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前言

新人轻点喷~
在网上看到好多基本都是循环写的，逐个像素操作，比较慢，这里尝试用numpy库的where函数写了一下，速度提升了好多（呃~~~~~）
RGB到HSI空间及HSI到RGB空间的原理和公式书上都有，就不列出来了（我看的是刚雷萨斯第四版）
直接上代码

代码

# -*- coding:utf-8 -*-
"""
作者：YJH
日期：2021年10月28日
"""
import matplotlib.pyplot as plt
import cv2 as cv
import numpy as np# 显示汉字用
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False# 定义显示一张图片函数
def imshow(image):if image.ndim == 2:plt.imshow(image, cmap='gray')                     # 指定为灰度图像else:plt.imshow(cv.cvtColor(image, cv.COLOR_BGR2RGB))# 定义坐标数字字体及大小
def label_def():plt.xticks(fontproperties='Times New Roman', size=8)plt.yticks(fontproperties='Times New Roman', size=8)plt.axis('off')                                     # 关坐标，可选# 读取图片
img_orig = cv.imread('strawberries_coffee_full_color.tif', 1)    # 读取彩色图片# RGB到HSI的变换
def rgb2hsi(image):b, g, r = cv.split(image)                    # 读取通道r = r / 255.0                                # 归一化g = g / 255.0b = b / 255.0eps = 1e-6                                   # 防止除零img_i = (r + g + b) / 3                      # I分量img_h = np.zeros(r.shape, dtype=np.float32)img_s = np.zeros(r.shape, dtype=np.float32)min_rgb = np.zeros(r.shape, dtype=np.float32)# 获取RGB中最小值min_rgb = np.where((r <= g) & (r <= b), r, min_rgb)min_rgb = np.where((g <= r) & (g <= b), g, min_rgb)min_rgb = np.where((b <= g) & (b <= r), b, min_rgb)img_s = 1 - 3*min_rgb/(r+g+b+eps)                                            # S分量num = ((r-g) + (r-b))/2den = np.sqrt((r-g)**2 + (r-b)*(g-b))theta = np.arccos(num/(den+eps))img_h = np.where((b-g) > 0, 2*np.pi - theta, theta)                           # H分量img_h = img_h/(2*np.pi)                                                       # 归一化temp_s = img_s - np.min(img_s)temp_i = img_i - np.min(img_i)img_s = temp_s/np.max(temp_s)img_i = temp_i/np.max(temp_i)image_hsi = cv.merge([img_h, img_s, img_i])return img_h, img_s, img_i, image_hsi# HSI到RGB的变换
def hsi2rgb(image_hsi):eps = 1e-6                                                                  # 防止除零img_h, img_s, img_i = cv.split(image_hsi)image_out = np.zeros((img_h.shape[0], img_h.shape[1], 3))img_h = img_h*2*np.piimg_r = np.zeros(img_h.shape, dtype=np.float32)img_g = np.zeros(img_h.shape, dtype=np.float32)img_b = np.zeros(img_h.shape, dtype=np.float32)# 扇区1img_b = np.where((img_h >= 0) & (img_h < 2 * np.pi / 3), img_i * (1 - img_s), img_b)img_r = np.where((img_h >= 0) & (img_h < 2 * np.pi / 3),img_i * (1 + img_s * np.cos(img_h) / (np.cos(np.pi/3 - img_h))), img_r)img_g = np.where((img_h >= 0) & (img_h < 2 * np.pi / 3), 3 * img_i - (img_r + img_b), img_g)# 扇区2img_r = np.where((img_h >= 2*np.pi/3) & (img_h < 4*np.pi/3), img_i * (1 - img_s), img_r)img_g = np.where((img_h >= 2*np.pi/3) & (img_h < 4*np.pi/3),img_i * (1 + img_s * np.cos(img_h) / (np.cos(np.pi/3 - img_h))), img_g)img_b = np.where((img_h >= 2*np.pi/3) & (img_h < 4*np.pi/3), 3 * img_i - (img_r + img_g), img_b)# 扇区3img_g = np.where((img_h >= 4*np.pi/3) & (img_h <= 2*np.pi), img_i * (1 - img_s), img_g)img_b = np.where((img_h >= 4*np.pi/3) & (img_h <= 2*np.pi),img_i * (1 + img_s * np.cos(img_h) / (np.cos(np.pi/3 - img_h))), img_b)img_r = np.where((img_h >= 4*np.pi/3) & (img_h <= 2*np.pi), 3 * img_i - (img_b + img_g), img_r)temp_r = img_r - np.min(img_r)img_r = temp_r/np.max(temp_r)temp_g = img_g - np.min(img_g)img_g = temp_g/np.max(temp_g)temp_b = img_b - np.min(img_b)img_b = temp_b/np.max(temp_b)image_out = cv.merge((img_b, img_g, img_r))                   # 按RGB合并，后面不用转换通道print(image_out.shape)return image_outif __name__ == '__main__':                                       # 运行当前函数h, s, i, hsi = rgb2hsi(img_orig)                             # RGB到HSI的变换img_revise = np.float32(hsi2rgb(hsi))                        # HSI复原到RGB# h, s, i = cv.split(cv.cvtColor(img_orig, cv.COLOR_BGR2HSV))       # 自带库函数HSV模型im_b, im_g, im_r = cv.split(img_orig)                        # 获取RGB通道数据plt.subplot(241), plt.imshow(cv.cvtColor(img_orig, cv.COLOR_BGR2RGB)), plt.title('原始图'), label_def()plt.subplot(242), plt.imshow(im_r, 'gray'), plt.title('R'), label_def()plt.subplot(243), plt.imshow(im_g, 'gray'), plt.title('G'), label_def()plt.subplot(244), plt.imshow(im_b, 'gray'), plt.title('B'), label_def()plt.subplot(245), plt.imshow(hsi), plt.title('HSI图'), label_def()plt.subplot(246), plt.imshow(h, 'gray'), plt.title('H(色调)'), label_def()plt.subplot(247), plt.imshow(s, 'gray'), plt.title('S(饱和度)'), label_def()plt.subplot(248), plt.imshow(i, 'gray'), plt.title('I(亮度)'), label_def()plt.show()plt.subplot(121), plt.imshow(cv.cvtColor(img_orig, cv.COLOR_BGR2RGB)), plt.title('原RGB'), label_def()plt.subplot(122), plt.imshow(cv.cvtColor(img_orig, cv.COLOR_BGR2RGB)), plt.title('HSI重建RGB'), label_def()plt.show()

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运行结果

欢迎大家批评指正~~~