image-classifier-ts
Version:
Command line tool to auto-classify images, renaming them with appropriate labels. Uses Node and Google Vision API.
165 lines • 8.54 kB
JavaScript
;
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();
_.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 };
}
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.ImageClassifier = void 0;
// - replace callbacks with promise - async/await
var fs = require("fs");
var os = require("os");
var path = require("path");
var ImageProperties_1 = require("../model/ImageProperties");
var Nodash_1 = require("../utils/Nodash");
var StringUtils_1 = require("../utils/StringUtils");
var sharp = require("sharp");
var vision = require("@google-cloud/vision");
var visionClient = new vision.ImageAnnotatorClient();
var ImageClassifier;
(function (ImageClassifier) {
function classifyImage(properties, options, outputter) {
return __awaiter(this, void 0, void 0, function () {
return __generator(this, function (_a) {
return [2 /*return*/, new Promise(function (resolve, reject) {
classifyImageWithResize(properties, options, outputter, function (error) {
outputter.error("error with file ".concat(properties.imagePath), error);
reject(error);
}, function (newProperties) { return resolve(newProperties); });
})];
});
});
}
ImageClassifier.classifyImage = classifyImage;
function classifyImageWithResize(properties, options, outputter, handleError, done) {
outputter.infoVerbose("detecting labels in image: '".concat(properties.imagePath, "'"));
var activeProperties = ImageProperties_1.ImageProperties.withFileSizeMb(properties, getFilesizeInMegaBytes(properties.imagePath));
if (activeProperties.fileSizeMb === null) {
handleError("could not get file size for image '".concat(activeProperties.imagePath, "'"));
done(activeProperties);
return;
}
if (activeProperties.fileSizeMb > 0.5) {
outputter.infoVerbose(" 'file is large! - ".concat(activeProperties.fileSizeMb, " Mb - will use resized copy..."));
resizeImage(activeProperties.imagePath, outputter, handleError, function (resizedImagePath, err) {
if (err) {
handleError(err);
}
else {
if (!resizedImagePath) {
throw new Error("unexpected: newImagePath is not set");
}
classifySmallImage(activeProperties, options, outputter, handleError, done);
}
});
}
else {
classifySmallImage(activeProperties, options, outputter, handleError, done);
}
}
function getSmallerFilePath(filePath) {
return path.join(os.tmpdir(), "".concat(path.basename(filePath), ".resized").concat(path.extname(filePath)));
}
var getFilesizeInMegaBytes = function (filename) {
var stats = fs.statSync(filename);
var fileSizeInBytes = stats.size;
return fileSizeInBytes / (1024 * 1024);
};
// ref: https://github.com/lovell/sharp
var resizeImage = function (filePath, outputter, handleError, cb) {
var outPath = getSmallerFilePath(filePath);
// read file to avoid issue where sharp does not release the file lock!
fs.readFile(filePath, function (err, data) {
if (err) {
outputter.error("Error reading file " + filePath, err);
cb(null, err);
}
else {
sharp(data)
.resize(800)
.toFile(outPath, function (sharpError) {
if (sharpError) {
outputter.error("Error resizing file " + filePath, sharpError);
handleError(sharpError);
}
cb(outPath, sharpError);
});
}
});
};
var _classifyImage = function (imagePath, options, outputter, handleError, cb) {
outputter.infoVerbose(" classifying image:", imagePath);
// ref: https://github.com/googleapis/nodejs-vision/blob/master/samples/detect.js
// ref: https://cloud.google.com/nodejs/docs/reference/vision/0.22.x/
//
// ref: .\node_modules\@google-cloud\vision\src\index.js
visionClient
.labelDetection(imagePath)
.then(function (results) {
var labels = results[0].labelAnnotations;
var topNLabels = null;
if (labels && labels.length > 0) {
// note: results already have the best one first:
var topLabels = Nodash_1.Nodash.take(labels.filter(function (l) { return l.score >= options.minScore && isLabelOk(l.description); }), options.topNLabels).map(function (l) { return StringUtils_1.StringUtils.replaceAll(l.description, " ", "-"); });
if (topLabels.length > 0) {
topNLabels = topLabels;
}
}
else {
outputter.errorVerbose("no labels returned from Google Vision API");
}
// TODO xxx replace cb with async await
cb(topNLabels);
})
.catch(function (err) {
handleError(err);
});
};
// ignore generic labels like: vertebrate -> take the next one...
var isLabelOk = function (label) {
return ["vertebrate"].indexOf(label) === -1;
};
var classifySmallImage = function (properties, options, outputter, handleError, done) {
_classifyImage(properties.imagePath, options, outputter, handleError, function (topNLabels) {
if (topNLabels) {
var activeProperties = ImageProperties_1.ImageProperties.withTopLabels(properties, topNLabels);
done(activeProperties);
}
else {
handleError("No labels were returned from classification (Google Vision API)");
}
});
};
})(ImageClassifier = exports.ImageClassifier || (exports.ImageClassifier = {}));
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