XSegUtil apply xseg now checks model face type

mainscripts/XSegUtil.py

@@ -11,7 +11,7 @@ from core.interact import interact as io
 from core.leras import nn
 from DFLIMG import *
 from facelib import XSegNet, LandmarksProcessor, FaceType
-
+import pickle
 
 def apply_xseg(input_path, model_path):
     if not input_path.exists():
@@ -20,20 +20,36 @@ def apply_xseg(input_path, model_path):
     if not model_path.exists():
         raise ValueError(f'{model_path} not found. Please ensure it exists.')
         
+    face_type = None
+    
+    model_dat = model_path / 'XSeg_data.dat'
+    if model_dat.exists():
+        dat = pickle.loads( model_dat.read_bytes() )
+        dat_options = dat.get('options', None)
+        if dat_options is not None:
+            face_type = dat_options.get('face_type', None)
+        
+        
+        
+    if face_type is None:
         face_type = io.input_str ("XSeg model face type", 'same', ['h','mf','f','wf','head','same'], help_message="Specify face type of trained XSeg model. For example if XSeg model trained as WF, but faceset is HEAD, specify WF to apply xseg only on WF part of HEAD. Default is 'same'").lower()
         if face_type == 'same':
             face_type = None
-    else:
+    
+    if face_type is not None:
         face_type = {'h'  : FaceType.HALF,
                      'mf' : FaceType.MID_FULL,
                      'f'  : FaceType.FULL,
                      'wf' : FaceType.WHOLE_FACE,
                      'head' : FaceType.HEAD}[face_type]
+                     
     io.log_info(f'Applying trained XSeg model to {input_path.name}/ folder.')
 
     device_config = nn.DeviceConfig.ask_choose_device(choose_only_one=True)
     nn.initialize(device_config)
         
+    
+    
     xseg = XSegNet(name='XSeg', 
                     load_weights=True,
                     weights_file_root=model_path,