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 nn.tf_default_device_name): self.model = FaceEnhancer() self.model.load_weights (model_path) with tf.device ('/CPU:0' if run_on_cpu else nn.tf_default_device_name): 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), interpolation=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) ), interpolation=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), interpolation=cv2.INTER_LANCZOS4) else: if preupscale_rate != 1.0: final_img = cv2.resize (final_img, (tw,th), interpolation=cv2.INTER_LANCZOS4) if not is_tanh: final_img = np.clip( final_img/2+0.5, 0, 1 ) return final_img """