对RGB图像进行颜色校正然后转化为HSI图像的代码

以下是对RGB图像进行颜色校正并转换为HSI图像的代码示例（使用Python和OpenCV库）：

import cv2
import numpy as np

# 读取待校正图像
img = cv2.imread('input.jpg')

# 获取颜色校正目标
calibration_target = cv2.imread('calibration_target.jpg')

# 将图像转换为Lab颜色空间
img_lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)
calibration_target_lab = cv2.cvtColor(calibration_target, cv2.COLOR_BGR2Lab)

# 计算待校正图像和校正目标的均值和标准差
img_mean, img_std = cv2.meanStdDev(img_lab)
calibration_target_mean, calibration_target_std = cv2.meanStdDev(calibration_target_lab)

# 对每个颜色通道进行均值和标准差的调整
adjusted_lab = np.zeros_like(img_lab)
for i in range(3):
    adjusted_lab[...,i] = (img_lab[...,i] - img_mean[i]) * calibration_target_std[i] / img_std[i] + calibration_target_mean[i]

# 将Lab颜色空间下的图像转换回RGB颜色空间
adjusted_img = cv2.cvtColor(adjusted_lab, cv2.COLOR_Lab2BGR)

# 将RGB图像转换为HSI图像
adjusted_img_hsi = cv2.cvtColor(adjusted_img, cv2.COLOR_BGR2HSV)

# 显示结果
cv2.imshow('input', img)
cv2.imshow('calibration target', calibration_target)
cv2.imshow('adjusted', adjusted_img)
cv2.imshow('adjusted HSI', adjusted_img_hsi)
cv2.waitKey(0)
cv2.destroyAllWindows()

需要注意的是，在将RGB图像转换为HSI图像时，需要使用OpenCV库中的`cv2.cvtColor()`函数，并将参数设置为`cv2.COLOR_BGR2HSV`。