrCallback === 'object' && - optionsOrCallback.canvas instanceof HTMLCanvasElement - ) { + } else if (typeof optionsOrCallback === 'object' && optionsOrCallback.canvas instanceof HTMLCanvasElement) { imgToSegment = optionsOrCallback.canvas; // Handle p5.js image + } else if (typeof optionsOrCallback === 'object' && optionsOrCallback.elt instanceof HTMLVideoElement) { + imgToSegment = optionsOrCallback.elt; // Handle p5.js image } else if (!(this.video instanceof HTMLVideoElement)) { // Handle unsupported input throw new Error( @@ -304,6 +298,7 @@ class BodyPix { * @return {Object} a result object with maskBackground, maskPerson, raw */ async segmentInternal(imgToSegment, segmentationOptions) { + await this.ready; await tf.nextFrame(); @@ -350,31 +345,25 @@ class BodyPix { let imgToSegment = this.video; let callback; let segmentationOptions = this.config; - + // Handle the image to predict if (typeof optionsOrCallback === 'function') { imgToSegment = this.video; callback = optionsOrCallback; // clean the following conditional statement up! - } else if (optionsOrCallback instanceof HTMLImageElement) { - imgToSegment = optionsOrCallback; - } else if ( - typeof optionsOrCallback === 'object' && - optionsOrCallback.elt instanceof HTMLImageElement - ) { - imgToSegment = optionsOrCallback.elt; // Handle p5.js image - } else if (optionsOrCallback instanceof HTMLCanvasElement) { - imgToSegment = optionsOrCallback; - } else if ( - typeof optionsOrCallback === 'object' && - optionsOrCallback.elt instanceof HTMLCanvasElement - ) { + } else if (optionsOrCallback instanceof HTMLImageElement + || optionsOrCallback instanceof HTMLCanvasElement + || optionsOrCallback instanceof HTMLVideoElement + || optionsOrCallback instanceof ImageData) { + imgToSegment = optionsOrCallback; + } else if (typeof optionsOrCallback === 'object' && (optionsOrCallback.elt instanceof HTMLImageElement + || optionsOrCallback.elt instanceof HTMLCanvasElement + || optionsOrCallback.elt instanceof ImageData)){ imgToSegment = optionsOrCallback.elt; // Handle p5.js image - } else if ( - typeof optionsOrCallback === 'object' && - optionsOrCallback.canvas instanceof HTMLCanvasElement - ) { + } else if (typeof optionsOrCallback === 'object' && optionsOrCallback.canvas instanceof HTMLCanvasElement) { imgToSegment = optionsOrCallback.canvas; // Handle p5.js image + } else if (typeof optionsOrCallback === 'object' && optionsOrCallback.elt instanceof HTMLVideoElement) { + imgToSegment = optionsOrCallback.elt; // Handle p5.js image } else if (!(this.video instanceof HTMLVideoElement)) { // Handle unsupported input throw new Error( diff --git a/src/BodyPix/index_test.js b/src/BodyPix/index_test.js index a3d8279b6..b1c767a31 100644 --- a/src/BodyPix/index_test.js +++ b/src/BodyPix/index_test.js @@ -31,6 +31,22 @@ describe('bodyPix', () => { return canvas; } + async function getImageData() { + const arr = new Uint8ClampedArray(40000); + + // Iterate through every pixel + for (let i = 0; i < arr.length; i += 4) { + arr[i + 0] = 0; // R value + arr[i + 1] = 190; // G value + arr[i + 2] = 0; // B value + arr[i + 3] = 255; // A value + } + + // Initialize a new ImageData object + const img = new ImageData(arr, 200); + return img; + } + beforeEach(async () => { jasmine.DEFAULT_TIMEOUT_INTERVAL = 5000; bp = await bodyPix(); @@ -42,6 +58,14 @@ describe('bodyPix', () => { expect(bp.config.segmentationThreshold).toBe(BODYPIX_DEFAULTS.segmentationThreshold); }); + it('segment takes ImageData', async () => { + const img = await getImageData(); + const results = await bp.segment(img); + // 200 * 50 == 10,000 * 4 == 40,000 the size of the array + expect(results.raw.width).toBe(200); + expect(results.raw.height).toBe(50); + }); + describe('segmentation', () => { it('Should segment an image of a Harriet Tubman with a width and height of 128', async () => { const img = await getImage(); diff --git a/src/CharRNN/index_test.js b/src/CharRNN/index_test.js index 04be17df6..573c64e2c 100644 --- a/src/CharRNN/index_test.js +++ b/src/CharRNN/index_test.js @@ -21,7 +21,7 @@ const RNN_DEFAULTS = { const RNN_OPTIONS = { seed: 'the meaning of pizza is: ', - length: 30, + length: 10, temperature: 0.7 } @@ -54,7 +54,7 @@ describe('charRnn', () => { it('generates content that follows the set options', async() => { const result = await rnn.generate(RNN_OPTIONS); - expect(result.sample.length).toBe(30); + expect(result.sample.length).toBe(RNN_OPTIONS.length); }); }); }); diff --git a/src/DCGAN/index.js b/src/DCGAN/index.js index a706523bc..7fe91adf1 100644 --- a/src/DCGAN/index.js +++ b/src/DCGAN/index.js @@ -10,7 +10,7 @@ This version is based on alantian's TensorFlow.js implementation: https://github import * as tf from '@tensorflow/tfjs'; import callCallback from '../utils/callcallback'; -import * as p5Utils from '../utils/p5Utils'; +import p5Utils from '../utils/p5Utils'; // Default pre-trained face model @@ -57,21 +57,33 @@ class DCGANBase { /** * Generates a new image * @param {function} callback - a callback function handle the results of generate + * @param {object} latentVector - an array containing the latent vector; otherwise use random vector * @return {object} a promise or the result of the callback function. */ - async generate(callback) { + async generate(callback, latentVector) { await this.ready; - return callCallback(this.generateInternal(), callback); + return callCallback(this.generateInternal(latentVector), callback); } /** * Computes what will become the image tensor * @param {number} latentDim - the number of latent dimensions to pass through + * @param {object} latentVector - an array containing the latent vector; otherwise use random vector * @return {object} a tensor */ - async compute(latentDim) { + async compute(latentDim, latentVector) { const y = tf.tidy(() => { - const z = tf.randomNormal([1, latentDim]); + let z; + if(Array.isArray(latentVector) === false) { + z = tf.randomNormal([1, latentDim]); + } + else { + const buffer = tf.buffer([1, latentDim]); + for(let count = 0; count < latentDim; count+=1) { + buffer.set(latentVector[count], 0, count); + } + z = buffer.toTensor(); + } // TBD: should model be a parameter to compute or is it ok to reference this.model here? const yDim = this.model.predict(z).squeeze().transpose([1, 2, 0]).div(tf.scalar(2)).add(tf.scalar(0.5)); return yDim; @@ -82,13 +94,14 @@ class DCGANBase { /** * Takes the tensor from compute() and returns an object of the generate image data + * @param {object} latentVector - an array containing the latent vector; otherwise use random vector * @return {object} includes blob, raw, and tensor. if P5 exists, then a p5Image */ - async generateInternal() { + async generateInternal(latentVector) { const { modelLatentDim } = this.modelInfo; - const imageTensor = await this.compute(modelLatentDim); + const imageTensor = await this.compute(modelLatentDim, latentVector); // get the raw data from tensor const raw = await tf.browser.toPixels(imageTensor); @@ -126,7 +139,6 @@ class DCGANBase { } const DCGAN = (modelPath, cb) => { - if (typeof modelPath !== 'string') { throw new Error(`Please specify a path to a "manifest.json" file: \n "models/face/manifest.json" \n\n diff --git a/src/FaceApi/index.js b/src/FaceApi/index.js new file mode 100644 index 000000000..4294eb50c --- /dev/null +++ b/src/FaceApi/index.js @@ -0,0 +1,435 @@ +// Copyright (c) 2019 ml5 +// +// This software is released under the MIT License. +// https://opensource.org/licenses/MIT + +/* eslint prefer-destructuring: ["error", {AssignmentExpression: {array: false}}] */ +/* eslint no-await-in-loop: "off" */ + +/* + * FaceApi: real-time face recognition, and landmark detection + * Ported and integrated from all the hard work by: https://github.com/justadudewhohacks/face-api.js?files=1 + */ + +import * as tf from '@tensorflow/tfjs'; +import * as faceapi from 'face-api.js'; +import callCallback from '../utils/callcallback'; + +const DEFAULTS = { + withLandmarks: true, + withDescriptors: true, + minConfidence: 0.5, + MODEL_URLS: { + Mobilenetv1Model: 'https://raw.githubusercontent.com/ml5js/ml5-data-and-models/face-api/models/faceapi/ssd_mobilenetv1_model-weights_manifest.json', + FaceLandmarkModel: 'https://raw.githubusercontent.com/ml5js/ml5-data-and-models/face-api/models/faceapi/face_landmark_68_model-weights_manifest.json', + FaceLandmark68TinyNet: 'https://raw.githubusercontent.com/ml5js/ml5-data-and-models/face-api/models/faceapi/face_landmark_68_tiny_model-weights_manifest.json', + FaceRecognitionModel: 'https://raw.githubusercontent.com/ml5js/ml5-data-and-models/face-api/models/faceapi/face_recognition_model-weights_manifest.json', + } +} + +class FaceApiBase { + /** + * Create FaceApi. + * @param {HTMLVideoElement} video - An HTMLVideoElement. + * @param {object} options - An object with options. + * @param {function} callback - A callback to be called when the model is ready. + */ + constructor(video, options, callback) { + this.video = video; + this.model = null; + this.modelReady = false; + this.config = { + minConfidence: this.checkUndefined(options.minConfidence, DEFAULTS.minConfidence), + withLandmarks: this.checkUndefined(options.withLandmarks, DEFAULTS.withLandmarks), + withDescriptors: this.checkUndefined(options.withDescriptors, DEFAULTS.withDescriptors), + MODEL_URLS: { + Mobilenetv1Model: this.checkUndefined(options.Mobilenetv1Model, DEFAULTS.MODEL_URLS.Mobilenetv1Model), + FaceLandmarkModel: this.checkUndefined(options.FaceLandmarkModel, DEFAULTS.MODEL_URLS.FaceLandmarkModel), + FaceLandmark68TinyNet: this.checkUndefined(options.FaceLandmark68TinyNet, DEFAULTS.MODEL_URLS.FaceLandmark68TinyNet), + FaceRecognitionModel: this.checkUndefined(options.FaceRecognitionModel, DEFAULTS.MODEL_URLS.FaceRecognitionModel), + } + } + + this.ready = callCallback(this.loadModel(), callback); + } + + /** + * Load the model and set it to this.model + * @return {this} the BodyPix model. + */ + async loadModel() { + const modelOptions = [ + "Mobilenetv1Model", + "FaceLandmarkModel", + "FaceLandmark68TinyNet", + "FaceRecognitionModel", + ]; + + Object.keys(this.config.MODEL_URLS).forEach(item => { + if (modelOptions.includes(item)) { + this.config.MODEL_URLS[item] = this.getModelPath(this.config.MODEL_URLS[item]); + } + }); + + const { + Mobilenetv1Model, + FaceLandmarkModel, + FaceRecognitionModel, + } = this.config.MODEL_URLS; + + this.model = faceapi; + + const SsdMobilenetv1Options = this.model.SsdMobilenetv1Options({ minConfidence: this.minConfidence }) + await this.model.loadSsdMobilenetv1Model(Mobilenetv1Model, SsdMobilenetv1Options) + await this.model.loadFaceLandmarkModel(FaceLandmarkModel) + // await this.model.loadFaceLandmarkTinyModel(FaceLandmark68TinyNet) + await this.model.loadFaceRecognitionModel(FaceRecognitionModel) + + this.modelReady = true; + return this; + } + + + /** + * .detect() - classifies multiple features by default + * @param {*} optionsOrCallback + * @param {*} configOrCallback + * @param {*} cb + */ + async detect(optionsOrCallback, configOrCallback, cb) { + let imgToClassify = this.video; + let callback; + let faceApiOptions = this.config; + + // Handle the image to predict + if (typeof optionsOrCallback === 'function') { + imgToClassify = this.video; + callback = optionsOrCallback; + // clean the following conditional statement up! + } else if (optionsOrCallback instanceof HTMLImageElement + || optionsOrCallback instanceof HTMLCanvasElement + || optionsOrCallback instanceof ImageData) { + imgToClassify = optionsOrCallback; + } else if (typeof optionsOrCallback === 'object' && (optionsOrCallback.elt instanceof HTMLImageElement + || optionsOrCallback.elt instanceof HTMLCanvasElement + || optionsOrCallback.elt instanceof ImageData)){ + imgToClassify = optionsOrCallback.elt; // Handle p5.js image + } else if (typeof optionsOrCallback === 'object' && optionsOrCallback.canvas instanceof HTMLCanvasElement) { + imgToClassify = optionsOrCallback.canvas; // Handle p5.js image + } else if (!(this.video instanceof HTMLVideoElement)) { + // Handle unsupported input + throw new Error( + 'No input image provided. If you want to classify a video, pass the video element in the constructor. ', + ); + } + + if (typeof configOrCallback === 'object') { + faceApiOptions = configOrCallback; + } else if (typeof configOrCallback === 'function') { + callback = configOrCallback; + } + + if (typeof cb === 'function') { + callback = cb; + } + + return callCallback(this.detectInternal(imgToClassify, faceApiOptions), callback); + } + + /** + * Detects multiple internal function + * @param {HTMLImageElement || HTMLVideoElement} imgToClassify + * @param {Object} faceApiOptions + */ + async detectInternal(imgToClassify, faceApiOptions) { + await this.ready; + await tf.nextFrame(); + + if (this.video && this.video.readyState === 0) { + await new Promise(resolve => { + this.video.onloadeddata = () => resolve(); + }); + } + + // sets the return options if any are passed in during .detect() or .detectSingle() + this.config = this.setReturnOptions(faceApiOptions); + + const { + withLandmarks, + withDescriptors, + } = this.config + + let result; + + if (withLandmarks) { + if (withDescriptors) { + result = await this.model.detectAllFaces(imgToClassify).withFaceLandmarks().withFaceDescriptors(); + } else { + result = await this.model.detectAllFaces(imgToClassify).withFaceLandmarks() + } + + } else if (!withLandmarks) { + result = await this.model.detectAllFaces(imgToClassify) + } else { + result = await this.model.detectAllFaces(imgToClassify).withFaceLandmarks().withFaceDescriptors(); + } + + + // always resize the results to the input image size + result = this.resizeResults(result, imgToClassify.width, imgToClassify.height) + // assign the {parts} object after resizing + result = this.landmarkParts(result); + + return result + } + + /** + * .detecSinglet() - classifies a single feature with higher accuracy + * @param {*} optionsOrCallback + * @param {*} configOrCallback + * @param {*} cb + */ + async detectSingle(optionsOrCallback, configOrCallback, cb) { + let imgToClassify = this.video; + let callback; + let faceApiOptions = this.config; + + // Handle the image to predict + if (typeof optionsOrCallback === 'function') { + imgToClassify = this.video; + callback = optionsOrCallback; + // clean the following conditional statement up! + } else if (optionsOrCallback instanceof HTMLImageElement + || optionsOrCallback instanceof HTMLCanvasElement + || optionsOrCallback instanceof ImageData) { + imgToClassify = optionsOrCallback; + } else if (typeof optionsOrCallback === 'object' && (optionsOrCallback.elt instanceof HTMLImageElement + || optionsOrCallback.elt instanceof HTMLCanvasElement + || optionsOrCallback.elt instanceof ImageData)){ + imgToClassify = optionsOrCallback.elt; // Handle p5.js image + } else if (typeof optionsOrCallback === 'object' && optionsOrCallback.canvas instanceof HTMLCanvasElement) { + imgToClassify = optionsOrCallback.canvas; // Handle p5.js image + } else if (!(this.video instanceof HTMLVideoElement)) { + // Handle unsupported input + throw new Error( + 'No input image provided. If you want to classify a video, pass the video element in the constructor. ', + ); + } + + if (typeof configOrCallback === 'object') { + faceApiOptions = configOrCallback; + } else if (typeof configOrCallback === 'function') { + callback = configOrCallback; + } + + if (typeof cb === 'function') { + callback = cb; + } + + return callCallback(this.detectSingleInternal(imgToClassify, faceApiOptions), callback); + } + + /** + * Detects only a single feature + * @param {HTMLImageElement || HTMLVideoElement} imgToClassify + * @param {Object} faceApiOptions + */ + async detectSingleInternal(imgToClassify, faceApiOptions) { + await this.ready; + await tf.nextFrame(); + + if (this.video && this.video.readyState === 0) { + await new Promise(resolve => { + this.video.onloadeddata = () => resolve(); + }); + } + + // sets the return options if any are passed in during .detect() or .detectSingle() + this.config = this.setReturnOptions(faceApiOptions); + + const { + withLandmarks, + withDescriptors + } = this.config + + let result; + if (withLandmarks) { + if (withDescriptors) { + result = await this.model.detectSingleFace(imgToClassify).withFaceLandmarks().withFaceDescriptor(); + } else { + result = await this.model.detectSingleFace(imgToClassify).withFaceLandmarks() + } + + } else if (!withLandmarks) { + result = await this.model.detectSingleFace(imgToClassify) + } else { + result = await this.model.detectSingleFace(imgToClassify).withFaceLandmarks().withFaceDescriptor(); + } + + // always resize the results to the input image size + result = this.resizeResults(result, imgToClassify.width, imgToClassify.height) + + // assign the {parts} object after resizing + result = this.landmarkParts(result); + + return result + } + + /** + * Check if the given _param is undefined, otherwise return the _default + * @param {*} _param + * @param {*} _default + */ + checkUndefined(_param, _default) { + return _param !== undefined ? _param : _default; + } + + /** + * Checks if the given string is an absolute or relative path and returns + * the path to the modelJson + * @param {String} absoluteOrRelativeUrl + */ + getModelPath(absoluteOrRelativeUrl) { + const modelJsonPath = this.isAbsoluteURL(absoluteOrRelativeUrl) ? absoluteOrRelativeUrl : window.location.pathname + absoluteOrRelativeUrl + return modelJsonPath; + } + + /** + * Sets the return options for .detect() or .detectSingle() in case any are given + * @param {Object} faceApiOptions + */ + setReturnOptions(faceApiOptions) { + const output = Object.assign({}, this.config); + const options = ["withLandmarks", "withDescriptors"]; + + options.forEach(prop => { + if (faceApiOptions[prop] !== undefined) { + this.config[prop] = faceApiOptions[prop] + } else { + output[prop] = this.config[prop]; + } + }) + + return output; + } + + /** + * Resize results to size of input image + * @param {*} str + */ + resizeResults(detections, width, height) { + if (width === undefined || height === undefined) { + throw new Error('width and height must be defined') + } + return this.model.resizeResults(detections, { + "width": width, + "height": height + }) + } + + /* eslint class-methods-use-this: "off" */ + isAbsoluteURL(str) { + const pattern = new RegExp('^(?:[a-z]+:)?//', 'i'); + return !!pattern.test(str); + } + + /** + * get parts from landmarks + * @param {*} result + */ + landmarkParts(result) { + let output; + // multiple detections is an array + if (Array.isArray(result) === true) { + output = result.map(item => { + // if landmarks exist return parts + const newItem = Object.assign({}, item); + if (newItem.landmarks) { + const { + landmarks + } = newItem; + newItem.parts = { + mouth: landmarks.getMouth(), + nose: landmarks.getNose(), + leftEye: landmarks.getLeftEye(), + leftEyeBrow: landmarks.getLeftEyeBrow(), + rightEye: landmarks.getRightEye(), + rightEyeBrow: landmarks.getRightEyeBrow(), + jawOutline: landmarks.getJawOutline(), + } + } else { + newItem.parts = { + mouth: [], + nose: [], + leftEye: [], + leftEyeBrow: [], + rightEye: [], + rightEyeBrow: [], + jawOutline: [], + } + } + return newItem; + }) + // single detection is an object + } else { + output = Object.assign({}, result); + if (output.landmarks) { + const { + landmarks + } = result; + output.parts = { + mouth: landmarks.getMouth(), + nose: landmarks.getNose(), + leftEye: landmarks.getLeftEye(), + leftEyeBrow: landmarks.getLeftEyeBrow(), + rightEye: landmarks.getRightEye(), + rightEyeBrow: landmarks.getRightEyeBrow() + } + } else { + output.parts = { + mouth: [], + nose: [], + leftEye: [], + leftEyeBrow: [], + rightEye: [], + rightEyeBrow: [] + } + } + } + + return output; + } + +} + +const faceApi = (videoOrOptionsOrCallback, optionsOrCallback, cb) => { + let video; + let options = {}; + let callback = cb; + + if (videoOrOptionsOrCallback instanceof HTMLVideoElement) { + video = videoOrOptionsOrCallback; + } else if ( + typeof videoOrOptionsOrCallback === 'object' && + videoOrOptionsOrCallback.elt instanceof HTMLVideoElement + ) { + video = videoOrOptionsOrCallback.elt; // Handle a p5.js video element + } else if (typeof videoOrOptionsOrCallback === 'object') { + options = videoOrOptionsOrCallback; + } else if (typeof videoOrOptionsOrCallback === 'function') { + callback = videoOrOptionsOrCallback; + } + + if (typeof optionsOrCallback === 'object') { + + options = optionsOrCallback; + console.log(options) + } else if (typeof optionsOrCallback === 'function') { + callback = optionsOrCallback; + } + + const instance = new FaceApiBase(video, options, callback); + return callback ? instance : instance.ready; +} + +export default faceApi; \ No newline at end of file diff --git a/src/FaceApi/index_test.js b/src/FaceApi/index_test.js new file mode 100644 index 000000000..35b965733 --- /dev/null +++ b/src/FaceApi/index_test.js @@ -0,0 +1,63 @@ +// Copyright (c) 2018 ml5 +// +// This software is released under the MIT License. +// https://opensource.org/licenses/MIT + +const { + faceApi +} = ml5; + +const FACEAPI_DEFAULTS = { + withLandmarks: true, + withDescriptors: true, + MODEL_URLS: { + Mobilenetv1Model: 'https://raw.githubusercontent.com/ml5js/ml5-data-and-models/face-api/models/faceapi/ssd_mobilenetv1_model-weights_manifest.json', + FaceLandmarkModel: 'https://raw.githubusercontent.com/ml5js/ml5-data-and-models/face-api/models/faceapi/face_landmark_68_model-weights_manifest.json', + FaceLandmark68TinyNet: 'https://raw.githubusercontent.com/ml5js/ml5-data-and-models/face-api/models/faceapi/face_landmark_68_tiny_model-weights_manifest.json', + FaceRecognitionModel: 'https://raw.githubusercontent.com/ml5js/ml5-data-and-models/face-api/models/faceapi/face_recognition_model-weights_manifest.json', + } +} + + +describe('faceApi', () => { + let faceapi; + + async function getImage() { + const img = new Image(); + img.crossOrigin = true; + img.src = 'https://raw.githubusercontent.com/ml5js/ml5-examples/development/p5js/FaceApi/FaceApi_Image_Landmarks/assets/frida.jpg'; + await new Promise((resolve) => { + img.onload = resolve; + }); + return img; + } + + // async function getCanvas() { + // const img = await getImage(); + // const canvas = document.createElement('canvas'); + // canvas.width = img.width; + // canvas.height = img.height; + // canvas.getContext('2d').drawImage(img, 0, 0); + // return canvas; + // } + + beforeEach(async () => { + jasmine.DEFAULT_TIMEOUT_INTERVAL = 15000; + faceapi = await faceApi(); + }); + + it('Should create faceApi with all the defaults', async () => { + expect(faceapi.config.withLandmarks).toBe(FACEAPI_DEFAULTS.withLandmarks); + expect(faceapi.config.withDescriptors).toBe(FACEAPI_DEFAULTS.withDescriptors); + }); + + describe('landmarks', () => { + it('Should get landmarks for Frida', async () => { + const img = await getImage(); + await faceapi.detectSingle(img) + .then(results => { + expect(results.landmarks).toEqual(jasmine.any(Object)); + }) + }); + }); +}); \ No newline at end of file diff --git a/src/FeatureExtractor/Mobilenet.js b/src/FeatureExtractor/Mobilenet.js index 99d1372c7..cfd392897 100644 --- a/src/FeatureExtractor/Mobilenet.js +++ b/src/FeatureExtractor/Mobilenet.js @@ -336,10 +336,16 @@ class Mobilenet { let imgToPredict; let callback; - if (inputOrCallback instanceof HTMLImageElement || inputOrCallback instanceof HTMLVideoElement || inputOrCallback instanceof HTMLCanvasElement) { + if (inputOrCallback instanceof HTMLImageElement + || inputOrCallback instanceof HTMLVideoElement + || inputOrCallback instanceof HTMLCanvasElement + || inputOrCallback instanceof ImageData) { imgToPredict = inputOrCallback; } else if (typeof inputOrCallback === 'object' && - (inputOrCallback.elt instanceof HTMLImageElement || inputOrCallback.elt instanceof HTMLVideoElement || inputOrCallback.elt instanceof HTMLCanvasElement)) { + (inputOrCallback.elt instanceof HTMLImageElement + || inputOrCallback.elt instanceof HTMLVideoElement + || inputOrCallback.elt instanceof HTMLCanvasElement + || inputOrCallback.elt instanceof ImageData)) { imgToPredict = inputOrCallback.elt; // p5.js image element } else if (typeof inputOrCallback === 'function') { imgToPredict = this.video; @@ -385,10 +391,16 @@ class Mobilenet { async predict(inputOrCallback, cb) { let imgToPredict; let callback; - if (inputOrCallback instanceof HTMLImageElement || inputOrCallback instanceof HTMLVideoElement || inputOrCallback instanceof HTMLCanvasElement) { + if (inputOrCallback instanceof HTMLImageElement + || inputOrCallback instanceof HTMLVideoElement + || inputOrCallback instanceof HTMLCanvasElement + || inputOrCallback instanceof ImageData) { imgToPredict = inputOrCallback; } else if (typeof inputOrCallback === 'object' && - (inputOrCallback.elt instanceof HTMLImageElement || inputOrCallback.elt instanceof HTMLVideoElement || inputOrCallback.elt instanceof HTMLCanvasElement)) { + (inputOrCallback.elt instanceof HTMLImageElement + || inputOrCallback.elt instanceof HTMLVideoElement + || inputOrCallback.elt instanceof HTMLCanvasElement + || inputOrCallback.elt instanceof ImageData)) { imgToPredict = inputOrCallback.elt; // p5.js image element } else if (typeof inputOrCallback === 'function') { imgToPredict = this.video; @@ -520,9 +532,15 @@ class Mobilenet { infer(input, endpoint) { let imgToPredict; let endpointToPredict; - if (input instanceof HTMLImageElement || input instanceof HTMLVideoElement || input instanceof HTMLCanvasElement || input instanceof ImageData) { + if (input instanceof HTMLImageElement + || input instanceof HTMLVideoElement + || input instanceof HTMLCanvasElement + || input instanceof ImageData) { imgToPredict = input; - } else if (typeof input === 'object' && (input.elt instanceof HTMLImageElement || input.elt instanceof HTMLVideoElement || input.elt instanceof HTMLCanvasElement)) { + } else if (typeof input === 'object' && (input.elt instanceof HTMLImageElement + || input.elt instanceof HTMLVideoElement + || input.elt instanceof HTMLCanvasElement + || input.elt instanceof ImageData)) { imgToPredict = input.elt; // p5.js image/canvas/video element } else { throw new Error('No input image found.'); diff --git a/src/ImageClassifier/darknet.js b/src/ImageClassifier/darknet.js index ceda257b2..b07e2ea30 100644 --- a/src/ImageClassifier/darknet.js +++ b/src/ImageClassifier/darknet.js @@ -17,9 +17,15 @@ const DEFAULTS = { function preProcess(img, size) { let image; if (!(img instanceof tf.Tensor)) { - if (img instanceof HTMLImageElement || img instanceof HTMLVideoElement || img instanceof HTMLCanvasElement) { + if (img instanceof HTMLImageElement + || img instanceof HTMLVideoElement + || img instanceof HTMLCanvasElement + || img instanceof ImageData) { image = tf.browser.fromPixels(img); - } else if (typeof img === 'object' && (img.elt instanceof HTMLImageElement || img.elt instanceof HTMLVideoElement || img.elt instanceof HTMLCanvasElement)) { + } else if (typeof img === 'object' && (img.elt instanceof HTMLImageElement + || img.elt instanceof HTMLVideoElement + || img.elt instanceof HTMLCanvasElement + || img.elt instanceof ImageData)) { image = tf.browser.fromPixels(img.elt); // Handle p5.js image and video. } } else { diff --git a/src/ImageClassifier/doodlenet.js b/src/ImageClassifier/doodlenet.js index 3bfc3e922..924676b1f 100644 --- a/src/ImageClassifier/doodlenet.js +++ b/src/ImageClassifier/doodlenet.js @@ -15,9 +15,15 @@ const DEFAULTS = { function preProcess(img, size) { let image; if (!(img instanceof tf.Tensor)) { - if (img instanceof HTMLImageElement || img instanceof HTMLVideoElement || img instanceof HTMLCanvasElement) { + if (img instanceof HTMLImageElement + || img instanceof HTMLVideoElement + || img instanceof HTMLCanvasElement + || img instanceof ImageData) { image = tf.browser.fromPixels(img); - } else if (typeof img === 'object' && (img.elt instanceof HTMLImageElement || img.elt instanceof HTMLVideoElement || img.elt instanceof HTMLCanvasElement)) { + } else if (typeof img === 'object' && (img.elt instanceof HTMLImageElement + || img.elt instanceof HTMLVideoElement + || img.elt instanceof HTMLCanvasElement + || img.elt instanceof ImageData)) { image = tf.browser.fromPixels(img.elt); // Handle p5.js image, video and canvas. } } else { @@ -28,8 +34,9 @@ function preProcess(img, size) { if (normalized.shape[0] !== size || normalized.shape[1] !== size) { resized = tf.image.resizeBilinear(normalized, [size, size]); } - const [r, g, b] = tf.split(resized, 3, 2); - const gray = (r.add(g).add(b)).div(tf.scalar(3)).round(); // Get average r,g,b color value and round to 0 or 1 + + const [r, g, b] = tf.split(resized, 3, 3); + const gray = (r.add(g).add(b)).div(tf.scalar(3)).floor(); // Get average r,g,b color value and round to 0 or 1 const batched = gray.reshape([1, size, size, 1]); return batched; } diff --git a/src/ImageClassifier/index.js b/src/ImageClassifier/index.js index 708c6548f..1359ca68b 100644 --- a/src/ImageClassifier/index.js +++ b/src/ImageClassifier/index.js @@ -16,7 +16,7 @@ import { imgToTensor } from '../utils/imageUtilities'; const DEFAULTS = { mobilenet: { - version: 1, + version: 2, alpha: 1.0, topk: 3, }, @@ -27,7 +27,7 @@ const MODEL_OPTIONS = ['mobilenet', 'darknet', 'darknet-tiny', 'doodlenet']; class ImageClassifier { /** * Create an ImageClassifier. - * @param {modelNameOrUrl} modelNameOrUrl - The name or the URL of the model to use. Current model name options + * @param {modelNameOrUrl} modelNameOrUrl - The name or the URL of the model to use. Current model name options * are: 'mobilenet', 'darknet', 'darknet-tiny', and 'doodlenet'. * @param {HTMLVideoElement} video - An HTMLVideoElement. * @param {object} options - An object with options. @@ -76,7 +76,8 @@ class ImageClassifier { */ async loadModel(modelUrl) { if (modelUrl) this.model = await this.loadModelFrom(modelUrl); - else this.model = await this.modelToUse.load(this.version, this.alpha); + else this.model = await this.modelToUse.load({version: this.version, alpha: this.alpha}); + return this; } @@ -87,16 +88,44 @@ class ImageClassifier { if (r.ml5Specs) { this.mapStringToIndex = r.ml5Specs.mapStringToIndex; } + }) + // When loading model generated by Teachable Machine 2.0, the r.ml5Specs is missing, + // which is causing imageClassifier failing to display lables. + // In this case, labels are stored in path/./metadata.json + // Therefore, I'm fetching the metadata and feeding the labels into this.mapStringToIndex + // by Yang Yang, yy2473@nyu.edu, Oct 2, 2019 + .then(()=>{ + if (this.mapStringToIndex.length === 0) { + const split = path.split("/"); + const prefix = split.slice(0, split.length - 1).join("/"); + const metadataUrl = `${prefix}/metadata.json`; + fetch(metadataUrl) + .then((res) => { + if (!res.ok) { + console.log("Tried to fetch metadata.json, but it seems to be missing."); + throw Error(res.statusText); + } + return res; + }) + .then(metadataJson => metadataJson.json()) + .then((metadataJson)=> { + if (metadataJson.labels){ + this.mapStringToIndex = metadataJson.labels; + } + }) + .catch(() => console.log("Error when loading metadata.json")); + } }); + // end of the Oct 2, 2019 fix this.model = await tf.loadLayersModel(path); return this.model; } /** * Classifies the given input and returns an object with labels and confidence - * @param {HTMLImageElement | HTMLCanvasElement | HTMLVideoElement} imgToPredict - + * @param {HTMLImageElement | HTMLCanvasElement | HTMLVideoElement} imgToPredict - * takes an image to run the classification on. - * @param {number} numberOfClasses - a number of labels to return for the image + * @param {number} numberOfClasses - a number of labels to return for the image * classification. * @return {object} an object with {label, confidence}. */ @@ -119,11 +148,13 @@ class ImageClassifier { }); } + // Process the images + const imageResize = [IMAGE_SIZE, IMAGE_SIZE]; + const processedImg = imgToTensor(imgToPredict, imageResize); + if (this.modelUrl) { await tf.nextFrame(); const predictedClasses = tf.tidy(() => { - const imageResize = [IMAGE_SIZE, IMAGE_SIZE]; - const processedImg = imgToTensor(imgToPredict, imageResize); const predictions = this.model.predict(processedImg); return Array.from(predictions.as1D().dataSync()); }); @@ -136,16 +167,17 @@ class ImageClassifier { }).sort((a, b) => b.confidence - a.confidence); return results; } + return this.model - .classify(imgToPredict, numberOfClasses) + .classify(processedImg, numberOfClasses) .then(classes => classes.map(c => ({ label: c.className, confidence: c.probability }))); } /** * Classifies the given input and takes a callback to handle the results - * @param {HTMLImageElement | HTMLCanvasElement | object | function | number} inputNumOrCallback - + * @param {HTMLImageElement | HTMLCanvasElement | object | function | number} inputNumOrCallback - * takes any of the following params - * @param {HTMLImageElement | HTMLCanvasElement | object | function | number} numOrCallback - + * @param {HTMLImageElement | HTMLCanvasElement | object | function | number} numOrCallback - * takes any of the following params * @param {function} cb - a callback function that handles the results of the function. * @return {function} a promise or the results of a given callback, cb. @@ -162,32 +194,21 @@ class ImageClassifier { } else if (typeof inputNumOrCallback === 'number') { imgToPredict = this.video; numberOfClasses = inputNumOrCallback; - } else if (inputNumOrCallback instanceof HTMLImageElement) { - imgToPredict = inputNumOrCallback; - } else if ( - typeof inputNumOrCallback === 'object' && - inputNumOrCallback.elt instanceof HTMLImageElement - ) { - imgToPredict = inputNumOrCallback.elt; // Handle p5.js image - } else if (inputNumOrCallback instanceof HTMLCanvasElement) { + } else if (inputNumOrCallback instanceof HTMLVideoElement + || inputNumOrCallback instanceof HTMLImageElement + || inputNumOrCallback instanceof HTMLCanvasElement + || inputNumOrCallback instanceof ImageData) { imgToPredict = inputNumOrCallback; } else if ( typeof inputNumOrCallback === 'object' && - inputNumOrCallback.elt instanceof HTMLCanvasElement - ) { - imgToPredict = inputNumOrCallback.elt; // Handle p5.js image - } else if ( - typeof inputNumOrCallback === 'object' && - inputNumOrCallback.canvas instanceof HTMLCanvasElement + (inputNumOrCallback.elt instanceof HTMLVideoElement + || inputNumOrCallback.elt instanceof HTMLImageElement + || inputNumOrCallback.elt instanceof HTMLCanvasElement + || inputNumOrCallback.elt instanceof ImageData) ) { + imgToPredict = inputNumOrCallback.elt; // Handle p5.js image + } else if (typeof inputNumOrCallback === 'object' && inputNumOrCallback.canvas instanceof HTMLCanvasElement) { imgToPredict = inputNumOrCallback.canvas; // Handle p5.js image - } else if (inputNumOrCallback instanceof HTMLVideoElement) { - imgToPredict = inputNumOrCallback; - } else if ( - typeof inputNumOrCallback === 'object' && - inputNumOrCallback.elt instanceof HTMLVideoElement - ) { - imgToPredict = inputNumOrCallback.elt; // Handle p5.js video } else if (!(this.video instanceof HTMLVideoElement)) { // Handle unsupported input throw new Error( diff --git a/src/ImageClassifier/index_test.js b/src/ImageClassifier/index_test.js index cd9b4ec00..31b0b4bdb 100644 --- a/src/ImageClassifier/index_test.js +++ b/src/ImageClassifier/index_test.js @@ -13,7 +13,7 @@ const DEFAULTS = { batchSize: 0.4, topk: 3, alpha: 1, - version: 1, + version: 2, }; describe('imageClassifier', () => { @@ -37,7 +37,7 @@ describe('imageClassifier', () => { } beforeEach(async () => { - jasmine.DEFAULT_TIMEOUT_INTERVAL = 5000; + jasmine.DEFAULT_TIMEOUT_INTERVAL = 15000; classifier = await imageClassifier('MobileNet', undefined, {}); }); diff --git a/src/KMeans/index.js b/src/KMeans/index.js new file mode 100644 index 000000000..d450828d1 --- /dev/null +++ b/src/KMeans/index.js @@ -0,0 +1,176 @@ +// Copyright (c) 2019 ml5 +// +// This software is released under the MIT License. +// https://opensource.org/licenses/MIT + +/* eslint "no-param-reassign": [2, { "props": false }] */ +/* +K-Means Algorithm (with Euclidian distance). +*/ + +import * as tf from '@tensorflow/tfjs'; +import callCallback from '../utils/callcallback'; +import { randomSample } from '../utils/random'; + +const DEFAULTS = { + 'k': 3, + 'maxIter': 5, + 'threshold': 0.5, +}; + +/** +* Read in a csv file from a path to its location. +* @param {string} path +*/ +async function readCsv(path) { + const myCsv = tf.data.csv(path); + const loadedData = await myCsv.toArray(); + return loadedData; +} + +/** +* Load and flatten an array of arrays, an array of objects, or a string +* path to a csv. +* @param {string || array || object} inputData +*/ +async function loadDataset(inputData) { + let data; + if (typeof inputData === 'string') { + data = await readCsv(inputData); + } else { + data = inputData; + } + const dataFlat = data.map((d) => { + return Object.values(d) + }); + return dataFlat; +} + + +class KMeans { + /** + * Create a K-Means. + * @param {String || array || object} dataset - The dataset to cluster. + * @param {options} options - An object describing a model's parameters: + * - k: number of clusters + * - maxIter: Max number of iterations to try before forcing convergence. + * - threshold: Threshold for updated centriod distance before declaring convergence. + * @param {function} callback - Optional. A callback to be called once + * the model has loaded. If no callback is provided, it will return a + * promise that will be resolved once the model has loaded. + */ + constructor(dataset, options, callback) { + this.config = { + k: options.k || DEFAULTS.k, + maxIter: options.maxIter || DEFAULTS.maxIter, + threshold: options.threshold || DEFAULTS.threshold + }; + this.ready = callCallback(this.load(dataset), callback); + } + + /** + * Load dataset, find initial centroids, and run model. + * @param {string || array || object} dataset + */ + async load(dataset) { + this.dataset = await loadDataset(dataset); + this.dataTensor = tf.tensor2d(this.dataset); + this.dataset.forEach(d => { + const tensors = tf.tensor1d(Object.values(d)); + d.tensor = tensors; + }); + this.centroids = tf.tensor2d(randomSample(this.dataset, this.config.k, false)); + this.fit(); + return this; + } + + /** + * Run K-Means algorithm. + */ + fit() { + this.getClosestCentroids() + this.recenterCentroids(); + let centroidDistance = KMeans.getEuclidianDistance(this.centroids, this.centroidsOld); + let iteration = 0; + while(centroidDistance > this.config.threshold && iteration < this.config.maxIter) { + this.getClosestCentroids(); + this.recenterCentroids(); + centroidDistance = KMeans.getEuclidianDistance(this.centroids, this.centroidsOld); + iteration += 1; + } + } + + /** + * Find closest centroids to each observation and store as attribute. + */ + getClosestCentroids() { + // find closest initial tensor + this.dataset.forEach(d => { + const minCentroid = this.closestCentroid(d.tensor); + d.centroid = minCentroid; + }) + } + + /** + * Load and flatten an array of arrays, an array of objects, or a string + * path to a csv. + * @param {string || array || object} inputData + */ + closestCentroid(dataTensor) { + const dist = this.centroids.squaredDifference(dataTensor).sum(1).sqrt(); + const minCentroid = dist.argMin().arraySync(); + return minCentroid; + } + + /** + * Assing `value` to a cluster. + * @param {array || object} value + */ + classify(value) { + // input must be array or object + const valueTensor = tf.tensor1d(Object.values(value)); + const minCentroid = this.closestCentroid(valueTensor); + return minCentroid; + } + + /** + * Recenter each centroid. + */ + recenterCentroids() { + // store previous run's centroids for convergence + this.centroidsOld = this.centroids; + // recenter each centroid + this.centroids = tf.stack(this.centroids.unstack().map((centroid, k) => { + // subset centroid to its cluster + const centroidK = this.dataset.filter(d => d.centroid === k); + // conver to tensor + const centroidKTensor = centroidK.map(d => d.tensor); + if (centroidKTensor.length === 0) { + return centroid; + } else if (centroidKTensor.length === 1) { + return centroidKTensor[0]; + } + // grab mean for for cluster + const newCentroids = tf.tidy(() => tf.stack(centroidKTensor).mean(0)); + return newCentroids + })) + } + + /** + * Calculate the Euclidian distance between two tensors. + * @param {tf.tensor} tensor1 + * @param {tf.tensor} tensor2 + */ + static getEuclidianDistance(tensor1, tensor2) { + // calculate euclidian distance between two arrays + const distTensor = tf.tidy(() => { + const distance = tf.squaredDifference(tensor1, tensor2).sum().sqrt(); + return distance.dataSync() + }) + return distTensor[0]; + } +} + +const kmeans = (dataset, options, callback) => new KMeans(dataset, options, callback); + +export default kmeans; diff --git a/src/KMeans/index_test.js b/src/KMeans/index_test.js new file mode 100644 index 000000000..c90cb960c --- /dev/null +++ b/src/KMeans/index_test.js @@ -0,0 +1,53 @@ +// Copyright (c) 2019 ml5 +// +// This software is released under the MIT License. +// https://opensource.org/licenses/MIT + +const { + kmeans +} = ml5; + +const KMEANS_DEFAULTS = { + 'k': 2, + 'maxIter': 5, + 'threshold': 0.5, +}; + +describe('kMeans', () => { + let kmeansModel; + const dataurl = 'https://raw.githubusercontent.com/ml5js/ml5-examples/development/d3/KMeans/KMeans_GaussianClusterDemo/data/gaussian2d_2clusters.csv' + beforeEach(async () => { + jasmine.DEFAULT_TIMEOUT_INTERVAL = 10000; + kmeansModel = await kmeans(dataurl, KMEANS_DEFAULTS, (err, result) => { + return; + }); + await kmeansModel.load(dataurl) + }); + + + it('Should create kmeans with all the defaults', async () => { + expect(kmeansModel.config.k).toBe(KMEANS_DEFAULTS.k); + expect(kmeansModel.config.maxIter).toBe(KMEANS_DEFAULTS.maxIter); + expect(kmeansModel.config.threshold).toBe(KMEANS_DEFAULTS.threshold); + }); + + it('kmeans dataset gaussian 2d: Should have length 200', async () => { + // await kmeansModel.load(dataurl) + expect(kmeansModel.dataset.length).toBe(200); + }); + + it('kmeans dataset gaussian 2d: Should have 2 unique centroids', async () => { + // await kmeansModel.load(dataurl) + const centroids = kmeansModel.dataset.map(val => val.centroid); + const unique = [...new Set(centroids)].length + expect(unique).toBe(2); + }); + + + + + + + + +}); \ No newline at end of file diff --git a/src/NeuralNetwork/NeuralNetworkData.js b/src/NeuralNetwork/NeuralNetworkData.js new file mode 100644 index 000000000..2b58b4bfc --- /dev/null +++ b/src/NeuralNetwork/NeuralNetworkData.js @@ -0,0 +1,620 @@ +import * as tf from '@tensorflow/tfjs'; +import { + saveBlob +} from '../utils/io'; +// import * as tfvis from '@tensorflow/tfjs-vis'; +// import callCallback from '../utils/callcallback'; + +/* eslint class-methods-use-this: ["error", { "exceptMethods": ["shuffle", "normalizeArray"] }] */ +class NeuralNetworkData { + constructor(options) { + this.config = options; + this.meta = { + // number of units - varies depending on input data type + inputUnits: null, + outputUnits: null, + // objects describing input/output data by property name + inputs: {}, // { name1: {dtype}, name2: {dtype} } + outputs: {}, // { name1: {dtype} } + isNormalized: false, + } + + this.data = { + raw: [], + tensor: { + inputs: null, // tensor + outputs: null, // tensor + inputMax: null, // tensor + inputMin: null, // tensor + outputMax: null, // tensor + outputMin: null, // tensor + }, + inputMax: null, // array or number + inputMin: null, // array or number + outputMax: null, // array or number + outputMin: null, // array or number + } + + // TODO: temp fix - check normalizationOptions + if (options.dataOptions.normalizationOptions !== null && options.dataOptions.normalizationOptions !== undefined) { + const items = ['inputMax', 'inputMin', 'outputMax', 'outputMin']; + items.forEach(prop => { + if (options.dataOptions.normalizationOptions[prop] !== null && options.dataOptions.normalizationOptions[prop] !== undefined) { + this.data[prop] = options.dataOptions.normalizationOptions[prop] + } + }) + } + + } + + + + + /** + * load data + */ + async loadData() { + const { + dataUrl + } = this.config.dataOptions; + if (dataUrl.endsWith('.csv')) { + await this.loadCSVInternal(); + } else if (dataUrl.endsWith('.json')) { + await this.loadJSONInternal(); + } else if (dataUrl.includes('blob')) { + await this.loadBlobInternal() + } else { + console.log('Not a valid data format. Must be csv or json') + } + } + + /** + * load csv + * TODO: pass to loadJSONInternal() + */ + async loadCSVInternal() { + const path = this.config.dataOptions.dataUrl; + const myCsv = tf.data.csv(path); + const loadedData = await myCsv.toArray(); + const json = { + entries: loadedData + } + this.loadJSONInternal(json); + } + + /** + * load json data + * @param {*} parsedJson + */ + async loadJSONInternal(parsedJson) { + const { + dataUrl + } = this.config.dataOptions; + const outputLabels = this.config.dataOptions.outputs; + const inputLabels = this.config.dataOptions.inputs; + + let json; + // handle loading parsedJson + if (parsedJson instanceof Object) { + json = parsedJson; + } else { + const data = await fetch(dataUrl); + json = await data.json(); + } + + // TODO: recurse through the object to find + // which object contains the + let parentProp; + if (Object.keys(json).includes('entries')) { + parentProp = 'entries' + } else if (Object.keys(json).includes('data')) { + parentProp = 'data' + } else { + console.log(`your data must be contained in an array in \n + a property called 'entries' or 'data'`); + return; + } + + const dataArray = json[parentProp]; + + this.data.raw = dataArray.map((item) => { + + const output = { + xs: {}, + ys: {} + } + // TODO: keep an eye on the order of the + // property name order if you use the order + // later on in the code! + const props = Object.keys(item); + + props.forEach(prop => { + if (inputLabels.includes(prop)) { + output.xs[prop] = item[prop] + } + + if (outputLabels.includes(prop)) { + output.ys[prop] = item[prop] + } + }) + + return output; + }) + + // set the data types for the inputs and outputs + this.setDTypes(); + + } + + /** + * load a blob and check if it is json + */ + async loadBlobInternal() { + try { + const data = await fetch(this.config.dataUrl); + const text = await data.text(); + if (this.isJsonString(text)) { + const json = JSON.parse(text); + await this.loadJSONInternal(json); + } else { + const json = this.csvJSON(text); + await this.loadJSONInternal(json); + } + } catch (err) { + console.log('mmm might be passing in a string or something!', err) + } + } + + /** + * sets the data types of the data we're using + * important for handling oneHot + */ + setDTypes() { + // this.meta.inputs + const sample = this.data.raw[0]; + const xs = Object.keys(sample.xs); + const ys = Object.keys(sample.ys); + + xs.forEach((prop) => { + this.meta.inputs[prop] = { + dtype: typeof sample.xs[prop] + } + }); + + ys.forEach((prop) => { + this.meta.outputs[prop] = { + dtype: typeof sample.ys[prop] + } + }); + + } + + /** + * Get the input and output units + * + */ + getIOUnits() { + let inputUnits = 0; + let outputUnits = 0; + + // TODO: turn these into functions! + // calc the number of inputs/output units + Object.entries(this.meta.inputs).forEach(arr => { + const { + dtype + } = arr[1]; + const prop = arr[0]; + if (dtype === 'number') { + inputUnits += 1; + } else if (dtype === 'string') { + const uniqueVals = [...new Set(this.data.raw.map(obj => obj.xs[prop]))] + // Store the unqiue values + this.meta.inputs[prop].uniqueValues = uniqueVals; + const onehotValues = [...new Array(uniqueVals.length).fill(null).map((item, idx) => idx)]; + + const oneHotEncodedValues = tf.oneHot(tf.tensor1d(onehotValues, 'int32'), uniqueVals.length); + const oneHotEncodedValuesArray = oneHotEncodedValues.arraySync(); + + this.meta.inputs[prop].legend = {}; + uniqueVals.forEach((uVal, uIdx) => { + this.meta.inputs[prop].legend[uVal] = oneHotEncodedValuesArray[uIdx] + }); + + // increment the number of inputs/outputs + inputUnits += uniqueVals.length; + } + }) + + + // calc the number of inputs/output units + Object.entries(this.meta.outputs).forEach(arr => { + const { + dtype + } = arr[1]; + const prop = arr[0]; + if (dtype === 'number') { + outputUnits += 1; + } else if (dtype === 'string') { + const uniqueVals = [...new Set(this.data.raw.map(obj => obj.ys[prop]))] + // Store the unqiue values + this.meta.outputs[prop].uniqueValues = uniqueVals; + const onehotValues = [...new Array(uniqueVals.length).fill(null).map((item, idx) => idx)]; + + const oneHotEncodedValues = tf.oneHot(tf.tensor1d(onehotValues, 'int32'), uniqueVals.length); + const oneHotEncodedValuesArray = oneHotEncodedValues.arraySync(); + + this.meta.outputs[prop].legend = {}; + uniqueVals.forEach((uVal, uIdx) => { + this.meta.outputs[prop].legend[uVal] = oneHotEncodedValuesArray[uIdx] + }); + + // increment the number of inputs/outputs + outputUnits += uniqueVals.length; + } + }) + + this.meta.inputUnits = inputUnits; + this.meta.outputUnits = outputUnits; + } + + /** + * Takes in a number or array and then either returns + * the array or returns an array of ['input0','input1'] + * the array or returns an array of ['output0','output1'] + * @param {*} val + * @param {*} inputType + */ + // eslint-disable-next-line class-methods-use-this + createNamedIO(val, inputType) { + const arr = (val instanceof Array) ? val : [...new Array(val).fill(null).map((item, idx) => `${inputType}${idx}`)] + return arr; + } + + /** + * checks whether or not a string is a json + * @param {*} str + */ + // eslint-disable-next-line class-methods-use-this + isJsonString(str) { + try { + JSON.parse(str); + } catch (e) { + return false; + } + return true; + } + + + /** + * Creates a csv from a strin + * @param {*} csv + */ + // via: http://techslides.com/convert-csv-to-json-in-javascript + // eslint-disable-next-line class-methods-use-this + csvJSON(csv) { + + const lines = csv.split("\n"); + + const result = []; + + const headers = lines[0].split(","); + + for (let i = 1; i < lines.length; i += 1) { + + const obj = {}; + const currentline = lines[i].split(","); + + for (let j = 0; j < headers.length; j += 1) { + obj[headers[j]] = currentline[j]; + } + result.push(obj); + } + + return { + entries: result + } + } + + /** + * Takes data as an array + * @param {*} xArray + * @param {*} yArray + */ + addData(xInputs, yInputs) { + let inputs = {}; + let outputs = {}; + + + + if(Array.isArray(xInputs)){ + xInputs.forEach((item, idx) => { + // TODO: get the label from the inputs? + // const label = `input${idx}`; + const label = this.config.dataOptions.inputs[idx]; + inputs[label] = item; + }); + } else if (typeof xInputs === 'object') { + inputs = xInputs; + } else { + console.log('input provided is not supported or does not match your output label specifications'); + } + + if(Array.isArray(yInputs)){ + yInputs.forEach((item, idx) => { + // TODO: get the label from the outputs? + // const label = `output${idx}`; + const label = this.config.dataOptions.outputs[idx]; + outputs[label] = item; + }); + } else if (typeof yInputs === 'object') { + outputs = yInputs; + } else { + console.log('input provided is not supported or does not match your output label specifications'); + } + + this.data.raw.push({ + xs: inputs, + ys: outputs + }); + } + + /** + * normalize the data.raw + */ + normalize() { + + // always make sure to check set the data types + this.setDTypes(); + // always make sure that the IO units are set + this.getIOUnits(); + + // do the things...! + const { + inputTensor, + outputTensor + } = this.convertRawToTensor(); + + // run normalize on the new tensors + const { + normalizedInputs, + normalizedOutputs, + inputMax, + inputMin, + outputMax, + outputMin + } = this.normalizeInternal(inputTensor, outputTensor); + + // set the tensor data to the normalized inputs + this.data.tensor = { + inputs: normalizedInputs, + outputs: normalizedOutputs, + inputMax, + inputMin, + outputMax, + outputMin, + } + + // set the input/output Min and max values as numbers + this.data.inputMin = inputMin.arraySync(); + this.data.inputMax = inputMax.arraySync(); + this.data.outputMax = outputMax.arraySync(); + this.data.outputMin = outputMin.arraySync(); + + // set isNormalized to true if this function is called + this.meta.isNormalized = true; + } + + /** + * + */ + normalizeInternal(inputTensor, outputTensor) { + // inputTensor.print() + // outputTensor.print() + + // 4. Get the min and max values for normalization + // TODO: allow people to submit their own normalization values! + const { + inputMax, + inputMin, + outputMax, + outputMin} = this.setIOStats(inputTensor, outputTensor); + + // 5. create a normalized tensor + const normalizedInputs = inputTensor.sub(inputMin).div(inputMax.sub(inputMin)); + const normalizedOutputs = outputTensor.sub(outputMin).div(outputMax.sub(outputMin)); + + return { + normalizedInputs, + normalizedOutputs, + inputMin, + inputMax, + outputMax, + outputMin + } + } + + /** + * Assemble the data for training + */ + warmUp() { + // always make sure to check set the data types + this.setDTypes(); + // always make sure that the IO units are set + this.getIOUnits(); + + // do the things...! + const { + inputTensor, + outputTensor + } = this.convertRawToTensor(); + + const { + inputMax, + inputMin, + outputMax, + outputMin} = this.setIOStats(inputTensor, outputTensor); + + this.data.tensor = { + inputs: inputTensor, + outputs: outputTensor, + inputMax, + inputMin, + outputMax, + outputMin, + } + + // set the input/output Min and max values as numbers + this.data.inputMin = inputMin.arraySync(); + this.data.inputMax = inputMax.arraySync(); + this.data.outputMax = outputMax.arraySync(); + this.data.outputMin = outputMin.arraySync(); + + } + + /** + * get the min and max values of the input and output data + * @param {*} inputTensor + * @param {*} outputTensor + */ + setIOStats(inputTensor, outputTensor){ + let inputMax; + let inputMin; + let outputMax; + let outputMin; + // TODO: this is terrible and can be handled way better! + // REFACTOR THIS!!! + if (this.config.architecture.task === 'regression') { + if (this.config.dataOptions.normalizationOptions instanceof Object) { + // if there is an object with normalizationOptions + inputMax = this.data.inputMax !== null ? tf.tensor1d(this.data.inputMax) : inputTensor.max(0); + inputMin = this.data.inputMin !== null ? tf.tensor1d(this.data.inputMin) : inputTensor.min(0); + outputMax = this.data.outputMax !== null ? tf.tensor1d(this.data.outputMax) : outputTensor.max(0); + outputMin = this.data.outputMin !== null ? tf.tensor1d(this.data.outputMin) : outputTensor.min(0); + } else { + // if the task is a regression, return all the + // output stats as an array + inputMax = inputTensor.max(0); + inputMin = inputTensor.min(0); + outputMax = outputTensor.max(0); + outputMin = outputTensor.min(0); + } + } else if (this.config.architecture.task === 'classification') { + if (this.config.dataOptions.normalizationOptions instanceof Object) { + // if there is an object with normalizationOptions + inputMax = this.data.inputMax !== null ? tf.tensor1d(this.data.inputMax) : inputTensor.max(0); + inputMin = this.data.inputMin !== null ? tf.tensor1d(this.data.inputMin) : inputTensor.min(0); + outputMax = this.data.outputMax !== null ? tf.tensor1d(this.data.outputMax) : outputTensor.max(); + outputMin = this.data.outputMin !== null ? tf.tensor1d(this.data.outputMin) : outputTensor.min(); + } else { + // if the task is a classification, return the single value + inputMax = inputTensor.max(0); + inputMin = inputTensor.min(0); + outputMax = outputTensor.max(); + outputMin = outputTensor.min(); + } + } + // return the values calculated here + return { + inputMax, + inputMin, + outputMax, + outputMin + } + } + + /** + * onehot encode values + */ + convertRawToTensor() { + // console.log(this.meta) + + // Given the inputs and output types, + // now create the input and output tensors + // 1. start by creating a matrix + const inputs = [] + const outputs = []; + + // 2. encode the values + // iterate through each entry and send the correct + // oneHot encoded values or the numeric value + this.data.raw.forEach((item) => { + let inputRow = []; + let outputRow = []; + const { + xs, + ys + } = item; + + // Create the inputs matrix + Object.entries(xs).forEach((valArray) => { + const prop = valArray[0]; + const val = valArray[1]; + const { + dtype + } = this.meta.inputs[prop]; + + if (dtype === 'number') { + inputRow.push(val); + } else if (dtype === 'string') { + const oneHotArray = this.meta.inputs[prop].legend[val]; + inputRow = [...inputRow, ...oneHotArray]; + } + + }); + + // Create the outputs matrix + Object.entries(ys).forEach((valArray) => { + const prop = valArray[0]; + const val = valArray[1]; + const { + dtype + } = this.meta.outputs[prop]; + + if (dtype === 'number') { + outputRow.push(val); + } else if (dtype === 'string') { + const oneHotArray = this.meta.outputs[prop].legend[val]; + outputRow = [...outputRow, ...oneHotArray]; + } + + }); + + inputs.push(inputRow); + outputs.push(outputRow); + + }); + + // console.log(inputs, outputs) + // 3. convert to tensors + const inputTensor = tf.tensor(inputs, [this.data.raw.length, this.meta.inputUnits]); + const outputTensor = tf.tensor(outputs, [this.data.raw.length, this.meta.outputUnits]); + + return { + inputTensor, + outputTensor + } + + } + + + async saveData(name){ + const today = new Date(); + const date = `${String(today.getFullYear())}-${String(today.getMonth()+1)}-${String(today.getDate())}`; + const time = `${String(today.getHours())}-${String(today.getMinutes())}-${String(today.getSeconds())}`; + const datetime = `${date}_${time}`; + + let dataName = datetime; + if (name) dataName = name; + + const output = { + data: this.data.raw + } + + await saveBlob(JSON.stringify(output), `${dataName}.json`, 'text/plain'); + } + + +} // end of class + + +export default NeuralNetworkData; \ No newline at end of file diff --git a/src/NeuralNetwork/NeuralNetworkDefaults.js b/src/NeuralNetwork/NeuralNetworkDefaults.js new file mode 100644 index 000000000..7b81ded55 --- /dev/null +++ b/src/NeuralNetwork/NeuralNetworkDefaults.js @@ -0,0 +1,18 @@ +const DEFAULTS = { + task: 'regression', + activationHidden: 'sigmoid', + activationOutput: 'sigmoid', + debug: false, + learningRate: 0.25, + inputs: 2, + outputs: 1, + noVal: null, + hiddenUnits: 16, + modelMetrics: ['accuracy'], + modelLoss: 'meanSquaredError', + modelOptimizer: null, + batchSize: 64, + epochs: 32, +} + +export default DEFAULTS; \ No newline at end of file diff --git a/src/NeuralNetwork/NeuralNetworkVis.js b/src/NeuralNetwork/NeuralNetworkVis.js new file mode 100644 index 000000000..e24d80cad --- /dev/null +++ b/src/NeuralNetwork/NeuralNetworkVis.js @@ -0,0 +1,139 @@ +// import * as tf from '@tensorflow/tfjs'; +import * as tfvis from '@tensorflow/tfjs-vis'; +// https://js.tensorflow.org/api_vis/latest/#render.barchart + +class NeuralNetworkVis { + + constructor() { + // TODO: + this.config = { + height: 300, + }; + + // store tfvis here for now so people can access it + // through ml5? + this.tfvis = tfvis; + } + + /** + * creates a scatterplot from 1 input variable and 1 output variable + * @param {*} inputLabel + * @param {*} outputLabel + * @param {*} data + */ + scatterplot(inputLabel, outputLabel, data) { + + const values = data.map(item => { + return { + x: item.xs[inputLabel], + y: item.ys[outputLabel] + } + }); + + const visOptions = { + name: 'debug mode' + } + const chartOptions = { + xLabel: 'X', + yLabel: 'Y', + height: this.config.height + } + + tfvis.render.scatterplot(visOptions, values, chartOptions) + + } + + /** + * creates a scatterplot from all input variables and all output variables + * @param {*} inputLabels + * @param {*} outputLabels + * @param {*} data + */ + scatterplotAll(inputLabels, outputLabels, data) { + let values = []; + + inputLabels.forEach(inputLabel => { + outputLabels.forEach(outputLabel => { + + const val = data.map(item => { + return { + x: item.xs[inputLabel], + y: item.ys[outputLabel] + } + }); + + values = [...values, ...val]; + }) + }) + + const visOptions = { + name: 'debug mode' + } + + const chartOptions = { + xLabel: 'X', + yLabel: 'Y', + height: this.config.height + } + + tfvis.render.scatterplot(visOptions, values, chartOptions) + } + + /** + * creates a barchart from 1 input label and 1 output label + * @param {*} inputLabel + * @param {*} outputLabel + * @param {*} data + */ + barchart(inputLabel, outputLabel, data) { + + const values = data.map(item => { + return { + value: item.xs[inputLabel], + index: item.ys[outputLabel] + } + }); + + const chartOptions = { + xLabel: 'label', + yLabel: 'value', + height: this.config.height + } + + console.log(chartOptions) + // Render to visor + const surface = { + name: 'Bar chart' + }; + tfvis.render.barchart(surface, values); + } + + /** + * create a confusion matrix + * @param {*} inputLabels + * @param {*} outputLabels + * @param {*} data + */ + // confusionMatrix(inputLabels, outputLabels, data) { + + // } + + + /** + * Visualize the training of the neural net + */ + trainingVis(){ + return tfvis.show.fitCallbacks({ + name: 'Training Performance' + }, + ['loss', 'accuracy'], { + height: this.config.height, + callbacks: ['onEpochEnd'] + } + ) + } + + +} + +export default NeuralNetworkVis \ No newline at end of file diff --git a/src/NeuralNetwork/index.js b/src/NeuralNetwork/index.js new file mode 100644 index 000000000..eaf39da75 --- /dev/null +++ b/src/NeuralNetwork/index.js @@ -0,0 +1,940 @@ +// Copyright (c) 2019 ml5 +// +// This software is released under the MIT License. +// https://opensource.org/licenses/MIT + +/* +Generic NeuralNetwork class +*/ + +import * as tf from '@tensorflow/tfjs'; +import callCallback from '../utils/callcallback'; +import { + saveBlob +} from '../utils/io'; +// import { input } from '@tensorflow/tfjs'; +import DEFAULTS from './NeuralNetworkDefaults'; +import NeuralNetworkData from './NeuralNetworkData'; +import NeuralNetworkVis from './NeuralNetworkVis'; + +class NeuralNetwork { + /** + * Create a Neural Network. + * @param {object} options - An object with options. + */ + constructor(options, callback) { + // model config + this.config = { + // debugging + debug: options.debug || DEFAULTS.debug, + // architecture + architecture: { + task: options.task || DEFAULTS.task, + // array of layers, the last is always the output layer + layers: options.layers || [], + // array of activations corresponding to the layer number + activations: [], + // hiddenUnits + hiddenUnits: options.hiddenUnits || DEFAULTS.hiddenUnits, + // the units of the layers will come from the config.dataOptions + }, + training: { + // defined either on instantiation or in .train(options) + batchSize: options.batchSize || DEFAULTS.batchSize, + epochs: options.epochs || DEFAULTS.epochs, + // will depend on the config.architecture.task + learningRate: options.learningRate || DEFAULTS.learningRate, + modelMetrics: options.modelMetrics || DEFAULTS.modelMetrics, + modelLoss: options.modelLoss || DEFAULTS.modelLoss, + modelOptimizer: options.modelOptimizer || DEFAULTS.modelOptimizer, + }, + // data + dataOptions: { + dataUrl: options.dataUrl || null, + inputs: options.inputs || DEFAULTS.inputs, + outputs: options.outputs || DEFAULTS.outputs, + // TODO: adding option for normalization + normalizationOptions: options.normalizationOptions || null + }, + + } + + + // TODO: maybe we create a set of configs for + // regression vs. classification + // set the default activations: + if (this.config.architecture.task === 'regression') { + // current defaults are for regression + const activationHidden = options.activationHidden || DEFAULTS.activationHidden; + const activationOutput = options.activationOutput || DEFAULTS.activationOutput; + + this.config.training.modelOptimizer = options.modelOptimizer || tf.train.adam(this.config.training.learningRate); + this.config.architecture.activations = [activationHidden, activationOutput]; + } else if (this.config.architecture.task === 'classification') { + // set classification specs different from regression in DEFAULTS + const activationHidden = options.activationHidden || DEFAULTS.activationHidden; + const activationOutput = options.activationOutput || 'softmax'; + + this.config.architecture.activations = [activationHidden, activationOutput]; + this.config.training.modelLoss = options.modelLoss || 'categoricalCrossentropy'; + this.config.training.modelOptimizer = options.modelOptimizer || tf.train.sgd(this.config.training.learningRate); + } else { + console.log(`task not defined. please set task: classification OR regression`); + } + + // vis class + this.vis = new NeuralNetworkVis(); + // data class + this.data = new NeuralNetworkData(this.config); + // check if the model is ready + this.ready = false; + // the model + this.model = null; + + // initialize + this.init(callback); + + } + + /** + * ---------------------------------------- + * --- model creation / initialization ---- + * ---------------------------------------- + */ + + /** + * Initialize the model creation + */ + init(callback) { + // Create the model based on data or the inputs/outputs + if (this.config.dataOptions.dataUrl !== null) { + this.ready = this.createModelFromData(callback); + } else { + // --- set the input/output units --- + const inputIOUnits = this.initializeIOUnits(this.config.dataOptions.inputs, 'inputs'); + this.data.meta.inputUnits = inputIOUnits.units; + this.data.config.dataOptions.inputs = inputIOUnits.labels; + + const outputIOUnits = this.initializeIOUnits(this.config.dataOptions.outputs, 'outputs'); + this.data.meta.outputUnits = outputIOUnits.units; + this.data.config.dataOptions.outputs = outputIOUnits.labels; + + this.ready = true; + } + } + + /** + * if the inputs is a number + * then set the inputUnits as the number + * and then create an array of input labels + * if not, then use what is given + * @param {*} input + * @param {*} ioType + */ + initializeIOUnits(input, ioType) { + let units; + let labels; + let ioLabel; + + if (ioType === 'outputs') { + ioLabel = 'output' + } else { + ioLabel = 'input' + } + + if (typeof input === 'number') { + units = input; + labels = this.data.createNamedIO(input, ioLabel); + } else if (Array.isArray(input)) { + units = input.length; + labels = input; + } else { + console.log(`${ioType} in this format are not supported`) + } + + return { + units, + labels + }; + + } + + /** + * create Model + */ + createModel() { + + switch (this.config.architecture.task) { + case 'regression': + // if the layers are not defined default to a + // neuralnet with 2 layers + this.defineModelLayers(); + return this.createModelInternal(); + case 'classification': + // if the layers are not defined default to a + // neuralnet with 2 layers + this.defineModelLayers(); + return this.createModelInternal(); + default: + console.log('no model exists for this type of task yet!'); + return tf.sequential(); + } + } + + /** + * Define the model layers + */ + defineModelLayers() { + if (!this.config.architecture.layers.length > 0) { + this.config.architecture.layers = []; + + const { + activations, + hiddenUnits + } = this.config.architecture; + + const hidden = tf.layers.dense({ + units: hiddenUnits, + inputShape: [this.data.meta.inputUnits], + activation: activations[0], + }); + + const output = tf.layers.dense({ + units: this.data.meta.outputUnits, + activation: activations[1], + }); + + this.config.architecture.layers = [hidden, output]; + + } + } + + + createModelInternal() { + const model = tf.sequential(); + + // add the layers to the model as defined in config.architecture.layers + this.config.architecture.layers.forEach(layer => { + model.add(layer); + }); + + // compile the model + const { + modelOptimizer, + modelLoss, + modelMetrics + } = this.config.training; + + model.compile({ + optimizer: modelOptimizer, + loss: modelLoss, + metrics: modelMetrics, + }); + + return model; + } + + /** + * create model from data + * @param {*} callback + */ + createModelFromData(callback) { + return callCallback(this.createModelFromDataInternal(), callback) + } + + /** + * Creates model architecture from the loaded data + */ + async createModelFromDataInternal() { + // load the data + await this.data.loadData(); + // check the input columns for data type to + // calculate the total number of inputs + // and outputs + this.data.getIOUnits(); + } + + /** + * ---------------------------------------- + * ----- adding data / training ----------- + * ---------------------------------------- + */ + /** + * Adds an endpoint to call data.addData() + * @param {*} xs + * @param {*} ys + */ + addData(xs, ys) { + this.data.addData(xs, ys); + } + + /** + * normalize the data.raw + */ + normalizeData() { + this.data.normalize(); + } + + + /** + * User-facing neural network training + * @param {*} optionsOrCallback + * @param {*} callback + */ + train(optionsOrCallback, optionsOrWhileTraining, callback) { + let options; + let whileTrainingCb; + let finishedTrainingCb; + if (typeof optionsOrCallback === 'object' && + typeof optionsOrWhileTraining === 'function' && + typeof callback === 'function' + ) { + options = optionsOrCallback; + whileTrainingCb = optionsOrWhileTraining; + finishedTrainingCb = callback; + } else if (typeof optionsOrCallback === 'object' && + typeof optionsOrWhileTraining === 'function') { + options = optionsOrCallback; + whileTrainingCb = null; + finishedTrainingCb = optionsOrWhileTraining; + } else if (typeof optionsOrCallback === 'function' && + typeof optionsOrWhileTraining === 'function' + ) { + options = {}; + whileTrainingCb = optionsOrCallback; + finishedTrainingCb = optionsOrWhileTraining; + } else { + options = {}; + whileTrainingCb = null; + finishedTrainingCb = optionsOrCallback; + } + + return callCallback(this.trainInternal(options, whileTrainingCb), finishedTrainingCb); + } + + /** + * Train the neural network + * @param {*} options + */ + async trainInternal(options, whileTrainingCallback) { + // get batch size and epochs + const batchSize = options.batchSize || this.config.batchSize; + const epochs = options.epochs || this.config.epochs; + + // placeholder for whiletraining callback; + let whileTraining; + // if debug is true, show tf vis during model training + // if not, then use whileTraining + let modelFitCallbacks; + + // Get the inputs and outputs from the data object + const { + inputs, + outputs + } = this.data.data.tensor; + + // placeholder for xs and ys data for training + let xs; + let ys; + + // check if data are normalized, run the data.warmUp before training + if (!this.data.meta.isNormalized) { + this.data.warmUp(); + } + + // Create the model when train is called + // important that this comes after checking if .isNormalized + this.model = this.createModel(); + + // check if a whileTrainingCallback was passed + if (typeof whileTrainingCallback === 'function') { + whileTraining = whileTrainingCallback; + } else { + whileTraining = () => null; + } + + // check if the inputs are tensors, if not, convert! + if (!(inputs instanceof tf.Tensor)) { + xs = tf.tensor(inputs) + ys = tf.tensor(outputs) + } else { + xs = inputs; + ys = outputs; + } + + // check if the debug mode is on to specify model fit callbacks + if (this.config.debug) { + modelFitCallbacks = [ + this.vis.trainingVis(), + { + onEpochEnd: whileTraining + } + ] + } else { + modelFitCallbacks = [{ + onEpochEnd: whileTraining + }] + } + + // train the model + await this.model.fit(xs, ys, { + shuffle: true, + batchSize, + epochs, + validationSplit: 0.1, + callbacks: modelFitCallbacks + }); + // dispose of the xs and ys + xs.dispose(); + ys.dispose(); + } + + + /** + * ---------------------------------------- + * ----- prediction / classification------- + * ---------------------------------------- + */ + /** + * Classify() + * Runs the classification if the neural network is doing a + * classification task + * @param {*} input + * @param {*} callback + */ + classify(input, callback) { + return callCallback(this.predictInternal(input), callback); + } + + /** + * classifyMultiple() + * Runs the classification task on multiple inputs + * @param {*} input + * @param {*} callback + */ + classifyMultiple(input, callback) { + return callCallback(this.predictMultipleInternal(input), callback); + } + + /** + * User-facing prediction function + * @param {*} input + * @param {*} callback + */ + predict(input, callback) { + return callCallback(this.predictInternal(input), callback); + } + + /** + * User-facing prediction function for multiple inputs + * @param {*} input + * @param {*} callback + */ + predictMultiple(input, callback) { + return callCallback(this.predictMultipleInternal(input), callback); + } + + /** + * Make a prediction based on the given input + * @param {*} sample + */ + async predictMultipleInternal(sampleList) { + + // 1. Handle the input sample + const inputData = []; + sampleList.forEach(sample => { + let inputRow = []; + if (sample instanceof Array) { + inputRow = sample; + } else if (sample instanceof Object) { + // TODO: make sure that the input order is preserved! + const headers = this.data.config.dataOptions.inputs; + inputRow = headers.map(prop => { + return sample[prop] + }); + } + inputData.push(inputRow); + }); + + + // 2. onehot encode the sample if necessary + const encodedInput = []; + + sampleList.forEach(item => { + let encodedInputRow = []; + Object.entries(this.data.meta.inputs).forEach((arr) => { + const prop = arr[0]; + const { + dtype + } = arr[1]; + + // to ensure that we get the value in the right order + const valIndex = this.data.config.dataOptions.inputs.indexOf(prop); + const val = item[valIndex]; + + if (dtype === 'number') { + let normVal; + // if the data has not been normalized, just send in the raw sample + if (!this.data.meta.isNormalized) { + normVal = val; + } else { + const { + inputMin, + inputMax + } = this.data.data; + normVal = val; + if (inputMin && inputMax) { + normVal = (val - inputMin[valIndex]) / (inputMax[valIndex] - inputMin[valIndex]); + } + } + encodedInputRow.push(normVal); + } else if (dtype === 'string') { + const { + legend + } = arr[1]; + const onehotVal = legend[val] + encodedInputRow = [...encodedInputRow, ...onehotVal] + } + + encodedInput.push(encodedInputRow); + }); + + }); + + + + // Step 3: make the prediction + const xs = tf.tensor(encodedInput, [encodedInput.length, this.data.meta.inputUnits]); + const ys = this.model.predict(xs); + + // ys.print(); + + // Step 4: Convert the outputs back to the recognizable format + let results = []; + + if (this.config.architecture.task === 'classification') { + const predictions = await ys.array(); + // TODO: Check to see if this fails with numeric values + // since no legend exists + const outputData = predictions.map(prediction => { + return Object.entries(this.data.meta.outputs).map((arr) => { + const { + legend + } = arr[1]; + // TODO: the order of the legend items matters + // Likey this means instead of `.push()`, + // we should do .unshift() + // alternatively we can use 'reverse()' here. + return Object.entries(legend).map((legendArr, idx) => { + const prop = legendArr[0]; + return { + label: prop, + confidence: prediction[idx] + } + }).sort((a, b) => b.confidence - a.confidence); + })[0]; + }) + // NOTE: we are doing a funky javascript thing + // setting an array as results, then adding + // .tensor as a property of that array object + results = outputData; + results.tensor = ys; + + } else if (this.config.architecture.task === 'regression') { + const predictions = await ys.array(); + + + + const outputData = predictions.map(prediction => { + return Object.entries(this.data.meta.outputs).map((item, idx) => { + const prop = item[0]; + const { + outputMin, + outputMax + } = this.data.data; + let val; + if (!this.data.meta.isNormalized) { + val = prediction[idx] + } else { + val = (prediction[idx] * (outputMax[idx] - outputMin[idx])) + outputMin[idx]; + } + + return { + value: val, + label: prop + } + }); + }) + + + // NOTE: we are doing a funky javascript thing + // setting an array as results, then adding + // .tensor as a property of that array object + results = outputData; + results.tensor = ys; + } + + xs.dispose(); + return results; + + } + + /** + * Make a prediction based on the given input + * @param {*} sample + */ + async predictInternal(sample) { + // 1. Handle the input sample + // either an array of values in order of the inputs + // OR an JSON object of key/values + // console.log(sample) + + let inputData = []; + if (sample instanceof Array) { + inputData = sample; + } else if (sample instanceof Object) { + // TODO: make sure that the input order is preserved! + const headers = this.data.config.dataOptions.inputs; + inputData = headers.map(prop => { + return sample[prop] + }); + } + + + // 2. onehot encode the sample if necessary + let encodedInput = []; + + Object.entries(this.data.meta.inputs).forEach((arr) => { + const prop = arr[0]; + const { + dtype + } = arr[1]; + + // to ensure that we get the value in the right order + const valIndex = this.data.config.dataOptions.inputs.indexOf(prop); + const val = inputData[valIndex]; + + if (dtype === 'number') { + let normVal; + // if the data has not been normalized, just send in the raw sample + if (!this.data.meta.isNormalized) { + normVal = val; + } else { + const { + inputMin, + inputMax + } = this.data.data; + normVal = val; + if (inputMin && inputMax) { + normVal = (val - inputMin[valIndex]) / (inputMax[valIndex] - inputMin[valIndex]); + } + } + encodedInput.push(normVal); + } else if (dtype === 'string') { + const { + legend + } = arr[1]; + const onehotVal = legend[val] + encodedInput = [...encodedInput, ...onehotVal] + } + + }) + + const xs = tf.tensor(encodedInput, [1, this.data.meta.inputUnits]); + const ys = this.model.predict(xs); + + let results = []; + + if (this.config.architecture.task === 'classification') { + const predictions = await ys.data(); + // TODO: Check to see if this fails with numeric values + // since no legend exists + const outputData = Object.entries(this.data.meta.outputs).map((arr) => { + const { + legend + } = arr[1]; + // TODO: the order of the legend items matters + // Likey this means instead of `.push()`, + // we should do .unshift() + // alternatively we can use 'reverse()' here. + return Object.entries(legend).map((legendArr, idx) => { + const prop = legendArr[0]; + return { + label: prop, + confidence: predictions[idx] + } + }).sort((a, b) => b.confidence - a.confidence); + })[0]; + + // NOTE: we are doing a funky javascript thing + // setting an array as results, then adding + // .tensor as a property of that array object + results = outputData; + results.tensor = ys; + + } else if (this.config.architecture.task === 'regression') { + const predictions = await ys.data(); + + + const outputData = Object.entries(this.data.meta.outputs).map((item, idx) => { + const prop = item[0]; + const { + outputMin, + outputMax + } = this.data.data; + let val; + if (!this.data.meta.isNormalized) { + val = predictions[idx] + } else { + val = (predictions[idx] * (outputMax[idx] - outputMin[idx])) + outputMin[idx]; + } + + return { + value: val, + label: prop + } + }); + + + // NOTE: we are doing a funky javascript thing + // setting an array as results, then adding + // .tensor as a property of that array object + results = outputData; + results.tensor = ys; + } + + xs.dispose(); + return results; + + + } + + + + + /** + * ---------------------------------------- + * ----- Exporting / Saving --------------- + * ---------------------------------------- + */ + + /** + * Calls this.data.saveData() to save data out to a json file + * @param {*} callback + * @param {*} name + */ + async saveData(nameOrCallback, callback) { + let cb; + let outputName; + + // check the inputs + if (typeof nameOrCallback === 'string' && callback) { + outputName = nameOrCallback + cb = callback; + } else if (typeof nameOrCallback === 'string' && !callback) { + cb = null; + outputName = nameOrCallback + } else if (typeof nameOrCallback === 'function') { + cb = nameOrCallback + outputName = undefined; + } + + // save the data out + await this.data.saveData(outputName); + + if (typeof cb === 'function') { + cb(); + } + } + + /** + * loadData from fileinput or path + * @param {*} filesOrPath + * @param {*} callback + */ + async loadData(filesOrPath = null, callback) { + + let loadedData; + if (typeof filesOrPath !== 'string') { + const file = filesOrPath[0]; + const fr = new FileReader(); + fr.readAsText(file); + if (file.name.includes('.json')) { + const temp = await file.text(); + loadedData = JSON.parse(temp); + } else { + console.log('data must be a json object containing an array called "data" or "entries') + } + } else { + loadedData = await fetch(filesOrPath); + const text = await loadedData.text(); + if (this.data.isJsonString(text)) { + loadedData = JSON.parse(text); + } else { + console.log('Whoops! something went wrong. Either this kind of data is not supported yet or there is an issue with .loadData') + } + } + + // check if a data or entries property exists + if (loadedData.data) { + this.data.data.raw = loadedData.data; + } else if (loadedData.entries) { + this.data.data.raw = loadedData.entries; + } else { + console.log('data must be a json object containing an array called "data" or "entries') + } + + if (callback) { + callback(); + } + } + + + /** + * Save the model and weights + * @param {*} callback + * @param {*} name + */ + async save(callback, name) { + this.model.save(tf.io.withSaveHandler(async (data) => { + let modelName = 'model'; + if (name) modelName = name; + + this.weightsManifest = { + modelTopology: data.modelTopology, + weightsManifest: [{ + paths: [`./${modelName}.weights.bin`], + weights: data.weightSpecs, + }] + }; + + const dataMeta = { + data: { + inputMin: this.data.data.inputMin, + inputMax: this.data.data.inputMax, + outputMin: this.data.data.outputMin, + outputMax: this.data.data.outputMax, + }, + meta: this.data.meta + } + + await saveBlob(data.weightData, `${modelName}.weights.bin`, 'application/octet-stream'); + await saveBlob(JSON.stringify(this.weightsManifest), `${modelName}.json`, 'text/plain'); + await saveBlob(JSON.stringify(dataMeta), `${modelName}_meta.json`, 'text/plain'); + if (callback) { + callback(); + } + })); + } + + /** + * Load the model and weights in from a file + * @param {*} filesOrPath + * @param {*} callback + */ + async load(filesOrPath = null, callback) { + + if (filesOrPath instanceof FileList) { + + const files = await Promise.all( + Array.from(filesOrPath).map( async (file) => { + if (file.name.includes('model.json')) { + return {name:"model", file} + } else if (file.name.includes('_meta.json')) { + const modelMetadata = await file.text(); + return {name: "metadata", file:modelMetadata} + } else if (file.name.includes('.bin')) { + return {name:"weights", file} + } + return {name:null, file:null} + }) + ) + + const model = files.find(item => item.name === 'model').file; + const modelMetadata = JSON.parse(files.find(item => item.name === 'metadata').file); + const weights = files.find(item => item.name === 'weights').file; + + // set the metainfo + this.data.data.inputMax = modelMetadata.data.inputMax; + this.data.data.inputMin = modelMetadata.data.inputMin; + this.data.data.outputMax = modelMetadata.data.outputMax; + this.data.data.outputMin = modelMetadata.data.outputMin; + this.data.meta = modelMetadata.meta; + + // load the model + this.model = await tf.loadLayersModel(tf.io.browserFiles([model, weights])); + + } else if(filesOrPath instanceof Object){ + // filesOrPath = {model: URL, metadata: URL, weights: URL} + + let modelMetadata = await fetch(filesOrPath.metadata); + modelMetadata = await modelMetadata.text(); + modelMetadata = JSON.parse(modelMetadata); + + let modelJson = await fetch(filesOrPath.model); + modelJson = await modelJson.text(); + const modelJsonFile = new File([modelJson], 'model.json', {type: 'application/json'}); + + let weightsBlob = await fetch(filesOrPath.weights); + weightsBlob = await weightsBlob.blob(); + const weightsBlobFile = new File([weightsBlob], 'model.weights.bin', {type: 'application/macbinary'}); + + this.data.data.inputMax = modelMetadata.data.inputMax; + this.data.data.inputMin = modelMetadata.data.inputMin; + this.data.data.outputMax = modelMetadata.data.outputMax; + this.data.data.outputMin = modelMetadata.data.outputMin; + this.data.meta = modelMetadata.meta; + + this.model = await tf.loadLayersModel(tf.io.browserFiles([modelJsonFile, weightsBlobFile])); + + } else { + const metaPath = `${filesOrPath.substring(0, filesOrPath.lastIndexOf("/"))}/model_meta.json`; + let modelMetadata = await fetch(metaPath); + modelMetadata = await modelMetadata.json(); + + this.data.data.inputMax = modelMetadata.data.inputMax; + this.data.data.inputMin = modelMetadata.data.inputMin; + this.data.data.outputMax = modelMetadata.data.outputMax; + this.data.data.outputMin = modelMetadata.data.outputMin; + this.data.meta = modelMetadata.meta; + + this.model = await tf.loadLayersModel(filesOrPath); + } + if (callback) { + callback(); + } + return this.model; + } + +} + + + + +/** + * Create an instance of the NeuralNetwork + * @param {*} inputsOrOptions + * @param {*} outputsOrCallback + * @param {*} callback + */ +const neuralNetwork = (inputsOrOptions, outputsOrCallback, callback) => { + + let options; + let cb; + + if (inputsOrOptions instanceof Object) { + options = inputsOrOptions; + cb = outputsOrCallback; + } else { + options = { + inputs: inputsOrOptions, + outputs: outputsOrCallback, + }; + cb = callback; + } + + const instance = new NeuralNetwork(options, cb); + return instance; +}; + +export default neuralNetwork; \ No newline at end of file diff --git a/src/NeuralNetwork/index_test.js b/src/NeuralNetwork/index_test.js new file mode 100644 index 000000000..fdb079270 --- /dev/null +++ b/src/NeuralNetwork/index_test.js @@ -0,0 +1,63 @@ +// Copyright (c) 2019 ml5 +// +// This software is released under the MIT License. +// https://opensource.org/licenses/MIT + + +const { + neuralNetwork +} = ml5; + +const NN_DEFAULTS = { + task: 'regression', + activationHidden: 'sigmoid', + activationOutput: 'sigmoid', + debug: false, + learningRate: 0.25, + inputs: 2, + outputs: 1, + noVal: null, + hiddenUnits: 16, + modelMetrics: ['accuracy'], + modelLoss: 'meanSquaredError', + modelOptimizer: null, + batchSize: 64, + epochs: 32, +} + + +describe('neuralNetwork', () => { + let nn; + + beforeEach(async () => { + jasmine.DEFAULT_TIMEOUT_INTERVAL = 15000; + nn = await neuralNetwork(); + }); + + it('Should create neuralNetwork with all the defaults', async () => { + expect(nn.config.debug).toBe(NN_DEFAULTS.debug); + // architecture defaults + expect(nn.config.architecture.task).toBe(NN_DEFAULTS.task); + + // expect(nn.config.architecture.layers).toBe(); + // expect(nn.config.architecture.activations).toBe(NN_DEFAULTS.activations); + + // training defaults + expect(nn.config.training.batchSize).toBe(NN_DEFAULTS.batchSize); + expect(nn.config.training.epochs).toBe(NN_DEFAULTS.epochs); + expect(nn.config.training.learningRate).toBe(NN_DEFAULTS.learningRate); + + // expect(nn.config.training.modelMetrics).toBe(NN_DEFAULTS.modelMetrics); + expect(nn.config.training.modelLoss).toBe(NN_DEFAULTS.modelLoss); + // expect(nn.config.training.modelOptimizer).toBe(); + + // data defaults + // expect(nn.config.dataOptions.dataUrl).toBe(); + // expect(nn.config.dataOptions.inputs).toBe(NN_DEFAULTS.inputs); + // expect(nn.config.dataOptions.outputs).toBe(NN_DEFAULTS.outputs); + + // expect(nn.config.dataOptions.normalizationOptions).toBe(); + + }); + +}); diff --git a/src/PoseNet/index.js b/src/PoseNet/index.js index 7a6fd49aa..184df5bfa 100644 --- a/src/PoseNet/index.js +++ b/src/PoseNet/index.js @@ -14,6 +14,7 @@ import * as posenet from '@tensorflow-models/posenet'; import callCallback from '../utils/callcallback'; const DEFAULTS = { + architecture: 'MobileNetV1', imageScaleFactor: 0.3, outputStride: 16, flipHorizontal: false, @@ -22,12 +23,16 @@ const DEFAULTS = { scoreThreshold: 0.5, nmsRadius: 20, detectionType: 'multiple', + inputResolution: 513, multiplier: 0.75, + quantBytes: 2 }; class PoseNet extends EventEmitter { /** * @typedef {Object} options + * @property {string} architecture - default 'MobileNetV1', + * @property {number} inputResolution - default 257, * @property {number} imageScaleFactor - default 0.3 * @property {number} outputStride - default 16 * @property {boolean} flipHorizontal - default false @@ -36,7 +41,8 @@ class PoseNet extends EventEmitter { * @property {number} scoreThreshold - default 0.5 * @property {number} nmsRadius - default 20 * @property {String} detectionType - default single - * @property {multiplier} nmsRadius - default 0.75 + * @property {multiplier} nmsRadius - default 0.75, + * @property {multiplier} quantBytes - default 2, */ /** * Create a PoseNet model. @@ -55,18 +61,42 @@ class PoseNet extends EventEmitter { * @type {String} * @public */ - this.detectionType = detectionType || DEFAULTS.detectionType; + this.architecture = options.architecture || DEFAULTS.architecture; + this.detectionType = detectionType || options.detectionType || DEFAULTS.detectionType; this.imageScaleFactor = options.imageScaleFactor || DEFAULTS.imageScaleFactor; this.outputStride = options.outputStride || DEFAULTS.outputStride; this.flipHorizontal = options.flipHorizontal || DEFAULTS.flipHorizontal; + this.scoreThreshold = options.scoreThreshold || DEFAULTS.scoreThreshold; this.minConfidence = options.minConfidence || DEFAULTS.minConfidence; + this.maxPoseDetections = options.maxPoseDetections || DEFAULTS.maxPoseDetections; this.multiplier = options.multiplier || DEFAULTS.multiplier; + this.inputResolution = options.inputResolution || DEFAULTS.inputResolution; + this.quantBytes = options.quantBytes || DEFAULTS.quantBytes; + this.nmsRadius = options.nmsRadius || DEFAULTS.nmsRadius; this.ready = callCallback(this.load(), callback); // this.then = this.ready.then; } async load() { - this.net = await posenet.load(this.multiplier); + let modelJson; + if(this.architecture.toLowerCase() === 'mobilenetv1'){ + modelJson = { + architecture: this.architecture, + outputStride: this.outputStride, + inputResolution: this.inputResolution, + multiplier: this.multiplier, + quantBytes: this.quantBytes + } + } else { + modelJson = { + architecture: this.architecture, + outputStride: this.outputStride, + inputResolution: this.inputResolution, + quantBytes: this.quantBytes + } + } + + this.net = await posenet.load(modelJson); if (this.video) { if (this.video.readyState === 0) { @@ -100,6 +130,26 @@ class PoseNet extends EventEmitter { return newPose; } + getInput(inputOr){ + let input; + if (inputOr instanceof HTMLImageElement + || inputOr instanceof HTMLVideoElement + || inputOr instanceof HTMLCanvasElement + || inputOr instanceof ImageData) { + input = inputOr; + } else if (typeof inputOr === 'object' && (inputOr.elt instanceof HTMLImageElement + || inputOr.elt instanceof HTMLVideoElement + || inputOr.elt instanceof ImageData)) { + input = inputOr.elt; // Handle p5.js image and video + } else if (typeof inputOr === 'object' && inputOr.canvas instanceof HTMLCanvasElement) { + input = inputOr.canvas; // Handle p5.js image + } else { + input = this.video; + } + + return input; + } + /** * Given an image or video, returns an array of objects containing pose estimations * using single or multi-pose detection. @@ -108,16 +158,9 @@ class PoseNet extends EventEmitter { */ /* eslint max-len: ["error", { "code": 180 }] */ async singlePose(inputOr, cb) { - let input; - if (inputOr instanceof HTMLImageElement || inputOr instanceof HTMLVideoElement) { - input = inputOr; - } else if (typeof inputOr === 'object' && (inputOr.elt instanceof HTMLImageElement || inputOr.elt instanceof HTMLVideoElement)) { - input = inputOr.elt; // Handle p5.js image and video - } else { - input = this.video; - } + const input = this.getInput(inputOr); - const pose = await this.net.estimateSinglePose(input, this.imageScaleFactor, this.flipHorizontal, this.outputStride); + const pose = await this.net.estimateSinglePose(input, {flipHorizontal: this.flipHorizontal}); const poseWithParts = this.mapParts(pose); const result = [{ pose:poseWithParts, skeleton: this.skeleton(pose.keypoints) }]; this.emit('pose', result); @@ -140,17 +183,15 @@ class PoseNet extends EventEmitter { * @param {function} cb */ async multiPose(inputOr, cb) { - let input; + const input = this.getInput(inputOr); - if (inputOr instanceof HTMLImageElement || inputOr instanceof HTMLVideoElement) { - input = inputOr; - } else if (typeof inputOr === 'object' && (inputOr.elt instanceof HTMLImageElement || inputOr.elt instanceof HTMLVideoElement)) { - input = inputOr.elt; // Handle p5.js image and video - } else { - input = this.video; - } + const poses = await this.net.estimateMultiplePoses(input, { + flipHorizontal: this.flipHorizontal, + maxDetections: this.maxPoseDetections, + scoreThreshold: this.scoreThreshold, + nmsRadius: this.nmsRadius + }); - const poses = await this.net.estimateMultiplePoses(input, this.imageScaleFactor, this.flipHorizontal, this.outputStride); const posesWithParts = poses.map(pose => (this.mapParts(pose))); const result = posesWithParts.map(pose => ({ pose, skeleton: this.skeleton(pose.keypoints) })); this.emit('pose', result); @@ -184,11 +225,14 @@ const poseNet = (videoOrOptionsOrCallback, optionsOrCallback, cb) => { if (typeof optionsOrCallback === 'object') { options = optionsOrCallback; - } else if (typeof optionsOrCallback === 'function') { - callback = optionsOrCallback; } else if (typeof optionsOrCallback === 'string') { detectionType = optionsOrCallback; } + + if (typeof optionsOrCallback === 'function') { + callback = optionsOrCallback; + } + return new PoseNet(video, options, detectionType, callback); }; diff --git a/src/PoseNet/index_test.js b/src/PoseNet/index_test.js index f6d3dd63b..29f2c3fe1 100644 --- a/src/PoseNet/index_test.js +++ b/src/PoseNet/index_test.js @@ -8,11 +8,18 @@ const { poseNet } = ml5; const POSENET_IMG = 'https://github.com/ml5js/ml5-adjacent/raw/master/02_ImageClassification_Video/starter.png'; const POSENET_DEFAULTS = { + architecture: 'MobileNetV1', imageScaleFactor: 0.3, + outputStride: 16, flipHorizontal: false, minConfidence: 0.5, + maxPoseDetections: 5, + scoreThreshold: 0.5, + nmsRadius: 20, detectionType: 'multiple', + inputResolution: 513, multiplier: 0.75, + quantBytes: 2 }; describe('PoseNet', () => { @@ -32,11 +39,12 @@ describe('PoseNet', () => { }); it('instantiates poseNet', () => { + expect(net.architecture).toBe(POSENET_DEFAULTS.architecture); expect(net.imageScaleFactor).toBe(POSENET_DEFAULTS.imageScaleFactor); - expect(net.flipHorizontal).toBe(POSENET_DEFAULTS.flipHorizontal); - expect(net.minConfidence).toBe(POSENET_DEFAULTS.minConfidence); - expect(net.detectionType).toBe(POSENET_DEFAULTS.detectionType); + expect(net.outputStride).toBe(POSENET_DEFAULTS.outputStride); + expect(net.inputResolution).toBe(POSENET_DEFAULTS.inputResolution); expect(net.multiplier).toBe(POSENET_DEFAULTS.multiplier); + expect(net.quantBytes).toBe(POSENET_DEFAULTS.quantBytes); }); // it('detects poses in image', async () => { diff --git a/src/StyleTransfer/index.js b/src/StyleTransfer/index.js index 658a4ba48..79f50743b 100644 --- a/src/StyleTransfer/index.js +++ b/src/StyleTransfer/index.js @@ -109,7 +109,9 @@ class StyleTransfer extends Video { inputOrCallback instanceof HTMLImageElement || inputOrCallback instanceof ImageData) { input = inputOrCallback; - } else if (typeof inputOrCallback === 'object' && (inputOrCallback.elt instanceof HTMLVideoElement || inputOrCallback.elt instanceof HTMLImageElement)) { + } else if (typeof inputOrCallback === 'object' && (inputOrCallback.elt instanceof HTMLVideoElement + || inputOrCallback.elt instanceof HTMLImageElement + || inputOrCallback.elt instanceof ImageData)) { input = inputOrCallback.elt; } else if (typeof inputOrCallback === 'function') { input = this.video; diff --git a/src/UNET/index.js b/src/UNET/index.js index 879efe4b1..f6ecfdc0f 100644 --- a/src/UNET/index.js +++ b/src/UNET/index.js @@ -11,6 +11,7 @@ import * as tf from '@tensorflow/tfjs'; import callCallback from '../utils/callcallback'; import { array3DToImage } from '../utils/imageUtilities'; import Video from '../utils/Video'; +import p5Utils from '../utils/p5Utils'; const URL = 'https://raw.githubusercontent.com/zaidalyafeai/HostedModels/master/unet-128/model.json'; const imageSize = 128; @@ -40,10 +41,10 @@ class UNET extends Video { } // check if p5js - static checkP5() { - if (typeof window !== 'undefined' && window.p5 && window.p5.Image && typeof window.p5.Image === 'function') return true; - return false; - } + // static checkP5() { + // if (typeof window !== 'undefined' && window.p5 && window.p5.Image && typeof window.p5.Image === 'function') return true; + // return false; + // } async segment(inputOrCallback, cb) { await this.ready; @@ -51,9 +52,12 @@ class UNET extends Video { let callback = cb; if (inputOrCallback instanceof HTMLImageElement - || inputOrCallback instanceof HTMLVideoElement) { + || inputOrCallback instanceof HTMLVideoElement + || inputOrCallback instanceof ImageData) { imgToPredict = inputOrCallback; - } else if (typeof inputOrCallback === 'object' && (inputOrCallback.elt instanceof HTMLImageElement || inputOrCallback.elt instanceof HTMLVideoElement)) { + } else if (typeof inputOrCallback === 'object' && (inputOrCallback.elt instanceof HTMLImageElement + || inputOrCallback.elt instanceof HTMLVideoElement + || inputOrCallback.elt instanceof ImageData)) { imgToPredict = inputOrCallback.elt; } else if (typeof inputOrCallback === 'function') { imgToPredict = this.video; @@ -105,8 +109,10 @@ class UNET extends Video { const raw = await tf.browser.toPixels(tensor); let image; - if (UNET.checkP5()) { - image = window.loadImage(dom.src); + if (p5Utils.checkP5()) { + const blob1 = await p5Utils.rawToBlob(raw, imageSize, imageSize); + const p5Image1 = await p5Utils.blobToP5Image(blob1); + image = p5Image1; } return { diff --git a/src/YOLO/index.js b/src/YOLO/index.js index 822d3e339..9a9646c6c 100644 --- a/src/YOLO/index.js +++ b/src/YOLO/index.js @@ -74,9 +74,13 @@ class YOLOBase extends Video { let imgToPredict; let callback = cb; - if (inputOrCallback instanceof HTMLImageElement || inputOrCallback instanceof HTMLVideoElement) { + if (inputOrCallback instanceof HTMLImageElement + || inputOrCallback instanceof HTMLVideoElement + || inputOrCallback instanceof ImageData) { imgToPredict = inputOrCallback; - } else if (typeof inputOrCallback === 'object' && (inputOrCallback.elt instanceof HTMLImageElement || inputOrCallback.elt instanceof HTMLVideoElement)) { + } else if (typeof inputOrCallback === 'object' && (inputOrCallback.elt instanceof HTMLImageElement + || inputOrCallback.elt instanceof HTMLVideoElement + || inputOrCallback.elt instanceof ImageData)) { imgToPredict = inputOrCallback.elt; // Handle p5.js image and video. } else if (typeof inputOrCallback === 'function') { imgToPredict = this.video; diff --git a/src/YOLO/index_test.js b/src/YOLO/index_test.js index ac72b7541..56293b2fc 100644 --- a/src/YOLO/index_test.js +++ b/src/YOLO/index_test.js @@ -23,6 +23,22 @@ describe('YOLO', () => { return img; } + async function getImageData() { + const arr = new Uint8ClampedArray(40000); + + // Iterate through every pixel + for (let i = 0; i < arr.length; i += 4) { + arr[i + 0] = 0; // R value + arr[i + 1] = 190; // G value + arr[i + 2] = 0; // B value + arr[i + 3] = 255; // A value + } + + // Initialize a new ImageData object + const img = new ImageData(arr, 200); + return img; + } + beforeEach(async () => { jasmine.DEFAULT_TIMEOUT_INTERVAL = 100000; yolo = await YOLO(); @@ -40,4 +56,10 @@ describe('YOLO', () => { const detection = await yolo.detect(robin); expect(detection[0].label).toBe('bird'); }); + + it('detects takes ImageData', async () => { + const img = await getImageData(); + const detection = await yolo.detect(img); + expect(detection).toEqual([]); + }); }); diff --git a/src/index.js b/src/index.js index e1c535829..076402c86 100644 --- a/src/index.js +++ b/src/index.js @@ -24,6 +24,10 @@ import preloadRegister from './utils/p5PreloadHelper'; import { version } from '../package.json'; import sentiment from './Sentiment'; import bodyPix from './BodyPix'; +import neuralNetwork from './NeuralNetwork'; +import faceApi from './FaceApi'; +import kmeans from './KMeans'; +import p5Utils from './utils/p5Utils'; const withPreload = { charRNN, @@ -31,6 +35,7 @@ const withPreload = { DCGAN, featureExtractor, imageClassifier, + kmeans, soundClassifier, pitchDetection, pix2pix, @@ -40,13 +45,15 @@ const withPreload = { word2vec, YOLO, uNet, + sentiment, + bodyPix, + faceApi, }; -module.exports = Object.assign({}, preloadRegister(withPreload), { +module.exports = Object.assign({p5Utils}, preloadRegister(withPreload), { KNNClassifier, ...imageUtils, tf, version, - sentiment, - bodyPix, + neuralNetwork, }); diff --git a/src/utils/p5Utils.js b/src/utils/p5Utils.js index d14d21053..8c4ea62ee 100644 --- a/src/utils/p5Utils.js +++ b/src/utils/p5Utils.js @@ -3,64 +3,100 @@ // This software is released under the MIT License. // https://opensource.org/licenses/MIT +class P5Util { + constructor() { + this.p5Instance = window; + } -// This function will check if the p5 is in the environment -const checkP5 = () => { - if (typeof window !== 'undefined' && window.p5 && window.p5.Image && typeof window.p5.Image === 'function') return true; - return false; -}; + /** + * Set p5 instance globally. + * @param {Object} p5Instance + */ + setP5Instance(p5Instance) { + this.p5Instance = p5Instance; + } -// Convert a canvas to Blob -const getBlob = (inputCanvas) => { - return new Promise((resolve) => { - inputCanvas.toBlob((blob) => { - resolve(blob); - }); - }); -}; + /** + * This function will check if the p5 is in the environment + * Either it is in the p5Instance mode OR it is in the window + * @returns {boolean} if it is in p5 + */ + checkP5() { + // typeof this.p5Instance !== 'undefined' && this.p5Instance.p5 && this.p5Instance.p5.Image && typeof this.p5Instance.p5.Image === 'function' + if (typeof this.p5Instance !== 'undefined' && + typeof this.p5Instance.loadImage === 'function' || + typeof this.p5Instance.p5 !== 'undefined' && + typeof this.p5Instance.p5.Image !== 'undefined' && + typeof this.p5Instance.p5.Image === 'function') return true; + return false + } + /** + * Convert a canvas to Blob + * @param {HTMLCanvasElement} inputCanvas + * @returns {Blob} blob object + */ + /* eslint class-methods-use-this: ["error", { "exceptMethods": ["getBlob"] }] */ + getBlob(inputCanvas) { + return new Promise((resolve) => { + inputCanvas.toBlob((blob) => { + resolve(blob); + }); + }); + }; -// Load Image Async -const loadAsync = (url) => { - return new Promise((resolve) => { - /* global loadImage */ - loadImage(url, (img) => { - resolve(img); + /** + * Load image in async way. + * @param {String} url + */ + loadAsync(url) { + return new Promise((resolve) => { + this.p5Instance.loadImage(url, (img) => { + resolve(img); + }); }); - }); -}; + }; -// Convert a bytes-array to a Blob Object -const rawToBlob = async (raws, x, y) => { - // console.log(raws, x, y) - const arr = Array.from(raws) - const canvas = document.createElement('canvas'); // Consider using offScreenCanvas when it is ready? - const ctx = canvas.getContext('2d'); + /** + * convert raw bytes to blob object + * @param {Array} raws + * @param {number} x + * @param {number} y + * @returns {Blob} + */ + async rawToBlob(raws, x, y) { + const arr = Array.from(raws) + const canvas = document.createElement('canvas'); // Consider using offScreenCanvas when it is ready? + const ctx = canvas.getContext('2d'); - canvas.width = x; - canvas.height = y; + canvas.width = x; + canvas.height = y; - const imgData = ctx.createImageData(x, y); - const { data } = imgData; + const imgData = ctx.createImageData(x, y); + const { data } = imgData; - for (let i = 0; i < x * y * 4; i += 1 ) data[i] = arr[i]; - ctx.putImageData(imgData, 0, 0); + for (let i = 0; i < x * y * 4; i += 1 ) data[i] = arr[i]; + ctx.putImageData(imgData, 0, 0); - const blob = await getBlob(canvas); - return blob; -}; + const blob = await this.getBlob(canvas); + return blob; + }; -// Conver Blob to P5.Image -const blobToP5Image = async (blob) => { - if (checkP5()) { - const p5Img = await loadAsync(URL.createObjectURL(blob)); - return p5Img; - } - return null; -}; + /** + * Conver Blob to P5.Image + * @param {Blob} blob + * @param {Object} p5Img + */ + async blobToP5Image(blob) { + if (this.checkP5()) { + const p5Img = await this.loadAsync(URL.createObjectURL(blob)); + return p5Img; + } + return null; + }; -export { - checkP5, - rawToBlob, - blobToP5Image } + +const p5Utils = new P5Util(); + +export default p5Utils; diff --git a/src/utils/random.js b/src/utils/random.js index 10e8fb919..38aad48d8 100644 --- a/src/utils/random.js +++ b/src/utils/random.js @@ -37,4 +37,19 @@ const randomGaussian = (mean = 0, sd = 1) => { return (y1 * sd) + mean; }; -export { randomFloat, randomInt, randomGaussian }; +// Returns a random sample (either with or without replacement) of size k from an array +const randomSample = (arr, k, withReplacement = false) => { + let sample; + if (withReplacement === true) { // sample with replacement + sample = Array.from({length: k}, () => arr[Math.floor(Math.random() * arr.length)]); + } else { // sample without replacement + if (k > arr.length) { + throw new RangeError('Sample size must be less than or equal to array length when sampling without replacement.') + } + sample = arr.map(a => [a, Math.random()]).sort((a, b) => { + return a[1] < b[1] ? -1 : 1;}).slice(0, k).map(a => a[0]); + }; + return sample; +}; + +export { randomFloat, randomInt, randomGaussian, randomSample }; diff --git a/webpack.dev.babel.js b/webpack.dev.babel.js index 2d97397e5..489a79114 100644 --- a/webpack.dev.babel.js +++ b/webpack.dev.babel.js @@ -13,7 +13,8 @@ export default merge(common, { devtool: 'inline-source-map', devServer: { watchContentBase: true, - contentBase: join(__dirname, './dist') + contentBase: join(__dirname, './dist'), + disableHostCheck: true, }, plugins: [ new HtmlWebpackPlugin({