model_used is now per file, not per face, implemented split of default_model to default_refimg_model and default_scan_model in settings, and default_refimg_model actualy works when creating refimgs in person.py. The model_used in face_file_link is based on default settings model and will scan with cnn if chosen and store that in DB as needed. Need viewer to allow changing per file / not just default for future scans

pa_job_manager.py

@@ -190,11 +190,12 @@ class Settings(Base):
     import_path = Column(String)
     storage_path = Column(String)
     recycle_bin_path = Column(String)
-    default_model = Column(Integer,ForeignKey('ai_model.id'), unique=True, nullable=False)
+    default_refimg_model = Column(Integer,ForeignKey('ai_model.id'), unique=True, nullable=False)
+    default_scan_model = Column(Integer,ForeignKey('ai_model.id'), unique=True, nullable=False)
     default_threshold = Column(Integer)
 
     def __repr__(self):
-        return f"<id: {self.id}, import_path: {self.import_path}, recycle_bin_path: {self.recycle_bin_path}, default_model: {self.default_model}, default_threshold: {self.default_threshold}>"
+        return f"<id: {self.id}, import_path: {self.import_path}, recycle_bin_path: {self.recycle_bin_path}, default_refimg_model: {self.default_refimg_model}, default_scan_model: {self.default_scan_model}, default_threshold: {self.default_threshold}>"
         
 class PersonRefimgLink(Base):
     __tablename__ = "person_refimg_link"
@@ -225,6 +226,7 @@ class Refimg(Base):
     orig_w = Column(Integer)
     orig_h = Column(Integer)
     face_locn = Column(String)
+    model_used = Column(Integer, ForeignKey("ai_model.id") )
 
     def __repr__(self):
         return f"<id: {self.id}, fname: {self.fname}, created_on: {self.created_on}>"
@@ -259,7 +261,6 @@ 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 )
-    model_used = Column(Integer, ForeignKey("ai_model.id") )
     face_distance = Column(Integer)
 
     def __repr__(self):
@@ -981,6 +982,10 @@ def JobRunAIOn(job):
     else:
         job.refimgs=session.query(Refimg).join(PersonRefimgLink).join(Person).filter(Person.tag==which_person).all()
 
+    if not job.refimgs:
+        FinishJob(job, "Failed Processesing AI - the person(s) you chose has no reference images!", "Failed" )
+        return
+
     # start by working out how many images in this selection we will need face match on 
     job.num_files = 0
     for jex in job.extra:
@@ -1060,25 +1065,6 @@ def GenHashAndThumb(job, e):
     e.file_details.last_hash_date = time.time()
     return
 
-def lookForPersonInImage(job, person, unknown_encoding, e):
-    FinishJob( job, "THIS CODE HAS BEEN REMOVED, need to use new Face* tables, and rethink", "Failed" )
-    return
-
-
-def generateUnknownEncodings(im):
-    unknown_image = numpy.array(im)
-    face_locations = face_recognition.face_locations(unknown_image)
-    if not face_locations:
-        return None
-    unknown_encodings = face_recognition.face_encodings(unknown_image, known_face_locations=face_locations)
-    return unknown_encodings
-
-
-def compareAI(known_encoding, unknown_encoding):
-    results = face_recognition.compare_faces([known_encoding], unknown_encoding, tolerance=0.55)
-    return results
-
-
 def ProcessFilesInDir(job, e, file_func, count_dirs):
     if DEBUG==1:
         print( f"DEBUG: ProcessFilesInDir: {e.FullPathOnFS()}")
@@ -1353,11 +1339,11 @@ def InitialValidationChecks():
     FinishJob(job,"Finished Initial Validation Checks")
     return
 
-def AddFaceToFile( locn_data, face_data, file_eid ):
+def AddFaceToFile( locn_data, face_data, file_eid, model ):
     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 )
+    ffl = FaceFileLink( face_id=face.id, file_eid=file_eid, model_used=model )
     session.add(ffl)
     session.commit()
     return face
@@ -1367,10 +1353,10 @@ def DelFacesForFile( eid ):
     session.commit()
     return
 
-def MatchRefimgToFace( refimg_id, face_id, model, face_dist ):
+def MatchRefimgToFace( refimg_id, face_id, face_dist ):
     # remove any match to this face from previous attempts, and 'replace' with new one
     session.query(FaceRefimgLink).filter(FaceRefimgLink.face_id==face_id).delete()
-    rfl = FaceRefimgLink( refimg_id = refimg_id, face_id = face_id, model_used=model, face_distance=face_dist )
+    rfl = FaceRefimgLink( refimg_id = refimg_id, face_id = face_id, face_distance=face_dist )
     session.add(rfl)
     session.commit()
     return
@@ -1398,24 +1384,24 @@ 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" )
         im = face_recognition.load_image_file(e.FullPathOnFS())
-        # TODO: use setting to use model
-        face_locations = face_recognition.face_locations(im)
+        model=AIModel.query.get(model)
+        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 )
+            AddFaceToFile( locn, face, e.id, model )
         file_h.faces_created_on = time.time()
     session.commit()
 
-    # get default_model from settings (test this)
-    settings = session.query(Settings).first()
-    model=settings.default_model
-    threshold = settings.default_threshold
-
     faces = session.query(Face).join(FaceFileLink).filter(FaceFileLink.file_eid==e.id).all()
     # if there are no faces for this file, then dont go any futher
     if not faces:
@@ -1437,7 +1423,7 @@ def ScanFileForPerson( job, e, force=False ):
     for face in faces:
         who, fd = BestFaceMatch(dist, face.id, threshold )
         if who != None:
-            MatchRefimgToFace( who, face.id, model, fd )
+            MatchRefimgToFace( who, face.id, fd )
             AddLogForJob(job, f'WE MATCHED: {name[who]} with file: {e.name} - face distance of {fd}')
             del( dist[who] )
     return