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https://github.com/iperov/DeepFaceLab.git
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76ca79216e
Removed the wait at first launch for most graphics cards. Increased speed of training by 10-20%, but you have to retrain all models from scratch. SAEHD: added option 'use float16' Experimental option. Reduces the model size by half. Increases the speed of training. Decreases the accuracy of the model. The model may collapse or not train. Model may not learn the mask in large resolutions. true_face_training option is replaced by "True face power". 0.0000 .. 1.0 Experimental option. Discriminates the result face to be more like the src face. Higher value - stronger discrimination. Comparison - https://i.imgur.com/czScS9q.png
41 lines
1.6 KiB
Python
41 lines
1.6 KiB
Python
import cv2
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import numpy as np
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from core import imagelib
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from facelib import FaceType, LandmarksProcessor
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from core.cv2ex import *
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def process_frame_info(frame_info, inp_sh):
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img_uint8 = cv2_imread (frame_info.filename)
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img_uint8 = imagelib.normalize_channels (img_uint8, 3)
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img = img_uint8.astype(np.float32) / 255.0
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img_mat = LandmarksProcessor.get_transform_mat (frame_info.landmarks_list[0], inp_sh[0], face_type=FaceType.FULL_NO_ALIGN)
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img = cv2.warpAffine( img, img_mat, inp_sh[0:2], borderMode=cv2.BORDER_REPLICATE, flags=cv2.INTER_CUBIC )
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return img
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def MergeFaceAvatar (predictor_func, predictor_input_shape, cfg, prev_temporal_frame_infos, frame_info, next_temporal_frame_infos):
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inp_sh = predictor_input_shape
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prev_imgs=[]
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next_imgs=[]
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for i in range(cfg.temporal_face_count):
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prev_imgs.append( process_frame_info(prev_temporal_frame_infos[i], inp_sh) )
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next_imgs.append( process_frame_info(next_temporal_frame_infos[i], inp_sh) )
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img = process_frame_info(frame_info, inp_sh)
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prd_f = predictor_func ( prev_imgs, img, next_imgs )
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if cfg.super_resolution_mode != 0:
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prd_f = cfg.superres_func(cfg.super_resolution_mode, prd_f)
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if cfg.sharpen_mode != 0 and cfg.sharpen_amount != 0:
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prd_f = cfg.sharpen_func ( prd_f, cfg.sharpen_mode, 3, cfg.sharpen_amount)
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out_img = np.clip(prd_f, 0.0, 1.0)
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if cfg.add_source_image:
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out_img = np.concatenate ( [cv2.resize ( img, (prd_f.shape[1], prd_f.shape[0]) ),
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out_img], axis=1 )
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return (out_img*255).astype(np.uint8)
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