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two pass prediction on merger, added pre_sharpen modes (gaussion + unsharpen)

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Jan 2021-11-26 12:09:15 +01:00
commit 555e8964e0
5 changed files with 62 additions and 7 deletions
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2
core/imagelib/__init__.py
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@ -18,7 +18,7 @@ from .common import random_crop, normalize_channels, cut_odd_image, overlay_alph
from .SegIEPolys import *
from .blursharpen import LinearMotionBlur, blursharpen
from .blursharpen import LinearMotionBlur, blursharpen, gaussian_sharpen, unsharpen_mask
from .filters import apply_random_rgb_levels, \
apply_random_overlay_triangle, \

14
core/imagelib/blursharpen.py
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@ -35,4 +35,18 @@ def blursharpen (img, sharpen_mode=0, kernel_size=3, amount=100):
n = max(n-10,0)
return img
return img
def gaussian_sharpen(img, amount=100, sigma=1.0):
img = cv2.addWeighted(img, 1.0 + (0.05 * amount), cv2.GaussianBlur(img, (0, 0), sigma), -(0.05 * amount), 0)
return np.clip(img, 0, 1, out=img)
def unsharpen_mask(img, amount=100, sigma=0.0, threshold = (5.0 / 255.0)):
radius = max(1, round(img.shape[0] * (amount / 100)))
kernel_size = int((radius * 2) + 1)
kernel_size = (kernel_size, kernel_size)
blur = cv2.GaussianBlur(img, kernel_size, sigma)
low_contrast_mask = (abs(img - blur) < threshold).astype("float32")
sharpened = (img * (1.0 + (0.05 * amount))) + (blur * -(0.05 * amount))
img = (img * (1.0 - low_contrast_mask)) + (sharpened * low_contrast_mask)
return img

3
merger/InteractiveMergerSubprocessor.py
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@ -330,11 +330,12 @@ class InteractiveMergerSubprocessor(Subprocessor):
'z' : lambda cfg,shift_pressed: cfg.toggle_masked_hist_match(),
'x' : lambda cfg,shift_pressed: cfg.toggle_mask_mode(),
'c' : lambda cfg,shift_pressed: cfg.toggle_color_transfer_mode(),
'n' : lambda cfg,shift_pressed: cfg.toggle_sharpen_mode(),
'n' : lambda cfg,shift_pressed: cfg.toggle_sharpen_mode_multi(shift_pressed),
'9' : lambda cfg,shift_pressed: cfg.add_pre_sharpen_power(1),
'8' : lambda cfg,shift_pressed: cfg.add_pre_sharpen_power(-1),
'(' : lambda cfg,shift_pressed: cfg.add_morph_power(1),
'*' : lambda cfg,shift_pressed: cfg.add_morph_power(-1),
'b' : lambda cfg,shift_pressed: cfg.toggle_two_pass(),
}
self.masked_keys = list(self.masked_keys_funcs.keys())

17
merger/MergeMasked.py
View file

@ -62,16 +62,29 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
dst_face_mask_a_0 = np.clip(dst_face_mask_a_0, 0, 1)
if cfg.pre_sharpen_power != 0:
dst_face_bgr = cv2.addWeighted(dst_face_bgr, 1.0 + (0.05 * cfg.pre_sharpen_power), cv2.GaussianBlur(dst_face_bgr, (0, 0), 1.0), -(0.05 * cfg.pre_sharpen_power), 0)
dst_face_bgr = np.clip(dst_face_bgr, 0, 1, out=dst_face_bgr)
if cfg.pre_sharpen_mode:
dst_face_bgr = imagelib.gaussian_sharpen(dst_face_bgr, amount=cfg.pre_sharpen_power)
else:
dst_face_bgr = imagelib.unsharpen_mask(dst_face_bgr, amount=cfg.pre_sharpen_power)
#dst_face_bgr = cv2.addWeighted(dst_face_bgr, 1.0 + (0.05 * cfg.pre_sharpen_power), cv2.GaussianBlur(dst_face_bgr, (0, 0), 1.0), -(0.05 * cfg.pre_sharpen_power), 0)
#dst_face_bgr = np.clip(dst_face_bgr, 0, 1, out=dst_face_bgr)
predictor_input_bgr = cv2.resize (dst_face_bgr, (input_size,input_size) )
predicted = predictor_func (predictor_input_bgr, func_morph_factor = cfg.morph_power/100.0) if cfg.is_morphable else predictor_func (predictor_input_bgr)
prd_face_bgr = np.clip (predicted[0], 0, 1.0)
prd_face_mask_a_0 = np.clip (predicted[1], 0, 1.0)
prd_face_dst_mask_a_0 = np.clip (predicted[2], 0, 1.0)
if cfg.two_pass:
predicted_2 = predictor_func (prd_face_bgr, func_morph_factor = 1.0) if cfg.is_morphable else predictor_func (prd_face_bgr)
prd_face_bgr = np.clip (predicted_2[0], 0, 1.0)
prd_face_mask_a_0 = np.clip (predicted_2[1], 0, 1.0)
prd_face_dst_mask_a_0 = np.clip (predicted_2[2], 0, 1.0)
if cfg.super_resolution_power != 0:
prd_face_bgr_enhanced = face_enhancer_func(prd_face_bgr, is_tanh=True, preserve_size=False)

33
merger/MergerConfig.py
View file

@ -97,6 +97,9 @@ mask_mode_dict = {0:'full',
ctm_dict = { 0: "None", 1:"rct", 2:"lct", 3:"mkl", 4:"mkl-m", 5:"idt", 6:"idt-m", 7:"sot-m", 8:"mix-m" }
ctm_str_dict = {None:0, "rct":1, "lct":2, "mkl":3, "mkl-m":4, "idt":5, "idt-m":6, "sot-m":7, "mix-m":8 }
pre_sharpen_dict = {0:'gaussian', 1:'unsharpen_mask'}
class MergerConfigMasked(MergerConfig):
def __init__(self, face_type=FaceType.FULL,
@ -115,6 +118,8 @@ class MergerConfigMasked(MergerConfig):
bicubic_degrade_power = 0,
color_degrade_power = 0,
pre_sharpen_power = 0,
pre_sharpen_mode=0,
two_pass = False,
morph_power = 100,
is_morphable = False,
**kwargs
@ -145,7 +150,9 @@ class MergerConfigMasked(MergerConfig):
self.image_denoise_power = image_denoise_power
self.bicubic_degrade_power = bicubic_degrade_power
self.color_degrade_power = color_degrade_power
self.two_pass = two_pass
self.pre_sharpen_power = pre_sharpen_power
self.pre_sharpen_mode = pre_sharpen_mode
self.morph_power = morph_power
self.is_morphable = is_morphable
@ -158,7 +165,22 @@ class MergerConfigMasked(MergerConfig):
def toggle_masked_hist_match(self):
if self.mode == 'hist-match':
self.masked_hist_match = not self.masked_hist_match
def toggle_two_pass(self):
self.two_pass = not self.two_pass
def toggle_sharpen_mode_multi(self, pre_sharpen=False):
if pre_sharpen:
self.toggle_sharpen_mode_presharpen()
else:
self.toggle_sharpen_mode()
def toggle_sharpen_mode_presharpen(self):
a = list( pre_sharpen_dict.keys() )
self.pre_sharpen_mode = a[ (a.index(self.pre_sharpen_mode)+1) % len(a) ]
def add_hist_match_threshold(self, diff):
if self.mode == 'hist-match' or self.mode == 'seamless-hist-match':
self.hist_match_threshold = np.clip ( self.hist_match_threshold+diff , 0, 255)
@ -227,7 +249,8 @@ class MergerConfigMasked(MergerConfig):
self.erode_mask_modifier = np.clip ( io.input_int ("Choose erode mask modifier", 0, add_info="-400..400"), -400, 400)
self.blur_mask_modifier = np.clip ( io.input_int ("Choose blur mask modifier", 0, add_info="0..400"), 0, 400)
self.motion_blur_power = np.clip ( io.input_int ("Choose motion blur power", 0, add_info="0..100"), 0, 100)
self.two_pass = io.input_bool("Use two pass mode?", False, help_message="Can enhance results by feeding network output again to network.")
self.pre_sharpen_power = np.clip (io.input_int ("Choose pre_sharpen power", 0, help_message="Can enhance results by pre sharping before feeding it to the network.", add_info="0..100" ), 0, 200)
if self.is_morphable:
@ -270,6 +293,8 @@ class MergerConfigMasked(MergerConfig):
self.bicubic_degrade_power == other.bicubic_degrade_power and \
self.color_degrade_power == other.color_degrade_power and \
self.pre_sharpen_power == other.pre_sharpen_power and \
self.pre_sharpen_mode == other.pre_sharpen_mode and \
self.two_pass == other.two_pass and \
self.morph_power == other.morph_power and \
self.is_morphable == other.is_morphable
@ -308,8 +333,10 @@ class MergerConfigMasked(MergerConfig):
f"""color_degrade_power: {self.color_degrade_power}\n""")
r += f"""pre_sharpen_power: {self.pre_sharpen_power}\n"""
r += f"""pre_sharpen_mode: {pre_sharpen_dict[self.pre_sharpen_mode]}\n"""
r += f"""two_pass: {self.two_pass}\n"""
r += f"""morph_power: {self.morph_power}\n"""
r += f"""is_morphable: {self.is_morphable}\n"""
#r += f"""is_morphable: {self.is_morphable}\n"""
r += "================"