从电脑上读入的灰度图像，并分别计算图像的均值、方差等统计特征，并计算图像的直方图特征并以图形方式显示图像的直方图（用C或C++语言实现）不能用opencv和其他现成的库函数操作；。

好的，以下是一个计算灰度图像均值、方差和直方图的代码示例，以及用gnuplot绘制直方图的方法：

#include <fstream>
#include <iostream>
#include <vector>
#include <algorithm>

// BMP文件头结构体
#pragma pack(push, 1)
struct BMPHeader {
    uint16_t bfType;
    uint32_t bfSize;
    uint16_t bfReserved1;
    uint16_t bfReserved2;
    uint32_t bfOffBits;
};
#pragma pack(pop)

// BMP信息头结构体
struct BMPInfoHeader {
    uint32_t biSize;
    int32_t biWidth;
    int32_t biHeight;
    uint16_t biPlanes;
    uint16_t biBitCount;
    uint32_t biCompression;
    uint32_t biSizeImage;
    int32_t biXPelsPerMeter;
    int32_t biYPelsPerMeter;
    uint32_t biClrUsed;
    uint32_t biClrImportant;
};

// 读取灰度图像数据
unsigned char* read_gray_bmp(const char* filename, int& width, int& height) {
    std::ifstream file(filename, std::ios::binary);
    if (!file) {
        std::cerr << "Error: cannot open file \"" << filename << "\"" << std::endl;
        return nullptr;
    }

    BMPHeader header;
    BMPInfoHeader info_header;

    // 读取文件头和信息头
    file.read((char*)&header, sizeof(header));
    file.read((char*)&info_header, sizeof(info_header));

    // 检查文件类型是否为BMP
    if (header.bfType != 0x4d42) {
        std::cerr << "Error: \"" << filename << "\" is not a BMP file" << std::endl;
        return nullptr;
    }

    // 检查位图压缩类型是否为无压缩
    if (info_header.biCompression != 0) {
        std::cerr << "Error: \"" << filename << "\" is a compressed BMP file" << std::endl;
        return nullptr;
    }

    // 检查像素位数是否为8
    if (info_header.biBitCount != 8) {
        std::cerr << "Error: \"" << filename << "\" is not a gray BMP file" << std::endl;
        return nullptr;
    }

    // 读取调色板
    unsigned char palette[1024];
    file.read((char*)palette, sizeof(palette));

    // 读取图像数据
    width = info_header.biWidth;
    height = info_header.biHeight;
    int row_size = ((width * info_header.biBitCount + 31) / 32) * 4;
    unsigned char* data = new unsigned char[width * height];
    file.seekg(header.bfOffBits, std::ios::beg);
    for (int y = 0; y < height; ++y) {
        file.read((char*)(data + width * (height - y - 1)), width);
        file.seekg(row_size - width, std::ios::cur);
    }

    return data;
}

// 计算均值和方差
void calc_mean_variance(const unsigned char* data, int width, int height, double& mean, double& variance) {
    int count = width * height;
    double sum = 0.0, sum2 = 0.0;
    for (int i = 0; i < count; ++i) {
        sum += data[i];
        sum2 += data[i] * data[i];
    }
    mean = sum / count;
    variance = sum2 / count - mean * mean;
}

// 计算直方图
std::vector<int> calc_histogram(const unsigned char* data, int width, int height, int num_bins) {
    int count = width * height;
    std::vector<int> histogram(num_bins);
    int bin_size = 256 / num_bins;
    for (int i = 0; i < count; ++i) {
        int bin = data[i] / bin_size;
        if (bin >= num_bins) {
            bin = num_bins - 1;
        }
        ++histogram[bin];
    }
    return histogram;
}

// 绘制直方图
void plot_histogram(const std::vector<int>& histogram) {
    // 生成gnuplot脚本文件
    std::ofstream gnufile("histogram.gnu");
    gnufile << "set terminal pngcairo size 800,600" << std::endl;
    gnufile << "set output \"histogram.png\"" << std::endl;
    gnufile << "set boxwidth 0.9" << std::endl;
    gnufile << "set style fill solid" << std::endl;
    gnufile << "unset key" << std::endl;
    gnufile << "plot '-' using 1:2 with boxes lc rgb \"#808080\"" << std::endl;
    for (int i = 0; i < histogram.size(); ++i) {
        gnufile << i << ' ' << histogram[i] << std::endl;
    }
    gnufile << "e" << std::endl;

    // 执行gnuplot脚本并生成直方图
    system("gnuplot histogram.gnu");
}

int main() {
    int width, height;
    unsigned char* data = read_gray_bmp("image.bmp", width, height);
    if (!data) {
        return 1;
    }

    double mean, variance;
    calc_mean_variance(data, width, height, mean, variance);
    std::cout << "Mean: " << mean << std::endl;
    std::cout << "Variance: " << variance << std::endl;

    std::vector<int> histogram = calc_histogram(data, width, height, 16);
    plot_histogram(histogram);

    delete[] data;
    return 0;
}

在这个例子中，我们首先读取灰度图像数据，然后计算图像的均值和方差，并计算图像的直方图。为了绘制直方图，我们使用gnuplot生成一个脚本文件，然后执行该脚本文件，生成PNG格式的直方图图像。您需要安装gnuplot来运行这个例子。希望这可以帮助您解决问题！