文章目录
- 基本步骤
- 1、定位图片中的脸
- 2、在面部ROI中检测出关键的面部结构
- 什么是ROI
- 补充函数rect_to_bb，将rect转成坐标点
- 补充函数shape__to__np
- 补充函数resive
- 主要代码
- 导入相关的包
- 初始化面部检测器和面部特征预测器
- 打开图片并读取，将之转换为的灰度图片，固定大小
- 调用加载好的检测器，对目标进行检测
- 遍历所有是别人出来的人脸
- 输出修改之后的图片
- 最终的代码
- 实验效果
- 分析与总结
基本步骤
1、定位图片中的脸
- 面部检测可以使用很多方式实现，比如说OpenCV内嵌的Haar级联，预训练好的HOG+ 先行SVM对象检测，或者使用深度学习算法进行面部识别。无论什么方法，我们最终都是要获得标定面部的选区
2、在面部ROI中检测出关键的面部结构
- 主要给出对应面部选区，我们就可以进行面部特征点的检测。有很多不同的面部特征点检测器，但是所有的方法基本上都是针对以下几个器官：嘴、左右眼睫毛、左右眼、鼻子、下巴等。
- Dlib库中常用的面部特征检测器是One millisecond face alignment with an ensemble of regression trees。这个方法使用了一个人工标注的测试集，标注的内容是围绕面部特征的特定的坐标，坐标表示的是像素点之间的距离。有了这个训练集，就可以训练出来一个集成的回归树，用来检测面部的特征。这个特征检测器的可以进行实时高精度检测。
- 如果想要的更加深入的了解这个技术，可以通过连接，读相关的文章，配合Dlib的官方文档。
- 文章的连接
- Dlib官方文档
- 在Dlib库中的预先训练好的面部特征检测是针对人脸结构上的68个特征点，分布如下。
什么是ROI
- 图像处理中，从被处理图像以方框、圆、椭圆等不规则多边形方式勾勒出的需要处理的区域，成为感兴趣区域，ROI。
补充函数rect_to_bb，将rect转成坐标点
- 描述：将检测器检测出来的rect转换成具体的长方形框的坐标点
- 原理：detecor返回的值是rect，数据的形式是（x,y,height,width）
def rect_to_bb(rect):
# take a bounding predicted by dlib and convert it
# to the format (x, y, w, h) as we would normally do
# with OpenCV
x = rect.left()
y = rect.top()
w = rect.right() - x
h = rect.bottom() - y
# return a tuple of (x, y, w, h)
return (x, y, w, h)补充函数shape__to__np
- 描述：将包含的68个面部区域的坐标点的shape转为numpy数组
def shape_to_np(shape, dtype="int"):
# initialize the list of (x, y)-coordinates
coords = np.zeros((68, 2), dtype=dtype)
# loop over the 68 facial landmarks and convert them
# to a 2-tuple of (x, y)-coordinates
for i in range(0, 68):
coords[i] = (shape.part(i).x, shape.part(i).y)
# return the list of (x, y)-coordinates
return coords补充函数resive
- 描述：将图片按照要求设定大小
- 参数：image是cv2.imread的对象
width和height是新指定的大小参数def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
# initialize the dimensions of the image to be resized and
# grab the image size
dim = None
(h, w) = image.shape[:2]
# if both the width and height are None, then return the
# original image
if width is None and height is None:
return image
# check to see if the width is None
if width is None:
# calculate the ratio of the height and construct the
# dimensions
r = height / float(h)
dim = (int(w * r), height)
# otherwise, the height is None
else:
# calculate the ratio of the width and construct the
# dimensions
r = width / float(w)
dim = (width, int(h * r))
# resize the image
resized = cv2.resize(image, dim, interpolation=inter)
# return the resized image
return resized主要代码
导入相关的包
# import the necessary packages
import numpy as np
import argparse
import dlib
import cv2初始化面部检测器和面部特征预测器
# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor
# 初始化基于HOG预测的预先训练好的检测器
detector = dlib.get_frontal_face_detector()
# 使用的shape-predictor去加载的下载的面部特征训练器
# 括号里的是检测器的路径
predictor = dlib.shape_predictor("the path of the detector")打开图片并读取，将之转换为的灰度图片，固定大小
# 使用opencv打开图片
image = cv2.imread("1.jpg")
# 统一图片的大小
image = imutils.resize(image,width = 500)
# 将图片转为灰度图片，将BGR图片转为灰度图片
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)- 具体resize函数
def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
# initialize the dimensions of the image to be resized and
# grab the image size
dim = None
(h, w) = image.shape[:2]
# if both the width and height are None, then return the
# original image
if width is None and height is None:
return image
# check to see if the width is None
if width is None:
# calculate the ratio of the height and construct the
# dimensions
r = height / float(h)
dim = (int(w * r), height)
# otherwise, the height is None
else:
# calculate the ratio of the width and construct the
# dimensions
r = width / float(w)
dim = (width, int(h * r))
# resize the image
resized = cv2.resize(image, dim, interpolation=inter)
# return the resized image
return resized- imread的具体的输出，每一个像素点是以（r，g，b）的形式进行保存的，结果如下
原图片的shape输出，对应的是heightweightchannel，总共是rgb三个颜色的通道，像素点是711*474
- 修改之后的图片尺寸
- 转换之后的灰度图片，仅仅只有一个单通道
调用加载好的检测器，对目标进行检测
- 第一个参数是需要检测的图片
- 第二个参数是图片的层数，这里是单层图片，只有一个灰度层
# detect face in the grayscale face
# detecting the bounding box of faces in our image
# the second parameter is the number of image pyramid layer
# prior applying the detector we must upscaling the image
rects = detector(gray,1)
- rects的结果是的坐标和weight和height两对参数
遍历所有是别人出来的人脸
for (i,rect) in enumerate(rects):
# i对应的是目标的索引，rect对应是每一个框的起始点坐标和长宽
# 定位人脸的关键点，返回的定位之后的68个关键点的位置
shape = predictor(gray,rect)
# shape是输出之后坐标点，是（68，2），68个点，每个点都是二维的，将所有的坐标点转为numpy数组
shape = face_utils.shape_to_np(shape)
# 将rect检测人脸的边框转为绘制矩形框的具体位置
(x,y,w,h) = face_utils.rect_to_bb(rect)
# 绘制人脸的矩形框
cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),2)
# 设置矩形框的文字部分
cv2.putText(image,"Face #{}".format(i+1),(x-10,y-10),
cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,255,0),2)
# 循环遍历所有的点，将之在原图上进行标注
for (x,y) in shape:
cv2.circle(image,(x,y),1,(0,0,255),-1)- 具体的函数face_utils.shape_to_np的具体代码，将shape的输出结果转为numpy数组
def shape_to_np(shape, dtype="int"):
# initialize the list of (x, y)-coordinates
coords = np.zeros((68, 2), dtype=dtype)
# loop over the 68 facial landmarks and convert them
# to a 2-tuple of (x, y)-coordinates
for i in range(0, 68):
coords[i] = (shape.part(i).x, shape.part(i).y)
# return the list of (x, y)-coordinates
return coords输出修改之后的图片
# show the output image with the face detections + facial landmarks
cv2.imshow("Output",image)
cv2.waitKey(0)最终的代码
# import the necessary packages
# import argparse
import cv2
import dlib
import imutils
# the package below from the writer
from imutils import face_utils
# intialize dlib face detector adn then create the facial landmark predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
# load the image,resize it and convert it into grayscale
# resice the size of the image into 500 width
# image = cv2.imread(args["image"])
image = cv2.imread("1.jpg")
image = imutils.resize(image,width = 500)
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# detect face in the grayscale face
# detecting the bounding box of faces in our image
# the second parameter is the number of image pyramid layer
# prior applying the detector we must upscaling the image
rects = detector(gray,1)
# Given the coordinates of the face in the image
# loop over the face detections
for (i,rect) in enumerate(rects):
# determine the facial landmarks for the face region
# convert the coordiantes of the facial landmark to numpy array
# predictor is to detect the facila landmark
shape = predictor(gray,rect)
# convert the dlib objects to a numpy array
shape = face_utils.shape_to_np(shape)
# convert dlib's rectangle to a OpenCV-style bounding box(x,y,w,h)
# then draw the face bounding box
(x,y,w,h) = face_utils.rect_to_bb(rect)
cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),2)
# show the face number
cv2.putText(image,"Face #{}".format(i+1),(x-10,y-10),
cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,255,0),2)
# loop over the (x,y)-coordinates for the facial lanmarks
# and draw the on the image
for (x,y) in shape:
cv2.circle(image,(x,y),1,(0,0,255),-1)
# show the output image with the face detections + facial landmarks
cv2.imshow("Output",image)
cv2.waitKey(0)实验效果
分析与总结
里面有一个的imutils的包下载地址，不过下不了也没关系，我已经把对应原函数附在了对应调用的地方，你们可以自己改一下
本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
