├── .gitignore
├── CODE_OF_CONDUCT.md
├── LICENSE
├── README.md
├── SECURITY.md
├── lib
    ├── index.js
    └── index.umd.js
├── package-lock.json
├── package.json
├── rollup.config.js
├── src
    └── index.js
└── test
    ├── index.html
    ├── index.test.js
    └── od_output.json


/.gitignore:
--------------------------------------------------------------------------------
1 | node_modules/


--------------------------------------------------------------------------------
/CODE_OF_CONDUCT.md:
--------------------------------------------------------------------------------
 1 | # Microsoft Open Source Code of Conduct
 2 | 
 3 | This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
 4 | 
 5 | Resources:
 6 | 
 7 | - [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
 8 | - [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
 9 | - Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns
10 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 |     MIT License
 2 | 
 3 |     Copyright (c) Microsoft Corporation.
 4 | 
 5 |     Permission is hereby granted, free of charge, to any person obtaining a copy
 6 |     of this software and associated documentation files (the "Software"), to deal
 7 |     in the Software without restriction, including without limitation the rights
 8 |     to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 |     copies of the Software, and to permit persons to whom the Software is
10 |     furnished to do so, subject to the following conditions:
11 | 
12 |     The above copyright notice and this permission notice shall be included in all
13 |     copies or substantial portions of the Software.
14 | 
15 |     THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 |     IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 |     FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 |     AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 |     LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 |     OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 |     SOFTWARE
22 | 


--------------------------------------------------------------------------------
/README.md:
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 1 | # customvision-tfjs
 2 | NPM package for TensorFlow.js models exported from Custom Vision Service
 3 | 
 4 | This package is for web browsers. If you are looking for a library to run on Node.js environment, please use [customvision-tfjs-node](https://github.com/microsoft/customvision-tfjs-node).
 5 | 
 6 | ## Install
 7 | ```sh
 8 | npm install @microsoft/customvision-tfjs
 9 | ```
10 | 
11 | Or, if you would like to use CDN,
12 | 
13 | ```html
14 | <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@3.12.0/dist/tf.min.js"></script>
15 | <script src="https://unpkg.com/@microsoft/customvision-tfjs@1.3.0"></script>
16 | ```
17 | 
18 | ## Usage
19 | 
20 | ```html
21 | <img id="image" src="test_image.jpg" />
22 | ```
23 | 
24 | ### Classification
25 | ```js
26 | import * as cvstfjs from '@microsoft/customvision-tfjs';
27 | 
28 | let model = new cvstfjs.ClassificationModel();
29 | await model.loadModelAsync('model.json');
30 | const image = document.getElementById('image');
31 | const result = await model.executeAsync(image);
32 | ```
33 | 
34 | The result is a 1D-array of probabilities.
35 | 
36 | ### Object Detection
37 | ```js
38 | import * as cvstfjs from '@microsoft/customvision-tfjs';
39 | 
40 | let model = new cvstfjs.ObjectDetectionModel();
41 | await model.loadModelAsync('model.json');
42 | const image = document.getElementById('image');
43 | const result = await model.executeAsync(image);
44 | ```
45 | 
46 | The result has 3 arrays.
47 | ```js
48 | 
49 | [
50 | 	[[0.1, 0.3, 0.4, 0.3], [0.2, 0.4, 0.8, 0.9]], // bounding boxes (x1, y1, x2, y2)
51 | 	[0.2, 0.3], // probabilities
52 | 	[1, 4] // class ids
53 | ]
54 | ```
55 | 
56 | # References
57 | * [sample scripts](https://github.com/Azure-Samples/customvision-export-samples)
58 | 
59 | # Contributing
60 | 
61 | This project welcomes contributions and suggestions.  Most contributions require you to agree to a
62 | Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
63 | the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
64 | 
65 | When you submit a pull request, a CLA bot will automatically determine whether you need to provide
66 | a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
67 | provided by the bot. You will only need to do this once across all repos using our CLA.
68 | 
69 | This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
70 | For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
71 | contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
72 | 


--------------------------------------------------------------------------------
/SECURITY.md:
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 1 | <!-- BEGIN MICROSOFT SECURITY.MD V0.0.3 BLOCK -->
 2 | 
 3 | ## Security
 4 | 
 5 | Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/).
 6 | 
 7 | If you believe you have found a security vulnerability in any Microsoft-owned repository that meets Microsoft's [Microsoft's definition of a security vulnerability](https://docs.microsoft.com/en-us/previous-versions/tn-archive/cc751383(v=technet.10)) of a security vulnerability, please report it to us as described below.
 8 | 
 9 | ## Reporting Security Issues
10 | 
11 | **Please do not report security vulnerabilities through public GitHub issues.**
12 | 
13 | Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report).
14 | 
15 | If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com).  If possible, encrypt your message with our PGP key; please download it from the the [Microsoft Security Response Center PGP Key page](https://www.microsoft.com/en-us/msrc/pgp-key-msrc).
16 | 
17 | You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc).
18 | 
19 | Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:
20 | 
21 |   * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)
22 |   * Full paths of source file(s) related to the manifestation of the issue
23 |   * The location of the affected source code (tag/branch/commit or direct URL)
24 |   * Any special configuration required to reproduce the issue
25 |   * Step-by-step instructions to reproduce the issue
26 |   * Proof-of-concept or exploit code (if possible)
27 |   * Impact of the issue, including how an attacker might exploit the issue
28 | 
29 | This information will help us triage your report more quickly.
30 | 
31 | If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://microsoft.com/msrc/bounty) page for more details about our active programs.
32 | 
33 | ## Preferred Languages
34 | 
35 | We prefer all communications to be in English.
36 | 
37 | ## Policy
38 | 
39 | Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/en-us/msrc/cvd).
40 | 
41 | <!-- END MICROSOFT SECURITY.MD BLOCK -->
42 | 


--------------------------------------------------------------------------------
/lib/index.js:
--------------------------------------------------------------------------------
  1 | "use strict";
  2 | 
  3 | var _interopRequireDefault = require("@babel/runtime/helpers/interopRequireDefault");
  4 | 
  5 | var _typeof = require("@babel/runtime/helpers/typeof");
  6 | 
  7 | Object.defineProperty(exports, "__esModule", {
  8 |   value: true
  9 | });
 10 | exports.ObjectDetectionModel = exports.ClassificationModel = void 0;
 11 | 
 12 | var _regenerator = _interopRequireDefault(require("@babel/runtime/regenerator"));
 13 | 
 14 | var _slicedToArray2 = _interopRequireDefault(require("@babel/runtime/helpers/slicedToArray"));
 15 | 
 16 | var _inherits2 = _interopRequireDefault(require("@babel/runtime/helpers/inherits"));
 17 | 
 18 | var _possibleConstructorReturn2 = _interopRequireDefault(require("@babel/runtime/helpers/possibleConstructorReturn"));
 19 | 
 20 | var _getPrototypeOf2 = _interopRequireDefault(require("@babel/runtime/helpers/getPrototypeOf"));
 21 | 
 22 | var _asyncToGenerator2 = _interopRequireDefault(require("@babel/runtime/helpers/asyncToGenerator"));
 23 | 
 24 | var _classCallCheck2 = _interopRequireDefault(require("@babel/runtime/helpers/classCallCheck"));
 25 | 
 26 | var _createClass2 = _interopRequireDefault(require("@babel/runtime/helpers/createClass"));
 27 | 
 28 | var tf = _interopRequireWildcard(require("@tensorflow/tfjs"));
 29 | 
 30 | function _getRequireWildcardCache(nodeInterop) { if (typeof WeakMap !== "function") return null; var cacheBabelInterop = new WeakMap(); var cacheNodeInterop = new WeakMap(); return (_getRequireWildcardCache = function _getRequireWildcardCache(nodeInterop) { return nodeInterop ? cacheNodeInterop : cacheBabelInterop; })(nodeInterop); }
 31 | 
 32 | function _interopRequireWildcard(obj, nodeInterop) { if (!nodeInterop && obj && obj.__esModule) { return obj; } if (obj === null || _typeof(obj) !== "object" && typeof obj !== "function") { return { default: obj }; } var cache = _getRequireWildcardCache(nodeInterop); if (cache && cache.has(obj)) { return cache.get(obj); } var newObj = {}; var hasPropertyDescriptor = Object.defineProperty && Object.getOwnPropertyDescriptor; for (var key in obj) { if (key !== "default" && Object.prototype.hasOwnProperty.call(obj, key)) { var desc = hasPropertyDescriptor ? Object.getOwnPropertyDescriptor(obj, key) : null; if (desc && (desc.get || desc.set)) { Object.defineProperty(newObj, key, desc); } else { newObj[key] = obj[key]; } } } newObj.default = obj; if (cache) { cache.set(obj, newObj); } return newObj; }
 33 | 
 34 | function _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = (0, _getPrototypeOf2.default)(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = (0, _getPrototypeOf2.default)(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return (0, _possibleConstructorReturn2.default)(this, result); }; }
 35 | 
 36 | function _isNativeReflectConstruct() { if (typeof Reflect === "undefined" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === "function") return true; try { Boolean.prototype.valueOf.call(Reflect.construct(Boolean, [], function () {})); return true; } catch (e) { return false; } }
 37 | 
 38 | var Model = /*#__PURE__*/function () {
 39 |   function Model() {
 40 |     (0, _classCallCheck2.default)(this, Model);
 41 |     this.NEW_OD_OUTPUT_TENSORS = ['detected_boxes', 'detected_scores', 'detected_classes'];
 42 |   }
 43 | 
 44 |   (0, _createClass2.default)(Model, [{
 45 |     key: "loadModelAsync",
 46 |     value: function () {
 47 |       var _loadModelAsync = (0, _asyncToGenerator2.default)( /*#__PURE__*/_regenerator.default.mark(function _callee(modelUrl) {
 48 |         var options,
 49 |             _args = arguments;
 50 |         return _regenerator.default.wrap(function _callee$(_context) {
 51 |           while (1) {
 52 |             switch (_context.prev = _context.next) {
 53 |               case 0:
 54 |                 options = _args.length > 1 && _args[1] !== undefined ? _args[1] : null;
 55 |                 _context.next = 3;
 56 |                 return tf.loadGraphModel(modelUrl, options);
 57 | 
 58 |               case 3:
 59 |                 this.model = _context.sent;
 60 |                 this.input_size = this.model.inputs[0].shape[1];
 61 |                 this.is_new_od_model = this.model.outputs.length == 3;
 62 |                 this.is_rgb_input = this.model.metadata && this.model.metadata['Image.BitmapPixelFormat'] == 'Rgb8' || this.is_new_od_model;
 63 | 
 64 |               case 7:
 65 |               case "end":
 66 |                 return _context.stop();
 67 |             }
 68 |           }
 69 |         }, _callee, this);
 70 |       }));
 71 | 
 72 |       function loadModelAsync(_x) {
 73 |         return _loadModelAsync.apply(this, arguments);
 74 |       }
 75 | 
 76 |       return loadModelAsync;
 77 |     }()
 78 |   }, {
 79 |     key: "dispose",
 80 |     value: function dispose() {
 81 |       this.model.dispose();
 82 |     }
 83 |   }, {
 84 |     key: "executeAsync",
 85 |     value: function () {
 86 |       var _executeAsync = (0, _asyncToGenerator2.default)( /*#__PURE__*/_regenerator.default.mark(function _callee2(pixels) {
 87 |         var _this = this;
 88 | 
 89 |         var inputs, outputs, arrays;
 90 |         return _regenerator.default.wrap(function _callee2$(_context2) {
 91 |           while (1) {
 92 |             switch (_context2.prev = _context2.next) {
 93 |               case 0:
 94 |                 inputs = tf.tidy(function () {
 95 |                   return pixels instanceof tf.Tensor ? pixels : _this._preprocess(tf.browser.fromPixels(pixels, 3));
 96 |                 });
 97 |                 _context2.next = 3;
 98 |                 return this.model.executeAsync(inputs, this.is_new_od_model ? this.NEW_OD_OUTPUT_TENSORS : null);
 99 | 
100 |               case 3:
101 |                 outputs = _context2.sent;
102 |                 tf.dispose(inputs);
103 |                 _context2.next = 7;
104 |                 return !Array.isArray(outputs) ? outputs.array() : Promise.all(outputs.map(function (t) {
105 |                   return t.array();
106 |                 }));
107 | 
108 |               case 7:
109 |                 arrays = _context2.sent;
110 |                 tf.dispose(outputs);
111 |                 return _context2.abrupt("return", this._postprocess(arrays));
112 | 
113 |               case 10:
114 |               case "end":
115 |                 return _context2.stop();
116 |             }
117 |           }
118 |         }, _callee2, this);
119 |       }));
120 | 
121 |       function executeAsync(_x2) {
122 |         return _executeAsync.apply(this, arguments);
123 |       }
124 | 
125 |       return executeAsync;
126 |     }()
127 |   }, {
128 |     key: "_postprocess",
129 |     value: function () {
130 |       var _postprocess2 = (0, _asyncToGenerator2.default)( /*#__PURE__*/_regenerator.default.mark(function _callee3(outputs) {
131 |         return _regenerator.default.wrap(function _callee3$(_context3) {
132 |           while (1) {
133 |             switch (_context3.prev = _context3.next) {
134 |               case 0:
135 |                 return _context3.abrupt("return", outputs);
136 | 
137 |               case 1:
138 |               case "end":
139 |                 return _context3.stop();
140 |             }
141 |           }
142 |         }, _callee3);
143 |       }));
144 | 
145 |       function _postprocess(_x3) {
146 |         return _postprocess2.apply(this, arguments);
147 |       }
148 | 
149 |       return _postprocess;
150 |     }()
151 |   }]);
152 |   return Model;
153 | }();
154 | 
155 | var ObjectDetectionModel = /*#__PURE__*/function (_Model) {
156 |   (0, _inherits2.default)(ObjectDetectionModel, _Model);
157 | 
158 |   var _super = _createSuper(ObjectDetectionModel);
159 | 
160 |   function ObjectDetectionModel() {
161 |     var _this2;
162 | 
163 |     (0, _classCallCheck2.default)(this, ObjectDetectionModel);
164 |     _this2 = _super.call(this);
165 |     _this2.ANCHORS = [0.573, 0.677, 1.87, 2.06, 3.34, 5.47, 7.88, 3.53, 9.77, 9.17];
166 |     return _this2;
167 |   }
168 | 
169 |   (0, _createClass2.default)(ObjectDetectionModel, [{
170 |     key: "_preprocess",
171 |     value: function _preprocess(image) {
172 |       var rgb_image = tf.image.resizeBilinear(image.expandDims().toFloat(), [this.input_size, this.input_size]);
173 |       return this.is_rgb_input ? rgb_image : rgb_image.reverse(-1); // RGB->BGR for old models
174 |     }
175 |   }, {
176 |     key: "_postprocess",
177 |     value: function () {
178 |       var _postprocess3 = (0, _asyncToGenerator2.default)( /*#__PURE__*/_regenerator.default.mark(function _callee4(outputs) {
179 |         var _this3 = this;
180 | 
181 |         var _tf$tidy, _tf$tidy2, boxes, scores, classes, selected_indices, tensor_results, results;
182 | 
183 |         return _regenerator.default.wrap(function _callee4$(_context4) {
184 |           while (1) {
185 |             switch (_context4.prev = _context4.next) {
186 |               case 0:
187 |                 if (!(outputs.length == 3)) {
188 |                   _context4.next = 2;
189 |                   break;
190 |                 }
191 | 
192 |                 return _context4.abrupt("return", outputs);
193 | 
194 |               case 2:
195 |                 _tf$tidy = tf.tidy(function () {
196 |                   // TODO: Need more efficient implmentation
197 |                   var num_anchor = _this3.ANCHORS.length / 2;
198 |                   var channels = outputs[0][0][0].length;
199 |                   var height = outputs[0].length;
200 |                   var width = outputs[0][0].length;
201 |                   var num_class = channels / num_anchor - 5;
202 |                   var boxes = [];
203 |                   var scores = [];
204 |                   var classes = [];
205 | 
206 |                   for (var grid_y = 0; grid_y < height; grid_y++) {
207 |                     for (var grid_x = 0; grid_x < width; grid_x++) {
208 |                       var offset = 0;
209 | 
210 |                       for (var i = 0; i < num_anchor; i++) {
211 |                         var x = (_this3._logistic(outputs[0][grid_y][grid_x][offset++]) + grid_x) / width;
212 |                         var y = (_this3._logistic(outputs[0][grid_y][grid_x][offset++]) + grid_y) / height;
213 |                         var w = Math.exp(outputs[0][grid_y][grid_x][offset++]) * _this3.ANCHORS[i * 2] / width;
214 |                         var h = Math.exp(outputs[0][grid_y][grid_x][offset++]) * _this3.ANCHORS[i * 2 + 1] / height;
215 |                         var objectness = tf.scalar(_this3._logistic(outputs[0][grid_y][grid_x][offset++]));
216 |                         var class_probabilities = tf.tensor1d(outputs[0][grid_y][grid_x].slice(offset, offset + num_class)).softmax();
217 |                         offset += num_class;
218 |                         class_probabilities = class_probabilities.mul(objectness);
219 |                         var max_index = class_probabilities.argMax();
220 |                         boxes.push([x - w / 2, y - h / 2, x + w / 2, y + h / 2]);
221 |                         scores.push(class_probabilities.max().dataSync()[0]);
222 |                         classes.push(max_index.dataSync()[0]);
223 |                       }
224 |                     }
225 |                   }
226 | 
227 |                   boxes = tf.tensor2d(boxes);
228 |                   scores = tf.tensor1d(scores);
229 |                   classes = tf.tensor1d(classes);
230 |                   return [boxes, scores, classes];
231 |                 }), _tf$tidy2 = (0, _slicedToArray2.default)(_tf$tidy, 3), boxes = _tf$tidy2[0], scores = _tf$tidy2[1], classes = _tf$tidy2[2];
232 |                 _context4.next = 5;
233 |                 return tf.image.nonMaxSuppressionAsync(boxes, scores, 10);
234 | 
235 |               case 5:
236 |                 selected_indices = _context4.sent;
237 |                 tensor_results = [boxes.gather(selected_indices), scores.gather(selected_indices), classes.gather(selected_indices)];
238 |                 _context4.next = 9;
239 |                 return tensor_results[0].array();
240 | 
241 |               case 9:
242 |                 _context4.t0 = _context4.sent;
243 |                 _context4.next = 12;
244 |                 return tensor_results[1].array();
245 | 
246 |               case 12:
247 |                 _context4.t1 = _context4.sent;
248 |                 _context4.next = 15;
249 |                 return tensor_results[2].array();
250 | 
251 |               case 15:
252 |                 _context4.t2 = _context4.sent;
253 |                 results = [_context4.t0, _context4.t1, _context4.t2];
254 |                 tf.dispose([boxes, scores, classes]);
255 |                 tf.dispose(selected_indices);
256 |                 tf.dispose(tensor_results);
257 |                 return _context4.abrupt("return", results);
258 | 
259 |               case 21:
260 |               case "end":
261 |                 return _context4.stop();
262 |             }
263 |           }
264 |         }, _callee4);
265 |       }));
266 | 
267 |       function _postprocess(_x4) {
268 |         return _postprocess3.apply(this, arguments);
269 |       }
270 | 
271 |       return _postprocess;
272 |     }()
273 |   }, {
274 |     key: "_logistic",
275 |     value: function _logistic(x) {
276 |       if (x > 0) {
277 |         return 1 / (1 + Math.exp(-x));
278 |       } else {
279 |         var e = Math.exp(x);
280 |         return e / (1 + e);
281 |       }
282 |     }
283 |   }]);
284 |   return ObjectDetectionModel;
285 | }(Model);
286 | 
287 | exports.ObjectDetectionModel = ObjectDetectionModel;
288 | 
289 | var ClassificationModel = /*#__PURE__*/function (_Model2) {
290 |   (0, _inherits2.default)(ClassificationModel, _Model2);
291 | 
292 |   var _super2 = _createSuper(ClassificationModel);
293 | 
294 |   function ClassificationModel() {
295 |     (0, _classCallCheck2.default)(this, ClassificationModel);
296 |     return _super2.apply(this, arguments);
297 |   }
298 | 
299 |   (0, _createClass2.default)(ClassificationModel, [{
300 |     key: "_preprocess",
301 |     value: function _preprocess(image) {
302 |       // CenterCrop
303 |       var _image$shape$slice = image.shape.slice(0, 2),
304 |           _image$shape$slice2 = (0, _slicedToArray2.default)(_image$shape$slice, 2),
305 |           h = _image$shape$slice2[0],
306 |           w = _image$shape$slice2[1];
307 | 
308 |       var top = h > w ? (h - w) / 2 : 0;
309 |       var left = h > w ? 0 : (w - h) / 2;
310 |       var size = Math.min(h, w);
311 |       var rgb_image = tf.image.cropAndResize(image.expandDims().toFloat(), [[top / h, left / w, (top + size) / h, (left + size) / w]], [0], [this.input_size, this.input_size]);
312 |       return this.is_rgb_input ? rgb_image : rgb_image.reverse(-1); // RGB->BGR for old models
313 |     }
314 |   }]);
315 |   return ClassificationModel;
316 | }(Model);
317 | 
318 | exports.ClassificationModel = ClassificationModel;


--------------------------------------------------------------------------------
/lib/index.umd.js:
--------------------------------------------------------------------------------
  1 | (function (global, factory) {
  2 |   typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('@tensorflow/tfjs')) :
  3 |   typeof define === 'function' && define.amd ? define(['exports', '@tensorflow/tfjs'], factory) :
  4 |   (global = global || self, factory(global.cvstfjs = {}, global.tf));
  5 | }(this, (function (exports, tf) { 'use strict';
  6 | 
  7 |   class Model {
  8 |     constructor() {
  9 |       this.NEW_OD_OUTPUT_TENSORS = ['detected_boxes', 'detected_scores', 'detected_classes'];
 10 |     }
 11 | 
 12 |     async loadModelAsync(modelUrl, options = null) {
 13 |       this.model = await tf.loadGraphModel(modelUrl, options);
 14 |       this.input_size = this.model.inputs[0].shape[1];
 15 |       this.is_new_od_model = this.model.outputs.length == 3;
 16 |       this.is_rgb_input = (this.model.metadata && this.model.metadata['Image.BitmapPixelFormat'] == 'Rgb8') || this.is_new_od_model;
 17 |     }
 18 | 
 19 |     dispose() {
 20 |       this.model.dispose();
 21 |     }
 22 | 
 23 |     async executeAsync(pixels) {
 24 |       const inputs = tf.tidy(() => { return pixels instanceof tf.Tensor ? pixels : this._preprocess(tf.browser.fromPixels(pixels, 3)); });
 25 |       const outputs = await this.model.executeAsync(inputs, this.is_new_od_model ? this.NEW_OD_OUTPUT_TENSORS : null);
 26 |       tf.dispose(inputs);
 27 |       const arrays = await (!Array.isArray(outputs) ? outputs.array() : Promise.all(outputs.map(t => t.array())));
 28 |       tf.dispose(outputs);
 29 |       return this._postprocess(arrays);
 30 |     }
 31 | 
 32 |     async _postprocess(outputs) {
 33 |       return outputs;
 34 |     }
 35 |   }
 36 | 
 37 |   class ObjectDetectionModel extends Model {
 38 |     constructor() {
 39 |       super();
 40 |       this.ANCHORS = [0.573, 0.677, 1.87, 2.06, 3.34, 5.47, 7.88, 3.53, 9.77, 9.17];
 41 |     }
 42 | 
 43 |     _preprocess(image) {
 44 |       const rgb_image = tf.image.resizeBilinear(image.expandDims().toFloat(), [this.input_size, this.input_size]);
 45 |       return this.is_rgb_input ? rgb_image : rgb_image.reverse(-1); // RGB->BGR for old models
 46 |     }
 47 | 
 48 |     async _postprocess(outputs) {
 49 |       if (outputs.length == 3) {
 50 |           return outputs; // New model doesn't need post processing.
 51 |       }
 52 | 
 53 |       const [boxes, scores, classes] = tf.tidy(() => {
 54 |         // TODO: Need more efficient implmentation
 55 |         const num_anchor = this.ANCHORS.length / 2;
 56 |         const channels = outputs[0][0][0].length;
 57 |         const height = outputs[0].length;
 58 |         const width = outputs[0][0].length;
 59 | 
 60 |         const num_class = channels / num_anchor - 5;
 61 | 
 62 |         let boxes = [];
 63 |         let scores = [];
 64 |         let classes = [];
 65 | 
 66 |         for (var grid_y = 0; grid_y < height; grid_y++) {
 67 |           for (var grid_x = 0; grid_x < width; grid_x++) {
 68 |             let offset = 0;
 69 | 
 70 |             for (var i = 0; i < num_anchor; i++) {
 71 |               let x = (this._logistic(outputs[0][grid_y][grid_x][offset++]) + grid_x) / width;
 72 |               let y = (this._logistic(outputs[0][grid_y][grid_x][offset++]) + grid_y) / height;
 73 |               let w = Math.exp(outputs[0][grid_y][grid_x][offset++]) * this.ANCHORS[i * 2] / width;
 74 |               let h = Math.exp(outputs[0][grid_y][grid_x][offset++]) * this.ANCHORS[i * 2 + 1] / height;
 75 | 
 76 |               let objectness = tf.scalar(this._logistic(outputs[0][grid_y][grid_x][offset++]));
 77 |               let class_probabilities = tf.tensor1d(outputs[0][grid_y][grid_x].slice(offset, offset + num_class)).softmax();
 78 |               offset += num_class;
 79 | 
 80 |               class_probabilities = class_probabilities.mul(objectness);
 81 |               let max_index = class_probabilities.argMax();
 82 |               boxes.push([x - w / 2, y - h / 2, x + w / 2, y + h / 2]);
 83 |               scores.push(class_probabilities.max().dataSync()[0]);
 84 |               classes.push(max_index.dataSync()[0]);
 85 |             }
 86 |           }
 87 |         }
 88 |         boxes = tf.tensor2d(boxes);
 89 |         scores = tf.tensor1d(scores);
 90 |         classes = tf.tensor1d(classes);
 91 | 
 92 |         return [boxes, scores, classes];
 93 |       });
 94 | 
 95 |       const selected_indices = await tf.image.nonMaxSuppressionAsync(boxes, scores, 10);
 96 |       const tensor_results = [boxes.gather(selected_indices), scores.gather(selected_indices), classes.gather(selected_indices)];
 97 |       const results = [await tensor_results[0].array(), await tensor_results[1].array(), await tensor_results[2].array()];
 98 |       tf.dispose([boxes, scores, classes]);
 99 |       tf.dispose(selected_indices);
100 |       tf.dispose(tensor_results);
101 |       return results;
102 |     }
103 | 
104 |     _logistic(x) {
105 |       if (x > 0) {
106 |           return (1 / (1 + Math.exp(-x)));
107 |       } else {
108 |           const e = Math.exp(x);
109 |           return e / (1 + e);
110 |       }
111 |     }
112 |   }
113 | 
114 |   class ClassificationModel extends Model {
115 |     _preprocess(image) {
116 |       // CenterCrop
117 |       const [h, w] = image.shape.slice(0, 2);
118 |       const top = h > w ? (h - w) / 2 : 0;
119 |       const left = h > w ? 0 : (w - h) / 2;
120 |       const size = Math.min(h, w);
121 |       const rgb_image = tf.image.cropAndResize(image.expandDims().toFloat(), [[top / h, left / w, (top+size) / h, (left+size) / w]], [0], [this.input_size, this.input_size]);
122 | 
123 |       return this.is_rgb_input ? rgb_image : rgb_image.reverse(-1); // RGB->BGR for old models
124 |     }
125 |   }
126 | 
127 |   exports.ClassificationModel = ClassificationModel;
128 |   exports.ObjectDetectionModel = ObjectDetectionModel;
129 | 
130 |   Object.defineProperty(exports, '__esModule', { value: true });
131 | 
132 | })));
133 | 


--------------------------------------------------------------------------------
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2710 |         "strip-ansi": "^6.0.0"
2711 |       },
2712 |       "dependencies": {
2713 |         "ansi-styles": {
2714 |           "version": "4.3.0",
2715 |           "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-4.3.0.tgz",
2716 |           "integrity": "sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg==",
2717 |           "dev": true,
2718 |           "requires": {
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2720 |           }
2721 |         },
2722 |         "color-convert": {
2723 |           "version": "2.0.1",
2724 |           "resolved": "https://registry.npmjs.org/color-convert/-/color-convert-2.0.1.tgz",
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2726 |           "dev": true,
2727 |           "requires": {
2728 |             "color-name": "~1.1.4"
2729 |           }
2730 |         },
2731 |         "color-name": {
2732 |           "version": "1.1.4",
2733 |           "resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.4.tgz",
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2735 |           "dev": true
2736 |         }
2737 |       }
2738 |     },
2739 |     "wrappy": {
2740 |       "version": "1.0.2",
2741 |       "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
2742 |       "integrity": "sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=",
2743 |       "dev": true
2744 |     },
2745 |     "y18n": {
2746 |       "version": "5.0.8",
2747 |       "resolved": "https://registry.npmjs.org/y18n/-/y18n-5.0.8.tgz",
2748 |       "integrity": "sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA=="
2749 |     },
2750 |     "yargs": {
2751 |       "version": "16.2.0",
2752 |       "resolved": "https://registry.npmjs.org/yargs/-/yargs-16.2.0.tgz",
2753 |       "integrity": "sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==",
2754 |       "dev": true,
2755 |       "requires": {
2756 |         "cliui": "^7.0.2",
2757 |         "escalade": "^3.1.1",
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2759 |         "require-directory": "^2.1.1",
2760 |         "string-width": "^4.2.0",
2761 |         "y18n": "^5.0.5",
2762 |         "yargs-parser": "^20.2.2"
2763 |       }
2764 |     },
2765 |     "yargs-parser": {
2766 |       "version": "20.2.4",
2767 |       "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.4.tgz",
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2769 |       "dev": true
2770 |     },
2771 |     "yargs-unparser": {
2772 |       "version": "2.0.0",
2773 |       "resolved": "https://registry.npmjs.org/yargs-unparser/-/yargs-unparser-2.0.0.tgz",
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2775 |       "dev": true,
2776 |       "requires": {
2777 |         "camelcase": "^6.0.0",
2778 |         "decamelize": "^4.0.0",
2779 |         "flat": "^5.0.2",
2780 |         "is-plain-obj": "^2.1.0"
2781 |       }
2782 |     },
2783 |     "yocto-queue": {
2784 |       "version": "0.1.0",
2785 |       "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
2786 |       "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
2787 |       "dev": true
2788 |     }
2789 |   }
2790 | }
2791 | 


--------------------------------------------------------------------------------
/package.json:
--------------------------------------------------------------------------------
 1 | {
 2 |   "name": "@microsoft/customvision-tfjs",
 3 |   "version": "1.3.0",
 4 |   "description": "Library for Tensorflow.js models exported from customvision.ai",
 5 |   "main": "lib/index.js",
 6 |   "unpkg": "lib/index.umd.js",
 7 |   "scripts": {
 8 |     "build": "babel src --out-dir lib && rollup -c"
 9 |   },
10 |   "files": [
11 |     "lib/index.js",
12 |     "lib/index.umd.js"
13 |   ],
14 |   "babel": {
15 |     "plugins": [
16 |       "@babel/plugin-transform-runtime"
17 |     ],
18 |     "presets": [
19 |       [
20 |         "@babel/preset-env",
21 |         {
22 |           "targets": [
23 |             ">0.1%"
24 |           ]
25 |         }
26 |       ]
27 |     ]
28 |   },
29 |   "author": "",
30 |   "license": "MIT",
31 |   "devDependencies": {
32 |     "@babel/cli": "^7.16.0",
33 |     "@babel/core": "^7.16.5",
34 |     "@babel/plugin-transform-runtime": "^7.16.5",
35 |     "@babel/preset-env": "^7.16.5",
36 |     "mocha": "^10.2.0",
37 |     "rollup": "^1.26.0"
38 |   },
39 |   "dependencies": {
40 |     "@babel/runtime": "^7.16.5",
41 |     "@tensorflow/tfjs": "^3.12.0"
42 |   },
43 |   "repository": {
44 |     "type": "git",
45 |     "url": "https://github.com/microsoft/customvision-tfjs.git"
46 |   }
47 | }
48 | 


--------------------------------------------------------------------------------
/rollup.config.js:
--------------------------------------------------------------------------------
 1 | export default {
 2 |     input: './src/index.js',
 3 |     external: ['@tensorflow/tfjs'],
 4 |     output: {
 5 | 	file: './lib/index.umd.js',
 6 | 	format: 'umd',
 7 | 	name: 'cvstfjs',
 8 | 	globals: {
 9 | 	    '@tensorflow/tfjs': 'tf'
10 | 	}
11 |     }
12 | }
13 | 


--------------------------------------------------------------------------------
/src/index.js:
--------------------------------------------------------------------------------
  1 | import * as tf from '@tensorflow/tfjs';
  2 | 
  3 | class Model {
  4 |   constructor() {
  5 |     this.NEW_OD_OUTPUT_TENSORS = ['detected_boxes', 'detected_scores', 'detected_classes'];
  6 |   }
  7 | 
  8 |   async loadModelAsync(modelUrl, options = null) {
  9 |     this.model = await tf.loadGraphModel(modelUrl, options);
 10 |     this.input_size = this.model.inputs[0].shape[1];
 11 |     this.is_new_od_model = this.model.outputs.length == 3;
 12 |     this.is_rgb_input = (this.model.metadata && this.model.metadata['Image.BitmapPixelFormat'] == 'Rgb8') || this.is_new_od_model;
 13 |   }
 14 | 
 15 |   dispose() {
 16 |     this.model.dispose();
 17 |   }
 18 | 
 19 |   async executeAsync(pixels) {
 20 |     const inputs = tf.tidy(() => { return pixels instanceof tf.Tensor ? pixels : this._preprocess(tf.browser.fromPixels(pixels, 3)); });
 21 |     const outputs = await this.model.executeAsync(inputs, this.is_new_od_model ? this.NEW_OD_OUTPUT_TENSORS : null);
 22 |     tf.dispose(inputs);
 23 |     const arrays = await (!Array.isArray(outputs) ? outputs.array() : Promise.all(outputs.map(t => t.array())));
 24 |     tf.dispose(outputs);
 25 |     return this._postprocess(arrays);
 26 |   }
 27 | 
 28 |   async _postprocess(outputs) {
 29 |     return outputs;
 30 |   }
 31 | }
 32 | 
 33 | export class ObjectDetectionModel extends Model {
 34 |   constructor() {
 35 |     super();
 36 |     this.ANCHORS = [0.573, 0.677, 1.87, 2.06, 3.34, 5.47, 7.88, 3.53, 9.77, 9.17];
 37 |   }
 38 | 
 39 |   _preprocess(image) {
 40 |     const rgb_image = tf.image.resizeBilinear(image.expandDims().toFloat(), [this.input_size, this.input_size]);
 41 |     return this.is_rgb_input ? rgb_image : rgb_image.reverse(-1); // RGB->BGR for old models
 42 |   }
 43 | 
 44 |   async _postprocess(outputs) {
 45 |     if (outputs.length == 3) {
 46 |         return outputs; // New model doesn't need post processing.
 47 |     }
 48 | 
 49 |     const [boxes, scores, classes] = tf.tidy(() => {
 50 |       // TODO: Need more efficient implmentation
 51 |       const num_anchor = this.ANCHORS.length / 2;
 52 |       const channels = outputs[0][0][0].length;
 53 |       const height = outputs[0].length;
 54 |       const width = outputs[0][0].length;
 55 | 
 56 |       const num_class = channels / num_anchor - 5;
 57 | 
 58 |       let boxes = [];
 59 |       let scores = [];
 60 |       let classes = [];
 61 | 
 62 |       for (var grid_y = 0; grid_y < height; grid_y++) {
 63 |         for (var grid_x = 0; grid_x < width; grid_x++) {
 64 |           let offset = 0;
 65 | 
 66 |           for (var i = 0; i < num_anchor; i++) {
 67 |             let x = (this._logistic(outputs[0][grid_y][grid_x][offset++]) + grid_x) / width;
 68 |             let y = (this._logistic(outputs[0][grid_y][grid_x][offset++]) + grid_y) / height;
 69 |             let w = Math.exp(outputs[0][grid_y][grid_x][offset++]) * this.ANCHORS[i * 2] / width;
 70 |             let h = Math.exp(outputs[0][grid_y][grid_x][offset++]) * this.ANCHORS[i * 2 + 1] / height;
 71 | 
 72 |             let objectness = tf.scalar(this._logistic(outputs[0][grid_y][grid_x][offset++]));
 73 |             let class_probabilities = tf.tensor1d(outputs[0][grid_y][grid_x].slice(offset, offset + num_class)).softmax();
 74 |             offset += num_class;
 75 | 
 76 |             class_probabilities = class_probabilities.mul(objectness);
 77 |             let max_index = class_probabilities.argMax();
 78 |             boxes.push([x - w / 2, y - h / 2, x + w / 2, y + h / 2]);
 79 |             scores.push(class_probabilities.max().dataSync()[0]);
 80 |             classes.push(max_index.dataSync()[0]);
 81 |           }
 82 |         }
 83 |       }
 84 |       boxes = tf.tensor2d(boxes);
 85 |       scores = tf.tensor1d(scores);
 86 |       classes = tf.tensor1d(classes);
 87 | 
 88 |       return [boxes, scores, classes];
 89 |     });
 90 | 
 91 |     const selected_indices = await tf.image.nonMaxSuppressionAsync(boxes, scores, 10);
 92 |     const tensor_results = [boxes.gather(selected_indices), scores.gather(selected_indices), classes.gather(selected_indices)];
 93 |     const results = [await tensor_results[0].array(), await tensor_results[1].array(), await tensor_results[2].array()];
 94 |     tf.dispose([boxes, scores, classes])
 95 |     tf.dispose(selected_indices);
 96 |     tf.dispose(tensor_results);
 97 |     return results;
 98 |   }
 99 | 
100 |   _logistic(x) {
101 |     if (x > 0) {
102 |         return (1 / (1 + Math.exp(-x)));
103 |     } else {
104 |         const e = Math.exp(x);
105 |         return e / (1 + e);
106 |     }
107 |   }
108 | }
109 | 
110 | export class ClassificationModel extends Model {
111 |   _preprocess(image) {
112 |     // CenterCrop
113 |     const [h, w] = image.shape.slice(0, 2);
114 |     const top = h > w ? (h - w) / 2 : 0;
115 |     const left = h > w ? 0 : (w - h) / 2;
116 |     const size = Math.min(h, w);
117 |     const rgb_image = tf.image.cropAndResize(image.expandDims().toFloat(), [[top / h, left / w, (top+size) / h, (left+size) / w]], [0], [this.input_size, this.input_size]);
118 | 
119 |     return this.is_rgb_input ? rgb_image : rgb_image.reverse(-1); // RGB->BGR for old models
120 |   }
121 | }
122 | 


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/test/index.html:
--------------------------------------------------------------------------------
 1 | <!DOCTYPE html>
 2 | <html lang="en">
 3 |   <head>
 4 |     <meta charset="utf-8" />
 5 |     <title>customvision-tfjs Tests</title>
 6 |     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
 7 |     <link rel="stylesheet" href="https://unpkg.com/mocha/mocha.css" />
 8 |   </head>
 9 |   <body>
10 |     <div id="mocha"></div>
11 |     <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"></script>
12 |     <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.4.1/jquery.min.js"></script>
13 |     <script src="https://unpkg.com/chai/chai.js"></script>
14 |     <script src="https://unpkg.com/mocha/mocha.js"></script>
15 | 
16 |     <script class="mocha-init">
17 |       mocha.setup('bdd');
18 |       mocha.checkLeaks();
19 |     </script>
20 |     <script src="../lib/index.umd.js"></script>
21 |     <script src="index.test.js"></script>
22 |     <script class="mocha-exec">
23 |       mocha.run();
24 |     </script>
25 |   </body>
26 |   </html>
27 | 


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/test/index.test.js:
--------------------------------------------------------------------------------
 1 | describe('ObjectDetection', () => {
 2 |   it('can construct model', () => {
 3 |     const model = new cvstfjs.ObjectDetectionModel();
 4 |   });
 5 | 
 6 |   it('can postprocess correctly', (done) => {
 7 |     const model = new cvstfjs.ObjectDetectionModel();
 8 |     $.getJSON('od_output.json', async (data) => {
 9 |       const results = await model._postprocess([data]);
10 |       console.log(results);
11 |       chai.assert(results[2][0] === 2);
12 |       done();
13 |     });
14 |   }).timeout(10000);
15 | });
16 | 
17 | describe('Classification', () => {
18 |   it('can construct model', () => {
19 |     const model = new cvstfjs.ClassificationModel();
20 |   });
21 | });
22 | 


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