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normalize_input simplified

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Sefik Ilkin Serengil 2021-08-01 22:51:55 +03:00
parent 7743629762
commit d1e16f68b5
1 changed files with 27 additions and 32 deletions
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deepface/commons/functions.py
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@ -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':
#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"):
elif(normalization=="Facenet2018"):
# 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