initial

models/ConverterImage.py

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+from models import ConverterBase
+from facelib import LandmarksProcessor
+from facelib import FaceType
+
+import cv2
+import numpy as np
+from utils import image_utils
+
+'''
+predictor: 
+    input:  [predictor_input_size, predictor_input_size, BGR]
+    output: [predictor_input_size, predictor_input_size, BGR]
+'''
+
+class ConverterImage(ConverterBase):
+
+    #override
+    def __init__(self,  predictor,
+                        predictor_input_size=0, 
+                        output_size=0,               
+                        **in_options):
+                        
+        super().__init__(predictor)
+         
+        self.predictor_input_size = predictor_input_size
+        self.output_size = output_size   
+  
+    #override
+    def get_mode(self):
+        return ConverterBase.MODE_IMAGE
+        
+    #override
+    def dummy_predict(self):
+        self.predictor ( np.zeros ( (self.predictor_input_size, self.predictor_input_size,3), dtype=np.float32) )
+        
+    #override
+    def 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 )
+        predicted_bgr = self.predictor ( predictor_input_bgr )
+
+        output = cv2.resize ( predicted_bgr, (self.output_size, self.output_size), cv2.INTER_LANCZOS4 )
+        if debug:
+            return (img_bgr,output,)
+        return output