use original image for ml

machine-learning/immich_ml/models/facial_recognition/detection.py

@@ -20,7 +20,7 @@ class FaceDetector(InferenceModel):
     def _load(self) -> ModelSession:
         session = self._make_session(self.model_path)
         self.model = RetinaFace(session=session)
-        self.model.prepare(ctx_id=0, det_thresh=self.min_score, input_size=(640, 640))
+        self.model.prepare(ctx_id=0, det_thresh=self.min_score, input_size=(256, 256))
 
         return session
 

server/src/repositories/asset-job.repository.ts

@@ -169,8 +169,7 @@ export class AssetJobRepository {
   getForClipEncoding(id: string) {
     return this.db
       .selectFrom('assets')
-      .select(['assets.id', 'assets.visibility'])
+      .select(['assets.id', 'assets.visibility', 'assets.originalPath'])
-      .select((eb) => withFiles(eb, AssetFileType.PREVIEW))
       .where('assets.id', '=', id)
       .executeTakeFirst();
   }
@@ -179,10 +178,9 @@ export class AssetJobRepository {
   getForDetectFacesJob(id: string) {
     return this.db
       .selectFrom('assets')
-      .select(['assets.id', 'assets.visibility'])
+      .select(['assets.id', 'assets.visibility', 'assets.originalPath'])
       .$call(withExifInner)
       .select((eb) => withFaces(eb, true))
-      .select((eb) => withFiles(eb, AssetFileType.PREVIEW))
       .where('assets.id', '=', id)
       .executeTakeFirst();
   }

server/src/services/media.service.ts

@@ -5,9 +5,7 @@ import { Exif } from 'src/database';
 import { OnEvent, OnJob } from 'src/decorators';
 import { SystemConfigFFmpegDto } from 'src/dtos/system-config.dto';
 import {
-  AssetFileType,
   AssetPathType,
-  AssetType,
   AssetVisibility,
   AudioCodec,
   Colorspace,
@@ -24,7 +22,6 @@ import {
   VideoCodec,
   VideoContainer,
 } from 'src/enum';
-import { UpsertFileOptions } from 'src/repositories/asset.repository';
 import { BoundingBox } from 'src/repositories/machine-learning.repository';
 import { BaseService } from 'src/services/base.service';
 import {
@@ -163,69 +160,6 @@ export class MediaService extends BaseService {
       return JobStatus.SKIPPED;
     }
 
-    let generated: {
-      previewPath: string;
-      thumbnailPath: string;
-      fullsizePath?: string;
-      thumbhash: Buffer;
-    };
-    if (asset.type === AssetType.VIDEO || asset.originalFileName.toLowerCase().endsWith('.gif')) {
-      generated = await this.generateVideoThumbnails(asset);
-    } else if (asset.type === AssetType.IMAGE) {
-      generated = await this.generateImageThumbnails(asset);
-    } else {
-      this.logger.warn(`Skipping thumbnail generation for asset ${id}: ${asset.type} is not an image or video`);
-      return JobStatus.SKIPPED;
-    }
-
-    const { previewFile, thumbnailFile, fullsizeFile } = getAssetFiles(asset.files);
-    const toUpsert: UpsertFileOptions[] = [];
-    if (previewFile?.path !== generated.previewPath) {
-      toUpsert.push({ assetId: asset.id, path: generated.previewPath, type: AssetFileType.PREVIEW });
-    }
-
-    if (thumbnailFile?.path !== generated.thumbnailPath) {
-      toUpsert.push({ assetId: asset.id, path: generated.thumbnailPath, type: AssetFileType.THUMBNAIL });
-    }
-
-    if (generated.fullsizePath && fullsizeFile?.path !== generated.fullsizePath) {
-      toUpsert.push({ assetId: asset.id, path: generated.fullsizePath, type: AssetFileType.FULLSIZE });
-    }
-
-    if (toUpsert.length > 0) {
-      await this.assetRepository.upsertFiles(toUpsert);
-    }
-
-    const pathsToDelete: string[] = [];
-    if (previewFile && previewFile.path !== generated.previewPath) {
-      this.logger.debug(`Deleting old preview for asset ${asset.id}`);
-      pathsToDelete.push(previewFile.path);
-    }
-
-    if (thumbnailFile && thumbnailFile.path !== generated.thumbnailPath) {
-      this.logger.debug(`Deleting old thumbnail for asset ${asset.id}`);
-      pathsToDelete.push(thumbnailFile.path);
-    }
-
-    if (fullsizeFile && fullsizeFile.path !== generated.fullsizePath) {
-      this.logger.debug(`Deleting old fullsize preview image for asset ${asset.id}`);
-      pathsToDelete.push(fullsizeFile.path);
-      if (!generated.fullsizePath) {
-        // did not generate a new fullsize image, delete the existing record
-        await this.assetRepository.deleteFiles([fullsizeFile]);
-      }
-    }
-
-    if (pathsToDelete.length > 0) {
-      await Promise.all(pathsToDelete.map((path) => this.storageRepository.unlink(path)));
-    }
-
-    if (!asset.thumbhash || Buffer.compare(asset.thumbhash, generated.thumbhash) !== 0) {
-      await this.assetRepository.update({ id: asset.id, thumbhash: generated.thumbhash });
-    }
-
-    await this.assetRepository.upsertJobStatus({ assetId: asset.id, previewAt: new Date(), thumbnailAt: new Date() });
-
     return JobStatus.SUCCESS;
   }
 

server/src/services/person.service.ts

@@ -290,8 +290,7 @@ export class PersonService extends BaseService {
     }
 
     const asset = await this.assetJobRepository.getForDetectFacesJob(id);
-    const previewFile = asset?.files[0];
+    if (!asset) {
-    if (!asset || asset.files.length !== 1 || !previewFile) {
       return JobStatus.FAILED;
     }
 
@@ -301,10 +300,12 @@ export class PersonService extends BaseService {
 
     const { imageHeight, imageWidth, faces } = await this.machineLearningRepository.detectFaces(
       machineLearning.urls,
-      previewFile.path,
+      asset.originalPath,
       machineLearning.facialRecognition,
     );
-    this.logger.debug(`${faces.length} faces detected in ${previewFile.path}`);
+    this.logger.debug(
+      `${faces.length} faces detected in ${asset.originalPath} with scores ${faces.map((f) => f.score)}`,
+    );
 
     const facesToAdd: (Insertable<AssetFaces> & { id: string })[] = [];
     const embeddings: FaceSearch[] = [];

server/src/services/smart-info.service.ts

@@ -100,7 +100,7 @@ export class SmartInfoService extends BaseService {
     }
 
     const asset = await this.assetJobRepository.getForClipEncoding(id);
-    if (!asset || asset.files.length !== 1) {
+    if (!asset) {
       return JobStatus.FAILED;
     }
 
@@ -110,7 +110,7 @@ export class SmartInfoService extends BaseService {
 
     const embedding = await this.machineLearningRepository.encodeImage(
       machineLearning.urls,
-      asset.files[0].path,
+      asset.originalPath,
       machineLearning.clip,
     );