[编程开发]STB image读取学习

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。

 

采用的加解码库:stb_image

官方:http://nothings.org/

 

stb_image.h用于解析图片格式:

JPG, PNG, TGA, BMP, PSD, GIF, HDR, PIC

stb_image_write.h用于保存图片格式:

PNG, TGA, BMP, HDR

 

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:

#include <iostream>
#include <algorithm>

#include <cstdint>
#include <numeric>
#include <math.h>
#include <io.h>

//使用stbImage http://nothings.org/
#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"
#define STB_IMAGE_WRITE_IMPLEMENTATION
#include "stb_image_write.h" 

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

//是否使用OMP方式计时
#define USE_OMP 0

#if  USE_OMP
#include <omp.h>
auto const epoch = omp_get_wtime();
double now() {
	return omp_get_wtime() - epoch; 
};
#else
#include <chrono>
auto const epoch = std::chrono::steady_clock::now();
double now() {
	return std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::steady_clock::now() - epoch).count() / 1000.0;
};
#endif

//计时函数
template<typename FN>
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png,也可以调用stbi_write_bmp 保存为bmp
	stbi_write_png(saveFile.c_str(), Width, Height, Channels, Output, 0);

#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 WidthStep = Width*Channels;
	if (Channels == 1)
	{
		for (unsigned int Y = 0; Y < Height; Y++)
		{
			unsigned char*     pOutput = Output + (Y * WidthStep);
			unsigned char*     pInput = Input + (Y * WidthStep);
			for (unsigned int X = 0; X < Width; X++)
			{
				pOutput[0] = 255 - pInput[0];

				//下一个像素点
				pInput += Channels;
				pOutput += Channels;
			}
		}
	}
	else 	if (Channels == 3 || Channels == 4)
	{
		for (unsigned int Y = 0; Y < Height; Y++)
		{
			unsigned char*     pOutput = Output + (Y * WidthStep);
			unsigned char*     pInput = Input + (Y * WidthStep);
			for (unsigned int X = 0; X < Width; X++)
			{
				pOutput[0] = 255 - pInput[0];
				pOutput[1] = 255 - pInput[1];
				pOutput[2] = 255 - pInput[2];
				//通道数为4时，不处理A通道反色(pOutput[3] = 255 - pInput[3];)
				//下一个像素点
				pInput += Channels;
				pOutput += Channels;
			}
		}
	}

}

//本人博客:http://tntmonks.cnblogs.com/转载请注明出处.

int main(int argc, char **argv) {

	std::cout << "Image Processing " << std::endl;
	std::cout << "Demo By Gaozhihan (Build 2016-03-22)" << 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: 
" << 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.png", 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_FREE(outputImg);
			outputImg = NULL;
		}

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

	getchar();
	std::cout << "按任意键退出程序 
" << std::endl;
	return 0;
}

　　

示例具体流程为：

加载图片->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。

 

示例代码下载:

http://files.cnblogs.com/files/tntmonks/imageProcDemo.zip