opencv:使用dlib进行人脸检测(python)

uncle_ll 2019-12-13

python 算法 Image 人脸识别 OpenCV 人脸检测 dlib

人脸检测

随着人脸识别,人脸支付,换脸等业务等爆发,多的人都将目光放在人脸方面的研究上。可以说,人脸检测是目前所有目标检测子方向中被研究的最充分的问题之一,它在安防监控,人机交互,金融支付,社交和娱乐等方面有很强的应用价值,也是整个人脸识别算法的第一步。

问题描述

人脸检测的目标就是从图像中找到所有的人脸对应的位置,算法结果输出的是人脸在图像中所处的坐标。有些算法还会有其它的一些信息,比如性别,年龄,面部情绪等。详细的发展过程网上有很多的参考资料,这里不作过多的介绍。
在这里插入图片描述

Dlib

DLIB是包含机器学习算法和工具,一个现代化的C ++工具包。它在工业界和学术界使用非常广泛,包括机器人,嵌入式设备,移动电话,和高性能的计算环境。 DLIB有开源许可,因此可以在任何应用程序中免费使用。
详细介绍: http://dlib.net/python/index.html
实现的功能有很多:
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使用起来也是比较简单的,首先进行安装:

pip install dlib
pip install opencv-python

关于人脸检测这块的函数是get_frontal_face_detector
写一个测试脚本:

import cv2
import sys
import dlib

detector = dlib.get_frontal_face_detector()  # init detector

img_file = sys.argv[1]
img = cv2.imread(img_file)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # convert to gray img to speed
faces = detector(img_gray, 1)  # detect input img, para 1 means 1 times upsamle
for face in faces:  # may be many faces in one image
    print(face)
    y1 = face.bottom()  # detect box bottom y value
    y2 = face.top()  # top y value
    x1 = face.left()  # left x value
    x2 = face.right()  # right x value
    print(x1, x2, y1, y2)
    # add detect box in image
    cv2.rectangle(img,(int(x1),int(y1)),(int(x2),int(y2)),(0,255,0),3)
    
cv2.imshow('new.jpg', img)
cv2.waitKey(0)
python test.py image1

单人情况下,image1:
!在这里插入图片描述
结果:

[(161, 247) (546, 632)]
161 546 632 247

在这里插入图片描述
多人情况下,img2:
在这里插入图片描述
结果:
在这里插入图片描述

关于get_frontal_face_detector的使用参数可以看下官方例子:

#!/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
#
#   Compiling dlib should work on any operating system so long as you have
#   CMake installed.  On Ubuntu, this can be done easily by running the
#   command:
#       sudo apt-get install cmake
#
#   Also note that this example requires Numpy which can be installed
#   via the command:
#       pip install numpy

import sys

import dlib

detector = dlib.get_frontal_face_detector()
win = dlib.image_window()

for f in sys.argv[1:]:
    print("Processing file: {}".format(f))
    img = dlib.load_rgb_image(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 = dlib.load_rgb_image(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]))

重点说明第二个参数,设置为1表示一次上采样,对原图进行上采样放大,能够使得检测器检测出更多的人脸。也可以设置为其它值,比如2,表示进行两次上采样。

参考

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