Files
Abschluss-Projekt/backend/services/generate-json-yolox.js
2025-11-28 09:35:02 +01:00

176 lines
7.9 KiB
JavaScript

const TrainingProject = require('../models/TrainingProject.js');
const TrainingProjectDetails = require('../models/TrainingProjectDetails.js')
const LabelStudioProject = require('../models/LabelStudioProject.js')
const Annotation = require('../models/Annotation.js')
const Images = require('../models/Images.js')
const fs = require('fs');
async function generateTrainingJson(trainingId){
// trainingId is now project_details_id
const trainingProjectDetails = await TrainingProjectDetails.findByPk(trainingId);
if (!trainingProjectDetails) throw new Error('No TrainingProjectDetails found for project_details_id ' + trainingId);
const detailsObj = trainingProjectDetails.get({ plain: true });
// Get parent project for name
const trainingProject = await TrainingProject.findByPk(detailsObj.project_id);
// Get split percentages (assume they are stored as train_percent, valid_percent, test_percent)
const trainPercent = detailsObj.train_percent || 85;
const validPercent = detailsObj.valid_percent || 10;
const testPercent = detailsObj.test_percent || 5;
let cocoImages = [];
let cocoAnnotations = [];
let cocoCategories = [];
let categoryMap = {};
let categoryId = 0;
let imageid = 0;
let annotationid = 0;
for (const cls of detailsObj.class_map) {
const asgMap = [];
const listAsg = cls[1];
for(const asg of listAsg){
asgMap.push ({ original: asg[0], mapped: asg[1] });
// Build category list and mapping
if (asg[1] && !(asg[1] in categoryMap)) {
categoryMap[asg[1]] = categoryId;
cocoCategories.push({ id: categoryId, name: asg[1], supercategory: '' });
categoryId++;
}
}
const images = await Images.findAll({ where: { project_id: cls[0] } });
for(const image of images){
imageid += 1;
let fileName = image.image_path;
if (fileName.includes('%20')) {
fileName = fileName.replace(/%20/g, ' ');
}
if (fileName && fileName.startsWith('/data/local-files/?d=')) {
fileName = fileName.replace('/data/local-files/?d=', '');
fileName = fileName.replace('/home/kitraining/home/kitraining/', '');
}
if (fileName && fileName.startsWith('home/kitraining/To_Annotate/')) {
fileName = fileName.replace('home/kitraining/To_Annotate/','');
}
// Get annotations for this image
const annotations = await Annotation.findAll({ where: { image_id: image.image_id } });
// Use image.width and image.height from DB (populated from original_width/original_height)
cocoImages.push({
id: imageid,
file_name: fileName,
width: image.width || 0,
height: image.height || 0
});
for (const annotation of annotations) {
// Translate class name using asgMap
let mappedClass = annotation.Label;
for (const mapEntry of asgMap) {
if (annotation.Label === mapEntry.original) {
mappedClass = mapEntry.mapped;
break;
}
}
// Only add annotation if mappedClass is valid
if (mappedClass && mappedClass in categoryMap) {
annotationid += 1;
let area = 0;
if (annotation.width && annotation.height) {
area = annotation.width * annotation.height;
}
cocoAnnotations.push({
id: annotationid,
image_id: imageid,
category_id: categoryMap[mappedClass],
bbox: [annotation.x, annotation.y, annotation.width, annotation.height],
area: area,
iscrowd: annotation.iscrowd || 0
});
}
}
}
}
// Shuffle images for random split using seed
function seededRandom(seed) {
let x = Math.sin(seed++) * 10000;
return x - Math.floor(x);
}
function shuffle(array, seed) {
for (let i = array.length - 1; i > 0; i--) {
const j = Math.floor(seededRandom(seed + i) * (i + 1));
[array[i], array[j]] = [array[j], array[i]];
}
}
// Use seed from detailsObj if present, else default to 42
const splitSeed = detailsObj.seed !== undefined && detailsObj.seed !== null ? Number(detailsObj.seed) : 42;
shuffle(cocoImages, splitSeed);
// Split images
const totalImages = cocoImages.length;
const trainCount = Math.floor(totalImages * trainPercent / 100);
const validCount = Math.floor(totalImages * validPercent / 100);
const testCount = totalImages - trainCount - validCount;
const trainImages = cocoImages.slice(0, trainCount);
const validImages = cocoImages.slice(trainCount, trainCount + validCount);
const testImages = cocoImages.slice(trainCount + validCount);
// Helper to get image ids for each split
const trainImageIds = new Set(trainImages.map(img => img.id));
const validImageIds = new Set(validImages.map(img => img.id));
const testImageIds = new Set(testImages.map(img => img.id));
// Split annotations
const trainAnnotations = cocoAnnotations.filter(ann => trainImageIds.has(ann.image_id));
const validAnnotations = cocoAnnotations.filter(ann => validImageIds.has(ann.image_id));
const testAnnotations = cocoAnnotations.filter(ann => testImageIds.has(ann.image_id));
// Build final COCO JSONs with info section
const buildCocoJson = (images, annotations, categories) => ({
images,
annotations,
categories
});
// Build COCO JSONs with info section
const trainJson = buildCocoJson(trainImages, trainAnnotations, cocoCategories);
const validJson = buildCocoJson(validImages, validAnnotations, cocoCategories);
const testJson = buildCocoJson(testImages, testAnnotations, cocoCategories);
// Create output directory: projectname/trainingid/annotations
const projectName = trainingProject && trainingProject.name ? trainingProject.name.replace(/\s+/g, '_') : `project_${detailsObj.project_id}`;
const outDir = `${projectName}/${trainingId}`;
const annotationsDir = `/home/kitraining/To_Annotate/annotations`;
if (!fs.existsSync(annotationsDir)) {
fs.mkdirSync(annotationsDir, { recursive: true });
}
// Write to files in the annotations directory
const trainPath = `${annotationsDir}/coco_project_${trainingId}_train.json`;
const validPath = `${annotationsDir}/coco_project_${trainingId}_valid.json`;
const testPath = `${annotationsDir}/coco_project_${trainingId}_test.json`;
fs.writeFileSync(trainPath, JSON.stringify(trainJson, null, 2));
fs.writeFileSync(validPath, JSON.stringify(validJson, null, 2));
fs.writeFileSync(testPath, JSON.stringify(testJson, null, 2));
console.log(`COCO JSON splits written to ${annotationsDir} for trainingId ${trainingId}`);
// Also generate inference exp.py in the same output directory as exp.py (project folder in workspace)
const { generateYoloxInferenceExp } = require('./generate-yolox-exp');
const path = require('path');
const projectFolder = path.join(__dirname, '..', projectName, String(trainingId));
if (!fs.existsSync(projectFolder)) {
fs.mkdirSync(projectFolder, { recursive: true });
}
const inferenceExpPath = path.join(projectFolder, 'exp_infer.py');
generateYoloxInferenceExp(trainingId).then(expContent => {
fs.writeFileSync(inferenceExpPath, expContent);
console.log(`Inference exp.py written to ${inferenceExpPath}`);
}).catch(err => {
console.error('Failed to generate inference exp.py:', err);
});
}
module.exports = {generateTrainingJson};