manual extractor: increased FPS,

sort by final : now you can specify target number of images,
converter: fix seamless mask and exception,
huge refactoring

converters/Converter.py

@@ -3,21 +3,15 @@ import copy
 You can implement your own Converter, check example ConverterMasked.py
 '''
 
-class ConverterBase(object):
+class Converter(object):
-    MODE_FACE = 0
+    TYPE_FACE = 0                   #calls convert_face
-    MODE_IMAGE = 1
+    TYPE_IMAGE = 1                  #calls convert_image without landmarks
-    MODE_IMAGE_WITH_LANDMARKS = 2
+    TYPE_IMAGE_WITH_LANDMARKS = 2   #calls convert_image with landmarks
     
     #overridable
-    def __init__(self, predictor):
+    def __init__(self, predictor_func, type):
-        self.predictor = predictor
+        self.predictor_func = predictor_func
-
+        self.type = type
-    #overridable
-    def get_mode(self):
-        #MODE_FACE calls convert_face
-        #MODE_IMAGE calls convert_image without landmarks
-        #MODE_IMAGE_WITH_LANDMARKS calls convert_image with landmarks
-        return ConverterBase.MODE_FACE
         
     #overridable
     def convert_face (self, img_bgr, img_face_landmarks, debug):
@@ -40,7 +34,7 @@ class ConverterBase(object):
     def copy(self):
         return copy.copy(self)
         
-    def copy_and_set_predictor(self, predictor):
+    def copy_and_set_predictor(self, predictor_func):
         result = self.copy()
-        result.predictor = predictor
+        result.predictor_func = predictor_func
         return result

converters/ConverterImage.py

@@ -1,4 +1,4 @@
-from models import ConverterBase
+from .Converter import Converter
 from facelib import LandmarksProcessor
 from facelib import FaceType
 
@@ -7,38 +7,33 @@ import numpy as np
 from utils import image_utils
 
 '''
-predictor: 
+predictor_func: 
     input:  [predictor_input_size, predictor_input_size, BGR]
     output: [predictor_input_size, predictor_input_size, BGR]
 '''
 
-class ConverterImage(ConverterBase):
+class ConverterImage(Converter):
 
     #override
-    def __init__(self,  predictor,
+    def __init__(self,  predictor_func,
                         predictor_input_size=0, 
-                        output_size=0,               
+                        output_size=0):
-                        **in_options):
                         
-        super().__init__(predictor)
+        super().__init__(predictor_func, Converter.TYPE_IMAGE)
          
         self.predictor_input_size = predictor_input_size
         self.output_size = output_size   
-  
-    #override
-    def get_mode(self):
-        return ConverterBase.MODE_IMAGE
         
     #override
     def dummy_predict(self):
-        self.predictor ( np.zeros ( (self.predictor_input_size, self.predictor_input_size,3), dtype=np.float32) )
+        self.predictor_func ( np.zeros ( (self.predictor_input_size, self.predictor_input_size,3), dtype=np.float32) )
         
     #override
     def convert_image (self, img_bgr, img_landmarks, debug):
         img_size = img_bgr.shape[1], img_bgr.shape[0]
 
         predictor_input_bgr = cv2.resize ( img_bgr, (self.predictor_input_size, self.predictor_input_size), cv2.INTER_LANCZOS4 )
-        predicted_bgr = self.predictor ( predictor_input_bgr )
+        predicted_bgr = self.predictor_func ( predictor_input_bgr )
 
         output = cv2.resize ( predicted_bgr, (self.output_size, self.output_size), cv2.INTER_LANCZOS4 )
         if debug:

converters/ConverterMasked.py

@@ -1,10 +1,10 @@
-from models import ConverterBase
+from .Converter import Converter
 from facelib import LandmarksProcessor
 from facelib import FaceType
 import cv2
 import numpy as np
 from utils import image_utils
-from utils.console_utils import *
+from interact import interact as io
 
 '''
 default_mode = {1:'overlay',
@@ -14,10 +14,10 @@ default_mode = {1:'overlay',
              5:'seamless-hist-match',
              6:'raw'}
 '''
-class ConverterMasked(ConverterBase):
+class ConverterMasked(Converter):
 
     #override
-    def __init__(self,  predictor,
+    def __init__(self,  predictor_func,
                         predictor_input_size=0, 
                         output_size=0, 
                         face_type=FaceType.FULL,
@@ -26,16 +26,15 @@ class ConverterMasked(ConverterBase):
                         base_blur_mask_modifier = 0,
                         default_erode_mask_modifier = 0,
                         default_blur_mask_modifier = 0,
-                        clip_hborder_mask_per = 0,
+                        clip_hborder_mask_per = 0):
-                        **in_options):
                         
-        super().__init__(predictor)
+        super().__init__(predictor_func, Converter.TYPE_FACE)
         self.predictor_input_size = predictor_input_size
         self.output_size = output_size
         self.face_type = face_type 
         self.clip_hborder_mask_per = clip_hborder_mask_per
         
-        mode = input_int ("Choose mode: (1) overlay, (2) hist match, (3) hist match bw, (4) seamless, (5) seamless hist match, (6) raw. Default - %d : " % (default_mode) , default_mode)
+        mode = io.input_int ("Choose mode: (1) overlay, (2) hist match, (3) hist match bw, (4) seamless, (5) seamless hist match, (6) raw. Default - %d : " % (default_mode) , default_mode)
         
         mode_dict = {1:'overlay',
                      2:'hist-match',
@@ -47,7 +46,7 @@ class ConverterMasked(ConverterBase):
         self.mode = mode_dict.get (mode, mode_dict[default_mode] )
         
         if self.mode == 'raw':
-            mode = input_int ("Choose raw mode: (1) rgb, (2) rgb+mask (default), (3) mask only, (4) predicted only : ", 2)
+            mode = io.input_int ("Choose raw mode: (1) rgb, (2) rgb+mask (default), (3) mask only, (4) predicted only : ", 2)
             self.raw_mode = {1:'rgb',
                              2:'rgb-mask',
                              3:'mask-only',
@@ -55,37 +54,33 @@ class ConverterMasked(ConverterBase):
         
         if self.mode != 'raw':
             if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
-                self.masked_hist_match = input_bool("Masked hist match? (y/n skip:y) : ", True)
+                self.masked_hist_match = io.input_bool("Masked hist match? (y/n skip:y) : ", True)
             
             if self.mode == 'hist-match' or self.mode == 'hist-match-bw' or self.mode == 'seamless-hist-match':
-                self.hist_match_threshold = np.clip ( input_int("Hist match threshold [0..255] (skip:255) :  ", 255), 0, 255)
+                self.hist_match_threshold = np.clip ( io.input_int("Hist match threshold [0..255] (skip:255) :  ", 255), 0, 255)
             
-        self.use_predicted_mask = input_bool("Use predicted mask? (y/n skip:y) : ", True)
+        self.use_predicted_mask = io.input_bool("Use predicted mask? (y/n skip:y) : ", True)
             
         if self.mode != 'raw':
-            self.erode_mask_modifier = base_erode_mask_modifier + np.clip ( input_int ("Choose erode mask modifier [-200..200] (skip:%d) : " % (default_erode_mask_modifier), default_erode_mask_modifier), -200, 200)
+            self.erode_mask_modifier = base_erode_mask_modifier + np.clip ( io.input_int ("Choose erode mask modifier [-200..200] (skip:%d) : " % (default_erode_mask_modifier), default_erode_mask_modifier), -200, 200)
-            self.blur_mask_modifier = base_blur_mask_modifier + np.clip ( input_int ("Choose blur mask modifier [-200..200] (skip:%d) : " % (default_blur_mask_modifier), default_blur_mask_modifier), -200, 200)
+            self.blur_mask_modifier = base_blur_mask_modifier + np.clip ( io.input_int ("Choose blur mask modifier [-200..200] (skip:%d) : " % (default_blur_mask_modifier), default_blur_mask_modifier), -200, 200)
             
             self.seamless_erode_mask_modifier = 0
             if self.mode == 'seamless' or self.mode == 'seamless-hist-match':
-                self.seamless_erode_mask_modifier = np.clip ( input_int ("Choose seamless erode mask modifier [-100..100] (skip:0) : ", 0), -100, 100)
+                self.seamless_erode_mask_modifier = np.clip ( io.input_int ("Choose seamless erode mask modifier [-100..100] (skip:0) : ", 0), -100, 100)
           
-        self.output_face_scale = np.clip ( 1.0 + input_int ("Choose output face scale modifier [-50..50] (skip:0) : ", 0)*0.01, 0.5, 1.5)
+        self.output_face_scale = np.clip ( 1.0 + io.input_int ("Choose output face scale modifier [-50..50] (skip:0) : ", 0)*0.01, 0.5, 1.5)
-        self.color_transfer_mode = input_str ("Apply color transfer to predicted face? Choose mode ( rct/lct skip:None ) : ", None, ['rct','lct'])
+        self.color_transfer_mode = io.input_str ("Apply color transfer to predicted face? Choose mode ( rct/lct skip:None ) : ", None, ['rct','lct'])
 
         if self.mode != 'raw':
-            self.final_image_color_degrade_power = np.clip (  input_int ("Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100)
+            self.final_image_color_degrade_power = np.clip (  io.input_int ("Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100)
-            self.alpha = input_bool("Export png with alpha channel? (y/n skip:n) : ", False)
+            self.alpha = io.input_bool("Export png with alpha channel? (y/n skip:n) : ", False)
             
-        print ("")
+        io.log_info ("")
-  
-    #override
-    def get_mode(self):
-        return ConverterBase.MODE_FACE
         
     #override
     def dummy_predict(self):
-        self.predictor ( np.zeros ( (self.predictor_input_size,self.predictor_input_size,4), dtype=np.float32 ) )
+        self.predictor_func ( np.zeros ( (self.predictor_input_size,self.predictor_input_size,4), dtype=np.float32 ) )
         
     #override
     def convert_face (self, img_bgr, img_face_landmarks, debug):        
@@ -106,7 +101,7 @@ class ConverterMasked(ConverterBase):
         predictor_input_mask_a_0 = cv2.resize (dst_face_mask_a_0, (self.predictor_input_size,self.predictor_input_size))
         predictor_input_mask_a   = np.expand_dims (predictor_input_mask_a_0, -1) 
         
-        predicted_bgra = self.predictor ( np.concatenate( (predictor_input_bgr, predictor_input_mask_a), -1) )
+        predicted_bgra = self.predictor_func ( np.concatenate( (predictor_input_bgr, predictor_input_mask_a), -1) )
 
         prd_face_bgr      = np.clip (predicted_bgra[:,:,0:3], 0, 1.0 )
         prd_face_mask_a_0 = np.clip (predicted_bgra[:,:,3], 0.0, 1.0)
@@ -129,11 +124,13 @@ class ConverterMasked(ConverterBase):
         
         img_face_mask_aaa [ img_face_mask_aaa <= 0.1 ] = 0.0
             
-        img_face_mask_flatten_aaa = img_face_mask_aaa.copy()
+        if self.mode == 'seamless' or self.mode == 'seamless-hist-match':
-        img_face_mask_flatten_aaa[img_face_mask_flatten_aaa > 0.9] = 1.0
+            img_face_seamless_mask_aaa = img_face_mask_aaa.copy()
-
+            img_face_seamless_mask_aaa[img_face_seamless_mask_aaa > 0.9] = 1.0
-        maxregion = np.argwhere(img_face_mask_flatten_aaa==1.0)        
+            img_face_seamless_mask_aaa[img_face_seamless_mask_aaa <= 0.9] = 0.0
-
+            
+        maxregion = np.argwhere(img_face_mask_aaa > 0.9)
+        
         out_img = img_bgr.copy()
         
         if self.mode == 'raw':
@@ -155,24 +152,24 @@ class ConverterMasked(ConverterBase):
                 maxy,maxx = maxregion.max(axis=0)[:2]
                 
                 if debug:
-                    print ("maxregion.size: %d, minx:%d, maxx:%d miny:%d, maxy:%d" % (maxregion.size, minx, maxx, miny, maxy  ) )
+                    io.log_info ("maxregion.size: %d, minx:%d, maxx:%d miny:%d, maxy:%d" % (maxregion.size, minx, maxx, miny, maxy  ) )
                 
                 lenx = maxx - minx
                 leny = maxy - miny
                 if lenx >= 4 and leny >= 4:
-                    masky = int(minx+(lenx//2))
+                    maskx = int(minx+(lenx//2))
-                    maskx = int(miny+(leny//2))
+                    masky = int(miny+(leny//2))
                     lowest_len = min (lenx, leny)
                     
                     if debug:
-                        print ("lowest_len = %f" % (lowest_len) )
+                        io.log_info ("lowest_len = %f" % (lowest_len) )
                   
                     img_mask_blurry_aaa = img_face_mask_aaa
 
                     if self.erode_mask_modifier != 0:
                         ero  = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*self.erode_mask_modifier )
                         if debug:
-                            print ("erode_size = %d" % (ero) )                    
+                            io.log_info ("erode_size = %d" % (ero) )                    
                         if ero > 0:
                             img_mask_blurry_aaa = cv2.erode(img_mask_blurry_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )                        
                         elif ero < 0:
@@ -181,12 +178,13 @@ class ConverterMasked(ConverterBase):
                     if self.seamless_erode_mask_modifier != 0:
                         ero  = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*self.seamless_erode_mask_modifier )
                         if debug:
-                            print ("seamless_erode_size = %d" % (ero) )
+                            io.log_info ("seamless_erode_size = %d" % (ero) )
                         if ero > 0:
-                            img_face_mask_flatten_aaa = cv2.erode(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
+                            img_face_seamless_mask_aaa = cv2.erode(img_face_seamless_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
                         elif ero < 0:
-                            img_face_mask_flatten_aaa = cv2.dilate(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
+                            img_face_seamless_mask_aaa = cv2.dilate(img_face_seamless_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
-
+                        img_face_seamless_mask_aaa = np.clip (img_face_seamless_mask_aaa, 0, 1)
+         
                     if self.clip_hborder_mask_per > 0: #clip hborder before blur
                         prd_hborder_rect_mask_a = np.ones ( prd_face_mask_a.shape, dtype=np.float32)        
                         prd_border_size = int ( prd_hborder_rect_mask_a.shape[1] * self.clip_hborder_mask_per )
@@ -205,7 +203,7 @@ class ConverterMasked(ConverterBase):
                     if self.blur_mask_modifier > 0:
                         blur = int( lowest_len * 0.10 * 0.01*self.blur_mask_modifier )
                         if debug:
-                            print ("blur_size = %d" % (blur) )
+                            io.log_info ("blur_size = %d" % (blur) )
                         if blur > 0:
                             img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa, (blur, blur) )                    
                         
@@ -276,8 +274,12 @@ class ConverterMasked(ConverterBase):
                         if debug:
                             debugs += [out_img.copy()]
 
-                        out_img = cv2.seamlessClone( (out_img*255).astype(np.uint8), (img_bgr*255).astype(np.uint8), (img_face_mask_flatten_aaa*255).astype(np.uint8), (masky,maskx) , cv2.NORMAL_CLONE )
+                        try:
-                        out_img = out_img.astype(dtype=np.float32) / 255.0
+                            out_img = cv2.seamlessClone( (out_img*255).astype(np.uint8), (img_bgr*255).astype(np.uint8), (img_face_seamless_mask_aaa*255).astype(np.uint8), (maskx,masky) , cv2.NORMAL_CLONE )
+                            out_img = out_img.astype(dtype=np.float32) / 255.0
+                        except:
+                            #seamlessClone may fail in some cases
+                            pass
                         
                         if debug:
                             debugs += [out_img.copy()]

converters/__init__.py

@@ -0,0 +1,3 @@
+from .Converter import Converter
+from .ConverterMasked import ConverterMasked
+from .ConverterImage import ConverterImage

facelib/LandmarksExtractor.py

@@ -3,10 +3,6 @@ import os
 import cv2
 from pathlib import Path
 
-from utils import std_utils
-
-
-
 def transform(point, center, scale, resolution):
     pt = np.array ( [point[0], point[1], 1.0] )            
     h = 200.0 * scale
@@ -123,8 +119,7 @@ class LandmarksExtractor(object):
             image = crop(input_image, center, scale).transpose ( (2,0,1) ).astype(np.float32) / 255.0
             image = np.expand_dims(image, 0)
             
-            with std_utils.suppress_stdout_stderr():
+            predicted = self.keras_model.predict (image)
-                predicted = self.keras_model.predict (image)
                 
             pts_img = get_pts_from_predict ( predicted[-1][0], center, scale)
             pts_img = [ ( int(pt[0]), int(pt[1]) ) for pt in pts_img ]             

facelib/LandmarksProcessor.py

@@ -134,7 +134,7 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
         elif face_type == FaceType.HEAD:
             padding = (output_size / 64) * 24
         else:
-            raise ValueError ('wrong face_type')
+            raise ValueError ('wrong face_type: ', face_type)
         
         mat = umeyama(image_landmarks[17:], landmarks_2D, True)[0:2]
         mat = mat * (output_size - 2 * padding)

interact/__init__.py

@@ -0,0 +1 @@
+from .interact import interact

interact/interact.py

@@ -0,0 +1,236 @@
+import os
+import sys
+import time
+import types
+import multiprocessing
+import cv2
+from tqdm import tqdm
+
+class Interact(object):
+    EVENT_LBUTTONDOWN = 1
+    EVENT_MOUSEWHEEL = 2
+    
+    def __init__(self):
+        self.named_windows = {}
+        self.capture_mouse_windows = {}
+        self.capture_keys_windows = {}        
+        self.mouse_events = {}
+        self.key_events = {}
+        self.pg_bar = None
+    
+    def log_info(self, msg, end='\n'):
+        print (msg, end=end)
+        
+    def log_err(self, msg, end='\n'):
+        print (msg, end=end)        
+    
+    def named_window(self, wnd_name):
+        if wnd_name not in self.named_windows:
+            #we will show window only on first show_image
+            self.named_windows[wnd_name] = 0
+            
+        else: print("named_window: ", wnd_name, " already created.")
+        
+    def destroy_all_windows(self):
+        if len( self.named_windows ) != 0:
+            cv2.destroyAllWindows()
+            self.named_windows = {}
+            self.capture_mouse_windows = {}
+            self.capture_keys_windows = {}
+            self.mouse_events = {}
+            self.key_events = {}
+        
+    def show_image(self, wnd_name, img):
+        if wnd_name in self.named_windows:
+            if self.named_windows[wnd_name] == 0:
+                self.named_windows[wnd_name] = 1
+                cv2.namedWindow(wnd_name)                
+                if wnd_name in self.capture_mouse_windows:
+                    self.capture_mouse(wnd_name)
+                
+            cv2.imshow (wnd_name, img)
+        else: print("show_image: named_window ", wnd_name, " not found.")
+
+    def capture_mouse(self, wnd_name):
+        def onMouse(event, x, y, flags, param):
+            (inst, wnd_name) = param            
+            if event == cv2.EVENT_LBUTTONDOWN: ev = Interact.EVENT_LBUTTONDOWN
+            elif event == cv2.EVENT_MOUSEWHEEL: ev = Interact.EVENT_MOUSEWHEEL
+            else: ev = 0
+            inst.add_mouse_event (wnd_name, x, y, ev, flags) 
+
+        if wnd_name in self.named_windows:
+            self.capture_mouse_windows[wnd_name] = True            
+            if self.named_windows[wnd_name] == 1:
+                cv2.setMouseCallback(wnd_name, onMouse, (self,wnd_name) )
+        else: print("capture_mouse: named_window ", wnd_name, " not found.")
+
+    def capture_keys(self, wnd_name):
+        if wnd_name in self.named_windows:
+            if wnd_name not in self.capture_keys_windows:
+                self.capture_keys_windows[wnd_name] = True
+            else: print("capture_keys: already set for window ", wnd_name)
+        else: print("capture_keys: named_window ", wnd_name, " not found.")
+
+    def progress_bar(self, desc, total, leave=True):
+        if self.pg_bar is None:
+            self.pg_bar = tqdm( total=total, desc=desc, leave=leave, ascii=True )
+        else: print("progress_bar: already set.")
+
+    def progress_bar_inc(self, c):
+        if self.pg_bar is not None:
+            self.pg_bar.n += c
+            self.pg_bar.refresh()
+        else: print("progress_bar not set.")
+
+    def progress_bar_close(self):
+        if self.pg_bar is not None:
+            self.pg_bar.close()
+            self.pg_bar = None
+        else: print("progress_bar not set.")
+    
+    def progress_bar_generator(self, data, desc, leave=True):
+        for x in tqdm( data, desc=desc, leave=leave, ascii=True ):
+            yield x
+    
+    def process_messages(self, sleep_time=0):
+        has_windows = False
+        has_capture_keys = False
+
+        if len(self.named_windows) != 0:
+            has_windows = True
+            
+        if len(self.capture_keys_windows) != 0:
+            has_capture_keys = True
+                
+        if has_windows or has_capture_keys:
+            wait_key_time = int(sleep_time / 1000) if sleep_time != 0 else 1
+            key = cv2.waitKey(1) & 0xFF
+        else:
+            if sleep_time != 0:
+                time.sleep(sleep_time)
+        
+        if has_capture_keys and key != 255:
+            for wnd_name in self.capture_keys_windows:
+                self.add_key_event (wnd_name, key)
+    
+    def wait_any_key(self):
+        cv2.waitKey(0)
+    
+    def add_mouse_event(self, wnd_name, x, y, ev, flags):
+        if wnd_name not in self.mouse_events: 
+            self.mouse_events[wnd_name] = []
+        self.mouse_events[wnd_name] += [ (x, y, ev, flags) ]
+        
+    def add_key_event(self, wnd_name, key):
+        if wnd_name not in self.key_events: 
+            self.key_events[wnd_name] = []
+        self.key_events[wnd_name] += [ (key,) ]
+
+    def get_mouse_events(self, wnd_name):
+        ar = self.mouse_events.get(wnd_name, [])
+        self.mouse_events[wnd_name] = []        
+        return ar
+        
+    def get_key_events(self, wnd_name):
+        ar = self.key_events.get(wnd_name, [])
+        self.key_events[wnd_name] = []        
+        return ar
+        
+    def input_number(self, s, default_value, valid_list=None, help_message=None):
+        while True:
+            try:
+                inp = input(s)
+                if len(inp) == 0:
+                    raise ValueError("")
+                    
+                if help_message is not None and inp == '?':
+                    print (help_message)
+                    continue
+                
+                i = float(inp)
+                if (valid_list is not None) and (i not in valid_list):
+                    return default_value
+                return i
+            except:
+                print (default_value)
+                return default_value
+                
+    def input_int(self,s, default_value, valid_list=None, help_message=None):
+        while True:
+            try:
+                inp = input(s)
+                if len(inp) == 0:
+                    raise ValueError("")
+                    
+                if help_message is not None and inp == '?':
+                    print (help_message)
+                    continue
+                
+                i = int(inp)
+                if (valid_list is not None) and (i not in valid_list):
+                    return default_value
+                return i
+            except:
+                print (default_value)
+                return default_value
+            
+    def input_bool(self, s, default_value, help_message=None):
+        while True:
+            try:
+                inp = input(s)
+                if len(inp) == 0:
+                    raise ValueError("")
+                    
+                if help_message is not None and inp == '?':
+                    print (help_message)
+                    continue
+                    
+                return bool ( {"y":True,"n":False,"1":True,"0":False}.get(inp.lower(), default_value) )
+            except:
+                print ( "y" if default_value else "n" )
+                return default_value
+            
+    def input_str(self, s, default_value, valid_list=None, help_message=None):
+        while True:        
+            try:
+                inp = input(s)
+                if len(inp) == 0:
+                    raise ValueError("")
+                    
+                if help_message is not None and inp == '?':
+                    print (help_message)
+                    continue
+                    
+                if (valid_list is not None) and (inp.lower() not in valid_list):
+                    return default_value
+                return inp
+            except:
+                print (default_value)
+                return default_value
+                
+    def input_process(self, stdin_fd, sq, str):
+        sys.stdin = os.fdopen(stdin_fd)
+        try:
+            inp = input (str)
+            sq.put (True)
+        except:
+            sq.put (False)
+            
+    def input_in_time (self, str, max_time_sec):
+        sq = multiprocessing.Queue()
+        p = multiprocessing.Process(target=self.input_process, args=( sys.stdin.fileno(), sq, str))
+        p.start()
+        t = time.time()
+        inp = False
+        while True:
+            if not sq.empty():
+                inp = sq.get()
+                break
+            if time.time() - t > max_time_sec:
+                break
+        p.terminate()
+        sys.stdin = os.fdopen( sys.stdin.fileno() )
+        return inp
+
+interact = Interact()

joblib/SubprocessFunctionCaller.py

@@ -0,0 +1,42 @@
+import time
+import multiprocessing
+
+class SubprocessFunctionCaller(object):
+    class CliFunction(object):
+        def __init__(self, s2c, c2s, lock):
+            self.s2c = s2c
+            self.c2s = c2s
+            self.lock = lock
+            
+        def __call__(self, value):
+            self.lock.acquire()
+            self.c2s.put (value)
+            while True:
+                if not self.s2c.empty():
+                    obj = self.s2c.get()
+                    self.lock.release()
+                    return obj
+                time.sleep(0.005)
+    
+    class HostProcessor(object):
+        def __init__(self, s2c, c2s, func):
+            self.s2c = s2c
+            self.c2s = c2s
+            self.func = func
+            
+        def process_messages(self):
+            while not self.c2s.empty():
+                obj = self.c2s.get()
+                result = self.func (obj)
+                self.s2c.put (result)
+                
+    @staticmethod
+    def make_pair( func ):
+        s2c = multiprocessing.Queue()
+        c2s = multiprocessing.Queue()
+        lock = multiprocessing.Lock()
+        
+        host_processor = SubprocessFunctionCaller.HostProcessor (s2c, c2s, func)
+        cli_func = SubprocessFunctionCaller.CliFunction (s2c, c2s, lock)
+        
+        return host_processor, cli_func

joblib/SubprocessorBase.py

@@ -0,0 +1,255 @@
+import traceback
+import multiprocessing
+import time
+import sys
+from interact import interact as io
+                
+class Subprocessor(object):
+        
+    class Cli(object):
+        def __init__ ( self, client_dict ):
+            self.s2c = multiprocessing.Queue()
+            self.c2s = multiprocessing.Queue()
+            self.p = multiprocessing.Process(target=self._subprocess_run, args=(client_dict,) )
+            self.p.daemon = True
+            self.p.start()
+            
+            self.state = None
+            self.sent_time = None
+            self.sent_data = None
+            self.name = None
+            self.host_dict = None
+        
+        def kill(self):
+            self.p.terminate()
+            self.p.join()
+                
+        #overridable optional
+        def on_initialize(self, client_dict):
+            #initialize your subprocess here using client_dict
+            pass
+            
+        #overridable optional
+        def on_finalize(self):
+            #finalize your subprocess here
+            pass
+            
+        #overridable
+        def process_data(self, data):
+            #process 'data' given from host and return result
+            raise NotImplementedError
+     
+        #overridable optional
+        def get_data_name (self, data):
+            #return string identificator of your 'data'
+            return "undefined"
+            
+        def log_info(self, msg): self.c2s.put ( {'op': 'log_info', 'msg':msg } )
+        def log_err(self, msg):  self.c2s.put ( {'op': 'log_err' , 'msg':msg } )
+        def progress_bar_inc(self, c): self.c2s.put ( {'op': 'progress_bar_inc' , 'c':c } )
+        
+        def _subprocess_run(self, client_dict):
+            data = None
+            s2c, c2s = self.s2c, self.c2s
+            try:
+                self.on_initialize(client_dict)
+
+                c2s.put ( {'op': 'init_ok'} )
+
+                while True:
+                    msg = s2c.get()
+                    op = msg.get('op','')
+                    if op == 'data':
+                        data = msg['data']
+                        result = self.process_data (data)
+                        c2s.put ( {'op': 'success', 'data' : data, 'result' : result} )                        
+                        data = None
+                    elif op == 'close':
+                        break
+
+                    time.sleep(0.001)
+                        
+                self.on_finalize()
+                c2s.put ( {'op': 'finalized'} )
+         
+            except Exception as e:
+                if data is not None:        
+                    print ('Exception while process data [%s]: %s' % (self.get_data_name(data), traceback.format_exc()) )
+                else:
+                    print ('Exception: %s' % (traceback.format_exc()) )
+
+                c2s.put ( {'op': 'error', 'data' : data} )
+
+    #overridable
+    def __init__(self, name, SubprocessorCli_class, no_response_time_sec = 60):
+        if not issubclass(SubprocessorCli_class, Subprocessor.Cli):
+            raise ValueError("SubprocessorCli_class must be subclass of Subprocessor.Cli")
+
+        self.name = name
+        self.SubprocessorCli_class = SubprocessorCli_class        
+        self.no_response_time_sec = no_response_time_sec
+
+    #overridable    
+    def process_info_generator(self):
+        #yield per process (name, host_dict, client_dict)
+        raise NotImplementedError
+
+    #overridable optional
+    def on_clients_initialized(self):
+        #logic when all subprocesses initialized and ready
+        pass
+        
+    #overridable optional
+    def on_clients_finalized(self):
+        #logic when all subprocess finalized
+        pass
+        
+    #overridable        
+    def get_data(self, host_dict):
+        #return data for processing here
+        raise NotImplementedError
+    
+    #overridable
+    def on_data_return (self, host_dict, data):
+        #you have to place returned 'data' back to your queue  
+        raise NotImplementedError
+        
+    #overridable
+    def on_result (self, host_dict, data, result):
+        #your logic what to do with 'result' of 'data'
+        raise NotImplementedError
+    
+    #overridable
+    def get_result(self):
+        #return result that will be returned in func run()
+        raise NotImplementedError
+ 
+    #overridable
+    def on_tick(self):
+        #tick in main loop
+        pass
+ 
+    def run(self):
+        self.clis = []
+            
+        #getting info about name of subprocesses, host and client dicts, and spawning them
+        for name, host_dict, client_dict in self.process_info_generator():            
+            try:
+                cli = self.SubprocessorCli_class(client_dict)
+                cli.state = 1
+                cli.sent_time = time.time()
+                cli.sent_data = None
+                cli.name = name
+                cli.host_dict = host_dict
+                
+                self.clis.append (cli)
+                
+            except:
+                raise Exception ("Unable to start subprocess %s" % (name))
+
+        if len(self.clis) == 0:
+            raise Exception ("Unable to start Subprocessor '%s' " % (self.name))
+            
+        #waiting subprocesses their success(or not) initialization
+        while True:
+            for cli in self.clis[:]:
+                while not cli.c2s.empty():
+                    obj = cli.c2s.get()
+                    op = obj.get('op','')                        
+                    if op == 'init_ok':
+                        cli.state = 0
+                    elif op == 'error':
+                        cli.kill()
+                        self.clis.remove(cli)
+                        break
+            if all ([cli.state == 0 for cli in self.clis]):
+                break                
+            io.process_messages(0.005)
+            
+        if len(self.clis) == 0:
+            raise Exception ( "Unable to start subprocesses." )
+        
+        #ok some processes survived, initialize host logic        
+        
+        self.on_clients_initialized()
+ 
+        #main loop of data processing
+        while True:
+            for cli in self.clis[:]:
+                while not cli.c2s.empty():
+                    obj = cli.c2s.get()
+                    op = obj.get('op','')
+                    
+                    if op == 'success':
+                        #success processed data, return data and result to on_result
+                        self.on_result (cli.host_dict, obj['data'], obj['result'])
+                        self.sent_data = None
+                        cli.state = 0
+                    elif op == 'error':
+                        #some error occured while process data, returning chunk to on_data_return
+                        if 'data' in obj.keys():
+                            self.on_data_return (cli.host_dict, obj['data'] )
+                        #and killing process
+                        cli.kill()
+                        self.clis.remove(cli)
+                    elif op == 'log_info':
+                        io.log_info(obj['msg'])
+                    elif op == 'log_err':
+                        io.log_err(obj['msg'])
+                    elif op == 'progress_bar_inc':
+                        io.progress_bar_inc(obj['c'])
+                        
+            for cli in self.clis[:]:
+                if cli.state == 0:
+                    #free state of subprocess, get some data from get_data 
+                    data = self.get_data(cli.host_dict)
+                    if data is not None:
+                        #and send it to subprocess
+                        cli.s2c.put ( {'op': 'data', 'data' : data} )
+                        cli.sent_time = time.time()
+                        cli.sent_data = data
+                        cli.state = 1
+                    
+                elif cli.state == 1:
+                    if self.no_response_time_sec != 0 and (time.time() - cli.sent_time) > self.no_response_time_sec:
+                        #subprocess busy too long
+                        print ( '%s doesnt response, terminating it.' % (cli.name) )
+                        self.on_data_return (cli.host_dict, cli.sent_data )
+                        cli.kill()
+                        self.clis.remove(cli)
+                        
+            if all ([cli.state == 0 for cli in self.clis]):
+                #all subprocesses free and no more data available to process, ending loop
+                break                
+            io.process_messages(0.005)
+            self.on_tick()
+        
+        #gracefully terminating subprocesses
+        for cli in self.clis[:]:
+            cli.s2c.put ( {'op': 'close'} )
+            cli.sent_time = time.time()
+            
+        while True:
+            for cli in self.clis[:]:
+                terminate_it = False
+                while not cli.c2s.empty():
+                    obj = cli.c2s.get()
+                    obj_op = obj['op']                    
+                    if obj_op == 'finalized':
+                        terminate_it = True
+                        break
+                        
+                if self.no_response_time_sec != 0 and (time.time() - cli.sent_time) > self.no_response_time_sec:                
+                    terminate_it = True
+                    
+                if terminate_it:
+                    cli.state = 2
+                    cli.kill()
+                    
+            if all ([cli.state == 2 for cli in self.clis]):
+                break
+        
+        #finalizing host logic and return result
+        self.on_clients_finalized()
+         
+        return self.get_result()

joblib/__init__.py

@@ -0,0 +1,2 @@
+from .SubprocessorBase import Subprocessor
+from .SubprocessFunctionCaller import SubprocessFunctionCaller

main.py

@@ -1,5 +1,6 @@
 import os
 import sys
+import time
 import argparse
 from utils import Path_utils
 from utils import os_utils
@@ -12,35 +13,26 @@ class fixPathAction(argparse.Action):
     def __call__(self, parser, namespace, values, option_string=None):
         setattr(namespace, self.dest, os.path.abspath(os.path.expanduser(values)))
 
-def str2bool(v):
-    if v.lower() in ('yes', 'true', 't', 'y', '1'):
-        return True
-    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
-        return False
-    else:
-        raise argparse.ArgumentTypeError('Boolean value expected.')
-        
 if __name__ == "__main__":
     os_utils.set_process_lowest_prio()   
     parser = argparse.ArgumentParser()    
-    parser.add_argument('--tf-suppress-std', action="store_true", dest="tf_suppress_std", default=False, help="Suppress tensorflow initialization info. May not works on some python builds such as anaconda python 3.6.4. If you can fix it, you are welcome.")
+
- 
     subparsers = parser.add_subparsers()
     
     def process_extract(arguments):
-        from mainscripts import Extractor        
+        from mainscripts import Extractor      
-        Extractor.main (
+        Extractor.main( arguments.input_dir, 
-            input_dir=arguments.input_dir, 
+                        arguments.output_dir, 
-            output_dir=arguments.output_dir, 
+                        arguments.debug,
-            debug=arguments.debug,
+                        arguments.detector,   
-            face_type=arguments.face_type,
+                        arguments.manual_fix,
-            detector=arguments.detector,
+                        arguments.manual_output_debug_fix,
-            multi_gpu=arguments.multi_gpu,
+                        arguments.manual_window_size, 
-            cpu_only=arguments.cpu_only,
+                        face_type=arguments.face_type,
-            manual_fix=arguments.manual_fix,
+                        device_args={'cpu_only'  : arguments.cpu_only,
-            manual_output_debug_fix=arguments.manual_output_debug_fix,
+                                     'multi_gpu' : arguments.multi_gpu,
-            manual_window_size=arguments.manual_window_size
+                                    }
-            )
+                      )
         
     extract_parser = subparsers.add_parser( "extract", help="Extract the faces from a pictures.")
     extract_parser.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
@@ -79,24 +71,26 @@ if __name__ == "__main__":
     util_parser.add_argument('--add-landmarks-debug-images', action="store_true", dest="add_landmarks_debug_images", default=False, help="Add landmarks debug image for aligned faces.")
     util_parser.set_defaults (func=process_util)
     
-    def process_train(arguments):      
+    def process_train(arguments):
-        from mainscripts import Trainer
+        args = {'training_data_src_dir'  : arguments.training_data_src_dir, 
-        Trainer.main (
+                'training_data_dst_dir'  : arguments.training_data_dst_dir, 
-            training_data_src_dir=arguments.training_data_src_dir, 
+                'model_path'             : arguments.model_dir,
-            training_data_dst_dir=arguments.training_data_dst_dir, 
+                'model_name'             : arguments.model_name,
-            model_path=arguments.model_dir, 
+                'no_preview'             : arguments.no_preview,
-            model_name=arguments.model_name,
+                'debug'                  : arguments.debug,                
-            debug              = arguments.debug,
+                }                
-            #**options
+        device_args = {'cpu_only'  : arguments.cpu_only,
-            force_gpu_idx = arguments.force_gpu_idx,
+                       'force_gpu_idx' : arguments.force_gpu_idx,
-            cpu_only      = arguments.cpu_only
+                       }
-            )
+        from mainscripts import Trainer           
-        
+        Trainer.main(args, device_args)
+
     train_parser = subparsers.add_parser( "train", help="Trainer") 
     train_parser.add_argument('--training-data-src-dir', required=True, action=fixPathAction, dest="training_data_src_dir", help="Dir of src-set.")
     train_parser.add_argument('--training-data-dst-dir', required=True, action=fixPathAction, dest="training_data_dst_dir", help="Dir of dst-set.")
     train_parser.add_argument('--model-dir', required=True, action=fixPathAction, dest="model_dir", help="Model dir.")
     train_parser.add_argument('--model', required=True, dest="model_name", choices=Path_utils.get_all_dir_names_startswith ( Path(__file__).parent / 'models' , 'Model_'), help="Type of model")
+    train_parser.add_argument('--no-preview', action="store_true", dest="no_preview", default=False, help="Disable preview window.")
     train_parser.add_argument('--debug', action="store_true", dest="debug", default=False, help="Debug samples.")  
     train_parser.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Train on CPU.")
     train_parser.add_argument('--force-gpu-idx', type=int, dest="force_gpu_idx", default=-1, help="Force to choose this GPU idx.")
@@ -104,17 +98,18 @@ if __name__ == "__main__":
     train_parser.set_defaults (func=process_train)
     
     def process_convert(arguments):
+        args = {'input_dir'   : arguments.input_dir, 
+                'output_dir'  : arguments.output_dir, 
+                'aligned_dir' : arguments.aligned_dir,
+                'model_dir'   : arguments.model_dir,
+                'model_name'  : arguments.model_name,
+                'debug'       : arguments.debug,                
+                }                
+        device_args = {'cpu_only'  : arguments.cpu_only,
+                       'force_gpu_idx' : arguments.force_gpu_idx,
+                       }
         from mainscripts import Converter
-        Converter.main (
+        Converter.main (args, device_args)
-            input_dir=arguments.input_dir, 
-            output_dir=arguments.output_dir, 
-            aligned_dir=arguments.aligned_dir,
-            model_dir=arguments.model_dir, 
-            model_name=arguments.model_name, 
-            debug = arguments.debug,
-            force_gpu_idx = arguments.force_gpu_idx,
-            cpu_only = arguments.cpu_only
-            )
         
     convert_parser = subparsers.add_parser( "convert", help="Converter") 
     convert_parser.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
@@ -134,14 +129,27 @@ if __name__ == "__main__":
     parser.set_defaults(func=bad_args)
     
     arguments = parser.parse_args()
-    if arguments.tf_suppress_std:
-        os.environ['TF_SUPPRESS_STD'] = '1'
 
     #os.environ['force_plaidML'] = '1'
     
     arguments.func(arguments)
 
     print ("Done.")
+
+    """
+    Suppressing error with keras 2.2.4+ on python exit:
+    
+        Exception ignored in: <bound method BaseSession._Callable.__del__ of <tensorflow.python.client.session.BaseSession._Callable object at 0x000000001BDEA9B0>>
+        Traceback (most recent call last):
+        File "D:\DeepFaceLab\_internal\bin\lib\site-packages\tensorflow\python\client\session.py", line 1413, in __del__
+        AttributeError: 'NoneType' object has no attribute 'raise_exception_on_not_ok_status'
+        
+    reproduce: https://github.com/keras-team/keras/issues/11751 ( still no solution )
+    """
+    outnull_file = open(os.devnull, 'w')
+    os.dup2 ( outnull_file.fileno(), sys.stderr.fileno() )
+    sys.stderr = outnull_file     
+    
     
 '''
 import code

mainscripts/Converter.py

@@ -4,7 +4,6 @@ import traceback
 from pathlib import Path
 from utils import Path_utils
 import cv2
-from tqdm import tqdm
 from utils.DFLPNG import DFLPNG
 from utils.DFLJPG import DFLJPG
 from utils.cv2_utils import *
@@ -13,219 +12,196 @@ import shutil
 import numpy as np
 import time
 import multiprocessing
-from models import ConverterBase
+from converters import Converter
+from joblib import Subprocessor, SubprocessFunctionCaller
+from interact import interact as io
 
-class model_process_predictor(object):
+class ConvertSubprocessor(Subprocessor):
-    def __init__(self, sq, cq, lock):
+    class Cli(Subprocessor.Cli):
-        self.sq = sq
-        self.cq = cq
-        self.lock = lock
         
-    def __call__(self, face):
+        #override
-        self.lock.acquire()
+        def on_initialize(self, client_dict):
-        
+            io.log_info ('Running on %s.' % (client_dict['device_name']) )
-        self.sq.put ( {'op': 'predict', 'face' : face} )
+            self.device_idx  = client_dict['device_idx']
-        while True:
+            self.device_name = client_dict['device_name']
-            if not self.cq.empty():
+            self.converter   = client_dict['converter']
-                obj = self.cq.get()
+            self.output_path = Path(client_dict['output_dir']) if 'output_dir' in client_dict.keys() else None        
-                obj_op = obj['op']
+            self.alignments  = client_dict['alignments']
-                if obj_op == 'predict_result':
+            self.debug       = client_dict['debug']
-                    self.lock.release()
-                    return obj['result']
-            time.sleep(0.005)
-        
-def model_process(stdin_fd, model_name, model_dir, in_options, sq, cq):
-    sys.stdin = os.fdopen(stdin_fd)
-    
-    try:    
-        model_path = Path(model_dir)
-        
-        import models 
-        model = models.import_model(model_name)(model_path, **in_options)
-        converter = model.get_converter(**in_options)
-        converter.dummy_predict()
-        
-        cq.put ( {'op':'init', 'converter' : converter.copy_and_set_predictor( None ) } )
 
-        while True:
+            from nnlib import nnlib          
-            while not sq.empty():
+            #model process ate all GPU mem,
-                obj = sq.get()
+            #so we cannot use GPU for any TF operations in converter processes
-                obj_op = obj['op']
+            #therefore forcing active_DeviceConfig to CPU only
-                if obj_op == 'predict':
+            nnlib.active_DeviceConfig = nnlib.DeviceConfig (cpu_only=True)
-                    result = converter.predictor ( obj['face'] )
+            
-                    cq.put ( {'op':'predict_result', 'result':result} )
+            return None
-            time.sleep(0.005)        
+            
-    except Exception as e:
+        #override
-        print ( 'Error: %s' % (str(e)))
+        def process_data(self, data):
-        traceback.print_exc()
+            filename_path = Path(data)
-    
+            files_processed = 1
-    
+            faces_processed = 0
-    
+                
-from utils.SubprocessorBase import SubprocessorBase
+            output_filename_path = self.output_path / (filename_path.stem + '.png')
-class ConvertSubprocessor(SubprocessorBase):
 
+            if self.converter.type == Converter.TYPE_FACE and filename_path.stem not in self.alignments.keys():                    
+                if not self.debug:
+                    self.log_info ( 'no faces found for %s, copying without faces' % (filename_path.name) )
+                    shutil.copy ( str(filename_path), str(output_filename_path) )
+            else:
+                image = (cv2_imread(str(filename_path)) / 255.0).astype(np.float32)
+
+                if self.converter.type == Converter.TYPE_IMAGE:
+                    image = self.converter.convert_image(image, None, self.debug)
+                    if self.debug:
+                        raise NotImplementedError
+                        #for img in image:
+                        #    io.show_image ('Debug convert', img )
+                        #    cv2.waitKey(0)
+                    faces_processed = 1
+                elif self.converter.type == Converter.TYPE_IMAGE_WITH_LANDMARKS:
+                    if filename_path.suffix == '.png':
+                        dflimg = DFLPNG.load( str(filename_path) )
+                    elif filename_path.suffix == '.jpg':
+                        dflimg = DFLJPG.load ( str(filename_path) )
+                    else:
+                        dflimg = None
+                        
+                    if dflimg is not None:
+                        image_landmarks = dflimg.get_landmarks()
+                                
+                        image = self.converter.convert_image(image, image_landmarks, self.debug)
+                        
+                        if self.debug:
+                            raise NotImplementedError
+                            #for img in image:
+                            #    io.show_image ('Debug convert', img )
+                            #    cv2.waitKey(0)
+                        faces_processed = 1
+                    else:
+                        self.log_err ("%s is not a dfl image file" % (filename_path.name) ) 
+                        
+                elif self.converter.type == Converter.TYPE_FACE:
+                    faces = self.alignments[filename_path.stem]
+                    
+                    if self.debug:
+                        debug_images = []
+                        
+                    for face_num, image_landmarks in enumerate(faces):
+                        try:
+                            if self.debug:
+                                self.log_info ( '\nConverting face_num [%d] in file [%s]' % (face_num, filename_path) )
+
+                            if self.debug:
+                                debug_images += self.converter.convert_face(image, image_landmarks, self.debug)
+                            else:
+                                image = self.converter.convert_face(image, image_landmarks, self.debug) 
+                                
+                        except Exception as e:
+                            self.log_info ( 'Error while converting face_num [%d] in file [%s]: %s' % (face_num, filename_path, str(e)) )
+                            traceback.print_exc()
+                            
+                    if self.debug:
+                        return (1, debug_images)
+                    
+                    faces_processed = len(faces)
+                        
+                if not self.debug:
+                    cv2_imwrite (str(output_filename_path), (image*255).astype(np.uint8) )
+                
+                
+            return (0, files_processed, faces_processed)
+            
+        #overridable
+        def get_data_name (self, data):
+            #return string identificator of your data
+            return data
+        
     #override
-    def __init__(self, converter, input_path_image_paths, output_path, alignments, debug = False, **in_options): 
+    def __init__(self, converter, input_path_image_paths, output_path, alignments, debug = False): 
-        super().__init__('Converter', 86400 if debug == True else 60)    
+        super().__init__('Converter', ConvertSubprocessor.Cli, 86400 if debug == True else 60)    
-        self.converter = converter
+
-        self.input_path_image_paths = input_path_image_paths
+        self.converter = converter        
+        self.host_processor, self.cli_func = SubprocessFunctionCaller.make_pair ( self.converter.predictor_func )
+        self.process_converter = self.converter.copy_and_set_predictor(self.cli_func)
+        
+        self.input_data = self.input_path_image_paths = input_path_image_paths
         self.output_path = output_path
         self.alignments = alignments
         self.debug = debug
-        self.in_options = in_options
+        
-        self.input_data = self.input_path_image_paths
         self.files_processed = 0
         self.faces_processed = 0
         
     #override
     def process_info_generator(self):
         r = [0] if self.debug else range( min(multiprocessing.cpu_count(), 6) )
-        
-        
 
         for i in r:
             yield 'CPU%d' % (i), {}, {'device_idx': i,
                                       'device_name': 'CPU%d' % (i), 
-                                      'converter' : self.converter, 
+                                      'converter' : self.process_converter, 
                                       'output_dir' : str(self.output_path), 
                                       'alignments' : self.alignments,
-                                      'debug': self.debug,
+                                      'debug': self.debug
-                                      'in_options': self.in_options
                                       }
-     
+
-    #override
+    #overridable optional
-    def get_no_process_started_message(self):
+    def on_clients_initialized(self):
-        return 'Unable to start CPU processes.'
+        if self.debug:
+            io.named_window ("Debug convert")
+                
+        io.progress_bar ("Converting", len (self.input_data) )
+        
+    #overridable optional
+    def on_clients_finalized(self):
+        io.progress_bar_close()
+        
+        if self.debug:
+            io.destroy_all_windows()
         
     #override
-    def onHostGetProgressBarDesc(self):
+    def get_data(self, host_dict):
-        return "Converting"
-        
-    #override
-    def onHostGetProgressBarLen(self):
-        return len (self.input_data)
-        
-    #override
-    def onHostGetData(self, host_dict):
         if len (self.input_data) > 0:
             return self.input_data.pop(0)            
         return None
     
     #override
-    def onHostDataReturn (self, host_dict, data):
+    def on_data_return (self, host_dict, data):
         self.input_data.insert(0, data)   
-        
-    #overridable
-    def onClientGetDataName (self, data):
-        #return string identificator of your data
-        return data
-        
-    #override
-    def onClientInitialize(self, client_dict):
-        print ('Running on %s.' % (client_dict['device_name']) )
-        self.device_idx  = client_dict['device_idx']
-        self.device_name = client_dict['device_name']
-        self.converter   = client_dict['converter']
-        self.output_path = Path(client_dict['output_dir']) if 'output_dir' in client_dict.keys() else None        
-        self.alignments  = client_dict['alignments']
-        self.debug       = client_dict['debug']
-        
-        from nnlib import nnlib          
-        #model process ate all GPU mem,
-        #so we cannot use GPU for any TF operations in converter processes (for example image_utils.TFLabConverter)
-        #therefore forcing active_DeviceConfig to CPU only
-        nnlib.active_DeviceConfig = nnlib.DeviceConfig (cpu_only=True)
-        
-        return None
 
     #override
-    def onClientFinalize(self):
+    def on_result (self, host_dict, data, result):
-        pass
+        if result[0] == 0:
+            self.files_processed += result[0]
+            self.faces_processed += result[1]
+        elif result[0] == 1:
+            for img in result[1]:
+                io.show_image ('Debug convert', (img*255).astype(np.uint8) )
+                io.wait_any_key()
+        io.progress_bar_inc(1)
+    
+    #override
+    def on_tick(self):
+        self.host_processor.process_messages()
         
     #override
-    def onClientProcessData(self, data):
+    def get_result(self):
-        filename_path = Path(data)
-
-        files_processed = 1
-        faces_processed = 0
-            
-        output_filename_path = self.output_path / (filename_path.stem + '.png')
-
-        if self.converter.get_mode() == ConverterBase.MODE_FACE and filename_path.stem not in self.alignments.keys():                    
-            if not self.debug:
-                print ( 'no faces found for %s, copying without faces' % (filename_path.name) )
-                shutil.copy ( str(filename_path), str(output_filename_path) )
-        else:
-            image = (cv2_imread(str(filename_path)) / 255.0).astype(np.float32)
-
-            if self.converter.get_mode() == ConverterBase.MODE_IMAGE:
-                image = self.converter.convert_image(image, None, self.debug)
-                if self.debug:
-                    for img in image:
-                        cv2.imshow ('Debug convert', img )
-                        cv2.waitKey(0)
-                faces_processed = 1
-            elif self.converter.get_mode() == ConverterBase.MODE_IMAGE_WITH_LANDMARKS:
-                if filename_path.suffix == '.png':
-                    dflimg = DFLPNG.load( str(filename_path), throw_on_no_embedded_data=True )
-                elif filename_path.suffix == '.jpg':
-                    dflimg = DFLJPG.load ( str(filename_path), throw_on_no_embedded_data=True )
-                else:
-                    raise Exception ("%s is not a dfl image file" % (filename_path.name) ) 
-            
-                image_landmarks = dflimg.get_landmarks()
-                        
-                image = self.converter.convert_image(image, image_landmarks, self.debug)
-                if self.debug:
-                    for img in image:
-                        cv2.imshow ('Debug convert', img )
-                        cv2.waitKey(0)
-                faces_processed = 1
-            elif self.converter.get_mode() == ConverterBase.MODE_FACE:
-                faces = self.alignments[filename_path.stem]
-                for face_num, image_landmarks in enumerate(faces):
-                    try:
-                        if self.debug:
-                            print ( '\nConverting face_num [%d] in file [%s]' % (face_num, filename_path) )
-                        
-                        image = self.converter.convert_face(image, image_landmarks, self.debug)     
-                        if self.debug:
-                            for img in image:
-                                cv2.imshow ('Debug convert', (img*255).astype(np.uint8) )
-                                cv2.waitKey(0)
-                    except Exception as e:
-                        print ( 'Error while converting face_num [%d] in file [%s]: %s' % (face_num, filename_path, str(e)) )
-                        traceback.print_exc()
-                faces_processed = len(faces)
-                    
-            if not self.debug:
-                cv2_imwrite (str(output_filename_path), (image*255).astype(np.uint8) )
-            
-            
-        return (files_processed, faces_processed)
-        
-    #override
-    def onHostResult (self, host_dict, data, result):
-        self.files_processed += result[0]
-        self.faces_processed += result[1]    
-        return 1
-             
-    #override
-    def onFinalizeAndGetResult(self):
         return self.files_processed, self.faces_processed
         
-def main (input_dir, output_dir, model_dir, model_name, aligned_dir=None, **in_options):
+def main (args, device_args):
-    print ("Running converter.\r\n")
+    io.log_info ("Running converter.\r\n")
     
-    debug = in_options['debug']
+    aligned_dir = args.get('aligned_dir', None)
     
     try:
-        input_path = Path(input_dir)
+        input_path = Path(args['input_dir'])
-        output_path = Path(output_dir)        
+        output_path = Path(args['output_dir'])
-        model_path = Path(model_dir)
+        model_path = Path(args['model_dir'])
         
         if not input_path.exists():
-            print('Input directory not found. Please ensure it exists.')
+            io.log_err('Input directory not found. Please ensure it exists.')
             return
 
         if output_path.exists():
@@ -233,126 +209,116 @@ def main (input_dir, output_dir, model_dir, model_name, aligned_dir=None, **in_o
                 Path(filename).unlink()
         else:
             output_path.mkdir(parents=True, exist_ok=True)
-            
+         
-        
-            
         if not model_path.exists():
-            print('Model directory not found. Please ensure it exists.')
+            io.log_err('Model directory not found. Please ensure it exists.')
             return
-   
+            
-        model_sq = multiprocessing.Queue()
+        import models 
-        model_cq = multiprocessing.Queue()
+        model = models.import_model( args['model_name'] )(model_path, device_args=device_args)
-        model_lock = multiprocessing.Lock()
+        converter = model.get_converter()
-        model_p = multiprocessing.Process(target=model_process, args=( sys.stdin.fileno(), model_name, model_dir, in_options, model_sq, model_cq))
+        converter.dummy_predict()
-        model_p.start()
-        
-        while True:
-            if not model_cq.empty():
-                obj = model_cq.get()
-                obj_op = obj['op']
-                if obj_op == 'init':
-                    converter = obj['converter']
-                    break
 
         alignments = None
         
-        if converter.get_mode() == ConverterBase.MODE_FACE:            
+        if converter.type == Converter.TYPE_FACE:
             if aligned_dir is None:
-                print('Aligned directory not found. Please ensure it exists.')
+                io.log_err('Aligned directory not found. Please ensure it exists.')
                 return 
                 
             aligned_path = Path(aligned_dir)
             if not aligned_path.exists():
-                print('Aligned directory not found. Please ensure it exists.')
+                io.log_err('Aligned directory not found. Please ensure it exists.')
                 return 
                 
             alignments = {}
             
             aligned_path_image_paths = Path_utils.get_image_paths(aligned_path)
-            for filepath in tqdm(aligned_path_image_paths, desc="Collecting alignments", ascii=True ):
+            for filepath in io.progress_bar_generator(aligned_path_image_paths, "Collecting alignments"):
                 filepath = Path(filepath)
                 
                 if filepath.suffix == '.png':
-                    dflimg = DFLPNG.load( str(filepath), print_on_no_embedded_data=True )
+                    dflimg = DFLPNG.load( str(filepath) )
                 elif filepath.suffix == '.jpg':
-                    dflimg = DFLJPG.load ( str(filepath), print_on_no_embedded_data=True )
+                    dflimg = DFLJPG.load ( str(filepath) )
                 else:
-                    print ("%s is not a dfl image file" % (filepath.name) ) 
+                    dflimg = None
-                
+                    
+                if dflimg is None:
+                    io.log_err ("%s is not a dfl image file" % (filepath.name) ) 
+                    continue
+                    
                 source_filename_stem = Path( dflimg.get_source_filename() ).stem
                 if source_filename_stem not in alignments.keys():
                     alignments[ source_filename_stem ] = []
 
                 alignments[ source_filename_stem ].append (dflimg.get_source_landmarks())
-        
+
-            
-            #interpolate landmarks
-            #from facelib import LandmarksProcessor
-            #from facelib import FaceType
-            #a = sorted(alignments.keys())
-            #a_len = len(a)
-            #
-            #box_pts = 3            
-            #box = np.ones(box_pts)/box_pts
-            #for i in range( a_len ):
-            #    if i >= box_pts and i <= a_len-box_pts-1:
-            #        af0 = alignments[ a[i] ][0] ##first face
-            #        m0 = LandmarksProcessor.get_transform_mat (af0, 256, face_type=FaceType.FULL)      
-            #    
-            #        points = []
-            #        
-            #        for j in range(-box_pts, box_pts+1):
-            #            af = alignments[ a[i+j] ][0] ##first face
-            #            m = LandmarksProcessor.get_transform_mat (af, 256, face_type=FaceType.FULL)                    
-            #            p = LandmarksProcessor.transform_points (af, m)
-            #            points.append (p)
-            #    
-            #        points = np.array(points)
-            #        points_len = len(points)
-            #        t_points = np.transpose(points, [1,0,2])
-            #        
-            #        p1 = np.array ( [ int(np.convolve(x[:,0], box, mode='same')[points_len//2]) for x in t_points ] )
-            #        p2 = np.array ( [ int(np.convolve(x[:,1], box, mode='same')[points_len//2]) for x in t_points ] )
-            #        
-            #        new_points = np.concatenate( [np.expand_dims(p1,-1),np.expand_dims(p2,-1)], -1 )
-            #        
-            #        alignments[ a[i] ][0]  = LandmarksProcessor.transform_points (new_points, m0, True).astype(np.int32)
-            
         files_processed, faces_processed = ConvertSubprocessor ( 
-                    converter              = converter.copy_and_set_predictor( model_process_predictor(model_sq,model_cq,model_lock) ), 
+                    converter              = converter,
                     input_path_image_paths = Path_utils.get_image_paths(input_path), 
                     output_path            = output_path,
-                    alignments             = alignments,  
+                    alignments             = alignments,
-                    **in_options ).process()
+                    debug                  = args.get('debug',False)
+                    ).run()
 
-        model_p.terminate()
+        model.finalize()
-        
-        '''            
-        if model_name == 'AVATAR':
-            output_path_image_paths = Path_utils.get_image_paths(output_path)
-            
-            last_ok_frame = -1
-            for filename in output_path_image_paths:
-                filename_path = Path(filename)
-                stem = Path(filename).stem
-                try:
-                    frame = int(stem)
-                except:
-                    raise Exception ('Aligned avatars must be created from indexed sequence files.')
-                    
-                if frame-last_ok_frame > 1:
-                    start = last_ok_frame + 1
-                    end = frame - 1
-                    
-                    print ("Filling gaps: [%d...%d]" % (start, end) )
-                    for i in range (start, end+1):                    
-                        shutil.copy ( str(filename), str( output_path / ('%.5d%s' % (i, filename_path.suffix ))  ) )
-                    
-                last_ok_frame = frame
-        '''
         
     except Exception as e:
         print ( 'Error: %s' % (str(e)))
         traceback.print_exc()
+        
+'''            
+if model_name == 'AVATAR':
+    output_path_image_paths = Path_utils.get_image_paths(output_path)
     
-   
+    last_ok_frame = -1
+    for filename in output_path_image_paths:
+        filename_path = Path(filename)
+        stem = Path(filename).stem
+        try:
+            frame = int(stem)
+        except:
+            raise Exception ('Aligned avatars must be created from indexed sequence files.')
+            
+        if frame-last_ok_frame > 1:
+            start = last_ok_frame + 1
+            end = frame - 1
+            
+            print ("Filling gaps: [%d...%d]" % (start, end) )
+            for i in range (start, end+1):                    
+                shutil.copy ( str(filename), str( output_path / ('%.5d%s' % (i, filename_path.suffix ))  ) )
+            
+        last_ok_frame = frame
+'''
+#interpolate landmarks
+#from facelib import LandmarksProcessor
+#from facelib import FaceType
+#a = sorted(alignments.keys())
+#a_len = len(a)
+#
+#box_pts = 3            
+#box = np.ones(box_pts)/box_pts
+#for i in range( a_len ):
+#    if i >= box_pts and i <= a_len-box_pts-1:
+#        af0 = alignments[ a[i] ][0] ##first face
+#        m0 = LandmarksProcessor.get_transform_mat (af0, 256, face_type=FaceType.FULL)      
+#    
+#        points = []
+#        
+#        for j in range(-box_pts, box_pts+1):
+#            af = alignments[ a[i+j] ][0] ##first face
+#            m = LandmarksProcessor.get_transform_mat (af, 256, face_type=FaceType.FULL)                    
+#            p = LandmarksProcessor.transform_points (af, m)
+#            points.append (p)
+#    
+#        points = np.array(points)
+#        points_len = len(points)
+#        t_points = np.transpose(points, [1,0,2])
+#        
+#        p1 = np.array ( [ int(np.convolve(x[:,0], box, mode='same')[points_len//2]) for x in t_points ] )
+#        p2 = np.array ( [ int(np.convolve(x[:,1], box, mode='same')[points_len//2]) for x in t_points ] )
+#        
+#        new_points = np.concatenate( [np.expand_dims(p1,-1),np.expand_dims(p2,-1)], -1 )
+#        
+#        alignments[ a[i] ][0]  = LandmarksProcessor.transform_points (new_points, m0, True).astype(np.int32)

mainscripts/Extractor.py

@@ -15,10 +15,137 @@ import facelib
 from facelib import FaceType
 from facelib import LandmarksProcessor
 from nnlib import nnlib
+from joblib import Subprocessor
+from interact import interact as io
+        
+class ExtractSubprocessor(Subprocessor):
+        
+    class Cli(Subprocessor.Cli):
 
-from utils.SubprocessorBase import SubprocessorBase
+        #override
-class ExtractSubprocessor(SubprocessorBase):
+        def on_initialize(self, client_dict):
+            self.log_info ('Running on %s.' % (client_dict['device_name']) )
+            self.type         = client_dict['type']
+            self.image_size   = client_dict['image_size']
+            self.face_type    = client_dict['face_type']
+            self.device_idx   = client_dict['device_idx']
+            self.cpu_only     = client_dict['device_type'] == 'CPU'
+            self.output_path  = Path(client_dict['output_dir']) if 'output_dir' in client_dict.keys() else None        
+            self.debug        = client_dict['debug']
+            self.detector     = client_dict['detector']
 
+            self.cached_image = (None, None)
+            
+            self.e = None
+            device_config = nnlib.DeviceConfig ( cpu_only=self.cpu_only, force_gpu_idx=self.device_idx, allow_growth=True)
+            if self.type == 'rects':
+                if self.detector is not None:
+                    if self.detector == 'mt':
+                        nnlib.import_all (device_config)
+                        self.e = facelib.MTCExtractor()                            
+                    elif self.detector == 'dlib':
+                        nnlib.import_dlib (device_config)
+                        self.e = facelib.DLIBExtractor(nnlib.dlib)
+                    else:
+                        raise ValueError ("Wrond detector type.")
+                        
+                    if self.e is not None:
+                        self.e.__enter__()
+                    
+            elif self.type == 'landmarks':
+                nnlib.import_all (device_config)
+                self.e = facelib.LandmarksExtractor(nnlib.keras)
+                self.e.__enter__()
+                
+            elif self.type == 'final':
+                pass
+                
+        #override
+        def on_finalize(self):
+            if self.e is not None:
+                self.e.__exit__()
+                
+        #override
+        def process_data(self, data):
+            filename_path = Path( data[0] )
+
+            filename_path_str = str(filename_path)
+            if self.cached_image[0] == filename_path_str:
+                image = self.cached_image[1]
+            else:
+                image = cv2_imread( filename_path_str )
+                self.cached_image = ( filename_path_str, image )
+            
+            if image is None:
+                self.log_err ( 'Failed to extract %s, reason: cv2_imread() fail.' % ( str(filename_path) ) )
+            else:
+                if self.type == 'rects':
+                    rects = self.e.extract_from_bgr (image)
+                    return [str(filename_path), rects]
+
+                elif self.type == 'landmarks':
+                    rects = data[1]   
+                    landmarks = self.e.extract_from_bgr (image, rects)                    
+                    return [str(filename_path), landmarks]
+
+                elif self.type == 'final':
+                    src_dflimg = None
+                    (h,w,c) = image.shape                
+                    if h == w:
+                        #extracting from already extracted jpg image?
+                        if filename_path.suffix == '.jpg':
+                            src_dflimg = DFLJPG.load ( str(filename_path) )
+                
+                    result = []
+                    faces = data[1]
+                    
+                    if self.debug:
+                        debug_output_file = '{}{}'.format( str(Path(str(self.output_path) + '_debug') / filename_path.stem),  '.jpg')
+                        debug_image = image.copy()
+                        
+                    for (face_idx, face) in enumerate(faces):         
+                        output_file = '{}_{}{}'.format(str(self.output_path / filename_path.stem), str(face_idx), '.jpg')
+                        
+                        rect = face[0]
+                        image_landmarks = np.array(face[1])
+
+                        if self.debug:
+                            LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, self.image_size, self.face_type)
+
+                        if self.face_type == FaceType.MARK_ONLY:                        
+                            face_image = image
+                            face_image_landmarks = image_landmarks
+                        else:
+                            image_to_face_mat = LandmarksProcessor.get_transform_mat (image_landmarks, self.image_size, self.face_type)       
+                            face_image = cv2.warpAffine(image, image_to_face_mat, (self.image_size, self.image_size), cv2.INTER_LANCZOS4)
+                            face_image_landmarks = LandmarksProcessor.transform_points (image_landmarks, image_to_face_mat)
+                        
+                        if src_dflimg is not None:
+                            #if extracting from dflimg just copy it in order not to lose quality
+                            shutil.copy ( str(filename_path), str(output_file) )
+                        else:
+                            cv2_imwrite(output_file, face_image, [int(cv2.IMWRITE_JPEG_QUALITY), 85] )
+
+                        DFLJPG.embed_data(output_file, face_type = FaceType.toString(self.face_type),
+                                                       landmarks = face_image_landmarks.tolist(),
+                                                       source_filename = filename_path.name,
+                                                       source_rect=  rect,
+                                                       source_landmarks = image_landmarks.tolist()
+                                            )  
+                            
+                        result.append (output_file)
+                        
+                    if self.debug:
+                        cv2_imwrite(debug_output_file, debug_image, [int(cv2.IMWRITE_JPEG_QUALITY), 50] )
+                        
+                    return result       
+            return None
+             
+        #overridable
+        def get_data_name (self, data):
+            #return string identificator of your data
+            return data[0]
+            
     #override
     def __init__(self, input_data, type, image_size, face_type, debug, multi_gpu=False, cpu_only=False, manual=False, manual_window_size=0, detector=None, output_path=None ): 
         self.input_data = input_data
@@ -35,34 +162,36 @@ class ExtractSubprocessor(SubprocessorBase):
         self.result = []
 
         no_response_time_sec = 60 if not self.manual else 999999
-        super().__init__('Extractor', no_response_time_sec)           
+        super().__init__('Extractor', ExtractSubprocessor.Cli, no_response_time_sec)
 
     #override
-    def onHostClientsInitialized(self):
+    def on_clients_initialized(self):
         if self.manual == True:
             self.wnd_name = 'Manual pass'
-            cv2.namedWindow(self.wnd_name)
+            io.named_window(self.wnd_name)
-
+            io.capture_mouse(self.wnd_name)
-            self.landmarks = None
+            io.capture_keys(self.wnd_name)
-            self.param_x = -1
-            self.param_y = -1
-            self.param_rect_size = -1
-            self.param = {'x': 0, 'y': 0, 'rect_size' : 100, 'rect_locked' : False, 'redraw_needed' : False }
             
-            def onMouse(event, x, y, flags, param):        
+            self.cache_original_image = (None, None)
-                if event == cv2.EVENT_MOUSEWHEEL:
+            self.cache_image = (None, None)
-                    mod = 1 if flags > 0 else -1 
+            self.cache_text_lines_img = (None, None)
-                    diff = 1 if param['rect_size'] <= 40 else np.clip(param['rect_size'] / 10, 1, 10)
+            
-                    param['rect_size'] = max (5, param['rect_size'] + diff*mod)                    
+            self.landmarks = None
-                elif event == cv2.EVENT_LBUTTONDOWN:
+            self.x = 0
-                    param['rect_locked'] = not param['rect_locked']
+            self.y = 0
-                    param['redraw_needed'] = True
+            self.rect_size = 100
-                elif not param['rect_locked']:
+            self.rect_locked = False
-                    param['x'] = x
+            self.redraw_needed = True
-                    param['y'] = y
+            
-                    
+        io.progress_bar (None, len (self.input_data))
-            cv2.setMouseCallback(self.wnd_name, onMouse, self.param)
+            
-    
+    #override
+    def on_clients_finalized(self):
+        if self.manual == True:
+            io.destroy_all_windows()
+            
+        io.progress_bar_close()
+        
     def get_devices_for_type (self, type, multi_gpu, cpu_only):
         if not cpu_only and (type == 'rects' or type == 'landmarks'):
             if type == 'rects' and self.detector == 'mt' and nnlib.device.backend == "plaidML":
@@ -86,8 +215,11 @@ class ExtractSubprocessor(SubprocessorBase):
                         yield (idx, 'GPU', dev_name, dev_vram)
 
         if cpu_only and (type == 'rects' or type == 'landmarks'):
-            for i in range( min(8, multiprocessing.cpu_count() // 2) ):
+            if self.manual:
-                yield (i, 'CPU', 'CPU%d' % (i), 0 )
+                yield (0, 'CPU', 'CPU', 0 )
+            else:
+                for i in range( min(8, multiprocessing.cpu_count() // 2) ):
+                    yield (i, 'CPU', 'CPU%d' % (i), 0 )
                 
         if type == 'final':
             for i in range( min(8, multiprocessing.cpu_count()) ):
@@ -108,25 +240,9 @@ class ExtractSubprocessor(SubprocessorBase):
             client_dict['device_name'] = device_name
             client_dict['device_type'] = device_type            
             yield client_dict['device_name'], {}, client_dict
-    
+
-                    
     #override
-    def get_no_process_started_message(self):
+    def get_data(self, host_dict):
-        if (self.type == 'rects' or self.type == 'landmarks'):
-            print ( 'You have no capable GPUs. Try to close programs which can consume VRAM, and run again.')
-        elif self.type == 'final':
-            print ( 'Unable to start CPU processes.')
-        
-    #override
-    def onHostGetProgressBarDesc(self):
-        return None
-        
-    #override
-    def onHostGetProgressBarLen(self):
-        return len (self.input_data)
-        
-    #override
-    def onHostGetData(self, host_dict):
         if not self.manual:
             if len (self.input_data) > 0:
                 return self.input_data.pop(0)    
@@ -146,33 +262,68 @@ class ExtractSubprocessor(SubprocessorBase):
                     if len(faces) > 0:
                         self.rect, self.landmarks = faces.pop()
                         
-                        self.param['rect_locked'] = True
+                        self.rect_locked = True
+                        self.redraw_needed = True
                         faces.clear()
-                        self.param['rect_size'] = ( self.rect[2] - self.rect[0] ) / 2
+                        self.rect_size = ( self.rect[2] - self.rect[0] ) / 2
-                        self.param['x'] = ( ( self.rect[0] + self.rect[2] ) / 2 ) * self.view_scale
+                        self.x = ( self.rect[0] + self.rect[2] ) / 2
-                        self.param['y'] = ( ( self.rect[1] + self.rect[3] ) / 2 ) * self.view_scale
+                        self.y = ( self.rect[1] + self.rect[3] ) / 2
-                        
 
                 if len(faces) == 0:
-                    self.original_image = cv2_imread(filename)
+                    if self.cache_original_image[0] == filename:
+                        self.original_image = self.cache_original_image[1]
+                    else:
+                        self.original_image = cv2_imread( filename )
+                        self.cache_original_image = (filename, self.original_image )
                     
                     (h,w,c) = self.original_image.shape
-                    self.view_scale = 1.0 if self.manual_window_size == 0 else self.manual_window_size / ( h * (16.0/9.0) ) 
+                    self.view_scale = 1.0 if self.manual_window_size == 0 else self.manual_window_size / ( h * (16.0/9.0) )
-                    self.original_image = cv2.resize (self.original_image, ( int(w*self.view_scale), int(h*self.view_scale) ), interpolation=cv2.INTER_LINEAR)    
-                    (h,w,c) = self.original_image.shape
                     
-                    self.text_lines_img = (image_utils.get_draw_text_lines ( self.original_image, (0,0, self.original_image.shape[1], min(100, self.original_image.shape[0]) ),
+                    if self.cache_image[0] == (h,w,c) + (self.view_scale,filename):
-                                                    [   'Match landmarks with face exactly. Click to confirm/unconfirm selection',
+                        self.image = self.cache_image[1]
-                                                        '[Enter] - confirm face landmarks and continue',
+                    else:                                            
-                                                        '[Space] - confirm as unmarked frame and continue',
+                        self.image = cv2.resize (self.original_image, ( int(w*self.view_scale), int(h*self.view_scale) ), interpolation=cv2.INTER_LINEAR)    
-                                                        '[Mouse wheel] - change rect',
+                        self.cache_image = ( (h,w,c) + (self.view_scale,filename), self.image )
-                                                        '[,] [.]- prev frame, next frame',
+ 
-                                                        '[Q] - skip remaining frames'
+                    (h,w,c) = self.image.shape
-                                                    ], (1, 1, 1) )*255).astype(np.uint8)           
+                    
+                    sh = (0,0, w, min(100, h) )                    
+                    if self.cache_text_lines_img[0] == sh:
+                        self.text_lines_img = self.cache_text_lines_img[1]
+                    else:
+                        self.text_lines_img = (image_utils.get_draw_text_lines ( self.image, sh,
+                                                        [   'Match landmarks with face exactly. Click to confirm/unconfirm selection',
+                                                            '[Enter] - confirm face landmarks and continue',
+                                                            '[Space] - confirm as unmarked frame and continue',
+                                                            '[Mouse wheel] - change rect',
+                                                            '[,] [.]- prev frame, next frame',
+                                                            '[Q] - skip remaining frames'
+                                                        ], (1, 1, 1) )*255).astype(np.uint8)
+                                                        
+                        self.cache_text_lines_img = (sh, self.text_lines_img)
 
                     while True:
-                        key = cv2.waitKey(1) & 0xFF
+                        new_x = self.x
+                        new_y = self.y
+                        new_rect_size = self.rect_size
                         
+                        mouse_events = io.get_mouse_events(self.wnd_name)
+                        for ev in mouse_events:
+                            (x, y, ev, flags) = ev
+                            if ev == io.EVENT_MOUSEWHEEL and not self.rect_locked:
+                                mod = 1 if flags > 0 else -1 
+                                diff = 1 if new_rect_size <= 40 else np.clip(new_rect_size / 10, 1, 10)
+                                new_rect_size = max (5, new_rect_size + diff*mod)                    
+                            elif ev == io.EVENT_LBUTTONDOWN:
+                                self.rect_locked = not self.rect_locked
+                                self.redraw_needed = True
+                            elif not self.rect_locked:
+                                new_x = np.clip (x, 0, w-1) / self.view_scale
+                                new_y = np.clip (y, 0, h-1) / self.view_scale
+                                
+                        key_events = io.get_key_events(self.wnd_name)
+                        key, = key_events[-1] if len(key_events) > 0 else (0,)
+
                         if key == ord('\r') or key == ord('\n'):
                             faces.append ( [(self.rect), self.landmarks] )
                             is_frame_done = True
@@ -183,200 +334,81 @@ class ExtractSubprocessor(SubprocessorBase):
                         elif key == ord('.'):
                             allow_remark_faces = True
                             # Only save the face if the rect is still locked
-                            if self.param['rect_locked']:
+                            if self.rect_locked:
                                 faces.append ( [(self.rect), self.landmarks] )
                             is_frame_done = True
                             break
                         elif key == ord(',')  and len(self.result) > 0:
                             # Only save the face if the rect is still locked
-                            if self.param['rect_locked']:
+                            if self.rect_locked:
                                 faces.append ( [(self.rect), self.landmarks] )
                             go_to_prev_frame = True
                             break
                         elif key == ord('q'):
                             skip_remaining = True
                             break
-                            
+                        
-                        new_param_x = np.clip (self.param['x'], 0, w-1) / self.view_scale
+                        if self.x != new_x or \
-                        new_param_y = np.clip (self.param['y'], 0, h-1) / self.view_scale
+                           self.y != new_y or \
-                        new_param_rect_size = self.param['rect_size']                        
+                           self.rect_size != new_rect_size or \
-                                
+                           self.redraw_needed:
-                        if self.param_x != new_param_x or \
+                            self.x = new_x
-                           self.param_y != new_param_y or \
+                            self.y = new_y
-                           self.param_rect_size != new_param_rect_size or \
+                            self.rect_size = new_rect_size
-                           self.param['redraw_needed']:
-                           
-                            self.param_x = new_param_x
-                            self.param_y = new_param_y
-                            self.param_rect_size = new_param_rect_size
 
-                            self.rect = ( int(self.param_x-self.param_rect_size), 
+                            self.rect = ( int(self.x-self.rect_size), 
-                                          int(self.param_y-self.param_rect_size), 
+                                          int(self.y-self.rect_size), 
-                                          int(self.param_x+self.param_rect_size), 
+                                          int(self.x+self.rect_size), 
-                                          int(self.param_y+self.param_rect_size) )
+                                          int(self.y+self.rect_size) )
                                           
                             return [filename, [self.rect]]
-                            
+                        
+                        io.process_messages(0.0001)
                 else:
                     is_frame_done = True
                     
                 if is_frame_done:
                     self.result.append ( data )
                     self.input_data.pop(0)
-                    self.inc_progress_bar(1)
+                    io.progress_bar_inc(1)
-                    self.param['redraw_needed'] = True
+                    self.redraw_needed = True
-                    self.param['rect_locked'] = False
+                    self.rect_locked = False
                 elif go_to_prev_frame:
                     self.input_data.insert(0, self.result.pop() )
-                    self.inc_progress_bar(-1)
+                    io.progress_bar_inc(-1)
                     allow_remark_faces = True
-                    self.param['redraw_needed'] = True
+                    self.redraw_needed = True
-                    self.param['rect_locked'] = False
+                    self.rect_locked = False
                 elif skip_remaining:
-                    if self.param['rect_locked']:
+                    if self.rect_locked:
                         faces.append ( [(self.rect), self.landmarks] )
                     while len(self.input_data) > 0:
                         self.result.append( self.input_data.pop(0) )
-                        self.inc_progress_bar(1)
+                        io.progress_bar_inc(1)
 
         return None
     
     #override
-    def onHostDataReturn (self, host_dict, data):
+    def on_data_return (self, host_dict, data):
         if not self.manual:
             self.input_data.insert(0, data)   
-        
-    #override
-    def onClientInitialize(self, client_dict):
-        self.safe_print ('Running on %s.' % (client_dict['device_name']) )
-        self.type         = client_dict['type']
-        self.image_size   = client_dict['image_size']
-        self.face_type    = client_dict['face_type']
-        self.device_idx   = client_dict['device_idx']
-        self.cpu_only     = client_dict['device_type'] == 'CPU'
-        self.output_path  = Path(client_dict['output_dir']) if 'output_dir' in client_dict.keys() else None        
-        self.debug        = client_dict['debug']
-        self.detector     = client_dict['detector']
-
-        self.e = None
-        device_config = nnlib.DeviceConfig ( cpu_only=self.cpu_only, force_gpu_idx=self.device_idx, allow_growth=True)
-        if self.type == 'rects':
-            if self.detector is not None:
-                if self.detector == 'mt':
-                    nnlib.import_all (device_config)
-                    self.e = facelib.MTCExtractor()                            
-                elif self.detector == 'dlib':
-                    nnlib.import_dlib (device_config)
-                    self.e = facelib.DLIBExtractor(nnlib.dlib)
-                self.e.__enter__()
-                
-        elif self.type == 'landmarks':
-            nnlib.import_all (device_config)
-            self.e = facelib.LandmarksExtractor(nnlib.keras)
-            self.e.__enter__()
-            
-        elif self.type == 'final':
-            pass
-        
-        return None
 
     #override
-    def onClientFinalize(self):
+    def on_result (self, host_dict, data, result):
-        if self.e is not None:
-            self.e.__exit__()
-        
-    #override
-    def onClientProcessData(self, data):
-        filename_path = Path( data[0] )
-
-        image = cv2_imread( str(filename_path) )
-
-        if image is None:
-            print ( 'Failed to extract %s, reason: cv2_imread() fail.' % ( str(filename_path) ) )
-        else:
-            if self.type == 'rects':
-                rects = self.e.extract_from_bgr (image)
-                return [str(filename_path), rects]
-
-            elif self.type == 'landmarks':
-                rects = data[1]   
-                landmarks = self.e.extract_from_bgr (image, rects)                    
-                return [str(filename_path), landmarks]
-
-            elif self.type == 'final':
-                src_dflimg = None
-                (h,w,c) = image.shape                
-                if h == w:
-                    #extracting from already extracted jpg image?
-                    if filename_path.suffix == '.jpg':
-                        src_dflimg = DFLJPG.load ( str(filename_path) )
-            
-                result = []
-                faces = data[1]
-                
-                if self.debug:
-                    debug_output_file = '{}{}'.format( str(Path(str(self.output_path) + '_debug') / filename_path.stem),  '.jpg')
-                    debug_image = image.copy()
-                    
-                for (face_idx, face) in enumerate(faces):         
-                    output_file = '{}_{}{}'.format(str(self.output_path / filename_path.stem), str(face_idx), '.jpg')
-                    
-                    rect = face[0]
-                    image_landmarks = np.array(face[1])
-
-                    if self.debug:
-                        LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, self.image_size, self.face_type)
-
-                    if self.face_type == FaceType.MARK_ONLY:                        
-                        face_image = image
-                        face_image_landmarks = image_landmarks
-                    else:
-                        image_to_face_mat = LandmarksProcessor.get_transform_mat (image_landmarks, self.image_size, self.face_type)       
-                        face_image = cv2.warpAffine(image, image_to_face_mat, (self.image_size, self.image_size), cv2.INTER_LANCZOS4)
-                        face_image_landmarks = LandmarksProcessor.transform_points (image_landmarks, image_to_face_mat)
-                    
-                    if src_dflimg is not None:
-                        #if extracting from dflimg just copy it in order not to lose quality
-                        shutil.copy ( str(filename_path), str(output_file) )
-                    else:
-                        cv2_imwrite(output_file, face_image, [int(cv2.IMWRITE_JPEG_QUALITY), 85] )
-
-                    DFLJPG.embed_data(output_file, face_type = FaceType.toString(self.face_type),
-                                                   landmarks = face_image_landmarks.tolist(),
-                                                   source_filename = filename_path.name,
-                                                   source_rect=  rect,
-                                                   source_landmarks = image_landmarks.tolist()
-                                        )  
-                        
-                    result.append (output_file)
-                    
-                if self.debug:
-                    cv2_imwrite(debug_output_file, debug_image, [int(cv2.IMWRITE_JPEG_QUALITY), 50] )
-                    
-                return result       
-        return None
-
-        #overridable
-    def onClientGetDataName (self, data):
-        #return string identificator of your data
-        return data[0]
-        
-    #override
-    def onHostResult (self, host_dict, data, result):
         if self.manual == True:
             self.landmarks = result[1][0][1]
                                         
-            (h,w,c) = self.original_image.shape
+            (h,w,c) = self.image.shape
-            image = cv2.addWeighted (self.original_image,1.0,self.text_lines_img,1.0,0)                                
+            image = cv2.addWeighted (self.image,1.0,self.text_lines_img,1.0,0)                                
             view_rect = (np.array(self.rect) * self.view_scale).astype(np.int).tolist()
             view_landmarks  = (np.array(self.landmarks) * self.view_scale).astype(np.int).tolist()
             
-            if self.param_rect_size <= 40:
+            if self.rect_size <= 40:
                 scaled_rect_size = h // 3 if w > h else w // 3
 
-                p1 = (self.param_x - self.param_rect_size, self.param_y - self.param_rect_size)
+                p1 = (self.x - self.rect_size, self.y - self.rect_size)
-                p2 = (self.param_x + self.param_rect_size, self.param_y - self.param_rect_size)
+                p2 = (self.x + self.rect_size, self.y - self.rect_size)
-                p3 = (self.param_x - self.param_rect_size, self.param_y + self.param_rect_size)
+                p3 = (self.x - self.rect_size, self.y + self.rect_size)
                 
                 wh = h if h < w else w                
                 np1 = (w / 2 - wh / 4, h / 2 - wh / 4)
@@ -389,12 +421,11 @@ class ExtractSubprocessor(SubprocessorBase):
      
             LandmarksProcessor.draw_rect_landmarks (image, view_rect, view_landmarks, self.image_size, self.face_type)
 
-            if self.param['rect_locked']:
+            if self.rect_locked:
                 LandmarksProcessor.draw_landmarks(image, view_landmarks, (255,255,0) )
-            self.param['redraw_needed'] = False
+            self.redraw_needed = False
             
-            cv2.imshow (self.wnd_name, image)
+            io.show_image (self.wnd_name, image)
-            return 0
         else:
             if self.type == 'rects':
                 self.result.append ( result )
@@ -403,98 +434,91 @@ class ExtractSubprocessor(SubprocessorBase):
             elif self.type == 'final':
                 self.result += result
                          
-            return 1
+            io.progress_bar_inc(1)
 
     #override
-    def onFinalizeAndGetResult(self):
+    def get_result(self):
-        if self.manual == True:
-            cv2.destroyAllWindows()
         return self.result
 
-class DeletedFilesSearcherSubprocessor(SubprocessorBase):
+
+class DeletedFilesSearcherSubprocessor(Subprocessor):
+    class Cli(Subprocessor.Cli):
+        #override
+        def on_initialize(self, client_dict):
+            self.debug_paths_stems = client_dict['debug_paths_stems']
+            return None
+            
+        #override
+        def process_data(self, data):  
+            input_path_stem = Path(data[0]).stem        
+            return any ( [ input_path_stem == d_stem for d_stem in self.debug_paths_stems] )
+            
+        #override
+        def get_data_name (self, data):
+            #return string identificator of your data
+            return data[0]
+            
     #override
     def __init__(self, input_paths, debug_paths ): 
         self.input_paths = input_paths
         self.debug_paths_stems = [ Path(d).stem for d in debug_paths]        
         self.result = []
-        super().__init__('DeletedFilesSearcherSubprocessor', 60)           
+        super().__init__('DeletedFilesSearcherSubprocessor', DeletedFilesSearcherSubprocessor.Cli, 60)           
         
     #override
     def process_info_generator(self):    
-        for i in range(0, min(multiprocessing.cpu_count(), 8) ):
+        for i in range(min(multiprocessing.cpu_count(), 8)):
-            yield 'CPU%d' % (i), {}, {'device_idx': i,
+            yield 'CPU%d' % (i), {}, {'debug_paths_stems' : self.debug_paths_stems}
-                                      'device_name': 'CPU%d' % (i), 
-                                      'debug_paths_stems' : self.debug_paths_stems
-                                      }
 
     #override
-    def get_no_process_started_message(self):
+    def on_clients_initialized(self):
-        print ( 'Unable to start CPU processes.')
+        io.progress_bar ("Searching deleted files", len (self.input_paths))
-        
+            
     #override
-    def onHostGetProgressBarDesc(self):
+    def on_clients_finalized(self):
-        return "Searching deleted files"
+        io.progress_bar_close()
-        
+
     #override
-    def onHostGetProgressBarLen(self):
+    def get_data(self, host_dict):
-        return len (self.input_paths)
-        
-    #override
-    def onHostGetData(self, host_dict):
         if len (self.input_paths) > 0:        
             return [self.input_paths.pop(0)]        
         return None
     
     #override
-    def onHostDataReturn (self, host_dict, data):
+    def on_data_return (self, host_dict, data):
         self.input_paths.insert(0, data[0])   
-        
+     
     #override
-    def onClientInitialize(self, client_dict):
+    def on_result (self, host_dict, data, result):
-        self.debug_paths_stems = client_dict['debug_paths_stems']
-        return None
-
-    #override
-    def onClientProcessData(self, data):  
-        input_path_stem = Path(data[0]).stem        
-        return any ( [ input_path_stem == d_stem for d_stem in self.debug_paths_stems] )
-
-    #override
-    def onClientGetDataName (self, data):
-        #return string identificator of your data
-        return data[0]
-        
-    #override
-    def onHostResult (self, host_dict, data, result):
         if result == False:
             self.result.append( data[0] )
-        return 1
+        io.progress_bar_inc(1)
 
     #override
-    def onFinalizeAndGetResult(self):
+    def get_result(self):
         return self.result
-        
-'''
-detector
-    'dlib'
-    'mt'
-    'manual'
-
-face_type
-    'full_face'
-    'avatar'
-'''
-def main (input_dir, output_dir, debug, detector='mt', multi_gpu=True, cpu_only=False, manual_fix=False, manual_output_debug_fix=False, manual_window_size=1368, image_size=256, face_type='full_face'):
-    print ("Running extractor.\r\n")
 
+def main(input_dir,
+         output_dir,
+         debug=False,
+         detector='mt',
+         manual_fix=False,
+         manual_output_debug_fix=False,
+         manual_window_size=1368,
+         image_size=256,
+         face_type='full_face',
+         device_args={}):
+         
     input_path = Path(input_dir)
     output_path = Path(output_dir)
     face_type = FaceType.fromString(face_type)
+    
+    multi_gpu = device_args.get('multi_gpu', False)
+    cpu_only = device_args.get('cpu_only', False)
 
     if not input_path.exists():
-        print('Input directory not found. Please ensure it exists.')
+        raise ValueError('Input directory not found. Please ensure it exists.')
-        return
+    
-        
     if output_path.exists():
         if not manual_output_debug_fix:
             for filename in Path_utils.get_image_paths(output_path):
@@ -505,19 +529,17 @@ def main (input_dir, output_dir, debug, detector='mt', multi_gpu=True, cpu_only=
     if manual_output_debug_fix:
         debug = True
         detector = 'manual'
-        print('Performing re-extract frames which were deleted from _debug directory.')
+        io.log_info('Performing re-extract frames which were deleted from _debug directory.')
         
-    input_path_image_paths = Path_utils.get_image_unique_filestem_paths(input_path, verbose=True)
+    input_path_image_paths = Path_utils.get_image_unique_filestem_paths(input_path, verbose_print_func=io.log_info)
-    
     if debug:
         debug_output_path = Path(str(output_path) + '_debug')
         
         if manual_output_debug_fix:
             if not debug_output_path.exists():
-                print ("%s not found " % ( str(debug_output_path) ))
+                raise ValueError("%s not found " % ( str(debug_output_path) ))
-                return
+
-            
+            input_path_image_paths = DeletedFilesSearcherSubprocessor (input_path_image_paths, Path_utils.get_image_paths(debug_output_path) ).run()
-            input_path_image_paths = DeletedFilesSearcherSubprocessor ( input_path_image_paths, Path_utils.get_image_paths(debug_output_path) ).process()
             input_path_image_paths = sorted (input_path_image_paths)            
         else:
             if debug_output_path.exists():
@@ -530,29 +552,29 @@ def main (input_dir, output_dir, debug, detector='mt', multi_gpu=True, cpu_only=
     faces_detected = 0
     if images_found != 0:    
         if detector == 'manual':
-            print ('Performing manual extract...')
+            io.log_info ('Performing manual extract...')
-            extracted_faces = ExtractSubprocessor ([ (filename,[]) for filename in input_path_image_paths ], 'landmarks', image_size, face_type, debug, cpu_only=cpu_only, manual=True, manual_window_size=manual_window_size).process()
+            extracted_faces = ExtractSubprocessor ([ (filename,[]) for filename in input_path_image_paths ], 'landmarks', image_size, face_type, debug, cpu_only=cpu_only, manual=True, manual_window_size=manual_window_size).run()
         else:
-            print ('Performing 1st pass...')
+            io.log_info ('Performing 1st pass...')
-            extracted_rects = ExtractSubprocessor ([ (x,) for x in input_path_image_paths ], 'rects', image_size, face_type, debug, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, detector=detector).process()
+            extracted_rects = ExtractSubprocessor ([ (x,) for x in input_path_image_paths ], 'rects', image_size, face_type, debug, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, detector=detector).run()
                 
-            print ('Performing 2nd pass...')
+            io.log_info ('Performing 2nd pass...')
-            extracted_faces = ExtractSubprocessor (extracted_rects, 'landmarks', image_size, face_type, debug, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False).process()
+            extracted_faces = ExtractSubprocessor (extracted_rects, 'landmarks', image_size, face_type, debug, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False).run()
                 
             if manual_fix:
-                print ('Performing manual fix...')
+                io.log_info ('Performing manual fix...')
                 
                 if all ( np.array ( [ len(data[1]) > 0 for data in extracted_faces] ) == True ):
-                    print ('All faces are detected, manual fix not needed.')
+                    io.log_info ('All faces are detected, manual fix not needed.')
                 else:
-                    extracted_faces = ExtractSubprocessor (extracted_faces, 'landmarks', image_size, face_type, debug, manual=True, manual_window_size=manual_window_size).process()
+                    extracted_faces = ExtractSubprocessor (extracted_faces, 'landmarks', image_size, face_type, debug, manual=True, manual_window_size=manual_window_size).run()
 
         if len(extracted_faces) > 0:
-            print ('Performing 3rd pass...')
+            io.log_info ('Performing 3rd pass...')
-            final_imgs_paths = ExtractSubprocessor (extracted_faces, 'final', image_size, face_type, debug, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, output_path=output_path).process()
+            final_imgs_paths = ExtractSubprocessor (extracted_faces, 'final', image_size, face_type, debug, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, output_path=output_path).run()
             faces_detected = len(final_imgs_paths)
             
-    print('-------------------------')
+    io.log_info ('-------------------------')
-    print('Images found:        %d' % (images_found) )
+    io.log_info ('Images found:        %d' % (images_found) )
-    print('Faces detected:      %d' % (faces_detected) )
+    io.log_info ('Faces detected:      %d' % (faces_detected) )
-    print('-------------------------')
+    io.log_info ('-------------------------')

mainscripts/Trainer.py

@@ -1,32 +1,34 @@
 import sys
 import traceback
 import queue
-import colorsys
+import threading
 import time
 import numpy as np
 import itertools
-
 from pathlib import Path
 from utils import Path_utils
 from utils import image_utils    
 import cv2
 import models
+from interact import interact as io
 
-def trainerThread (input_queue, output_queue, training_data_src_dir, training_data_dst_dir, model_path, model_name, save_interval_min=15, debug=False, **in_options):
+def trainerThread (s2c, c2s, args, device_args):
-
     while True:
         try: 
-            training_data_src_path = Path(training_data_src_dir)
+            training_data_src_path = Path( args.get('training_data_src_dir', '') )
-            training_data_dst_path = Path(training_data_dst_dir)
+            training_data_dst_path = Path( args.get('training_data_dst_dir', '') )
-            model_path = Path(model_path)
+            model_path = Path( args.get('model_path', '') )
+            model_name = args.get('model_name', '')
+            save_interval_min = 15            
+            debug = args.get('debug', '')
             
             if not training_data_src_path.exists():
-                print( 'Training data src directory does not exist.')
+                io.log_err('Training data src directory does not exist.')
-                return
+                break
                 
             if not training_data_dst_path.exists():
-                print( 'Training data dst directory does not exist.')
+                io.log_err('Training data dst directory does not exist.')
-                return
+                break
                 
             if not model_path.exists():
                 model_path.mkdir(exist_ok=True)
@@ -36,7 +38,7 @@ def trainerThread (input_queue, output_queue, training_data_src_dir, training_da
                         training_data_src_path=training_data_src_path, 
                         training_data_dst_path=training_data_dst_path, 
                         debug=debug,
-                        **in_options)
+                        device_args=device_args)
             
             is_reached_goal = model.is_reached_epoch_goal()
             is_upd_save_time_after_train = False
@@ -48,10 +50,10 @@ def trainerThread (input_queue, output_queue, training_data_src_dir, training_da
             def send_preview():
                 if not debug:                        
                     previews = model.get_previews()                
-                    output_queue.put ( {'op':'show', 'previews': previews, 'epoch':model.get_epoch(), 'loss_history': model.get_loss_history().copy() } )
+                    c2s.put ( {'op':'show', 'previews': previews, 'epoch':model.get_epoch(), 'loss_history': model.get_loss_history().copy() } )
                 else:
                     previews = [( 'debug, press update for new', model.debug_one_epoch())]
-                    output_queue.put ( {'op':'show', 'previews': previews} )
+                    c2s.put ( {'op':'show', 'previews': previews} )
             
             
             if model.is_first_run():
@@ -59,11 +61,11 @@ def trainerThread (input_queue, output_queue, training_data_src_dir, training_da
                 
             if model.get_target_epoch() != 0:
                 if is_reached_goal:
-                    print ('Model already trained to target epoch. You can use preview.')
+                    io.log_info('Model already trained to target epoch. You can use preview.')
                 else:
-                    print('Starting. Target epoch: %d. Press "Enter" to stop training and save model.' % ( model.get_target_epoch()  ) )
+                    io.log_info('Starting. Target epoch: %d. Press "Enter" to stop training and save model.' % ( model.get_target_epoch()  ) )
             else: 
-                print('Starting. Press "Enter" to stop training and save model.')
+                io.log_info('Starting. Press "Enter" to stop training and save model.')
  
             last_save_time = time.time()
             
@@ -75,12 +77,12 @@ def trainerThread (input_queue, output_queue, training_data_src_dir, training_da
                             #save resets plaidML programs, so upd last_save_time only after plaidML rebuild them
                             last_save_time = time.time()
                         
-                        print (loss_string, end='\r')
+                        io.log_info (loss_string, end='\r')
                         if model.get_target_epoch() != 0 and model.is_reached_epoch_goal():
-                            print ('Reached target epoch.')
+                            io.log_info ('Reached target epoch.')
                             model_save()
                             is_reached_goal = True
-                            print ('You can use preview now.')
+                            io.log_info ('You can use preview now.')
 
                 if not is_reached_goal and (time.time() - last_save_time) >= save_interval_min*60:
                     last_save_time = time.time()
@@ -95,8 +97,8 @@ def trainerThread (input_queue, output_queue, training_data_src_dir, training_da
                 if debug:
                     time.sleep(0.005)
                     
-                while not input_queue.empty():
+                while not s2c.empty():
-                    input = input_queue.get()
+                    input = s2c.get()
                     op = input['op']
                     if op == 'save':
                         model_save()
@@ -120,131 +122,142 @@ def trainerThread (input_queue, output_queue, training_data_src_dir, training_da
             print ('Error: %s' % (str(e)))
             traceback.print_exc()
         break
-    output_queue.put ( {'op':'close'} )
+    c2s.put ( {'op':'close'} )
 
-def previewThread (input_queue, output_queue):
-    
-    
-    previews = None
-    loss_history = None
-    selected_preview = 0
-    update_preview = False
-    is_showing = False
-    is_waiting_preview = False
-    show_last_history_epochs_count = 0    
-    epoch = 0
-    while True:      
-        if not input_queue.empty():
-            input = input_queue.get()
-            op = input['op']
-            if op == 'show':
-                is_waiting_preview = False
-                loss_history = input['loss_history'] if 'loss_history' in input.keys() else None
-                previews = input['previews'] if 'previews' in input.keys() else None
-                epoch = input['epoch'] if 'epoch' in input.keys() else 0
-                if previews is not None:
-                    max_w = 0
-                    max_h = 0
-                    for (preview_name, preview_rgb) in previews:
-                        (h, w, c) = preview_rgb.shape
-                        max_h = max (max_h, h)
-                        max_w = max (max_w, w)
-                    
-                    max_size = 800
-                    if max_h > max_size:
-                        max_w = int( max_w / (max_h / max_size) )
-                        max_h = max_size
-
-                    #make all previews size equal
-                    for preview in previews[:]:
-                        (preview_name, preview_rgb) = preview
-                        (h, w, c) = preview_rgb.shape
-                        if h != max_h or w != max_w:
-                            previews.remove(preview)
-                            previews.append ( (preview_name, cv2.resize(preview_rgb, (max_w, max_h))) )
-                    selected_preview = selected_preview % len(previews)
-                    update_preview = True
-            elif op == 'close':
-                break
-                
-        if update_preview:
-            update_preview = False
-
-            selected_preview_name = previews[selected_preview][0]
-            selected_preview_rgb = previews[selected_preview][1]
-            (h,w,c) = selected_preview_rgb.shape
-            
-            # HEAD
-            head_lines = [
-                '[s]:save [enter]:exit',
-                '[p]:update [space]:next preview [l]:change history range',
-                'Preview: "%s" [%d/%d]' % (selected_preview_name,selected_preview+1, len(previews) )
-                ] 
-            head_line_height = 15
-            head_height = len(head_lines) * head_line_height
-            head = np.ones ( (head_height,w,c) ) * 0.1
-              
-            for i in range(0, len(head_lines)):
-                t = i*head_line_height
-                b = (i+1)*head_line_height
-                head[t:b, 0:w] += image_utils.get_text_image (  (w,head_line_height,c) , head_lines[i], color=[0.8]*c )
-                
-            final = head
    
-            if loss_history is not None:                
+       
-                if show_last_history_epochs_count == 0:
+def main(args, device_args):
-                    loss_history_to_show = loss_history
+    io.log_info ("Running trainer.\r\n")
-                else:
-                    loss_history_to_show = loss_history[-show_last_history_epochs_count:]
-                    
-                lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, epoch, w, c)
-                final = np.concatenate ( [final, lh_img], axis=0 )
-
-            final = np.concatenate ( [final, selected_preview_rgb], axis=0 )
-            final = np.clip(final, 0, 1)
-            
-            cv2.imshow ( 'Training preview', (final*255).astype(np.uint8) )
-            is_showing = True
-        
-        if is_showing:
-            key = cv2.waitKey(100)
-        else:
-            time.sleep(0.1)
-            key = 0
-
-        if key == ord('\n') or key == ord('\r'):
-            output_queue.put ( {'op': 'close'} )
-        elif key == ord('s'):
-            output_queue.put ( {'op': 'save'} )
-        elif key == ord('p'):
-            if not is_waiting_preview:
-                is_waiting_preview = True
-                output_queue.put ( {'op': 'preview'} )
-        elif key == ord('l'):
-            if show_last_history_epochs_count == 0:
-                show_last_history_epochs_count = 5000
-            elif show_last_history_epochs_count == 5000:
-                show_last_history_epochs_count = 10000
-            elif show_last_history_epochs_count == 10000:
-                show_last_history_epochs_count = 50000
-            elif show_last_history_epochs_count == 50000:
-                show_last_history_epochs_count = 100000    
-            elif show_last_history_epochs_count == 100000:
-                show_last_history_epochs_count = 0                
-            update_preview = True
-        elif key == ord(' '):
-            selected_preview = (selected_preview + 1) % len(previews)
-            update_preview = True
-            
-    cv2.destroyAllWindows()
     
-def main (training_data_src_dir, training_data_dst_dir, model_path, model_name, **in_options):
+    no_preview = args.get('no_preview', False)
-    print ("Running trainer.\r\n")
     
-    output_queue = queue.Queue()
+    s2c = queue.Queue()
-    input_queue = queue.Queue()
+    c2s = queue.Queue()
-    import threading
+    
-    thread = threading.Thread(target=trainerThread, args=(output_queue, input_queue, training_data_src_dir, training_data_dst_dir, model_path, model_name), kwargs=in_options )
+    thread = threading.Thread(target=trainerThread, args=(s2c, c2s, args, device_args) )
     thread.start()
 
-    previewThread (input_queue, output_queue)
+    if no_preview:
+        while True:      
+            if not c2s.empty():
+                input = c2s.get()
+                op = input.get('op','')
+                if op == 'close':
+                    break
+            io.process_messages(0.1)
+    else:    
+        wnd_name = "Training preview"
+        io.named_window(wnd_name)
+        io.capture_keys(wnd_name)
+        
+        previews = None
+        loss_history = None
+        selected_preview = 0
+        update_preview = False
+        is_showing = False
+        is_waiting_preview = False
+        show_last_history_epochs_count = 0    
+        epoch = 0
+        while True:      
+            if not c2s.empty():
+                input = c2s.get()
+                op = input['op']
+                if op == 'show':
+                    is_waiting_preview = False
+                    loss_history = input['loss_history'] if 'loss_history' in input.keys() else None
+                    previews = input['previews'] if 'previews' in input.keys() else None
+                    epoch = input['epoch'] if 'epoch' in input.keys() else 0
+                    if previews is not None:
+                        max_w = 0
+                        max_h = 0
+                        for (preview_name, preview_rgb) in previews:
+                            (h, w, c) = preview_rgb.shape
+                            max_h = max (max_h, h)
+                            max_w = max (max_w, w)
+                        
+                        max_size = 800
+                        if max_h > max_size:
+                            max_w = int( max_w / (max_h / max_size) )
+                            max_h = max_size
+
+                        #make all previews size equal
+                        for preview in previews[:]:
+                            (preview_name, preview_rgb) = preview
+                            (h, w, c) = preview_rgb.shape
+                            if h != max_h or w != max_w:
+                                previews.remove(preview)
+                                previews.append ( (preview_name, cv2.resize(preview_rgb, (max_w, max_h))) )
+                        selected_preview = selected_preview % len(previews)
+                        update_preview = True
+                elif op == 'close':
+                    break
+                    
+            if update_preview:
+                update_preview = False
+
+                selected_preview_name = previews[selected_preview][0]
+                selected_preview_rgb = previews[selected_preview][1]
+                (h,w,c) = selected_preview_rgb.shape
+                
+                # HEAD
+                head_lines = [
+                    '[s]:save [enter]:exit',
+                    '[p]:update [space]:next preview [l]:change history range',
+                    'Preview: "%s" [%d/%d]' % (selected_preview_name,selected_preview+1, len(previews) )
+                    ] 
+                head_line_height = 15
+                head_height = len(head_lines) * head_line_height
+                head = np.ones ( (head_height,w,c) ) * 0.1
+                  
+                for i in range(0, len(head_lines)):
+                    t = i*head_line_height
+                    b = (i+1)*head_line_height
+                    head[t:b, 0:w] += image_utils.get_text_image (  (w,head_line_height,c) , head_lines[i], color=[0.8]*c )
+                    
+                final = head
+       
+                if loss_history is not None:                
+                    if show_last_history_epochs_count == 0:
+                        loss_history_to_show = loss_history
+                    else:
+                        loss_history_to_show = loss_history[-show_last_history_epochs_count:]
+                        
+                    lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, epoch, w, c)
+                    final = np.concatenate ( [final, lh_img], axis=0 )
+
+                final = np.concatenate ( [final, selected_preview_rgb], axis=0 )
+                final = np.clip(final, 0, 1)
+                
+                io.show_image( wnd_name, (final*255).astype(np.uint8) )
+                is_showing = True
+            
+            key_events = io.get_key_events(wnd_name)
+            key, = key_events[-1] if len(key_events) > 0 else (0,)
+                    
+            if key == ord('\n') or key == ord('\r'):
+                s2c.put ( {'op': 'close'} )
+            elif key == ord('s'):
+                s2c.put ( {'op': 'save'} )
+            elif key == ord('p'):
+                if not is_waiting_preview:
+                    is_waiting_preview = True
+                    s2c.put ( {'op': 'preview'} )
+            elif key == ord('l'):
+                if show_last_history_epochs_count == 0:
+                    show_last_history_epochs_count = 5000
+                elif show_last_history_epochs_count == 5000:
+                    show_last_history_epochs_count = 10000
+                elif show_last_history_epochs_count == 10000:
+                    show_last_history_epochs_count = 50000
+                elif show_last_history_epochs_count == 50000:
+                    show_last_history_epochs_count = 100000    
+                elif show_last_history_epochs_count == 100000:
+                    show_last_history_epochs_count = 0                
+                update_preview = True
+            elif key == ord(' '):
+                selected_preview = (selected_preview + 1) % len(previews)
+                update_preview = True
+            
+            io.process_messages(0.1)
+            
+        io.destroy_all_windows()

mainscripts/Util.py

@@ -1,28 +1,21 @@
-import os
+import cv2
-import sys
-import operator
-import numpy as np
-import cv2
-from tqdm import tqdm
-from shutil import copyfile
-
 from pathlib import Path
 from utils import Path_utils
-from utils import image_utils
 from utils.DFLPNG import DFLPNG
 from utils.DFLJPG import DFLJPG
 from utils.cv2_utils import *
 from facelib import LandmarksProcessor
-from utils.SubprocessorBase import SubprocessorBase
+from interact import interact as io
-import multiprocessing
 
 def convert_png_to_jpg_file (filepath):
     filepath = Path(filepath)
     
     if filepath.suffix != '.png': 
-        return  
+        return
-    dflpng = DFLPNG.load (str(filepath), print_on_no_embedded_data=True)
+        
-    if dflpng is None: 
+    dflpng = DFLPNG.load (str(filepath) )
+    if dflpng is None:
+        print ("%s is not a dfl image file" % (filepath.name) ) 
         return
     
     dfl_dict = dflpng.getDFLDictData()
@@ -45,27 +38,30 @@ def convert_png_to_jpg_folder (input_path):
 
     print ("Converting PNG to JPG...\r\n")
 
-    for filepath in tqdm( Path_utils.get_image_paths(input_path), desc="Converting", ascii=True):
+    for filepath in io.progress_bar_generator( Path_utils.get_image_paths(input_path), "Converting"):
         filepath = Path(filepath)
         convert_png_to_jpg_file(filepath)
         
 def add_landmarks_debug_images(input_path):
     print ("Adding landmarks debug images...")
 
-    for filepath in tqdm( Path_utils.get_image_paths(input_path), desc="Processing", ascii=True):
+    for filepath in io.progress_bar_generator( Path_utils.get_image_paths(input_path), "Processing"):
         filepath = Path(filepath)
  
         img = cv2_imread(str(filepath))
  
         if filepath.suffix == '.png':
-            dflimg = DFLPNG.load( str(filepath), print_on_no_embedded_data=True )
+            dflimg = DFLPNG.load( str(filepath) )
         elif filepath.suffix == '.jpg':
-            dflimg = DFLJPG.load ( str(filepath), print_on_no_embedded_data=True )
+            dflimg = DFLJPG.load ( str(filepath) )
         else:
+            dflimg = None
+            
+        if dflimg is None:
             print ("%s is not a dfl image file" % (filepath.name) ) 
             continue
 
-        if not (dflimg is None or img is None):
+        if img is not None:
             face_landmarks = dflimg.get_landmarks()
             LandmarksProcessor.draw_landmarks(img, face_landmarks)
             

models/ModelBase.py

@@ -7,31 +7,34 @@ from pathlib import Path
 from utils import Path_utils
 from utils import std_utils
 from utils import image_utils
-from utils.console_utils import *
 from utils.cv2_utils import *
 import numpy as np
 import cv2
 from samples import SampleGeneratorBase
 from nnlib import nnlib
+from interact import interact as io
 '''
 You can implement your own model. Check examples.
 '''
 class ModelBase(object):
 
     #DONT OVERRIDE
-    def __init__(self, model_path, training_data_src_path=None, training_data_dst_path=None, debug = False, force_gpu_idx=-1, **in_options):
+    def __init__(self, model_path, training_data_src_path=None, training_data_dst_path=None, debug = False, device_args = None):
-    
+        
-        if force_gpu_idx == -1: 
+        device_args['force_gpu_idx'] = device_args.get('force_gpu_idx',-1)
+        
+        if device_args['force_gpu_idx'] == -1: 
             idxs_names_list = nnlib.device.getValidDevicesIdxsWithNamesList()
             if len(idxs_names_list) > 1:
-                print ("You have multi GPUs in a system: ")
+                io.log_info ("You have multi GPUs in a system: ")
                 for idx, name in idxs_names_list:
-                    print ("[%d] : %s" % (idx, name) )
+                    io.log_info ("[%d] : %s" % (idx, name) )
 
-                force_gpu_idx = input_int("Which GPU idx to choose? ( skip: best GPU ) : ", -1, [ x[0] for x in idxs_names_list] )
+                device_args['force_gpu_idx'] = io.input_int("Which GPU idx to choose? ( skip: best GPU ) : ", -1, [ x[0] for x in idxs_names_list] )
-        self.force_gpu_idx = force_gpu_idx
+        self.device_args = device_args
     
-        print ("Loading model...")
+        io.log_info ("Loading model...")
+            
         self.model_path = model_path
         self.model_data_path = Path( self.get_strpath_storage_for_file('data.dat') )
         
@@ -46,9 +49,7 @@ class ModelBase(object):
         self.dst_data_generator = None
         self.debug = debug
         self.is_training_mode = (training_data_src_path is not None and training_data_dst_path is not None)
-        
+
-        self.supress_std_once = os.environ.get('TF_SUPPRESS_STD', '0') == '1'
-        
         self.epoch = 0
         self.options = {}
         self.loss_history = []
@@ -61,40 +62,40 @@ class ModelBase(object):
                 self.loss_history = model_data['loss_history'] if 'loss_history' in model_data.keys() else []
                 self.sample_for_preview = model_data['sample_for_preview']  if 'sample_for_preview' in model_data.keys() else None
 
-        ask_override = self.is_training_mode and self.epoch != 0 and input_in_time ("Press enter in 2 seconds to override model settings.", 2)
+        ask_override = self.is_training_mode and self.epoch != 0 and io.input_in_time ("Press enter in 2 seconds to override model settings.", 2)
         
         if self.epoch == 0: 
-            print ("\nModel first run. Enter model options as default for each run.")
+            io.log_info ("\nModel first run. Enter model options as default for each run.")
         
         if self.epoch == 0 or ask_override: 
             default_write_preview_history = False if self.epoch == 0 else self.options.get('write_preview_history',False)
-            self.options['write_preview_history'] = input_bool("Write preview history? (y/n ?:help skip:n/default) : ", default_write_preview_history, help_message="Preview history will be writed to <ModelName>_history folder.")
+            self.options['write_preview_history'] = io.input_bool("Write preview history? (y/n ?:help skip:n/default) : ", default_write_preview_history, help_message="Preview history will be writed to <ModelName>_history folder.")
         else:
             self.options['write_preview_history'] = self.options.get('write_preview_history', False)
             
         if self.epoch == 0 or ask_override: 
-            self.options['target_epoch'] = max(0, input_int("Target epoch (skip:unlimited/default) : ", 0))
+            self.options['target_epoch'] = max(0, io.input_int("Target epoch (skip:unlimited/default) : ", 0))
         else:
             self.options['target_epoch'] = self.options.get('target_epoch', 0)
         
         if self.epoch == 0 or ask_override: 
             default_batch_size = 0 if self.epoch == 0 else self.options.get('batch_size',0)
-            self.options['batch_size'] = max(0, input_int("Batch_size (?:help skip:0/default) : ", default_batch_size, help_message="Larger batch size is always better for NN's generalization, but it can cause Out of Memory error. Tune this value for your videocard manually."))
+            self.options['batch_size'] = max(0, io.input_int("Batch_size (?:help skip:0/default) : ", default_batch_size, help_message="Larger batch size is always better for NN's generalization, but it can cause Out of Memory error. Tune this value for your videocard manually."))
         else:
             self.options['batch_size'] = self.options.get('batch_size', 0)
         
         if self.epoch == 0: 
-            self.options['sort_by_yaw'] = input_bool("Feed faces to network sorted by yaw? (y/n ?:help skip:n) : ", False, help_message="NN will not learn src face directions that don't match dst face directions." )
+            self.options['sort_by_yaw'] = io.input_bool("Feed faces to network sorted by yaw? (y/n ?:help skip:n) : ", False, help_message="NN will not learn src face directions that don't match dst face directions." )
         else:
             self.options['sort_by_yaw'] = self.options.get('sort_by_yaw', False)
             
         if self.epoch == 0: 
-            self.options['random_flip'] = input_bool("Flip faces randomly? (y/n ?:help skip:y) : ", True, help_message="Predicted face will look more naturally without this option, but src faceset should cover all face directions as dst faceset.")
+            self.options['random_flip'] = io.input_bool("Flip faces randomly? (y/n ?:help skip:y) : ", True, help_message="Predicted face will look more naturally without this option, but src faceset should cover all face directions as dst faceset.")
         else:
             self.options['random_flip'] = self.options.get('random_flip', True)
         
         if self.epoch == 0: 
-            self.options['src_scale_mod'] = np.clip( input_int("Src face scale modifier % ( -30...30, ?:help skip:0) : ", 0, help_message="If src face shape is wider than dst, try to decrease this value to get a better result."), -30, 30)
+            self.options['src_scale_mod'] = np.clip( io.input_int("Src face scale modifier % ( -30...30, ?:help skip:0) : ", 0, help_message="If src face shape is wider than dst, try to decrease this value to get a better result."), -30, 30)
         else:            
             self.options['src_scale_mod'] = self.options.get('src_scale_mod', 0)
              
@@ -116,10 +117,10 @@ class ModelBase(object):
             
         self.onInitializeOptions(self.epoch == 0, ask_override)
         
-        nnlib.import_all ( nnlib.DeviceConfig(allow_growth=False, force_gpu_idx=self.force_gpu_idx, **in_options) )
+        nnlib.import_all ( nnlib.DeviceConfig(allow_growth=False, **self.device_args) )
         self.device_config = nnlib.active_DeviceConfig
 
-        self.onInitialize(**in_options)
+        self.onInitialize()
         
         self.options['batch_size'] = self.batch_size
         
@@ -128,10 +129,10 @@ class ModelBase(object):
         
         if self.is_training_mode:
             if self.write_preview_history:
-                if self.force_gpu_idx == -1:
+                if self.device_args['force_gpu_idx'] == -1:
                     self.preview_history_path = self.model_path / ( '%s_history' % (self.get_model_name()) )
                 else:
-                    self.preview_history_path = self.model_path / ( '%d_%s_history' % (self.force_gpu_idx, self.get_model_name()) )
+                    self.preview_history_path = self.model_path / ( '%d_%s_history' % (self.device_args['force_gpu_idx'], self.get_model_name()) )
                 
                 if not self.preview_history_path.exists():
                     self.preview_history_path.mkdir(exist_ok=True)
@@ -141,11 +142,11 @@ class ModelBase(object):
                             Path(filename).unlink()
         
             if self.generator_list is None:
-                raise Exception( 'You didnt set_training_data_generators()')
+                raise ValueError( 'You didnt set_training_data_generators()')
             else:
                 for i, generator in enumerate(self.generator_list):
                     if not isinstance(generator, SampleGeneratorBase):
-                        raise Exception('training data generator is not subclass of SampleGeneratorBase')
+                        raise ValueError('training data generator is not subclass of SampleGeneratorBase')
                         
             if (self.sample_for_preview is None) or (self.epoch == 0):
                 self.sample_for_preview = self.generate_next_sample()
@@ -181,14 +182,14 @@ class ModelBase(object):
         model_summary_text += ["========================="]  
         model_summary_text = "\r\n".join (model_summary_text) 
         self.model_summary_text = model_summary_text       
-        print(model_summary_text)
+        io.log_info(model_summary_text)
         
     #overridable
     def onInitializeOptions(self, is_first_run, ask_override):
         pass
        
     #overridable
-    def onInitialize(self, **in_options):
+    def onInitialize(self):
         '''
         initialize your keras models
         
@@ -221,10 +222,9 @@ class ModelBase(object):
         return Path(inspect.getmodule(self).__file__).parent.name.rsplit("_", 1)[1]
         
     #overridable
-    def get_converter(self, **in_options):
+    def get_converter(self):
-        #return existing or your own converter which derived from base        
+        raise NotImplementeError
-        from .ConverterBase import ConverterBase
+        #return existing or your own converter which derived from base
-        return ConverterBase(self, **in_options) 
      
     def get_target_epoch(self):
         return self.target_epoch
@@ -258,17 +258,10 @@ class ModelBase(object):
         return self.onGetPreview (self.sample_for_preview)[0][1] #first preview, and bgr
        
     def save(self):    
-        print ("Saving...")
+        io.log_info ("Saving...")
         
-        if self.supress_std_once:
-            supressor = std_utils.suppress_stdout_stderr()
-            supressor.__enter__()
-         
         Path( self.get_strpath_storage_for_file('summary.txt') ).write_text(self.model_summary_text)  
         self.onSave()
-        
-        if self.supress_std_once:
-            supressor.__exit__()
             
         model_data = {
             'epoch': self.epoch,
@@ -310,11 +303,7 @@ class ModelBase(object):
     def generate_next_sample(self):
         return [next(generator) for generator in self.generator_list]
 
-    def train_one_epoch(self):    
+    def train_one_epoch(self):
-        if self.supress_std_once:
-            supressor = std_utils.suppress_stdout_stderr()
-            supressor.__enter__()
-            
         sample = self.generate_next_sample()        
         epoch_time = time.time()        
         losses = self.onTrainOneEpoch(sample, self.generator_list)        
@@ -322,11 +311,7 @@ class ModelBase(object):
         self.last_sample = sample
         
         self.loss_history.append ( [float(loss[1]) for loss in losses] )
-        
+
-        if self.supress_std_once:
-            supressor.__exit__()
-            self.supress_std_once = False
-                  
         if self.write_preview_history:
             if self.epoch % 10 == 0:            
                 preview = self.get_static_preview()
@@ -377,10 +362,10 @@ class ModelBase(object):
         return self.generator_list
         
     def get_strpath_storage_for_file(self, filename):
-        if self.force_gpu_idx == -1:
+        if self.device_args['force_gpu_idx'] == -1:
             return str( self.model_path / ( self.get_model_name() + '_' + filename) )
         else:
-            return str( self.model_path / ( str(self.force_gpu_idx) + '_' + self.get_model_name() + '_' + filename) )
+            return str( self.model_path / ( str(self.device_args['force_gpu_idx']) + '_' + self.get_model_name() + '_' + filename) )
 
     def set_vram_batch_requirements (self, d):
         #example d = {2:2,3:4,4:8,5:16,6:32,7:32,8:32,9:48} 

models/Model_DF/Model.py

@@ -4,7 +4,7 @@ from nnlib import nnlib
 from models import ModelBase
 from facelib import FaceType
 from samples import *
-from utils.console_utils import *
+from interact import interact as io
 
 class Model(ModelBase):
 
@@ -12,12 +12,12 @@ class Model(ModelBase):
     def onInitializeOptions(self, is_first_run, ask_override):        
         if is_first_run or ask_override:
             def_pixel_loss = self.options.get('pixel_loss', False)
-            self.options['pixel_loss'] = input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k epochs to enhance fine details and decrease face jitter.")
+            self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k epochs to enhance fine details and decrease face jitter.")
         else:
             self.options['pixel_loss'] = self.options.get('pixel_loss', False)
             
     #override
-    def onInitialize(self, **in_options):
+    def onInitialize(self):
         exec(nnlib.import_all(), locals(), globals())
         self.set_vram_batch_requirements( {4.5:4} )
                 
@@ -113,15 +113,14 @@ class Model(ModelBase):
         return np.concatenate ( (x,mx), -1 )
         
     #override
-    def get_converter(self, **in_options):
+    def get_converter(self):
-        from models import ConverterMasked            
+        from converters import ConverterMasked            
         return ConverterMasked(self.predictor_func, 
                                predictor_input_size=128, 
                                output_size=128, 
                                face_type=FaceType.FULL, 
                                base_erode_mask_modifier=30,
-                               base_blur_mask_modifier=0,
+                               base_blur_mask_modifier=0)
-                               **in_options)
         
     def Build(self, input_layer):
         exec(nnlib.code_import_all, locals(), globals())

models/Model_H128/Model.py

@@ -4,14 +4,14 @@ from nnlib import nnlib
 from models import ModelBase
 from facelib import FaceType
 from samples import *
-from utils.console_utils import *
+from interact import interact as io
 
 class Model(ModelBase):
 
     #override
     def onInitializeOptions(self, is_first_run, ask_override):        
         if is_first_run:
-            self.options['lighter_ae'] = input_bool ("Use lightweight autoencoder? (y/n, ?:help skip:n) : ", False, help_message="Lightweight autoencoder is faster, requires less VRAM, sacrificing overall quality. If your GPU VRAM <= 4, you should to choose this option.")
+            self.options['lighter_ae'] = io.input_bool ("Use lightweight autoencoder? (y/n, ?:help skip:n) : ", False, help_message="Lightweight autoencoder is faster, requires less VRAM, sacrificing overall quality. If your GPU VRAM <= 4, you should to choose this option.")
         else:
             default_lighter_ae = self.options.get('created_vram_gb', 99) <= 4 #temporally support old models, deprecate in future
             if 'created_vram_gb' in self.options.keys():
@@ -20,12 +20,12 @@ class Model(ModelBase):
             
         if is_first_run or ask_override:
             def_pixel_loss = self.options.get('pixel_loss', False)
-            self.options['pixel_loss'] = input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k epochs to enhance fine details and decrease face jitter.")
+            self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k epochs to enhance fine details and decrease face jitter.")
         else:
             self.options['pixel_loss'] = self.options.get('pixel_loss', False)
             
     #override
-    def onInitialize(self, **in_options):
+    def onInitialize(self):
         exec(nnlib.import_all(), locals(), globals())        
         self.set_vram_batch_requirements( {2.5:4} )
                 
@@ -125,15 +125,14 @@ class Model(ModelBase):
         return np.concatenate ( (x,mx), -1 )
 
     #override
-    def get_converter(self, **in_options):
+    def get_converter(self):
-        from models import ConverterMasked
+        from converters import ConverterMasked
         return ConverterMasked(self.predictor_func, 
                                predictor_input_size=128, 
                                output_size=128, 
                                face_type=FaceType.HALF,
                                base_erode_mask_modifier=100,
-                               base_blur_mask_modifier=100,
+                               base_blur_mask_modifier=100)
-                               **in_options)
     
     def Build(self, lighter_ae):
         exec(nnlib.code_import_all, locals(), globals())

models/Model_H64/Model.py

@@ -4,14 +4,14 @@ from nnlib import nnlib
 from models import ModelBase
 from facelib import FaceType
 from samples import *
-from utils.console_utils import *
+from interact import interact as io
 
 class Model(ModelBase):
 
     #override
     def onInitializeOptions(self, is_first_run, ask_override):        
         if is_first_run:
-            self.options['lighter_ae'] = input_bool ("Use lightweight autoencoder? (y/n, ?:help skip:n) : ", False, help_message="Lightweight autoencoder is faster, requires less VRAM, sacrificing overall quality. If your GPU VRAM <= 4, you should to choose this option.")
+            self.options['lighter_ae'] = io.input_bool ("Use lightweight autoencoder? (y/n, ?:help skip:n) : ", False, help_message="Lightweight autoencoder is faster, requires less VRAM, sacrificing overall quality. If your GPU VRAM <= 4, you should to choose this option.")
         else:
             default_lighter_ae = self.options.get('created_vram_gb', 99) <= 4 #temporally support old models, deprecate in future
             if 'created_vram_gb' in self.options.keys():
@@ -20,12 +20,12 @@ class Model(ModelBase):
             
         if is_first_run or ask_override:
             def_pixel_loss = self.options.get('pixel_loss', False)
-            self.options['pixel_loss'] = input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k epochs to enhance fine details and decrease face jitter.")
+            self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k epochs to enhance fine details and decrease face jitter.")
         else:
             self.options['pixel_loss'] = self.options.get('pixel_loss', False)
             
     #override
-    def onInitialize(self, **in_options):
+    def onInitialize(self):
         exec(nnlib.import_all(), locals(), globals())
         self.set_vram_batch_requirements( {1.5:4} )
         
@@ -127,15 +127,14 @@ class Model(ModelBase):
         return np.concatenate ( (x,mx), -1 )
 
     #override
-    def get_converter(self, **in_options):
+    def get_converter(self):
-        from models import ConverterMasked
+        from converters import ConverterMasked
         return ConverterMasked(self.predictor_func,
                                predictor_input_size=64, 
                                output_size=64, 
                                face_type=FaceType.HALF, 
                                base_erode_mask_modifier=100,
-                               base_blur_mask_modifier=100,
+                               base_blur_mask_modifier=100)
-                               **in_options)
         
     def Build(self, lighter_ae):
         exec(nnlib.code_import_all, locals(), globals())

models/Model_LIAEF128/Model.py

@@ -4,7 +4,7 @@ from nnlib import nnlib
 from models import ModelBase
 from facelib import FaceType
 from samples import *
-from utils.console_utils import *
+from interact import interact as io
 
 class Model(ModelBase):
 
@@ -12,12 +12,12 @@ class Model(ModelBase):
     def onInitializeOptions(self, is_first_run, ask_override):        
         if is_first_run or ask_override:
             def_pixel_loss = self.options.get('pixel_loss', False)
-            self.options['pixel_loss'] = input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k epochs to enhance fine details and decrease face jitter.")
+            self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k epochs to enhance fine details and decrease face jitter.")
         else:
             self.options['pixel_loss'] = self.options.get('pixel_loss', False)
             
     #override
-    def onInitialize(self, **in_options):
+    def onInitialize(self):
         exec(nnlib.import_all(), locals(), globals())
         self.set_vram_batch_requirements( {4.5:4} )
 
@@ -121,15 +121,14 @@ class Model(ModelBase):
         return np.concatenate ( (x,mx), -1 )
         
     #override
-    def get_converter(self, **in_options):
+    def get_converter(self):
-        from models import ConverterMasked
+        from converters import ConverterMasked
         return ConverterMasked(self.predictor_func, 
                                predictor_input_size=128, 
                                output_size=128, 
                                face_type=FaceType.FULL, 
                                base_erode_mask_modifier=30,
-                               base_blur_mask_modifier=0,
+                               base_blur_mask_modifier=0)
-                               **in_options)
       
     def Build(self, input_layer):
         exec(nnlib.code_import_all, locals(), globals())

models/Model_SAE/Model.py

@@ -4,7 +4,7 @@ from nnlib import nnlib
 from models import ModelBase
 from facelib import FaceType
 from samples import *
-from utils.console_utils import *
+from interact import interact as io
 
 #SAE - Styled AutoEncoder
 class SAEModel(ModelBase):
@@ -27,15 +27,15 @@ class SAEModel(ModelBase):
         default_face_type = 'f'
         
         if is_first_run:
-            resolution = input_int("Resolution ( 64-256 ?:help skip:128) : ", default_resolution, help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 16.")
+            resolution = io.input_int("Resolution ( 64-256 ?:help skip:128) : ", default_resolution, help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 16.")
             resolution = np.clip (resolution, 64, 256)            
             while np.modf(resolution / 16)[0] != 0.0:
                 resolution -= 1
             self.options['resolution'] = resolution
             
-            self.options['face_type'] = input_str ("Half or Full face? (h/f, ?:help skip:f) : ", default_face_type, ['h','f'], help_message="Half face has better resolution, but covers less area of cheeks.").lower()            
+            self.options['face_type'] = io.input_str ("Half or Full face? (h/f, ?:help skip:f) : ", default_face_type, ['h','f'], help_message="Half face has better resolution, but covers less area of cheeks.").lower()            
-            self.options['learn_mask'] = input_bool ("Learn mask? (y/n, ?:help skip:y) : ", True, help_message="Learning mask can help model to recognize face directions. Learn without mask can reduce model size, in this case converter forced to use 'not predicted mask' that is not smooth as predicted. Model with style values can be learned without mask and produce same quality result.")
+            self.options['learn_mask'] = io.input_bool ("Learn mask? (y/n, ?:help skip:y) : ", True, help_message="Learning mask can help model to recognize face directions. Learn without mask can reduce model size, in this case converter forced to use 'not predicted mask' that is not smooth as predicted. Model with style values can be learned without mask and produce same quality result.")
-            self.options['archi'] = input_str ("AE architecture (df, liae, vg ?:help skip:%s) : " % (default_archi) , default_archi, ['df','liae','vg'], help_message="'df' keeps faces more natural. 'liae' can fix overly different face shapes. 'vg' - currently testing.").lower()
+            self.options['archi'] = io.input_str ("AE architecture (df, liae, vg ?:help skip:%s) : " % (default_archi) , default_archi, ['df','liae','vg'], help_message="'df' keeps faces more natural. 'liae' can fix overly different face shapes. 'vg' - currently testing.").lower()
         else:
             self.options['resolution'] = self.options.get('resolution', default_resolution)
             self.options['face_type'] = self.options.get('face_type', default_face_type)
@@ -45,17 +45,17 @@ class SAEModel(ModelBase):
         default_ae_dims = 256 if self.options['archi'] == 'liae' else 512
         default_ed_ch_dims = 42
         if is_first_run:
-            self.options['ae_dims'] = np.clip ( input_int("AutoEncoder dims (32-1024 ?:help skip:%d) : " % (default_ae_dims) , default_ae_dims, help_message="More dims are better, but requires more VRAM. You can fine-tune model size to fit your GPU." ), 32, 1024 )
+            self.options['ae_dims'] = np.clip ( io.input_int("AutoEncoder dims (32-1024 ?:help skip:%d) : " % (default_ae_dims) , default_ae_dims, help_message="More dims are better, but requires more VRAM. You can fine-tune model size to fit your GPU." ), 32, 1024 )
-            self.options['ed_ch_dims'] = np.clip ( input_int("Encoder/Decoder dims per channel (21-85 ?:help skip:%d) : " % (default_ed_ch_dims) , default_ed_ch_dims, help_message="More dims are better, but requires more VRAM. You can fine-tune model size to fit your GPU." ), 21, 85 )
+            self.options['ed_ch_dims'] = np.clip ( io.input_int("Encoder/Decoder dims per channel (21-85 ?:help skip:%d) : " % (default_ed_ch_dims) , default_ed_ch_dims, help_message="More dims are better, but requires more VRAM. You can fine-tune model size to fit your GPU." ), 21, 85 )
         else:
             self.options['ae_dims'] = self.options.get('ae_dims', default_ae_dims)
             self.options['ed_ch_dims'] = self.options.get('ed_ch_dims', default_ed_ch_dims)
             
         if is_first_run:
-            self.options['lighter_encoder'] = input_bool ("Use lightweight encoder? (y/n, ?:help skip:n) : ", False, help_message="Lightweight encoder is 35% faster, requires less VRAM, but sacrificing overall quality.")
+            self.options['lighter_encoder'] = io.input_bool ("Use lightweight encoder? (y/n, ?:help skip:n) : ", False, help_message="Lightweight encoder is 35% faster, requires less VRAM, but sacrificing overall quality.")
             
             if self.options['archi'] != 'vg':
-                self.options['multiscale_decoder'] = input_bool ("Use multiscale decoder? (y/n, ?:help skip:y) : ", True, help_message="Multiscale decoder helps to get better details.")
+                self.options['multiscale_decoder'] = io.input_bool ("Use multiscale decoder? (y/n, ?:help skip:y) : ", True, help_message="Multiscale decoder helps to get better details.")
         else:
             self.options['lighter_encoder'] = self.options.get('lighter_encoder', False)
             
@@ -66,14 +66,14 @@ class SAEModel(ModelBase):
         default_bg_style_power = 0.0  
         if is_first_run or ask_override:
             def_pixel_loss = self.options.get('pixel_loss', False)
-            self.options['pixel_loss'] = input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 15-25k epochs to enhance fine details and decrease face jitter.")
+            self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 15-25k epochs to enhance fine details and decrease face jitter.")
         
             default_face_style_power = default_face_style_power if is_first_run else self.options.get('face_style_power', default_face_style_power)
-            self.options['face_style_power'] = np.clip ( input_number("Face style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_face_style_power), default_face_style_power, 
+            self.options['face_style_power'] = np.clip ( io.input_number("Face style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_face_style_power), default_face_style_power, 
                                                                                help_message="Learn to transfer face style details such as light and color conditions. Warning: Enable it only after 10k epochs, when predicted face is clear enough to start learn style. Start from 0.1 value and check history changes."), 0.0, 100.0 )            
                             
             default_bg_style_power = default_bg_style_power if is_first_run else self.options.get('bg_style_power', default_bg_style_power)
-            self.options['bg_style_power'] = np.clip ( input_number("Background style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_bg_style_power), default_bg_style_power, 
+            self.options['bg_style_power'] = np.clip ( io.input_number("Background style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_bg_style_power), default_bg_style_power, 
                                                                                help_message="Learn to transfer image around face. This can make face more like dst."), 0.0, 100.0 )            
         else:
             self.options['pixel_loss'] = self.options.get('pixel_loss', False)
@@ -81,7 +81,7 @@ class SAEModel(ModelBase):
             self.options['bg_style_power'] = self.options.get('bg_style_power', default_bg_style_power)
 
     #override
-    def onInitialize(self, **in_options):
+    def onInitialize(self):
         exec(nnlib.import_all(), locals(), globals())
         SAEModel.initialize_nn_functions()
         
@@ -427,9 +427,7 @@ class SAEModel(ModelBase):
         return np.concatenate ( [prd[0], prd[1]], -1 )
         
     #override
-    def get_converter(self, **in_options):
+    def get_converter(self):
-        from models import ConverterMasked
-
         base_erode_mask_modifier = 30 if self.options['face_type'] == 'f' else 100
         base_blur_mask_modifier = 0 if self.options['face_type'] == 'f' else 100
         
@@ -439,6 +437,7 @@ class SAEModel(ModelBase):
         
         face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF
         
+        from converters import ConverterMasked
         return ConverterMasked(self.predictor_func, 
                                predictor_input_size=self.options['resolution'],
                                output_size=self.options['resolution'],
@@ -448,8 +447,7 @@ class SAEModel(ModelBase):
                                base_blur_mask_modifier=base_blur_mask_modifier,
                                default_erode_mask_modifier=default_erode_mask_modifier,
                                default_blur_mask_modifier=default_blur_mask_modifier,
-                               clip_hborder_mask_per=0.0625 if self.options['face_type'] == 'f' else 0,
+                               clip_hborder_mask_per=0.0625 if self.options['face_type'] == 'f' else 0)
-                               **in_options)
                                
     @staticmethod
     def initialize_nn_functions():

models/__init__.py

@@ -1,7 +1,4 @@
 from .ModelBase import ModelBase
-from .ConverterBase import ConverterBase
-from .ConverterMasked import ConverterMasked
-from .ConverterImage import ConverterImage
 
 def import_model(name):
     module = __import__('Model_'+name, globals(), locals(), [], 1)

samples/SampleLoader.py

@@ -2,7 +2,6 @@ import traceback
 from enum import IntEnum
 import cv2
 import numpy as np
-from tqdm import tqdm
 from pathlib import Path
 
 from utils import Path_utils
@@ -14,6 +13,7 @@ from .Sample import SampleType
 
 from facelib import FaceType
 from facelib import LandmarksProcessor
+from interact import interact as io
 
 class SampleLoader:
     cache = dict()
@@ -29,7 +29,7 @@ class SampleLoader:
 
         if            sample_type == SampleType.IMAGE:
             if  datas[sample_type] is None:  
-                datas[sample_type] = [ Sample(filename=filename) for filename in tqdm( Path_utils.get_image_paths(samples_path), desc="Loading", ascii=True ) ]
+                datas[sample_type] = [ Sample(filename=filename) for filename in io.progress_bar_generator( Path_utils.get_image_paths(samples_path), "Loading") ]
 
         elif          sample_type == SampleType.FACE:
             if  datas[sample_type] is None:  
@@ -55,16 +55,17 @@ class SampleLoader:
     def upgradeToFaceSamples ( samples ):
         sample_list = []
         
-        for s in tqdm( samples, desc="Loading", ascii=True ):
+        for s in io.progress_bar_generator(samples, "Loading"):
             s_filename_path = Path(s.filename)
             try:
                 if s_filename_path.suffix == '.png':
-                    dflimg = DFLPNG.load ( str(s_filename_path), print_on_no_embedded_data=True )
+                    dflimg = DFLPNG.load ( str(s_filename_path) )
-                    if dflimg is None: continue
                 elif s_filename_path.suffix == '.jpg':
-                    dflimg = DFLJPG.load ( str(s_filename_path), print_on_no_embedded_data=True )
+                    dflimg = DFLJPG.load ( str(s_filename_path) )
-                    if dflimg is None: continue
                 else:
+                    dflimg = None
+                    
+                if dflimg is None:
                     print ("%s is not a dfl image file required for training" % (s_filename_path.name) ) 
                     continue
                     
@@ -87,7 +88,7 @@ class SampleLoader:
         yaw_samples_len = len(yaw_samples)
         
         sample_list = []
-        for i in tqdm( range(yaw_samples_len), desc="Sorting", ascii=True ):
+        for i in io.progress_bar_generator( range(yaw_samples_len), "Sorting"):
             if yaw_samples[i] is not None:
                 for s in yaw_samples[i]:
                     s_t = []
@@ -123,7 +124,7 @@ class SampleLoader:
 
         yaws_sample_list = [None]*gradations
         
-        for i in tqdm( range(0, gradations), desc="Sorting", ascii=True ):
+        for i in io.progress_bar_generator(range(gradations), "Sorting"):
             yaw = lowest_yaw + i*diff_rot_per_grad
             next_yaw = lowest_yaw + (i+1)*diff_rot_per_grad
 

utils/DFLJPG.py

@@ -113,7 +113,7 @@ class DFLJPG(object):
         return None
         
     @staticmethod
-    def load(filename, print_on_no_embedded_data=False, throw_on_no_embedded_data=False):
+    def load(filename):
         inst = DFLJPG.load_raw (filename)
         inst.dfl_dict = None
         
@@ -141,10 +141,6 @@ class DFLJPG(object):
             inst.dfl_dict['face_type'] = FaceType.toString (FaceType.FULL)
         
         if inst.dfl_dict == None:
-            if print_on_no_embedded_data:
-                print ( "No DFL data found in %s" % (filename) )
-            if throw_on_no_embedded_data:
-                raise ValueError("No DFL data found in %s" % (filename) )
             return None
         
         return inst

utils/DFLPNG.py

@@ -248,7 +248,7 @@ class DFLPNG(object):
         return inst
         
     @staticmethod
-    def load(filename, print_on_no_embedded_data=False, throw_on_no_embedded_data=False):
+    def load(filename):
         inst = DFLPNG.load_raw (filename)
         inst.fcwp_dict = inst.getDFLDictData()
         
@@ -256,10 +256,6 @@ class DFLPNG(object):
             inst.fcwp_dict['face_type'] = FaceType.toString (FaceType.FULL)
         
         if inst.fcwp_dict == None:
-            if print_on_no_embedded_data:
-                print ( "No DFL data found in %s" % (filename) )
-            if throw_on_no_embedded_data:
-                raise ValueError("No DFL data found in %s" % (filename) )
             return None
         
         return inst

utils/Path_utils.py

@@ -13,7 +13,7 @@ def get_image_paths(dir_path):
                 result.append(x.path)
     return result
 
-def get_image_unique_filestem_paths(dir_path, verbose=False):
+def get_image_unique_filestem_paths(dir_path, verbose_print_func=None):
     result = get_image_paths(dir_path)   
     result_dup = set()    
     
@@ -21,8 +21,8 @@ def get_image_unique_filestem_paths(dir_path, verbose=False):
         f_stem = Path(f).stem
         if f_stem in result_dup:            
             result.remove(f)
-            if verbose:
+            if verbose_print_func is not None:
-                print ("Duplicate filenames are not allowed, skipping: %s" % Path(f).name )            
+                verbose_print_func ("Duplicate filenames are not allowed, skipping: %s" % Path(f).name )            
             continue            
         result_dup.add(f_stem)
             

utils/SubprocessorBase.py

@@ -1,257 +0,0 @@
-import traceback
-from tqdm import tqdm
-import multiprocessing
-import time
-import sys
-
-class SubprocessorBase(object):
-
-    #overridable
-    def __init__(self, name, no_response_time_sec = 60):
-        self.name = name
-        self.no_response_time_sec = no_response_time_sec
-        self.is_host = True
-        
-    #overridable    
-    def process_info_generator(self):
-        #yield name, host_dict, client_dict - per process
-        yield 'first process', {}, {}
-        
-    #overridable
-    def get_no_process_started_message(self):
-        return "No process started."
-
-    #overridable
-    def onHostGetProgressBarDesc(self):
-        return "Processing"
-        
-    #overridable
-    def onHostGetProgressBarLen(self):
-        return 0
-        
-    #overridable        
-    def onHostGetData(self, host_dict):
-        #return data here
-        return None
-    
-    #overridable
-    def onHostDataReturn (self, host_dict, data):
-        #input_data.insert(0, obj['data'])   
-        pass
-    
-    #overridable
-    def onClientInitialize(self, client_dict):
-        #return fail message or None if ok
-        return None
-        
-    #overridable
-    def onClientFinalize(self):
-        pass
-        
-    #overridable
-    def onClientProcessData(self, data):
-        #return result object
-        return None  
-        
-    #overridable
-    def onClientGetDataName (self, data):
-        #return string identificator of your data
-        return "undefined"
-        
-    #overridable
-    def onHostClientsInitialized(self):
-        pass
-        
-    #overridable
-    def onHostResult (self, host_dict, data, result):
-        #return count of progress bar update
-        return 1
-    
-    #overridable
-    def onFinalizeAndGetResult(self):
-        return None
-        
-    def inc_progress_bar(self, c):        
-        if self.is_host:
-            self.progress_bar.n += c
-            self.progress_bar.refresh()
-        else:
-            self.cq.put ( {'op': 'inc_bar', 'c':c} )
-    
-    def safe_print(self, msg):
-        self.print_lock.acquire()
-        print (msg)
-        self.print_lock.release()
-    
-    def process(self):
-        #returns start_return
-        
-        self.processes = []
-        
-        self.print_lock = multiprocessing.Lock()        
-        for name, host_dict, client_dict in self.process_info_generator():            
-            sq = multiprocessing.Queue()
-            cq = multiprocessing.Queue()
-            
-            client_dict.update ( {'print_lock' : self.print_lock} ) 
-            try:
-                p = multiprocessing.Process(target=self.subprocess, args=(sq,cq,client_dict))
-                p.daemon = True
-                p.start()
-                self.processes.append ( { 'process' : p,
-                                          'sq' : sq,
-                                          'cq' : cq,
-                                          'state' : 'busy',
-                                          'sent_time': time.time(),
-                                          'name': name,
-                                          'host_dict' : host_dict
-                                        } )
-            except:
-                print ("Unable to start subprocess %s" % (name))
-
-        if len(self.processes) == 0:
-            raise Exception ("Unable to start Subprocessor '%s' " % (self.name))
-
-        while True:
-            for p in self.processes[:]:
-                while not p['cq'].empty():
-                    obj = p['cq'].get()
-                    obj_op = obj['op']
-                        
-                    if obj_op == 'init_ok':
-                        p['state'] = 'free'
-                    elif obj_op == 'error':                       
-                        if obj['close'] == True:
-                            p['process'].terminate()
-                            p['process'].join()
-                            self.processes.remove(p)
-                            break                
-                    
-            if all ([ p['state'] == 'free' for p in self.processes ] ):
-                break
-                
-        if len(self.processes) == 0:
-            print ( self.get_no_process_started_message() )
-            return None
-        
-        self.progress_bar = tqdm( total=self.onHostGetProgressBarLen(), desc=self.onHostGetProgressBarDesc(), ascii=True )                  
-        self.onHostClientsInitialized()        
-        
-        try: 
-            while True:
-                for p in self.processes[:]:
-                    while not p['cq'].empty():
-                        obj = p['cq'].get()
-                        obj_op = obj['op']
-                        
-                        if obj_op == 'success':   
-                            data = obj['data']
-                            result = obj['result']
-                            
-                            c = self.onHostResult (p['host_dict'], data, result)                        
-                            if c > 0:
-                                self.progress_bar.update(c)
-                                
-                            p['state'] = 'free'
-                        elif obj_op == 'inc_bar':                            
-                            self.inc_progress_bar(obj['c'])
-                        elif obj_op == 'error':
-                            if 'data' in obj.keys():
-                                self.onHostDataReturn (p['host_dict'], obj['data'] )
-            
-                            if obj['close'] == True:
-                                p['sq'].put ( {'op': 'close'} )
-                                p['process'].join()
-                                self.processes.remove(p)
-                                break
-                            p['state'] = 'free'
-                            
-                for p in self.processes[:]:
-                    if p['state'] == 'free':
-                        data = self.onHostGetData(p['host_dict'])
-                        if data is not None:
-                            p['sq'].put ( {'op': 'data', 'data' : data} )
-                            p['sent_time'] = time.time()
-                            p['sent_data'] = data
-                            p['state'] = 'busy'
-                        
-                    elif p['state'] == 'busy':
-                        if self.no_response_time_sec != 0 and (time.time() - p['sent_time']) > self.no_response_time_sec:
-                            print ( '%s doesnt response, terminating it.' % (p['name']) )
-                            self.onHostDataReturn (p['host_dict'], p['sent_data'] )
-                            p['process'].terminate()
-                            self.processes.remove(p)
-                            
-                if all ([p['state'] == 'free' for p in self.processes]):
-                    break
-                    
-                time.sleep(0.005)
-        except:
-            print ("Exception occured in Subprocessor.start(): %s" % (traceback.format_exc()) )
-        
-        self.progress_bar.close()
-        
-        for p in self.processes[:]:
-            p['sq'].put ( {'op': 'close'} )
-            p['sent_time'] = time.time()
-            
-        while True:
-            for p in self.processes[:]:
-                terminate_it = False
-                while not p['cq'].empty():
-                    obj = p['cq'].get()
-                    obj_op = obj['op']                    
-                    if obj_op == 'finalized':
-                        terminate_it = True
-                        break
-                        
-                if self.no_response_time_sec != 0 and (time.time() - p['sent_time']) > self.no_response_time_sec:                
-                    terminate_it = True
-                    
-                if terminate_it:
-                    p['state'] = 'finalized'
-                    p['process'].terminate()
-                    
-            if all ([p['state'] == 'finalized' for p in self.processes]):
-                break
-         
-        return self.onFinalizeAndGetResult()
-
-    def subprocess(self, sq, cq, client_dict):    
-        self.is_host = False
-        self.print_lock = client_dict['print_lock']
-        self.cq = cq
-        try:
-            fail_message = self.onClientInitialize(client_dict)
-        except:
-            fail_message = 'Exception while initialization: %s' % (traceback.format_exc())
-    
-        if fail_message is None:
-            cq.put ( {'op': 'init_ok'} )
-        else:
-            print (fail_message)
-            cq.put ( {'op': 'error', 'close': True} )
-            return 
-
-        while True:
-            obj = sq.get()
-            obj_op = obj['op']
-
-            if obj_op == 'data':
-                data = obj['data']
-                try:
-                    result = self.onClientProcessData (data)
-                    cq.put ( {'op': 'success', 'data' : data, 'result' : result} )                    
-                except:
-                    
-                    print ( 'Exception while process data [%s]: %s' % (self.onClientGetDataName(data), traceback.format_exc()) )
-                    cq.put ( {'op': 'error', 'close': True, 'data' : data } )
-            elif obj_op == 'close':
-                break
-                
-            time.sleep(0.005)
-                
-        self.onClientFinalize()
-        cq.put ( {'op': 'finalized'} )
-        while True:
-            time.sleep(0.1)

utils/console_utils.py

@@ -1,100 +0,0 @@
-import os
-import sys
-import time
-import multiprocessing
-
-def input_number(s, default_value, valid_list=None, help_message=None):
-    while True:
-        try:
-            inp = input(s)
-            if len(inp) == 0:
-                raise ValueError("")
-                
-            if help_message is not None and inp == '?':
-                print (help_message)
-                continue
-            
-            i = float(inp)
-            if (valid_list is not None) and (i not in valid_list):
-                return default_value
-            return i
-        except:
-            print (default_value)
-            return default_value
-            
-def input_int(s, default_value, valid_list=None, help_message=None):
-    while True:
-        try:
-            inp = input(s)
-            if len(inp) == 0:
-                raise ValueError("")
-                
-            if help_message is not None and inp == '?':
-                print (help_message)
-                continue
-            
-            i = int(inp)
-            if (valid_list is not None) and (i not in valid_list):
-                return default_value
-            return i
-        except:
-            print (default_value)
-            return default_value
-        
-def input_bool(s, default_value, help_message=None):
-    while True:
-        try:
-            inp = input(s)
-            if len(inp) == 0:
-                raise ValueError("")
-                
-            if help_message is not None and inp == '?':
-                print (help_message)
-                continue
-                
-            return bool ( {"y":True,"n":False,"1":True,"0":False}.get(inp.lower(), default_value) )
-        except:
-            print ( "y" if default_value else "n" )
-            return default_value
-        
-def input_str(s, default_value, valid_list=None, help_message=None):
-    while True:        
-        try:
-            inp = input(s)
-            if len(inp) == 0:
-                raise ValueError("")
-                
-            if help_message is not None and inp == '?':
-                print (help_message)
-                continue
-                
-            if (valid_list is not None) and (inp.lower() not in valid_list):
-                return default_value
-            return inp
-        except:
-            print (default_value)
-            return default_value
-            
-def input_process(stdin_fd, sq, str):
-    sys.stdin = os.fdopen(stdin_fd)
-    try:
-        inp = input (str)
-        sq.put (True)
-    except:
-        sq.put (False)
-        
-def input_in_time (str, max_time_sec):
-    sq = multiprocessing.Queue()
-    p = multiprocessing.Process(target=input_process, args=( sys.stdin.fileno(), sq, str))
-    p.start()
-    t = time.time()
-    inp = False
-    while True:
-        if not sq.empty():
-            inp = sq.get()
-            break
-        if time.time() - t > max_time_sec:
-            break
-    p.terminate()
-    sys.stdin = os.fdopen( sys.stdin.fileno() )
-    return inp