initial

facelib/DLIBExtractor.py

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+import numpy as np
+import os
+import cv2
+
+from pathlib import Path
+
+class DLIBExtractor(object):   
+    def __init__(self, dlib):
+        self.scale_to = 1850 
+        #3100 eats ~1.687GB VRAM on 2GB 730 desktop card, but >4Gb on 6GB card, 
+        #but 3100 doesnt work on 2GB 850M notebook card, I cant understand this behaviour
+        #1850 works on 2GB 850M notebook card, works faster than 3100, produces good result
+        self.dlib = dlib
+
+    def __enter__(self):   
+        self.dlib_cnn_face_detector = self.dlib.cnn_face_detection_model_v1( str(Path(__file__).parent / "mmod_human_face_detector.dat") )
+        self.dlib_cnn_face_detector ( np.zeros ( (self.scale_to, self.scale_to, 3), dtype=np.uint8), 0 ) 
+        return self
+        
+    def __exit__(self, exc_type=None, exc_value=None, traceback=None):
+        del self.dlib_cnn_face_detector
+        return False #pass exception between __enter__ and __exit__ to outter level
+        
+    def extract_from_bgr (self, input_image):
+        input_image = input_image[:,:,::-1].copy()
+        (h, w, ch) = input_image.shape
+
+        detected_faces = []    
+        input_scale = self.scale_to / (w if w > h else h)
+        input_image = cv2.resize (input_image, ( int(w*input_scale), int(h*input_scale) ), interpolation=cv2.INTER_LINEAR)
+        detected_faces = self.dlib_cnn_face_detector(input_image, 0)
+
+        result = []        
+        for d_rect in detected_faces:
+            if type(d_rect) == self.dlib.mmod_rectangle:
+                d_rect = d_rect.rect            
+            left, top, right, bottom = d_rect.left(), d_rect.top(), d_rect.right(), d_rect.bottom()
+            result.append ( (int(left/input_scale), int(top/input_scale), int(right/input_scale), int(bottom/input_scale)) )
+
+        return result