前言

调用训练好的卷积神经网络（CNN）模型进行人脸检测。
模型下载链接：http://dlib.net/files/mmod_human_face_detector.dat.bz2

程序

注：使用了opencv和dlib，需要自行配置环境。

# -*- coding: utf-8 -*-
import sys
import dlib
import cv2# 导入cnn模型
cnn_face_detector = dlib.cnn_face_detection_model_v1(sys.argv[1])for f in sys.argv[2:]:# opencv 读取图片，并显示img = cv2.imread(f, cv2.IMREAD_COLOR)# opencv的bgr格式图片转换成rgb格式b, g, r = cv2.split(img)img2 = cv2.merge([r, g, b])# 进行检测dets = cnn_face_detector(img, 1)# 打印检测到的人脸数print("Number of faces detected: {}".format(len(dets)))# 遍历返回的结果# 返回的结果是一个mmod_rectangles对象。这个对象包含有2个成员变量：dlib.rectangle类，表示对象的位置；dlib.confidence，表示置信度。for i, d in enumerate(dets):face = d.rectprint("Detection {}: Left: {} Top: {} Right: {} Bottom: {} Confidence: {}".format(i, face.left(), face.top(), face.right(), d.rect.bottom(), d.confidence))# 在图片中标出人脸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)k = cv2.waitKey(0)
cv2.destroyAllWindows()

程序中已经有注释，也可以参考python dlib学习（一）：人脸检测。

运行结果

命令行下，输入：

python cnn_face_detector.py mmod_human_face_detector.dat jobs.jpg obama.jpg silicon_valley.jpg

目录下有以下文件：

结果截图：
（运行速度有点慢，要多等一下）
虽然dlib也支持cuda，怕我的笔记本扛不住还是用的CPU跑的。

官方例程

#!/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 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 objectThe 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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python dlib学习（三）：调用cnn人脸检测

前言

程序

运行结果

官方例程

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