自助建站的优点与缺点,定位网站关键词,重庆信息网招聘,visual studio做的网站本文主要介绍三个点#xff1a; 1. 如何单独建立一个工程#xff0c;使用dlib的人脸检测功能。 2. 提高人脸检测率的两个方法 3. 加速人脸检测的方法 下面围绕这几个点展开叙述。
建人脸检测工程
1 . 首先我们先使用上期说的examples里的人脸检测。 我们只要将face_de…本文主要介绍三个点 1. 如何单独建立一个工程使用dlib的人脸检测功能。 2. 提高人脸检测率的两个方法 3. 加速人脸检测的方法 下面围绕这几个点展开叙述。
建人脸检测工程
1 . 首先我们先使用上期说的examples里的人脸检测。 我们只要将face_detection_ex设为启动项即可运行。效果如下 2. 建立单独的工程。像其他正常的方法建立一般的工程。然后 在项目 属性中选择C/C : 常规-》附加包含目录填写之前准备好的dlib的include的路径我这里是D:\dlib_win32\include 预处理器定义 WIN32 _WINDOWS DLIB_PNG_SUPPORT DLIB_JPEG_SUPPORT NDEBUG DLIB_HAVE_AVX 链接器-》常规-》附加库目录填写你要加的库目录。我这里是 D:\dlib_win32\lib 输入-》附加依赖项dlib.lib 命令行-》其它选项/arch:AVX 最后在配置属性-》调试中添加你要检测的图片的路径 main.cpp可以使用dlib提供的官方示例
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/*This example program shows how to find frontal human faces in an image. Inparticular, this program shows how you can take a list of images from thecommand line and display each on the screen with red boxes overlaid on eachhuman face.The examples/faces folder contains some jpg images of people. You can runthis program on them and see the detections by executing the following command:./face_detection_ex faces/*.jpgThis face detector is made using the now classic Histogram of OrientedGradients (HOG) feature combined with a linear classifier, an image pyramid,and sliding window detection scheme. This type of object detector is fairlygeneral and capable of detecting many types of semi-rigid objects inaddition to human faces. Therefore, if you are interested in making yourown object detectors then read the fhog_object_detector_ex.cpp exampleprogram. It shows how to use the machine learning tools which were used tocreate dlibs face detector. Finally, note that the face detector is fastest when compiled with at leastSSE2 instructions enabled. So if you are using a PC with an Intel or AMDchip then you should enable at least SSE2 instructions. If you are usingcmake to compile this program you can enable them by using one of thefollowing commands when you create the build project:cmake path_to_dlib_root/examples -DUSE_SSE2_INSTRUCTIONSONcmake path_to_dlib_root/examples -DUSE_SSE4_INSTRUCTIONSONcmake path_to_dlib_root/examples -DUSE_AVX_INSTRUCTIONSONThis will set the appropriate compiler options for GCC, clang, VisualStudio, or the Intel compiler. If you are using another compiler then youneed to consult your compilers manual to determine how to enable theseinstructions. Note that AVX is the fastest but requires a CPU from at least2011. SSE4 is the next fastest and is supported by most current machines.
*/#include dlib/image_processing/frontal_face_detector.h
#include dlib/gui_widgets.h
#include dlib/image_io.h
#include iostreamusing namespace dlib;
using namespace std;// ----------------------------------------------------------------------------------------int main(int argc, char** argv)
{ try{if (argc 1){cout Give some image files as arguments to this program. endl;return 0;}frontal_face_detector detector get_frontal_face_detector();image_window win;// Loop over all the images provided on the command line.for (int i 1; i argc; i){cout processing image argv[i] endl;array2dunsigned char img;load_image(img, argv[i]);// Make the image bigger by a factor of two. This is useful since// the face detector looks for faces that are about 80 by 80 pixels// or larger. Therefore, if you want to find faces that are smaller// than that then you need to upsample the image as we do here by// calling pyramid_up(). So this will allow it to detect faces that// are at least 40 by 40 pixels in size. We could call pyramid_up()// again to find even smaller faces, but note that every time we// upsample the image we make the detector run slower since it must// process a larger image.pyramid_up(img);// Now tell the face detector to give us a list of bounding boxes// around all the faces it can find in the image.std::vectorrectangle dets detector(img);cout Number of faces detected: dets.size() endl;// Now we show the image on the screen and the face detections as// red overlay boxes.win.clear_overlay();win.set_image(img);win.add_overlay(dets, rgb_pixel(255,0,0));cout Hit enter to process the next image... endl;cin.get();}}catch (exception e){cout \nexception thrown! endl;cout e.what() endl;}
}// ----------------------------------------------------------------------------------------
然后就可以人脸检测了。如下是我的效果。
提高人脸检测率的两个方法
确保检测图片是检测器的两倍。这第一点是十分有用的因为脸部检测器搜寻的人脸大小是80*80或者更大。 因此如果你想找到比80*80小的人脸需要将检测图片进行上采样我们可以调用pyramid_up()函数。 执行一次pyramid_up()我们能检测40*40大小的了如果我们想检测更小的人脸那还需要再次执行pyramid_up()函数。 注意上采样后速度会减慢*/ pyramid_up(img);//对图像进行上采用检测更小的人脸。在程序中使用 array2drgb_pixel img; 取代 array2dunsigned char img;
这个我试验过了有些图片使用’rgb_pixel‘就检测不出来‘unsigned \ char’就可以。可能是前者使用的rgb信息而后者只使用了灰度信息。
加速人脸检测
可以参考这两篇文章。这也是为什么我们要在命令行-》其它选项/arch:AVX 加的原因。
跨平台使用Intrinsic函数范例1——使用SSE、AVX指令集 处理 单精度浮点数组求和支持vc、gcc兼容Windows、Linux、MacWhy is dlib slow?
参考文献
http://dlib.net/http://blog.csdn.net/sunshine_in_moon/article/details/50149339
