film-poster-genres/src/lib/DataPreprocessor.mjs

"use strict";

import fs from 'fs';
import path from 'path';

import tf from '@tensorflow/tfjs-node';

import genres from './Genres.mjs';

class DataPreprocessor {
	constructor(dir_input) {
		if(!fs.existsSync(dir_input))
			throw new Error(`Error: The input directory '${dir_input}' does not exist.`);
		
		this.dir_input_train = path.join(dir_input, "train");
		this.dir_input_validate = path.join(dir_input, "validate");
		
		if(!fs.existsSync(this.dir_input_train))
			throw new Error(`Error: Failed to locate the directory containing the training data.`);
		if(!fs.existsSync(this.dir_input_validate))
			throw new Error(`Error: Failed to locate the directory containing the validation data.`);
		
		this.genres_count = genres.length;
	}
	
	genre2id(genre_name) {
		return genres.indexOf(genre_name);
	}
	
	id2genre(id) {
		return genres[id];
	}
	
	async *data_from_dir(dirpath) {
		if(!fs.existsSync(dirpath))
			throw new Error(`[DataPreprocessor/dataset_dir] Error: The specified directory does not exist.`);
		
		let filenames = await fs.promises.readdir(dirpath);
		for(let filename of filenames) {
			let imagetensor = tf.node.decodeImage(
				await fs.promises.readFile(path.join(dirpath, filename)),
				3 // channels
			);
			if(imagetensor.shape[0] == 256
				&& imagetensor.shape[1] == 256
				&& imagetensor.shape[0] == 3)
				throw new Error(`Error: Loaded image has a shape of [${imagetensor.shape.join(", ")}], but a shape of [ 256, 256, 3 ] was expected.`);
			
			// Strip the file extension, then split into an array of genres, and finally remove the id from the beginning
			let next_genres = filename.replace(/\.[a-zA-Z]+$/, "")
				.split(",")
				.slice(1)
				.map(this.genre2id);
			
			
			let genres_pretensor = Array(this.genres_count).fill(0);
			for(let genre_id of next_genres)
				genres_pretensor[genre_id] = 1;
			
			let genres_tensor = tf.tensor(genres_pretensor);
			
			// console.log(`>>>>>>>>> output shapes: ${imagetensor.shape}, ${genres_tensor.shape}`);
			
			yield {
				xs: imagetensor,
				ys: genres_tensor
			};
		}
	}
	
	dataset_train() {
		return tf.data.generator(this.data_from_dir.bind(this, this.dir_input_train));
	}
	
	dataset_validate() {
		return tf.data.generator(this.data_from_dir.bind(this, this.dir_input_validate));
	}
}

export default DataPreprocessor;