分享 C++图像处理的代码简易示例

采用Decoder:stb_image

https://github.com/nothings/stb/blob/master/stb_image.h

采用Encoder:tiny_jpeg
https://github.com/serge-rgb/TinyJPEG/blob/master/tiny_jpeg.h

stb_image.h用于解析图片格式:
JPG, PNG, TGA, BMP, PSD, GIF, HDR, PIC

tiny_jpeg.h用于保存JPG格式。

附带处理耗时计算,示例演示了一个简单的反色处理算法,并简单注释了一下部分逻辑。

完整代码:

//如果是Windows的话，调用系统API ShellExecuteA打开图片
#if defined(_MSC_VER)
#define _CRT_SECURE_NO_WARNINGS
#include <windows.h>
#define USE_SHELL_OPEN
#endif

#define STB_IMAGE_STATIC
#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"
//ref:https://github.com/nothings/stb/blob/master/stb_image.h
#define TJE_IMPLEMENTATION
#include "tiny_jpeg.h"
//ref:https://github.com/serge-rgb/TinyJPEG/blob/master/tiny_jpeg.h
#include <math.h>
#include <io.h>
#include <iostream>
#include <string>
#include <chrono>

//计时
auto const epoch = std::chrono::steady_clock::now();
static double now()
{
    return std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::steady_clock::now() - epoch).count() / 1000.0;
};

template <typename FN>
static double bench(const FN &fn)
{
    auto took = -now();
    return (fn(), took + now());
}

//存储当前传入文件位置的变量
std::string m_curFilePath;

//加载图片
void loadImage(const char *filename, unsigned char *&Output, int &Width, int &Height, int &Channels)
{
    Output = stbi_load(filename, &Width, &Height, &Channels, );
}
//保存图片
void saveImage(const char *filename, int Width, int Height, int Channels, unsigned char *Output, bool open = true)
{
    std::string saveFile = m_curFilePath;
    saveFile += filename;
    //保存为jpg
    if (!tje_encode_to_file(saveFile.c_str(), Width, Height, Channels, Output))
    {
        fprintf(stderr, "写入 JPEG 文件失败.\n");
        return;
    }

#ifdef USE_SHELL_OPEN
    if (open)
        ShellExecuteA(NULL, "open", saveFile.c_str(), NULL, NULL, SW_SHOW);
#else
    //其他平台暂不实现
#endif
}

//取当前传入的文件位置
void getCurrentFilePath(const char *filePath, std::string &curFilePath)
{
    char drive[_MAX_DRIVE];
    char dir[_MAX_DIR];
    char fname[_MAX_FNAME];
    char ext[_MAX_EXT];
    curFilePath.clear();
    _splitpath_s(filePath, drive, dir, fname, ext);
    curFilePath += drive;
    curFilePath += dir;
    curFilePath += fname;
    curFilePath += "_";
}

//算法处理,这里以一个反色作为例子
void processImage(unsigned char *Input, unsigned char *Output, unsigned int Width, unsigned int Height, unsigned int Channels)
{
    int Stride = Width * Channels;
    )
    {
        ; Y < Height; Y++)
        {
            unsigned char *scanLineOut = Output + (Y * Stride);
            unsigned char *scanLineIn = Input + (Y * Stride);
            ; X < Width; X++)
            {
                scanLineOut[] =  - scanLineIn[];

                scanLineIn++;
                scanLineOut++;
            }
        }
    }
     || Channels == )
    {
        ; Y < Height; Y++)
        {
            unsigned char *scanLineOut = Output + (Y * Stride);
            unsigned char *scanLineIn = Input + (Y * Stride);
            ; X < Width; X++)
            {
                scanLineOut[] =  - scanLineIn[];
                scanLineOut[] =  - scanLineIn[];
                scanLineOut[] =  - scanLineIn[];
                //通道数为4时，不处理A通道反色(scanLineOut[3] =  255- scanLineIn[3];
                scanLineIn += Channels;
                scanLineOut += Channels;
            }
        }
    }
}

int main(int argc, char **argv)
{
    std::cout << "Image Processing " << std::endl;
    std::cout << "博客:http://cpuimage.cnblogs.com/" << std::endl;
    std::cout << "支持解析如下图片格式:" << std::endl;
    std::cout << "JPG, PNG, TGA, BMP, PSD, GIF, HDR, PIC" << std::endl;

    //检查参数是否正确
    )
    {
        std::cout << "参数错误。" << std::endl;
        std::cout << "请拖放文件到可执行文件上，或使用命令行：imageProc.exe 图片" << std::endl;
        std::cout << "例如: imageProc.exe d:\\image.jpg" << std::endl;

        ;
    }

    std::];
    //检查输入的文件是否存在
    ) == -)
    {
        std::cout << "输入的文件不存在，参数错误！" << std::endl;
    }

    getCurrentFilePath(szfile.c_str(), m_curFilePath);

    ;                    //图片宽度
    ;                   //图片高度
    ;                 //图片通道数
    unsigned char *inputImage = NULL; //输入图片指针

    double nLoadTime = bench([&] {
        //加载图片
        loadImage(szfile.c_str(), inputImage, Width, Height, Channels);
    });
    std::cout << ) << " 毫秒" << std::endl;
    ) && (Width != ) && (Height != ))
    {
        //分配与载入同等内存用于处理后输出结果
        unsigned char *outputImg = (unsigned char *)stbi__malloc(Width * Channels * Height * sizeof(unsigned char));
        if (inputImage)
        {
            //如果图片加载成功，则将内容复制给输出内存，方便处理
            memcpy(outputImg, inputImage, Width * Channels * Height);
        }
        else
        {
            std::cout << " 加载文件: \n"
                << szfile.c_str() << " 失败!" << std::endl;
        }

        double nProcessTime = bench([&] {
            //处理算法
            processImage(inputImage, outputImg, Width, Height, Channels);
        });
        std::cout << ) << " 毫秒" << std::endl;

        //保存处理后的图片
        double nSaveTime = bench([&] {
            saveImage("_done.jpg", Width, Height, Channels, outputImg);
        });
        std::cout << ) << " 毫秒" << std::endl;

        //释放占用的内存
        if (outputImg)
        {
            stbi_image_free(outputImg);
            outputImg = NULL;
        }

        if (inputImage)
        {
            stbi_image_free(inputImage);
            inputImage = NULL;
        }
    }
    else
    {
        std::cout << " 加载文件: \n"     << szfile.c_str() << " 失败!" << std::endl;
    }

    getchar();
    std::cout << "按任意键退出程序 \n" << std::endl;

    return EXIT_SUCCESS;
}

示例具体流程为：

加载图片(拖放文件到可执行文件上)->算法处理->保存图片->打开保存图片(仅Windows)

并对 加载，处理，保存 这三个环节都进行了耗时计算并输出。

参考资源:http://www.cnblogs.com/tntmonks/p/5305844.html

后续博主将基于该模板，分享基于cpu的图像算法系列文章。