122 lines
3.4 KiB
Python
122 lines
3.4 KiB
Python
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# built-in dependencies
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from typing import Tuple
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# 3rd party
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import numpy as np
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import cv2
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# project dependencies
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from deepface.commons import package_utils
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tf_major_version = package_utils.get_tf_major_version()
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if tf_major_version == 1:
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from keras.preprocessing import image
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elif tf_major_version == 2:
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from tensorflow.keras.preprocessing import image
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def normalize_input(img: np.ndarray, normalization: str = "base") -> np.ndarray:
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"""Normalize input image.
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Args:
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img (numpy array): the input image.
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normalization (str, optional): the normalization technique. Defaults to "base",
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for no normalization.
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Returns:
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numpy array: the normalized image.
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"""
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# issue 131 declares that some normalization techniques improves the accuracy
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if normalization == "base":
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return img
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# @trevorgribble and @davedgd contributed this feature
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# restore input in scale of [0, 255] because it was normalized in scale of
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# [0, 1] in preprocess_face
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img *= 255
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if normalization == "raw":
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pass # return just restored pixels
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elif normalization == "Facenet":
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mean, std = img.mean(), img.std()
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img = (img - mean) / std
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elif normalization == "Facenet2018":
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# simply / 127.5 - 1 (similar to facenet 2018 model preprocessing step as @iamrishab posted)
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img /= 127.5
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img -= 1
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elif normalization == "VGGFace":
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# mean subtraction based on VGGFace1 training data
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img[..., 0] -= 93.5940
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img[..., 1] -= 104.7624
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img[..., 2] -= 129.1863
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elif normalization == "VGGFace2":
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# mean subtraction based on VGGFace2 training data
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img[..., 0] -= 91.4953
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img[..., 1] -= 103.8827
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img[..., 2] -= 131.0912
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elif normalization == "ArcFace":
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# Reference study: The faces are cropped and resized to 112×112,
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# and each pixel (ranged between [0, 255]) in RGB images is normalised
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# by subtracting 127.5 then divided by 128.
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img -= 127.5
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img /= 128
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else:
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raise ValueError(f"unimplemented normalization type - {normalization}")
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return img
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def resize_image(img: np.ndarray, target_size: Tuple[int, int]) -> np.ndarray:
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"""
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Resize an image to expected size of a ml model with adding black pixels.
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Args:
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img (np.ndarray): pre-loaded image as numpy array
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target_size (tuple): input shape of ml model
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Returns:
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img (np.ndarray): resized input image
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"""
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factor_0 = target_size[0] / img.shape[0]
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factor_1 = target_size[1] / img.shape[1]
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factor = min(factor_0, factor_1)
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dsize = (
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int(img.shape[1] * factor),
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int(img.shape[0] * factor),
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)
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img = cv2.resize(img, dsize)
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diff_0 = target_size[0] - img.shape[0]
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diff_1 = target_size[1] - img.shape[1]
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# Put the base image in the middle of the padded image
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img = np.pad(
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img,
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(
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(diff_0 // 2, diff_0 - diff_0 // 2),
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(diff_1 // 2, diff_1 - diff_1 // 2),
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(0, 0),
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),
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"constant",
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)
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# double check: if target image is not still the same size with target.
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if img.shape[0:2] != target_size:
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img = cv2.resize(img, target_size)
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# make it 4-dimensional how ML models expect
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img = image.img_to_array(img)
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img = np.expand_dims(img, axis=0)
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if img.max() > 1:
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img = (img.astype(np.float32) / 255.0).astype(np.float32)
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return img
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