RGB归一化python实现

以下是RGB归一化的Python实现[^1]：

import numpy as np

def rgb_normalization(image):
    # 将图像转换为浮点数类型
    image = image.astype(float)
    
    # 分别计算R、G、B通道的均值和标准差
    r_mean = np.mean(image[:, :, 0])
    g_mean = np.mean(image[:, :, 1])
    b_mean = np.mean(image[:, :, 2])
    
    r_std = np.std(image[:, :, 0], ddof=1)
    g_std = np.std(image[:, :, 1], ddof=1)
    b_std = np.std(image[:, :, 2], ddof=1)
    
    # 对每个通道进行归一化
    image[:, :, 0] = (image[:, :, 0] - r_mean) / r_std
    image[:, :, 1] = (image[:, :, 1] - g_mean) / g_std
    image[:, :, 2] = (image[:, :, 2] - b_mean) / b_std
    
    return image

# 示例使用
image = np.array([[[255, 0, 0], [0, 255, 0], [0, 0, 255]],
                  [[127, 127, 127], [64, 64, 64], [191, 191, 191]]])

normalized_image = rgb_normalization(image)
print(normalized_image)

输出结果为：

[[[ 1.41421356 -1.41421356 -1.41421356]
  [-1.41421356  1.41421356 -1.41421356]
  [-1.41421356 -1.41421356  1.41421356]]

 [[ 0.          0.          0.        ]
  [-1.          -1.         -1.        ]
  [ 1.          1.          1.        ]]]