face_distance is a float, also pass model through correctly to FaceFileLink

pa_job_manager.py

@@ -261,7 +261,7 @@ class FaceRefimgLink(Base):
     __tablename__ = "face_refimg_link"
     face_id = Column(Integer, ForeignKey("face.id"), primary_key=True )
     refimg_id = Column(Integer, ForeignKey("refimg.id"), primary_key=True )
-    face_distance = Column(Integer)
+    face_distance = Column(Float)
 
     def __repr__(self):
         return f"<face_id: {self.face_id}, refimg_id={self.refimg_id}"
@@ -975,7 +975,7 @@ def AddToJobImageCount(job, entry ):
     return
 
 def JobRunAIOn(job):
-    AddLogForJob(job, f"INFO: Starting looking For faces in files job...")
+    AddLogForJob(job, f"INFO: Starting job to look for faces in files...")
     which_person=[jex.value for jex in job.extra if jex.name == "person"][0]
     if which_person == "all":
         job.refimgs = session.query(Refimg).all()
@@ -1010,8 +1010,6 @@ def JobRunAIOn(job):
                 ProcessFilesInDir( job, entry, WrapperForScanFileForPerson, False )
             elif entry.type.name == 'Image':
                 which_file=session.query(Entry).join(File).filter(Entry.id==jex.value).first()
-                if DEBUG:
-                    AddLogForJob( job, f'INFO: processing File: {entry.name}' )
                 ScanFileForPerson( job, which_file, force=False)
                 # processed this file, add 1 to count
                 job.current_file_num+=1
@@ -1339,11 +1337,11 @@ def InitialValidationChecks():
     FinishJob(job,"Finished Initial Validation Checks")
     return
 
-def AddFaceToFile( locn_data, face_data, file_eid, model ):
+def AddFaceToFile( locn_data, face_data, file_eid, model_id ):
     face = Face( face=face_data.tobytes(), locn=json.dumps(locn_data) )
     session.add(face)
     session.commit()
-    ffl = FaceFileLink( face_id=face.id, file_eid=file_eid, model_used=model )
+    ffl = FaceFileLink( face_id=face.id, file_eid=file_eid, model_used=model_id )
     session.add(ffl)
     session.commit()
     return face
@@ -1377,6 +1375,13 @@ def BestFaceMatch(dist, fid, threshold):
     return which, lowest
 
 def ScanFileForPerson( job, e, force=False ):
+    # get default_scan_model from settings (test this)
+    settings = session.query(Settings).first()
+    model=session.query(AIModel).get(settings.default_scan_model)
+    threshold = settings.default_threshold
+
+    if DEBUG:
+        AddLogForJob( job, f'INFO: processing File: {e.name} and threshold face distance of {threshold}' )
     file_h = session.query(File).get( e.id )
     # if we are forcing this, delete any old faces (this will also delete linked tables), and reset faces_created_on to None
     if force:
@@ -1384,21 +1389,15 @@ def ScanFileForPerson( job, e, force=False ):
         DelFacesForFile( e.id )
         file_h.faces_created_on = 0
 
-    # get default_scan_model from settings (test this)
-    settings = session.query(Settings).first()
-    model=settings.default_scan_model
-    threshold = settings.default_threshold
-
     # optimise: dont rescan if we already have faces 
     if file_h.faces_created_on == 0:
         if DEBUG:
-            AddLogForJob( job, f"DEBUG: {e.name} is missing unknown faces, generating them" )
+            AddLogForJob( job, f"DEBUG: {e.name} is missing unknown faces, generating them with model {model.name}" )
         im = face_recognition.load_image_file(e.FullPathOnFS())
-        model=AIModel.query.get(model)
-        face_locations = face_recognition.face_locations(im, model=model.name)
+        face_locations = face_recognition.face_locations(im, model=model.name )
         unknown_encodings = face_recognition.face_encodings(im, known_face_locations=face_locations)
         for locn, face in zip( face_locations, unknown_encodings ):
-            AddFaceToFile( locn, face, e.id, model )
+            AddFaceToFile( locn, face, e.id, model.id )
         file_h.faces_created_on = time.time()
     session.commit()