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* feat: add OCR functionality and related configurations * chore: update labeler configuration for machine learning files * feat(i18n): enhance OCR model descriptions and add orientation classification and unwarping features * chore: update Dockerfile to include ccache for improved build performance * feat(ocr): enhance OCR model configuration with orientation classification and unwarping options, update PaddleOCR integration, and improve response structure * refactor(ocr): remove OCR_CLEANUP job from enum and type definitions * refactor(ocr): remove obsolete OCR entity and migration files, and update asset job status and schema to accommodate new OCR table structure * refactor(ocr): update OCR schema and response structure to use individual coordinates instead of bounding box, and adjust related service and repository files * feat: enhance OCR configuration and functionality - Updated OCR settings to include minimum detection box score, minimum detection score, and minimum recognition score. - Refactored PaddleOCRecognizer to utilize new scoring parameters. - Introduced new database tables for asset OCR data and search functionality. - Modified related services and repositories to support the new OCR features. - Updated translations for improved clarity in settings UI. * sql changes * use rapidocr * change dto * update web * update lock * update api * store positions as normalized floats * match column order in db * update admin ui settings descriptions fix max resolution key set min threshold to 0.1 fix bind * apply config correctly, adjust defaults * unnecessary model type * unnecessary sources * fix(ocr): switch RapidOCR lang type from LangDet to LangRec * fix(ocr): expose lang_type (LangRec.CH) and font_path on OcrOptions for RapidOCR * fix(ocr): make OCR text search case- and accent-insensitive using ILIKE + unaccent * fix(ocr): add OCR search fields * fix: Add OCR database migration and update ML prediction logic. * trigrams are already case insensitive * add tests * format * update migrations * wrong uuid function * linting * maybe fix medium tests * formatting * fix weblate check * openapi * sql * minor fixes * maybe fix medium tests part 2 * passing medium tests * format web * readd sql * format dart * disabled in e2e * chore: translation ordering --------- Co-authored-by: mertalev <101130780+mertalev@users.noreply.github.com> Co-authored-by: Alex Tran <alex.tran1502@gmail.com>
87 lines
2.7 KiB
TypeScript
87 lines
2.7 KiB
TypeScript
import { Injectable } from '@nestjs/common';
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import { JOBS_ASSET_PAGINATION_SIZE } from 'src/constants';
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import { OnJob } from 'src/decorators';
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import { AssetVisibility, JobName, JobStatus, QueueName } from 'src/enum';
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import { OCR } from 'src/repositories/machine-learning.repository';
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import { BaseService } from 'src/services/base.service';
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import { JobItem, JobOf } from 'src/types';
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import { isOcrEnabled } from 'src/utils/misc';
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@Injectable()
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export class OcrService extends BaseService {
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@OnJob({ name: JobName.OcrQueueAll, queue: QueueName.Ocr })
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async handleQueueOcr({ force }: JobOf<JobName.OcrQueueAll>): Promise<JobStatus> {
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const { machineLearning } = await this.getConfig({ withCache: false });
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if (!isOcrEnabled(machineLearning)) {
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return JobStatus.Skipped;
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}
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if (force) {
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await this.ocrRepository.deleteAll();
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}
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let jobs: JobItem[] = [];
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const assets = this.assetJobRepository.streamForOcrJob(force);
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for await (const asset of assets) {
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jobs.push({ name: JobName.Ocr, data: { id: asset.id } });
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if (jobs.length >= JOBS_ASSET_PAGINATION_SIZE) {
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await this.jobRepository.queueAll(jobs);
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jobs = [];
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}
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}
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await this.jobRepository.queueAll(jobs);
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return JobStatus.Success;
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}
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@OnJob({ name: JobName.Ocr, queue: QueueName.Ocr })
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async handleOcr({ id }: JobOf<JobName.Ocr>): Promise<JobStatus> {
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const { machineLearning } = await this.getConfig({ withCache: true });
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if (!isOcrEnabled(machineLearning)) {
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return JobStatus.Skipped;
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}
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const asset = await this.assetJobRepository.getForOcr(id);
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if (!asset || !asset.previewFile) {
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return JobStatus.Failed;
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}
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if (asset.visibility === AssetVisibility.Hidden) {
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return JobStatus.Skipped;
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}
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const ocrResults = await this.machineLearningRepository.ocr(asset.previewFile, machineLearning.ocr);
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await this.ocrRepository.upsert(id, this.parseOcrResults(id, ocrResults));
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await this.assetRepository.upsertJobStatus({ assetId: id, ocrAt: new Date() });
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this.logger.debug(`Processed ${ocrResults.text.length} OCR result(s) for ${id}`);
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return JobStatus.Success;
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}
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private parseOcrResults(id: string, { box, boxScore, text, textScore }: OCR) {
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const ocrDataList = [];
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for (let i = 0; i < text.length; i++) {
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const boxOffset = i * 8;
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ocrDataList.push({
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assetId: id,
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x1: box[boxOffset],
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y1: box[boxOffset + 1],
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x2: box[boxOffset + 2],
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y2: box[boxOffset + 3],
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x3: box[boxOffset + 4],
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y3: box[boxOffset + 5],
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x4: box[boxOffset + 6],
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y4: box[boxOffset + 7],
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boxScore: boxScore[i],
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textScore: textScore[i],
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text: text[i],
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});
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}
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return ocrDataList;
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}
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}
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