import operator
from pathlib import Path
import cv2
import numpy as np
from core.leras import nn
class FaceEnhancer(object):
"""
x4 face enhancer
"""
def __init__(self, place_model_on_cpu=False, run_on_cpu=False):
nn.initialize(data_format="NHWC")
tf = nn.tf
class FaceEnhancer (nn.ModelBase):
def __init__(self, name='FaceEnhancer'):
super().__init__(name=name)
def on_build(self):
self.conv1 = nn.Conv2D (3, 64, kernel_size=3, strides=1, padding='SAME')
self.dense1 = nn.Dense (1, 64, use_bias=False)
self.dense2 = nn.Dense (1, 64, use_bias=False)
self.e0_conv0 = nn.Conv2D (64, 64, kernel_size=3, strides=1, padding='SAME')
self.e0_conv1 = nn.Conv2D (64, 64, kernel_size=3, strides=1, padding='SAME')
self.e1_conv0 = nn.Conv2D (64, 112, kernel_size=3, strides=1, padding='SAME')
self.e1_conv1 = nn.Conv2D (112, 112, kernel_size=3, strides=1, padding='SAME')
self.e2_conv0 = nn.Conv2D (112, 192, kernel_size=3, strides=1, padding='SAME')
self.e2_conv1 = nn.Conv2D (192, 192, kernel_size=3, strides=1, padding='SAME')
self.e3_conv0 = nn.Conv2D (192, 336, kernel_size=3, strides=1, padding='SAME')
self.e3_conv1 = nn.Conv2D (336, 336, kernel_size=3, strides=1, padding='SAME')
self.e4_conv0 = nn.Conv2D (336, 512, kernel_size=3, strides=1, padding='SAME')
self.e4_conv1 = nn.Conv2D (512, 512, kernel_size=3, strides=1, padding='SAME')
self.center_conv0 = nn.Conv2D (512, 512, kernel_size=3, strides=1, padding='SAME')
self.center_conv1 = nn.Conv2D (512, 512, kernel_size=3, strides=1, padding='SAME')
self.center_conv2 = nn.Conv2D (512, 512, kernel_size=3, strides=1, padding='SAME')
self.center_conv3 = nn.Conv2D (512, 512, kernel_size=3, strides=1, padding='SAME')
self.d4_conv0 = nn.Conv2D (1024, 512, kernel_size=3, strides=1, padding='SAME')
self.d4_conv1 = nn.Conv2D (512, 512, kernel_size=3, strides=1, padding='SAME')
self.d3_conv0 = nn.Conv2D (848, 512, kernel_size=3, strides=1, padding='SAME')
self.d3_conv1 = nn.Conv2D (512, 512, kernel_size=3, strides=1, padding='SAME')
self.d2_conv0 = nn.Conv2D (704, 288, kernel_size=3, strides=1, padding='SAME')
self.d2_conv1 = nn.Conv2D (288, 288, kernel_size=3, strides=1, padding='SAME')
self.d1_conv0 = nn.Conv2D (400, 160, kernel_size=3, strides=1, padding='SAME')
self.d1_conv1 = nn.Conv2D (160, 160, kernel_size=3, strides=1, padding='SAME')
self.d0_conv0 = nn.Conv2D (224, 96, kernel_size=3, strides=1, padding='SAME')
self.d0_conv1 = nn.Conv2D (96, 96, kernel_size=3, strides=1, padding='SAME')
self.out1x_conv0 = nn.Conv2D (96, 48, kernel_size=3, strides=1, padding='SAME')
self.out1x_conv1 = nn.Conv2D (48, 3, kernel_size=3, strides=1, padding='SAME')
self.dec2x_conv0 = nn.Conv2D (96, 96, kernel_size=3, strides=1, padding='SAME')
self.dec2x_conv1 = nn.Conv2D (96, 96, kernel_size=3, strides=1, padding='SAME')
self.out2x_conv0 = nn.Conv2D (96, 48, kernel_size=3, strides=1, padding='SAME')
self.out2x_conv1 = nn.Conv2D (48, 3, kernel_size=3, strides=1, padding='SAME')
self.dec4x_conv0 = nn.Conv2D (96, 72, kernel_size=3, strides=1, padding='SAME')
self.dec4x_conv1 = nn.Conv2D (72, 72, kernel_size=3, strides=1, padding='SAME')
self.out4x_conv0 = nn.Conv2D (72, 36, kernel_size=3, strides=1, padding='SAME')
self.out4x_conv1 = nn.Conv2D (36, 3 , kernel_size=3, strides=1, padding='SAME')
def forward(self, inp):
bgr, param, param1 = inp
x = self.conv1(bgr)
a = self.dense1(param)
a = tf.reshape(a, (-1,1,1,64) )
b = self.dense2(param1)
b = tf.reshape(b, (-1,1,1,64) )
x = tf.nn.leaky_relu(x+a+b, 0.1)
x = tf.nn.leaky_relu(self.e0_conv0(x), 0.1)
x = e0 = tf.nn.leaky_relu(self.e0_conv1(x), 0.1)
x = tf.nn.avg_pool(x, [1,2,2,1], [1,2,2,1], "VALID")
x = tf.nn.leaky_relu(self.e1_conv0(x), 0.1)
x = e1 = tf.nn.leaky_relu(self.e1_conv1(x), 0.1)
x = tf.nn.avg_pool(x, [1,2,2,1], [1,2,2,1], "VALID")
x = tf.nn.leaky_relu(self.e2_conv0(x), 0.1)
x = e2 = tf.nn.leaky_relu(self.e2_conv1(x), 0.1)
x = tf.nn.avg_pool(x, [1,2,2,1], [1,2,2,1], "VALID")
x = tf.nn.leaky_relu(self.e3_conv0(x), 0.1)
x = e3 = tf.nn.leaky_relu(self.e3_conv1(x), 0.1)
x = tf.nn.avg_pool(x, [1,2,2,1], [1,2,2,1], "VALID")
x = tf.nn.leaky_relu(self.e4_conv0(x), 0.1)
x = e4 = tf.nn.leaky_relu(self.e4_conv1(x), 0.1)
x = tf.nn.avg_pool(x, [1,2,2,1], [1,2,2,1], "VALID")
x = tf.nn.leaky_relu(self.center_conv0(x), 0.1)
x = tf.nn.leaky_relu(self.center_conv1(x), 0.1)
x = tf.nn.leaky_relu(self.center_conv2(x), 0.1)
x = tf.nn.leaky_relu(self.center_conv3(x), 0.1)
x = tf.concat( [nn.resize2d_bilinear(x), e4], -1 )
x = tf.nn.leaky_relu(self.d4_conv0(x), 0.1)
x = tf.nn.leaky_relu(self.d4_conv1(x), 0.1)
x = tf.concat( [nn.resize2d_bilinear(x), e3], -1 )
x = tf.nn.leaky_relu(self.d3_conv0(x), 0.1)
x = tf.nn.leaky_relu(self.d3_conv1(x), 0.1)
x = tf.concat( [nn.resize2d_bilinear(x), e2], -1 )
x = tf.nn.leaky_relu(self.d2_conv0(x), 0.1)
x = tf.nn.leaky_relu(self.d2_conv1(x), 0.1)
x = tf.concat( [nn.resize2d_bilinear(x), e1], -1 )
x = tf.nn.leaky_relu(self.d1_conv0(x), 0.1)
x = tf.nn.leaky_relu(self.d1_conv1(x), 0.1)
x = tf.concat( [nn.resize2d_bilinear(x), e0], -1 )
x = tf.nn.leaky_relu(self.d0_conv0(x), 0.1)
x = d0 = tf.nn.leaky_relu(self.d0_conv1(x), 0.1)
x = tf.nn.leaky_relu(self.out1x_conv0(x), 0.1)
x = self.out1x_conv1(x)
out1x = bgr + tf.nn.tanh(x)
x = d0
x = tf.nn.leaky_relu(self.dec2x_conv0(x), 0.1)
x = tf.nn.leaky_relu(self.dec2x_conv1(x), 0.1)
x = d2x = nn.resize2d_bilinear(x)
x = tf.nn.leaky_relu(self.out2x_conv0(x), 0.1)
x = self.out2x_conv1(x)
out2x = nn.resize2d_bilinear(out1x) + tf.nn.tanh(x)
x = d2x
x = tf.nn.leaky_relu(self.dec4x_conv0(x), 0.1)
x = tf.nn.leaky_relu(self.dec4x_conv1(x), 0.1)
x = d4x = nn.resize2d_bilinear(x)
x = tf.nn.leaky_relu(self.out4x_conv0(x), 0.1)
x = self.out4x_conv1(x)
out4x = nn.resize2d_bilinear(out2x) + tf.nn.tanh(x)
return out4x
model_path = Path(__file__).parent / "FaceEnhancer.npy"
if not model_path.exists():
raise Exception("Unable to load FaceEnhancer.npy")
with tf.device ('/CPU:0' if place_model_on_cpu else '/GPU:0'):
self.model = FaceEnhancer()
self.model.load_weights (model_path)
with tf.device ('/CPU:0' if run_on_cpu else '/GPU:0'):
self.model.build_for_run ([ (tf.float32, nn.get4Dshape (192,192,3) ),
(tf.float32, (None,1,) ),
(tf.float32, (None,1,) ),
])
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
ih,iw,ic = inp_img.shape
h,w,c = ih,iw,ic
th,tw = h*up_res, w*up_res
t_padding = 0
b_padding = 0
l_padding = 0
r_padding = 0
if h < patch_size:
t_padding = (patch_size-h)//2
b_padding = (patch_size-h) - t_padding
if w < patch_size:
l_padding = (patch_size-w)//2
r_padding = (patch_size-w) - l_padding
if t_padding != 0:
inp_img = np.concatenate ([ np.zeros ( (t_padding,w,c), dtype=np.float32 ), inp_img ], axis=0 )
h,w,c = inp_img.shape
if b_padding != 0:
inp_img = np.concatenate ([ inp_img, np.zeros ( (b_padding,w,c), dtype=np.float32 ) ], axis=0 )
h,w,c = inp_img.shape
if l_padding != 0:
inp_img = np.concatenate ([ np.zeros ( (h,l_padding,c), dtype=np.float32 ), inp_img ], axis=1 )
h,w,c = inp_img.shape
if r_padding != 0:
inp_img = np.concatenate ([ inp_img, np.zeros ( (h,r_padding,c), dtype=np.float32 ) ], axis=1 )
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.run( [ 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 t_padding+b_padding+l_padding+r_padding != 0:
final_img = final_img [t_padding*up_res:(h-b_padding)*up_res, l_padding*up_res:(w-r_padding)*up_res,:]
if preserve_size:
final_img = cv2.resize (final_img, (iw,ih), cv2.INTER_LANCZOS4)
if not is_tanh:
final_img = np.clip( final_img/2+0.5, 0, 1 )
return final_img
"""
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
th,tw = h*up_res, w*up_res
preupscale_rate = 1.0
if h < patch_size or w < patch_size:
preupscale_rate = 1.0 / ( max(h,w) / patch_size )
if preupscale_rate != 1.0:
inp_img = cv2.resize (inp_img, ( int(w*preupscale_rate), int(h*preupscale_rate) ), cv2.INTER_LANCZOS4)
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.run( [ 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)
else:
if preupscale_rate != 1.0:
final_img = cv2.resize (final_img, (tw,th), cv2.INTER_LANCZOS4)
if not is_tanh:
final_img = np.clip( final_img/2+0.5, 0, 1 )
return final_img
"""