64位+win7+Python3.6+dlib19.7检测人脸 详细图文教程
2017-11-16 23:02
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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
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.bz2mmod_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.jpg Processing file: C:/local/dlib-19.7/examples/faces/2008_007676.jpg Number of faces detected: 7 Detection 0: Left: 225 Top: 53 Right: 264 Bottom: 93 Confidence: 1.0600713491439 82 Detection 1: Left: 193 Top: 113 Right: 232 Bottom: 153 Confidence: 1.05377495288 84888 Detection 2: Left: 261 Top: 125 Right: 300 Bottom: 165 Confidence: 1.05047667026 51978 Detection 3: Left: 365 Top: 129 Right: 404 Bottom: 169 Confidence: 1.04934203624 72534 Detection 4: Left: 131 Top: 74 Right: 178 Bottom: 122 Confidence: 1.042161345481 8726 Detection 5: Left: 313 Top: 117 Right: 352 Bottom: 157 Confidence: 1.02432358264 9231 Detection 6: Left: 100 Top: 130 Right: 156 Bottom: 187 Confidence: 1.02354717254 63867 Hit 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 sys import dlib from skimage import io if 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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