Added new face type : head

Now you can replace the head.
Example: https://www.youtube.com/watch?v=xr5FHd0AdlQ
Requirements:
	Post processing skill in Adobe After Effects or Davinci Resolve.
Usage:
1)	Find suitable dst footage with the monotonous background behind head
2)	Use “extract head” script
3)	Gather rich src headset from only one scene (same color and haircut)
4)	Mask whole head for src and dst using XSeg editor
5)	Train XSeg
6)	Apply trained XSeg mask for src and dst headsets
7)	Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. You can use pretrained model for head. Minimum recommended resolution for head is 224.
8)	Extract multiple tracks, using Merger:
a.	Raw-rgb
b.	XSeg-prd mask
c.	XSeg-dst mask
9)	Using AAE or DavinciResolve, do:
a.	Hide source head using XSeg-prd mask: content-aware-fill, clone-stamp, background retraction, or other technique
b.	Overlay new head using XSeg-dst mask

Warning: Head faceset can be used for whole_face or less types of training only with XSeg masking.

XSegEditor: added button ‘view trained XSeg mask’, so you can see which frames should be masked to improve mask quality.

DFLIMG/DFLJPG.py

@@ -1,13 +1,16 @@
 import pickle
 import struct
+import traceback
 
 import cv2
 import numpy as np
 
+from core import imagelib
+from core.imagelib import SegIEPolys
 from core.interact import interact as io
 from core.structex import *
 from facelib import FaceType
-from core.imagelib import SegIEPolys
+
 
 class DFLJPG(object):
     def __init__(self, filename):
@@ -148,7 +151,7 @@ class DFLJPG(object):
 
             return inst
         except Exception as e:
-            print (e)
+            io.log_err (f'Exception occured while DFLJPG.load : {traceback.format_exc()}')
             return None
 
     def has_data(self):
@@ -165,10 +168,10 @@ class DFLJPG(object):
         data = b""
 
         dict_data = self.dfl_dict
-        
+
         # Remove None keys
         for key in list(dict_data.keys()):
-            if dict_data[key] is None:                
+            if dict_data[key] is None:
                 dict_data.pop(key)
 
         for chunk in self.chunks:
@@ -242,52 +245,58 @@ class DFLJPG(object):
         return None
     def set_image_to_face_mat(self, image_to_face_mat):   self.dfl_dict['image_to_face_mat'] = image_to_face_mat
 
-    def get_seg_ie_polys(self): 
+    def get_seg_ie_polys(self):
         d = self.dfl_dict.get('seg_ie_polys',None)
         if d is not None:
             d = SegIEPolys.load(d)
         else:
             d = SegIEPolys()
-            
+
         return d
-        
+
     def set_seg_ie_polys(self, seg_ie_polys):
-        if seg_ie_polys is not None:        
+        if seg_ie_polys is not None:
             if not isinstance(seg_ie_polys, SegIEPolys):
                 raise ValueError('seg_ie_polys should be instance of SegIEPolys')
-            
+
             if seg_ie_polys.has_polys():
                 seg_ie_polys = seg_ie_polys.dump()
             else:
                 seg_ie_polys = None
-        
+
         self.dfl_dict['seg_ie_polys'] = seg_ie_polys
 
-    def get_xseg_mask(self): 
+    def get_xseg_mask(self):
         mask_buf = self.dfl_dict.get('xseg_mask',None)
         if mask_buf is None:
             return None
-            
+
         img = cv2.imdecode(mask_buf, cv2.IMREAD_UNCHANGED)
         if len(img.shape) == 2:
             img = img[...,None]
-            
-        
+
         return img.astype(np.float32) / 255.0
-        
-        
+
+
     def set_xseg_mask(self, mask_a):
         if mask_a is None:
             self.dfl_dict['xseg_mask'] = None
             return
-            
-        ret, buf = cv2.imencode( '.png', np.clip( mask_a*255, 0, 255 ).astype(np.uint8) )
+
+        mask_a = imagelib.normalize_channels(mask_a, 1)
+        img_data = np.clip( mask_a*255, 0, 255 ).astype(np.uint8)
+
+        data_max_len = 4096
+
+        ret, buf = cv2.imencode('.png', img_data)
+
+        if not ret or len(buf) > data_max_len:
+            for jpeg_quality in range(100,-1,-1):
+                ret, buf = cv2.imencode( '.jpg', img_data, [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_quality] )
+                if ret and len(buf) <= data_max_len:
+                    break
+
         if not ret:
-            raise Exception("unable to generate PNG data for set_xseg_mask")
-        
+            raise Exception("set_xseg_mask: unable to generate image data for set_xseg_mask")
+
         self.dfl_dict['xseg_mask'] = buf
-
-
-
-
-