OpenCV cv :: Mat to std :: ifstream for base64 encoding

Question

说实话,我很惊讶到目前为止没有人遇到过这种情况.我正在将一张来自OpenCV的图片加载到cv :: Mat中,在我通过套接字发送之前,我想要base64编码.

对于base64,我使用的是libb64,因为它是Debian/Ubuntu的原生,并且易于使用且速度非常快.编码函数将std :: ifstream作为参数,并输出std :: ofstream.

#include <opencv2/opencv.hpp>
#include <b64/encode.h>
#include <fstream>

using namespace cv;
Mat image;
image = imread( "picture.jpg", CV_LOAD_IMAGE_COLOR );

if ( image.data )
{
    std::ifstream instream( ???, std::ios_base::in | std::ios_base::binary);
    std::ofstream outstream;        

    // Convert Matrix to ifstream
    // ...

    base64::encoder E;
    E.encode( instream, outstream );

    // Now put it in a string, and send it over a socket...
}

我真的不知道如何填充cv :: Mat的内流.谷歌搜索,我发现我可以通过列和行迭代cv :: Mat,并得到每个(我假设的像素)RGB值:

for ( int j = 0; j < image.rows; j++ )
{
    for ( int i = 0; i < image.cols; i++ )
    {
        unsigned char b = input [ image.step * j + i ] ;
        unsigned char g = input [ image.step * j + i + 1 ];
        unsigned char r = input [ image.step * j + i + 2 ];
    }
}

这是继续下去的正确方法吗？有更优雅的方式吗？

Answer 1

为了能够通过HTTP发送图像,您还需要对其宽度,高度和类型进行编码.您需要将序列Mat化为流并使用libb64对该流进行编码.另一方面,您需要解码该流并反序列化图像以检索它.

我实现了一个小型测试程序,它使用std::stringstream缓冲区进行序列化和反序列化.我选择了它,因为它同时扩展std::istream和std::ostream其libb64用途.

该serialize函数将a序列cv::Mat化为a std::stringstream.在其中,我写了图像的宽度,高度,类型,缓冲区的大小和缓冲区本身.

该deserialize功能正好相反.它读取缓冲区的宽度,高度,类型,大小和缓冲区.它没有那么高效,因为它需要分配一个临时缓冲区来从字符串流中读取数据.此外,它需要克隆图像,以便它不依赖于临时缓冲区,它将处理自己的内存分配.我敢肯定,通过一些修补,它可以提高效率.

main函数加载图像,对其进行序列化,使用libb64对其进行编码,然后对其进行解码,对其进行反序列化并在窗口中显示.这应该模拟你想要做的事情.

// Serialize a cv::Mat to a stringstream
stringstream serialize(Mat input)
{
    // We will need to also serialize the width, height, type and size of the matrix
    int width = input.cols;
    int height = input.rows;
    int type = input.type();
    size_t size = input.total() * input.elemSize();

    // Initialize a stringstream and write the data
    stringstream ss;
    ss.write((char*)(&width), sizeof(int));
    ss.write((char*)(&height), sizeof(int));
    ss.write((char*)(&type), sizeof(int));
    ss.write((char*)(&size), sizeof(size_t));

    // Write the whole image data
    ss.write((char*)input.data, size);

    return ss;
}

// Deserialize a Mat from a stringstream
Mat deserialize(stringstream& input)
{
    // The data we need to deserialize
    int width = 0;
    int height = 0;
    int type = 0;
    size_t size = 0;

    // Read the width, height, type and size of the buffer
    input.read((char*)(&width), sizeof(int));
    input.read((char*)(&height), sizeof(int));
    input.read((char*)(&type), sizeof(int));
    input.read((char*)(&size), sizeof(size_t));

    // Allocate a buffer for the pixels
    char* data = new char[size];
    // Read the pixels from the stringstream
    input.read(data, size);

    // Construct the image (clone it so that it won't need our buffer anymore)
    Mat m = Mat(height, width, type, data).clone();

    // Delete our buffer
    delete[]data;

    // Return the matrix
    return m;
}

void main()
{
    // Read a test image
    Mat input = imread("D:\\test\\test.jpg");

    // Serialize the input image to a stringstream
    stringstream serializedStream = serialize(input);

    // Base64 encode the stringstream
    base64::encoder E;
    stringstream encoded;
    E.encode(serializedStream, encoded);

    // Base64 decode the stringstream
    base64::decoder D;
    stringstream decoded;
    D.decode(encoded, decoded);

    // Deserialize the image from the decoded stringstream
    Mat deserialized = deserialize(decoded);

    // Show the retrieved image
    imshow("Retrieved image", deserialized);
    waitKey(0);
}