[ { "path": ".devcontainer/Dockerfile", "content": "# See here for image contents: https://github.com/microsoft/vscode-dev-containers/tree/v0.183.0/containers/typescript-node/.devcontainer/base.Dockerfile\n\n# [Choice] Node.js version: 16, 14, 12\nARG VARIANT=\"16-buster\"\nFROM mcr.microsoft.com/vscode/devcontainers/typescript-node:0-${VARIANT}\n\n# [Optional] Uncomment this section to install additional OS packages.\n# RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \\\n# && apt-get -y install --no-install-recommends \n\n# [Optional] Uncomment if you want to install an additional version of node using nvm\n# ARG EXTRA_NODE_VERSION=10\n# RUN su node -c \"source /usr/local/share/nvm/nvm.sh && nvm install ${EXTRA_NODE_VERSION}\"\n\n# [Optional] Uncomment if you want to install more global node packages\n# RUN su node -c \"npm install -g \"\n" }, { "path": ".devcontainer/devcontainer.json", "content": "// For format details, see https://aka.ms/devcontainer.json. For config options, see the README at:\n// https://github.com/microsoft/vscode-dev-containers/tree/v0.183.0/containers/typescript-node\n{\n\t\"name\": \"Node.js & TypeScript\",\n\t\"build\": {\n\t\t\"dockerfile\": \"Dockerfile\",\n\t\t// Update 'VARIANT' to pick a Node version: 12, 14, 16\n\t\t\"args\": {\n\t\t\t\"VARIANT\": \"16\"\n\t\t}\n\t},\n\t// Set *default* container specific settings.json values on container create.\n\t\"settings\": {},\n\t// Add the IDs of extensions you want installed when the container is created.\n\t\"extensions\": [\n\t\t\"dbaeumer.vscode-eslint\"\n\t],\n\t// Use 'forwardPorts' to make a list of ports inside the container available locally.\n\t// \"forwardPorts\": [],\n\t// Use 'postCreateCommand' to run commands after the container is created.\n\t// \"postCreateCommand\": \"yarn install\",\n\t// Comment out connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root.\n\t// \"remoteUser\": \"node\"\n}" }, { "path": ".editorconfig", "content": "[*.js]\nindent_style = space\nindent_size = 2\n" }, { "path": ".eslintrc.js", "content": "module.exports = {\n env: {\n browser: true,\n node: true\n },\n extends: 'eslint:recommended',\n globals: {\n Promise: false,\n define: false,\n Uint8ClampedArray: false\n },\n rules: {\n indent: ['error', 2],\n 'linebreak-style': ['error', 'unix'],\n quotes: ['error', 'single'],\n semi: ['error', 'always']\n },\n overrides: [\n {\n files: ['test/*.js', 'examples/*.js'],\n rules: { 'no-console': 'off' },\n env: {\n browser: true,\n node: true,\n mocha: true\n },\n globals: {\n smartcrop: false,\n Benchmark: false,\n $: false,\n chai: false,\n debugDraw: false,\n Uint8Array: false,\n _: false,\n cv: false,\n tracking: false,\n faceapi: false\n }\n }\n ]\n};\n" }, { "path": ".gitattributes", "content": ".gitattributes export-ignore \n.gitignore export-ignore\nkarma.conf.js export-ignore\nGruntfile.js export-ignore\nCONTRIBUTING.md export-ignore\nexamples/images/*.jpg export-ignore\nscripts/ export-ignore\n.npmignore export-ignore\n.travis.yml export-ignore\n.vimrc export-ignore\n\n" }, { "path": ".github/workflows/publish.yml", "content": "name: Publish\n\non:\n release:\n types: [published]\n\njobs:\n publish:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v2\n - uses: actions/setup-node@v2\n with:\n node-version: 14\n registry-url: 'https://registry.npmjs.org' \n - run: npm ci\n - run: npm publish\n env:\n NPM_TOKEN: ${{ secrets.NPM_TOKEN }}\n NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}" }, { "path": ".github/workflows/tests.yml", "content": "name: Tests\n\non:\n push:\n branches: [ main ]\n pull_request:\n branches: [ '*' ]\n\njobs:\n tests:\n\n runs-on: ubuntu-latest\n\n strategy:\n matrix:\n node-version: [16.x]\n\n steps:\n - uses: actions/checkout@v2\n - name: Use Node.js ${{ matrix.node-version }}\n uses: actions/setup-node@v2\n with:\n node-version: ${{ matrix.node-version }}\n - run: npm ci\n - run: npm run lint\n - run: npm test\n env:\n SAUCE_ACCESS_KEY: ${{ secrets.SAUCE_ACCESS_KEY }}\n SAUCE_USERNAME: ${{ secrets.SAUCE_USERNAME }}" }, { "path": ".gitignore", "content": "node_modules\n.500px-consumer-key\ndist\nrelease\n" }, { "path": ".prettierrc", "content": "{\n \"singleQuote\": true\n}\n" }, { "path": ".vimrc", "content": "set shiftwidth=2\nautocmd FileType javascript setlocal expandtab shiftwidth=4 softtabstop=4\n" }, { "path": "CONTRIBUTING.md", "content": "# How to contribute\n\n## Reporting Bugs\n\nWhen reporting a bug please provide **all** the information needed to quickly reproduce the problem.\n\nThis includes:\n\n* The browser & version used\n* A URL to reproduce the error\n* Other steps other necessary to reproduce the error\n\nIf I can't reproduce your problem it will likely not get fixed.\n\n## Contributing Code\n\n### Before Hacking\n\nTo avoid wasting your time please communicate before starting to hack\non a new feature / change.\nCreating an issue describing your plans is a good start.\n\n### Before submitting a pull request\n\n* Try to make your code look like the code around it.\n* Provide tests where appropriate.\n* Run eslint & prettier on the files you changed, fix any issues related to your changes.\n* Run the tests, make sure they are green\n\n### Please Avoid\n\n* Reformat my code to fit your personal preferences.\n\n### License\n\nBy contributing your code, you agree to license your contribution under the MIT License.\n\n## Setting up the development environment\n\nBefore you can start working you'll need to have node.js and git installed.\n\n```\n$ git clone\n$ cd normalmap.js\n$ npm install -g grunt jshint jscs\n$ npm install\n$ grunt\n```\n\nI work on GNU/Linux. Things might not work on windows.\n\n## Running the tests\n\nThe tests for smartcrop are fairly simplistic at the moment.\n\nYou can run them by opening the [/test/](http://localhost:8000/test/)\nfolder after running `grunt`.\n\nThe tests show the actual output next to the expected output and a diff.\n\n## Code of Conduct\n\nNone. Just be who you are.\n" }, { "path": "Gruntfile.js", "content": "module.exports = function(grunt) {\n grunt.initConfig({\n connect: {\n options: {\n // useAvailablePort: true,\n port: 8000,\n hostname: '*',\n base: '.',\n // keepalive: true,\n livereload: true\n // debug: true,\n },\n server: {\n options: {\n open: 'http://localhost:8000/examples/testsuite.html'\n }\n },\n test: {\n options: {\n open: 'http://localhost:8000/test/'\n }\n }\n },\n watch: {\n options: {\n livereload: true\n },\n static: {\n files: ['smartcrop.js', 'examples/*', 'test/*'],\n options: {\n livereload: true\n }\n }\n },\n rsync: {\n options: {\n recursive: true\n },\n release: {\n options: {\n src: 'examples smartcrop.js doc/example.jpg',\n dest: '/var/www/static/sandbox/2014/smartcrop/',\n host: '29a.ch',\n port: '22',\n dryRun: false\n }\n }\n },\n karma: {\n unit: {\n configFile: 'karma.conf.js'\n }\n }\n });\n grunt.registerTask('default', ['connect:server', 'watch']);\n grunt.registerTask('test', ['connect:test', 'watch']);\n grunt.registerTask(\n 'fetchSamples',\n 'fetch sample images from 500px api',\n function() {\n var done = this.async();\n var API500px = require('500px').API500px;\n var http = require('http');\n var fs = require('fs');\n var consumerKey = grunt.file\n .read('.500px-consumer-key')\n .replace(/\\n$/, '');\n var api500px = new API500px(consumerKey);\n // jscs:disable requireCamelCaseOrUpperCaseIdentifiers\n var options = {\n rpp: 100,\n image_size: 4,\n license_type: 4,\n sort: 'rating'\n };\n\n api500px.photos.searchByTerm('', options, function(error, results) {\n var downloaded = 0;\n var samples = results.photos.map(function(photo) {\n var name = 'images/' + photo.id * 1 + '.jpg';\n var f = fs.createWriteStream('examples/' + name);\n http.get(photo.image_url, function(response) {\n response.pipe(f);\n });\n f.on('finish', function() {\n f.close();\n if (++downloaded == samples.length) {\n done();\n }\n });\n return {\n id: photo.id,\n name: photo.name,\n url: name,\n attribution: photo.user.fullname,\n thumb: photo.image_url.replace('4.jpg', '2.jpg'),\n href: 'http://500px.com/photo/' + photo.id\n };\n });\n grunt.file.write(\n 'examples/images/images.json',\n JSON.stringify(samples)\n );\n // jscs:enable\n });\n }\n );\n grunt.loadNpmTasks('grunt-contrib-watch');\n grunt.loadNpmTasks('grunt-contrib-connect');\n grunt.loadNpmTasks('grunt-rsync');\n};\n" }, { "path": "LICENSE", "content": "Copyright (c) 2016 Jonas Wagner\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "README.md", "content": "# smartcrop.js\n\n![example workflow](https://github.com/jwagner/smartcrop.js/actions/workflows/tests.yml/badge.svg)\n\nSmartcrop.js implements an algorithm to find good crops for images.\nIt can be used in the browser, in node or via a CLI.\n\n![Example](https://29a.ch/sandbox/2014/smartcrop/example.jpg)\nImage: [https://www.flickr.com/photos/endogamia/5682480447/](https://www.flickr.com/photos/endogamia/5682480447) by Leon F. Cabeiro (N. Feans), licensed under CC-BY-2.0\n\n> **Note**\n> I'm currently working on a more advanced version of smartcrop.js based on machine learning. As part of that I'm looking for a large dataset of manually cropped images. If you know of such a dataset, please [let me know](https://29a.ch/about).\n\n## Demos\n\n- [Smartcrop.js Test Suite](https://29a.ch/sandbox/2014/smartcrop/examples/testsuite.html), contains over 100 images, **heavy**.\n- [Smartcrop.js Test Bed](https://29a.ch/sandbox/2014/smartcrop/examples/testbed.html), allows you to test smartcrop with your own images and different face detection libraries.\n- [Automatic Photo transitions](https://29a.ch/sandbox/2014/smartcrop/examples/slideshow.html), automatically creates Ken Burns transitions for a slide show.\n\n## Simple Example\n\n```javascript\n// you pass in an image as well as the width & height of the crop you\n// want to optimize.\nsmartcrop.crop(image, { width: 100, height: 100 }).then(function(result) {\n console.log(result);\n});\n```\n\nOutput:\n\n```javascript\n// smartcrop will output you its best guess for a crop\n// you can now use this data to crop the image.\n{topCrop: {x: 300, y: 200, height: 200, width: 200}}\n```\n\n## Download/ Installation\n\n`npm install smartcrop`\nor just download [smartcrop.js](https://raw.githubusercontent.com/jwagner/smartcrop.js/master/smartcrop.js) from the git repository.\n\nSmarcrop requires support for [Promises](http://caniuse.com/#feat=promises),\nuse a [polyfill](https://github.com/taylorhakes/promise-polyfill) for unsupported browsers or set `smartcrop.Promise` to your favorite promise implementation\n(I recommend [bluebird](http://bluebirdjs.com/)).\n\n\n## Consider avoiding crops using dont-crop\n\nIf you are interested in using smartcrop.js to crop your images you should also consider to avoid cropping them by using [dont-crop](https://github.com/jwagner/dont-crop/).\nDont-crop gives you matching gradients and colors to pad and complement your images.\n\n![Example](https://29a.ch/images/dont-crop.cache-399897619c3de2e0.jpg)\n\n## Command Line Interface\n\nThe [smartcrop-cli](https://github.com/jwagner/smartcrop-cli) offers command line interface to smartcrop.js.\n\n## Node\n\nYou can use smartcrop from nodejs via either [smartcrop-gm](https://github.com/jwagner/smartcrop-gm) (which is using image magick via gm) or [smartcrop-sharp](https://github.com/jwagner/smartcrop-sharp) (which is using libvips via sharp).\nThe [smartcrop-cli](https://github.com/jwagner/smartcrop-cli) can be used as an example of using smartcrop from node.\n\n## Stability\n\nWhile _smartcrop.js_ is a small personal project it is currently being used on high traffic production sites.\nIt has a basic set of automated tests and a test coverage of close to 100%.\nThe tests run in all modern browsers thanks to [saucelabs](https://saucelabs.com/).\nIf in any doubt the code is short enough to perform a quick review yourself.\n\n## Algorithm Overview\n\nSmartcrop.js works using fairly dumb image processing. In short:\n\n1. Find edges using laplace\n1. Find regions with a color like skin\n1. Find regions high in saturation\n1. Boost regions as specified by options (for example detected faces)\n1. Generate a set of candidate crops using a sliding window\n1. Rank them using an importance function to focus the detail in the center\n and avoid it in the edges.\n1. Output the candidate crop with the highest rank\n\n## Face detection\n\nThe smartcrop algorithm itself is designed to be simple, relatively fast, small and generic.\n\nIn many cases it does make sense to add face detection to it to ensure faces get the priority they deserve.\n\nThere are multiple javascript libraries which can be easily integrated into smartcrop.js.\n\n- [ccv js](https://github.com/liuliu/ccv) / [jquery.facedetection](http://facedetection.jaysalvat.com/)\n- [tracking.js](https://trackingjs.com/examples/face_hello_world.html)\n- [opencv.js](https://docs.opencv.org/3.3.1/d5/d10/tutorial_js_root.html)\n- [node-opencv](https://github.com/peterbraden/node-opencv)\n\nYou can experiment with all of these in the [smartcrop.js testbed](https://29a.ch/sandbox/2014/smartcrop/examples/testbed.html)\n\nOn the client side I would recommend using tracking.js because it's small and simple. Opencv.js is compiled from c++ and very heavy (~7.6MB of javascript + 900kb of data).\njquery.facedetection has dependency on jquery and from my limited experience seems to perform worse than the others.\n\nOn the server side node-opencv can be quicker but comes with some [annoying issues](https://github.com/peterbraden/node-opencv/issues/415) as well.\n\nIt's also worth noting that all of these libraries are based on the now dated [viola-jones](https://en.wikipedia.org/wiki/Viola%E2%80%93Jones_object_detection_framework) object detection framework.\nIt would be interesting to see how more [state of the art](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/WiderFace_Results.html) techniques could be implemented in browser friendly javascript.\n\n## Supported Module Formats\n\n- CommonJS\n- AMD\n- global export / window\n\n## Supported Browsers\n\nSee [caniuse.com/canvas](http://caniuse.com/canvas).\nA [polyfill](https://github.com/taylorhakes/promise-polyfill) for\n[Promises](http://caniuse.com/#feat=promises) is recommended if you need to support old browsers.\n\n## API\n\n### smartcrop.crop(image, options)\n\nFind the best crop for _image_ using _options_.\n\n**image:** anything ctx.drawImage() accepts, usually HTMLImageElement, HTMLCanvasElement or HTMLVideoElement.\n\nKeep in mind that [origin policies](https://en.wikipedia.org/wiki/Same-origin_policy) apply to the image source.\nYou may not use cross-domain images without [CORS](https://en.wikipedia.org/wiki/Cross-origin_resource_sharing) clearance.\n\n**options:** [cropOptions](#cropOptions)\n\n**returns:** A promise for a [cropResult](#cropResult).\n\n### cropOptions\n\n**minScale:** minimal scale of the crop rect, set to 1.0 to prevent smaller than necessary crops (lowers the risk of chopping things off).\n\n**width:** width of the crop you want to use.\n\n**height:** height of the crop you want to use.\n\n**boost:** optional array of regions whose 'interestingness' you want to boost (for example faces). See [boost](#boost);\n\n**ruleOfThirds:** optional boolean if set to false it will turn off the rule of thirds composition weight.\n\n**debug _(internal)_:** if true, cropResults will contain a debugCanvas and the complete results array.\n\nThere are many more (for now undocumented) options available.\nCheck the [source](smartcrop.js#L32) and be advised that they might change in the future.\n\n### cropResult\n\nResult of the promise returned by smartcrop.crop.\n\n```javascript\n{\n topCrop: crop;\n}\n```\n\n### crop\n\nAn individual crop.\n\n```javascript\n{\n x: 11, // pixels from the left side\n y: 20, // pixels from the top\n width: 1, // pixels\n height: 1 // pixels\n}\n```\n\n### boost\n\nDescribes a region to boost. A usage example of this is to take\ninto account faces in the image. See [smartcrop-cli](https://github.com/jwagner/smartcrop-cli) for an example on how to integrate face detection.\n\n```javascript\n{\n x: 11, // pixels from the left side\n y: 20, // pixels from the top\n width: 32, // pixels\n height: 32, // pixels\n weight: 1 // in the range [0, 1]\n}\n```\n\nNote that the impact the boost has is proportional to it's weight and area.\n\n## Tests\n\nYou can run the tests using `grunt test`. Alternatively you can also just run grunt (the default task) and open \n\n## Benchmark\n\nThere are benchmarks for both the browser (test/benchmark.html) and node (node test/benchmark-node.js [requires node-canvas])\nboth powered by [benchmark.js](http://benchmarkjs.com).\n\nIf you just want some rough numbers: It takes **< 20 ms** to find a **square crop** of a **640x427px** picture on an i7.\nIn other words, it's fine to run it on one image, it's suboptimal to run it on an entire gallery on page load.\n\n## Contributors\n\n- [Christian Muehlhaeuser](https://github.com/muesli)\n\n## Ports, Alternatives\n\n- [connect-thumbs](https://github.com/inadarei/connect-thumbs) Middleware for connect.js that supports smartcrop.js by [Irakli Nadareishvili](https://github.com/inadarei/connect-thumbs)\n- [smartcrop-java](https://github.com/QuadFlask/smartcrop-java) by [QuadFlask](https://github.com/QuadFlask/)\n- [smartcrop-android](https://github.com/QuadFlask/smartcrop-android) by [QuadFlask](https://github.com/QuadFlask/)\n- [smartcrop.go](https://github.com/muesli/smartcrop) by [Christian Muehlhaeuser](https://github.com/muesli)\n- [smartcrop.py](https://github.com/hhatto/smartcrop.py) by [Hideo Hattori](http://www.hexacosa.net/about/)\n- [smartcrop-rails](https://github.com/sadiqmmm/smartcrop-rails) smartcrop wrapped in a ruby gem by [Mohammed Sadiq](https://github.com/sadiqmmm/)\n- [smartcrop.net](https://github.com/softawaregmbh/smartcrop.net) c# .net port by [softaware gmbh](https://www.softaware.at/)\n- [dont-crop](https://github.com/jwagner/dont-crop/) a library to avoid cropping by padding images with matching colors or gradients\n\n## Version history\n\n### 2.0.5\nFix `TS1046: Top-level declarations in .d.ts files must start with either a 'declare' or 'export' modifier.`.\n\n### 2.0.4\nTypescript type definitions.\n\n### 2.0.2\n\nIn short: It's a lot faster when calculating bigger crops.\nThe quality of the crops should be comparable but the results\nare going to be different so this will be a major release.\n\n### 1.1.1\n\nRemoved useless files from npm package.\n\n### 1.1\n\nCreating github releases. Added options.input which is getting passed along to iop.open.\n\n### 1.0\n\nRefactoring/cleanup to make it easier to use with node.js (dropping the node-canvas dependency) and enable support for boosts which can be used to do face detection.\nThis is a 1.0 in the semantic meaning (denoting backwards incompatible API changes).\nIt does not denote a finished product.\n\n## License\n\nCopyright (c) 2018 Jonas Wagner, licensed under the MIT License (enclosed)\n" }, { "path": "bower.json", "content": "{\n \"name\": \"smartcrop\",\n \"main\": \"smartcrop.js\",\n \"authors\": [\n \"Jonas Wagner \"\n ],\n \"description\": \"Content aware image cropping\",\n \"moduleType\": [\n \"amd\",\n \"globals\",\n \"node\"\n ],\n \"license\": \"MIT\",\n \"homepage\": \"https://github.com/jwagner/smartcrop.js\",\n \"ignore\": [\n \"**/.*\",\n \"node_modules\",\n \"bower_components\",\n \"test\",\n \"doc\",\n \"tests\",\n \"karma.conf.js\",\n \"Gruntfile.js\",\n \"examples\"\n ],\n \"devDependencies\": {\n \"underscore\": \"~1.6.0\",\n \"jquery\": \"~2.1.0\",\n \"jasmine\": \"~2.0.0\",\n \"mocha\": \"~1.18.2\",\n \"chai\": \"~1.9.1\"\n }\n}\n" }, { "path": "examples/face-api/tiny_face_detector_model-weights_manifest.json", "content": 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http://www.opensource.org/licenses/mit-license.html\n * Forked at ref_send.js version: 2009-05-11\n *\n * With parts by Mark Miller\n * Copyright (C) 2011 Google Inc.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\n(function (definition) {\n // Turn off strict mode for this function so we can assign to global.Q\n /* jshint strict: false */\n\n // This file will function properly as a \n \n \n \n \n \n\n\n" }, { "path": "examples/slideshow.js", "content": "/* global jQuery, Q */\njQuery(function($) {\n var transitionend =\n 'transitionend webkitTransitionEnd oTransitionEnd MSTransitionEnd';\n\n var slide = $('#slide');\n var prevSlide = $('#prev-slide');\n var images = [];\n var width = slide.width();\n var height = slide.height();\n\n for (var i = 0; i < 8; i++) {\n images.push({ url: 'images/slideshow/' + i + '.jpg' });\n }\n\n function loadImage(src) {\n var d = Q.defer();\n var i = new Image();\n\n i.onload = function() {\n d.resolve(i);\n };\n i.onerror = function(e) {\n d.reject(e);\n };\n i.src = src;\n return d.promise;\n }\n\n function smartcrop(img, options) {\n var d = Q.defer();\n smartcrop.crop(img, options, d.resolve.bind(d));\n return d.promise;\n }\n\n function slideShow(images) {\n var i = 0;\n\n function next() {\n showSlide(images[i], next);\n i = (i + 1) % images.length;\n }\n\n images = _.chain(images)\n .shuffle()\n .head(10)\n .value();\n\n Q.all(\n images.map(function(i) {\n return loadImage(i.url).then(function(img) {\n i.img = img;\n var options = {\n width: width * 0.1,\n height: height * 0.1,\n ruleOfThirds: false\n };\n return Q.all([\n smartcrop(\n img,\n _.extend({ maxScale: 0.8, minScale: 0.7 }, options)\n ).then(function(result) {\n i.from = result;\n }),\n smartcrop(img, _.extend({ minScale: 1 }, options)).then(function(\n result\n ) {\n i.to = result;\n })\n ]);\n });\n })\n ).then(next);\n }\n\n function showSlide(image, done) {\n var img = image.img;\n function transform(crop) {\n var s = width / crop.width;\n var x = crop.x;\n var y = crop.y;\n var t = 'scale(' + s + ') translate(-' + x + 'px, -' + y + 'px)';\n return {\n '-webkit-transform': t,\n transform: t,\n '-webkit-transform-origin': '0 0',\n 'transform-origin': '0 0'\n };\n }\n\n // zooming out usually works better, but some change is good too\n var from, to;\n if (\n (image.from.topCrop.score.total + 0.1) * 1.002 >\n image.to.topCrop.score.total + 0.1\n ) {\n from = image.from;\n to = image.to;\n } else {\n from = image.to;\n to = image.from;\n }\n window.from = from;\n window.to = to;\n var last = $('img', slide);\n if (last[0]) {\n prevSlide.empty().append(last.remove());\n last.width();\n last.css('opacity', '0');\n }\n slide.empty().append(img);\n $(img)\n .css(transform(from.topCrop))\n .css('opacity', 1)\n .width(); // reflow\n $(img)\n .on(transitionend, _.once(done))\n .css(transform(to.topCrop));\n\n img.onerror = done;\n img.src = image.url;\n }\n\n slideShow(images);\n});\n" }, { "path": "examples/smartcrop-debug.js", "content": "// eslint-disable-next-line no-unused-vars\nfunction debugDraw(result, showCrop) {\n var topCrop = result.debugTopCrop;\n var options = result.debugOptions;\n var output = result.debugOutput;\n var canvas = document.createElement('canvas');\n canvas.width = output.width;\n canvas.height = output.height;\n var ctx = canvas.getContext('2d');\n ctx.fillStyle = 'rgba(255, 0, 0, 0.1)';\n ctx.fillRect(topCrop.x, topCrop.y, topCrop.width, topCrop.height);\n var debugOutput = ctx.createImageData(output.width, output.height);\n debugOutput.data.set(output.data);\n for (var y = 0; y < output.height; y++) {\n for (var x = 0; x < output.width; x++) {\n var p = (y * output.width + x) * 4;\n if (showCrop) {\n var I = smartcrop.importance(options, topCrop, x, y);\n if (I > 0) {\n debugOutput.data[p + 1] += I * 32;\n }\n\n if (I < 0) {\n debugOutput.data[p] += I * -64;\n }\n }\n // visualize alpha (boost) as magenta\n var boost = debugOutput.data[p + 3] / 2;\n debugOutput.data[p] += boost;\n debugOutput.data[p + 2] += boost;\n debugOutput.data[p + 3] = 255;\n }\n }\n\n ctx.putImageData(debugOutput, 0, 0);\n ctx.strokeStyle = 'rgba(255, 0, 0, 0.8)';\n if (showCrop) {\n ctx.strokeRect(topCrop.x, topCrop.y, topCrop.width, topCrop.height);\n }\n if (options.boost) {\n ctx.strokeStyle = 'rgba(255, 255, 255, 1.0)';\n for (var i = 0; i < options.boost.length; i++) {\n var b = options.boost[i];\n ctx.strokeRect(b.x, b.y, b.width, b.height);\n }\n }\n return canvas;\n}\n" }, { "path": "examples/style.css", "content": "html {\n width: 100%;\n min-height: 100%;\n}\nbody {\n font-family: sans-serif;\n max-width: 1400px;\n padding: 8px;\n margin: auto;\n}\n.dark body {\n max-width: 1240px;\n}\n.dark {\n background: #111;\n color: #888;\n}\n.dark a {\n color: #8af;\n}\n\nh1 {\n font-weight: normal;\n}\n\np {\n max-width: 900px;\n}\ninput[type='file'] {\n display: block;\n}\ninput[type='range'] {\n width: 300px;\n}\n\nimg,\ncanvas {\n padding: 8px;\n box-sizing: border-box;\n max-width: 100%;\n margin: auto;\n}\n\n.output {\n width: 50%;\n float: left;\n}\n\n.testsuite img {\n max-width: 40%;\n}\n.testsuite > div {\n margin-top: 2em;\n}\n\n.testsuite > .testsuite-image-title {\n margin-top: 0;\n}\n\n.sidebar {\n width: 50%;\n float: left;\n height: 600px;\n}\n\n#slide,\n#prev-slide {\n max-width: 100%;\n width: 1280px;\n height: 720px;\n overflow: hidden;\n}\n#slide {\n background: #ccc;\n}\n#prev-slide {\n position: absolute;\n z-index: 1;\n}\n\n#prev-slide img {\n transition: all 1s linear;\n -webkit-transition: all 1s linear;\n opacity: 0.9;\n max-width: none;\n padding: 0;\n margin: 0;\n}\n\n#slide img {\n transition: all 5s linear;\n -webkit-transition: all 5s linear;\n /*transition-delay: 1s;*/\n /*-webkit-transition-delay: 1s; [> Safari <]*/\n opacity: 1;\n max-width: none;\n padding: 0;\n margin: 0;\n}\n\n.opencv-loading {\n color: #666;\n position: relative;\n}\n\n@keyframes spinner {\n to {\n transform: rotate(360deg);\n }\n}\n\n.opencv-loading:after {\n content: '';\n box-sizing: border-box;\n display: inline-block;\n width: 1ex;\n height: 1ex;\n margin-left: 1em;\n border-radius: 50%;\n border: 2px solid #ccc;\n border-top-color: #333;\n animation: spinner 0.6s linear infinite;\n}\n\n.drop {\n background: #eee;\n padding: 1em;\n margin-bottom: 2em;\n}\n" }, { "path": "examples/testbed.html", "content": "\n\n\n\n \n smartcrop.js testbed\n \n \n\n\n\n

smartcrop.js testbed

\n

\n smartcrop.js is a content aware image cropping library. On this page you\n can test it with your own images. You can learn more about the library on\n it's github page.\n

\n
\n
\n
\n

Drop images on this page to analyze them.

\n \n
\n \n \n \n

\n \n

\n

Face Detection

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\n \n
\n
\n \n
\n
\n \n
\n
\n
\n
\n \n
\n
\n
\n

All Crops

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\n
\n \n \n \n \n \n \n \n

Example Photo by Makhmutova Dina on Unsplash

\n \n\n\n" }, { "path": "examples/testbed.js", "content": "(function() {\n var canvas = $('canvas')[0];\n var form = document.forms[0];\n var ctx = canvas.getContext('2d');\n var img;\n\n $('html')\n .on('dragover', function(e) {\n e.preventDefault();\n return false;\n })\n .on('drop', function(e) {\n var files = e.originalEvent.dataTransfer.files;\n handleFiles(files);\n return false;\n });\n\n $('input[type=file]').change(function() {\n handleFiles(this.files);\n });\n\n function handleFiles(files) {\n if (files.length > 0) {\n var file = files[0];\n if (\n typeof FileReader !== 'undefined' &&\n file.type.indexOf('image') != -1\n ) {\n var reader = new FileReader();\n // Note: addEventListener doesn't work in Google Chrome for this event\n reader.onload = function(evt) {\n load(evt.target.result);\n };\n reader.readAsDataURL(file);\n }\n }\n }\n\n load('images/unsplash/makhmutova-dina-1580313-unsplash.jpg');\n\n $('input[type=range]').on(\n 'input',\n _.debounce(function() {\n $(this)\n .next('.value')\n .text($(this).val());\n run();\n }, 500)\n );\n\n $('input[type=radio], input[type=checkbox]').on(\n 'change',\n _.debounce(function() {\n run();\n })\n );\n\n function load(src) {\n img = new Image();\n img.onload = function() {\n run();\n };\n img.src = src;\n }\n\n function run() {\n if (!img) return;\n var options = {\n width: form.width.value * 1,\n height: form.height.value * 1,\n minScale: form.minScale.value * 1,\n ruleOfThirds: form.ruleOfThirds.checked,\n debug: true\n };\n\n var faceDetection = $('input[name=faceDetection]:checked', form).val();\n var analyzeOptions = analyze.bind(this, options);\n\n switch (faceDetection) {\n case 'opencv':\n faceDetectionOpenCV(options, analyzeOptions);\n break;\n case 'face-api':\n faceDetectionFaceAPI(options, analyzeOptions);\n break;\n default:\n analyzeOptions();\n }\n }\n\n function prescaleImage(image, maxDimension, callback) {\n // tracking.js is very slow on big images so make sure the image is reasonably small\n var width = image.naturalWidth || image.width;\n var height = image.naturalHeight || image.height;\n if (width < maxDimension && height < maxDimension)\n return callback(image, 1);\n var scale = Math.min(maxDimension / width, maxDimension / height);\n var canvas = document.createElement('canvas');\n canvas.width = ~~(width * scale);\n canvas.height = ~~(height * scale);\n canvas.getContext('2d').drawImage(image, 0, 0, canvas.width, canvas.height);\n var result = document.createElement('img');\n result.onload = function() {\n callback(result, scale);\n };\n result.src = canvas.toDataURL();\n }\n\n var getFaceAPIDetector = _.memoize(function() {\n return faceapi.nets.tinyFaceDetector.loadFromUri('face-api');\n });\n\n function faceDetectionFaceAPI(options, callback) {\n getFaceAPIDetector()\n .then(\n function() {\n return faceapi.tinyFaceDetector(img);\n },\n function(err) {\n console.error(err);\n alert('Could not load models');\n }\n )\n .then(\n function(faces) {\n options.boost = faces.map(function(face) {\n var box = face.box;\n return {\n x: box.x,\n y: box.y,\n width: box.width,\n height: box.height,\n weight: 1.0\n };\n });\n callback(options);\n },\n function(err) {\n console.error(err);\n alert('could not detectAllFaces');\n }\n );\n }\n\n function faceDetectionOpenCV(options, callback) {\n prescaleImage(img, 768, function(img, scale) {\n var src = cv.imread(img);\n var gray = new cv.Mat();\n cv.cvtColor(src, gray, cv.COLOR_RGBA2GRAY, 0);\n var faces = new cv.RectVector();\n var faceCascade = new cv.CascadeClassifier();\n // load pre-trained classifiers\n faceCascade.load('haarcascade_frontalface_default.xml');\n console.log(faceCascade);\n // detect faces\n var msize = new cv.Size(0, 0);\n faceCascade.detectMultiScale(gray, faces, 1.1, 3, 0, msize, msize);\n options.boost = [];\n for (var i = 0; i < faces.size(); ++i) {\n var face = faces.get(i);\n options.boost.push({\n x: face.x / scale,\n y: face.y / scale,\n width: face.width / scale,\n height: face.height / scale,\n weight: 1.0\n });\n }\n src.delete();\n gray.delete();\n faceCascade.delete();\n faces.delete();\n callback(options);\n });\n }\n\n function analyze(options) {\n console.log('analyze', options);\n smartcrop.crop(img, options, draw);\n }\n\n function draw(result) {\n var selectedCrop = result.topCrop;\n $('.crops')\n .empty()\n .append(\n _.sortBy(result.crops, function(c) {\n return -c.score.total;\n }).map(function(crop) {\n return $('

')\n .text(\n 'Score: ' +\n ~~(crop.score.total * 10000000) +\n ', ' +\n crop.x +\n 'x' +\n crop.y\n )\n .hover(\n function() {\n drawCrop(crop);\n },\n function() {\n drawCrop(selectedCrop);\n }\n )\n .click(function() {\n selectedCrop = crop;\n drawCrop(selectedCrop);\n })\n .data('crop', crop);\n })\n );\n\n drawCrop(selectedCrop);\n $('#debug')\n .empty()\n .append(debugDraw(result, true));\n }\n\n function drawCrop(crop) {\n canvas.width = img.width;\n canvas.height = img.height;\n ctx.drawImage(img, 0, 0);\n ctx.strokeStyle = 'red';\n ctx.lineWidth = 4;\n ctx.strokeRect(crop.x, crop.y, crop.width, crop.height);\n }\n\n window.openCvReady = function() {\n console.log('opencv code ready');\n loadCascade(\n 'haarcascade_frontalface_default.xml',\n 'https://unpkg.com/opencv.js@1.2.1/tests/haarcascade_frontalface_default.xml',\n function() {\n console.log('opencv ready');\n document.querySelector('.opencv-loading input').disabled = false;\n document.querySelector('.opencv-loading').className = '';\n }\n );\n };\n\n function loadCascade(path, url, callback) {\n var request = new XMLHttpRequest();\n request.open('GET', url, true);\n request.responseType = 'arraybuffer';\n request.onload = function() {\n if (request.readyState === 4) {\n if (request.status === 200) {\n var data = new Uint8Array(request.response);\n cv.FS_createDataFile('/', path, data, true, false, false);\n callback();\n } else {\n self.printError(\n 'Failed to load ' + url + ' status: ' + request.status\n );\n }\n }\n };\n request.send();\n }\n})();\n" }, { "path": "examples/testsuite.html", "content": "\n\n\n\n \n smartcrop.js testsuite\n \n \n\n\n\n

smartcrop.js test page

\n

smartcrop.js is a content aware image cropping library. On this page you can see how it operates on a large set of\n (>100) images. This page is designed to show you both the strengths and weaknesses of this open source library.\n Please note that this\n page is running just smartcrop.js without any additional face detection. You can play with face detection in the testbed.\n

\n

\n You can learn more about the library on its github page. You\n can try it out on your on images using the testbed.

\n

\n
\n
Selfie by Myself ;)
\n
\n
Cats in the\n Spring by ziamoku_woodpile
\n
\n
Kitty, Daisy and\n Lewis at the Garage by Paul Hudson
\n
\n
Where Have All the Good\n Times Gone? by N. Feans
\n \n \n \n \n \n\n\n" }, { "path": "examples/testsuite.js", "content": "// prepare base perf object\nif (typeof window.performance === 'undefined') {\n window.performance = {};\n}\n\nif (!window.performance.now) {\n var nowOffset = Date.now();\n\n if (performance.timing && performance.timing.navigationStart) {\n nowOffset = performance.timing.navigationStart;\n }\n\n window.performance.now = function now() {\n return Date.now() - nowOffset;\n };\n}\n\nvar processed = {};\nvar options = { debug: true, width: 250, height: 250 };\n$.getJSON('images/images.json', function(images) {\n $('body').append(\n images.map(function(image) {\n return $('
')\n .append($('').attr('src', image.url))\n .append(\n $('
')\n .append(\n $('')\n .text(image.name)\n .attr('href', image.href)\n )\n .append(\n $('').text(\n ' by ' + image.attribution\n )\n )\n );\n })\n );\n\n var totalTime = 0;\n var totalmpix = 0;\n var totalCrops = 0;\n\n $('img').each(function() {\n $(this).on('load', function() {\n window.setTimeout(\n function() {\n var img = this;\n if (processed[img.src]) return;\n processed[img.src] = true;\n var t = performance.now();\n smartcrop.crop(img, options, function(result) {\n totalTime += (performance.now() - t) / 1e3;\n totalmpix += img.naturalWidth * img.naturalHeight / 1e6;\n totalCrops++;\n $('#perf').text(\n 'processed ' +\n totalCrops +\n ' images, ' +\n Math.round(totalTime * 1000 / totalCrops) +\n ' ms/image, ' +\n Math.round(100 * totalmpix / totalTime) / 100 +\n ' mega pixel/s'\n );\n // console.log(img.src, result);\n var crop = result.topCrop;\n var canvas = $('')[0];\n var ctx = canvas.getContext('2d');\n canvas.width = options.width;\n canvas.height = options.height;\n ctx.drawImage(\n img,\n crop.x,\n crop.y,\n crop.width,\n crop.height,\n 0,\n 0,\n canvas.width,\n canvas.height\n );\n\n $(img)\n .after(canvas)\n .after(debugDraw(result, true));\n // .parent()\n // .append($('
').text(JSON.stringify(crop.score)));\n          });\n        }.bind(this),\n        100\n      );\n    });\n    if (this.complete) {\n      $(this).trigger('load');\n    }\n  });\n});\n"
  },
  {
    "path": "examples/tracking-face-min.js",
    "content": "/**\n * tracking.js - A modern approach for Computer Vision on the web.\n * @author Eduardo Lundgren \n * @version v1.0.0\n * @link http://trackingjs.com\n * @license BSD\n */\ntracking.ViolaJones.classifiers.face=new 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  },
  {
    "path": "examples/tracking-min.js",
    "content": "/**\n * tracking - A modern approach for Computer Vision on the web.\n * @author Eduardo Lundgren \n * @version v1.1.2\n * @link http://trackingjs.com\n * @license BSD\n */\n!function(t,r){t.tracking=t.tracking||{},tracking.inherits=function(t,r){function n(){}n.prototype=r.prototype,t.superClass_=r.prototype,t.prototype=new n,t.prototype.constructor=t,t.base=function(t,n){var e=Array.prototype.slice.call(arguments,2);return r.prototype[n].apply(t,e)}},tracking.initUserMedia_=function(r,n){t.navigator.getUserMedia({video:!0,audio:!(!n||!n.audio)},function(n){try{r.src=t.URL.createObjectURL(n)}catch(e){r.src=n}},function(){throw Error(\"Cannot capture user camera.\")})},tracking.isNode=function(t){return t.nodeType||this.isWindow(t)},tracking.isWindow=function(t){return!!(t&&t.alert&&t.document)},tracking.one=function(t,r){return this.isNode(t)?t:(r||document).querySelector(t)},tracking.track=function(t,r,n){if(t=tracking.one(t),!t)throw new Error(\"Element not found, try a different element or selector.\");if(!r)throw new Error(\"Tracker not specified, try `tracking.track(element, new tracking.FaceTracker())`.\");switch(t.nodeName.toLowerCase()){case\"canvas\":return this.trackCanvas_(t,r,n);case\"img\":return this.trackImg_(t,r,n);case\"video\":return n&&n.camera&&this.initUserMedia_(t,n),this.trackVideo_(t,r,n);default:throw new Error(\"Element not supported, try in a canvas, img, or video.\")}},tracking.trackCanvas_=function(t,r){var n=this,e=new tracking.TrackerTask(r);return e.on(\"run\",function(){n.trackCanvasInternal_(t,r)}),e.run()},tracking.trackCanvasInternal_=function(t,r){var n=t.width,e=t.height,i=t.getContext(\"2d\"),a=i.getImageData(0,0,n,e);r.track(a.data,n,e)},tracking.trackImg_=function(t,r){var n=t.width,e=t.height,i=document.createElement(\"canvas\");i.width=n,i.height=e;var a=new tracking.TrackerTask(r);return a.on(\"run\",function(){tracking.Canvas.loadImage(i,t.src,0,0,n,e,function(){tracking.trackCanvasInternal_(i,r)})}),a.run()},tracking.trackVideo_=function(r,n){var e,i,a=document.createElement(\"canvas\"),o=a.getContext(\"2d\"),c=function(){e=r.offsetWidth,i=r.offsetHeight,a.width=e,a.height=i};c(),r.addEventListener(\"resize\",c);var s,g=function(){s=t.requestAnimationFrame(function(){if(r.readyState===r.HAVE_ENOUGH_DATA){try{o.drawImage(r,0,0,e,i)}catch(t){}tracking.trackCanvasInternal_(a,n)}g()})},h=new tracking.TrackerTask(n);return h.on(\"stop\",function(){t.cancelAnimationFrame(s)}),h.on(\"run\",function(){g()}),h.run()},t.URL||(t.URL=t.URL||t.webkitURL||t.msURL||t.oURL),navigator.getUserMedia||(navigator.getUserMedia=navigator.getUserMedia||navigator.webkitGetUserMedia||navigator.mozGetUserMedia||navigator.msGetUserMedia)}(window),function(){tracking.EventEmitter=function(){},tracking.EventEmitter.prototype.events_=null,tracking.EventEmitter.prototype.addListener=function(t,r){if(\"function\"!=typeof r)throw new TypeError(\"Listener must be a function\");return this.events_||(this.events_={}),this.emit(\"newListener\",t,r),this.events_[t]||(this.events_[t]=[]),this.events_[t].push(r),this},tracking.EventEmitter.prototype.listeners=function(t){return this.events_&&this.events_[t]},tracking.EventEmitter.prototype.emit=function(t){var r=this.listeners(t);if(r){for(var n=Array.prototype.slice.call(arguments,1),e=0;ee)return this;n.splice(e,1)}return this},tracking.EventEmitter.prototype.setMaxListeners=function(){throw new Error(\"Not implemented\")}}(),function(){tracking.Canvas={},tracking.Canvas.loadImage=function(t,r,n,e,i,a,o){var c=this,s=new window.Image;s.crossOrigin=\"*\",s.onload=function(){var r=t.getContext(\"2d\");t.width=i,t.height=a,r.drawImage(s,n,e,i,a),o&&o.call(c),s=null},s.src=r}}(),function(){tracking.DisjointSet=function(t){if(void 0===t)throw new Error(\"DisjointSet length not specified.\");this.length=t,this.parent=new Uint32Array(t);for(var r=0;t>r;r++)this.parent[r]=r},tracking.DisjointSet.prototype.length=null,tracking.DisjointSet.prototype.parent=null,tracking.DisjointSet.prototype.find=function(t){return this.parent[t]===t?t:this.parent[t]=this.find(this.parent[t])},tracking.DisjointSet.prototype.union=function(t,r){var n=this.find(t),e=this.find(r);this.parent[n]=e}}(),function(){tracking.Image={},tracking.Image.blur=function(t,r,n,e){if(e=Math.abs(e),1>=e)throw new Error(\"Diameter should be greater than 1.\");for(var i=e/2,a=Math.ceil(e)+(1-Math.ceil(e)%2),o=new Float32Array(a),c=(i+.5)/3,s=c*c,g=1/Math.sqrt(2*Math.PI*s),h=-1/(2*c*c),k=0,u=Math.floor(a/2),f=0;a>f;f++){var l=f-u,p=g*Math.exp(l*l*h);o[f]=p,k+=p}for(var v=0;vs;s++)for(var g=0;r>g;g++){var h=s*r*4+4*g,k=~~(.299*t[h]+.587*t[h+1]+.114*t[h+2]);if(e&&this.computePixelValueSAT_(e,r,s,g,k),i&&this.computePixelValueSAT_(i,r,s,g,k*k),a){var u=h-4*r,f=~~(.299*t[u]+.587*t[u+1]+.114*t[u+2]);this.computePixelValueRSAT_(a,r,s,g,k,f||0)}o&&this.computePixelValueSAT_(o,r,s,g,c[h])}},tracking.Image.computePixelValueRSAT_=function(t,r,n,e,i,a){var o=n*r+e;t[o]=(t[o-r-1]||0)+(t[o-r+1]||0)-(t[o-r-r]||0)+i+a},tracking.Image.computePixelValueSAT_=function(t,r,n,e,i){var a=n*r+e;t[a]=(t[a-r]||0)+(t[a-1]||0)+i-(t[a-r-1]||0)},tracking.Image.grayscale=function(t,r,n,e){for(var i=new Uint8ClampedArray(e?t.length:t.length>>2),a=0,o=0,c=0;n>c;c++)for(var s=0;r>s;s++){var g=.299*t[o]+.587*t[o+1]+.114*t[o+2];i[a++]=g,e&&(i[a++]=g,i[a++]=g,i[a++]=t[o+3]),o+=4}return i},tracking.Image.horizontalConvolve=function(t,r,n,e,i){for(var a=e.length,o=Math.floor(a/2),c=new Float32Array(r*n*4),s=i?1:0,g=0;n>g;g++)for(var h=0;r>h;h++){for(var k=g,u=h,f=4*(g*r+h),l=0,p=0,v=0,m=0,y=0;a>y;y++){var d=k,w=Math.min(r-1,Math.max(0,u+y-o)),T=4*(d*r+w),C=e[y];l+=t[T]*C,p+=t[T+1]*C,v+=t[T+2]*C,m+=t[T+3]*C}c[f]=l,c[f+1]=p,c[f+2]=v,c[f+3]=m+s*(255-m)}return c},tracking.Image.verticalConvolve=function(t,r,n,e,i){for(var a=e.length,o=Math.floor(a/2),c=new Float32Array(r*n*4),s=i?1:0,g=0;n>g;g++)for(var h=0;r>h;h++){for(var k=g,u=h,f=4*(g*r+h),l=0,p=0,v=0,m=0,y=0;a>y;y++){var d=Math.min(n-1,Math.max(0,k+y-o)),w=u,T=4*(d*r+w),C=e[y];l+=t[T]*C,p+=t[T+1]*C,v+=t[T+2]*C,m+=t[T+3]*C}c[f]=l,c[f+1]=p,c[f+2]=v,c[f+3]=m+s*(255-m)}return c},tracking.Image.separableConvolve=function(t,r,n,e,i,a){var o=this.verticalConvolve(t,r,n,i,a);return this.horizontalConvolve(o,r,n,e,a)},tracking.Image.sobel=function(t,r,n){t=this.grayscale(t,r,n,!0);for(var e=new Float32Array(r*n*4),i=new Float32Array([-1,0,1]),a=new Float32Array([1,2,1]),o=this.separableConvolve(t,r,n,i,a),c=this.separableConvolve(t,r,n,a,i),s=0;s0&&(s=new Int32Array(r*n)),tracking.Image.computeIntegralImage(t,r,n,k,u,f,s);for(var l=c[0],p=c[1],v=e*i,m=v*l|0,y=v*p|0;r>m&&n>y;){for(var d=v*a+.5|0,w=0;n-y>w;w+=d)for(var T=0;r-m>T;T+=d)o>0&&this.isTriviallyExcluded(o,s,w,T,r,m,y)||this.evalStages_(c,k,u,f,w,T,r,m,y,v)&&(h[g++]={width:m,height:y,x:T,y:w});v*=i,m=v*l|0,y=v*p|0}return this.mergeRectangles_(h)},tracking.ViolaJones.isTriviallyExcluded=function(t,r,n,e,i,a,o){var c=n*i+e,s=c+a,g=c+o*i,h=g+a,k=(r[c]-r[s]-r[g]+r[h])/(a*o*255);return t>k?!0:!1},tracking.ViolaJones.evalStages_=function(t,r,n,e,i,a,o,c,s,g){var h=1/(c*s),k=i*o+a,u=k+c,f=k+s*o,l=f+c,p=(r[k]-r[u]-r[f]+r[l])*h,v=(n[k]-n[u]-n[f]+n[l])*h-p*p,m=1;v>0&&(m=Math.sqrt(v));for(var y=t.length,d=2;y>d;){for(var w=0,T=t[d++],C=t[d++];C--;){for(var _=0,E=t[d++],M=t[d++],x=0;M>x;x++){var I,b,O,A,S=a+t[d++]*g+.5|0,D=i+t[d++]*g+.5|0,R=t[d++]*g+.5|0,j=t[d++]*g+.5|0,F=t[d++];E?(I=S-j+R+(D+R+j-1)*o,b=S+(D-1)*o,O=S-j+(D+j-1)*o,A=S+R+(D+R-1)*o,_+=(e[I]+e[b]-e[O]-e[A])*F):(I=D*o+S,b=I+R,O=I+j*o,A=O+R,_+=(r[I]-r[b]-r[O]+r[A])*F)}var L=t[d++],V=t[d++],U=t[d++];w+=L*m>_*h?V:U}if(T>w)return!1}return!0},tracking.ViolaJones.mergeRectangles_=function(t){for(var r=new tracking.DisjointSet(t.length),n=0;n=this.REGIONS_OVERLAP&&h/(u*(k/u))>=this.REGIONS_OVERLAP&&r.union(n,i)}}for(var f={},l=0;l>1)*(this.N>>5)),i=0,a=this.getRandomOffsets_(r),o=0,c=0;ch;h++)t[a[g++]+s]>1,a=n.length>>1,o=new Array(i),c=0;i>c;c++){for(var s=1/0,g=0,h=0;a>h;h++){for(var k=0,u=0,f=this.N>>5;f>u;u++)k+=tracking.Math.hammingWeight(r[c*f+u]^e[h*f+u]);s>k&&(s=k,g=h)}o[c]={index1:c,index2:g,keypoint1:[t[2*c],t[2*c+1]],keypoint2:[n[2*g],n[2*g+1]],confidence:1-s/this.N}}return o},tracking.Brief.reciprocalMatch=function(t,r,n,e){var i=[];if(0===t.length||0===n.length)return i;for(var a=tracking.Brief.match(t,r,n,e),o=tracking.Brief.match(n,e,t,r),c=0;cc;c++)for(var s=3;r-3>s;s++){for(var g=c*r+s,h=t[g],k=0;16>k;k++)a[k]=t[g+i[k]];this.isCorner(h,a,e)&&(o.push(s,c),s+=3)}return o},tracking.Fast.isBrighter=function(t,r,n){return t-r>n},tracking.Fast.isCorner=function(t,r,n){if(this.isTriviallyExcluded(r,t,n))return!1;for(var e=0;16>e;e++){for(var i=!0,a=!0,o=0;9>o;o++){var c=r[e+o&15];if(!this.isBrighter(t,c,n)&&(a=!1,i===!1))break;if(!this.isDarker(t,c,n)&&(i=!1,a===!1))break}if(a||i)return!0}return!1},tracking.Fast.isDarker=function(t,r,n){return r-t>n},tracking.Fast.isTriviallyExcluded=function(t,r,n){var e=0,i=t[8],a=t[12],o=t[4],c=t[0];return this.isBrighter(c,r,n)&&e++,this.isBrighter(o,r,n)&&e++,this.isBrighter(i,r,n)&&e++,this.isBrighter(a,r,n)&&e++,3>e&&(e=0,this.isDarker(c,r,n)&&e++,this.isDarker(o,r,n)&&e++,this.isDarker(i,r,n)&&e++,this.isDarker(a,r,n)&&e++,3>e)?!0:!1},tracking.Fast.getCircleOffsets_=function(t){if(this.circles_[t])return this.circles_[t];var r=new Int32Array(16);return r[0]=-t-t-t,r[1]=r[0]+1,r[2]=r[1]+t+1,r[3]=r[2]+t+1,r[4]=r[3]+t,r[5]=r[4]+t,r[6]=r[5]+t-1,r[7]=r[6]+t-1,r[8]=r[7]-1,r[9]=r[8]-1,r[10]=r[9]-t-1,r[11]=r[10]-t-1,r[12]=r[11]-t,r[13]=r[12]-t,r[14]=r[13]-t+1,r[15]=r[14]-t+1,this.circles_[t]=r,r}}(),function(){tracking.Math={},tracking.Math.distance=function(t,r,n,e){var i=n-t,a=e-r;return Math.sqrt(i*i+a*a)},tracking.Math.hammingWeight=function(t){return t-=t>>1&1431655765,t=(858993459&t)+(t>>2&858993459),16843009*(t+(t>>4)&252645135)>>24},tracking.Math.uniformRandom=function(t,r){return t+Math.random()*(r-t)},tracking.Math.intersectRect=function(t,r,n,e,i,a,o,c){return!(i>n||t>o||a>e||r>c)}}(),function(){tracking.Matrix={},tracking.Matrix.forEach=function(t,r,n,e,i){i=i||1;for(var a=0;n>a;a+=i)for(var o=0;r>o;o+=i){var c=a*r*4+4*o;e.call(this,t[c],t[c+1],t[c+2],t[c+3],c,a,o)}}}(),function(){tracking.EPnP={},tracking.EPnP.solve=function(t,r,n){}}(),function(){tracking.Tracker=function(){tracking.Tracker.base(this,\"constructor\")},tracking.inherits(tracking.Tracker,tracking.EventEmitter),tracking.Tracker.prototype.track=function(){}}(),function(){tracking.TrackerTask=function(t){if(tracking.TrackerTask.base(this,\"constructor\"),!t)throw new Error(\"Tracker instance not specified.\");this.setTracker(t)},tracking.inherits(tracking.TrackerTask,tracking.EventEmitter),tracking.TrackerTask.prototype.tracker_=null,tracking.TrackerTask.prototype.running_=!1,tracking.TrackerTask.prototype.getTracker=function(){return this.tracker_},tracking.TrackerTask.prototype.inRunning=function(){return this.running_},tracking.TrackerTask.prototype.setRunning=function(t){this.running_=t},tracking.TrackerTask.prototype.setTracker=function(t){this.tracker_=t},tracking.TrackerTask.prototype.run=function(){var t=this;if(!this.inRunning())return this.setRunning(!0),this.reemitTrackEvent_=function(r){t.emit(\"track\",r)},this.tracker_.on(\"track\",this.reemitTrackEvent_),this.emit(\"run\"),this},tracking.TrackerTask.prototype.stop=function(){return this.inRunning()?(this.setRunning(!1),this.emit(\"stop\"),this.tracker_.removeListener(\"track\",this.reemitTrackEvent_),this):void 0}}(),function(){tracking.ColorTracker=function(t){tracking.ColorTracker.base(this,\"constructor\"),\"string\"==typeof t&&(t=[t]),t&&(t.forEach(function(t){if(!tracking.ColorTracker.getColor(t))throw new Error('Color not valid, try `new tracking.ColorTracker(\"magenta\")`.')}),this.setColors(t))},tracking.inherits(tracking.ColorTracker,tracking.Tracker),tracking.ColorTracker.knownColors_={},tracking.ColorTracker.neighbours_={},tracking.ColorTracker.registerColor=function(t,r){tracking.ColorTracker.knownColors_[t]=r},tracking.ColorTracker.getColor=function(t){return tracking.ColorTracker.knownColors_[t]},tracking.ColorTracker.prototype.colors=[\"magenta\"],tracking.ColorTracker.prototype.minDimension=20,tracking.ColorTracker.prototype.maxDimension=1/0,tracking.ColorTracker.prototype.minGroupSize=30,tracking.ColorTracker.prototype.calculateDimensions_=function(t,r){for(var n=-1,e=-1,i=1/0,a=1/0,o=0;r>o;o+=2){var c=t[o],s=t[o+1];i>c&&(i=c),c>n&&(n=c),a>s&&(a=s),s>e&&(e=s)}return{width:n-i,height:e-a,x:i,y:a}},tracking.ColorTracker.prototype.getColors=function(){return this.colors},tracking.ColorTracker.prototype.getMinDimension=function(){return this.minDimension},tracking.ColorTracker.prototype.getMaxDimension=function(){return this.maxDimension},tracking.ColorTracker.prototype.getMinGroupSize=function(){return this.minGroupSize},tracking.ColorTracker.prototype.getNeighboursForWidth_=function(t){if(tracking.ColorTracker.neighbours_[t])return tracking.ColorTracker.neighbours_[t];var r=new Int32Array(8);return r[0]=4*-t,r[1]=4*-t+4,r[2]=4,r[3]=4*t+4,r[4]=4*t,r[5]=4*t-4,r[6]=-4,r[7]=4*-t-4,tracking.ColorTracker.neighbours_[t]=r,r},tracking.ColorTracker.prototype.mergeRectangles_=function(t){for(var r,n=[],e=this.getMinDimension(),i=this.getMaxDimension(),a=0;a=e&&o.height>=e&&o.width<=i&&o.height<=i&&n.push(o)}return n},tracking.ColorTracker.prototype.setColors=function(t){this.colors=t},tracking.ColorTracker.prototype.setMinDimension=function(t){this.minDimension=t},tracking.ColorTracker.prototype.setMaxDimension=function(t){this.maxDimension=t},tracking.ColorTracker.prototype.setMinGroupSize=function(t){this.minGroupSize=t},tracking.ColorTracker.prototype.track=function(t,r,n){var e=this,i=this.getColors();if(!i)throw new Error('Colors not specified, try `new tracking.ColorTracker(\"magenta\")`.');var a=[];i.forEach(function(i){a=a.concat(e.trackColor_(t,r,n,i))}),this.emit(\"track\",{data:a})},tracking.ColorTracker.prototype.trackColor_=function(n,e,i,a){var o,c,s,g,h,k=tracking.ColorTracker.knownColors_[a],u=new Int32Array(n.length>>2),f=new Int8Array(n.length),l=this.getMinGroupSize(),p=this.getNeighboursForWidth_(e),v=new Int32Array(n.length),m=[],y=-4;if(!k)return m;for(var d=0;i>d;d++)for(var w=0;e>w;w++)if(y+=4,!f[y]){for(o=0,h=-1,v[++h]=y,v[++h]=d,v[++h]=w,f[y]=1;h>=0;)if(s=v[h--],c=v[h--],g=v[h--],k(n[g],n[g+1],n[g+2],n[g+3],g,c,s)){u[o++]=s,u[o++]=c;for(var T=0;T=0&&i>_&&E>=0&&e>E&&(v[++h]=C,v[++h]=_,v[++h]=E,f[C]=1)}}if(o>=l){var M=this.calculateDimensions_(u,o);M&&(M.color=a,m.push(M))}}return this.mergeRectangles_(m)},tracking.ColorTracker.registerColor(\"cyan\",function(t,r,n){var e=50,i=70,a=t-0,o=r-255,c=n-255;return r-t>=e&&n-t>=i?!0:6400>a*a+o*o+c*c}),tracking.ColorTracker.registerColor(\"magenta\",function(t,r,n){var e=50,i=t-255,a=r-0,o=n-255;return t-r>=e&&n-r>=e?!0:19600>i*i+a*a+o*o}),tracking.ColorTracker.registerColor(\"yellow\",function(t,r,n){var e=50,i=t-255,a=r-255,o=n-0;return t-n>=e&&r-n>=e?!0:1e4>i*i+a*a+o*o});var t=new Int32Array([-1,-1,0,1,1,1,0,-1]),r=new Int32Array([0,1,1,1,0,-1,-1,-1])}(),function(){tracking.ObjectTracker=function(t){tracking.ObjectTracker.base(this,\"constructor\"),t&&(Array.isArray(t)||(t=[t]),Array.isArray(t)&&t.forEach(function(r,n){if(\"string\"==typeof r&&(t[n]=tracking.ViolaJones.classifiers[r]),!t[n])throw new Error('Object classifier not valid, try `new tracking.ObjectTracker(\"face\")`.')})),this.setClassifiers(t)},tracking.inherits(tracking.ObjectTracker,tracking.Tracker),tracking.ObjectTracker.prototype.edgesDensity=.2,tracking.ObjectTracker.prototype.initialScale=1,tracking.ObjectTracker.prototype.scaleFactor=1.25,tracking.ObjectTracker.prototype.stepSize=1.5,tracking.ObjectTracker.prototype.getClassifiers=function(){return this.classifiers},tracking.ObjectTracker.prototype.getEdgesDensity=function(){return this.edgesDensity},tracking.ObjectTracker.prototype.getInitialScale=function(){return this.initialScale},tracking.ObjectTracker.prototype.getScaleFactor=function(){return this.scaleFactor},tracking.ObjectTracker.prototype.getStepSize=function(){return this.stepSize},tracking.ObjectTracker.prototype.track=function(t,r,n){var e=this,i=this.getClassifiers();if(!i)throw new Error('Object classifier not specified, try `new tracking.ObjectTracker(\"face\")`.');var a=[];i.forEach(function(i){a=a.concat(tracking.ViolaJones.detect(t,r,n,e.getInitialScale(),e.getScaleFactor(),e.getStepSize(),e.getEdgesDensity(),i))}),this.emit(\"track\",{data:a})},tracking.ObjectTracker.prototype.setClassifiers=function(t){this.classifiers=t},tracking.ObjectTracker.prototype.setEdgesDensity=function(t){this.edgesDensity=t},tracking.ObjectTracker.prototype.setInitialScale=function(t){this.initialScale=t},tracking.ObjectTracker.prototype.setScaleFactor=function(t){this.scaleFactor=t},tracking.ObjectTracker.prototype.setStepSize=function(t){this.stepSize=t}}();"
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  },
  {
    "path": "karma.conf.js",
    "content": "// Karma configuration\n// Generated on Mon Jun 27 2016 18:36:52 GMT+0200 (CEST)\n\nmodule.exports = function(config) {\n  var launchers = {\n    sauceChromeLatest: {\n      base: 'SauceLabs',\n      browserName: 'chrome',\n      platform: 'Windows 11',\n      version: 'latest'\n    },\n    sauceFirefoxLatest: {\n      base: 'SauceLabs',\n      browserName: 'firefox',\n      platform: 'Windows 11',\n      version: 'latest'\n    },\n    sauceEdge: {\n      base: 'SauceLabs',\n      browserName: 'MicrosoftEdge',\n      platform: 'Windows 11',\n      version: 'latest'\n    },\n    sauceSafari: {\n      base: 'SauceLabs',\n      browserName: 'safari',\n      platform: 'macOS 13',\n      version: 'latest'\n    }\n    // sl_firefox: {\n    //   base: 'SauceLabs',\n    //   browserName: 'firefox',\n    //   version: '30'\n    // },\n    // sl_ios_safari: {\n    //   base: 'SauceLabs',\n    //   browserName: 'iphone',\n    //   platform: 'OS X 10.9',\n    //   version: '7.1'\n    // },\n    // sl_ie_11: {\n    //   base: 'SauceLabs',\n    //   browserName: 'internet explorer',\n    //   platform: 'Windows 8.1',\n    //   version: '11'\n    // }\n  };\n\n  config.set({\n\n    // base path that will be used to resolve all patterns (eg. files, exclude)\n    basePath: '',\n\n\n    // frameworks to use\n    // available frameworks: https://npmjs.org/browse/keyword/karma-adapter\n    frameworks: ['mocha'],\n\n\n    // list of files / patterns to load in the browser\n    files: [\n      'node_modules/promise-polyfill/dist/polyfill.js',\n      'node_modules/chai/chai.js',\n      'smartcrop.js',\n      'test/smartcrop.js',\n      { pattern: 'examples/images/flickr/kitty.jpg', included: false, served: true }\n    ],\n\n    proxies: {\n      '/examples/images/': '/base/examples/images/'\n    },\n\n\n    // list of files to exclude\n    exclude: [\n    ],\n\n\n    // preprocess matching files before serving them to the browser\n    // available preprocessors: https://npmjs.org/browse/keyword/karma-preprocessor\n    preprocessors: {\n    },\n\n\n    // test results reporter to use\n    // possible values: 'dots', 'progress'\n    // available reporters: https://npmjs.org/browse/keyword/karma-reporter\n    reporters: ['progress', 'saucelabs'],\n\n\n    // web server port\n    port: 9876,\n\n\n    // enable / disable colors in the output (reporters and logs)\n    colors: true,\n\n\n    // level of logging\n    // possible values: config.LOG_DISABLE || config.LOG_ERROR || config.LOG_WARN || config.LOG_INFO || config.LOG_DEBUG\n    logLevel: config.LOG_INFO,\n\n\n    // enable / disable watching file and executing tests whenever any file changes\n    autoWatch: true,\n\n\n    // start these browsers\n    // available browser launchers: https://npmjs.org/browse/keyword/karma-launcher\n    browsers: Object.keys(launchers),\n    customLaunchers: launchers,\n\n    // Continuous Integration mode\n    // if true, Karma captures browsers, runs the tests and exits\n    singleRun: true,\n\n    // Concurrency level\n    // how many browser should be started simultaneous\n    concurrency: Infinity\n  });\n};\n"
  },
  {
    "path": "package.json",
    "content": "{\n  \"name\": \"smartcrop\",\n  \"version\": \"2.0.5\",\n  \"description\": \"Content aware image cropping.\",\n  \"homepage\": \"https://github.com/jwagner/smartcrop.js\",\n  \"author\": \"Jonas Wagner  (http://29a.ch/)\",\n  \"main\": \"./smartcrop\",\n  \"files\": [\n    \"smartcrop.js\",\n    \"index.d.ts\"\n  ],\n  \"devDependencies\": {\n    \"benchmark\": \"^2.1.4\",\n    \"chai\": \"^4.2.0\",\n    \"eslint\": \"^8.56.0\",\n    \"@vladmandic/face-api\": \"^1.7.13\",\n    \"grunt\": \"^1.6.1\",\n    \"grunt-cli\": \"^1.4.3\",\n    \"grunt-contrib-connect\": \"~4.0.0\",\n    \"grunt-contrib-watch\": \"^1.1.0\",\n    \"grunt-rsync\": \"~3.0.0\",\n    \"karma\": \"^6.4.2\",\n    \"karma-chrome-launcher\": \"^3.2.0\",\n    \"karma-mocha\": \"^2.0.1\",\n    \"karma-sauce-launcher\": \"^4.3.6\",\n    \"microtime\": \"^3.1.1\",\n    \"mocha\": \"^9.0.2\",\n    \"prettier-eslint\": \"^16.3.0\",\n    \"promise-polyfill\": \"^8.3.0\"\n  },\n  \"overrides\": {\n    \"saucelabs\": \"^7.5.0\",\n    \"webdriverio\": \"^8.29.0\"\n  },\n  \"license\": \"MIT\",\n  \"repository\": {\n    \"type\": \"git\",\n    \"url\": \"https://github.com/jwagner/smartcrop.js.git\"\n  },\n  \"scripts\": {\n    \"start\": \"grunt\",\n    \"test\": \"karma start karma.conf.js && npx tsc -noEmit test/types.ts\",\n    \"lint\": \"eslint smartcrop.js test examples/slideshow.js examples/testbed.js examples/testsuite.js examples/smartcrop-debug.js\"\n  }\n}"
  },
  {
    "path": "scripts/release.sh",
    "content": "#!/bin/sh\nversion=\"$1\"\nif [ -z \"$version\" ]; then\n    echo \"Usage: release.sh version\"\n    exit\nfi\n\necho $version\n\njq \".version=\\\"$version\\\"\" < package.json > _package.json\nmv _package.json package.json\nname=$(jq -r \".name\" < package.json)\narchive=release/$name-$version.zip\n\nmkdir release\ngit archive -o $archive HEAD\n\ngit diff\ngit status\necho \"Confirm release with YES\"\nread confirmation\nif [ \"$confirmation\" != 'YES' ]; then\n    echo \"Ok, maybe not.\"\n    exit\nfi\ngit add -f package.json\ngit commit -am \"$version\"\ngit tag -a \"v$version\" -m \"release $version\"\ngit push origin \"v$version\"\ngrunt rsync\nnpm publish\nhub release create -a $archive \"v$version\"\n#git checkout master\n"
  },
  {
    "path": "scripts/sync-web.sh",
    "content": "#!/bin/bash\nfor f in examples/*.{html,js,css}; do\n  gzip -k $f\ndone\nrsync -vLr examples smartcrop.js doc/example.jpg x.29a.ch:/var/www/static/sandbox/2014/smartcrop/\nrm examples/*.gz\n"
  },
  {
    "path": "smartcrop.js",
    "content": "/**\n * smartcrop.js\n * A javascript library implementing content aware image cropping\n *\n * Copyright (C) 2018 Jonas Wagner\n *\n * Permission is hereby granted, free of charge, to any person obtaining\n * a copy of this software and associated documentation files (the\n * \"Software\"), to deal in the Software without restriction, including\n * without limitation the rights to use, copy, modify, merge, publish,\n * distribute, sublicense, and/or sell copies of the Software, and to\n * permit persons to whom the Software is furnished to do so, subject to\n * the following conditions:\n *\n * The above copyright notice and this permission notice shall be\n * included in all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\n * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\n * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\n * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE\n * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION\n * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION\n * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n */\n\n(function() {\n  'use strict';\n\n  var smartcrop = {};\n  // Promise implementation to use\n  function NoPromises() {\n    throw new Error('No native promises and smartcrop.Promise not set.');\n  }\n\n  smartcrop.Promise = typeof Promise !== 'undefined' ? Promise : NoPromises;\n\n  smartcrop.DEFAULTS = {\n    width: 0,\n    height: 0,\n    aspect: 0,\n    cropWidth: 0,\n    cropHeight: 0,\n    detailWeight: 0.2,\n    skinColor: [0.78, 0.57, 0.44],\n    skinBias: 0.01,\n    skinBrightnessMin: 0.2,\n    skinBrightnessMax: 1.0,\n    skinThreshold: 0.8,\n    skinWeight: 1.8,\n    saturationBrightnessMin: 0.05,\n    saturationBrightnessMax: 0.9,\n    saturationThreshold: 0.4,\n    saturationBias: 0.2,\n    saturationWeight: 0.1,\n    // Step * minscale rounded down to the next power of two should be good\n    scoreDownSample: 8,\n    step: 8,\n    scaleStep: 0.1,\n    minScale: 1.0,\n    maxScale: 1.0,\n    edgeRadius: 0.4,\n    edgeWeight: -20.0,\n    outsideImportance: -0.5,\n    boostWeight: 100.0,\n    ruleOfThirds: true,\n    prescale: true,\n    imageOperations: null,\n    canvasFactory: defaultCanvasFactory,\n    // Factory: defaultFactories,\n    debug: false\n  };\n\n  smartcrop.crop = function(inputImage, options_, callback) {\n    var options = extend({}, smartcrop.DEFAULTS, options_);\n\n    if (options.aspect) {\n      options.width = options.aspect;\n      options.height = 1;\n    }\n\n    if (options.imageOperations === null) {\n      options.imageOperations = canvasImageOperations(options.canvasFactory);\n    }\n\n    var iop = options.imageOperations;\n\n    var scale = 1;\n    var prescale = 1;\n\n    // open the image\n    return iop\n      .open(inputImage, options.input)\n      .then(function(image) {\n        // calculate desired crop dimensions based on the image size\n        if (options.width && options.height) {\n          scale = min(\n            image.width / options.width,\n            image.height / options.height\n          );\n          options.cropWidth = ~~(options.width * scale);\n          options.cropHeight = ~~(options.height * scale);\n          // Img = 100x100, width = 95x95, scale = 100/95, 1/scale > min\n          // don't set minscale smaller than 1/scale\n          // -> don't pick crops that need upscaling\n          options.minScale = min(\n            options.maxScale,\n            max(1 / scale, options.minScale)\n          );\n\n          // prescale if possible\n          if (options.prescale !== false) {\n            prescale = min(max(256 / image.width, 256 / image.height), 1);\n            if (prescale < 1) {\n              image = iop.resample(\n                image,\n                image.width * prescale,\n                image.height * prescale\n              );\n              options.cropWidth = ~~(options.cropWidth * prescale);\n              options.cropHeight = ~~(options.cropHeight * prescale);\n              if (options.boost) {\n                options.boost = options.boost.map(function(boost) {\n                  return {\n                    x: ~~(boost.x * prescale),\n                    y: ~~(boost.y * prescale),\n                    width: ~~(boost.width * prescale),\n                    height: ~~(boost.height * prescale),\n                    weight: boost.weight\n                  };\n                });\n              }\n            } else {\n              prescale = 1;\n            }\n          }\n        }\n        return image;\n      })\n      .then(function(image) {\n        return iop.getData(image).then(function(data) {\n          var result = analyse(options, data);\n\n          var crops = result.crops || [result.topCrop];\n          for (var i = 0, iLen = crops.length; i < iLen; i++) {\n            var crop = crops[i];\n            crop.x = ~~(crop.x / prescale);\n            crop.y = ~~(crop.y / prescale);\n            crop.width = ~~(crop.width / prescale);\n            crop.height = ~~(crop.height / prescale);\n          }\n          if (callback) callback(result);\n          return result;\n        });\n      });\n  };\n\n  // Check if all the dependencies are there\n  // todo:\n  smartcrop.isAvailable = function(options) {\n    if (!smartcrop.Promise) return false;\n\n    var canvasFactory = options ? options.canvasFactory : defaultCanvasFactory;\n\n    if (canvasFactory === defaultCanvasFactory) {\n      var c = document.createElement('canvas');\n      if (!c.getContext('2d')) {\n        return false;\n      }\n    }\n\n    return true;\n  };\n\n  function edgeDetect(i, o) {\n    var id = i.data;\n    var od = o.data;\n    var w = i.width;\n    var h = i.height;\n\n    for (var y = 0; y < h; y++) {\n      for (var x = 0; x < w; x++) {\n        var p = (y * w + x) * 4;\n        var lightness;\n\n        if (x === 0 || x >= w - 1 || y === 0 || y >= h - 1) {\n          lightness = sample(id, p);\n        } else {\n          lightness =\n            sample(id, p) * 4 -\n            sample(id, p - w * 4) -\n            sample(id, p - 4) -\n            sample(id, p + 4) -\n            sample(id, p + w * 4);\n        }\n\n        od[p + 1] = lightness;\n      }\n    }\n  }\n\n  function skinDetect(options, i, o) {\n    var id = i.data;\n    var od = o.data;\n    var w = i.width;\n    var h = i.height;\n\n    for (var y = 0; y < h; y++) {\n      for (var x = 0; x < w; x++) {\n        var p = (y * w + x) * 4;\n        var lightness = cie(id[p], id[p + 1], id[p + 2]) / 255;\n        var skin = skinColor(options, id[p], id[p + 1], id[p + 2]);\n        var isSkinColor = skin > options.skinThreshold;\n        var isSkinBrightness =\n          lightness >= options.skinBrightnessMin &&\n          lightness <= options.skinBrightnessMax;\n        if (isSkinColor && isSkinBrightness) {\n          od[p] =\n            (skin - options.skinThreshold) *\n            (255 / (1 - options.skinThreshold));\n        } else {\n          od[p] = 0;\n        }\n      }\n    }\n  }\n\n  function saturationDetect(options, i, o) {\n    var id = i.data;\n    var od = o.data;\n    var w = i.width;\n    var h = i.height;\n    for (var y = 0; y < h; y++) {\n      for (var x = 0; x < w; x++) {\n        var p = (y * w + x) * 4;\n\n        var lightness = cie(id[p], id[p + 1], id[p + 2]) / 255;\n        var sat = saturation(id[p], id[p + 1], id[p + 2]);\n\n        var acceptableSaturation = sat > options.saturationThreshold;\n        var acceptableLightness =\n          lightness >= options.saturationBrightnessMin &&\n          lightness <= options.saturationBrightnessMax;\n        if (acceptableLightness && acceptableSaturation) {\n          od[p + 2] =\n            (sat - options.saturationThreshold) *\n            (255 / (1 - options.saturationThreshold));\n        } else {\n          od[p + 2] = 0;\n        }\n      }\n    }\n  }\n\n  function applyBoosts(options, output) {\n    if (!options.boost) return;\n    var od = output.data;\n    for (var i = 0; i < output.width; i += 4) {\n      od[i + 3] = 0;\n    }\n    for (i = 0; i < options.boost.length; i++) {\n      applyBoost(options.boost[i], options, output);\n    }\n  }\n\n  function applyBoost(boost, options, output) {\n    var od = output.data;\n    var w = output.width;\n    var x0 = ~~boost.x;\n    var x1 = ~~(boost.x + boost.width);\n    var y0 = ~~boost.y;\n    var y1 = ~~(boost.y + boost.height);\n    var weight = boost.weight * 255;\n    for (var y = y0; y < y1; y++) {\n      for (var x = x0; x < x1; x++) {\n        var i = (y * w + x) * 4;\n        od[i + 3] += weight;\n      }\n    }\n  }\n\n  function generateCrops(options, width, height) {\n    var results = [];\n    var minDimension = min(width, height);\n    var cropWidth = options.cropWidth || minDimension;\n    var cropHeight = options.cropHeight || minDimension;\n    for (\n      var scale = options.maxScale;\n      scale >= options.minScale;\n      scale -= options.scaleStep\n    ) {\n      for (var y = 0; y + cropHeight * scale <= height; y += options.step) {\n        for (var x = 0; x + cropWidth * scale <= width; x += options.step) {\n          results.push({\n            x: x,\n            y: y,\n            width: cropWidth * scale,\n            height: cropHeight * scale\n          });\n        }\n      }\n    }\n    return results;\n  }\n\n  function score(options, output, crop) {\n    var result = {\n      detail: 0,\n      saturation: 0,\n      skin: 0,\n      boost: 0,\n      total: 0\n    };\n\n    var od = output.data;\n    var downSample = options.scoreDownSample;\n    var invDownSample = 1 / downSample;\n    var outputHeightDownSample = output.height * downSample;\n    var outputWidthDownSample = output.width * downSample;\n    var outputWidth = output.width;\n\n    for (var y = 0; y < outputHeightDownSample; y += downSample) {\n      for (var x = 0; x < outputWidthDownSample; x += downSample) {\n        var p =\n          (~~(y * invDownSample) * outputWidth + ~~(x * invDownSample)) * 4;\n        var i = importance(options, crop, x, y);\n        var detail = od[p + 1] / 255;\n\n        result.skin += (od[p] / 255) * (detail + options.skinBias) * i;\n        result.detail += detail * i;\n        result.saturation +=\n          (od[p + 2] / 255) * (detail + options.saturationBias) * i;\n        result.boost += (od[p + 3] / 255) * i;\n      }\n    }\n\n    result.total =\n      (result.detail * options.detailWeight +\n        result.skin * options.skinWeight +\n        result.saturation * options.saturationWeight +\n        result.boost * options.boostWeight) /\n      (crop.width * crop.height);\n    return result;\n  }\n\n  function importance(options, crop, x, y) {\n    if (\n      crop.x > x ||\n      x >= crop.x + crop.width ||\n      crop.y > y ||\n      y >= crop.y + crop.height\n    ) {\n      return options.outsideImportance;\n    }\n    x = (x - crop.x) / crop.width;\n    y = (y - crop.y) / crop.height;\n    var px = abs(0.5 - x) * 2;\n    var py = abs(0.5 - y) * 2;\n    // Distance from edge\n    var dx = Math.max(px - 1.0 + options.edgeRadius, 0);\n    var dy = Math.max(py - 1.0 + options.edgeRadius, 0);\n    var d = (dx * dx + dy * dy) * options.edgeWeight;\n    var s = 1.41 - sqrt(px * px + py * py);\n    if (options.ruleOfThirds) {\n      s += Math.max(0, s + d + 0.5) * 1.2 * (thirds(px) + thirds(py));\n    }\n    return s + d;\n  }\n  smartcrop.importance = importance;\n\n  function skinColor(options, r, g, b) {\n    var mag = sqrt(r * r + g * g + b * b);\n    var rd = r / mag - options.skinColor[0];\n    var gd = g / mag - options.skinColor[1];\n    var bd = b / mag - options.skinColor[2];\n    var d = sqrt(rd * rd + gd * gd + bd * bd);\n    return 1 - d;\n  }\n\n  function analyse(options, input) {\n    var result = {};\n    var output = new ImgData(input.width, input.height);\n\n    edgeDetect(input, output);\n    skinDetect(options, input, output);\n    saturationDetect(options, input, output);\n    applyBoosts(options, output);\n\n    var scoreOutput = downSample(output, options.scoreDownSample);\n\n    var topScore = -Infinity;\n    var topCrop = null;\n    var crops = generateCrops(options, input.width, input.height);\n\n    for (var i = 0, iLen = crops.length; i < iLen; i++) {\n      var crop = crops[i];\n      crop.score = score(options, scoreOutput, crop);\n      if (crop.score.total > topScore) {\n        topCrop = crop;\n        topScore = crop.score.total;\n      }\n    }\n\n    result.topCrop = topCrop;\n\n    if (options.debug && topCrop) {\n      result.crops = crops;\n      result.debugOutput = output;\n      result.debugOptions = options;\n      // Create a copy which will not be adjusted by the post scaling of smartcrop.crop\n      result.debugTopCrop = extend({}, result.topCrop);\n    }\n    return result;\n  }\n\n  function ImgData(width, height, data) {\n    this.width = width;\n    this.height = height;\n    if (data) {\n      this.data = new Uint8ClampedArray(data);\n    } else {\n      this.data = new Uint8ClampedArray(width * height * 4);\n    }\n  }\n  smartcrop.ImgData = ImgData;\n\n  function downSample(input, factor) {\n    var idata = input.data;\n    var iwidth = input.width;\n    var width = Math.floor(input.width / factor);\n    var height = Math.floor(input.height / factor);\n    var output = new ImgData(width, height);\n    var data = output.data;\n    var ifactor2 = 1 / (factor * factor);\n    for (var y = 0; y < height; y++) {\n      for (var x = 0; x < width; x++) {\n        var i = (y * width + x) * 4;\n\n        var r = 0;\n        var g = 0;\n        var b = 0;\n        var a = 0;\n\n        var mr = 0;\n        var mg = 0;\n\n        for (var v = 0; v < factor; v++) {\n          for (var u = 0; u < factor; u++) {\n            var j = ((y * factor + v) * iwidth + (x * factor + u)) * 4;\n            r += idata[j];\n            g += idata[j + 1];\n            b += idata[j + 2];\n            a += idata[j + 3];\n            mr = Math.max(mr, idata[j]);\n            mg = Math.max(mg, idata[j + 1]);\n            // unused\n            // mb = Math.max(mb, idata[j + 2]);\n          }\n        }\n        // this is some funky magic to preserve detail a bit more for\n        // skin (r) and detail (g). Saturation (b) does not get this boost.\n        data[i] = r * ifactor2 * 0.5 + mr * 0.5;\n        data[i + 1] = g * ifactor2 * 0.7 + mg * 0.3;\n        data[i + 2] = b * ifactor2;\n        data[i + 3] = a * ifactor2;\n      }\n    }\n    return output;\n  }\n  smartcrop._downSample = downSample;\n\n  function defaultCanvasFactory(w, h) {\n    var c = document.createElement('canvas');\n    c.width = w;\n    c.height = h;\n    return c;\n  }\n\n  function canvasImageOperations(canvasFactory) {\n    return {\n      // Takes imageInput as argument\n      // returns an object which has at least\n      // {width: n, height: n}\n      open: function(image) {\n        // Work around images scaled in css by drawing them onto a canvas\n        var w = image.naturalWidth || image.width;\n        var h = image.naturalHeight || image.height;\n        var c = canvasFactory(w, h);\n        var ctx = c.getContext('2d');\n        if (\n          image.naturalWidth &&\n          (image.naturalWidth != image.width ||\n            image.naturalHeight != image.height)\n        ) {\n          c.width = image.naturalWidth;\n          c.height = image.naturalHeight;\n        } else {\n          c.width = image.width;\n          c.height = image.height;\n        }\n        ctx.drawImage(image, 0, 0);\n        return smartcrop.Promise.resolve(c);\n      },\n      // Takes an image (as returned by open), and changes it's size by resampling\n      resample: function(image, width, height) {\n        return Promise.resolve(image).then(function(image) {\n          var c = canvasFactory(~~width, ~~height);\n          var ctx = c.getContext('2d');\n\n          ctx.drawImage(\n            image,\n            0,\n            0,\n            image.width,\n            image.height,\n            0,\n            0,\n            c.width,\n            c.height\n          );\n          return smartcrop.Promise.resolve(c);\n        });\n      },\n      getData: function(image) {\n        return Promise.resolve(image).then(function(c) {\n          var ctx = c.getContext('2d');\n          var id = ctx.getImageData(0, 0, c.width, c.height);\n          return new ImgData(c.width, c.height, id.data);\n        });\n      }\n    };\n  }\n  smartcrop._canvasImageOperations = canvasImageOperations;\n\n  // Aliases and helpers\n  var min = Math.min;\n  var max = Math.max;\n  var abs = Math.abs;\n  var sqrt = Math.sqrt;\n\n  function extend(o) {\n    for (var i = 1, iLen = arguments.length; i < iLen; i++) {\n      var arg = arguments[i];\n      if (arg) {\n        for (var name in arg) {\n          o[name] = arg[name];\n        }\n      }\n    }\n    return o;\n  }\n\n  // Gets value in the range of [0, 1] where 0 is the center of the pictures\n  // returns weight of rule of thirds [0, 1]\n  function thirds(x) {\n    x = (((x - 1 / 3 + 1.0) % 2.0) * 0.5 - 0.5) * 16;\n    return Math.max(1.0 - x * x, 0.0);\n  }\n\n  function cie(r, g, b) {\n    return 0.5126 * b + 0.7152 * g + 0.0722 * r;\n  }\n  function sample(id, p) {\n    return cie(id[p], id[p + 1], id[p + 2]);\n  }\n  function saturation(r, g, b) {\n    var maximum = max(r / 255, g / 255, b / 255);\n    var minimum = min(r / 255, g / 255, b / 255);\n\n    if (maximum === minimum) {\n      return 0;\n    }\n\n    var l = (maximum + minimum) / 2;\n    var d = maximum - minimum;\n\n    return l > 0.5 ? d / (2 - maximum - minimum) : d / (maximum + minimum);\n  }\n\n  // Amd\n  if (typeof define !== 'undefined' && define.amd)\n    define(function() {\n      return smartcrop;\n    });\n  // Common js\n  if (typeof exports !== 'undefined') exports.smartcrop = smartcrop;\n  else if (typeof navigator !== 'undefined')\n    // Browser\n    window.SmartCrop = window.smartcrop = smartcrop;\n  // Nodejs\n  if (typeof module !== 'undefined') {\n    module.exports = smartcrop;\n  }\n})();\n"
  },
  {
    "path": "test/benchmark-node.js",
    "content": "// npm install microtime canvas\n// then run from base directory:\n// node test/benchmark-node.js\n//\nvar Benchmark = require('benchmark'),\n  fs = require('fs'),\n  Canvas = require('canvas'),\n  SmartCrop = require('../smartcrop');\n\nvar img = new Canvas.Image();\nimg.src = fs.readFileSync('examples/images/flickr/kitty.jpg');\nfunction canvasFactory(w, h) {\n  return new Canvas(w, h);\n}\nvar benchmark = new Benchmark(\n  'SmartCrop.crop()',\n  function(deferred) {\n    SmartCrop.crop(\n      img,\n      { width: 256, height: 256, canvasFactory: canvasFactory },\n      function() {\n        deferred.resolve();\n      }\n    );\n  },\n  {\n    defer: true,\n    onComplete: function() {\n      console.log(benchmark.toString()); // eslint: ignore\n    }\n  }\n).run();\n"
  },
  {
    "path": "test/benchmark.html",
    "content": "\n\n  \n  smartcrop.js benchmark\n\n\n  
smartcrop.js benchmark
\n  
\n  
Running
\n  \n\n  \n  \n  \n  \n\n\n"
  },
  {
    "path": "test/benchmark.js",
    "content": "(function() {\n  var KITTY = '/examples/images/flickr/kitty.jpg',\n    raw = $('#raw'),\n    status = $('#status');\n\n  var benchmark = new Benchmark(\n      'smartcrop.crop()',\n      function(deferred) {\n        smartcrop.crop(img, { width: 256, height: 256 }, function() {\n          deferred.resolve();\n        });\n      },\n      {\n        defer: true,\n        maxTime: 30,\n        onCycle: function() {\n          status.text(benchmark.toString());\n        },\n        onComplete: function() {\n          console.log('complete', arguments);\n          status.text(benchmark.toString());\n          raw.text(JSON.stringify(benchmark.stats, null, ' '));\n        }\n      }\n    ),\n    img = new Image();\n  // add tests\n  img.src = KITTY;\n  img.onload = function() {\n    benchmark.run({ async: true });\n  };\n})();\n"
  },
  {
    "path": "test/index.html",
    "content": "\n\n  \n  smartcrop.js Mocha Tests\n  \n\n\n  
\n  \n  \n  \n  \n\n  \n  \n  \n\n\n"
  },
  {
    "path": "test/smartcrop.js",
    "content": "(function() {\n  mocha.setup('bdd');\n  var expect = chai.expect;\n\n  var KITTY = '/examples/images/flickr/kitty.jpg';\n  describe('smartcrop', function() {\n    var img;\n    beforeEach(function(done) {\n      img = new Image();\n      img.src = KITTY;\n      img.onload = function() {\n        done();\n      };\n    });\n    function validResult(result) {\n      expect(result.topCrop.x).to.be.within(\n        0,\n        img.width - result.topCrop.width\n      );\n      expect(result.topCrop.y).to.be.within(\n        0,\n        img.height - result.topCrop.height\n      );\n      expect(result.topCrop.width).to.be.within(1, img.width);\n      expect(result.topCrop.height).to.be.within(1, img.height);\n    }\n    function expectAspectRatio(result, expected) {\n      expect(result.topCrop.width / result.topCrop.height).to.be.closeTo(\n        expected,\n        0.01\n      );\n    }\n    describe('isAvailable', function() {\n      it('should return true when canvas is available', function() {\n        expect(smartcrop.isAvailable()).to.equal(true);\n      });\n    });\n    describe('crop', function() {\n      it('should do something sane', function() {\n        var c = document.createElement('canvas');\n        var ctx = c.getContext('2d');\n        c.width = 128;\n        c.height = 64;\n        ctx.fillStyle = 'white';\n        ctx.fillRect(0, 0, 128, 64);\n        ctx.fillStyle = 'red';\n        ctx.fillRect(96, 32, 16, 16);\n        return smartcrop\n          .crop(c, { width: 32, height: 32 })\n          .then(function(result) {\n            expectAspectRatio(result, 1);\n            expect(result.topCrop.x).to.be.lessThan(96);\n            expect(result.topCrop.y).to.be.lessThan(32);\n            expect(result.topCrop.x + result.topCrop.width).to.be.greaterThan(\n              112\n            );\n            expect(result.topCrop.y + result.topCrop.height).to.be.greaterThan(\n              48\n            );\n          });\n      });\n      it('should take into account ruleOfThirds', function() {\n        var c = document.createElement('canvas');\n        var ctx = c.getContext('2d');\n        c.width = 256;\n        c.height = 128;\n        ctx.fillStyle = 'white';\n        ctx.fillRect(0, 0, 256, 128);\n        ctx.fillStyle = 'red';\n        ctx.fillRect(128, 24, 8, 80);\n        var optionsThirds = {\n          width: 32,\n          height: 32,\n          ruleOfThirds: true,\n          debug: true\n        };\n        var optionsNoThirds = {\n          width: 32,\n          height: 32,\n          ruleOfThirds: false,\n          debug: true\n        };\n        return smartcrop.crop(c, optionsThirds).then(function(resultThirds) {\n          return smartcrop\n            .crop(c, optionsNoThirds)\n            .then(function(resultNoThirds) {\n              validResult(resultThirds);\n              validResult(resultNoThirds);\n              expectAspectRatio(resultNoThirds, 1);\n              expect(\n                resultNoThirds.topCrop.x + resultNoThirds.topCrop.width / 2\n              ).to.equal(128);\n              expect(\n                resultThirds.topCrop.x + resultThirds.topCrop.width / 2\n              ).to.equal(144);\n            });\n        });\n      });\n\n      it('should adhere to minScale', function() {\n        return smartcrop.crop(img, { minScale: 1 }).then(function(result) {\n          validResult(result);\n          expectAspectRatio(result, 1);\n          expect(result.topCrop.y).to.equal(0);\n          expect(result.topCrop.height).to.equal(img.height);\n        });\n      });\n      it('should take into account boost', function() {\n        var boost = [\n          {\n            x: img.width - 128,\n            y: img.height - 128,\n            width: 64,\n            height: 64,\n            weight: 1.0\n          }\n        ];\n        return smartcrop\n          .crop(img, { boost: boost, width: 64, height: 64 })\n          .then(function(result) {\n            validResult(result);\n            expect(result.topCrop.y).to.equal(0);\n            expect(result.topCrop.x).to.equal(200);\n            expect(result.topCrop.height).to.equal(img.height);\n          });\n      });\n      it('should take into account the aspect ratio', function() {\n        return smartcrop\n          .crop(img, { width: 128, height: 64 })\n          .then(function(result) {\n            validResult(result);\n            expectAspectRatio(result, 2.0 / 1);\n            return smartcrop\n              .crop(img, { width: 160, height: 90 })\n              .then(function(result) {\n                validResult(result);\n                expectAspectRatio(result, 16 / 9);\n              });\n          });\n      });\n\n      it('should crop the kitty', function() {\n        return smartcrop.crop(img, {}).then(function(result) {\n          validResult(result);\n        });\n      });\n    });\n    describe('iop', function() {\n      describe('open', function() {\n        it('passes input options', function() {\n          var iop = smartcrop._canvasImageOperations(\n            smartcrop.DEFAULTS.canvasFactory\n          );\n          var open = iop.open;\n          var inputOptions;\n          iop.open = function(image, inputOptions_) {\n            inputOptions = inputOptions_;\n            return open(image);\n          };\n          var options = {\n            imageOperations: iop,\n            input: { foo: 'bar' }\n          };\n          return smartcrop.crop(img, options).then(function(result) {\n            expect(inputOptions).to.equal(options.input);\n            validResult(result);\n          });\n        });\n      });\n    });\n\n    describe('_downSample', function() {\n      var input = {\n        width: 4,\n        height: 4,\n        data: mono2rgba([1, 2, 3, 4, 5, 6, 7, 8, 9, 8, 7, 6, 5, 4, 3, 2])\n      };\n      function mono2rgba(input) {\n        var output = new Uint8ClampedArray(input.length * 4);\n        for (var i = 0; i < input.length; i++) {\n          output[i * 4] = input[i];\n          output[i * 4 + 1] = input[i];\n          output[i * 4 + 2] = input[i];\n          output[i * 4 + 3] = input[i];\n        }\n        return output;\n      }\n      it('keeps the image the same at a factor of one', function() {\n        var output = smartcrop._downSample(input, 1);\n        expect(output.width).to.equal(input.width);\n        expect(output.height).to.equal(input.height);\n        expect(output.data).to.deep.equal(input.data);\n      });\n\n      it('samples down an image by a factor of two', function() {\n        var expectedOutputData = new Uint8ClampedArray([\n          5,\n          4,\n          4,\n          4,\n          7,\n          6,\n          6,\n          6,\n          8,\n          7,\n          6,\n          6,\n          6,\n          5,\n          4,\n          4\n        ]);\n        var output = smartcrop._downSample(input, 2);\n        expect(output.width).to.equal(input.width / 2);\n        expect(output.height).to.equal(input.height / 2);\n        expect(output.data).to.deep.equal(expectedOutputData);\n      });\n      it('keeps a constant value constant', function() {\n        var w = 59;\n        var h = 23;\n        var input = {\n          width: w,\n          height: h,\n          data: new Uint8ClampedArray(w * h * 4)\n        };\n        for (var i = 0; i < input.data.length; i++) {\n          input.data[i] = 119;\n        }\n        var output = smartcrop._downSample(input, 8);\n        expect(output.width).to.equal(~~(input.width / 8));\n        expect(output.height).to.equal(~~(input.height / 8));\n        for (i = 0; i < output.data.length; i++) {\n          expect(output.data[i]).to.be.within(118, 120);\n        }\n      });\n    });\n  });\n})();\n"
  },
  {
    "path": "test/types.ts",
    "content": "import SmartCrop from '..';\n\nconsole.log(SmartCrop.crop);"
  }
]