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converter:

Browse source 
fixed crashes

removed useless 'ebs' color transfer

changed keys for color degrade

added image degrade via denoise - same as denoise extracted data_dst.bat ,
but you can control this option directly in the interactive converter

added image degrade via bicubic downscale and upscale

SAEHD: default ae_dims for df now 256.
This commit is contained in:
Colombo 2019-11-09 15:12:35 +04:00
parent 374d8c2388
commit 770c70d778
8 changed files with 274 additions and 57 deletions
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32
converters/ConvertMasked.py
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@ -8,10 +8,8 @@ from facelib import FaceType, LandmarksProcessor
from interact import interact as io
from utils.cv2_utils import *
def ConvertMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmarks):
img_size = img_bgr.shape[1], img_bgr.shape[0]
img_face_mask_a = LandmarksProcessor.get_image_hull_mask (img_bgr.shape, img_face_landmarks)
if cfg.mode == 'original':
@ -85,7 +83,7 @@ def ConvertMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, i
full_face_fanchq_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, cfg.fanchq_input_size, face_type=FaceType.FULL)
dst_face_fanchq_bgr = cv2.warpAffine(img_bgr, full_face_fanchq_mat, (cfg.fanchq_input_size,)*2, flags=cv2.INTER_CUBIC )
dst_face_fanchq_mask = cfg.fanchq_extract_func( FaceType.FULL, dst_face_fanchq_bgr )
if cfg.face_type == FaceType.FULL:
FANCHQ_dst_face_mask_a_0 = cv2.resize (dst_face_fanchq_mask, (output_size,output_size), cv2.INTER_CUBIC)
else:
@ -110,7 +108,7 @@ def ConvertMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, i
prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_dst_face_mask_a_0
#elif cfg.mask_mode == 8: #FANCHQ-dst
# prd_face_mask_a_0 = FANCHQ_dst_face_mask_a_0
prd_face_mask_a_0[ prd_face_mask_a_0 < 0.001 ] = 0.0
prd_face_mask_a = prd_face_mask_a_0[...,np.newaxis]
@ -231,7 +229,7 @@ def ConvertMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, i
hist_match_2 = dst_face_bgr*hist_mask_a + white
hist_match_2[ hist_match_1 > 1.0 ] = 1.0
prd_face_bgr = imagelib.color_hist_match(hist_match_1, hist_match_2, cfg.hist_match_threshold )
prd_face_bgr = imagelib.color_hist_match(hist_match_1, hist_match_2, cfg.hist_match_threshold ).astype(dtype=np.float32)
if cfg.mode == 'hist-match-bw':
prd_face_bgr = prd_face_bgr.astype(dtype=np.float32)
@ -254,13 +252,11 @@ def ConvertMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, i
break
if cfg.mode == 'seamless2':
face_seamless = imagelib.seamless_clone ( prd_face_bgr, dst_face_bgr, img_face_seamless_mask_a )
out_img = cv2.warpAffine( face_seamless, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT )
else:
out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT )
out_img = np.clip(out_img, 0.0, 1.0)
if 'seamless' in cfg.mode and cfg.mode != 'seamless2':
@ -278,7 +274,8 @@ def ConvertMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, i
raise Exception("Seamless fail: " + e_str) #reraise MemoryError in order to reprocess this data by other processes
else:
print ("Seamless fail: " + e_str)
out_img = img_bgr*(1-img_face_mask_aaa) + (out_img*img_face_mask_aaa)
out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size) )
@ -322,6 +319,23 @@ def ConvertMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, i
if cfg.blursharpen_amount != 0:
out_face_bgr = cfg.blursharpen_func ( out_face_bgr, cfg.sharpen_mode, 3, cfg.blursharpen_amount)
if cfg.image_denoise_power != 0:
n = cfg.image_denoise_power
while n > 0:
img_bgr_denoised = cv2.medianBlur(img_bgr, 5)
if int(n / 100) != 0:
img_bgr = img_bgr_denoised
else:
pass_power = (n % 100) / 100.0
img_bgr = img_bgr*(1.0-pass_power)+img_bgr_denoised*pass_power
n = max(n-10,0)
if cfg.bicubic_degrade_power != 0:
p = 1.0 - cfg.bicubic_degrade_power / 101.0
img_bgr_downscaled = cv2.resize (img_bgr, ( int(img_size[0]*p), int(img_size[1]*p ) ), cv2.INTER_CUBIC)
img_bgr = cv2.resize (img_bgr_downscaled, img_size, cv2.INTER_CUBIC)
new_out = cv2.warpAffine( out_face_bgr, face_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT )
out_img = np.clip( img_bgr*(1-img_face_mask_aaa) + (new_out*img_face_mask_aaa) , 0, 1.0 )