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fixed mask editor

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added FacesetEnhancer
4.2.other) data_src util faceset enhance best GPU.bat
4.2.other) data_src util faceset enhance multi GPU.bat

FacesetEnhancer greatly increases details in your source face set,
same as Gigapixel enhancer, but in fully automatic mode.
In OpenCL build it works on CPU only.

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This commit is contained in:
Colombo
2019-12-26 21:27:10 +04:00
parent 3be223a265
commit d46fb5cfd3
6 changed files with 476 additions and 6 deletions
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facelib/FaceEnhancer.py
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import operator
from pathlib import Path
import cv2
import numpy as np
class FaceEnhancer(object):
"""
x4 face enhancer
"""
def __init__(self):
from nnlib import nnlib
exec( nnlib.import_all(), locals(), globals() )
model_path = Path(__file__).parent / "FaceEnhancer.h5"
if not model_path.exists():
return
bgr_inp = Input ( (192,192,3) )
t_param_inp = Input ( (1,) )
t_param1_inp = Input ( (1,) )
x = Conv2D (64, 3, strides=1, padding='same' )(bgr_inp)
a = Dense (64, use_bias=False) ( t_param_inp )
a = Reshape( (1,1,64) )(a)
b = Dense (64, use_bias=False ) ( t_param1_inp )
b = Reshape( (1,1,64) )(b)
x = Add()([x,a,b])
x = LeakyReLU(0.1)(x)
x = LeakyReLU(0.1)(Conv2D (64, 3, strides=1, padding='same' )(x))
x = e0 = LeakyReLU(0.1)(Conv2D (64, 3, strides=1, padding='same')(x))
x = AveragePooling2D()(x)
x = LeakyReLU(0.1)(Conv2D (112, 3, strides=1, padding='same')(x))
x = e1 = LeakyReLU(0.1)(Conv2D (112, 3, strides=1, padding='same')(x))
x = AveragePooling2D()(x)
x = LeakyReLU(0.1)(Conv2D (192, 3, strides=1, padding='same')(x))
x = e2 = LeakyReLU(0.1)(Conv2D (192, 3, strides=1, padding='same')(x))
x = AveragePooling2D()(x)
x = LeakyReLU(0.1)(Conv2D (336, 3, strides=1, padding='same')(x))
x = e3 = LeakyReLU(0.1)(Conv2D (336, 3, strides=1, padding='same')(x))
x = AveragePooling2D()(x)
x = LeakyReLU(0.1)(Conv2D (512, 3, strides=1, padding='same')(x))
x = e4 = LeakyReLU(0.1)(Conv2D (512, 3, strides=1, padding='same')(x))
x = AveragePooling2D()(x)
x = LeakyReLU(0.1)(Conv2D (512, 3, strides=1, padding='same')(x))
x = LeakyReLU(0.1)(Conv2D (512, 3, strides=1, padding='same')(x))
x = LeakyReLU(0.1)(Conv2D (512, 3, strides=1, padding='same')(x))
x = LeakyReLU(0.1)(Conv2D (512, 3, strides=1, padding='same')(x))
x = Concatenate()([ BilinearInterpolation()(x), e4 ])
x = LeakyReLU(0.1)(Conv2D (512, 3, strides=1, padding='same')(x))
x = LeakyReLU(0.1)(Conv2D (512, 3, strides=1, padding='same')(x))
x = Concatenate()([ BilinearInterpolation()(x), e3 ])
x = LeakyReLU(0.1)(Conv2D (512, 3, strides=1, padding='same')(x))
x = LeakyReLU(0.1)(Conv2D (512, 3, strides=1, padding='same')(x))
x = Concatenate()([ BilinearInterpolation()(x), e2 ])
x = LeakyReLU(0.1)(Conv2D (288, 3, strides=1, padding='same')(x))
x = LeakyReLU(0.1)(Conv2D (288, 3, strides=1, padding='same')(x))
x = Concatenate()([ BilinearInterpolation()(x), e1 ])
x = LeakyReLU(0.1)(Conv2D (160, 3, strides=1, padding='same')(x))
x = LeakyReLU(0.1)(Conv2D (160, 3, strides=1, padding='same')(x))
x = Concatenate()([ BilinearInterpolation()(x), e0 ])
x = LeakyReLU(0.1)(Conv2D (96, 3, strides=1, padding='same')(x))
x = d0 = LeakyReLU(0.1)(Conv2D (96, 3, strides=1, padding='same')(x))
x = LeakyReLU(0.1)(Conv2D (48, 3, strides=1, padding='same')(x))
x = Conv2D (3, 3, strides=1, padding='same', activation='tanh')(x)
out1x = Add()([bgr_inp, x])
x = d0
x = LeakyReLU(0.1)(Conv2D (96, 3, strides=1, padding='same')(x))
x = LeakyReLU(0.1)(Conv2D (96, 3, strides=1, padding='same')(x))
x = d2x = BilinearInterpolation()(x)
x = LeakyReLU(0.1)(Conv2D (48, 3, strides=1, padding='same')(x))
x = Conv2D (3, 3, strides=1, padding='same', activation='tanh')(x)
out2x = Add()([BilinearInterpolation()(out1x), x])
x = d2x
x = LeakyReLU(0.1)(Conv2D (72, 3, strides=1, padding='same')(x))
x = LeakyReLU(0.1)(Conv2D (72, 3, strides=1, padding='same')(x))
x = d4x = BilinearInterpolation()(x)
x = LeakyReLU(0.1)(Conv2D (36, 3, strides=1, padding='same')(x))
x = Conv2D (3, 3, strides=1, padding='same', activation='tanh')(x)
out4x = Add()([BilinearInterpolation()(out2x), x ])
self.model = keras.models.Model ( [bgr_inp,t_param_inp,t_param1_inp], [out4x] )
self.model.load_weights (str(model_path))
def enhance (self, inp_img, is_tanh=False, preserve_size=True):
if not is_tanh:
inp_img = np.clip( inp_img * 2 -1, -1, 1 )
param = np.array([0.2])
param1 = np.array([1.0])
up_res = 4
patch_size = 192
patch_size_half = patch_size // 2
h,w,c = inp_img.shape
i_max = w-patch_size+1
j_max = h-patch_size+1
final_img = np.zeros ( (h*up_res,w*up_res,c), dtype=np.float32 )
final_img_div = np.zeros ( (h*up_res,w*up_res,1), dtype=np.float32 )
x = np.concatenate ( [ np.linspace (0,1,patch_size_half*up_res), np.linspace (1,0,patch_size_half*up_res) ] )
x,y = np.meshgrid(x,x)
patch_mask = (x*y)[...,None]
j=0
while j < j_max:
i = 0
while i < i_max:
patch_img = inp_img[j:j+patch_size, i:i+patch_size,:]
x = self.model.predict( [ patch_img[None,...], param, param1 ] )[0]
final_img [j*up_res:(j+patch_size)*up_res, i*up_res:(i+patch_size)*up_res,:] += x*patch_mask
final_img_div[j*up_res:(j+patch_size)*up_res, i*up_res:(i+patch_size)*up_res,:] += patch_mask
if i == i_max-1:
break
i = min( i+patch_size_half, i_max-1)
if j == j_max-1:
break
j = min( j+patch_size_half, j_max-1)
final_img_div[final_img_div==0] = 1.0
final_img /= final_img_div
if preserve_size:
final_img = cv2.resize (final_img, (w,h), cv2.INTER_LANCZOS4)
if not is_tanh:
final_img = np.clip( final_img/2+0.5, 0, 1 )
return final_img