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https://github.com/iperov/DeepFaceLive
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code release
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iperov
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354 changed files with 826570 additions and 1 deletions
224
modelhub/DFLive/DFMModel.py
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modelhub/DFLive/DFMModel.py
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from pathlib import Path
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from typing import Iterator, List, Tuple, Union
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import numpy as np
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from xlib import onnxruntime as lib_ort
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from xlib import path as lib_path
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from xlib.image import ImageProcessor
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from xlib.net import ThreadFileDownloader
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from xlib.onnxruntime.device import ORTDeviceInfo
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class DFMModelInfo:
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def __init__(self, name : str, model_path : Path, url : str = None):
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self._name = name
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self._model_path = model_path
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self._url = url
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def get_name(self) -> str: return self._name
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def get_model_path(self) -> Path: return self._model_path
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def get_url(self) -> Union[str, None]: return self._url
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def __eq__(self, other):
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if self is not None and other is not None and isinstance(self, DFMModelInfo) and isinstance(other, DFMModelInfo):
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return self._name == other._name
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return False
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def __hash__(self):
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return self._name.__hash__()
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def __str__(self):
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return self._name
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def get_available_models_info(models_path : Path) -> List[DFMModelInfo]:
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# predefined list of celebs with urls
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dfm_models = [
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#DFMModelInfo(name='Tom Cruise', model_path=models_path / f'Tom Cruise.dfm', url=rf'https://github.com/iperov/DeepFaceLive/releases/download/test/TOM_CRISE.onnx'),#TODO https://github.com/iperov/DeepFaceLive/releases/download/dfm/TOM_CRUISE.dfm'),
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#DFMModelInfo(name='Vladimir Putin', model_path=models_path / f'Vladimir Putin.dfm', url=rf'https://github.com/iperov/DeepFaceLive/releases/download/dfm/VLADIMIR_PUTIN.dfm'),
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]
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# scan additional models in directory
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dfm_model_paths = [ celeb.get_model_path() for celeb in dfm_models]
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for dfm_path in lib_path.get_files_paths(models_path, extensions=['.dfm']):
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if dfm_path not in dfm_model_paths:
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dfm_models.append( DFMModelInfo(dfm_path.stem, model_path=dfm_path, ) )
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return dfm_models
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def get_available_devices() -> List[ORTDeviceInfo]:
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"""
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"""
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return lib_ort.get_available_devices_info()
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class DFMModel:
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def __init__(self, model_path : Path, device : ORTDeviceInfo = None):
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if device is None:
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device = lib_ort.get_cpu_device()
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self._model_path = model_path
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sess = self._sess = lib_ort.InferenceSession_with_device(str(model_path), device)
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inputs = sess.get_inputs()
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if len(inputs) == 0:
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raise Exception(f'Invalid model {model_path}')
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else:
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if 'in_face' not in inputs[0].name:
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raise Exception(f'Invalid model {model_path}')
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else:
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self._input_height, self._input_width = inputs[0].shape[1:3]
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self._model_type = 1
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if len(inputs) == 2:
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if 'morph_value' not in inputs[1].name:
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raise Exception(f'Invalid model {model_path}')
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self._model_type = 2
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elif len(inputs) > 2:
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raise Exception(f'Invalid model {model_path}')
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def get_model_path(self) -> Path: return self._model_path
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def get_input_res(self) -> Tuple[int, int]:
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return self._input_width, self._input_height
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def has_morph_value(self) -> bool:
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return self._model_type == 2
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def convert(self, img, morph_factor=0.75):
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"""
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img np.ndarray HW,HWC,NHWC uint8,float32
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morph_factor float used if model supports it
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returns
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img NHW3 same dtype as img
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celeb_mask NHW1 same dtype as img
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face_mask NHW1 same dtype as img
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"""
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ip = ImageProcessor(img)
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N,H,W,C = ip.get_dims()
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dtype = ip.get_dtype()
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img = ip.resize( (self._input_width,self._input_height) ).ch(3).to_ufloat32().get_image('NHWC')
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if self._model_type == 1:
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out_face_mask, out_celeb, out_celeb_mask = self._sess.run(None, {'in_face:0': img})
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elif self._model_type == 2:
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out_face_mask, out_celeb, out_celeb_mask = self._sess.run(None, {'in_face:0': img, 'morph_value:0':np.float32([morph_factor]) })
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out_celeb = ImageProcessor(out_celeb).resize((W,H)).ch(3).to_dtype(dtype).get_image('NHWC')
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out_celeb_mask = ImageProcessor(out_celeb_mask).resize((W,H)).ch(1).to_dtype(dtype).get_image('NHWC')
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out_face_mask = ImageProcessor(out_face_mask).resize((W,H)).ch(1).to_dtype(dtype).get_image('NHWC')
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return out_celeb, out_celeb_mask, out_face_mask
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class DFMModelInitializer:
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"""
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class to initialize DFMModel from DFMModelInfo
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use .process_events() to iterate initialization process with events
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"""
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class Events:
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prev_status_initializing : bool = False
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prev_status_downloading : bool = False
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prev_status_initialized : bool = False
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prev_status_error : bool = False
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new_status_initializing : bool = False
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new_status_downloading : bool = False
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new_status_initialized : bool = False
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new_status_error : bool = False
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download_progress : float = None
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error : str = None
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dfm_model : DFMModel = None
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def __init__(self, dfm_model_info : DFMModelInfo, device : ORTDeviceInfo = None ): self._gen = self._generator(dfm_model_info, device)
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def process_events(self) -> 'DFMModelInitializer.Events': return next(self._gen)
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def _generator(self, dfm_model_info : DFMModelInfo, device : ORTDeviceInfo = None) -> Iterator['DFMModelInitializer.Events']:
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"""
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Creates a generator object to initialize DFM model from provided parameters
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"""
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INITIALIZING, DOWNLOADING, INITIALIZED, ERROR, = range(4)
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downloader : ThreadFileDownloader = None
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status = None
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while True:
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events = DFMModelInitializer.Events()
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new_status = status
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if status is None:
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new_status = INITIALIZING
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elif status == INITIALIZING:
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model_path = dfm_model_info.get_model_path()
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if not model_path.exists():
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url = dfm_model_info.get_url()
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if url is None:
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new_status = ERROR
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events.error = 'Model file is not found and URL is not defined.'
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else:
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downloader = ThreadFileDownloader(url=url, savepath=model_path)
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new_status = DOWNLOADING
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else:
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error = None
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try:
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dfm_model = DFMModel(model_path, device)
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except Exception as e:
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error = str(e)
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if error is None:
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new_status = INITIALIZED
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events.dfm_model = dfm_model
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else:
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new_status = ERROR
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events.error = error
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elif status == DOWNLOADING:
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error = downloader.get_error()
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if error is None:
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progress = downloader.get_progress()
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if progress == 100.0:
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new_status = INITIALIZING
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else:
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events.download_progress = progress
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else:
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new_status = ERROR
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events.error = error
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if new_status != status:
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events.prev_status_initializing = status == INITIALIZING
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events.prev_status_downloading = status == DOWNLOADING
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events.prev_status_initialized = status == INITIALIZED
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events.prev_status_error = status == ERROR
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events.new_status_initializing = new_status == INITIALIZING
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events.new_status_downloading = new_status == DOWNLOADING
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events.new_status_initialized = new_status == INITIALIZED
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events.new_status_error = new_status == ERROR
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status = new_status
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yield events
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def DFMModel_from_path(model_path : Path, device : ORTDeviceInfo = None) -> DFMModel:
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"""
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instantiates DFMModel
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"""
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return DFMModel(model_path=model_path, device=device, __check=1)
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def DFMModel_from_info(dfm_model_info : DFMModelInfo, device : ORTDeviceInfo = None) -> DFMModelInitializer:
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"""
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instantiates DFMModelInitializer
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"""
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return DFMModelInitializer(dfm_model_info=dfm_model_info, device=device)
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