海思移植opencv+人脸检测+速度优化

[i=s] 本帖最后由 9crk 于 2014-12-25 22:32 编辑 [/i]

[b]1.确保ubuntu能上网[/b]

[b]2.安装cmake[/b] 代码: 全选 sudo apt-get install cmake-gui

[b]3.下载opencv2.4.9 Linux版源码，不要用最新的3.0.0[/b] http://opencv.org/downloads.html

[b]4.解压opencv源码[/b] 代码: 全选 unzip opencv-2.4.9.zip

[b]5.创建一个build目录用于编译和一个output目录[/b] 用于存放编译完成后的海思平台的opencv： 代码: 全选 xlab@xlab-dev:~/zhouhua/opencv/opencv-2.4.9$ ls 3rdparty LICENSE apps data include modules samples CMakeLists.txt README.md cmake doc index.rst platforms xlab@xlab-dev:~/zhouhua/opencv/opencv-2.4.9$ cd ..
xlab@xlab-dev:~/zhouhua/opencv$ ls build opencv-2.4.9 opencv-2.4.9.zip output xlab@xlab-dev:~/zhouhua/opencv$ mkdir build xlab@xlab-dev:~/zhouhua/opencv$ mkdir output

[b]6.执行cmake-gui[/b] 代码: 全选 xlab@xlab-dev:~/zhouhua/opencv/opencv-2.4.9$ cmake-gui

点击Browse Source选择~/zhouhua/opencv/opencv-2.4.9 点击Browse Build选择~/zhouhua/opencv/build 然后点击Configure

此时出现的对话框选择最后一项：Specify options for cross-compiling 下一步 Operating System填写 arm-hisiv100nptl-linux C填写arm-hisiv100nptl-linux-gcc C++填写arm-hisiv100nptl-linux-g++

下一步，然后等待Configuration done 然后在出现的列表中修改CMAKE_INSTALL_PREFIX为~/zhouhua/opencv/output 然后点击Generate 等待Generation done 即可关闭cmake软件。

[b]7.进入build目录执行make[/b] 代码: 全选 xlab@xlab-dev:~/zhouhua/opencv/build$ make

提示出错： 代码: 全选 ../../lib/libopencv_core.so: undefined reference to pthread_once' ../../lib/libopencv_core.so: undefined reference topthread_spin_lock' ../../lib/libopencv_core.so: undefined reference to pthread_spin_unlock' ../../lib/libopencv_core.so: undefined reference topthread_spin_init' ../../lib/libopencv_core.so: undefined reference to pthread_spin_trylock' ../../lib/libopencv_core.so: undefined reference topthread_spin_destroy'

修改CMakeCache.txt大约200行处 //Flags used by the linker. CMAKE_EXE_LINKER_FLAGS:STRING= -lpthread -lrt 继续make 可能出现如下错误 代码: 全选 CMake Error at /home/xlab/zhouhua/opencv/opencv-2.4.9/cmake/cl2cpp.cmake:50 (string): string does not recognize sub-command MD5

make[2]: [modules/ocl/opencl_kernels.cpp] Error 1 make[1]: [modules/ocl/CMakeFiles/opencv_ocl.dir/all] Error 2 make: *** [all] Error 2

删除/home/xlab/zhouhua/opencv/opencv-2.4.9/cmake/cl2cpp.cmake的第50行的内容即可。 继续make 完成后执行make install 代码: 全选 xlab@xlab-dev:~/zhouhua/opencv/output$ ls LICENSE bin include lib share

[b]8.得到了include和lib目录就可以编写程序了，来试试最常用的人脸检测吧[/b] 编写如下代码 代码: 全选 /*Author:9crk 2014-12-24***/

include "cv.h"

include "highgui.h"

include "stdio.h"

/**for time mesurement*****/

include <sys/time.h>

struct timeval tpstart,tpend; unsigned long timeuses; void timeRec() { gettimeofday(&tpstart,0); } int timeRep() { gettimeofday(&tpend,0); timeuses=(tpend.tv_sec-tpstart.tv_sec)*1000000+tpend.tv_usec-tpstart.tv_usec; printf("use time: %uus\n",timeuses); return timeuses; } /****end**/

int main(int argc, char argv[]) { IplImage img = NULL; IplImage cutImg = NULL; CvMemStorage storage = cvCreateMemStorage(0); CvHaarClassifierCascade cascade = (CvHaarClassifierCascade)cvLoad("./haarcascade_frontalface_alt2.xml", 0, 0, 0); CvSeq* faces;

img = cvLoadImage(argv[1], 0);

timeRec(); faces = cvHaarDetectObjects(img, cascade, storage, 1.2, 2, 0, cvSize(25,25) ); timeRep(); if (faces->total == 0){ printf("no face!\n"); } cvSetImageROI(img, ((CvRect)cvGetSeqElem( faces, 0))); cvSaveImage("face.bmp", img);
cvResetImageROI(img); printf("face detected! in face.bmp!\n"); }

为了方便，直接将库和头文件拷贝到编译器的目录下去 代码: 全选 xlab@xlab-dev:~/zhouhua/opencv/mytest$ sudo cp ../output/lib/ /opt/hisi-linux-nptl/arm-hisiv100-linux/arm-hisiv100-linux-uclibcgnueabi/lib/ xlab@xlab-dev:~/zhouhua/opencv/mytest$sudo cp ../output/include/ /opt/hisi-linux-nptl/arm-hisiv100-linux/arm-hisiv100-linux-uclibcgnueabi/include/ -r

然后编译：（由于版本比较高，用了opencv2的头文件，因此需要额外增加一个-I参数指定头文件目录） 代码: 全选 arm-hisiv100nptl-linux-g++ face.cpp -I/home/xlab/zhouhua/opencv/output/include/opencv -lopencv_highgui -lopencv_core -lopencv_imgproc -lpthread -lrt -lopencv_objdetect -o face

会提示一些warning，不用管。 编译成功，然后拷贝人脸分类器文件过来。 代码: 全选 xlab@xlab-dev:~/zhouhua/opencv/mytest$ cp ../output/share/OpenCV/haarcascades/haarcascade_frontalface_alt2.xml ./

再找个图片过来，我这里就用lena.jpg了。 然后nfs挂到开发板 到开发板端做软连接库到/lib目录下 代码: 全选 ln -s /mnt/nfs/zhouhua/opencv/output/lib/libopencv_imgproc.so /lib/libopencv_imgproc.so ln -s /mnt/nfs/zhouhua/opencv/output/lib/libopencv_objdetect.so /lib/libopencv_objdetect.so ln -s /mnt/nfs/zhouhua/opencv/output/lib/libopencv_highgui.so /lib/libopencv_highgui.so ln -s /mnt/nfs/zhouhua/opencv/output/lib/libopencv_core.so /lib/libopencv_core.so

然后到face所在的nfs目录去执行即可：

./face lena.jpg

use time: 31532724us face detected! in face.bmp! 由于参数没有优化，用了31秒才找到lena的脸。。 [attach]2049[/attach]

[b]9.速度优化[/b] 先修改一下图片长、宽为之前的1/4试试

./face lena.jpg

smallImg w=128 h=128 use time: 1179871us face detected! in face.bmp! 这次用了1.1秒

再修改检测参数 faces = cvHaarDetectObjects(smallImg, cascade, storage, 1.5, 4, 0, cvSize(25,25) );

./face lena.jpg

smallImg w=128 h=128 use time: 578169us face detected! in face.bmp! 这次用了578ms，检测出来的人脸大小是44x44的。

一般的应用应该够了，注意，此测试是在nfs下，如果拷贝到板子，速度会更快，当然，修改参数后的漏检率还需要测试。

下面贴出最后的代码（ps:之前的代码没有释放内存）

/*Author:9crk 2014-12-24***/

include "cv.h"

include "highgui.h"

include "stdio.h"

/**for time mesurement*****/

include <sys/time.h>

struct timeval tpstart,tpend; unsigned long timeuses; void timeRec() { gettimeofday(&tpstart,0); } int timeRep() { gettimeofday(&tpend,0); timeuses=(tpend.tv_sec-tpstart.tv_sec)*1000000+tpend.tv_usec-tpstart.tv_usec; printf("use time: %uus\n",timeuses); return timeuses; } /****end**/ int main(int argc, char argv[]) { IplImage img = NULL; IplImage* cutImg = NULL;

CvMemStorage storage = cvCreateMemStorage(0); CvHaarClassifierCascade cascade = (CvHaarClassifierCascade)cvLoad("./haarcascade_frontalface_alt2.xml", 0, 0, 0); CvSeq faces; //加载图像 img = cvLoadImage(argv[1], 0); //缩放到1/4大小 IplImage *smallImg = cvCreateImage(cvSize(img->width/4, img->height/4), 8, img->nChannels); printf("smallImg w=%d h=%d\n", smallImg->width, smallImg->height); cvResize(img, smallImg); //检测并计时 timeRec(); faces = cvHaarDetectObjects(smallImg, cascade, storage, 1.5, 4, 0, cvSize(25,25) ); timeRep();

if (faces->total == 0){
    printf("no face!\n");
}
//切取出头像
cvSetImageROI(smallImg, *((CvRect*)cvGetSeqElem( faces, 0))); 
cvSaveImage("face.bmp", smallImg);    
cvResetImageROI(smallImg);
//释放内存
cvReleaseImage(&img);
cvReleaseImage(&smallImg);
printf("face detected! in face.bmp!\n");

}