文章目錄

• `dlib`概述
• 人臉檢測
• • CPU版本人臉檢測算法
  • • 檢測步驟
    • 示例代碼：
  • CUDA版本人臉檢測算法
  • • 檢測步驟
    • 示例代碼
  • 類定義與接口源碼
  • • 人臉檢測中用到的重要的類(概述)
    • `fhog_object_detector`類接口定義
    • `rectangle`類接口定義
    • `cnn_face_detection_model_v1`類定義
    • `mmod_rectangle` `mmod_rectangles` `mmod_rectangless`

dlib概述

Dlib是一個包含機器學習算法的C++開源工具包。Dlib可以幫助您創(chuàng)建很多復雜的機器學習方面的軟件來幫助解決實際問題。目前Dlib已經(jīng)被廣泛的用在行業(yè)和學術領域，包括機器人，嵌入式設備，移動電話和大型高性能計算環(huán)境.

人臉檢測

CPU版本人臉檢測算法

檢測步驟

獲取hog detector

傳入image,數(shù)據(jù)格式為numpy.ndarray

(可選項) 獲取分數(shù)值和檢測列表

獲取人臉坐標

示例代碼：

import dlibfrom cv2 import cv2# step 1. create an object detector based on hog detector = dlib.get_frontal_face_detector() # _dlib_pybind11.fhog_object_detector# step 2. read an image using dlib or cv2 # note that the difference between the image data formated as numpy.ndarray read by dlib and cv2 is that dlib read it channels as *R G B* order while cv2 read as *B G R*,so you should do one more step to convert the image if using cv2 image_path = "sample.jpg" img = dlib.load_rgb_image(image_path) # img = cv2.imread(image_path) # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)# step 3. using the numpy.ndarray image data as input to detect the front face on the image # The 1 in the second argument indicates that we should upsample the image 1 time. # This will make everything bigger and allow us to detect more faces. detections = detector(img, 1) # List[_dlib_pybind11.rectangle]# step 3.1 (Optional) if you want to get more detail information,using function run() instead # detections, scores, idx = detector.run(img, 1, 0.5) # List[_dlib_pybind11.rectangle] List[int] List[int]# step 4. get point coordinates from the detection results # let's just fetch one instead all of the in a loop detection = detections[0] left,top,right,bottom = detection .left(),detection .top(),detection .right(),detection .bottom()# step x : now you can do whatever you want since you've already got what you want.

CUDA版本人臉檢測算法

檢測步驟

下載模型文件

加載模型文件，生成卷積神經(jīng)網(wǎng)絡人臉檢測對象

傳入image,數(shù)據(jù)格式為numpy.ndarray

(可選項) 獲取分數(shù)值和檢測列表

獲取人臉坐標

示例代碼

import dlib # step 1. make sure you have downloaded the correct model file face_detector_model_path = '../models/mmod_human_face_detector.dat'# step 2. load this model and create a cnn face detector face_detector = dlib.cnn_face_detection_model_v1(face_detector_model_path) # dlib.cnn_face_detection_model_v1# step 3. read an image using dlib or cv2 # note that the difference between the image data formated as numpy.ndarray read by dlib and cv2 is that dlib read it channels as *R G B* order while cv2 read as *B G R*,so you should do one more step to convert the image if using cv2 image_path = "sample.jpg" img = dlib.load_rgb_image(image_path) # img = cv2.imread(image_path) # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)# step 4. predict and detect detections = face_detector(img, 1) # dlib.mmod_rectangles# step 5. get just one of the rectangle instead all of them ,the type is mmod_rectangle detection = detections[0] # dlib.mmod_rectangle # the mmod_rectangle contains two parts : confidence and rect print(detection.confidence, detection.rect)# step 6.get face coordinates for just one as sample left,top,right,bottom = detection.rect.left(),detection.rect.top(),detection.rect.right(),detection.rect.bottom()# step x. do anything you would like to

類定義與接口源碼

人臉檢測中用到的重要的類(概述)

dlib.fhog_object_detector : hog模型的人臉檢測對象,常用方法: __call()__和run()

dlib.rectangle:人臉檢測結果，用于表示人臉的矩形區(qū)域,常用方法：left() right()``top()``bottom()

dlib.cnn_face_detection_model_v1:卷積神經(jīng)網(wǎng)絡模型的人臉檢測對象,常用方法: __call()__

dlib.mmod_rectangle:人臉檢測結果，包含了表示人臉的巨型區(qū)域以及檢測置信度,成員包含: rect和confidence,其中,rect為 dlib.rectangle類型,confidence為float類型

dlib.mmod_rectangles:包含多個 dlib.mmod_rectangle對象

類定義參考鏈接：Python API 鏈接

fhog_object_detector類接口定義

fhog_object_detector類在源碼中為C++類，這里使用偽代碼編譯觀察其接口與調(diào)用方法

class dlib.fhog_object_detector():"""This object represents a sliding window histogram-of-oriented-gradients based object detector."""def __call__(self: dlib.fhog_object_detector, image: array, upsample_num_times: int = 0L) -> dlib.rectangles:"""requiresimage is a numpy ndarray containing either an 8bit grayscale or RGB image.upsample_num_times >= 0ensuresThis 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."""def __init__(self: dlib.fhog_object_detector, arg0: unicode) -> None:'''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>>>.detection_window_heightdetection_window_widthnum_detectors'''passdef run(self: dlib.fhog_object_detector, image: array, upsample_num_times: int = 0L,adjust_threshold: float = 0.0) -> tuple:"""requiresimage is a numpy ndarray containing either an 8bit grayscale or RGB image.upsample_num_times >= 0ensuresThis 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."""passdef run_multiple(detectors: list, image: array, upsample_num_times: int = 0L, adjust_threshold: float = 0.0)->tuple:"""requiresdetectors is a list of detectors.image is a numpy ndarray containing either an 8bit grayscale or RGB image.upsample_num_times >= 0ensuresThis 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."""passdef save(self: dlib.fhog_object_detector, detector_output_filename: unicode)->None:'''Save a simple_object_detector to the provided path.'''pass

rectangle類接口定義

class dlib.rectangleThis object represents a rectangular area of an image.__init__(*args, **kwargs)Overloaded function.__init__(self: dlib.rectangle, left: int, top: int, right: int, bottom: int) -> None__init__(self: dlib.rectangle, rect: dlib::drectangle) -> None__init__(self: dlib.rectangle, rect: dlib.rectangle) -> None__init__(self: dlib.rectangle) -> Nonearea(self: dlib.rectangle) → intbl_corner(self: dlib.rectangle) → dlib.pointReturns the bottom left corner of the rectangle.bottom(self: dlib.rectangle) → intbr_corner(self: dlib.rectangle) → dlib.pointReturns the bottom right corner of the rectangle.center(self: dlib.rectangle) → dlib.pointcontains(*args, **kwargs)Overloaded function.contains(self: dlib.rectangle, point: dlib.point) -> boolcontains(self: dlib.rectangle, point: dlib.dpoint) -> boolcontains(self: dlib.rectangle, x: int, y: int) -> boolcontains(self: dlib.rectangle, rectangle: dlib.rectangle) -> booldcenter(self: dlib.rectangle) → dlib.pointheight(self: dlib.rectangle) → intintersect(self: dlib.rectangle, rectangle: dlib.rectangle) → dlib.rectangleis_empty(self: dlib.rectangle) → boolleft(self: dlib.rectangle) → intright(self: dlib.rectangle) → inttl_corner(self: dlib.rectangle) → dlib.pointReturns the top left corner of the rectangle.top(self: dlib.rectangle) → inttr_corner(self: dlib.rectangle) → dlib.pointReturns the top right corner of the rectangle.width(self: dlib.rectangle) → int

cnn_face_detection_model_v1類定義

class dlib.cnn_face_detection_model_v1This 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.__call__(*args, **kwargs)Overloaded function.__call__(self: dlib.cnn_face_detection_model_v1, imgs: list, upsample_num_times: int=0L, batch_size: int=128L) -> std::vector<std::vector<dlib::mmod_rect, std::allocator<dlib::mmod_rect> >, std::allocator<std::vector<dlib::mmod_rect, std::allocator<dlib::mmod_rect> > > >takes a list of images as input returning a 2d list of mmod rectangles__call__(self: dlib.cnn_face_detection_model_v1, img: array, upsample_num_times: int=0L) -> std::vector<dlib::mmod_rect, std::allocator<dlib::mmod_rect> >Find faces in an image using a deep learning model.Upsamples the image upsample_num_times before running the face detector.__init__(self: dlib.cnn_face_detection_model_v1, filename: unicode) → Non

mmod_rectangle mmod_rectangles mmod_rectangless

class dlib.mmod_rectangleWrapper around a rectangle object and a detection confidence score.__init__x.__init__(...) initializes x; see help(type(x)) for signatureconfidencerect class dlib.mmod_rectanglesAn array of mmod rectangle objects.__init__(*args, **kwargs)Overloaded function.__init__(self: dlib.mmod_rectangles) -> None__init__(self: dlib.mmod_rectangles, arg0: dlib.mmod_rectangles) -> NoneCopy constructor__init__(self: dlib.mmod_rectangles, arg0: iterable) -> Noneappend(self: dlib.mmod_rectangles, x: dlib.mmod_rectangle) → NoneAdd an item to the end of the listcount(self: dlib.mmod_rectangles, x: dlib.mmod_rectangle) → intReturn the number of times x appears in the listextend(*args, **kwargs)Overloaded function.extend(self: dlib.mmod_rectangles, L: dlib.mmod_rectangles) -> NoneExtend the list by appending all the items in the given listextend(self: dlib.mmod_rectangles, arg0: list) -> Noneinsert(self: dlib.mmod_rectangles, i: int, x: dlib.mmod_rectangle) → NoneInsert an item at a given position.pop(*args, **kwargs)Overloaded function.pop(self: dlib.mmod_rectangles) -> dlib.mmod_rectangleRemove and return the last itempop(self: dlib.mmod_rectangles, i: int) -> dlib.mmod_rectangleRemove and return the item at index iremove(self: dlib.mmod_rectangles, x: dlib.mmod_rectangle) → NoneRemove the first item from the list whose value is x. It is an error if there is no such item. class dlib.mmod_rectanglessA 2D array of mmod rectangle objects.__init__(*args, **kwargs)Overloaded function.__init__(self: dlib.mmod_rectangless) -> None__init__(self: dlib.mmod_rectangless, arg0: dlib.mmod_rectangless) -> NoneCopy constructor__init__(self: dlib.mmod_rectangless, arg0: iterable) -> Noneappend(self: dlib.mmod_rectangless, x: dlib.mmod_rectangles) → NoneAdd an item to the end of the listcount(self: dlib.mmod_rectangless, x: dlib.mmod_rectangles) → intReturn the number of times x appears in the listextend(*args, **kwargs)Overloaded function.extend(self: dlib.mmod_rectangless, L: dlib.mmod_rectangless) -> NoneExtend the list by appending all the items in the given listextend(self: dlib.mmod_rectangless, arg0: list) -> Noneinsert(self: dlib.mmod_rectangless, i: int, x: dlib.mmod_rectangles) → NoneInsert an item at a given position.pop(*args, **kwargs)Overloaded function.pop(self: dlib.mmod_rectangless) -> dlib.mmod_rectanglesRemove and return the last itempop(self: dlib.mmod_rectangless, i: int) -> dlib.mmod_rectanglesRemove and return the item at index iremove(self: dlib.mmod_rectangless, x: dlib.mmod_rectangles) → NoneRemove the first item from the list whose value is x. It is an error if there is no such item.

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