dlib庫的使用函數
0、利用dlib.get_frontal_face_detector函數實作人臉檢測可視化
CV之dlib：利用dlib.get_frontal_face_detector函數實作人臉檢測
1、hog提取特征的函數
dlib.get_frontal_face_detector() #人臉特征提取器，該函數是在C++裡面定義的
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 :
| a tuple of (list of detections, list of scores, list of weight_indices).
| 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 :
| - detectors is a list of detectors.
| - This function runs the list of object detectors at once on the input image and returns
| 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提取特征的函數
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.|
| cnn_face_detection_model_v1
| __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__( (object)arg1, (str)arg2) -> None
| __instance_size__ = 984
inline frontal_face_detector get_frontal_face_detector()