用python进行人脸识别

1、安装cv2

sudo apt-get install python-opencv opencv-data

2、 Haar特征分类器

Haar特征分类器就是一个XML文件，该文件中会描述人体各个部位的Haar特征值。包括人脸、眼睛、嘴唇等等。 
Haar特征分类器存放目录：OpenCV安装目录中的data/haarcascades目录下，经过apt安装的Haar特征分类器以下：

$ ll /usr/share/opencv/haarcascades/
总用量 19504
drwxr-xr-x 2 root root    4096 6月   2 22:01 ./
drwxr-xr-x 4 root root    4096 6月   2 22:01 ../
-rw-r--r-- 1 root root 1095035 9月  19  2014 haarcascade_eye_tree_eyeglasses.xml
-rw-r--r-- 1 root root  506314 9月  19  2014 haarcascade_eye.xml
-rw-r--r-- 1 root root  837462 9月  19  2014 haarcascade_frontalface_alt2.xml
-rw-r--r-- 1 root root 3644763 9月  19  2014 haarcascade_frontalface_alt_tree.xml
-rw-r--r-- 1 root root  919871 9月  19  2014 haarcascade_frontalface_alt.xml
-rw-r--r-- 1 root root 1254733 9月  19  2014 haarcascade_frontalface_default.xml
-rw-r--r-- 1 root root  636650 9月  19  2014 haarcascade_fullbody.xml
-rw-r--r-- 1 root root  323227 9月  19  2014 haarcascade_lefteye_2splits.xml
-rw-r--r-- 1 root root  531497 9月  19  2014 haarcascade_lowerbody.xml
-rw-r--r-- 1 root root  358385 9月  19  2014 haarcascade_mcs_eyepair_big.xml
-rw-r--r-- 1 root root  410204 9月  19  2014 haarcascade_mcs_eyepair_small.xml
-rw-r--r-- 1 root root  312877 9月  19  2014 haarcascade_mcs_leftear.xml
-rw-r--r-- 1 root root  777721 9月  19  2014 haarcascade_mcs_lefteye.xml
-rw-r--r-- 1 root root  719806 9月  19  2014 haarcascade_mcs_mouth.xml
-rw-r--r-- 1 root root 1585210 9月  19  2014 haarcascade_mcs_nose.xml
-rw-r--r-- 1 root root  324727 9月  19  2014 haarcascade_mcs_rightear.xml
-rw-r--r-- 1 root root 1383113 9月  19  2014 haarcascade_mcs_righteye.xml
-rw-r--r-- 1 root root 1522737 9月  19  2014 haarcascade_mcs_upperbody.xml
-rw-r--r-- 1 root root 1125633 9月  19  2014 haarcascade_profileface.xml
-rw-r--r-- 1 root root  324586 9月  19  2014 haarcascade_righteye_2splits.xml
-rw-r--r-- 1 root root  281945 9月  19  2014 haarcascade_smile.xml
-rw-r--r-- 1 root root 1046387 9月  19  2014 haarcascade_upperbody.xml

Haar特征值反映了图像的灰度变化状况。例如：脸部的一些特征能由矩形特征简单的描述，如：眼睛要比脸颊颜色要深，鼻梁两侧比鼻梁颜色要深，嘴巴比周围颜色要深等。

3、opencv api

要想使用opencv，就必须先知道其能干什么，怎么作。因而API的重要性便体现出来了。就本例而言，使用到的函数不多，也就普通的读取图片，灰度转换，显示图像，简单的编辑图像罢了。

读取图片：

import cv2
image = cv2.imread(imagepath)

 灰度转换

灰度转换的做用就是：转换成灰度的图片的计算强度得以下降。

import cv2
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

画图

opencv 的强大之处的一个体现就是其能够对图片进行任意编辑，处理。 
下面的这个函数最后一个参数指定的就是画笔的大小。

import cv2
cv2.rectangle(image,(x,y),(x+w,y+w),(0,255,0),2)

显示图像

编辑完的图像要么直接的被显示出来，要么就保存到物理的存储介质。

import cv2
cv2.imshow("Image Title",image)

 获取人脸识别训练数据

看似复杂，其实就是对于人脸特征的一些描述，这样opencv在读取完数据后很据训练中的样品数据，就能够感知读取到的图片上的特征，进而对图片进行人脸识别。

import cv2
face_cascade = cv2.CascadeClassifier(r'./haarcascade_frontalface_default.xml')

咱们能够在刚才的/usr/share/opencv/haarcascades/目录下复制，也能够到opencv在GitHub上共享出来的具备普适的训练好的数据。

训练数据参考地址：

https://github.com/opencv/opencv/tree/master/data/haarcascades

4、探测人脸

说白了，就是根据训练的数据来对新图片进行识别的过程。

import cv2

# 探测图片中的人脸

faces = face_cascade.detectMultiScale(
   gray,
   scaleFactor = 1.15,
   minNeighbors = 5,
   minSize = (5,5),
   # flags = cv2.cv.CV_HAAR_SCALE_IMAGE
)

咱们能够随意的指定里面参数的值，来达到不一样精度下的识别。返回值就是opencv对图片的探测结果的体现。

处理人脸探测的结果

结束了刚才的人脸探测，咱们就能够拿到返回值来作进一步的处理了。但这也不是说会多么的复杂，无非添加点特征值罢了。

import cv2

print ("发现{0}我的脸!".format(len(faces)))

for(x,y,w,h) in faces:
   cv2.rectangle(image,(x,y),(x+w,y+w),(0,255,0),2)

5、实例

# coding:utf-8

import sys
reload(sys)
sys.setdefaultencoding('utf8')

"""
opencv实现人脸识别
参考：
一、https://github.com/opencv/opencv/tree/master/data/haarcascades
二、http://www.cnblogs.com/hanson1/p/7105265.html

"""

import cv2

# 待检测的图片路径
imagepath="nba.jpg"

image = cv2.imread(imagepath)
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)


'''
# 获取人脸识别训练数据

对于人脸特征的一些描述，opencv在读取完数据后很据训练中的样品数据，
就能够感知读取到的图片上的特征，进而对图片进行人脸识别。
xml数据下载，
参考：https://github.com/opencv/opencv/tree/master/data/haarcascades
'''
face_cascade = cv2.CascadeClassifier(r'./haarcascade_frontalface_default.xml')

# 探测人脸
# 根据训练的数据来对新图片进行识别的过程。
faces = face_cascade.detectMultiScale(
  gray,
  scaleFactor = 1.15,
  minNeighbors = 5,
  minSize = (5,5),
  #flags = cv2.HAAR_SCALE_IMAGE
)

# 咱们能够随意的指定里面参数的值，来达到不一样精度下的识别。返回值就是opencv对图片的探测结果的体现。

# 处理人脸探测的结果
print ("发现{0}我的脸!".format(len(faces)))
for(x,y,w,h) in faces:
    cv2.rectangle(image,(x,y),(x+w,y+w),(0,255,0),2)
    # cv2.circle(image,((x+x+w)/2,(y+y+h)/2),w/2,(0,255,0),2)

cv2.imshow("image",image)
cv2.waitKey(0)
cv2.destroyAllWindows()

nba.jpg

运行：

/home/mymotif/PycharmProjects/py2/venv/bin/python /home/mymotif/PycharmProjects/py2/OpencvDemo/FaceRecognition3.py
发现5我的脸!

opencv2.4自带的训练数据集不能识别歪头的人脸：

上图只能识别到3我的脸、高圆圆的脸不能被识别。

6、识别眼睛

代码：

import cv2

face_cascade = cv2.CascadeClassifier("./haarcascade_frontalface_default.xml")
eye_cascade = cv2.CascadeClassifier('./haarcascade_eye.xml')

img = cv2.imread("gyy.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
    cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
    roi_gray = gray[y:y + h, x:x + w]
    roi_color = img[y:y + h, x:x + w]
    eyes = eye_cascade.detectMultiScale(roi_gray)
    for (ex, ey, ew, eh) in eyes:
        cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)

cv2.imwrite('./mygyy.jpg',img,)
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

haarcascade_eye.xml也是到/usr/share/opencv/haarcascades/haarcascade_eye.xml拷贝

图片gyy.jpg

识别后的输出图像：

7、使用dlib库：

安装：

pip install dlib -i http://mirrors.aliyun.com/pypi/simple --trusted-host mirrors.aliyun.com
pip install scikit-image -i http://mirrors.aliyun.com/pypi/simple --trusted-host mirrors.aliyun.com

得到训练模型数据：

wget http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2
wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2

解压后获得：dlib_face_recognition_resnet_model_v1.dat  shape_predictor_68_face_landmarks.dat

图片放在当前目录的faces字目录下:（这里仍是选择刚才的两个高圆圆的图片）

代码：

# -*- coding: utf-8 -*-
import sys
import dlib
import cv2
import os
import glob

current_path = os.getcwd()  # 获取当前路径
predictor_path = current_path + "/shape_predictor_68_face_landmarks.dat"
face_rec_model_path = current_path + "/dlib_face_recognition_resnet_model_v1.dat"
faces_folder_path = current_path + "/faces/"

detector = dlib.get_frontal_face_detector()
shape_predictor = dlib.shape_predictor(predictor_path)
face_rec_model = dlib.face_recognition_model_v1(face_rec_model_path)

for img_path in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
    print("Processing file: {}".format(img_path))
    # opencv 读取图片，并显示
    img = cv2.imread(img_path, 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])

    dets = detector(img, 1)
    print("Number of faces detected: {}".format(len(dets)))

    for index, face in enumerate(dets):
        print('face {}; left {}; top {}; right {}; bottom {}'.format(index, face.left(), face.top(), face.right(),
                                                                     face.bottom()))

        shape = shape_predictor(img2, face)
        for i, pt in enumerate(shape.parts()):
            # print('Part {}: {}'.format(i, pt))
            pt_pos = (pt.x, pt.y)
            cv2.circle(img, pt_pos, 2, (255, 0, 0), 1)
        # print(type(pt))
        # print("Part 0: {}, Part 1: {} ...".format(shape.part(0), shape.part(1)))
        cv2.namedWindow(img_path + str(index), cv2.WINDOW_AUTOSIZE)
        cv2.imshow(img_path + str(index), img)

        face_descriptor = face_rec_model.compute_face_descriptor(img2, shape)
        print(face_descriptor)

k = cv2.waitKey(0)
cv2.destroyAllWindows()

运行：

/home/mymotif/PycharmProjects/py3/venv/bin/python /home/mymotif/PycharmProjects/py3/OpencvDemo/my_face_recogniton.py
Processing file: /home/mymotif/PycharmProjects/py3/OpencvDemo/faces/gyy1.jpg
Number of faces detected: 1
face 0; left 882; top 263; right 1067; bottom 449
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Processing file: /home/mymotif/PycharmProjects/py3/OpencvDemo/faces/test4.jpg
Number of faces detected: 3
face 0; left 395; top 229; right 469; bottom 304
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face 1; left 308; top 191; right 370; bottom 253
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歪头的仍是没有被识别。

dlib另外一更清晰的列子（把识别后图片存到res_imgs子目录）

# _*_ coding: utf-8 _*_


import cv2
import numpy as np
import dlib


def rect_to_bb(rect):
    """
    :param rect: dlib 脸部区域检测输出
    :return: 返回一个矩形坐标
    """
    x = rect.left()
    y = rect.top()
    w = rect.right() - x
    h = rect.bottom() - y
    return x, y, w, h


def shape_to_np(shape, dtype="int"):
    """

    :param shape: dlib脸部特征检测的输出
    :param dtype:
    :return:
    """
    coords = np.zeros((68, 2), dtype=dtype)
    for i in range(0, 68):
            coords[i] = (shape.part(i).x, shape.part(i).y)

    return coords


def resize(image, width=1200):
    """

    :param image: 要检测的图片
    :param width:
    :return:
    """
    r = width * 1.0 / image.shape[1]
    dim = (width, int(image.shape[0] * r))
    resized = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
    return resized


def detect(image_file):
    """

    :param image_file: image_file_path
    :return:
    """
    count = 0
    image = cv2.imread('./faces/'+image_file)
    # image = resize(image, width=1200)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    detector = dlib.get_frontal_face_detector()
    rects = detector(gray, 1)

    predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

    for (i, rect) in enumerate(rects):
        count += 1
        shape = predictor(gray, rect)

        shape = shape_to_np(shape)

        (x, y, w, h) = rect_to_bb(rect)

        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
        for (x, y) in shape:
            cv2.circle(image, (x, y), 1, (0, 0, 255), -1)
    cv2.imwrite('./res_imgs/detect_res_'+image_file, image)
    return './res_imgs/detect_res_'+image_file, count


if __name__ == '__main__':
    detect('test4.jpg')

8、使用face_recognition

安装

pip install face_recognition -i http://mirrors.aliyun.com/pypi/simple --trusted-host mirrors.aliyun.com

下载源码：

git clone https://github.com/ageitgey/face_recognition

把要识别的图片gyy.jpg复制到下面目录下

~/face_recognition/examples

将find_faces_in_picuture.py中的图片文件名，原先是biden的，改为高圆圆的图片名，修改后保存为find_faces_in_picuture1.py：

from PIL import Image
import face_recognition

# Load the jpg file into a numpy array
image = face_recognition.load_image_file("gyy.jpg")

# Find all the faces in the image using the default HOG-based model.
# This method is fairly accurate, but not as accurate as the CNN model and not GPU accelerated.
# See also: find_faces_in_picture_cnn.py
face_locations = face_recognition.face_locations(image)

print("I found {} face(s) in this photograph.".format(len(face_locations)))

for face_location in face_locations:

    # Print the location of each face in this image
    top, right, bottom, left = face_location
    print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))

    # You can access the actual face itself like this:
    face_image = image[top:bottom, left:right]
    pil_image = Image.fromarray(face_image)
    pil_image.show()

运行结果：

/home/mymotif/PycharmProjects/my3/venv/bin/python /home/mymotif/PycharmProjects/my3/face_recognition-examples/find_faces_in_picture1.py
I found 1 face(s) in this photograph.
A face is located at pixel location Top: 263, Left: 882, Bottom: 449, Right: 1067

进程完成，退出码 0

ps：

把find_faces_in_picuture1.py和图片gyy.jpg移到其它目录执行会出错：

Traceback (most recent call last):
  File "/home/mymotif/PycharmProjects/my3/OpencvDemo/find_faces_in_picture1.py", line 5, in <module>
    image = face_recognition.load_image_file("gyy.jpg")
AttributeError: module 'face_recognition' has no attribute 'load_image_file'

进程完成，退出码 1

为何必需要在~/face_recognition/examples下作，还没找到出错缘由和解决方法，求指点。（经测试大部分目录只有find_faces_in_picuture1.py和gyy.jpg两个文件能够运行，有些会产生上述错误，换个新目录试试）

face_recognition：能够作到两张人脸对比参考：

应用一个基于Python的开源人脸识别库，face_recognition

python一行代码实现人脸识别