av图片php技巧_图像处理 YUV图像处理入门5

yuv转换为rgb有多种方法，比如公式法。
但是推举利用第三方库进行转换，比如ffmpeg，libyuv，opencv。
个中ffmpeg是专门的视频音频处理软件，libyuv是谷歌开拓的专门用于yuv基本图像处理（如旋转，缩放，格式转换）的视频库，libyuv紧张用于android端。

ffmpeg，libyuv，opencv都是开源的。
可以在网上查找资料。

本文大略先容ffmpeg和libyuv的安装，opencv的安装教程很多就不先容了。
详细见文章：

https://blog.csdn.net/weixin_39393712/article/details/79583274

下载最新的ffmpeg的dev版和share版，ffmpeg严格区分x64和x86。
下载网站为：

http://ffmpeg.zeranoe.com/builds/

Libyuv须要编译源文件，源文件地址：

https://chromium.googlesource.com/libyuv/libyuv/

https://github.com/seungrye/libyuv 。

编译步骤见：

https://blog.csdn.net/aabcd123456/article/details/78982528

得到源文件后先建立vs工程，然后将ffmpegdev版本文件夹中的include和lib全体目录复制到vs工程目录下。
如图所示：

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对付libyuv的libyuv文件和lib文件，将其分别复制到vs工程目录下的include目录和lib目录。
如图所示：

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常日include中包含的是所调用库头文件，lib包含的是静态链接库，当然ffmpeg须要将其动态链接库复制到vs工程目录下，即将ffmpeg，share版本文件夹中bin目录下对应的所有dll复制到项目路径下如图所示：：

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Dll和lib是windows系统下的动态链接库和静态链接库，linux系统下的静态链接库以.a结尾，linux系统下的动态链接库以.so或.so.y结尾。
详细可以见文章：

https://www.cnblogs.com/general001/articles/3567446.html

对付ffmpeg，libyuv在linux系统下的编译利用，通过编译下载干系源代码，通过cmake或者make命令进行项目构建。
推举利用cmake软件，cmake非常有用，运用十分广泛。
入门教程见：

http://www.cnblogs.com/52php/p/5681745.html

在windows平台下，通过vs就能够减少大量事情。
vs平台链接ffmpeg和libyuv的头文件和lib文件，先在项目工程属性>C/C++>常规>附加包含目录，添加include目录，但是ffmpeg有许多缺点，vs常日会开启SDL检讨后，某些警告会成为缺点。
以是将sdl检讨置为否。
如下图所示：

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接着在在项目工程属性>链接器>常规>附加库目录下，添加lib文件夹，如下图所示：

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末了如果利用ffmpeg和libyuv库，须要添加头文件完玉成部事情的配置。
代码如下：

extern "C"{#include "include\libavcodec\avcodec.h"#include "include\libavformat\avformat.h"#include "include\libavutil\channel_layout.h"#include "include\libavutil\common.h"#include "include\libavutil\imgutils.h"#include "include\libswscale\swscale.h"#include "include\libavutil\imgutils.h"#include "include\libavutil\opt.h"#include "include\libavutil\mathematics.h"#include "include\libavutil\samplefmt.h"//libyuv#include "include\libyuv\libyuv.h"};#pragma comment(lib, "avcodec.lib")#pragma comment(lib, "avformat.lib")#pragma comment(lib, "avdevice.lib")#pragma comment(lib, "avfilter.lib")#pragma comment(lib, "avutil.lib")#pragma comment(lib, "postproc.lib")#pragma comment(lib, "swresample.lib")#pragma comment(lib, "swscale.lib")//libyuv#pragma comment(lib, "yuv.lib")yuv420转rgb

接下来通过ffmpeg，libyuv，opencv实现yuv420转rgb，并进行性能剖析。
函数的代码如下所示：

/ @file 12 yuv_transform.cpp @author luohen @brief YUV image transform to opencv rgb image @date 2018-12-11 /#include "pch.h"#include <iostream>#include <opencv2/opencv.hpp>#include <time.h>extern "C"{#include "include\libavcodec\avcodec.h"#include "include\libavformat\avformat.h"#include "include\libavutil\channel_layout.h"#include "include\libavutil\common.h"#include "include\libavutil\imgutils.h"#include "include\libswscale\swscale.h"#include "include\libavutil\imgutils.h"#include "include\libavutil\opt.h"#include "include\libavutil\mathematics.h"#include "include\libavutil\samplefmt.h"//libyuv#include "include\libyuv\libyuv.h"};#pragma comment(lib, "avcodec.lib")#pragma comment(lib, "avformat.lib")#pragma comment(lib, "avdevice.lib")#pragma comment(lib, "avfilter.lib")#pragma comment(lib, "avutil.lib")#pragma comment(lib, "postproc.lib")#pragma comment(lib, "swresample.lib")#pragma comment(lib, "swscale.lib")//libyuv#pragma comment(lib, "yuv.lib")using namespace std;using namespace cv;/ @brief @param pYUV input yuv420 image @param pBGR24 output bgr24 image @param width width of input yuv420p image @param height height of input yuv420p image @return /bool ffmpeg_yuv2bgr(unsigned char pYUV, unsigned char pBGR24, int width, int height){ AVPicture pFrameYUV, pFrameBGR; avpicture_fill(&pFrameYUV, pYUV, AV_PIX_FMT_YUV420P, width, height); avpicture_fill(&pFrameBGR, pBGR24, AV_PIX_FMT_BGR24, width, height); struct SwsContext imgCtx = NULL; //初始化函数 //原图高，宽，图像类型；输出图高，宽，图像类型；算法种类；其他 imgCtx = sws_getContext(width, height, AV_PIX_FMT_YUV420P, width, height, AV_PIX_FMT_BGR24, SWS_BILINEAR, 0, 0, 0); if (imgCtx != NULL) { //实行函数 //函数返回值；输入图像指针数组，图像颜色通道数组；扫描出发点；扫描行数；输出图像指针数组，图像颜色通道数组； sws_scale(imgCtx, pFrameYUV.data, pFrameYUV.linesize, 0, height, pFrameBGR.data, pFrameBGR.linesize); //end if (imgCtx) { sws_freeContext(imgCtx); imgCtx = NULL; } return true; } else { sws_freeContext(imgCtx); imgCtx = NULL; return false; }}/ @brief transform function of ffmpeg @param w width of input yuv420p image @param h height of input yuv420p image @param pic input yuv image @return Mat output rgb image(opencv mat) /Mat yuv420_ffmpeg(int w, int h, unsigned char pic){ Mat bgrImg(h, w, CV_8UC3); unsigned char pBGR24 = new unsigned char[w h 3]; ffmpeg_yuv2bgr(pic, bgrImg.data, w, h); return bgrImg;}/ @brief transform function of libyuv @param w width of input yuv420p image @param h height of input yuv420p image @param pic input yuv image @return Mat output rgb image(opencv mat) /Mat yuv420_libyuv(int w, int h, unsigned char pic){ int size_src = w h 3 / 2; int size_dest = w h 4; //BGRA, A:Alpha(transparency，透明度) Mat matI420 = cv::Mat(h, w, CV_8UC4); libyuv::I420ToARGB((const uint8 )pic, w, (const uint8 )(pic + w h), w / 2, (const uint8 )(pic + w h 5 / 4), w / 2, matI420.data, w 4, w, h); //bgr Mat bgrImg; cvtColor(matI420, bgrImg, COLOR_BGRA2BGR); return bgrImg;}/ @brief @param w @param h @param pic @return Mat /Mat yuv420_opencv(int w, int h, unsigned char pic){ //创建YUV mat cv::Mat yuvImg; yuvImg.create(h 3 / 2, w, CV_8UC1); //数据保存为yuvImg.data memcpy(yuvImg.data, pic, w h 3 / 2 sizeof(unsigned char)); //转化为RGB图像 cv::Mat bgrImg; cv::cvtColor(yuvImg, bgrImg, CV_YUV2BGR_I420); return bgrImg;}/ @brief main @return int /int main(){ clock_t start, end; double endtime; //Frequency of reading image int count_frame = 300; //视频路径 char url = (char )"video/akiyo.yuv"; int w = 352, h = 288; FILE input_fp; if ((input_fp = fopen(url, "rb")) == NULL) { printf("%s open error!\n", url); return -1; } else { printf("%s open.\n", url); } unsigned char pYuvBuf = new unsigned char[w h 3 / 2]; fseek(input_fp, 0, SEEK_SET); //Timing starts start = clock(); Mat ffmpeg_mat; for (int i = 0; i < count_frame; i++) { fread(pYuvBuf, sizeof(unsigned char), w h 3 / 2, input_fp); ffmpeg_mat = yuv420_ffmpeg(w, h, pYuvBuf); } //Timing end end = clock(); endtime = (double)(end - start) / CLOCKS_PER_SEC; cout << "ffmpeg Total time:" << endtime << "s" << endl; cout << "ffmpeg Total time:" << endtime 1000 << "ms" << endl; fseek(input_fp, 0, SEEK_SET); start = clock(); Mat libyuv_mat; for (int i = 0; i < count_frame; i++) { fread(pYuvBuf, sizeof(unsigned char), w h 3 / 2, input_fp); libyuv_mat = yuv420_libyuv(w, h, pYuvBuf); } end = clock(); endtime = (double)(end - start) / CLOCKS_PER_SEC; cout << "libyuv Total time:" << endtime << "s" << endl; //s为单位 cout << "libyuv Total time:" << endtime 1000 << "ms" << endl; //ms为单位 fseek(input_fp, 0, SEEK_SET); start = clock(); Mat opencv_mat; for (int i = 0; i < count_frame; i++) { fread(pYuvBuf, sizeof(unsigned char), w h 3 / 2, input_fp); opencv_mat = yuv420_opencv(w, h, pYuvBuf); } end = clock(); endtime = (double)(end - start) / CLOCKS_PER_SEC; cout << "opencv Total time:" << endtime << "s" << endl; cout << "opencv Total time:" << endtime 1000 << "ms" << endl; system("pause"); return 0;}

调用函数为：

Mat yuv420_ffmpeg(int w, int h, unsigned char pic);Mat yuv420_libyuv(int w, int h, unsigned char pic);Mat yuv420_opencv(int w, int h, unsigned char pic);

这段代码紧张是分别用ffmpeg，libyuv，opencv实现yuv420转换为rgb，每种方法转换300张yuv420图像。
比拟三种方法转换所用韶光，结果如下：

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综合三种方法来说，ffmpeg速率最快，且ffmpeg最常用，因此推举利用ffmpeg。
如果仅仅对yuv图像进行处理或者android端，libyuv最为推举。
如果是安装ffmpeg或者libyuv较为麻烦，仅限于研究项目，建议利用opencv。

引用链接

[1] https://blog.csdn.net/weixin_39393712/article/details/79583274: https://blog.csdn.net/weixin_39393712/article/details/79583274

[2] http://ffmpeg.zeranoe.com/builds/ : http://ffmpeg.zeranoe.com/builds/

[3] https://chromium.googlesource.com/libyuv/libyuv/ : https://chromium.googlesource.com/libyuv/libyuv/

[4] https://github.com/seungrye/libyuv : https://github.com/seungrye/libyuv

[5] https://blog.csdn.net/aabcd123456/article/details/78982528 : https://blog.csdn.net/aabcd123456/article/details/78982528

[6] https://www.cnblogs.com/general001/articles/3567446.html : https://www.cnblogs.com/general001/articles/3567446.html

[7] http://www.cnblogs.com/52php/p/5681745.html : http://www.cnblogs.com/52php/p/5681745.html