python dlib学习（一）：人脸检测

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/hongbin_xu/article/details/78347484

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。

官方例程：

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]))