refactoring

main.py

@@ -79,12 +79,12 @@ def main():
     p.set_defaults(func=train_FaceAligner)
     
     def train_CTSOT(args):
-        from apps.trainers.CTSOT.CTSOTTrainerApp import run_app
-        run_app(userdata_path=Path(args.userdata_dir), faceset_path=Path(args.faceset_path))
+        from apps.trainers.CTSOT.CTSOTTrainerApp import CTSOTTrainerApp
+        CTSOTTrainerApp(workspace_path=Path(args.workspace_dir), faceset_path=Path(args.faceset_path))
 
     p = train_parsers.add_parser('CTSOT')
-    p.add_argument('--userdata-dir', default=None, action=fixPathAction, help="Directory to save app data.")
-    p.add_argument('--faceset-path', default=None, action=fixPathAction, help=".dfs path")
+    p.add_argument('--workspace-dir', default=None, action=fixPathAction, help="Workspace directory.")
+    p.add_argument('--faceset-path', default=None, action=fixPathAction, help=".dfs faceset path")
     p.set_defaults(func=train_CTSOT)
 
     def bad_args(arguments):

modelhub/DFLive/DFMModel.py

@@ -58,7 +58,7 @@ def get_available_devices() -> List[ORTDeviceInfo]:
 class DFMModel:
     def __init__(self, model_path : Path, device : ORTDeviceInfo = None):
         if device is None:
-            device = lib_ort.get_cpu_device()
+            device = lib_ort.get_cpu_device_info()
         self._model_path = model_path
 
         sess = self._sess = lib_ort.InferenceSession_with_device(str(model_path), device)

modelhub/torch/CTSOT/CTSOT.py

@@ -0,0 +1,121 @@
+from pathlib import Path
+from typing import Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from xlib.file import SplittedFile
+from xlib.torch import TorchDeviceInfo, get_cpu_device_info
+
+
+class ResBlock(nn.Module):
+    def __init__(self, ch):
+        super().__init__()
+        self.conv1 = nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1)
+        self.conv2 = nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1)
+        
+    def forward(self, inp):
+        x = inp
+        x = F.leaky_relu(self.conv1(x), 0.2)
+        x = F.leaky_relu(self.conv2(x) + inp, 0.2)
+        return x
+        
+class AutoEncoder(nn.Module):
+    def __init__(self, resolution, in_ch, ae_ch):
+        super().__init__()
+        self._resolution = resolution
+        self._in_ch = in_ch
+        self._ae_ch = ae_ch
+        
+        self.conv1 = nn.Conv2d(in_ch*1, in_ch*2, kernel_size=5, stride=2, padding=2)
+        self.conv2 = nn.Conv2d(in_ch*2, in_ch*4, kernel_size=5, stride=2, padding=2)
+        self.conv3 = nn.Conv2d(in_ch*4, in_ch*8, kernel_size=5, stride=2, padding=2)
+        self.conv4 = nn.Conv2d(in_ch*8, in_ch*8, kernel_size=5, stride=2, padding=2)
+        
+        self.dense_in = nn.Linear( in_ch*8 * ( resolution // (2**4) )**2, ae_ch)
+        self.dense_out = nn.Linear( ae_ch, in_ch*8 * ( resolution // (2**4) )**2 )
+        
+        self.up_conv4 = nn.ConvTranspose2d(in_ch*8, in_ch*8, kernel_size=3, stride=2, padding=1, output_padding=(1,1), )
+        self.up_conv3 = nn.ConvTranspose2d(in_ch*8, in_ch*4, kernel_size=3, stride=2, padding=1, output_padding=(1,1), )
+        self.up_conv2 = nn.ConvTranspose2d(in_ch*4, in_ch*2, kernel_size=3, stride=2, padding=1, output_padding=(1,1), )
+        self.up_conv1 = nn.ConvTranspose2d(in_ch*2, in_ch*1, kernel_size=3, stride=2, padding=1, output_padding=(1,1), )
+        
+        
+    def forward(self, inp):
+        x = inp
+        x = F.leaky_relu(self.conv1(x), 0.1)
+        x = F.leaky_relu(self.conv2(x), 0.1)
+        x = F.leaky_relu(self.conv3(x), 0.1)
+        x = F.leaky_relu(self.conv4(x), 0.1)
+        
+        x = x.view( (x.shape[0], np.prod(x.shape[1:])) )
+        
+        x = self.dense_in(x)
+        x = self.dense_out(x)
+        
+        x = x.view( (x.shape[0], self._in_ch*8, self._resolution // (2**4), self._resolution // (2**4)  ))
+        x = F.leaky_relu(self.up_conv4(x), 0.1)
+        x = F.leaky_relu(self.up_conv3(x), 0.1)
+        x = F.leaky_relu(self.up_conv2(x), 0.1)
+        x = F.leaky_relu(self.up_conv1(x), 0.1)
+        
+        # from xlib.console import diacon
+        # diacon.Diacon.stop()
+        # import code
+        # code.interact(local=dict(globals(), **locals()))
+        
+        
+        return x
+        
+        
+class CTSOTNet(nn.Module):
+    def __init__(self, resolution, in_ch=6, inner_ch=64, ae_ch=256, out_ch=3, res_block_count = 12):
+        super().__init__()
+        self.in_conv = nn.Conv2d(in_ch, inner_ch, kernel_size=1, stride=1, padding=0)
+        self.ae = AutoEncoder(resolution, inner_ch, ae_ch)
+        self.out_conv = nn.Conv2d(inner_ch, out_ch, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, img1_t, img2_t):
+        x = torch.cat([img1_t, img2_t], dim=1)
+                
+        x = self.in_conv(x)
+        
+        x = self.ae(x)
+        x = self.out_conv(x)
+        x = torch.tanh(x)
+        return x
+
+
+
+class CTSOT:
+    def __init__(self, device_info : TorchDeviceInfo = None, 
+                       state_dict : Union[dict, None] = None, 
+                       training : bool = False):
+        if device_info is None:
+            device_info = get_cpu_device_info()
+        self.device_info = device_info
+            
+        self._net = net = CTSOTNet()
+        
+        if state_dict is not None:
+            net.load_state_dict(state_dict)
+        
+        if training:
+            net.train()
+        else:
+            net.eval()
+            
+        self.set_device(device_info)
+    
+    def set_device(self, device_info : TorchDeviceInfo = None):
+        if device_info is None or device_info.is_cpu():
+            self._net.cpu()
+        else:
+            self._net.cuda(device_info.get_index())
+        
+    def get_state_dict(self):
+        return self.net.state_dict()
+        
+    def get_net(self) -> CTSOTNet:
+        return self._net

modelhub/torch/S3FD/S3FD.py

@@ -8,13 +8,13 @@ import torch.nn.functional as F
 from xlib import math as lib_math
 from xlib.file import SplittedFile
 from xlib.image import ImageProcessor
-from xlib.torch import TorchDeviceInfo, get_cpu_device
+from xlib.torch import TorchDeviceInfo, get_cpu_device_info
 
 
 class S3FD:
     def __init__(self, device_info : TorchDeviceInfo = None ):
         if device_info is None:
-            device_info = get_cpu_device()
+            device_info = get_cpu_device_info()
         self.device_info = device_info
         
         path = Path(__file__).parent / 'S3FD.pth'

modelhub/torch/__init__.py

@@ -1,2 +1,3 @@
-from .CenterFace.CenterFace import CenterFace, CenterFace_to_onnx
+from .CenterFace.CenterFace import CenterFace_to_onnx
 from .S3FD.S3FD import S3FD
+from .CTSOT.CTSOT import CTSOT, CTSOTNet

scripts/dev.py

@@ -55,12 +55,8 @@ def extract_FaceSynthetics(inputdir_path : Path, faceset_path : Path):
         raise ValueError('faceset_path must have .dfs extension.')
         
     filepaths = lib_path.get_files_paths(inputdir_path)
-
-    fs = lib_face.Faceset(faceset_path)
-    fs.recreate()
-
-
-    for filepath in lib_con.progress_bar_iterator(filepaths, 'Processing'):
+    fs = lib_face.Faceset(faceset_path, write_access=True, recreate=True)
+    for filepath in lib_con.progress_bar_iterator(filepaths, desc='Processing'):
         if filepath.suffix == '.txt':
 
             image_filepath = filepath.parent / f'{filepath.name.split("_")[0]}.png'
@@ -94,16 +90,12 @@ def extract_FaceSynthetics(inputdir_path : Path, faceset_path : Path):
             ufm.set_FRect(flmrks.get_FRect())
             ufm.add_FLandmarks2D(flmrks)
             
-            fs.add_UImage(uimg, format='png')
             fs.add_UFaceMark(ufm)            
+            fs.add_UImage(uimg, format='png')
             
-
-    fs.shrink()
+    fs.optimize()
     fs.close()
 
-    import code
-    code.interact(local=dict(globals(), **locals()))
-
 # seg_filepath = input_path / ( Path(image_filepath).stem + '_seg.png')
 # if not seg_filepath.exists():
 #     raise ValueError(f'{seg_filepath} does not exist')

xlib/avecl/_internal/op/gaussian_blur.py

@@ -15,7 +15,7 @@ def gaussian_blur (input_t : Tensor, sigma, dtype=None) -> Tensor:
 
     """
     if sigma <= 0.0:
-        return input_t.copy() #TODO
+        return input_t.copy() 
 
     device = input_t.get_device()
 

xlib/console/diacon/Diacon.py

@@ -3,75 +3,116 @@ import threading
 import time
 from enum import IntEnum
 from typing import Any, Callable, List, Tuple, Union
-
+from numbers import Number
 from ... import text as lib_text
 
 class EDlgMode(IntEnum):
-    UNDEFINED = 0
+    UNHANDLED = 0
     BACK = 1
     RELOAD = 2
     WRONG_INPUT = 3
-    SUCCESS = 4
+    HANDLED = 4
+
 
 
 class DlgChoice:
-    def __init__(self, name : str = None, row_desc : str = None):
-        super().__init__()
+    def __init__(self, name : str = None, 
+                       row_def : str = None, 
+                       on_choose : Callable = None):
         if len(name) == 0:
             raise ValueError('Zero len name is not valid.')
         self._name = name
-        self._row_desc = row_desc
+        self._row_def = row_def
+        self._on_choose = on_choose
 
     def get_name(self) -> Union[str, None]: return self._name
-    def get_row_desc(self) -> Union[str, None]: return self._row_desc
+    def get_row_def(self) -> Union[str, None]: return self._row_def
+    def get_on_choose(self) -> Callable: return self._on_choose
 
 class Dlg:
-    def __init__(self, title : str = None, has_go_back=True):
+    def __init__(self, on_recreate : Callable[ [], 'Dlg'] = None,
+                       on_back  : Callable = None,
+                       top_rows_def : Union[str, List[str]] = None, 
+                       bottom_rows_def : Union[str, List[str]] = None, 
+                       ):
         """
+        base class for Diacon dialogs.
+        """
+        self._on_recreate = on_recreate
+        self._on_back = on_back
         
+        self._top_rows_def = top_rows_def
+        self._bottom_rows_def = bottom_rows_def
+    
+    def recreate(self):
         """
-        self._title = title
-        self._has_go_back = has_go_back
+        """
+        if self._on_recreate is not None:
+            return self._on_recreate(self)
+        else:
+            raise Exception('on_recreate() is not defined.')
         
     def get_name(self) -> str: return self._name
     
-    def handle_user_input(self, s : str) -> EDlgMode:
-        """
+    def set_current(self, print=True):
+        Diacon.update_dlg(self, print=print)
     
+    def handle_user_input(self, s : str):
         """
-        s = s.strip()
+        """
+        mode = self.on_user_input(s.strip())
         
-        # ? and < available in any dialog, handle them first
-        s_len = len(s)
-        if s_len == 0:
+        if mode == EDlgMode.UNHANDLED:
+            mode = EDlgMode.RELOAD
+        if mode == EDlgMode.WRONG_INPUT:
+            print('\nWrong input')
+            mode = EDlgMode.RELOAD
+            
+        if mode == EDlgMode.RELOAD:
+            self.recreate().set_current()
+        if mode == EDlgMode.BACK:
+            if self._on_back is not None:
+                self._on_back(self)
+    
+    #overridable
+    def on_user_input(self, s : str) -> EDlgMode:
+        if len(s) == 0:
             return EDlgMode.RELOAD
-        if s_len == 1:
-            #if s == '?':
-            #    return EDlgMode.RELOAD
+        if self._on_back is not None and len(s) == 1:
             if s == '<':
                 return EDlgMode.BACK
-
-        return self.on_user_input(s)
+        return EDlgMode.UNHANDLED
         
     def print(self, table_width_max=80, col_spacing = 3):
         """
         print dialog
         """
-        
-        # Gather table lines
         table_def : List[str]= []
         
-        if self._has_go_back:
+        trd = self._top_rows_def
+        brd = self._bottom_rows_def
+        if trd is not None:
+            if not isinstance(trd, (list,tuple)):
+                trd = [trd]
+            table_def += trd
+
+        if self._on_back is not None:
             table_def.append('| < | Go back.')
 
         table_def.append('|99')
         table_def = self.on_print(table_def)
         
+        if brd is not None:
+            if not isinstance(brd, (list,tuple)):
+                brd = [brd]
+            table_def += brd
+
         table = lib_text.ascii_table(table_def, max_table_width=80,
-                                     left_border = None,
-                                     right_border = None,
+                                     left_border = '| ',
+                                     right_border = ' |',
                                      border = ' | ',
-                                     row_symbol = None)
+                                     row_symbol = None,
+                                     )
         print()
         print(table)
 
@@ -80,36 +121,102 @@ class Dlg:
     def on_print(self, table_lines : List[Tuple[str,str]]):
         return table_lines
 
+    
+
+class DlgNumber(Dlg):
+    def __init__(self, is_float : bool,
+                       current_value = None,
+                       min_value = None,
+                       max_value = None,
+                       clip_min_value = None,
+                       clip_max_value = None,
+                       on_value : Callable[ [Dlg, Number], None] = None,
+                       on_recreate : Callable[ [], 'Dlg'] = None,
+                       on_back : Callable = None,
+                       top_rows_def : Union[str, List[str]] = None, 
+                       bottom_rows_def : Union[str, List[str]] = None, ):
+        super().__init__(on_recreate=on_recreate, on_back=on_back, top_rows_def=top_rows_def, bottom_rows_def=bottom_rows_def)
+
+        if min_value is not None and max_value is not None and min_value > max_value:
+            raise ValueError('min_value > max_value')
+        if clip_min_value is not None and clip_max_value is not None and clip_min_value > clip_max_value:
+            raise ValueError('clip_min_value > clip_max_value')
+            
+        self._is_float = is_float
+        self._current_value = current_value
+        self._min_value = min_value
+        self._max_value = max_value
+        self._clip_min_value = clip_min_value
+        self._clip_max_value = clip_max_value
+        self._on_value = on_value
+
     #overridable
-    def on_user_input(self, s : str) -> EDlgMode:
-        """
-        handle user input
-        return False if input is invalid
-        """
-        return EDlgMode.UNDEFINED
+    def on_print(self, table_def : List[str]):
+
+        minv, maxv = self._min_value, self._max_value
+    
+        if self._is_float:
+            line = '| * | Enter float number'
+        else:
+            line = '| * | Enter integer number'
+        
+        if minv is not None and maxv is None:
+            line += f' in range: [{minv} ... )'
+        elif minv is None and maxv is not None:
+            line += f' in range: ( ... {maxv} ]'
+        elif minv is not None and maxv is not None:
+            line += f' in range: [{minv} ... {maxv} ]'
+    
+        table_def.append(line)
+    
+        return table_def
+
+    #overridable
+    def on_user_input(self, s : str) -> bool:
+        result = super().on_user_input(s)
+        if result == EDlgMode.UNHANDLED:
+            try:
+                print(s)
+                v = float(s) if self._is_float else int(s)
+
+                if self._min_value is not None:
+                    if v < self._min_value:
+                        return EDlgMode.WRONG_INPUT
+
+                if self._max_value is not None:
+                    if v > self._max_value:
+                        return EDlgMode.WRONG_INPUT
+
+                if self._clip_min_value is not None:
+                    if v < self._clip_min_value:
+                        v = self._clip_min_value
+
+                if self._clip_max_value is not None:
+                    if v > self._clip_max_value:
+                        v = self._clip_max_value
+                
+                if self._on_value is not None:
+                    self._on_value(self, v)
+                return EDlgMode.HANDLED
+            except:
+                return EDlgMode.WRONG_INPUT
+
+        return result
 
 class DlgChoices(Dlg):
-    def __init__(self, choices : List[DlgChoice], multiple_choices=False, title : str = None, has_go_back = True):
-        """
-
-        """
-        super().__init__(title=title, has_go_back=has_go_back)
+    def __init__(self, choices : List[DlgChoice],
+                       on_multi_choice : Callable[ [ List[DlgChoice] ], None] = None,
+                       on_recreate : Callable[ [Dlg], Dlg] = None,
+                       on_back : Callable = None,
+                       top_rows_def : Union[str, List[str]] = None, 
+                       bottom_rows_def : Union[str, List[str]] = None,
+                       ):
+        super().__init__(on_recreate=on_recreate, on_back=on_back, top_rows_def=top_rows_def, bottom_rows_def=bottom_rows_def)
         self._choices = choices
-        self._multiple_choices = multiple_choices
-
-        self._results = None
-        self._results_id = None
+        self._on_multi_choice = on_multi_choice
 
         self._short_names = [choice.get_name() for choice in choices]
 
-        # if any([x is not None for x in self._short_names]):
-        #     # Using short names from choices
-        #     if any([x is None for x in self._short_names]):
-        #         raise Exception('No short name for one of choices.')
-        #     if len(set(self._short_names)) != len(self._short_names):
-        #         raise ValueError(f'Contains duplicate short names: {self._short_names}')
-        # else:
-
         # Make short names for all choices
         names = [ choice.get_name() for choice in choices ]
         names_len = len(names)
@@ -139,27 +246,14 @@ class DlgChoices(Dlg):
                 break
         self._short_names = short_names
 
-
-
-    def get_selected_choices(self) -> List[DlgChoice]:
-        """
-        returns selected choices
-        """
-        return self._results
-
-    def get_selected_choices_id(self) -> List[int]:
-        """
-        returns selected choice
-        """
-        return self._results_id
-
     #overridable
     def on_print(self, table_def : List[str]):
+
         for short_name, choice in zip(self._short_names, self._choices):
             row_def = f'| {short_name}'
-            row_desc = choice.get_row_desc()
-            if row_desc is not None:
-                row_def += row_desc
+            x = choice.get_row_def()
+            if x is not None:
+                row_def += x
             table_def.append(row_def)
 
         return table_def
@@ -167,35 +261,36 @@ class DlgChoices(Dlg):
     #overridable
     def on_user_input(self, s : str) -> bool:
         result = super().on_user_input(s)
-        if result == EDlgMode.UNDEFINED:
+        if result == EDlgMode.UNHANDLED:
 
-            if self._multiple_choices:
+            if self._on_multi_choice is not None:
                 multi_s = s.split(',')
             else:
                 multi_s = [s]
 
-            results = []
-            results_id = []
+            choices_id = []
             for s in multi_s:
-                s = s.strip()
-
-                x = [ i for i,short_name in enumerate(self._short_names) if s == short_name  ]
+                x = [ i for i, short_name in enumerate(self._short_names) if s.strip() == short_name  ]
                 if len(x) == 0:
-                    # no short name match
+                    # No short name match
                     return EDlgMode.WRONG_INPUT
                 else:
                     id = x[0]
-                    results_id.append(id)
-                    results.append(self._choices[id])
+                    choices_id.append(id)
             
-            if len(set(results_id)) != len(results_id):
+            if len(set(choices_id)) != len(choices_id):
                 # Duplicate input
                 return EDlgMode.WRONG_INPUT
 
-            self._results = results
-            self._results_id = results_id
+            for id in choices_id:
+                on_choose = self._choices[id].get_on_choose()
+                if on_choose is not None:
+                    on_choose(self)
                     
-            return EDlgMode.SUCCESS
+            if self._on_multi_choice is not None:
+                self._on_multi_choice(choices_id)
+
+            return EDlgMode.HANDLED
 
         return result
 
@@ -204,42 +299,9 @@ class DlgChoices(Dlg):
 class _Diacon:
     """
     User dialog with via console.
-
-    Internal architecture:
-
-    [
-        Main-Thread
-
-        current thread from which __init__() called
-    ]
-
-    [
-        Dialog-Thread
-
-        separate thread where dialogs are handled and dynamically created
-
-        we need this thread, because main thread can be busy,
-        for example training neural network
-
-        calls on_dlg() provided with __init__
-
-        thus keep in mind on_dlg() works in separate thread
-
-        This thread must not be blocked inside on_dlg(),
-        because Diacon.stop() can be called that stops all threads.
-    ]
-
-    [
-        Input-Thread
-
-        separate thread where user input is accepted in non-blocking mode,
-        and transfered to processing thread
-    ]
     """
 
     def __init__(self):
-        self._on_dlg : Callable = None
-
         self._lock = threading.RLock()
         self._current_dlg : Dlg = None
         self._new_dlg : Dlg = None
@@ -250,11 +312,10 @@ class _Diacon:
         self._input_request = False
         self._input_result : str = None
 
-    def start(self, on_dlg : Callable):
+    def start(self):
         if self._started:
             raise Exception('Diacon already started.')
         self._started = True
-        self._on_dlg = on_dlg
 
         self._input_t = threading.Thread(target=self._input_thread, daemon=True)
         self._input_t.start()
@@ -285,8 +346,6 @@ class _Diacon:
 
 
     def _dialog_thread(self, ):
-        self._on_dlg(None, EDlgMode.RELOAD)
-
         while self._started:
 
             with self._lock:
@@ -303,13 +362,7 @@ class _Diacon:
                 if input_result is not None:
 
                     if self._current_dlg is not None:
-                        mode = self._current_dlg.handle_user_input(input_result)
-                        if mode == EDlgMode.WRONG_INPUT:
-                            print('\nWrong input')
-                            mode = EDlgMode.RELOAD
-                        if mode == EDlgMode.UNDEFINED:
-                            mode = EDlgMode.RELOAD
-                        self._on_dlg(self._current_dlg, mode)
+                        self._current_dlg.handle_user_input(input_result)                        
                         continue
 
             time.sleep(0.005)
@@ -332,6 +385,9 @@ class _Diacon:
         show current or set new Dialog
         Can be called from any thread.
         """
+        if not self._started:
+            self.start()
+            
         self._new_dlg = (new_dlg, print)
 
 Diacon = _Diacon()

xlib/console/diacon/__init__.py

@@ -1 +1 @@
-from .Diacon import Diacon, Dlg, DlgChoice, DlgChoices, EDlgMode
+from .Diacon import Diacon, Dlg, DlgChoice, DlgChoices, EDlgMode, DlgNumber

xlib/face/FMask.py

@@ -9,7 +9,7 @@ class FMask:
     def __init__(self, _from_pickled=False):
         """
         """
-        self._uuid : Union[bytes, None] = uuid.uuid4().bytes_le if not _from_pickled else None
+        self._uuid : Union[bytes, None] = uuid.uuid4().bytes if not _from_pickled else None
         self._mask_type : Union[FMask.Type, None] = None
         self._FImage_uuid : Union[bytes, None] = None
         

xlib/face/FaceWarper.py

@@ -52,9 +52,25 @@ class FaceWarper:
         self._rw_grid_tx         = rnd_state.uniform(*rw_grid_tx) if isinstance(rw_grid_tx, Iterable) else rw_grid_tx
         self._rw_grid_ty         = rnd_state.uniform(*rw_grid_ty) if isinstance(rw_grid_ty, Iterable) else rw_grid_ty
         
+        self._warp_rnd_mat = Affine2DUniMat.from_transformation(0.5, 0.5, self._rw_grid_rot_deg, 1.0+self._rw_grid_scale, self._rw_grid_tx, self._rw_grid_ty)
+        self._align_rnd_mat = Affine2DUniMat.from_transformation(0.5, 0.5, self._align_rot_deg, 1.0+self._align_scale, self._align_tx, self._align_ty)
+        
         self._rnd_state_state = rnd_state.get_state()
         self._cached = {}
         
+    def get_aligned_random_transform_mat(self) -> Affine2DUniMat:
+        """
+        returns Affine2DUniMat that represents transformation from aligned face to randomly transformed aligned face
+        """
+        mat1 = self._img_to_face_uni_mat
+        mat2 = (self._face_to_img_uni_mat * self._align_rnd_mat).invert()
+        
+        pts = [ [0,0], [1,0], [1,1]]
+        src_pts = mat1.transform_points(pts)
+        dst_pts = mat2.transform_points(pts)
+        
+        return Affine2DUniMat.from_3_pairs(src_pts, dst_pts)
+        
     def transform(self, img : np.ndarray, out_res : int, random_warp : bool = True) -> np.ndarray:
         """
         transform an image. 
@@ -91,9 +107,7 @@ class FaceWarper:
             face_warp_grid = FaceWarper._gen_random_warp_uni_grid_diff(out_res, self._rw_grid_cell_count, 0.12, rnd_state)
 
             # make a randomly transformable mat to transform face_warp_grid from face to image
-            face_warp_grid_mat = (self._face_to_img_uni_mat *
-                                  Affine2DUniMat.from_transformation(0.5, 0.5, self._rw_grid_rot_deg, 1.0+self._rw_grid_scale, self._rw_grid_tx, self._rw_grid_ty)
-                                  )
+            face_warp_grid_mat = self._face_to_img_uni_mat * self._warp_rnd_mat
 
             # warp face_warp_grid to the space of image and merge with image_grid
             image_grid += cv2.warpAffine(face_warp_grid, face_warp_grid_mat.to_exact_mat(out_res,out_res, W, H), (W,H), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
@@ -101,9 +115,10 @@ class FaceWarper:
         # scale uniform grid from to image size
         image_grid *= (H-1, W-1)
 
-        # apply random transormations for align mat
-        img_to_face_rnd_mat = (self._face_to_img_uni_mat * Affine2DMat.from_transformation(0.5, 0.5, self._align_rot_deg, 1.0+self._align_scale, self._align_tx, self._align_ty)
-                                ).invert().to_exact_mat(W,H,out_res,out_res)
+        # apply random transformations for align mat
+        #img_to_face_rnd_uni_mat = (self._face_to_img_uni_mat * self._align_rnd_mat).invert()
+        
+        img_to_face_rnd_mat = (self._face_to_img_uni_mat * self._align_rnd_mat).invert().to_exact_mat(W,H,out_res,out_res)
 
         # warp image_grid to face space
         image_grid = cv2.warpAffine(image_grid, img_to_face_rnd_mat, (out_res,out_res), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE )

xlib/face/Faceset.py

@@ -1,19 +1,22 @@
 import pickle
-import sqlite3
+import uuid
 from pathlib import Path
-from typing import Generator, List, Union, Iterable
+from typing import Generator, Iterable, List, Union
 
 import cv2
+import h5py
 import numpy as np
 
+from .. import console as lib_con
 from .FMask import FMask
 from .UFaceMark import UFaceMark
 from .UImage import UImage
 from .UPerson import UPerson
 
+
 class Faceset:
 
-    def __init__(self, path = None):
+    def __init__(self, path = None, write_access=False, recreate=False):
         """
         Faceset is a class to store and manage face related data.
 
@@ -21,205 +24,155 @@ class Faceset:
 
             path       path to faceset .dfs file
 
+            write_access
+
+            recreate
+
         Can be pickled.
         """
+        self._f = None
+
         self._path = path = Path(path)
         if path.suffix != '.dfs':
             raise ValueError('Path must be a .dfs file')
 
-        self._conn = conn = sqlite3.connect(path, isolation_level=None)
-        self._cur = cur = conn.cursor()
+        if path.exists():
+            if write_access and recreate:
+                path.unlink()
+        elif not write_access:
+            raise FileNotFoundError(f'File {path} not found.')
 
-        cur = self._get_cursor()
-        cur.execute('BEGIN IMMEDIATE')
-        if not self._is_table_exists('FacesetInfo'):
-            self.recreate(shrink=False, _transaction=False)
-            cur.execute('COMMIT')
-            self.shrink()
-        else:
-            cur.execute('END')
+        self._mode = 'a' if write_access else 'r'
+        self._open()
 
     def __del__(self):
         self.close()
 
     def __getstate__(self):
-        return {'_path' : self._path}
+        return {'_path' : self._path, '_mode' : self._mode}
 
     def __setstate__(self, d):
-        self.__init__( d['_path'] )
+        self._f = None
+        self._path = d['_path']
+        self._mode = d['_mode']
+        self._open()
 
     def __repr__(self): return self.__str__()
     def __str__(self):
         return f"Faceset. UImage:{self.get_UImage_count()} UFaceMark:{self.get_UFaceMark_count()} UPerson:{self.get_UPerson_count()}"
 
-    def _is_table_exists(self, name):
-        return self._cur.execute(f"SELECT count(*) FROM sqlite_master WHERE type='table' AND name=?", [name]).fetchone()[0] != 0
+    def _open(self):
+        if self._f is None:
+            self._f = f = h5py.File(self._path, mode=self._mode)
+            self._UFaceMark_grp = f.require_group('UFaceMark')
+            self._UImage_grp = f.require_group('UImage')
+            self._UImage_image_data_grp = f.require_group('UImage_image_data')
+            self._UPerson_grp = f.require_group('UPerson')
 
-    def _get_cursor(self) -> sqlite3.Cursor: return self._cur
 
     def close(self):
-        if self._cur is not None:
-            self._cur.close()
-            self._cur = None
+        if self._f is not None:
+            self._f.close()
+            self._f = None
 
-        if self._conn is not None:
-            self._conn.close()
-            self._conn = None
-
-    def shrink(self):
-        self._cur.execute('VACUUM')
-
-    def recreate(self, shrink=True, _transaction=True):
+    def optimize(self, verbose=True):
         """
-        delete all data and recreate Faceset structure.
+        recreate Faceset with optimized structure.
         """
-        cur = self._get_cursor()
+        if verbose:
+            print(f'Optimizing {self._path.name}...')
 
-        if _transaction:
-            cur.execute('BEGIN IMMEDIATE')
+        tmp_path = self._path.parent / (self._path.stem + '_optimizing' + self._path.suffix)
 
-        for table_name, in cur.execute("SELECT name from sqlite_master where type = 'table';").fetchall():
-            cur.execute(f'DROP TABLE {table_name}')
+        tmp_fs = Faceset(tmp_path, write_access=True, recreate=True)
+        self._group_copy(tmp_fs._UFaceMark_grp, self._UFaceMark_grp, verbose=verbose)
+        self._group_copy(tmp_fs._UPerson_grp, self._UPerson_grp, verbose=verbose)
+        self._group_copy(tmp_fs._UImage_grp, self._UImage_grp, verbose=verbose)
+        self._group_copy(tmp_fs._UImage_image_data_grp, self._UImage_image_data_grp, verbose=verbose)
+        tmp_fs.close()
 
-        (cur.execute('CREATE TABLE FacesetInfo (version INT)')
-            .execute('INSERT INTO  FacesetInfo VALUES (1)')
+        self.close()
+        self._path.unlink()
+        tmp_path.rename(self._path)
+        self._open()
 
-            .execute('CREATE TABLE UImage (uuid BLOB, name TEXT, format TEXT, data BLOB)')
-            .execute('CREATE TABLE UPerson (uuid BLOB, data BLOB)')
-            .execute('CREATE TABLE UFaceMark (uuid BLOB, UImage_uuid BLOB, UPerson_uuid BLOB, data BLOB)')
-            )
+    def _group_copy(self, group_dst : h5py.Group, group_src : h5py.Group, verbose=True):
+        for key, value in lib_con.progress_bar_iterator(group_src.items(), desc=f'Copying {group_src.name} -> {group_dst.name}', suppress_print=not verbose):
+            d = group_dst.create_dataset(key, shape=value.shape, dtype=value.dtype )
+            d[:] = value[:]
+            for a_key, a_value in value.attrs.items():
+                d.attrs[a_key] = a_value
 
-        if _transaction:
-            cur.execute('COMMIT')
+    def _group_read_bytes(self, group : h5py.Group, key : str, check_key=True) -> Union[bytes, None]:
+        if check_key and key not in group:
+            return None
+        dataset = group[key]
+        data_bytes = bytearray(len(dataset))
+        dataset.read_direct(np.frombuffer(data_bytes, dtype=np.uint8))
+        return data_bytes
 
-        if shrink:
-            self.shrink()
+    def _group_write_bytes(self, group : h5py.Group, key : str, data : bytes, update_existing=True) -> Union[h5py.Dataset, None]:
+        if key in group:
+            if not update_existing:
+                return None
+            del group[key]
+
+        return group.create_dataset(key, data=np.frombuffer(data, dtype=np.uint8) )
 
     ###################
     ### UFaceMark
     ###################
-    def _UFaceMark_from_db_row(self, db_row) -> UFaceMark:
-        uuid, UImage_uuid, UPerson_uuid, data = db_row
-
-        ufm = UFaceMark()
-        ufm.restore_state(pickle.loads(data))
-        return ufm
-
-    def add_UFaceMark(self, ufacemark_or_list : UFaceMark):
+    def add_UFaceMark(self, ufacemark_or_list : UFaceMark, update_existing=True):
         """
         add or update UFaceMark in DB
         """
         if not isinstance(ufacemark_or_list, Iterable):
             ufacemark_or_list : List[UFaceMark] = [ufacemark_or_list]
 
-        cur = self._cur
-        cur.execute('BEGIN IMMEDIATE')
         for ufm in ufacemark_or_list:
-            uuid = ufm.get_uuid()
-            UImage_uuid = ufm.get_UImage_uuid()
-            UPerson_uuid = ufm.get_UPerson_uuid()
-
-            data = pickle.dumps(ufm.dump_state())
-
-            if cur.execute('SELECT COUNT(*) from UFaceMark where uuid=?', [uuid] ).fetchone()[0] != 0:
-                cur.execute('UPDATE UFaceMark SET UImage_uuid=?, UPerson_uuid=?, data=? WHERE uuid=?',
-                            [UImage_uuid, UPerson_uuid, data, uuid])
-            else:
-                cur.execute('INSERT INTO UFaceMark VALUES (?, ?, ?, ?)', [uuid, UImage_uuid, UPerson_uuid, data])
-        cur.execute('COMMIT')
+            self._group_write_bytes(self._UFaceMark_grp, ufm.get_uuid().hex(), pickle.dumps(ufm.dump_state()), update_existing=update_existing )
 
     def get_UFaceMark_count(self) -> int:
-        return self._cur.execute('SELECT COUNT(*) FROM UFaceMark').fetchone()[0]
+        return len(self._UFaceMark_grp.keys())
 
     def get_all_UFaceMark(self) -> List[UFaceMark]:
-        return [ self._UFaceMark_from_db_row(db_row) for db_row in self._cur.execute('SELECT * FROM UFaceMark').fetchall() ]
+        return [ UFaceMark.from_state(pickle.loads(self._group_read_bytes(self._UFaceMark_grp, key, check_key=False))) for key in self._UFaceMark_grp.keys() ]
+    
+    def get_all_UFaceMark_uuids(self) -> List[bytes]:
+        return [ uuid.UUID(key).bytes for key in self._UFaceMark_grp.keys() ]
      
     def get_UFaceMark_by_uuid(self, uuid : bytes) -> Union[UFaceMark, None]:
-        c = self._cur.execute('SELECT * FROM UFaceMark WHERE uuid=?', [uuid])
-        db_row = c.fetchone()
-        if db_row is None:
+        data = self._group_read_bytes(self._UFaceMark_grp, uuid.hex())
+        if data is None:
             return None
+        return UFaceMark.from_state(pickle.loads(data))
+
+    def delete_UFaceMark_by_uuid(self, uuid : bytes) -> bool:
+        key = uuid.hex()
+        if key in self._UFaceMark_grp:
+            del self._UFaceMark_grp[key]
+            return True
+        return False
     
-        return self._UFaceMark_from_db_row(db_row)
            
     def iter_UFaceMark(self) -> Generator[UFaceMark, None, None]:
         """
         returns Generator of UFaceMark
         """
-        for db_row in self._cur.execute('SELECT * FROM UFaceMark').fetchall():
-            yield self._UFaceMark_from_db_row(db_row)
+        for key in self._UFaceMark_grp.keys():
+            yield UFaceMark.from_state(pickle.loads(self._group_read_bytes(self._UFaceMark_grp, key, check_key=False)))
 
     def delete_all_UFaceMark(self):
         """
         deletes all UFaceMark from DB
         """
-        (self._cur.execute('BEGIN IMMEDIATE')
-                  .execute('DELETE FROM UFaceMark')
-                  .execute('COMMIT') )
-    ###################
-    ### UPerson
-    ###################
-    def _UPerson_from_db_row(self, db_row) -> UPerson:
-        uuid, data = db_row
-        up = UPerson()
-        up.restore_state(pickle.loads(data))
-        return up
-
-    def add_UPerson(self, uperson_or_list : UPerson):
-        """
-        add or update UPerson in DB
-        """
-        if not isinstance(uperson_or_list, Iterable):
-            uperson_or_list : List[UPerson] = [uperson_or_list]
-
-        cur = self._cur
-        cur.execute('BEGIN IMMEDIATE')
-        for uperson in uperson_or_list:
-            uuid = uperson.get_uuid()
-
-            data = pickle.dumps(uperson.dump_state())
-
-            if cur.execute('SELECT COUNT(*) from UPerson where uuid=?', [uuid]).fetchone()[0] != 0:
-                cur.execute('UPDATE UPerson SET data=? WHERE uuid=?', [data])
-            else:
-                cur.execute('INSERT INTO UPerson VALUES (?, ?)', [uuid, data])
-        cur.execute('COMMIT')
-
-    def get_UPerson_count(self) -> int:
-        return self._cur.execute('SELECT COUNT(*) FROM UPerson').fetchone()[0]
-
-    def get_all_UPerson(self) -> List[UPerson]:
-        return [ self._UPerson_from_db_row(db_row) for db_row in self._cur.execute('SELECT * FROM UPerson').fetchall() ]
-
-    def iter_UPerson(self) -> Generator[UPerson, None, None]:
-        """
-        iterator of all UPerson's
-        """
-        for db_row in self._cur.execute('SELECT * FROM UPerson').fetchall():
-            yield self._UPerson_from_db_row(db_row)
-
-    def delete_all_UPerson(self):
-        """
-        deletes all UPerson from DB
-        """
-        (self._cur.execute('BEGIN IMMEDIATE')
-                  .execute('DELETE FROM UPerson')
-                  .execute('COMMIT') )
+        for key in self._UFaceMark_grp.keys():
+            del self._UFaceMark_grp[key]
 
     ###################
     ### UImage
     ###################
-    def _UImage_from_db_row(self, db_row) -> UImage:
-        uuid, name, format, data_bytes = db_row
-        img = cv2.imdecode(np.frombuffer(data_bytes, dtype=np.uint8), flags=cv2.IMREAD_UNCHANGED)
-
-        uimg = UImage()
-        uimg.set_uuid(uuid)
-        uimg.set_name(name)
-        uimg.assign_image(img)
-        return uimg
-
-    def add_UImage(self, uimage_or_list : UImage, format : str = 'webp', quality : int = 100):
+    def add_UImage(self, uimage_or_list : UImage, format : str = 'png', quality : int = 100, update_existing=True):
         """
         add or update UImage in DB
 
@@ -239,9 +192,8 @@ class Faceset:
             raise ValueError('quality must be in range [0..100]')
 
         if not isinstance(uimage_or_list, Iterable):
-            uimage_or_list = [uimage_or_list]
+            uimage_or_list : List[UImage] = [uimage_or_list]
 
-        uimage_datas = []
         for uimage in uimage_or_list:
             if format == 'webp':
                 imencode_args = [int(cv2.IMWRITE_WEBP_QUALITY), quality]
@@ -251,44 +203,112 @@ class Faceset:
                 imencode_args = [int(cv2.IMWRITE_JPEG2000_COMPRESSION_X1000), quality*10]
             else:
                 imencode_args = []
+
             ret, data_bytes = cv2.imencode( f'.{format}', uimage.get_image(), imencode_args)
             if not ret:
                 raise Exception(f'Unable to encode image format {format}')
-            uimage_datas.append(data_bytes.data)
 
-        cur = self._cur
-        cur.execute('BEGIN IMMEDIATE')
-        for uimage, data in zip(uimage_or_list, uimage_datas):
-            uuid = uimage.get_uuid()
-            if cur.execute('SELECT COUNT(*) from UImage where uuid=?', [uuid] ).fetchone()[0] != 0:
-                cur.execute('UPDATE UImage SET name=?, format=?, data=? WHERE uuid=?', [uimage.get_name(), format, data, uuid])
-            else:
-                cur.execute('INSERT INTO UImage VALUES (?, ?, ?, ?)', [uuid, uimage.get_name(), format, data])
-        cur.execute('COMMIT')
+            key = uimage.get_uuid().hex()
 
-    def get_UImage_count(self) -> int: return self._cur.execute('SELECT COUNT(*) FROM UImage').fetchone()[0]
-    def get_UImage_by_uuid(self, uuid : Union[bytes, None]) -> Union[UImage, None]:
-        """
-        """
-        if uuid is None:
+            self._group_write_bytes(self._UImage_grp, key, pickle.dumps(uimage.dump_state(exclude_image=True)), update_existing=update_existing )
+            d = self._group_write_bytes(self._UImage_image_data_grp, key, data_bytes.data, update_existing=update_existing )
+            d.attrs['format'] = format
+            d.attrs['quality'] = quality
+
+    def get_UImage_count(self) -> int:
+        return len(self._UImage_grp.keys())
+
+    def get_all_UImage(self) -> List[UImage]:
+        return [ self._get_UImage_by_key(key) for key in self._UImage_grp.keys() ]
+        
+    def get_all_UImage_uuids(self) -> List[bytes]:
+        return [ uuid.UUID(key).bytes for key in self._UImage_grp.keys() ]
+        
+    def _get_UImage_by_key(self, key, check_key=True) -> Union[UImage, None]:
+        data = self._group_read_bytes(self._UImage_grp, key, check_key=check_key)
+        if data is None:
             return None
+        uimg = UImage.from_state(pickle.loads(data))
 
-        db_row = self._cur.execute('SELECT * FROM UImage where uuid=?', [uuid]).fetchone()
-        if db_row is None:
-            return None
-        return self._UImage_from_db_row(db_row)
+        image_data = self._group_read_bytes(self._UImage_image_data_grp, key, check_key=check_key)
+        if image_data is not None:
+            uimg.assign_image (cv2.imdecode(np.frombuffer(image_data, dtype=np.uint8), flags=cv2.IMREAD_UNCHANGED))
 
-    def iter_UImage(self) -> Generator[UImage, None, None]:
+        return uimg
+
+    def get_UImage_by_uuid(self, uuid : bytes) -> Union[UImage, None]:
+        return self._get_UImage_by_key(uuid.hex())
+
+    def delete_UImage_by_uuid(self, uuid : bytes):
+        key = uuid.hex()
+        if key in self._UImage_grp:
+            del self._UImage_grp[key]
+        if key in self._UImage_image_data_grp:
+            del self._UImage_image_data_grp[key]
+
+    def iter_UImage(self, include_key=False) -> Generator[UImage, None, None]:
         """
-        iterator of all UImage's
+        returns Generator of UImage
         """
-        for db_row in self._cur.execute('SELECT * FROM UImage').fetchall():
-            yield self._UImage_from_db_row(db_row)
+        for key in self._UImage_grp.keys():
+            uimg = self._get_UImage_by_key(key, check_key=False)
+            yield (uimg, key) if include_key else uimg
 
     def delete_all_UImage(self):
         """
         deletes all UImage from DB
         """
-        (self._cur.execute('BEGIN IMMEDIATE')
-                  .execute('DELETE FROM UImage')
-                  .execute('COMMIT') )
+        for key in self._UImage_grp.keys():
+            del self._UImage_grp[key]
+        for key in self._UImage_image_data_grp.keys():
+            del self._UImage_image_data_grp[key]
+
+    ###################
+    ### UPerson
+    ###################
+
+    def add_UPerson(self, uperson_or_list : UPerson, update_existing=True):
+        """
+        add or update UPerson in DB
+        """
+        if not isinstance(uperson_or_list, Iterable):
+            uperson_or_list : List[UPerson] = [uperson_or_list]
+
+        for uperson in uperson_or_list:
+            self._group_write_bytes(self._UPerson_grp, uperson.get_uuid().hex(), pickle.dumps(uperson.dump_state()), update_existing=update_existing )
+
+    def get_UPerson_count(self) -> int:
+        return len(self._UPerson_grp.keys())
+
+    def get_all_UPerson(self) -> List[UPerson]:
+        return [ UPerson.from_state(pickle.loads(self._group_read_bytes(self._UPerson_grp, key, check_key=False))) for key in self._UPerson_grp.keys() ]
+    
+    def get_all_UPerson_uuids(self) -> List[bytes]:
+        return [ uuid.UUID(key).bytes for key in self._UPerson_grp.keys() ]
+    
+    def get_UPerson_by_uuid(self, uuid : bytes) -> Union[UPerson, None]:
+        data = self._group_read_bytes(self._UPerson_grp, uuid.hex())
+        if data is None:
+            return None
+        return UPerson.from_state(pickle.loads(data))
+
+    def delete_UPerson_by_uuid(self, uuid : bytes) -> bool:
+        key = uuid.hex()
+        if key in self._UPerson_grp:
+            del self._UPerson_grp[key]
+            return True
+        return False
+
+    def iter_UPerson(self) -> Generator[UPerson, None, None]:
+        """
+        returns Generator of UPerson
+        """
+        for key in self._UPerson_grp.keys():
+            yield UPerson.from_state(pickle.loads(self._group_read_bytes(self._UPerson_grp, key, check_key=False)))
+
+    def delete_all_UPerson(self):
+        """
+        deletes all UPerson from DB
+        """
+        for key in self._UPerson_grp.keys():
+            del self._UPerson_grp[key]

xlib/face/UFaceMark.py

@@ -26,6 +26,12 @@ class UFaceMark(IState):
     def __str__(self):
         return f"UFaceMark UUID:[...{self.get_uuid()[-4:].hex()}]"
     
+    @staticmethod
+    def from_state(state : dict) -> 'UFaceMark':
+        ufm = UFaceMark()
+        ufm.restore_state(state)
+        return ufm
+    
     def restore_state(self, state : dict):
         self._uuid = state.get('_uuid', None)
         self._UImage_uuid = state.get('_UImage_uuid', None)
@@ -45,7 +51,7 @@ class UFaceMark(IState):
 
     def get_uuid(self) -> Union[bytes, None]:
         if self._uuid is None:
-            self._uuid = uuid.uuid4().bytes_le
+            self._uuid = uuid.uuid4().bytes
         return self._uuid
 
     def set_uuid(self, uuid : Union[bytes, None]):
@@ -72,6 +78,16 @@ class UFaceMark(IState):
         self._FRect = face_urect
 
     def get_all_FLandmarks2D(self) -> List[FLandmarks2D]: return self._FLandmarks2D_list
+    
+    def get_FLandmarks2D_best(self) -> Union[FLandmarks2D, None]:
+        """get best available FLandmarks2D """
+        lmrks = self.get_FLandmarks2D_by_type(ELandmarks2D.L468)
+        if lmrks is None:
+            lmrks = self.get_FLandmarks2D_by_type(ELandmarks2D.L68)
+        if lmrks is None:
+            lmrks = self.get_FLandmarks2D_by_type(ELandmarks2D.L5)
+        return lmrks
+        
     def get_FLandmarks2D_by_type(self, type : ELandmarks2D) -> Union[FLandmarks2D, None]:
         """get FLandmarks2D from list by type"""
         if not isinstance(type, ELandmarks2D):

xlib/face/UImage.py

@@ -19,9 +19,30 @@ class UImage(IState):
     def __str__(self): return f"UImage UUID:[...{self.get_uuid()[-4:].hex()}] name:[{self._name}] image:[{ (self._image.shape, self._image.dtype) if self._image is not None else None}]"
     def __repr__(self): return self.__str__()
     
+    @staticmethod
+    def from_state(state : dict) -> 'UImage':
+        ufm = UImage()
+        ufm.restore_state(state)
+        return ufm
+        
+    def restore_state(self, state : dict):
+        self._uuid = state.get('_uuid', None)
+        self._name = state.get('_name', None)
+        self._image = state.get('_image', None)
+
+    def dump_state(self, exclude_image=False) -> dict:
+        d =  {'_uuid' : self._uuid,
+              '_name' : self._name,
+              }
+              
+        if not exclude_image:
+            d['_image'] = self._image
+            
+        return d
+                
     def get_uuid(self) -> Union[bytes, None]:
         if self._uuid is None:
-            self._uuid = uuid.uuid4().bytes_le
+            self._uuid = uuid.uuid4().bytes
         return self._uuid
 
     def set_uuid(self, uuid : Union[bytes, None]):

xlib/face/UPerson.py

@@ -15,6 +15,12 @@ class UPerson(IState):
     def __str__(self): return f"UPerson UUID:[...{self._uuid[-4:].hex()}] name:[{self._name}] age:[{self._age}]"
     def __repr__(self): return self.__str__()
 
+    @staticmethod
+    def from_state(state : dict) -> 'UPerson':
+        ufm = UPerson()
+        ufm.restore_state(state)
+        return ufm
+        
     def restore_state(self, state : dict):
         self._uuid = state.get('_uuid', None)
         self._name = state.get('_name', None)
@@ -28,7 +34,7 @@ class UPerson(IState):
 
     def get_uuid(self) -> Union[bytes, None]:
         if self._uuid is None:
-            self._uuid = uuid.uuid4().bytes_le
+            self._uuid = uuid.uuid4().bytes
         return self._uuid
 
     def set_uuid(self, uuid : Union[bytes, None]):

xlib/image/color_transfer/sot.py

@@ -2,12 +2,14 @@ import cv2
 import numpy as np
 import numpy.linalg as npla
 
-def sot(src,trg, steps=10, batch_size=30, reg_sigmaXY=16.0, reg_sigmaV=5.0):
+def sot(src,trg, mask=None, steps=10, batch_size=30, reg_sigmaXY=16.0, reg_sigmaV=5.0, return_diff=False):
     """
     Color Transform via Sliced Optimal Transfer, ported from https://github.com/dcoeurjo/OTColorTransfer
 
     src         - any float range any channel image
     dst         - any float range any channel image, same shape as src
+    mask        - 
+    
     steps       - number of solver steps
     batch_size  - solver batch size
     reg_sigmaXY - apply regularization and sigmaXY of filter, otherwise set to 0.0
@@ -39,8 +41,8 @@ def sot(src,trg, steps=10, batch_size=30, reg_sigmaXY=16.0, reg_sigmaV=5.0):
             dir = np.random.normal(size=c).astype(src_dtype)
             dir /= npla.norm(dir)
 
-            projsource = np.sum( new_src*dir, axis=-1).reshape ((h*w))
-            projtarget = np.sum( trg*dir, axis=-1).reshape ((h*w))
+            projsource = np.sum( new_src*dir*mask, axis=-1).reshape ((h*w))
+            projtarget = np.sum( trg*dir*mask, axis=-1).reshape ((h*w))
 
             idSource = np.argsort (projsource)
             idTarget = np.argsort (projtarget)
@@ -48,12 +50,18 @@ def sot(src,trg, steps=10, batch_size=30, reg_sigmaXY=16.0, reg_sigmaV=5.0):
             a = projtarget[idTarget]-projsource[idSource]
             for i_c in range(c):
                 advect[idSource,i_c] += a * dir[i_c]
-        new_src += advect.reshape( (h,w,c) ) / batch_size
+                
+        new_src += (advect.reshape( (h,w,c) ) * mask) / batch_size
 
     if reg_sigmaXY != 0.0:
         src_diff = new_src-src
         src_diff_filt = cv2.bilateralFilter (src_diff, 0, reg_sigmaV, reg_sigmaXY )
         if len(src_diff_filt.shape) == 2:
             src_diff_filt = src_diff_filt[...,None]
-        new_src = src + src_diff_filt
+        if return_diff:
+            return src_diff_filt
+        return src + src_diff_filt
+    else:
+        if return_diff:
+            return new_src-src
         return new_src

xlib/mp/MPSPSCMRRingData.py

@@ -37,7 +37,7 @@ class MPSPSCMRRingData:
 
         # Initialize first block at 0 index
         wid = 0
-        wid_uuid = uuid.uuid4().bytes_le
+        wid_uuid = uuid.uuid4().bytes
         wid_heap_offset = 0
         wid_data_size = 0
 
@@ -82,7 +82,7 @@ class MPSPSCMRRingData:
             raise Exception('data_size more than heap_size')
 
         fmv = FormattedMemoryViewIO(self._shared_mem.get_mv())
-        wid_uuid = uuid.uuid4().bytes_le
+        wid_uuid = uuid.uuid4().bytes
 
         if self._write_lock is not None:
             self._write_lock.acquire()

xlib/mp/MPWeakHeap.py

@@ -49,7 +49,7 @@ class MPWeakHeap:
 
         # Entire block
         fmv.seek(self._first_block_offset)
-        fmv.write_fmt('qq', self._heap_size-self._first_block_offset, 0), fmv.write(uuid.uuid4().bytes_le)
+        fmv.write_fmt('qq', self._heap_size-self._first_block_offset, 0), fmv.write(uuid.uuid4().bytes)
 
 
     def add_data(self, data : Union[bytes, bytearray, memoryview] ) -> 'MPWeakHeap.DataRef':
@@ -92,7 +92,7 @@ class MPWeakHeap:
                 if block_remain_size >= block_header_size:
                     # the remain space of the block is enough for next block, split the block
                     next_block_offset = cur_block_offset + block_new_size
-                    fmv.seek(next_block_offset), fmv.write_fmt('qq', block_remain_size, 0), fmv.write(uuid.uuid4().bytes_le)
+                    fmv.seek(next_block_offset), fmv.write_fmt('qq', block_remain_size, 0), fmv.write(uuid.uuid4().bytes)
                 else:
                     # otherwise do not split
                     next_block_offset = cur_block_offset + block_size
@@ -101,7 +101,7 @@ class MPWeakHeap:
                     block_new_size = block_size
 
                 # update current block structure
-                uid = uuid.uuid4().bytes_le
+                uid = uuid.uuid4().bytes
                 fmv.seek(cur_block_offset), fmv.write_fmt('qq', block_new_size, data_size ), fmv.write(uid)
 
                 # update ring_head_block_offset
@@ -135,7 +135,7 @@ class MPWeakHeap:
                     next_block_size, = fmv.get_fmt('q')
 
                     # erase data of next block
-                    fmv.write_fmt('qq', 0, 0), fmv.write(uuid.uuid4().bytes_le)
+                    fmv.write_fmt('qq', 0, 0), fmv.write(uuid.uuid4().bytes)
 
                     # overwrite current block size with expanded block size
                     fmv.seek(cur_block_offset)

xlib/mp/SPMTWorker.py

@@ -11,7 +11,6 @@ def _host_thread_proc(wref):
             break
         ref._host_process_messages(0.005)
         del ref
-    print('_host_thread_proc exit')
 
 class SPMTWorker:
     def __init__(self, *sub_args, **sub_kwargs):

xlib/onnxruntime/__init__.py

@@ -1,3 +1,3 @@
 from .device import (ORTDeviceInfo, get_available_devices_info,
-                     get_cpu_device)
+                     get_cpu_device_info)
 from .InferenceSession import InferenceSession_with_device

xlib/onnxruntime/device.py

@@ -67,33 +67,34 @@ class ORTDeviceInfo:
 
 _ort_devices_info = None
 
-def get_cpu_device() -> ORTDeviceInfo:
+def get_cpu_device_info() -> ORTDeviceInfo:
     return ORTDeviceInfo(index=-1, execution_provider='CPUExecutionProvider', name='CPU', total_memory=0, free_memory=0)
 
 def get_available_devices_info(include_cpu=True, cpu_only=False) -> List[ORTDeviceInfo]:
     """
     returns a list of available ORTDeviceInfo
     """
-    global _ort_devices_info
-    if _ort_devices_info is None:
-        _initialize_ort_devices()
     devices = []
     if not cpu_only: 
+        global _ort_devices_info
+        if _ort_devices_info is None:
+            _initialize_ort_devices_info()
+            _ort_devices_info = []
             for i in range ( int(os.environ.get('ORT_DEVICES_COUNT',0)) ):
-                devices.append ( ORTDeviceInfo(index=int(os.environ[f'ORT_DEVICE_{i}_INDEX']),
+                _ort_devices_info.append ( ORTDeviceInfo(index=int(os.environ[f'ORT_DEVICE_{i}_INDEX']),
                                             execution_provider=os.environ[f'ORT_DEVICE_{i}_EP'],
                                             name=os.environ[f'ORT_DEVICE_{i}_NAME'],
                                             total_memory=int(os.environ[f'ORT_DEVICE_{i}_TOTAL_MEM']),
                                             free_memory=int(os.environ[f'ORT_DEVICE_{i}_FREE_MEM']),
                                             ) )
-        if include_cpu or cpu_only:
-            devices.append(get_cpu_device())
-        _ort_devices_info = devices
+        devices += _ort_devices_info
+    if include_cpu:
+        devices.append(get_cpu_device_info())
 
-    return _ort_devices_info
+    return devices
 
 
-def _initialize_ort_devices():
+def _initialize_ort_devices_info():
     """
     Determine available ORT devices, and place info about them to os.environ,
     they will be available in spawned subprocesses.
@@ -184,4 +185,4 @@ def _initialize_ort_devices():
             os.environ[f'ORT_DEVICE_{i}_TOTAL_MEM'] = str(device['total_mem'])
             os.environ[f'ORT_DEVICE_{i}_FREE_MEM'] = str(device['free_mem'])
         
-_initialize_ort_devices()
+_initialize_ort_devices_info()

xlib/text/ascii_table.py

@@ -248,6 +248,7 @@ def ascii_table(table_def : List[str],
         lines.append(row_line)
     for sub_rows in rows:
         
+        
         for row in sub_rows:
             line = ''
 
@@ -288,7 +289,8 @@ def ascii_table(table_def : List[str],
                 line += right_border
 
             lines.append(line)
-        if row_line is not None:
+            
+        if len(sub_rows) != 0 and row_line is not None:
             lines.append(row_line)
 
     return '\n'.join(lines)

xlib/torch/__init__.py

@@ -1,2 +1,2 @@
-from .device import (TorchDeviceInfo, TorchDevicesInfo, get_available_devices,
-                     get_cpu_device)
+from .device import (TorchDeviceInfo, get_available_devices_info,
+                     get_cpu_device_info, get_device, get_device_info_by_index)

xlib/torch/device.py

@@ -1,4 +1,6 @@
-from typing import List
+from typing import List, Union
+
+import torch
 
 
 class TorchDeviceInfo:
@@ -45,96 +47,40 @@ class TorchDeviceInfo:
     def __repr__(self):
         return f'{self.__class__.__name__} object: ' + self.__str__()
 
-# class TorchDevicesInfo:
-#     """
-#     picklable list of TorchDeviceInfo
-#     """
-#     def __init__(self, devices : List[TorchDeviceInfo] = None):
-#         if devices is None:
-#             devices = []
-#         self._devices = devices
-
-#     def __getstate__(self):
-#         return self.__dict__.copy()
-
-#     def __setstate__(self, d):
-#         self.__init__()
-#         self.__dict__.update(d)
-
-#     def add(self, device_or_devices : TorchDeviceInfo):
-#         if isinstance(device_or_devices, TorchDeviceInfo):
-#             if device_or_devices not in self._devices:
-#                 self._devices.append(device_or_devices)
-#         elif isinstance(device_or_devices, TorchDevicesInfo):
-#             for device in device_or_devices:
-#                 self.add(device)
-
-#     def copy(self):
-#         return copy.deepcopy(self)
-
-#     def get_count(self): return len(self._devices)
-
-#     def get_largest_total_memory_device(self) -> TorchDeviceInfo:
-#         raise NotImplementedError()
-#         result = None
-#         idx_mem = 0
-#         for device in self._devices:
-#             mem = device.get_total_memory()
-#             if result is None or (mem is not None and mem > idx_mem):
-#                 result = device
-#                 idx_mem = mem
-#         return result
-
-#     def get_smallest_total_memory_device(self) -> TorchDeviceInfo:
-#         raise NotImplementedError()
-#         result = None
-#         idx_mem = sys.maxsize
-#         for device in self._devices:
-#             mem = device.get_total_memory()
-#             if result is None or (mem is not None and mem < idx_mem):
-#                 result = device
-#                 idx_mem = mem
-#         return result
-
-#     def __len__(self):
-#         return len(self._devices)
-
-#     def __getitem__(self, key):
-#         result = self._devices[key]
-#         if isinstance(key, slice):
-#             return self.__class__(result)
-#         return result
-
-#     def __iter__(self):
-#         for device in self._devices:
-#             yield device
-
-#     def __str__(self):  return f'{self.__class__.__name__}:[' + ', '.join([ device.__str__() for device in self._devices ]) + ']'
-#     def __repr__(self): return f'{self.__class__.__name__}:[' + ', '.join([ device.__repr__() for device in self._devices ]) + ']'
-
-
 _torch_devices = None
 
-def get_cpu_device() -> TorchDeviceInfo:
+def get_cpu_device_info() -> TorchDeviceInfo:
     return TorchDeviceInfo(index=-1, name='CPU', total_memory=0)
 
-def get_available_devices(include_cpu=True, cpu_only=False) -> List[TorchDeviceInfo]:
+def get_device_info_by_index(index) -> Union[TorchDeviceInfo, None]:
+    for device in get_available_devices_info(include_cpu=False):
+        if device.get_index() == index:
+            return device
+
+    return None
+
+def get_device(device_info : TorchDeviceInfo) -> torch.device:
+    if device_info.is_cpu():
+        return torch.device('cpu')
+    return torch.device(f'cuda:{device_info.get_index()}')
+        
+def get_available_devices_info(include_cpu=True, cpu_only=False) -> List[TorchDeviceInfo]:
     """
     returns a list of available TorchDeviceInfo
     """
+    devices = []
+    if not cpu_only:
         global _torch_devices
         if _torch_devices is None:
-        import torch
-        devices = []
             
-        if not cpu_only:
+            _torch_devices = []
             for i in range (torch.cuda.device_count()):
                 device_props = torch.cuda.get_device_properties(i)
-                devices.append ( TorchDeviceInfo(index=i, name=device_props.name, total_memory=device_props.total_memory))
+                _torch_devices.append ( TorchDeviceInfo(index=i, name=device_props.name, total_memory=device_props.total_memory))
+        devices += _torch_devices
 
-        if include_cpu or cpu_only:
-            devices.append ( get_cpu_device() )
+    if include_cpu:
+        devices.append ( get_cpu_device_info() )
 
-        _torch_devices = devices
-    return _torch_devices
+    return devices