Py之dlib：Python库之dlib库的简介、安装、使用方法详细攻略
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：
dlib pypi
dlib库
dlib c++ library
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！
CV之dlib：利用dlib.get_frontal_face_detector函数实现人脸检测
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__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()
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