python dlib学习（一）：人脸检测 |
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python dlib学习（一）：人脸检测
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前言
dlib毕竟是一个很有名的库了，有c++、Python的接口。使用dlib可以大大简化开发，比如人脸识别，特征点检测之类的工作都可以很轻松实现。同时也有很多基于dlib开发的应用和开源库，比如face_recogintion库（应用一个基于Python的开源人脸识别库，face_recognition）等等。 环境安装不算复杂，我只在Linux和win下跑过。安装配置不算难，直接贴链接了。 Linux下的安装在这篇博客中介绍了（应用一个基于Python的开源人脸识别库，face_recognition），不做赘述。 win下安装教程： python 安装dlib和boost Windows环境 安装dlib（python） 总结 程序注：程序中使用了python-opencv、dlib，使用前请配置好环境。 程序中已有注释。 # -*- coding: utf-8 -*- import sys import dlib import cv2 detector = dlib.get_frontal_face_detector() #获取人脸分类器 # 传入的命令行参数 for f in sys.argv[1:]: # opencv 读取图片，并显示 img = cv2.imread(f, cv2.IMREAD_COLOR) # 摘自官方文档： # image is a numpy ndarray containing either an 8bit grayscale or RGB image. # opencv读入的图片默认是bgr格式，我们需要将其转换为rgb格式；都是numpy的ndarray类。 b, g, r = cv2.split(img) # 分离三个颜色通道 img2 = cv2.merge([r, g, b]) # 融合三个颜色通道生成新图片 dets = detector(img, 1) #使用detector进行人脸检测 dets为返回的结果 print("Number of faces detected: {}".format(len(dets))) # 打印识别到的人脸个数 # enumerate是一个Python的内置方法，用于遍历索引 # index是序号；face是dets中取出的dlib.rectangle类的对象，包含了人脸的区域等信息 # left()、top()、right()、bottom()都是dlib.rectangle类的方法，对应矩形四条边的位置 for index, face in enumerate(dets): print('face {}; left {}; top {}; right {}; bottom {}'.format(index, face.left(), face.top(), face.right(), face.bottom())) # 在图片中标注人脸，并显示 left = face.left() top = face.top() right = face.right() bottom = face.bottom() cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0), 3) cv2.namedWindow(f, cv2.WINDOW_AUTOSIZE) cv2.imshow(f, img) # 等待按键，随后退出，销毁窗口 k = cv2.waitKey(0) cv2.destroyAllWindows() 运行结果运行程序，后缀是图片的名称。 最后附上官方程序： #!/usr/bin/python # The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt # # This example program shows how to find frontal human faces in an image. In # particular, it shows how you can take a list of images from the command # line and display each on the screen with red boxes overlaid on each human # face. # # 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: # ./face_detector.py ../examples/faces/*.jpg # # This face detector is made using the now classic Histogram of Oriented # Gradients (HOG) feature combined with a linear classifier, an image # pyramid, and sliding window detection scheme. This type of object detector # is fairly general and capable of detecting many types of semi-rigid objects # in addition to human faces. Therefore, if you are interested in making # your own object detectors then read the train_object_detector.py example # program. # # # 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 # if you have a CPU that supports AVX instructions, since this makes some # things run 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 detector = dlib.get_frontal_face_detector() win = dlib.image_window() for f in sys.argv[1:]: 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 = detector(img, 1) print("Number of faces detected: {}".format(len(dets))) for i, d in enumerate(dets): print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format( i, d.left(), d.top(), d.right(), d.bottom())) win.clear_overlay() win.set_image(img) win.add_overlay(dets) dlib.hit_enter_to_continue() # Finally, if you really want to you can ask the detector to tell you the score # for each detection. The score is bigger for more confident detections. # The third argument to run is an optional adjustment to the detection threshold, # where a negative value will return more detections and a positive value fewer. # Also, the idx tells you which of the face sub-detectors matched. This can be # used to broadly identify faces in different orientations. if (len(sys.argv[1:]) > 0): img = io.imread(sys.argv[1]) dets, scores, idx = detector.run(img, 1, -1) for i, d in enumerate(dets): print("Detection {}, score: {}, face_type:{}".format( d, scores[i], idx[i])) |
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