@tensorflow-models/coco-ssd
Version:
Object detection model (coco-ssd) in TensorFlow.js
644 lines (631 loc) • 21.4 kB
JavaScript
// @tensorflow/tfjs-models Copyright 2019 Google
(function (global, factory) {
typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('@tensorflow/tfjs')) :
typeof define === 'function' && define.amd ? define(['exports', '@tensorflow/tfjs'], factory) :
(factory((global.cocoSsd = {}),global.tf));
}(this, (function (exports,tf) { 'use strict';
/*! *****************************************************************************
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use
this file except in compliance with the License. You may obtain a copy of the
License at http://www.apache.org/licenses/LICENSE-2.0
THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
MERCHANTABLITY OR NON-INFRINGEMENT.
See the Apache Version 2.0 License for specific language governing permissions
and limitations under the License.
***************************************************************************** */
function __awaiter(thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
}
function __generator(thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
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case 4: _.label++; return { value: op[1], done: false };
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if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
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_.trys.pop(); continue;
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op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
}
function __read(o, n) {
var m = typeof Symbol === "function" && o[Symbol.iterator];
if (!m) return o;
var i = m.call(o), r, ar = [], e;
try {
while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);
}
catch (error) { e = { error: error }; }
finally {
try {
if (r && !r.done && (m = i["return"])) m.call(i);
}
finally { if (e) throw e.error; }
}
return ar;
}
var CLASSES = {
1: {
name: '/m/01g317',
id: 1,
displayName: 'person',
},
2: {
name: '/m/0199g',
id: 2,
displayName: 'bicycle',
},
3: {
name: '/m/0k4j',
id: 3,
displayName: 'car',
},
4: {
name: '/m/04_sv',
id: 4,
displayName: 'motorcycle',
},
5: {
name: '/m/05czz6l',
id: 5,
displayName: 'airplane',
},
6: {
name: '/m/01bjv',
id: 6,
displayName: 'bus',
},
7: {
name: '/m/07jdr',
id: 7,
displayName: 'train',
},
8: {
name: '/m/07r04',
id: 8,
displayName: 'truck',
},
9: {
name: '/m/019jd',
id: 9,
displayName: 'boat',
},
10: {
name: '/m/015qff',
id: 10,
displayName: 'traffic light',
},
11: {
name: '/m/01pns0',
id: 11,
displayName: 'fire hydrant',
},
13: {
name: '/m/02pv19',
id: 13,
displayName: 'stop sign',
},
14: {
name: '/m/015qbp',
id: 14,
displayName: 'parking meter',
},
15: {
name: '/m/0cvnqh',
id: 15,
displayName: 'bench',
},
16: {
name: '/m/015p6',
id: 16,
displayName: 'bird',
},
17: {
name: '/m/01yrx',
id: 17,
displayName: 'cat',
},
18: {
name: '/m/0bt9lr',
id: 18,
displayName: 'dog',
},
19: {
name: '/m/03k3r',
id: 19,
displayName: 'horse',
},
20: {
name: '/m/07bgp',
id: 20,
displayName: 'sheep',
},
21: {
name: '/m/01xq0k1',
id: 21,
displayName: 'cow',
},
22: {
name: '/m/0bwd_0j',
id: 22,
displayName: 'elephant',
},
23: {
name: '/m/01dws',
id: 23,
displayName: 'bear',
},
24: {
name: '/m/0898b',
id: 24,
displayName: 'zebra',
},
25: {
name: '/m/03bk1',
id: 25,
displayName: 'giraffe',
},
27: {
name: '/m/01940j',
id: 27,
displayName: 'backpack',
},
28: {
name: '/m/0hnnb',
id: 28,
displayName: 'umbrella',
},
31: {
name: '/m/080hkjn',
id: 31,
displayName: 'handbag',
},
32: {
name: '/m/01rkbr',
id: 32,
displayName: 'tie',
},
33: {
name: '/m/01s55n',
id: 33,
displayName: 'suitcase',
},
34: {
name: '/m/02wmf',
id: 34,
displayName: 'frisbee',
},
35: {
name: '/m/071p9',
id: 35,
displayName: 'skis',
},
36: {
name: '/m/06__v',
id: 36,
displayName: 'snowboard',
},
37: {
name: '/m/018xm',
id: 37,
displayName: 'sports ball',
},
38: {
name: '/m/02zt3',
id: 38,
displayName: 'kite',
},
39: {
name: '/m/03g8mr',
id: 39,
displayName: 'baseball bat',
},
40: {
name: '/m/03grzl',
id: 40,
displayName: 'baseball glove',
},
41: {
name: '/m/06_fw',
id: 41,
displayName: 'skateboard',
},
42: {
name: '/m/019w40',
id: 42,
displayName: 'surfboard',
},
43: {
name: '/m/0dv9c',
id: 43,
displayName: 'tennis racket',
},
44: {
name: '/m/04dr76w',
id: 44,
displayName: 'bottle',
},
46: {
name: '/m/09tvcd',
id: 46,
displayName: 'wine glass',
},
47: {
name: '/m/08gqpm',
id: 47,
displayName: 'cup',
},
48: {
name: '/m/0dt3t',
id: 48,
displayName: 'fork',
},
49: {
name: '/m/04ctx',
id: 49,
displayName: 'knife',
},
50: {
name: '/m/0cmx8',
id: 50,
displayName: 'spoon',
},
51: {
name: '/m/04kkgm',
id: 51,
displayName: 'bowl',
},
52: {
name: '/m/09qck',
id: 52,
displayName: 'banana',
},
53: {
name: '/m/014j1m',
id: 53,
displayName: 'apple',
},
54: {
name: '/m/0l515',
id: 54,
displayName: 'sandwich',
},
55: {
name: '/m/0cyhj_',
id: 55,
displayName: 'orange',
},
56: {
name: '/m/0hkxq',
id: 56,
displayName: 'broccoli',
},
57: {
name: '/m/0fj52s',
id: 57,
displayName: 'carrot',
},
58: {
name: '/m/01b9xk',
id: 58,
displayName: 'hot dog',
},
59: {
name: '/m/0663v',
id: 59,
displayName: 'pizza',
},
60: {
name: '/m/0jy4k',
id: 60,
displayName: 'donut',
},
61: {
name: '/m/0fszt',
id: 61,
displayName: 'cake',
},
62: {
name: '/m/01mzpv',
id: 62,
displayName: 'chair',
},
63: {
name: '/m/02crq1',
id: 63,
displayName: 'couch',
},
64: {
name: '/m/03fp41',
id: 64,
displayName: 'potted plant',
},
65: {
name: '/m/03ssj5',
id: 65,
displayName: 'bed',
},
67: {
name: '/m/04bcr3',
id: 67,
displayName: 'dining table',
},
70: {
name: '/m/09g1w',
id: 70,
displayName: 'toilet',
},
72: {
name: '/m/07c52',
id: 72,
displayName: 'tv',
},
73: {
name: '/m/01c648',
id: 73,
displayName: 'laptop',
},
74: {
name: '/m/020lf',
id: 74,
displayName: 'mouse',
},
75: {
name: '/m/0qjjc',
id: 75,
displayName: 'remote',
},
76: {
name: '/m/01m2v',
id: 76,
displayName: 'keyboard',
},
77: {
name: '/m/050k8',
id: 77,
displayName: 'cell phone',
},
78: {
name: '/m/0fx9l',
id: 78,
displayName: 'microwave',
},
79: {
name: '/m/029bxz',
id: 79,
displayName: 'oven',
},
80: {
name: '/m/01k6s3',
id: 80,
displayName: 'toaster',
},
81: {
name: '/m/0130jx',
id: 81,
displayName: 'sink',
},
82: {
name: '/m/040b_t',
id: 82,
displayName: 'refrigerator',
},
84: {
name: '/m/0bt_c3',
id: 84,
displayName: 'book',
},
85: {
name: '/m/01x3z',
id: 85,
displayName: 'clock',
},
86: {
name: '/m/02s195',
id: 86,
displayName: 'vase',
},
87: {
name: '/m/01lsmm',
id: 87,
displayName: 'scissors',
},
88: {
name: '/m/0kmg4',
id: 88,
displayName: 'teddy bear',
},
89: {
name: '/m/03wvsk',
id: 89,
displayName: 'hair drier',
},
90: {
name: '/m/012xff',
id: 90,
displayName: 'toothbrush',
}
};
var version = '0.1.1';
var BASE_PATH = 'https://storage.googleapis.com/tfjs-models/savedmodel/';
function load(base) {
if (base === void 0) { base = 'lite_mobilenet_v2'; }
return __awaiter(this, void 0, void 0, function () {
var objectDetection;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
if (tf == null) {
throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please " +
"also include @tensorflow/tfjs on the page before using this model.");
}
if (['mobilenet_v1', 'mobilenet_v2', 'lite_mobilenet_v2'].indexOf(base) ===
-1) {
throw new Error("ObjectDetection constructed with invalid base model " +
(base + ". Valid names are 'mobilenet_v1',") +
" 'mobilenet_v2' and 'lite_mobilenet_v2'.");
}
objectDetection = new ObjectDetection(base);
return [4, objectDetection.load()];
case 1:
_a.sent();
return [2, objectDetection];
}
});
});
}
var ObjectDetection = (function () {
function ObjectDetection(base) {
this.modelPath = "" + BASE_PATH + this.getPrefix(base) + "/model.json";
}
ObjectDetection.prototype.getPrefix = function (base) {
return base === 'lite_mobilenet_v2' ? "ssd" + base : "ssd_" + base;
};
ObjectDetection.prototype.load = function () {
return __awaiter(this, void 0, void 0, function () {
var _a, result;
var _this = this;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this;
return [4, tf.loadGraphModel(this.modelPath)];
case 1:
_a.model = _b.sent();
return [4, this.model.executeAsync(tf.zeros([1, 300, 300, 3]))];
case 2:
result = _b.sent();
result.map(function (t) { return __awaiter(_this, void 0, void 0, function () { return __generator(this, function (_a) {
switch (_a.label) {
case 0: return [4, t.data()];
case 1: return [2, _a.sent()];
}
}); }); });
result.map(function (t) { return __awaiter(_this, void 0, void 0, function () { return __generator(this, function (_a) {
return [2, t.dispose()];
}); }); });
return [2];
}
});
});
};
ObjectDetection.prototype.infer = function (img, maxNumBoxes) {
return __awaiter(this, void 0, void 0, function () {
var batched, height, width, result, scores, boxes, _a, maxScores, classes, prevBackend, indexTensor, indexes;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
batched = tf.tidy(function () {
if (!(img instanceof tf.Tensor)) {
img = tf.browser.fromPixels(img);
}
return img.expandDims(0);
});
height = batched.shape[1];
width = batched.shape[2];
return [4, this.model.executeAsync(batched)];
case 1:
result = _b.sent();
scores = result[0].dataSync();
boxes = result[1].dataSync();
batched.dispose();
tf.dispose(result);
_a = __read(this.calculateMaxScores(scores, result[0].shape[1], result[0].shape[2]), 2), maxScores = _a[0], classes = _a[1];
prevBackend = tf.getBackend();
tf.setBackend('cpu');
indexTensor = tf.tidy(function () {
var boxes2 = tf.tensor2d(boxes, [result[1].shape[1], result[1].shape[3]]);
return tf.image.nonMaxSuppression(boxes2, maxScores, maxNumBoxes, 0.5, 0.5);
});
indexes = indexTensor.dataSync();
indexTensor.dispose();
tf.setBackend(prevBackend);
return [2, this.buildDetectedObjects(width, height, boxes, maxScores, indexes, classes)];
}
});
});
};
ObjectDetection.prototype.buildDetectedObjects = function (width, height, boxes, scores, indexes, classes) {
var count = indexes.length;
var objects = [];
for (var i = 0; i < count; i++) {
var bbox = [];
for (var j = 0; j < 4; j++) {
bbox[j] = boxes[indexes[i] * 4 + j];
}
var minY = bbox[0] * height;
var minX = bbox[1] * width;
var maxY = bbox[2] * height;
var maxX = bbox[3] * width;
bbox[0] = minX;
bbox[1] = minY;
bbox[2] = maxX - minX;
bbox[3] = maxY - minY;
objects.push({
bbox: bbox,
class: CLASSES[classes[indexes[i]] + 1].displayName,
score: scores[indexes[i]]
});
}
return objects;
};
ObjectDetection.prototype.calculateMaxScores = function (scores, numBoxes, numClasses) {
var maxes = [];
var classes = [];
for (var i = 0; i < numBoxes; i++) {
var max = Number.MIN_VALUE;
var index = -1;
for (var j = 0; j < numClasses; j++) {
if (scores[i * numClasses + j] > max) {
max = scores[i * numClasses + j];
index = j;
}
}
maxes[i] = max;
classes[i] = index;
}
return [maxes, classes];
};
ObjectDetection.prototype.detect = function (img, maxNumBoxes) {
if (maxNumBoxes === void 0) { maxNumBoxes = 20; }
return __awaiter(this, void 0, void 0, function () {
return __generator(this, function (_a) {
return [2, this.infer(img, maxNumBoxes)];
});
});
};
ObjectDetection.prototype.dispose = function () {
if (this.model) {
this.model.dispose();
}
};
return ObjectDetection;
}());
exports.load = load;
exports.ObjectDetection = ObjectDetection;
exports.version = version;
Object.defineProperty(exports, '__esModule', { value: true });
})));