Matlab/C++源码实现RGB通道与HSV通道的转换（效果对比Halcon）

HSV通道的含义

HSV通道是指图像处理中的一种颜色模型，它由色调（Hue）、饱和度（Saturation）和明度（Value）三个通道组成。色调表示颜色的种类，饱和度表示颜色的纯度或鲜艳程度，明度表示颜色的亮度。HSV通道常用于图像处理中的颜色分析、颜色过滤、颜色调整等任务，它相对于其他颜色模型具有更直观和易于调节的特点，因此被广泛应用于计算机视觉和图像处理的领域。

Halcon算子例程

read_image (Image, 'D:/lena.jpg')
decompose3 (Image, ImageR, ImageG, ImageB)
trans_from_rgb (ImageR, ImageG, ImageB, ImageH, ImageS, ImageV, 'hsv')
trans_to_rgb (ImageH, ImageS, ImageV, ImageR1, ImageG1, ImageB1, 'hsv')
compose3 (ImageR1, ImageG1, ImageB1, MultiChannelImage)

这里先将三通道RGB三通道拆开成单独的通道，再将RGB与HSV通道互相转换，最后将三通道图像合并成RGB图像。
Halcon的图像效果是：

源代码实现

RGB转成HSV

这里需要注意的是,halcon这里将HSV三通道的取值范围作了说明，H通道的数值范围是0到2*pi,S通道的数值范围是0到1，V通道的数值范围是0到1。而常用的图像为BYTE字节型，数值范围是0到255，这里对公式做了修改，使Matlab得出的图像数据范围是0到255，可以直接显示，这里可以从matlab的workspace中看到计算过程。
以下便是使用Matlab实现trans_from_rgb的效果

trans_from_rgb (ImageR, ImageG, ImageB, ImageH, ImageS, ImageV, 'hsv')

Matlab源码

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%代码--RGB通道转HSV通道
%时间：2023.9
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clc;
image=imread('D:\lena.jpg');
[height,width,channels]=size(image);
figure;
imshow(image);
title('rgb-image');
image_R=image(:,:,1);
image_R=double(image_R);
image_G=image(:,:,2);
image_G=double(image_G);
image_B=image(:,:,3);
image_B=double(image_B);%%%转化成HSV通道
H_image = zeros(height,width);
S_image = zeros(height,width);
V_image = zeros(height,width);%%RGB转成HSV
for i=1:1:heightfor j=1:1:width%%%计算三通道的最大最小值计算image_matrix = [image_R(i,j), image_G(i,j), image_B(i,j)];maxValue = max(image_matrix);minValue = min(image_matrix);V_image(i,j) = maxValue;if(maxValue == minValue)S_image(i,j) = 0;H_image(i,j) = 0;else%%%计算饱和度S_image(i,j) = (maxValue - minValue)*255/minValue;%%%计算H通道if(maxValue == image_R(i,j))H_image(i,j) = 42.5.*(image_G(i,j) - image_B(i,j))./(maxValue - minValue);elseif(maxValue == image_G(i,j))H_image(i,j) = 42.5 * (2 + (image_B(i,j) - image_R(i,j)) / (maxValue - minValue));elseif(maxValue == image_B(i,j)) H_image(i,j) = 42.5 * (4 + (image_R(i,j) - image_G(i,j)) / (maxValue - minValue));endendend
end%%%RGB要取整
H_image = uint8(H_image);
S_image = uint8(S_image);
V_image = uint8(V_image);figure;
imshow(H_image);
title('H_image');figure;
imshow(S_image);
title('S_image');figure;
imshow(V_image);
title('V_image');

最终实现的效果是：

最终验证的效果与halcon效果一致；

同时，以上代码采用C++实现的话如下所示，这里为了保证精度，输出结果采用的是double类型，但是范围也是0到255之间，要显示的话，需要转化为unsigned char类型：

C++源码

//将RGB图像转化成HSV图像
/*
输入：  rData : r通道图像gData : g通道图像bData : b通道图像
输出：  hDoubleData : h通道图像, h通道采用double类型，保留精度sDoubleData : s通道图像，s通道采用double类型，保留精度vDoubleData : v通道图像，v通道采用double类型，保留精度
*/
void trans_from_rgb(unsigned char *rData, unsigned char *gData, unsigned char *bData, double *hDoubleData, double *sDoubleData, double *vDoubleData, int height, int width)
{if ((height <= 0) || (width <= 0))return;//在函数外部分配好内存空间if (rData == NULL || gData == NULL || bData == NULL || hDoubleData == NULL || sDoubleData == NULL || vDoubleData == NULL)return;int i;unsigned char minValue,maxValue;for (i = 0; i < width * height; i++){//V通道数据,三通道的最大值maxValue = std::max(std::max(rData[i], gData[i]), bData[i]);minValue = std::min(std::min(rData[i], gData[i]), bData[i]);vDoubleData[i] = maxValue;if (maxValue == minValue){sDoubleData[i] = 0;hDoubleData[i] = 0;}else{//S通道sDoubleData[i] = (maxValue - minValue)*255.0 / maxValue;//H通道if (maxValue == rData[i])hDoubleData[i] = 42.5 * (gData[i] - bData[i]) / (maxValue - minValue);    else if (maxValue == gData[i])hDoubleData[i] = 42.5 * (2 + (bData[i] - rData[i]) / (maxValue - minValue));else if (maxValue == bData[i])hDoubleData[i] = 42.5 * (4 + (rData[i] - gData[i]) / (maxValue - minValue));}}
}

HSV转成RGB

halcon给出的公式说明为：

Matlab源码

同样的，采用Matlab实现：

%%%%%%%%%%%%%%%%%%%%%%%%%%%
%代码--HSV通道转RGB通道
%时间：2023.9
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clc;
image1=imread('D:\lena.jpg');
[height,width,channels]=size(image1);
image_H=double(image1);
image2=imread('E:\S_image.bmp');
image_S=double(image2);
image3=imread('E:\V_image.bmp');
image_V=double(image3);%%%转化成RGB通道
R_image = zeros(height,width);
G_image = zeros(height,width);
B_image = zeros(height,width);%%%HSV转成RGB
for i=1:1:heightfor j=1:1:widthif(image_S(i,j) == 0)R_image(i,j) = image_V(i,j);G_image(i,j) = image_V(i,j);B_image(i,j) = image_V(i,j);else%%Hi = floor(image_H(i,j)*2*pi/255/deg2rad(60));    %%归一化到0到2*piHi = floor(image_H(i,j)*0.025);Hf = image_H(i,j)*0.025 - Hi;%%%%根据H的值，将C,X,m分别对应到RGB三个分量上if(Hi == 0)R_image(i,j) = image_V(i,j);G_image(i,j) = image_V(i,j) * (1 - image_S(i,j)/255*(1-Hf));B_image(i,j) = image_V(i,j) * (1 - image_S(i,j)/255);elseif(Hi == 1)R_image(i,j) = image_V(i,j) * (1 - image_S(i,j)/255*Hf);G_image(i,j) = image_V(i,j);B_image(i,j) = image_V(i,j) * (1 - image_S(i,j)/255);elseif(Hi == 2)R_image(i,j) = image_V(i,j) * (1 - image_S(i,j)/255);G_image(i,j) = image_V(i,j);B_image(i,j) = image_V(i,j) * (1 - image_S(i,j)/255*(1-Hf));elseif(Hi == 3)R_image(i,j) = image_V(i,j) * (1 - image_S(i,j)/255);G_image(i,j) = image_V(i,j) * (1 - image_S(i,j)/255*Hf);B_image(i,j) = image_V(i,j);elseif(Hi == 4)R_image(i,j) = image_V(i,j) * (1 - image_S(i,j)/255*(1-Hf));G_image(i,j) = image_V(i,j) * (1 - image_S(i,j)/255);B_image(i,j) = image_V(i,j);elseif(Hi == 5)R_image(i,j) = image_V(i,j);G_image(i,j) = image_V(i,j) * (1 - image_S(i,j)/255);B_image(i,j) = image_V(i,j) * (1 - image_S(i,j)/255*Hf);endendend
end%%%RGB要取整
R_image = uint8(R_image);
G_image = uint8(G_image);
B_image = uint8(B_image);figure;
imshow(R_image);
title('R_image');figure;
imshow(G_image);
title('G_image');figure;
imshow(B_image);
title('B_image');

最终实现的效果是：

可以看出，与Halcon效果一致；

C++源码

同样的，采用C++实现：

//将HSV图像转化成RGB图像
/*
输入：  hDoubleData : h通道图像 ,h通道采用double类型，保留精度sDoubleData : s通道图像 ,s通道采用double类型，保留精度vDoubleData : v通道图像 ,v通道采用double类型，保留精度
输出：  rDoubleData : r通道图像 ,r通道采用double类型，保留精度gDoubleData : g通道图像 ,g通道采用double类型，保留精度bDoubleData : b通道图像 ,b通道采用double类型，保留精度
*/
void trans_to_rgb(double *hDoubleData, double *sDoubleData, double *vDoubleData, double *rDoubleData, double *gDoubleData, double *bDoubleData, int height, int width)
{if ((height <= 0) || (width <= 0))return;if (rDoubleData == NULL || gDoubleData == NULL || bDoubleData == NULL || hDoubleData == NULL || sDoubleData == NULL || vDoubleData == NULL)return;int i;double Hi, Hf;for (i = 0; i < width * height; i++){if (sDoubleData[i] > 0){Hi = floor(hDoubleData[i] * 0.025);Hf = hDoubleData[i] * 0.025 - Hi;if (Hi == 0){rDoubleData[i] = vDoubleData[i];gDoubleData[i] = vDoubleData[i] * (1 - sDoubleData[i] / 255.0*(1 - Hf));bDoubleData[i] = vDoubleData[i] * (1 - sDoubleData[i] / 255.0);}else if (Hi == 1){rDoubleData[i] = vDoubleData[i] * (1 - sDoubleData[i] / 255.0 * Hf);gDoubleData[i] = vDoubleData[i];bDoubleData[i] = vDoubleData[i] * (1 - sDoubleData[i] / 255.0);}else if (Hi == 2){rDoubleData[i] = vDoubleData[i] * (1 - sDoubleData[i] / 255.0);gDoubleData[i] = vDoubleData[i];bDoubleData[i] = vDoubleData[i] * (1 - sDoubleData[i] / 255.0*(1 - Hf));}else if (Hi == 3){rDoubleData[i] = vDoubleData[i] * (1 - sDoubleData[i] / 255.0);gDoubleData[i] = vDoubleData[i] * (1 - sDoubleData[i] / 255.0 * Hf);bDoubleData[i] = vDoubleData[i];}else if (Hi == 4){rDoubleData[i] = vDoubleData[i] * (1 - sDoubleData[i] / 255.0*(1 - Hf));gDoubleData[i] = vDoubleData[i] * (1 - sDoubleData[i] / 255.0);bDoubleData[i] = vDoubleData[i];}else if (Hi == 5){rDoubleData[i] = vDoubleData[i];gDoubleData[i] = vDoubleData[i] * (1 - sDoubleData[i] / 255.0);bDoubleData[i] = vDoubleData[i] * (1 - sDoubleData[i] / 255.0 * Hf);}}else{rDoubleData[i] = gDoubleData[i] = bDoubleData[i] = vDoubleData[i];}}
}

通道拆分与合并C++源代码实现

三通道拆分

Halcon中，拆分三通道的算子为：

decompose3 (Image, ImageR, ImageG, ImageB)

对应的拆分三通道图像的C++函数为：

//拆分三通道图像
/*
输入：  srcData : 三通道图像，内存排列方式是BGRBGRBGR......
输出：  rData : r通道图像gData : g通道图像bData : b通道图像
*/
void decompose3(unsigned char *srcData, unsigned char *rData, unsigned char *gData, unsigned char *bData, int height, int width)
{if ((height <= 0) || (width <= 0))return;if (srcData == NULL || rData == NULL || gData == NULL || bData == NULL)return;int i;
#pragma omp parallel for num_threads(3)for (i = 0; i < width * height; i++){bData[i] = srcData[3 * i];gData[i] = srcData[3 * i + 1];rData[i] = srcData[3 * i + 2];}
}

三通道合并

Halcon中，拆分三通道的算子为：

compose3 (ImageR1, ImageG1, ImageB1, MultiChannelImage)

对应的合并三通道图像的C++函数为：

//合并三通道图像
/*
输入：  rData : r通道图像gData : g通道图像bData : b通道图像
输出：  bgrData:彩色图像,合并成BGRBGR.....排列
*/
void compose3(unsigned char *rData, unsigned char *gData, unsigned char *bData, unsigned char *bgrData, int height, int width)
{if ((height <= 0) || (width <= 0))return;if (bgrData == NULL || rData == NULL || gData == NULL || bData == NULL)return;int i;
#pragma omp parallel for num_threads(3)for (i = 0; i < width * height; i++){bgrData[3 * i] = bData[i];bgrData[3 * i + 1] = gData[i];bgrData[3 * i + 2] = rData[i];}
}