Files
immich/server/src/domain/smart-info/smart-info.service.ts
T
Mert bcc36d14a1 feat(ml)!: customizable ML settings (#3891)
* consolidated endpoints, added live configuration

* added ml settings to server

* added settings dashboard

* updated deps, fixed typos

* simplified modelconfig

updated tests

* Added ml setting accordion for admin page

updated tests

* merge `clipText` and `clipVision`

* added face distance setting

clarified setting

* add clip mode in request, dropdown for face models

* polished ml settings

updated descriptions

* update clip field on error

* removed unused import

* add description for image classification threshold

* pin safetensors for arm wheel

updated poetry lock

* moved dto

* set model type only in ml repository

* revert form-data package install

use fetch instead of axios

* added slotted description with link

updated facial recognition description

clarified effect of disabling tasks

* validation before model load

* removed unnecessary getconfig call

* added migration

* updated api

updated api

updated api

---------

Co-authored-by: Alex Tran <alex.tran1502@gmail.com>
2023-08-29 08:58:00 -05:00

108 lines
3.5 KiB
TypeScript

import { Inject, Injectable } from '@nestjs/common';
import { IAssetRepository, WithoutProperty } from '../asset';
import { usePagination } from '../domain.util';
import { IBaseJob, IEntityJob, IJobRepository, JobName, JOBS_ASSET_PAGINATION_SIZE } from '../job';
import { ISystemConfigRepository, SystemConfigCore } from '../system-config';
import { IMachineLearningRepository } from './machine-learning.interface';
import { ISmartInfoRepository } from './smart-info.repository';
@Injectable()
export class SmartInfoService {
private configCore: SystemConfigCore;
constructor(
@Inject(IAssetRepository) private assetRepository: IAssetRepository,
@Inject(ISystemConfigRepository) configRepository: ISystemConfigRepository,
@Inject(IJobRepository) private jobRepository: IJobRepository,
@Inject(ISmartInfoRepository) private repository: ISmartInfoRepository,
@Inject(IMachineLearningRepository) private machineLearning: IMachineLearningRepository,
) {
this.configCore = new SystemConfigCore(configRepository);
}
async handleQueueObjectTagging({ force }: IBaseJob) {
const { machineLearning } = await this.configCore.getConfig();
if (!machineLearning.enabled || !machineLearning.classification.enabled) {
return true;
}
const assetPagination = usePagination(JOBS_ASSET_PAGINATION_SIZE, (pagination) => {
return force
? this.assetRepository.getAll(pagination)
: this.assetRepository.getWithout(pagination, WithoutProperty.OBJECT_TAGS);
});
for await (const assets of assetPagination) {
for (const asset of assets) {
await this.jobRepository.queue({ name: JobName.CLASSIFY_IMAGE, data: { id: asset.id } });
}
}
return true;
}
async handleClassifyImage({ id }: IEntityJob) {
const { machineLearning } = await this.configCore.getConfig();
if (!machineLearning.enabled || !machineLearning.classification.enabled) {
return true;
}
const [asset] = await this.assetRepository.getByIds([id]);
if (!asset.resizePath) {
return false;
}
const tags = await this.machineLearning.classifyImage(
machineLearning.url,
{ imagePath: asset.resizePath },
machineLearning.classification,
);
await this.repository.upsert({ assetId: asset.id, tags });
return true;
}
async handleQueueEncodeClip({ force }: IBaseJob) {
const { machineLearning } = await this.configCore.getConfig();
if (!machineLearning.enabled || !machineLearning.clip.enabled) {
return true;
}
const assetPagination = usePagination(JOBS_ASSET_PAGINATION_SIZE, (pagination) => {
return force
? this.assetRepository.getAll(pagination)
: this.assetRepository.getWithout(pagination, WithoutProperty.CLIP_ENCODING);
});
for await (const assets of assetPagination) {
for (const asset of assets) {
await this.jobRepository.queue({ name: JobName.ENCODE_CLIP, data: { id: asset.id } });
}
}
return true;
}
async handleEncodeClip({ id }: IEntityJob) {
const { machineLearning } = await this.configCore.getConfig();
if (!machineLearning.enabled || !machineLearning.clip.enabled) {
return true;
}
const [asset] = await this.assetRepository.getByIds([id]);
if (!asset.resizePath) {
return false;
}
const clipEmbedding = await this.machineLearning.encodeImage(
machineLearning.url,
{ imagePath: asset.resizePath },
machineLearning.clip,
);
await this.repository.upsert({ assetId: asset.id, clipEmbedding: clipEmbedding });
return true;
}
}