if we use run_ai_on_path, then we remove all existing matches on the path, then go again -- allows us to change threshold and reset
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@@ -1449,6 +1449,7 @@ def JobRunAIOnPath(job):
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path_cnt=0
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for p in paths:
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d = session.query(Dir).join(PathDirLink).filter(PathDirLink.path_id==p.id).filter(Dir.rel_path=='').first()
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DelMatchesForDir( job, d.eid )
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job.extra.append( JobExtra( name=f"eid-{path_cnt}", value=f"{d.eid}" ) )
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path_cnt+=1
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JobRunAIOn(job)
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@@ -1927,6 +1928,28 @@ def AddFaceToFile( locn_data, face_data, file_eid, model_id, settings ):
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session.commit()
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return
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####################################################################################################################################
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# DelMatchesForDir(): quick func to delete any matched faces in the specified dir
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####################################################################################################################################
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def DelMatchesForDir( job, eid ):
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print("Recurse through dirs deleting face per file" )
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dir=session.query(Entry).get(eid)
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ProcessFilesInDir( job, dir, DelMatchesForFile, False )
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session.commit()
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return
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####################################################################################################################################
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# DelMatchesForFile(): quick func to delete any matched faces associated with the specified file
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####################################################################################################################################
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def DelMatchesForFile( job, ent ):
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if DEBUG:
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AddLogForJob(job, f'Remove any old matches in {ent.name}')
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session.execute( f"delete from face_refimg_link where face_id in (select face_id from face_file_link where file_eid = {ent.id})" )
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ent.file_details.last_ai_scan=0
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session.add(ent)
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return
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####################################################################################################################################
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# DelFacesForFile(): quick func to delete any faces associated with the specified file
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####################################################################################################################################
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