分享用于学习C++图像处理的代码示例
为了便于学习图像处理并研究图像算法，
俺写了一个适合初学者学习的小小框架。
麻雀虽小五脏俱全。
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, 0); } //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; if (Channels == 1) { for (unsigned int Y = 0; Y < Height; Y++) { unsigned char *scanLineOut = Output + (Y * Stride); unsigned char *scanLineIn = Input + (Y * Stride); for (unsigned int X = 0; X < Width; X++) { scanLineOut[0] = 255 - scanLineIn[0]; scanLineIn++; scanLineOut++; } } } else if (Channels == 3 || Channels == 4) { for (unsigned int Y = 0; Y < Height; Y++) { unsigned char *scanLineOut = Output + (Y * Stride); unsigned char *scanLineIn = Input + (Y * Stride); for (unsigned int X = 0; X < Width; X++) { scanLineOut[0] = 255 - scanLineIn[0]; scanLineOut[1] = 255 - scanLineIn[1]; scanLineOut[2] = 255 - scanLineIn[2]; //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 if (argc < 2) { 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; return 0; } std::string szfile = argv[1]; //检查输入的文件是否存在 if (_access(szfile.c_str(), 0) == -1) { std::cout << "输入的文件不存在，参数错误！" << std::endl; } getCurrentFilePath(szfile.c_str(), m_curFilePath); int Width = 0; //图片宽度 int Height = 0; //图片高度 int Channels = 0; //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 << " 加载耗时: " << int(nLoadTime * 1000) << " 毫秒" << std::endl; if ((Channels != 0) && (Width != 0) && (Height != 0)) { //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 << " 处理耗时: " << int(nProcessTime * 1000) << " 毫秒" << std::endl; //保存处理后的图片 double nSaveTime = bench([&] { saveImage("_done.jpg", Width, Height, Channels, outputImg); }); std::cout << " 保存耗时: " << int(nSaveTime * 1000) << " 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。
若有其他相关问题或者需求也可以邮件联系俺探讨。
邮箱地址是:
gaozhihan@vip.qq.com
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