mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 13:02:15 -07:00
046649e6be
* superb improved fanseg * _ * _ * added FANseg extractor for src and dst faces to use it in training * - * _ * _ * update to 'partial' func * _ * trained FANSeg_256_full_face.h5, new experimental models: AVATAR, RecycleGAN * _ * _ * _ * fix for TCC mode cards(tesla), was conflict with plaidML initialization. * _ * update manuals * _
50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
import copy
|
|
'''
|
|
You can implement your own Converter, check example ConverterMasked.py
|
|
'''
|
|
|
|
class Converter(object):
|
|
TYPE_FACE = 0 #calls convert_face
|
|
TYPE_FACE_AVATAR = 1 #calls convert_face with avatar_operator_face
|
|
TYPE_IMAGE = 2 #calls convert_image without landmarks
|
|
TYPE_IMAGE_WITH_LANDMARKS = 3 #calls convert_image with landmarks
|
|
|
|
#overridable
|
|
def __init__(self, predictor_func, type):
|
|
self.predictor_func = predictor_func
|
|
self.type = type
|
|
|
|
#overridable
|
|
def on_cli_initialize(self):
|
|
#cli initialization
|
|
pass
|
|
|
|
#overridable
|
|
def on_host_tick(self):
|
|
pass
|
|
|
|
#overridable
|
|
def cli_convert_face (self, img_bgr, img_face_landmarks, debug, avaperator_face_bgr=None, **kwargs):
|
|
#return float32 image
|
|
#if debug , return tuple ( images of any size and channels, ...)
|
|
return image
|
|
|
|
#overridable
|
|
def cli_convert_image (self, img_bgr, img_landmarks, debug):
|
|
#img_landmarks not None, if input image is png with embedded data
|
|
#return float32 image
|
|
#if debug , return tuple ( images of any size and channels, ...)
|
|
return image
|
|
|
|
#overridable
|
|
def dummy_predict(self):
|
|
#do dummy predict here
|
|
pass
|
|
|
|
def copy(self):
|
|
return copy.copy(self)
|
|
|
|
def copy_and_set_predictor(self, predictor_func):
|
|
result = self.copy()
|
|
result.predictor_func = predictor_func
|
|
return result
|