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# built-in dependencies
from typing import Tuple
# 3rd party
import numpy as np
import cv2
# project dependencies
from deepface.commons import package_utils
tf_major_version = package_utils.get_tf_major_version()
if tf_major_version == 1:
from keras.preprocessing import image
elif tf_major_version == 2:
from tensorflow.keras.preprocessing import image
def normalize_input(img: np.ndarray, normalization: str = "base") -> np.ndarray:
"""Normalize input image.
Args:
img (numpy array): the input image.
normalization (str, optional): the normalization technique. Defaults to "base",
for no normalization.
Returns:
numpy array: the normalized image.
"""
# issue 131 declares that some normalization techniques improves the accuracy
if normalization == "base":
return img
# @trevorgribble and @davedgd contributed this feature
# restore input in scale of [0, 255] because it was normalized in scale of
# [0, 1] in preprocess_face
img *= 255
if normalization == "raw":
pass # return just restored pixels
elif normalization == "Facenet":
mean, std = img.mean(), img.std()
img = (img - mean) / std
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
else:
raise ValueError(f"unimplemented normalization type - {normalization}")
return img
def resize_image(img: np.ndarray, target_size: Tuple[int, int]) -> np.ndarray:
"""
Resize an image to expected size of a ml model with adding black pixels.
Args:
img (np.ndarray): pre-loaded image as numpy array
target_size (tuple): input shape of ml model
Returns:
img (np.ndarray): resized input image
"""
factor_0 = target_size[0] / img.shape[0]
factor_1 = target_size[1] / img.shape[1]
factor = min(factor_0, factor_1)
dsize = (
int(img.shape[1] * factor),
int(img.shape[0] * factor),
)
img = cv2.resize(img, dsize)
diff_0 = target_size[0] - img.shape[0]
diff_1 = target_size[1] - img.shape[1]
# Put the base image in the middle of the padded image
img = np.pad(
img,
(
(diff_0 // 2, diff_0 - diff_0 // 2),
(diff_1 // 2, diff_1 - diff_1 // 2),
(0, 0),
),
"constant",
)
# double check: if target image is not still the same size with target.
if img.shape[0:2] != target_size:
img = cv2.resize(img, target_size)
# make it 4-dimensional how ML models expect
img = image.img_to_array(img)
img = np.expand_dims(img, axis=0)
if img.max() > 1:
img = (img.astype(np.float32) / 255.0).astype(np.float32)
return img
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