init

converters/Converter.py

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+import copy
+'''
+You can implement your own Converter, check example ConverterMasked.py
+'''
+
+class Converter(object):
+    TYPE_FACE = 0                   #calls convert_face
+    TYPE_FACE_AVATAR = 1            #calls convert_face with avatar_operator_face
+    TYPE_IMAGE = 2                  #calls convert_image without landmarks
+    TYPE_IMAGE_WITH_LANDMARKS = 3   #calls convert_image with landmarks
+
+    #overridable
+    def __init__(self, predictor_func, type):
+        self.predictor_func = predictor_func
+        self.type = type
+
+    #overridable
+    def on_cli_initialize(self):
+        #cli initialization
+        pass
+
+    #overridable
+    def on_host_tick(self):
+        pass
+
+    #overridable
+    def cli_convert_face (self, img_bgr, img_face_landmarks, debug, avaperator_face_bgr=None, **kwargs):
+        #return float32 image
+        #if debug , return tuple ( images of any size and channels, ...)
+        return image
+
+    #overridable
+    def cli_convert_image (self, img_bgr, img_landmarks, debug):
+        #img_landmarks not None, if input image is png with embedded data
+        #return float32 image
+        #if debug , return tuple ( images of any size and channels, ...)
+        return image
+
+    #overridable
+    def dummy_predict(self):
+        #do dummy predict here
+        pass
+
+    def copy(self):
+        return copy.copy(self)
+
+    def copy_and_set_predictor(self, predictor_func):
+        result = self.copy()
+        result.predictor_func = predictor_func
+        return result

converters/ConverterAvatar.py

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+import time
+
+import cv2
+import numpy as np
+
+from facelib import FaceType, LandmarksProcessor
+from joblib import SubprocessFunctionCaller
+from utils.pickle_utils import AntiPickler
+
+from .Converter import Converter
+
+class ConverterAvatar(Converter):
+
+    #override
+    def __init__(self,  predictor_func,
+                        predictor_input_size=0):
+
+        super().__init__(predictor_func, Converter.TYPE_FACE_AVATAR)
+
+        self.predictor_input_size = predictor_input_size
+
+        #dummy predict and sleep, tensorflow caching kernels. If remove it, conversion speed will be x2 slower
+        predictor_func ( np.zeros ( (predictor_input_size,predictor_input_size,3), dtype=np.float32 ), 
+                         np.zeros ( (predictor_input_size,predictor_input_size,1), dtype=np.float32 ) )
+        time.sleep(2)
+
+        predictor_func_host, predictor_func = SubprocessFunctionCaller.make_pair(predictor_func)
+        self.predictor_func_host = AntiPickler(predictor_func_host)
+        self.predictor_func = predictor_func
+
+    #overridable
+    def on_host_tick(self):
+        self.predictor_func_host.obj.process_messages()
+
+    #override
+    def cli_convert_face (self, img_bgr, img_face_landmarks, debug, avaperator_face_bgr=None, **kwargs):
+        if debug:
+            debugs = [img_bgr.copy()]
+
+        img_size = img_bgr.shape[1], img_bgr.shape[0]
+
+        img_face_mask_a = LandmarksProcessor.get_image_hull_mask (img_bgr.shape, img_face_landmarks)
+        img_face_mask_aaa = np.repeat(img_face_mask_a, 3, -1)
+
+        output_size = self.predictor_input_size        
+        face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=FaceType.FULL)
+
+        dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
+
+        predictor_input_dst_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (self.predictor_input_size,self.predictor_input_size), cv2.INTER_CUBIC )
+        prd_inp_dst_face_mask_a = predictor_input_dst_face_mask_a_0[...,np.newaxis]
+
+        prd_inp_avaperator_face_bgr = cv2.resize (avaperator_face_bgr, (self.predictor_input_size,self.predictor_input_size), cv2.INTER_CUBIC )
+
+        prd_face_bgr = self.predictor_func ( prd_inp_avaperator_face_bgr, prd_inp_dst_face_mask_a )
+
+        out_img = img_bgr.copy()
+        out_img = cv2.warpAffine( prd_face_bgr, face_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
+        out_img = np.clip(out_img, 0.0, 1.0)
+
+        if debug:
+            debugs += [out_img.copy()]
+
+        out_img = np.clip( img_bgr*(1-img_face_mask_aaa) + (out_img*img_face_mask_aaa) , 0, 1.0 )
+
+        if debug:
+            debugs += [out_img.copy()]
+
+
+        return debugs if debug else out_img

converters/ConverterImage.py

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+import time
+
+import cv2
+import numpy as np
+
+from facelib import FaceType, LandmarksProcessor
+from joblib import SubprocessFunctionCaller
+from utils.pickle_utils import AntiPickler
+
+from .Converter import Converter
+
+class ConverterImage(Converter):
+
+    #override
+    def __init__(self,  predictor_func,
+                        predictor_input_size=0):
+
+        super().__init__(predictor_func, Converter.TYPE_IMAGE)
+
+        self.predictor_input_size = predictor_input_size
+
+        #dummy predict and sleep, tensorflow caching kernels. If remove it, conversion speed will be x2 slower
+        predictor_func ( np.zeros ( (predictor_input_size,predictor_input_size,3), dtype=np.float32 ) )
+        time.sleep(2)
+
+        predictor_func_host, predictor_func = SubprocessFunctionCaller.make_pair(predictor_func)
+        self.predictor_func_host = AntiPickler(predictor_func_host)
+        self.predictor_func = predictor_func
+
+    #overridable
+    def on_host_tick(self):
+        self.predictor_func_host.obj.process_messages()
+
+    #override
+    def cli_convert_image (self, img_bgr, img_landmarks, debug):
+        img_size = img_bgr.shape[1], img_bgr.shape[0]
+
+        predictor_input_bgr = cv2.resize ( img_bgr, (self.predictor_input_size, self.predictor_input_size), cv2.INTER_LANCZOS4 )
+
+        if debug:
+            debugs = [predictor_input_bgr]
+
+        output = self.predictor_func ( predictor_input_bgr )
+
+        if debug:
+            return (predictor_input_bgr,output,)
+        if debug:
+            debugs += [out_img.copy()]
+
+        return debugs if debug else output