@tensorflow-models/coco-ssd
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Object detection model (coco-ssd) in TensorFlow.js
214 lines • 10.1 kB
JavaScript
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var __awaiter = (this && this.__awaiter) || function (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());
});
};
var __generator = (this && this.__generator) || function (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) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
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if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
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 };
}
};
var __read = (this && this.__read) || function (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;
};
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs");
var classes_1 = require("./classes");
var BASE_PATH = 'https://storage.googleapis.com/tfjs-models/savedmodel/';
var version_1 = require("./version");
exports.version = version_1.version;
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];
}
});
});
}
exports.load = load;
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_1.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.ObjectDetection = ObjectDetection;
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