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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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import imagelib
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from facelib import FaceType, LandmarksProcessor
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from utils.cv2_utils 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], flags=cv2.INTER_CUBIC )
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return img
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def ConvertFaceAvatar (cfg, prev_temporal_frame_infos, frame_info, next_temporal_frame_infos):
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inp_sh = cfg.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 = cfg.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, 0.003, 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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