updated pdf manuals for AVATAR model.

Avatar converter: added super resolution option.
All converters: super resolution DCSCN network is now replaced by RankSRGAN

converters/ConvertAvatar.py

@@ -26,6 +26,9 @@ def ConvertFaceAvatar (cfg, prev_temporal_frame_infos, frame_info, next_temporal
 
     prd_f = cfg.predictor_func ( prev_imgs, img, next_imgs )
 
+    if cfg.super_resolution_mode != 0:
+        prd_f = cfg.superres_func(cfg.super_resolution_mode, prd_f)
+        
     out_img = np.clip(prd_f, 0.0, 1.0)
 
     if cfg.add_source_image:

converters/ConvertMasked.py

@@ -27,7 +27,7 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
     out_merging_mask = None
 
     output_size = cfg.predictor_input_shape[0]
-    if cfg.super_resolution:
+    if cfg.super_resolution_mode != 0:
         output_size *= 2
 
     face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=cfg.face_type)
@@ -48,12 +48,12 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
         prd_face_bgr      = np.clip (predicted, 0, 1.0 )
         prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, cfg.predictor_input_shape[0:2] )
 
-    if cfg.super_resolution:
+    if cfg.super_resolution_mode:
         #if debug:
         #    tmp = cv2.resize (prd_face_bgr,  (output_size,output_size), cv2.INTER_CUBIC)
         #    debugs += [ np.clip( cv2.warpAffine( tmp, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
 
-        prd_face_bgr = cfg.dcscn_upscale_func(prd_face_bgr)
+        prd_face_bgr = cfg.superres_func(cfg.super_resolution_mode, prd_face_bgr)
         #if debug:
         #    debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
 
@@ -335,7 +335,7 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
                 k_size = int(frame_info.motion_power*cfg_mp)
                 if k_size >= 1:
                     k_size = np.clip (k_size+1, 2, 50)
-                    if cfg.super_resolution:
+                    if cfg.super_resolution_mode:
                         k_size *= 2
                     out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size) )
                     new_out_face_bgr = imagelib.LinearMotionBlur (out_face_bgr, k_size , frame_info.motion_deg)

converters/ConverterConfig.py

@@ -92,7 +92,7 @@ class ConverterConfigMasked(ConverterConfig):
         self.motion_blur_power = 0
         self.output_face_scale = 0
         self.color_transfer_mode = 0
-        self.super_resolution = False
+        self.super_resolution_mode = 0
         self.color_degrade_power = 0
         self.export_mask_alpha = False
 
@@ -118,9 +118,11 @@ class ConverterConfigMasked(ConverterConfig):
                                          2:'dst',
                                          4:'FAN-dst',
                                          7:'learned*FAN-dst'}
-
+                                         
         self.ctm_dict = { 0: "None", 1:"rct", 2:"lct" }
         self.ctm_str_dict = {None:0, "rct":1, "lct": 2 }
+        
+        self.super_res_dict = {0:"None", 1:'RankSRGAN'}
 
     def copy(self):
         return copy.copy(self)
@@ -158,8 +160,9 @@ class ConverterConfigMasked(ConverterConfig):
     def toggle_color_transfer_mode(self):
         self.color_transfer_mode = (self.color_transfer_mode+1) % 3
 
-    def toggle_super_resolution(self):
+    def toggle_super_resolution_mode(self):
-        self.super_resolution = not self.super_resolution
+        a = list( self.super_res_dict.keys() )
+        self.super_resolution_mode = a[ (a.index(self.super_resolution_mode)+1) % len(a) ]
 
     def add_color_degrade_power(self, diff):
         self.color_degrade_power = np.clip ( self.color_degrade_power+diff , 0, 100)
@@ -210,8 +213,13 @@ class ConverterConfigMasked(ConverterConfig):
             self.color_transfer_mode = io.input_str ("Apply color transfer to predicted face? Choose mode ( rct/lct skip:None ) : ", None, ['rct','lct'])
             self.color_transfer_mode = self.ctm_str_dict[self.color_transfer_mode]
 
-        self.super_resolution = io.input_bool("Apply super resolution? (y/n ?:help skip:n) : ", False, help_message="Enhance details by applying DCSCN network.")
+        s = """Choose super resolution mode: \n"""
+        for key in self.super_res_dict.keys():
+            s += f"""({key}) {self.super_res_dict[key]}\n"""
+        s += f"""?:help Default: {list(self.super_res_dict.keys())[0]} : """
+        self.super_resolution_mode = io.input_int (s, 0, valid_list=self.super_res_dict.keys(), help_message="Enhance details by applying superresolution network.")
 
+  
         if 'raw' not in self.mode:
             self.color_degrade_power = np.clip (  io.input_int ("Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100)
             self.export_mask_alpha = io.input_bool("Export png with alpha channel of the mask? (y/n skip:n) : ", False)
@@ -231,7 +239,7 @@ class ConverterConfigMasked(ConverterConfig):
                    self.motion_blur_power == other.motion_blur_power and \
                    self.output_face_scale == other.output_face_scale and \
                    self.color_transfer_mode == other.color_transfer_mode and \
-                   self.super_resolution == other.super_resolution and \
+                   self.super_resolution_mode == other.super_resolution_mode and \
                    self.color_degrade_power == other.color_degrade_power and \
                    self.export_mask_alpha == other.export_mask_alpha
 
@@ -264,7 +272,7 @@ class ConverterConfigMasked(ConverterConfig):
         if 'raw' not in self.mode:
             r += f"""color_transfer_mode: { self.ctm_dict[self.color_transfer_mode]}\n"""
 
-        r += f"""super_resolution: {self.super_resolution}\n"""
+        r += f"""super_resolution_mode: {self.super_res_dict[self.super_resolution_mode]}\n"""
 
         if 'raw' not in self.mode:
             r += (f"""color_degrade_power: {self.color_degrade_power}\n"""
@@ -289,6 +297,8 @@ class ConverterConfigFaceAvatar(ConverterConfig):
 
         #changeable params
         self.add_source_image = False
+        self.super_resolution_mode = 0
+        self.super_res_dict = {0:"None", 1:'RankSRGAN'}
 
     def copy(self):
         return copy.copy(self)
@@ -296,22 +306,33 @@ class ConverterConfigFaceAvatar(ConverterConfig):
     #override
     def ask_settings(self):
         self.add_source_image = io.input_bool("Add source image? (y/n ?:help skip:n) : ", False, help_message="Add source image for comparison.")
+        
+        s = """Choose super resolution mode: \n"""
+        for key in self.super_res_dict.keys():
+            s += f"""({key}) {self.super_res_dict[key]}\n"""
+        s += f"""?:help Default: {list(self.super_res_dict.keys())[0]} : """
+        self.super_resolution_mode = io.input_int (s, 0, valid_list=self.super_res_dict.keys(), help_message="Enhance details by applying superresolution network.")
 
     def toggle_add_source_image(self):
         self.add_source_image = not self.add_source_image
+        
+    def toggle_super_resolution_mode(self):
+        a = list( self.super_res_dict.keys() )
+        self.super_resolution_mode = a[ (a.index(self.super_resolution_mode)+1) % len(a) ]
 
     #override
     def __eq__(self, other):
         #check equality of changeable params
 
         if isinstance(other, ConverterConfigFaceAvatar):
-            return self.add_source_image == other.add_source_image
+            return self.add_source_image == other.add_source_image and \
-
+                   self.super_resolution_mode == other.super_resolution_mode
         return False
 
     #override
     def __str__(self):
         return ("ConverterConfig: \n"
                 f"add_source_image : {self.add_source_image}\n"
+                f"super_resolution_mode : {self.super_res_dict[self.super_resolution_mode]}\n"
                 "================"
                 )

imagelib/DCSCN.py

@@ -1,164 +0,0 @@
-import numpy as np
-import cv2
-from pathlib import Path
-from nnlib import nnlib
-from interact import interact as io
-
-class DCSCN():
-    def __init__(self):
-        exec( nnlib.import_all(), locals(), globals() )
-
-        inp_x = KL.Input([None, None, 1])
-        inp_x2 = KL.Input([None, None, 1])
-
-        x = inp_x
-        layers_count = 12
-        layers = []
-        for i in range(1,layers_count+1):
-            if i == 1:
-                output_feature_num = 196
-            else:
-                x1 = (i-1) / float(layers_count - 1)
-                y1 = x1 ** (1.0 / 1.5)
-                output_feature_num = int((196 - 48) * (1 - y1) + 48)
-            x = Conv2D(output_feature_num, kernel_size=3, strides=1, padding='same', name='CNN%d' % (i) ) (x)
-            x = PReLU(shared_axes=[1,2], name='CNN%d_prelu' % (i) ) (x)
-            layers.append(x)
-
-        x_concat = KL.Concatenate()(layers)
-
-        A1 = Conv2D(64, kernel_size=1, strides=1, padding='same', name='A1' ) (x_concat)
-        A1 = PReLU(shared_axes=[1,2], name='A1_prelu') (A1)
-
-        B1 = Conv2D(32, kernel_size=1, strides=1, padding='same', name='B1' ) (x_concat)
-        B1 = PReLU(shared_axes=[1,2], name='B1_prelu') (B1)
-
-        B2 = Conv2D(32, kernel_size=3, strides=1, padding='same', name='B2' ) (B1)
-        B2 = PReLU(shared_axes=[1,2], name='B2_prelu') (B2)
-
-        x = KL.Concatenate()([B2,A1])
-        x = Conv2D(96*4, kernel_size=3, strides=1, padding='same', name='Up_PS' )(x)
-        x = PixelShuffler()(x)
-        x = Conv2D(1, kernel_size=3, strides=1, padding='same', name='R_CNN1', use_bias=False )(x)
-        x = KL.Add()([x, inp_x2])
-        self.model = keras.models.Model ([inp_x, inp_x2], [x])
-        self.model.load_weights ( Path(__file__).parent / 'DCSCN.h5' )
-
-    def upscale(self, img, is_bgr=True, is_float=True):
-        if is_bgr:
-            img = img[...,::-1]
-
-        if is_float:
-            img = np.clip (img*255, 0, 255)
-
-        img_shape_len = len(img.shape)
-        h, w = img.shape[:2]
-        ch = img.shape[2] if len(img.shape) >= 3 else 1
-
-        nh, nw = h*2, w*2
-
-        img_x = self.convert_rgb_to_y(img)
-
-        img_bx = cv2.resize(img_x, (nh, nw), cv2.INTER_CUBIC)
-
-        ensemble = 8
-
-        output = np.zeros([nh,nw,1], dtype=np.float32)
-
-        for i in range(ensemble):
-            x = np.reshape( self.flip(img_x, i), (1,h,w,1) )
-            bx = np.reshape( self.flip(img_bx, i), (1,nh,nw,1) )
-            y = self.model.predict([x,bx])[0]
-            y = self.flip(y, i, invert=True)
-            output += y
-
-        output /= ensemble
-
-        bimg = cv2.resize(img, (nh, nw), cv2.INTER_CUBIC)
-        bimg_ycbcr = self.convert_rgb_to_ycbcr(bimg)
-
-        if ch > 1:
-            output = self.convert_y_and_cbcr_to_rgb(output, bimg_ycbcr[:, :, 1:3])
-
-        if is_float:
-            output = np.clip (output/255.0, 0, 1.0)
-
-        if is_bgr:
-            output = output[...,::-1]
-
-        return output
-
-    def convert_rgb_to_y(self, image):
-        if len(image.shape) <= 2 or image.shape[2] == 1:
-            return image
-
-        xform = np.array([[65.738 / 256.0, 129.057 / 256.0, 25.064 / 256.0]], dtype=np.float32)
-        y_image = image.dot(xform.T) + 16.0
-
-        return y_image
-
-
-    def convert_rgb_to_ycbcr(self, image):
-        if len(image.shape) <= 2 or image.shape[2] == 1:
-            return image
-
-        xform = np.array(
-            [[65.738 / 256.0, 129.057 / 256.0, 25.064 / 256.0],
-            [- 37.945 / 256.0, - 74.494 / 256.0, 112.439 / 256.0],
-            [112.439 / 256.0, - 94.154 / 256.0, - 18.285 / 256.0]], dtype=np.float32)
-
-        ycbcr_image = image.dot(xform.T)
-        ycbcr_image[:, :, 0] += 16.0
-        ycbcr_image[:, :, [1, 2]] += 128.0
-
-        return ycbcr_image
-
-    def convert_ycbcr_to_rgb(self,ycbcr_image):
-        rgb_image = np.zeros([ycbcr_image.shape[0], ycbcr_image.shape[1], 3], dtype=np.float32)
-
-        rgb_image[:, :, 0] = ycbcr_image[:, :, 0] - 16.0
-        rgb_image[:, :, [1, 2]] = ycbcr_image[:, :, [1, 2]] - 128.0
-        xform = np.array(
-            [[298.082 / 256.0, 0, 408.583 / 256.0],
-            [298.082 / 256.0, -100.291 / 256.0, -208.120 / 256.0],
-            [298.082 / 256.0, 516.412 / 256.0, 0]], dtype=np.float32)
-        rgb_image = rgb_image.dot(xform.T)
-
-        return rgb_image
-
-    def convert_y_and_cbcr_to_rgb(self,y_image, cbcr_image):
-        if len(y_image.shape) <= 2:
-            y_image = y_image.reshape[y_image.shape[0], y_image.shape[1], 1]
-
-        if len(y_image.shape) == 3 and y_image.shape[2] == 3:
-            y_image = y_image[:, :, 0:1]
-
-        ycbcr_image = np.zeros([y_image.shape[0], y_image.shape[1], 3], dtype=np.float32)
-        ycbcr_image[:, :, 0] = y_image[:, :, 0]
-        ycbcr_image[:, :, 1:3] = cbcr_image[:, :, 0:2]
-
-        return self.convert_ycbcr_to_rgb(ycbcr_image)
-
-    def flip(self, image, flip_type, invert=False):
-        if flip_type == 0:
-            return image
-        elif flip_type == 1:
-            return np.flipud(image)
-        elif flip_type == 2:
-            return np.fliplr(image)
-        elif flip_type == 3:
-            return np.flipud(np.fliplr(image))
-        elif flip_type == 4:
-            return np.rot90(image, 1 if invert is False else -1)
-        elif flip_type == 5:
-            return np.rot90(image, -1 if invert is False else 1)
-        elif flip_type == 6:
-            if invert is False:
-                return np.flipud(np.rot90(image))
-            else:
-                return np.rot90(np.flipud(image), -1)
-        elif flip_type == 7:
-            if invert is False:
-                return np.flipud(np.rot90(image, -1))
-            else:
-                return np.rot90(np.flipud(image), 1)

imagelib/RankSRGAN.py

@@ -0,0 +1,109 @@
+import numpy as np
+import cv2
+from pathlib import Path
+from nnlib import nnlib
+from interact import interact as io
+
+class RankSRGAN():
+    def __init__(self):
+        exec( nnlib.import_all(), locals(), globals() )
+
+        class PixelShufflerTorch(KL.Layer):
+            def __init__(self, size=(2, 2), data_format='channels_last', **kwargs):
+                super(PixelShufflerTorch, self).__init__(**kwargs)
+                self.data_format = data_format
+                self.size = size
+
+            def call(self, inputs):
+                input_shape = K.shape(inputs)
+                if K.int_shape(input_shape)[0] != 4:
+                    raise ValueError('Inputs should have rank 4; Received input shape:', str(K.int_shape(inputs)))
+
+                batch_size, h, w, c = input_shape[0], input_shape[1], input_shape[2], K.int_shape(inputs)[-1]
+                rh, rw = self.size
+                oh, ow = h * rh, w * rw
+                oc = c // (rh * rw)
+
+                out = inputs
+                out = K.permute_dimensions(out, (0, 3, 1, 2)) #NCHW
+
+                out = K.reshape(out, (batch_size, oc, rh, rw, h, w))
+                out = K.permute_dimensions(out, (0, 1, 4, 2, 5, 3))
+                out = K.reshape(out, (batch_size, oc, oh, ow))
+
+                out = K.permute_dimensions(out, (0, 2, 3, 1))
+                return out
+
+            def compute_output_shape(self, input_shape):
+                if len(input_shape) != 4:
+                    raise ValueError('Inputs should have rank ' + str(4) + '; Received input shape:', str(input_shape))
+
+                height = input_shape[1] * self.size[0] if input_shape[1] is not None else None
+                width = input_shape[2] * self.size[1] if input_shape[2] is not None else None
+                channels = input_shape[3] // self.size[0] // self.size[1]
+
+                if channels * self.size[0] * self.size[1] != input_shape[3]:
+                    raise ValueError('channels of input and size are incompatible')
+
+                return (input_shape[0],
+                        height,
+                        width,
+                        channels)
+
+            def get_config(self):
+                config = {'size': self.size,
+                        'data_format': self.data_format}
+                base_config = super(PixelShufflerTorch, self).get_config()
+
+                return dict(list(base_config.items()) + list(config.items()))
+
+        def res_block(inp, name_prefix):
+            x = inp
+            x = Conv2D (ndf, kernel_size=3, strides=1, padding='same', activation="relu", name=name_prefix+"0")(x)
+            x = Conv2D (ndf, kernel_size=3, strides=1, padding='same', name=name_prefix+"2")(x)
+            return Add()([inp,x])
+
+        ndf = 64
+        nb = 16
+        inp = Input ( (None, None,3) )
+        x = inp
+
+        x = x0 = Conv2D (ndf, kernel_size=3, strides=1, padding='same', name="model0")(x)
+        for i in range(nb):
+            x = res_block(x, "model1%.2d" %i )
+        x = Conv2D (ndf, kernel_size=3, strides=1, padding='same', name="model1160")(x)
+        x = Add()([x0,x])
+
+        x = ReLU() ( PixelShufflerTorch() ( Conv2D (ndf*4, kernel_size=3, strides=1, padding='same', name="model2")(x) ) )
+        x = ReLU() ( PixelShufflerTorch() ( Conv2D (ndf*4, kernel_size=3, strides=1, padding='same', name="model5")(x) ) )
+
+        x = Conv2D (ndf, kernel_size=3, strides=1, padding='same', activation="relu", name="model8")(x)
+        x = Conv2D (3,   kernel_size=3, strides=1, padding='same', name="model10")(x)
+        self.model = Model(inp, x )
+        self.model.load_weights ( Path(__file__).parent / 'RankSRGAN.h5')
+
+    def upscale(self, img, scale=2, is_bgr=True, is_float=True):
+        if scale not in [2,4]:
+            raise ValueError ("RankSRGAN: supported scale are 2 or 4.")
+
+        if not is_bgr:
+            img = img[...,::-1]
+
+        if not is_float:
+            img /= 255.0
+
+        h, w = img.shape[:2]
+        ch = img.shape[2] if len(img.shape) >= 3 else 1
+
+        output = self.model.predict([img[None,...]])[0]
+
+        if scale == 2:
+            output = cv2.resize (output, (w*scale, h*scale), cv2.INTER_CUBIC)
+
+        if not is_float:
+            output = np.clip (output * 255.0, 0, 255.0)
+
+        if not is_bgr:
+            output = output[...,::-1]
+
+        return output

imagelib/__init__.py

@@ -13,7 +13,7 @@ from .reduce_colors import reduce_colors
 
 from .color_transfer import color_hist_match, reinhard_color_transfer, linear_color_transfer
 
-from .DCSCN import DCSCN
+from .RankSRGAN import RankSRGAN
 
 from .common import normalize_channels, overlay_alpha_image
 

mainscripts/Converter.py

@@ -71,7 +71,7 @@ class ConvertSubprocessor(Subprocessor):
             self.device_idx  = client_dict['device_idx']
             self.device_name = client_dict['device_name']
             self.predictor_func = client_dict['predictor_func']
-            self.dcscn_upscale_func = client_dict['dcscn_upscale_func']
+            self.superres_func = client_dict['superres_func']
 
             #transfer and set stdin in order to work code.interact in debug subprocess
             stdin_fd         = client_dict['stdin_fd']
@@ -103,7 +103,8 @@ class ConvertSubprocessor(Subprocessor):
         def process_data(self, pf): #pf=ProcessingFrame
             cfg = pf.cfg.copy()
             cfg.predictor_func = self.predictor_func
-
+            cfg.superres_func = self.superres_func
+            
             frame_info = pf.frame_info
 
             filename = frame_info.filename
@@ -127,7 +128,6 @@ class ConvertSubprocessor(Subprocessor):
                     pf.image = img_bgr
             else:
                 if cfg.type == ConverterConfig.TYPE_MASKED:
-                    cfg.dcscn_upscale_func = self.dcscn_upscale_func
                     cfg.fanseg_input_size = self.fanseg_input_size
                     cfg.fanseg_extract_func = self.fanseg_extract_func
 
@@ -176,12 +176,14 @@ class ConvertSubprocessor(Subprocessor):
         self.converter_config.predictor_func = None
 
         self.dcscn = None
-        def DCSCN_upscale(*args, **kwargs):
+        self.ranksrgan = None
-            if self.dcscn is None:
+        def superres_func(mode, *args, **kwargs):
-                self.dcscn = imagelib.DCSCN()
+            if mode == 1:
-            return self.dcscn.upscale(*args, **kwargs)
+                if self.ranksrgan is None:
+                    self.ranksrgan = imagelib.RankSRGAN()
+                return self.ranksrgan.upscale(*args, **kwargs)
 
-        self.dcscn_host, self.dcscn_upscale_func = SubprocessFunctionCaller.make_pair(DCSCN_upscale)
+        self.dcscn_host, self.superres_func = SubprocessFunctionCaller.make_pair(superres_func)
 
         self.frames = frames
         self.output_path = output_path
@@ -205,7 +207,7 @@ class ConvertSubprocessor(Subprocessor):
             yield 'CPU%d' % (i), {}, {'device_idx': i,
                                       'device_name': 'CPU%d' % (i),
                                       'predictor_func': self.predictor_func,
-                                      'dcscn_upscale_func': self.dcscn_upscale_func,
+                                      'superres_func': self.superres_func,
                                       'stdin_fd': sys.stdin.fileno() if CONVERTER_DEBUG else None
                                       }
 
@@ -333,12 +335,14 @@ class ConvertSubprocessor(Subprocessor):
                                 elif chr_key == 'c':
                                     cfg.toggle_color_transfer_mode()
                                 elif chr_key == 'v':
-                                    cfg.toggle_super_resolution()
+                                    cfg.toggle_super_resolution_mode()
                                 elif chr_key == 'b':
                                     cfg.toggle_export_mask_alpha()
                             else:
                                 if chr_key == 's':
                                     cfg.toggle_add_source_image()
+                                elif chr_key == 'v':
+                                    cfg.toggle_super_resolution_mode()
 
                             if prev_cfg != cfg:
                                 io.log_info (cfg)