机器学习库dlib的C++编译和使用（windows和linux）



踏莎行

科技/金融/文学/游戏/美术/运动，游戏赏析公众号 youxizhimei

1 人赞同了该文章

dlib是一个C++编写的工具集，相比于深度学习库而言，dlib内部更多的是封装了很多传统机器学习计算函数，例如回归分析、支撑向量机、聚类，开箱即用，对外提供了C++和python两种接口。

本文通过一个C++调用dlib的demo来体验一下dlib这个强大的工具库。

获取

从官网dlib官网或者github地址dlib源码下载最新源码

这里用的是dlib-19.17

编译

支持windows和linux双平台编译

为了避免引入过多的依赖，解压源码后，打开dlib-19.17/dlib/CMakeLists.txt找到以下几行进行修改

if (DLIB_ISO_CPP_ONLY)
      option(DLIB_JPEG_SUPPORT ${DLIB_JPEG_SUPPORT_STR} OFF)
      option(DLIB_LINK_WITH_SQLITE3 ${DLIB_LINK_WITH_SQLITE3_STR} OFF)
      option(DLIB_USE_BLAS ${DLIB_USE_BLAS_STR} OFF)
      option(DLIB_USE_LAPACK ${DLIB_USE_LAPACK_STR} OFF)
      option(DLIB_USE_CUDA ${DLIB_USE_CUDA_STR} OFF)
      option(DLIB_PNG_SUPPORT ${DLIB_PNG_SUPPORT_STR} OFF)
      option(DLIB_GIF_SUPPORT ${DLIB_GIF_SUPPORT_STR} OFF)
      #option(DLIB_USE_FFTW ${DLIB_USE_FFTW_STR} OFF)
      option(DLIB_USE_MKL_FFT ${DLIB_USE_MKL_FFT_STR} OFF)
else()
      option(DLIB_JPEG_SUPPORT ${DLIB_JPEG_SUPPORT_STR} ON)
      option(DLIB_LINK_WITH_SQLITE3 ${DLIB_LINK_WITH_SQLITE3_STR} ON)
      option(DLIB_USE_BLAS ${DLIB_USE_BLAS_STR} ON)
      option(DLIB_USE_LAPACK ${DLIB_USE_LAPACK_STR} ON)
      option(DLIB_USE_CUDA ${DLIB_USE_CUDA_STR} ON)
      option(DLIB_PNG_SUPPORT ${DLIB_PNG_SUPPORT_STR} ON)
      option(DLIB_GIF_SUPPORT ${DLIB_GIF_SUPPORT_STR} ON)
      #option(DLIB_USE_FFTW ${DLIB_USE_FFTW_STR} ON)
      option(DLIB_USE_MKL_FFT ${DLIB_USE_MKL_FFT_STR} ON)
endif()

改为

if (DLIB_ISO_CPP_ONLY)
      option(DLIB_JPEG_SUPPORT ${DLIB_JPEG_SUPPORT_STR} OFF)
      option(DLIB_LINK_WITH_SQLITE3 ${DLIB_LINK_WITH_SQLITE3_STR} OFF)
      option(DLIB_USE_BLAS ${DLIB_USE_BLAS_STR} OFF)
      option(DLIB_USE_LAPACK ${DLIB_USE_LAPACK_STR} OFF)
      option(DLIB_USE_CUDA ${DLIB_USE_CUDA_STR} OFF)
      option(DLIB_PNG_SUPPORT ${DLIB_PNG_SUPPORT_STR} OFF)
      option(DLIB_GIF_SUPPORT ${DLIB_GIF_SUPPORT_STR} OFF)
      #option(DLIB_USE_FFTW ${DLIB_USE_FFTW_STR} OFF)
      option(DLIB_USE_MKL_FFT ${DLIB_USE_MKL_FFT_STR} OFF)
else()
      option(DLIB_JPEG_SUPPORT ${DLIB_JPEG_SUPPORT_STR} ON)
      option(DLIB_LINK_WITH_SQLITE3 ${DLIB_LINK_WITH_SQLITE3_STR} ON)
      option(DLIB_USE_BLAS ${DLIB_USE_BLAS_STR} OFF)
      option(DLIB_USE_LAPACK ${DLIB_USE_LAPACK_STR} OFF)
      option(DLIB_USE_CUDA ${DLIB_USE_CUDA_STR} OFF)
      option(DLIB_PNG_SUPPORT ${DLIB_PNG_SUPPORT_STR} ON)
      option(DLIB_GIF_SUPPORT ${DLIB_GIF_SUPPORT_STR} ON)
      #option(DLIB_USE_FFTW ${DLIB_USE_FFTW_STR} ON)
      option(DLIB_USE_MKL_FFT ${DLIB_USE_MKL_FFT_STR} OFF)
endif()

显式地禁止使用blas，lapack，cuda和mkl依赖

另外，确保编译器能支持C++11全部特性

windows

环境

win7/win10 64位
	
vs2015 update3 及以上
步骤

1，用cmake编译静态库

要用release版，计算效率快

windows下推荐用官网的命令来编译，也可以用cmake的gui工具

cd dlib-19.17
mkdir build_x64
cd build_x64
cmake -G "Visual Studio 14 2015 Win64" -T host=x64 ..
cmake --build . --config Release

会在build/dlib/Release目录生成静态链接库dlib19.17.0_release_64bit_msvc1900.lib，将其改名为dlib.lib

2，替换config.h

这一步非常重要，解决调用dlib时USER_ERROR__inconsistent_build_configuration__see_dlib_faq_2这个报错

需要将build/dlib/config.h文件拷贝到源码目录dlib-19.17/dlib进行覆盖

linux

环境

ubuntu14.04 64位
	
gcc4.8.1及以上
步骤

1，用cmake编译静态库

要用release版，计算效率快

cd dlib-19.17
mkdir build
cd build
cmake ..
cmake --build . --config Release

会在build/dlib/Release目录生成静态链接库libdlib.a

2，替换config.h

这一步非常重要，解决调用dlib时USER_ERROR__inconsistent_build_configuration__see_dlib_faq_2这个报错

需要将build/dlib/config.h文件拷贝到源码目录dlib-19.17/dlib进行覆盖

使用

创建项目，用cmake构建跨平台项目

目录结构

dlib_test

├── CMakeLists.txt

└── src

└── main.cpp

其中

CMakeLists.txt

用cmake编译，运行，注意demo程序也是采用64位release模式进行编译运行

project(dlib_test)
cmake_minimum_required(VERSION 2.8)

add_definitions(-std=c++11)

if (UNIX)
include_directories(
    /home/user/codetest/dlib-19.17
)
else()
include_directories(
    D:/Programs/dlib-19.17
)
endif()

aux_source_directory(./src DIR_SRCS)

if (UNIX)
link_directories(
    /home/user/codetest/dlib-19.17/build/dlib
)
else()
link_directories(
    D:/Programs/dlib-19.17/build_x64/dlib/Release
)
endif()

add_executable(dlib_test ${DIR_SRCS})
target_link_libraries(dlib_test dlib)

main.cpp

#include <iostream>
#include "dlib/svm.h"

using namespace std;
using namespace dlib;

int main()
{
	
	typedef matrix<double, 2, 1> sample_type;
	typedef radial_basis_kernel<sample_type> kernel_type;


	// Now we make objects to contain our samples and their respective labels.
	std::vector<sample_type> samples;
	std::vector<double> labels;

	// Now let's put some data into our samples and labels objects.  We do this
	// by looping over a bunch of points and labeling them according to their
	// distance from the origin.
	for (int r = -20; r <= 20; ++r)
	{
		for (int c = -20; c <= 20; ++c)
		{
			sample_type samp;
			samp(0) = r;
			samp(1) = c;
			samples.push_back(samp);

			// if this point is less than 10 from the origin
			if (sqrt((double)r*r + c*c) <= 10)
				labels.push_back(+1);
			else
				labels.push_back(-1);

		}
	}

	vector_normalizer<sample_type> normalizer;
	// Let the normalizer learn the mean and standard deviation of the samples.
	normalizer.train(samples);
	// now normalize each sample
	for (unsigned long i = 0; i < samples.size(); ++i)
		samples[i] = normalizer(samples[i]);

	randomize_samples(samples, labels);


	// here we make an instance of the svm_c_trainer object that uses our kernel
	// type.
	svm_c_trainer<kernel_type> trainer;

	cout << "doing cross validation" << endl;
	for (double gamma = 0.00001; gamma <= 1; gamma *= 5)
	{
		for (double C = 1; C < 100000; C *= 5)
		{
			// tell the trainer the parameters we want to use
			trainer.set_kernel(kernel_type(gamma));
			trainer.set_c(C);

			cout << "gamma: " << gamma << "    C: " << C;
			// Print out the cross validation accuracy for 3-fold cross validation using
			// the current gamma and C.  cross_validate_trainer() returns a row vector.
			// The first element of the vector is the fraction of +1 training examples
			// correctly classified and the second number is the fraction of -1 training
			// examples correctly classified.
			cout << "     cross validation accuracy: "
				<< cross_validate_trainer(trainer, samples, labels, 3);
		}
	}

	trainer.set_kernel(kernel_type(0.15625));
	trainer.set_c(5);
	typedef decision_function<kernel_type> dec_funct_type;
	typedef normalized_function<dec_funct_type> funct_type;

	// Here we are making an instance of the normalized_function object.  This
	// object provides a convenient way to store the vector normalization
	// information along with the decision function we are going to learn.  
	funct_type learned_function;
	learned_function.normalizer = normalizer;  // save normalization information
	learned_function.function = trainer.train(samples, labels); // perform the actual SVM training and save the results

																// print out the number of support vectors in the resulting decision function
	cout << "\nnumber of support vectors in our learned_function is "
		<< learned_function.function.basis_vectors.size() << endl;

	// Now let's try this decision_function on some samples we haven't seen before.
	sample_type sample;

	sample(0) = 3.123;
	sample(1) = 2;
	cout << "This is a +1 class example, the classifier output is " << learned_function(sample) << endl;

	sample(0) = 3.123;
	sample(1) = 9.3545;
	cout << "This is a +1 class example, the classifier output is " << learned_function(sample) << endl;

	sample(0) = 13.123;
	sample(1) = 9.3545;
	cout << "This is a -1 class example, the classifier output is " << learned_function(sample) << endl;

	sample(0) = 13.123;
	sample(1) = 0;
	cout << "This is a -1 class example, the classifier output is " << learned_function(sample) << endl;


	// We can also train a decision function that reports a well conditioned
	// probability instead of just a number > 0 for the +1 class and < 0 for the
	// -1 class.  An example of doing that follows:
	typedef probabilistic_decision_function<kernel_type> probabilistic_funct_type;
	typedef normalized_function<probabilistic_funct_type> pfunct_type;

	pfunct_type learned_pfunct;
	learned_pfunct.normalizer = normalizer;
	learned_pfunct.function = train_probabilistic_decision_function(trainer, samples, labels, 3);
	// Now we have a function that returns the probability that a given sample is of the +1 class.  

	// print out the number of support vectors in the resulting decision function.  
	// (it should be the same as in the one above)
	cout << "\nnumber of support vectors in our learned_pfunct is "
		<< learned_pfunct.function.decision_funct.basis_vectors.size() << endl;

	sample(0) = 3.123;
	sample(1) = 2;
	cout << "This +1 class example should have high probability.  Its probability is: "
		<< learned_pfunct(sample) << endl;

	sample(0) = 3.123;
	sample(1) = 9.3545;
	cout << "This +1 class example should have high probability.  Its probability is: "
		<< learned_pfunct(sample) << endl;

	sample(0) = 13.123;
	sample(1) = 9.3545;
	cout << "This -1 class example should have low probability.  Its probability is: "
		<< learned_pfunct(sample) << endl;

	sample(0) = 13.123;
	sample(1) = 0;
	cout << "This -1 class example should have low probability.  Its probability is: "
		<< learned_pfunct(sample) << endl;

	serialize("saved_function.dat") << learned_pfunct;

	// Now let's open that file back up and load the function object it contains.
	deserialize("saved_function.dat") >> learned_pfunct;

	cout << "\ncross validation accuracy with only 10 support vectors: "
		<< cross_validate_trainer(reduced2(trainer, 10), samples, labels, 3);

	// Let's print out the original cross validation score too for comparison.
	cout << "cross validation accuracy with all the original support vectors: "
		<< cross_validate_trainer(trainer, samples, labels, 3);

	// When you run this program you should see that, for this problem, you can
	// reduce the number of basis vectors down to 10 without hurting the cross
	// validation accuracy. 


	// To get the reduced decision function out we would just do this:
	learned_function.function = reduced2(trainer, 10).train(samples, labels);
	// And similarly for the probabilistic_decision_function: 
	learned_pfunct.function = train_probabilistic_decision_function(reduced2(trainer, 10), samples, labels, 3);

	return 0;
}


跑一个简单的svm的例子，借鉴自官方的example里面svm_c_ex.cpp

运行结果

doing cross validation
gamma: 1e-05    C: 1     cross validation accuracy: 0 1 
gamma: 1e-05    C: 5     cross validation accuracy: 0 1 
gamma: 1e-05    C: 25     cross validation accuracy: 0 1 
gamma: 1e-05    C: 125     cross validation accuracy: 0 1 
gamma: 1e-05    C: 625     cross validation accuracy: 0 1 
gamma: 1e-05    C: 3125     cross validation accuracy: 0 1 
gamma: 1e-05    C: 15625     cross validation accuracy: 0 1 
gamma: 1e-05    C: 78125     cross validation accuracy: 0 1 
gamma: 5e-05    C: 1     cross validation accuracy: 0 1 
gamma: 5e-05    C: 5     cross validation accuracy: 0 1 
gamma: 5e-05    C: 25     cross validation accuracy: 0 1 
gamma: 5e-05    C: 125     cross validation accuracy: 0 1 
gamma: 5e-05    C: 625     cross validation accuracy: 0 1 
gamma: 5e-05    C: 3125     cross validation accuracy: 0 1 
gamma: 5e-05    C: 15625     cross validation accuracy: 0 1 
gamma: 5e-05    C: 78125     cross validation accuracy: 0 1 
gamma: 0.00025    C: 1     cross validation accuracy: 0 1 
gamma: 0.00025    C: 5     cross validation accuracy: 0 1 
gamma: 0.00025    C: 25     cross validation accuracy: 0 1 
gamma: 0.00025    C: 125     cross validation accuracy: 0 1 
gamma: 0.00025    C: 625     cross validation accuracy: 0 1 
gamma: 0.00025    C: 3125     cross validation accuracy: 0 1 
gamma: 0.00025    C: 15625     cross validation accuracy: 0 1 
gamma: 0.00025    C: 78125     cross validation accuracy: 0.990476 0.991189 
gamma: 0.00125    C: 1     cross validation accuracy: 0 1 
gamma: 0.00125    C: 5     cross validation accuracy: 0 1 
gamma: 0.00125    C: 25     cross validation accuracy: 0 1 
gamma: 0.00125    C: 125     cross validation accuracy: 0 1 
gamma: 0.00125    C: 625     cross validation accuracy: 0 1 
gamma: 0.00125    C: 3125     cross validation accuracy: 0.980952 0.994126 
gamma: 0.00125    C: 15625     cross validation accuracy: 0.980952 0.991924 
gamma: 0.00125    C: 78125     cross validation accuracy: 0.984127  0.99486 
gamma: 0.00625    C: 1     cross validation accuracy: 0 1 
gamma: 0.00625    C: 5     cross validation accuracy: 0 1 
gamma: 0.00625    C: 25     cross validation accuracy: 0 1 
gamma: 0.00625    C: 125     cross validation accuracy: 0.980952  0.99486 
gamma: 0.00625    C: 625     cross validation accuracy: 0.980952 0.991924 
gamma: 0.00625    C: 3125     cross validation accuracy: 0.980952 0.995595 
gamma: 0.00625    C: 15625     cross validation accuracy: 0.987302 0.994126 
gamma: 0.00625    C: 78125     cross validation accuracy: 0.990476  0.99486 
gamma: 0.03125    C: 1     cross validation accuracy: 0 1 
gamma: 0.03125    C: 5     cross validation accuracy: 0.971429 0.996329 
gamma: 0.03125    C: 25     cross validation accuracy: 0.974603 0.992658 
gamma: 0.03125    C: 125     cross validation accuracy: 0.980952 0.996329 
gamma: 0.03125    C: 625     cross validation accuracy: 0.987302  0.99486 
gamma: 0.03125    C: 3125     cross validation accuracy: 0.990476  0.99486 
gamma: 0.03125    C: 15625     cross validation accuracy:  0.95873 0.995595 
gamma: 0.03125    C: 78125     cross validation accuracy: 0.996825 0.995595 
gamma: 0.15625    C: 1     cross validation accuracy: 0.952381 0.998532 
gamma: 0.15625    C: 5     cross validation accuracy: 0.993651 0.996329 
gamma: 0.15625    C: 25     cross validation accuracy: 0.990476 0.995595 
gamma: 0.15625    C: 125     cross validation accuracy: 0.980952  0.99486 
gamma: 0.15625    C: 625     cross validation accuracy: 0.949206 0.997797 
gamma: 0.15625    C: 3125     cross validation accuracy: 0.993651 0.998532 
gamma: 0.15625    C: 15625     cross validation accuracy: 0.987302        1 
gamma: 0.15625    C: 78125     cross validation accuracy: 0.990476 0.997797 
gamma: 0.78125    C: 1     cross validation accuracy: 0.952381 0.997797 
gamma: 0.78125    C: 5     cross validation accuracy: 0.974603 0.997797 
gamma: 0.78125    C: 25     cross validation accuracy: 0.974603        1 
gamma: 0.78125    C: 125     cross validation accuracy: 0.984127        1 
gamma: 0.78125    C: 625     cross validation accuracy: 0.987302        1 
gamma: 0.78125    C: 3125     cross validation accuracy: 0.987302        1 
gamma: 0.78125    C: 15625     cross validation accuracy: 0.987302 0.997797 
gamma: 0.78125    C: 78125     cross validation accuracy: 0.980952 0.998532 

number of support vectors in our learned_function is 209
This is a +1 class example, the classifier output is 2.71477
This is a +1 class example, the classifier output is -0.0102314
This is a -1 class example, the classifier output is -4.36211
This is a -1 class example, the classifier output is -2.16552

number of support vectors in our learned_pfunct is 209
This +1 class example should have high probability.  Its probability is: 1
This +1 class example should have high probability.  Its probability is: 0.465781
This -1 class example should have low probability.  Its probability is: 3.05246e-11
This -1 class example should have low probability.  Its probability is: 5.78323e-06

cross validation accuracy with only 10 support vectors: 0.993651  0.99486 
cross validation accuracy with all the original support vectors: 0.993651 0.996329 

使用opecv4.2.0和dlib19.12编译时出错：error: conversion from ‘const cv::Mat’ to non-scalar type ‘IplImage’
问题描述

我在使用opencv和dlib做人脸关键点检测时，遇到如标题所示的编译错误，库的具体版本也在标题中给出了。

原因分析

opencv从4.1.2更迭到4.2.0时不再支持Mat类型到IplImage类型的转换。结论来自cv::cvarrToMat link error and mat to Ipimage error!!!。
解决方案

目前github上最新的dlib(尚未发布)已经修复了这个问题。具体的，修改dlib源码中的cv_image.h的第37行，136行，从如下代码：

IplImage temp = img;

修改为
#if CV_MAJOR_VERSION > 3
	IplImage temp = cvIplImage(img);
#else
	IplImage temp = img;
#endif

以上修改引用自官方，可放心使用。

Microsoft Visual Studio：https://visualstudio.microsoft.com/zh-hans/downloads/

CMake：https://cmake.org/download/

Dlib：https://github.com/davisking/dlib

Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. See http://dlib.net for the main project documentation and API reference.

Dlib是一个现代的C ++工具箱，其中包含机器学习算法和工具，这些工具和工具可以用C ++创建复杂的软件来解决实际问题。 有关主要项目文档和API参考，请参见http://dlib.net。


一、C/C++编译环境
在Microsoft Visual Studio下载页面，可以通过安装完整版的Visual Studio，或者安装生成工具，安装过程中选择C++生成工具。

没有安装的话会出现以下错误：
windows CMake Error: CMAKE_C_COMPILER not set, after EnableLanguage
windows CMake Error: CMAKE_CXX_COMPILER not set, after EnableLanguage




二、安装CMake
在CMake官网下载CMake并安装，也可以通过pip install cmake安装（很慢）。

没有安装的话会提示缺少CMake。


三、安装Dlib
按照Dlib Github上的README安装就可以。

下载，解压，cd dlib-master，mkdir build，cd build，cmake ..，cmake --build .，cd ..，python setup.py install。

Py之dlib：Python库之dlib库的简介、安装、使用方法详细攻略

 

 

 

目录

dlib库的简介

dlib库的安装

dlib库的使用函数

0、利用dlib.get_frontal_face_detector函数实现人脸检测可视化

1、hog提取特征的函数

2、CNN提取特征的函数

 

 

dlib库的简介

    一个机器学习的开源库，包含了机器学习的很多算法，使用起来很方便，直接包含头文件即可，并且不依赖于其他库（自带图像编解码库源码）。Dlib可以帮助您创建很多复杂的机器学习方面的软件来帮助解决实际问题。目前Dlib已经被广泛的用在行业和学术领域,包括机器人,嵌入式设备,移动电话和大型高性能计算环境。

Dlib是一个使用现代C++技术编写的跨平台的通用库，遵守Boost Software licence. 主要特点如下： 

完善的文档：每个类每个函数都有详细的文档，并且提供了大量的示例代码，如果你发现文档描述不清晰或者没有文档，告诉作者，作者会立刻添加。 
	
可移植代码：代码符合ISO C++标准，不需要第三方库支持，支持win32、Linux、Mac OS X、Solaris、HPUX、BSDs 和 POSIX 系统 
	
线程支持：提供简单的可移植的线程API 
	
网络支持：提供简单的可移植的Socket API和一个简单的Http服务器 
	
图形用户界面：提供线程安全的GUI API 
	
数值算法：矩阵、大整数、随机数运算等 
	
机器学习算法：
	
图形模型算法： 
	
图像处理：支持读写Windows BMP文件，不同类型色彩转换 
	
数据压缩和完整性算法：CRC32、Md5、不同形式的PPM算法 
	
测试：线程安全的日志类和模块化的单元测试框架以及各种测试assert支持
	
一般工具：XML解析、内存管理、类型安全的big/little endian转换、序列化支持和容器类

	 



dlib pypi
dlib库
dlib c++ library

 

 

dlib库的安装

本博客提供三种方法进行安装

T1方法：pip install dlib

此方法是需要在你安装cmake、Boost环境的计算机使用

T2方法：conda install -c menpo dlib=18.18

此方法适合那些已经安装好conda库的环境的计算机使用，conda库的安装本博客有详细攻略，请自行翻看。

T3方法：pip install dlib-19.8.1-cp36-cp36m-win_amd64.whl

dlib库的whl文件——dlib-19.7.0-cp36-cp36m-win_amd64.rar

dlib-19.3.1-cp35-cp35m-win_amd64.whl







哈哈，大功告成！如有资料或问题需求，请留言！

 

 

dlib库的使用函数

0、利用dlib.get_frontal_face_detector函数实现人脸检测可视化

CV之dlib：利用dlib.get_frontal_face_detector函数实现人脸检测



 

1、hog提取特征的函数

dlib.get_frontal_face_detector()    #人脸特征提取器，该函数是在C++里面定义的

help(dlib.get_frontal_face_detector())
Help on fhog_object_detector in module dlib.dlib object:

class fhog_object_detector(Boost.Python.instance)
 |  This object represents a sliding window histogram-of-oriented-gradients based object detector.
 |
 |  Method resolution order:
 |      fhog_object_detector
 |      Boost.Python.instance
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __call__(...)
 |      __call__( (fhog_object_detector)arg1, (object)image [, (int)upsample_num_times=0]) -> rectangles :
 |          requires
 |              - image is a numpy ndarray containing either an 8bit grayscale or RGB
 |                image.
 |              - upsample_num_times >= 0
 |          ensures
 |              - This function runs the object detector on the input image and returns
 |                a list of detections.
 |              - Upsamples the image upsample_num_times before running the basic
 |                detector.
 |
 |  __getstate__(...)
 |      __getstate__( (fhog_object_detector)arg1) -> tuple
 |
 |  __init__(...)
 |      __init__( (object)arg1) -> None
 |
 |      __init__( (object)arg1, (str)arg2) -> object :
 |          Loads an object detector from a file that contains the output of the
 |          train_simple_object_detector() routine or a serialized C++ object of type
 |          object_detector<scan_fhog_pyramid<pyramid_down<6>>>.
 |
 |  __reduce__ = <unnamed Boost.Python function>(...)
 |
 |  __setstate__(...)
 |      __setstate__( (fhog_object_detector)arg1, (tuple)arg2) -> None
 |
 |  run(...)
 |      run( (fhog_object_detector)arg1, (object)image [, (int)upsample_num_times=0 [, (float)adjust_threshold=0.0]]) -> tuple :
 |          requires
 |              - image is a numpy ndarray containing either an 8bit grayscale or RGB
 |                image.
 |              - upsample_num_times >= 0
 |          ensures
 |              - This function runs the object detector on the input image and returns
 |                a tuple of (list of detections, list of scores, list of weight_indices).
 |              - Upsamples the image upsample_num_times before running the basic
 |                detector.
 |
 |  save(...)
 |      save( (fhog_object_detector)arg1, (str)detector_output_filename) -> None :
 |          Save a simple_object_detector to the provided path.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  run_multiple(...)
 |      run_multiple( (list)detectors, (object)image [, (int)upsample_num_times=0 [, (float)adjust_threshold=0.0]]) -> tuple :
 |          requires
 |              - detectors is a list of detectors.
 |              - image is a numpy ndarray containing either an 8bit grayscale or RGB
 |                image.
 |              - upsample_num_times >= 0
 |          ensures
 |              - This function runs the list of object detectors at once on the input image and returns
 |                a tuple of (list of detections, list of scores, list of weight_indices).
 |              - Upsamples the image upsample_num_times before running the basic
 |                detector.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __instance_size__ = 160
 |
 |  __safe_for_unpickling__ = True
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Boost.Python.instance:
 |
 |  __new__(*args, **kwargs) from Boost.Python.class
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Boost.Python.instance:
 |
 |  __dict__
 |
 |  __weakref__

2、CNN提取特征的函数

cnn_face_detector = dlib.cnn_face_detection_model_v1(cnn_face_detection_model)

help(dlib.cnn_face_detection_model_v1)
Help on class cnn_face_detection_model_v1 in module dlib.dlib:

class cnn_face_detection_model_v1(Boost.Python.instance)
 |  This object detects human faces in an image.  The constructor loads the face detection model from a file. You can download a pre-trained model from http://dlib.net/files/mmod_human_face_detector.dat.bz2.
 |
 |  Method resolution order:
 |      cnn_face_detection_model_v1
 |      Boost.Python.instance
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __call__(...)
 |      __call__( (cnn_face_detection_model_v1)arg1, (object)img [, (int)upsample_num_times=0]) -> mmod_rectangles :
 |          Find faces in an image using a deep learning model.
 |                    - Upsamples the image upsample_num_times before running the face
 |                      detector.
 |
 |      __call__( (cnn_face_detection_model_v1)arg1, (list)imgs [, (int)upsample_num_times=0 [, (int)batch_size=128]]) -> mmod_rectangless :
 |          takes a list of images as input returning a 2d list of mmod rectangles
 |
 |  __init__(...)
 |      __init__( (object)arg1, (str)arg2) -> None
 |
 |  __reduce__ = <unnamed Boost.Python function>(...)
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __instance_size__ = 984
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Boost.Python.instance:
 |
 |  __new__(*args, **kwargs) from Boost.Python.class
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Boost.Python.instance:
 |
 |  __dict__
 |
 |  __weakref__

 

inline frontal_face_detector get_frontal_face_detector()