64位+win7+Python3.6+dlib19.7检测人脸 详细图文教程

系统环境

win7旗舰sp1,64位机

python3.6.2

dlib19.7.0 （其他版本例子没有cnn_face_detector.py和里面对应的一些模块功能和函数，运行该实例会报错）

效果展示

官网

http://dlib.net/

https://pypi.python.org/pypi/dlib#downloads 

dlib-19.7.0-cp36-cp36m-win_amd64.whl (md5) 下载

https://github.com/davisking/dlib   Git下载

软件、模型和数据下载

链接：http://pan.baidu.com/s/1eRC8PpC 密码：8cfp

列表

应用软件

   dlib-19.7.0-cp36-cp36m-win_amd64.whl （pip本地安装使用）

   dlib-19.7.0.tar.gz  （解压，源文件和实例）

dlib19.7.0运行需要的模型（model）和训练、测试、测试数据（data）集合

   dlib_face_recognition_resnet_model_v1.dat.bz2

   mmod_human_face_detector.dat.bz2

   shape_predictor_5_face_landmarks.dat.bz2

   shape_predictor_68_face_landmarks.dat.bz2

   说明：.bz2文件解压后得到.dat文件，勿修改文件名（直接删除文件名后缀.bz2）,否则运行程序报错。

python各种库(whl格式)下载地址

  http://www.lfd.uci.edu/~gohlke/pythonlibs/
  https://pypi.python.org/pypi?%3Aaction=index

  http://dlib.net/files/

   说明：使用whl免去了下载、编译、匹配和调试的操作，节省了大量的时间。

运行命令

python    C:/local/dlib-19.7/python_examples/cnn_face_detector.py    C:/local/dlib-19.7/python_examples/mmod_human_face_detector.dat    C:/local/dlib-19.7/examples/faces/2008_007676.jpg

运行输出

(test) C:\Users\Administrator>python C:/local/dlib-19.7/python_examples/cnn_face_detector.py C:/local/dlib-19.7/python_examples/mmod_human_face_detector.dat C:/local/dlib-19.7/examples/faces/2008_007676.jpgProcessing file: C:/local/dlib-19.7/examples/faces/2008_007676.jpgNumber of faces detected: 7Detection 0: Left: 225 Top: 53 Right: 264 Bottom: 93 Confidence: 1.060071349143982Detection 1: Left: 193 Top: 113 Right: 232 Bottom: 153 Confidence: 1.0537749528884888Detection 2: Left: 261 Top: 125 Right: 300 Bottom: 165 Confidence: 1.0504766702651978Detection 3: Left: 365 Top: 129 Right: 404 Bottom: 169 Confidence: 1.0493420362472534Detection 4: Left: 131 Top: 74 Right: 178 Bottom: 122 Confidence: 1.0421613454818726Detection 5: Left: 313 Top: 117 Right: 352 Bottom: 157 Confidence: 1.024323582649231Detection 6: Left: 100 Top: 130 Right: 156 Bottom: 187 Confidence: 1.0235471725463867Hit enter to continue

源代码

cnn_face_detector.py

#!/usr/bin/python# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt##   This example shows how to run a CNN based face detector using dlib.  The#   example loads a pretrained model and uses it to find faces in images.  The#   CNN model is much more accurate than the HOG based model shown in the#   face_detector.py example, but takes much more computational power to#   run, and is meant to be executed on a GPU to attain reasonable speed.##   You can download the pre-trained model from:#       http://dlib.net/files/mmod_human_face_detector.dat.bz2##   The examples/faces folder contains some jpg images of people.  You can run#   this program on them and see the detections by executing the#   following command:#       ./cnn_face_detector.py mmod_human_face_detector.dat ../examples/faces/*.jpg### COMPILING/INSTALLING THE DLIB PYTHON INTERFACE#   You can install dlib using the command:#       pip install dlib##   Alternatively, if you want to compile dlib yourself then go into the dlib#   root folder and run:#       python setup.py install#   or#       python setup.py install --yes USE_AVX_INSTRUCTIONS --yes DLIB_USE_CUDA#   if you have a CPU that supports AVX instructions, you have an Nvidia GPU#   and you have CUDA installed since this makes things run *much* faster.##   Compiling dlib should work on any operating system so long as you have#   CMake and boost-python installed.  On Ubuntu, this can be done easily by#   running the command:#       sudo apt-get install libboost-python-dev cmake##   Also note that this example requires scikit-image which can be installed#   via the command:#       pip install scikit-image#   Or downloaded from http://scikit-image.org/download.html.import sysimport dlibfrom skimage import ioif len(sys.argv) < 3:    print(        "Call this program like this:\n"        "   ./cnn_face_detector.py mmod_human_face_detector.dat ../examples/faces/*.jpg\n"        "You can get the mmod_human_face_detector.dat file from:\n"        "    http://dlib.net/files/mmod_human_face_detector.dat.bz2")    exit()cnn_face_detector = dlib.cnn_face_detection_model_v1(sys.argv[1])win = dlib.image_window()for f in sys.argv[2:]:    print("Processing file: {}".format(f))    img = io.imread(f)    # 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.    dets = cnn_face_detector(img, 1)    '''    This detector returns a mmod_rectangles object. This object contains a list of mmod_rectangle objects.    These objects can be accessed by simply iterating over the mmod_rectangles object    The mmod_rectangle object has two member variables, a dlib.rectangle object, and a confidence score.        It is also possible to pass a list of images to the detector.        - like this: dets = cnn_face_detector([image list], upsample_num, batch_size = 128)    In this case it will return a mmod_rectangless object.    This object behaves just like a list of lists and can be iterated over.    '''    print("Number of faces detected: {}".format(len(dets)))    for i, d in enumerate(dets):        print("Detection {}: Left: {} Top: {} Right: {} Bottom: {} Confidence: {}".format(            i, d.rect.left(), d.rect.top(), d.rect.right(), d.rect.bottom(), d.confidence))    rects = dlib.rectangles()    rects.extend([d.rect for d in dets])    win.clear_overlay()    win.set_image(img)    win.add_overlay(rects)    dlib.hit_enter_to_continue()

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