normalize_input simplified

deepface/commons/functions.py

@@ -110,47 +110,42 @@ def normalize_input(img, normalization = 'base'):
 
 	if normalization == 'base':
 		return img
-	else: #@trevorgribble recommend the following idea
+	else:
+		#@trevorgribble and @davedgd contributed this feature
 
 		img *= 255 #restore input in scale of [0, 255] because it was normalized in scale of  [0, 1] in preprocess_face
 
-		if normalization == 'Facenet':
+		if normalization == 'raw':
+			pass #return just restored pixels
+
+		elif normalization == 'Facenet':
 			mean, std = img.mean(), img.std()
 			img = (img - mean) / std
 
-		elif normalization == 'v1':
+		elif(normalization=="Facenet2018"):
-			#BGR mean subtraction / 255 normalization
-			img[..., 0]-= 131.0912
-			img[..., 1] -= 103.8827
-			img[..., 2] -= 91.4953
-			img = img[..., ::-1]
-			img /= 255
-
-		elif(normalization =="v2"):
-			#RGB mean subtraction / 255 normalization
-			img[..., 0]-= 131.0912
-			img[..., 1] -= 103.8827
-			img[..., 2] -= 91.4953
-			img /= 255
-
-		elif(normalization =="v3"):
-			#BGR mean subtraction normalization
-			img[..., 0]-= 131.0912
-			img[..., 1] -= 103.8827
-			img[..., 2] -= 91.4953
-			img = img[..., ::-1]
-
-		elif(normalization =="v4"):
-			#RGB mean subtraction normalization
-			img[..., 0]-= 131.0912
-			img[..., 1] -= 103.8827
-			img[..., 2] -= 91.4953
-
-		elif(normalization=="v6"):
 			# simply / 127.5 - 1 (similar to facenet 2018 model preprocessing step as @iamrishab posted)
 			img /= 127.5
 			img -= 1
 
+		elif normalization == 'VGGFace':
+			# mean subtraction based on VGGFace1 training data
+			img[..., 0] -= 93.5940
+			img[..., 1] -= 104.7624
+			img[..., 2] -= 129.1863
+
+		elif(normalization == 'VGGFace2'):
+			# mean subtraction based on VGGFace2 training data
+			img[..., 0] -= 91.4953
+			img[..., 1] -= 103.8827
+			img[..., 2] -= 131.0912
+
+		elif(normalization == 'ArcFace'):
+			#Reference study: The faces are cropped and resized to 112×112,
+			#and each pixel (ranged between [0, 255]) in RGB images is normalised
+			#by subtracting 127.5 then divided by 128.
+			img -= 127.5
+			img /= 128
+
 	#-----------------------------
 
 	return img