tensorflow对图像分通道进行标准化
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2022-03-25 08:20:30
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tensorflow自带的tf.image.per_image_standardization会在整个图像通道上计算均值标准差,进而标准化。这里实现分别在r g b通道上单独标准化。
import tensorflow as tf
import numpy as np
import cv2
origin = cv2.resize(cv2.imread('/home/shuai/Desktop/tool/lena.jpg'), (400, 400))
a = tf.constant(origin, dtype=tf.float32)
# separate channel
a = tf.unstack(a, axis=2)
res = []
for i in a:
res.append(tf.squeeze(tf.image.per_image_standardization(tf.expand_dims(i, axis=2)), axis=2))
a = tf.stack(res, axis=2)
# all channel
b = tf.image.per_image_standardization(origin)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
a_, b_ = sess.run([a, b])
# numpy 分通道标准化图像
image = origin
mean1, mean2, mean3 = np.mean(image[:, :, 0]), np.mean(image[:, :, 1]), np.mean(image[:, :, 2])
std1, std2, std3 = np.std(image[:, :, 0]), np.std(image[:, :, 1]), np.std(image[:, :, 2])
i1 = (image[:, :, 0] - mean1) / std1
i2 = (image[:, :, 1] - mean2) / std2
i3 = (image[:, :, 2] - mean3) / std3
image = np.stack([i1, i2, i3], axis=2)
cv2.imshow('tf each channel', a_)
cv2.imshow('tf all channel', b_)
cv2.imshow('numpy', image)
cv2.imshow('origin', origin)
cv2.waitKey(-1)
cv2.destroyAllWindows()