对rgb和yuv分析三个通道的概率分布，并计算各自的熵

一、实验目的
对rgb和yuv分析三个通道的概率分布，并计算各自的熵。（编程实现）

二、注意事项
①两个文件的分辨率均为256*256，
②yuv为4:2:0采样空间
③存储格式为：rgb文件按每个像素BGR分量依次存放；YUV格式按照全部像素的Y数据块、U数据块和V数据块依次存放。

三、实现过程及结果

1、rgb图像分析

源程序：

#include<stdio.h>
#include<iostream>
#include<math.h>

using namespace std;

#define A  65536 //分辨率为256*256

int main()
{
	//打开文件,创建输出文件
	FILE* image, * red, * green, * blue;
	fopen_s(&image, "C:\\Users\\LENOVO\\source\\repos\\sjyslab1\\down.rgb", "rb");
	fopen_s(&red, "C:\\Users\\LENOVO\\source\\repos\\sjyslab1\\R.txt", "w");
	fopen_s(&green, "C:\\Users\\LENOVO\\source\\repos\\sjyslab1\\G.txt", "w");
	fopen_s(&blue, "C:\\Users\\LENOVO\\source\\repos\\sjyslab1\\B.txt", "w");

	//定义R、G、B分量
	unsigned char R[A] = { 0 }, G[A] = { 0 }, B[A] = { 0 };

	//定义频率分量
	double R_F[256] = { 0 }, G_F[256] = { 0 }, B_F[256] = { 0 };

	//定义熵
	double R_S = 0, G_S = 0, B_S = 0;

	//读取R、G、B三个分量
	unsigned char sum[256 * 256 * 3];
	fread(sum, 1, 256 * 256 * 3, image);

	for (int i = 0, j = 0; i < 256 * 256 * 3; i = i + 3, j++)
	{
		B[j] = *(sum + i);
		G[j] = *(sum + i + 1);
		R[j] = *(sum + i + 2);
	}
	//计数三通道各颜色值次数
	for (int i = 0; i < 256; i++)
	{
		for (int j = 0; j < A; j++)
		{
			if (int(R[j] == i)) { R_F[i]++; }
			if (int(G[j] == i)) { G_F[i]++; }
			if (int(B[j] == i)) { B_F[i]++; }
		}
	}
	//计算频率
	for (int i = 0; i < 256; i++)
	{
		R_F[i] = R_F[i] / (256 * 256);
		B_F[i] = B_F[i] / (256 * 256);
		G_F[i] = G_F[i] / (256 * 256);
	}
	//将频率写入文件
	fprintf(red, "值\t概率\n");
	for (int i = 0; i < 256; i++)
	{
		fprintf(red, "%d\t%f\n", i, R_F[i]);
	}
	fprintf(green, "值\t概率\n");
	for (int i = 0; i < 256; i++)
	{
		fprintf(green, "%d\t%f\n", i, G_F[i]);
	}
	fprintf(blue, "值\t概率\n");
	for (int i = 0; i < 256; i++)
	{
		fprintf(blue, "%d\t%f\n", i, B_F[i]);
	}
	//计算并输出熵
	for (int i = 0; i < 256; i++)
	{
		if (R_F[i] != 0) { R_S += -R_F[i] * log(R_F[i]) / log(2); }
		if (G_F[i] != 0) { G_S += -G_F[i] * log(G_F[i]) / log(2); }
		if (B_F[i] != 0) { B_S += -B_F[i] * log(B_F[i]) / log(2); }
	}
	cout << "R的熵为" << R_S << endl;
	cout << "G的熵为" << G_S << endl;
	cout << "B的熵为" << B_S << endl;

	fclose(image);
	fclose(red);
	fclose(green);
	fclose(blue);
	return 0;
}

运行结果：

输出的概率分布txt文件导入Excel后并制作成折线图：

2、yuv图像分析

源程序：

#include<stdio.h>
#include<iostream>
#include<math.h>

using namespace std;
#define A  65536 //分辨率为256*256

int main()
{
	//打开文件,创建输出文件
	FILE* image, * fY, * fU, * fV;
	fopen_s(&image, "C:\\Users\\LENOVO\\source\\repos\\sjyslab1\\down.yuv", "rb");
	fopen_s(&fY, "C:\\Users\\LENOVO\\source\\repos\\sjyslab1\\Y.txt", "w");
	fopen_s(&fU, "C:\\Users\\LENOVO\\source\\repos\\sjyslab1\\U.txt", "w");
	fopen_s(&fV, "C:\\Users\\LENOVO\\source\\repos\\sjyslab1\\V.txt", "w");

	unsigned char Y[A] = { 0 }, U[A / 4] = { 0 }, V[A / 4] = { 0 };
	//定义yuv分量

	double Y_F[256] = { 0 }, U_F[256] = { 0 }, V_F[256] = { 0 };
	//定义频率分量

	double Y_S = 0, U_S = 0, V_S = 0;
	//定义熵

	//分别读取YUV三个分量到数组中
	unsigned char Array[98304];
	fread(Array, 1, A * 1.5, image);
	for (int i = 0; i < A; i++)
	{
		Y[i] = *(Array + i);
	}

	for (int i = A; i < A * 1.25; i++)
	{
		U[i - 65536] = *(Array + i);
	}

	for (int i = A * 1.25; i < A * 1.5; i++)
	{
		V[i - 81920] = *(Array + i);
	}

	//计数三通道各颜色值次数
	for (int i = 0; i < A; i++)
	{
		Y_F[Y[i]]++;
	}
	for (int i = 0; i < (A / 4); i++)
	{
		U_F[U[i]]++;
	}
	for (int i = 0; i < (A / 4); i++)
	{
		V_F[V[i]]++;
	}

	//计算频率
	for (int i = 0; i < 256; i++)
	{
		Y_F[i] = Y_F[i] / (A);
		V_F[i] = V_F[i] / (A / 4);
		U_F[i] = U_F[i] / (A / 4);
	}
	//将频率写入文件
	fprintf(fY, "值\t概率\n");
	for (int i = 0; i < 256; i++)
	{
		fprintf(fY, "%d\t%f\n", i, Y_F[i]);
	}
	fprintf(fU, "值\t概率\n");
	for (int i = 0; i < 256; i++)
	{
		fprintf(fU, "%d\t%f\n", i, U_F[i]);
	}
	fprintf(fV, "值\t概率\n");
	for (int i = 0; i < 256; i++)
	{
		fprintf(fV, "%d\t%f\n", i, V_F[i]);
	}
	//计算并输出熵
	for (int i = 0; i < 256; i++)
	{
		if (Y_F[i] != 0) { Y_S += -Y_F[i] * log(Y_F[i]) / log(2); }
		if (U_F[i] != 0) { U_S += -U_F[i] * log(U_F[i]) / log(2); }
		if (V_F[i] != 0) { V_S += -V_F[i] * log(V_F[i]) / log(2); }
	}
	cout << "Y的熵为" << Y_S << endl;
	cout << "U的熵为" << U_S << endl;
	cout << "V的熵为" << V_S << endl;

	fclose(image);
	fclose(fY);
	fclose(fU);
	fclose(fV);
	return 0;
}

运行结果:

输出的概率分布txt文件导入Excel后并制作成折线图：