image-classifier
Version:
Machine Learning Image Classifier for NodeJS
207 lines • 10.3 kB
JavaScript
"use strict";
var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) {
if (k2 === undefined) k2 = k;
Object.defineProperty(o, k2, { enumerable: true, get: function() { return m[k]; } });
}) : (function(o, m, k, k2) {
if (k2 === undefined) k2 = k;
o[k2] = m[k];
}));
var __setModuleDefault = (this && this.__setModuleDefault) || (Object.create ? (function(o, v) {
Object.defineProperty(o, "default", { enumerable: true, value: v });
}) : function(o, v) {
o["default"] = v;
});
var __importStar = (this && this.__importStar) || function (mod) {
if (mod && mod.__esModule) return mod;
var result = {};
if (mod != null) for (var k in mod) if (k !== "default" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);
__setModuleDefault(result, mod);
return result;
};
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
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) : adopt(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 {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
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();
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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 };
}
};
var __importDefault = (this && this.__importDefault) || function (mod) {
return (mod && mod.__esModule) ? mod : { "default": mod };
};
var fsp = __importStar(require("fs/promises"));
var mobilenet = __importStar(require("@tensorflow-models/mobilenet"));
var knnClassifier = __importStar(require("@tensorflow-models/knn-classifier"));
var Tensorset_1 = __importDefault(require("tensorset/lib/Tensorset"));
var tf = __importStar(require("@tensorflow/tfjs-node"));
var ImageClassifier = /** @class */ (function () {
function ImageClassifier(mobilenet, classifier) {
this.mobilenet = mobilenet;
this.classifier = classifier;
}
ImageClassifier.create = function () {
return __awaiter(this, void 0, void 0, function () {
var classifier, _a, error_1;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
_b.trys.push([0, 2, , 3]);
classifier = knnClassifier.create();
_a = ImageClassifier.bind;
return [4 /*yield*/, mobilenet.load()];
case 1: return [2 /*return*/, new (_a.apply(ImageClassifier, [void 0, _b.sent(), classifier]))()];
case 2:
error_1 = _b.sent();
// ERROR: Mobilenet fails to load
throw error_1;
case 3: return [2 /*return*/];
}
});
});
};
ImageClassifier.load = function (datasetPath) {
return __awaiter(this, void 0, void 0, function () {
var classifier, dataset, tensorset, _a, error_2;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
_b.trys.push([0, 4, , 5]);
classifier = knnClassifier.create();
return [4 /*yield*/, fsp.readFile(datasetPath, { encoding: 'utf-8' })];
case 1:
dataset = _b.sent();
return [4 /*yield*/, Tensorset_1.default.parse(dataset)];
case 2:
tensorset = _b.sent();
classifier.setClassifierDataset(tensorset);
_a = ImageClassifier.bind;
return [4 /*yield*/, mobilenet.load()];
case 3: return [2 /*return*/, new (_a.apply(ImageClassifier, [void 0, _b.sent(), classifier]))()];
case 4:
error_2 = _b.sent();
// ERROR (Option 1): Attempts to load an invalid dataset
// ERROR (Option 2): Mobilenet fails to load
throw error_2;
case 5: return [2 /*return*/];
}
});
});
};
ImageClassifier.prototype.save = function (datasetDestination) {
return __awaiter(this, void 0, void 0, function () {
var dataset, data, error_3;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
_a.trys.push([0, 3, , 4]);
dataset = this.classifier.getClassifierDataset();
return [4 /*yield*/, Tensorset_1.default.stringify(dataset)];
case 1:
data = _a.sent();
return [4 /*yield*/, fsp.writeFile(datasetDestination, data)];
case 2:
_a.sent();
return [3 /*break*/, 4];
case 3:
error_3 = _a.sent();
// ERROR (Option 1): Destination path is not specified and there is no default path
// ERROR (Option 2): File could not be written
throw error_3;
case 4: return [2 /*return*/];
}
});
});
};
ImageClassifier.prototype.addExample = function (label, image) {
return __awaiter(this, void 0, void 0, function () {
var imageData, _a, tensor, error_4;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
_b.trys.push([0, 4, , 5]);
if (!(image instanceof Buffer)) return [3 /*break*/, 1];
_a = image;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, fsp.readFile(image)];
case 2:
_a = _b.sent();
_b.label = 3;
case 3:
imageData = _a;
tensor = this.mobilenet.infer(tf.node.decodeImage(new Uint8Array(imageData), 3), true);
this.classifier.addExample(tensor, label);
return [3 /*break*/, 5];
case 4:
error_4 = _b.sent();
// ERROR (Option 1): Failed to read file
// ERROR (Option 2): File was not a proper image
throw error_4;
case 5: return [2 /*return*/];
}
});
});
};
ImageClassifier.prototype.dropClassifier = function (label) {
this.classifier.clearClass(label);
};
ImageClassifier.prototype.predict = function (image) {
return __awaiter(this, void 0, void 0, function () {
var imageData, _a, tensor, error_5;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
_b.trys.push([0, 4, , 5]);
if (!(image instanceof Buffer)) return [3 /*break*/, 1];
_a = image;
return [3 /*break*/, 3];
case 1: return [4 /*yield*/, fsp.readFile(image)];
case 2:
_a = _b.sent();
_b.label = 3;
case 3:
imageData = _a;
tensor = this.mobilenet.infer(tf.node.decodeImage(new Uint8Array(imageData), 3), true);
return [2 /*return*/, this.classifier.predictClass(tensor)];
case 4:
error_5 = _b.sent();
// ERROR (Option 1): Failed to read file
// ERROR (Option 2): File was not a proper image
throw error_5;
case 5: return [2 /*return*/];
}
});
});
};
ImageClassifier.default = ImageClassifier;
return ImageClassifier;
}());
module.exports = ImageClassifier;
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