transfercolor via lab converter now implemented by tensorflow-cpu, which is x2 faster than skimage.

We cannot use GPU for lab converter in converter multiprocesses, because almost all VRAM ate by model process, so even 300Mb free VRAM not enough for tensorflow lab converter.
Removed skimage dependency.
Refactorings.

mainscripts/Extractor.py

@@ -241,7 +241,9 @@ class ExtractSubprocessor(SubprocessorBase):
         if self.type == 'rects':
             if self.detector is not None:
                 if self.detector == 'mt':
-                    self.tf = gpufmkmgr.import_tf ([self.device_idx], allow_growth=True)
+                
+                    self.gpu_config = gpufmkmgr.GPUConfig ( force_best_gpu_idx=self.device_idx, allow_growth=True)                
+                    self.tf = gpufmkmgr.import_tf ( self.gpu_config )
                     self.tf_session = gpufmkmgr.get_tf_session()
                     self.keras = gpufmkmgr.import_keras()
                     self.e = facelib.MTCExtractor(self.keras, self.tf, self.tf_session)                            
@@ -251,7 +253,8 @@ class ExtractSubprocessor(SubprocessorBase):
                 self.e.__enter__()
 
         elif self.type == 'landmarks':
-            self.tf = gpufmkmgr.import_tf([self.device_idx], allow_growth=True)
+            self.gpu_config = gpufmkmgr.GPUConfig ( force_best_gpu_idx=self.device_idx, allow_growth=True)                
+            self.tf = gpufmkmgr.import_tf ( self.gpu_config )
             self.tf_session = gpufmkmgr.get_tf_session()
             self.keras = gpufmkmgr.import_keras()
             self.e = facelib.LandmarksExtractor(self.keras)