mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-11 15:47:01 -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 * _
70 lines
2.8 KiB
Python
70 lines
2.8 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,1), 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, img_bgr, img_face_landmarks, debug, avaperator_face_bgr=None, **kwargs):
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if debug:
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debugs = [img_bgr.copy()]
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img_size = img_bgr.shape[1], img_bgr.shape[0]
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img_face_mask_a = LandmarksProcessor.get_image_hull_mask (img_bgr.shape, img_face_landmarks)
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img_face_mask_aaa = np.repeat(img_face_mask_a, 3, -1)
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output_size = self.predictor_input_size
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face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=FaceType.FULL)
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dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
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predictor_input_dst_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (self.predictor_input_size,self.predictor_input_size), cv2.INTER_CUBIC )
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prd_inp_dst_face_mask_a = predictor_input_dst_face_mask_a_0[...,np.newaxis]
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prd_inp_avaperator_face_bgr = cv2.resize (avaperator_face_bgr, (self.predictor_input_size,self.predictor_input_size), cv2.INTER_CUBIC )
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prd_face_bgr = self.predictor_func ( prd_inp_avaperator_face_bgr, prd_inp_dst_face_mask_a )
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out_img = img_bgr.copy()
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out_img = cv2.warpAffine( prd_face_bgr, face_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
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out_img = np.clip(out_img, 0.0, 1.0)
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if debug:
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debugs += [out_img.copy()]
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out_img = np.clip( img_bgr*(1-img_face_mask_aaa) + (out_img*img_face_mask_aaa) , 0, 1.0 )
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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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