mirror of
https://github.com/iperov/DeepFaceLab.git
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b72d5a3f9a
fixed error "dll load failed" on some systems Expanded eyebrows line of face masks. It does not affect mask of FAN-x converter mode.
61 lines
2.4 KiB
Python
61 lines
2.4 KiB
Python
import time
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import cv2
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import numpy as np
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from facelib import FaceType, LandmarksProcessor
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from joblib import SubprocessFunctionCaller
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from utils.pickle_utils import AntiPickler
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from .Converter import Converter
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class ConverterAvatar(Converter):
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#override
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def __init__(self, predictor_func,
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predictor_input_size=0):
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super().__init__(predictor_func, Converter.TYPE_FACE_AVATAR)
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self.predictor_input_size = predictor_input_size
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#dummy predict and sleep, tensorflow caching kernels. If remove it, conversion speed will be x2 slower
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predictor_func ( np.zeros ( (predictor_input_size,predictor_input_size,3), dtype=np.float32 ),
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np.zeros ( (predictor_input_size,predictor_input_size,3), dtype=np.float32 ),
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np.zeros ( (predictor_input_size,predictor_input_size,3), dtype=np.float32 ) )
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time.sleep(2)
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predictor_func_host, predictor_func = SubprocessFunctionCaller.make_pair(predictor_func)
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self.predictor_func_host = AntiPickler(predictor_func_host)
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self.predictor_func = predictor_func
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#overridable
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def on_host_tick(self):
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self.predictor_func_host.obj.process_messages()
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#override
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def cli_convert_face (self, f0, f0_lmrk, f1, f1_lmrk, f2, f2_lmrk, debug, **kwargs):
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if debug:
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debugs = []
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inp_size = self.predictor_input_size
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f0_mat = LandmarksProcessor.get_transform_mat (f0_lmrk, inp_size, face_type=FaceType.FULL_NO_ALIGN)
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f1_mat = LandmarksProcessor.get_transform_mat (f1_lmrk, inp_size, face_type=FaceType.FULL_NO_ALIGN)
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f2_mat = LandmarksProcessor.get_transform_mat (f2_lmrk, inp_size, face_type=FaceType.FULL_NO_ALIGN)
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inp_f0 = cv2.warpAffine( f0, f0_mat, (inp_size, inp_size), flags=cv2.INTER_CUBIC )
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inp_f1 = cv2.warpAffine( f1, f1_mat, (inp_size, inp_size), flags=cv2.INTER_CUBIC )
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inp_f2 = cv2.warpAffine( f2, f2_mat, (inp_size, inp_size), flags=cv2.INTER_CUBIC )
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prd_f = self.predictor_func ( inp_f0, inp_f1, inp_f2 )
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out_img = np.clip(prd_f, 0.0, 1.0)
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out_img = np.concatenate ( [cv2.resize ( inp_f1, (prd_f.shape[1], prd_f.shape[0]) ),
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out_img], axis=1 )
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if debug:
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debugs += [out_img.copy()]
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return debugs if debug else out_img
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