python人脸识别方法_Python3 利用face_recognition实现人脸识别的方法

前言

之前实践了下face++在线人脸识别版本，这回做一下离线版本。github 上面有关于face_recognition的相关资料，本人只是做个搬运工，对其中的一些内容进行搬运，对其中一些例子进行实现。

官方描述：

face_recognition是一个强大、简单、易上手的人脸识别开源项目，并且配备了完整的开发文档和应用案例，特别是兼容树莓派系统。本项目是世界上最简洁的人脸识别库，你可以使用Python和命令行工具提取、识别、操作人脸。本项目的人脸识别是基于业内领先的C++开源库 dlib中的深度学习模型，用Labeled Faces in the Wild人脸数据集进行测试，有高达99.38%的准确率。但对小孩和亚洲人脸的识别准确率尚待提升。

（关于兼容树莓派，以后有板子了再做一下）

下面两个链接划重点

环境配置

ubuntu16.04（其他环境的安装可以参考第一个链接，官方有说明）

pycharm(可忽略,怎么舒服怎么来)

python3

opencv(我的是4.1.2，三点几的版本应该也一样）

实际上只需要安装face_recognition，当然，没有opencv的也需要安装一下opencv

pip3 install face_recognition

图片准备

由于需要做一些图片的比对，因此需要准备一些图片，本文图片取自以下链接

https://www.zhihu.com/question/314169580/answer/872770507

接下来开始操作

官方还有提供命令行的操作（这个没去做），本文不做这个，我们只要是要在python中用face_recognition，因此定位到这一块。

这个api文档地址就是上面的第二个链接。进去之后可以看到：

part1.识别图片中的人是谁

代码

# part1

# 识别图片中的人是谁

import face_recognition

known_image = face_recognition.load_image_file("lyf1.jpg")

unknown_image = face_recognition.load_image_file("lyf2.jpg")

lyf_encoding = face_recognition.face_encodings(known_image)[0]

unknown_encoding = face_recognition.face_encodings(unknown_image)[0]

results = face_recognition.compare_faces([lyf_encoding], unknown_encoding)

# A list of True/False values indicating which known_face_encodings match the face encoding to check

print(type(results))

print(results)

if results[0] == True:

print("yes")

else:

print("no")

结果

[True]

yes

part2.从图片中找到人脸

代码

# part2

# 从图片中找到人脸（定位人脸位置）

import face_recognition

import cv2

image = face_recognition.load_image_file("lyf1.jpg")

face_locations_useCNN = face_recognition.face_locations(image,model='cnn')

# model – Which face detection model to use. “hog” is less accurate but faster on CPUs.

# “cnn” is a more accurate deep-learning model which is GPU/CUDA accelerated (if available). The default is “hog”.

face_locations_noCNN=face_recognition.face_locations(image)

# A list of tuples of found face locations in css (top, right, bottom, left) order

# 因为返回值的顺序是这样子的，因此在后面的for循环里面赋值要注意按这个顺序来

print("face_location_useCNN:")

print(face_locations_useCNN)

face_num1=len(face_locations_useCNN)

print(face_num1) # The number of faces

print("face_location_noCNN:")

print(face_locations_noCNN)

face_num2=len(face_locations_noCNN)

print(face_num2) # The number of faces

# 到这里为止，可以观察两种情况的坐标和人脸数，一般来说，坐标会不一样，但是检测出来的人脸数应该是一样的

# 也就是说face_num1　＝　face_num２；　face_locations_useCNN　和　face_locations_noCNN　不一样

org = cv2.imread("lyf1.jpg")

img = cv2.imread("lyf1.jpg")

cv2.imshow("lyf1.jpg",img) # 原始图片

# Go to get the data and draw the rectangle

# use CNN

for i in range(0,face_num1):

top = face_locations_useCNN[i][0]

right = face_locations_useCNN[i][1]

bottom = face_locations_useCNN[i][2]

left = face_locations_useCNN[i][3]

start = (left, top)

end = (right, bottom)

color = (0,255,255)

thickness = 2

cv2.rectangle(img, start, end, color, thickness) # opencv 里面画矩形的函数

# Show the result

cv2.imshow("useCNN",img)

# for face_location in face_locations_noCNN:

#

# # Print the location of each face in this image

# top, right, bottom, left = face_location

# # 等价于下面的这种写法

for i in range(0,face_num2):

top = face_locations_noCNN[i][0]

right = face_locations_noCNN[i][1]

bottom = face_locations_noCNN[i][2]

left = face_locations_noCNN[i][3]

start = (left, top)

end = (right, bottom)

color = (0,255,255)

thickness = 2

cv2.rectangle(org, start, end, color, thickness)

cv2.imshow("no cnn ",org)

cv2.waitKey(0)

cv2.destroyAllWindows()

结果

face_location_useCNN:

[(223, 470, 427, 266)]

1

face_location_noCNN:

[(242, 489, 464, 266)]

1

图片效果大致是这样

part3.找到人脸并将其裁剪打印出来（使用cnn定位人脸）

代码

# part3

# 找到人脸并将其裁剪打印出来（使用cnn定位人脸）

from PIL import Image

import face_recognition

# Load the jpg file into a numpy array

image = face_recognition.load_image_file("lyf1.jpg")

face_locations = face_recognition.face_locations(image, number_of_times_to_upsample=0, model="cnn")

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

for face_location in face_locations:

top, right, bottom, left = face_location

print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))

face_image = image[top:bottom, left:right]

pil_image = Image.fromarray(face_image)

pil_image.show()

结果

I found 1 face(s) in this photograph.

A face is located at pixel location Top: 205, Left: 276, Bottom: 440, Right: 512

图片效果大致是这样

part4.识别单张图片中人脸的关键点

代码

# part4　识别单张图片中人脸的关键点

from PIL import Image, ImageDraw

import face_recognition

# Load the jpg file into a numpy array

image = face_recognition.load_image_file("lyf1.jpg")

# Find all facial features in all the faces in the image

face_landmarks_list = face_recognition.face_landmarks(image)

# print(face_landmarks_list)

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

# Create a PIL imagedraw object so we can draw on the picture

pil_image = Image.fromarray(image)

d = ImageDraw.Draw(pil_image)

for face_landmarks in face_landmarks_list:

# Print the location of each facial feature in this image

for facial_feature in face_landmarks.keys():

print("The {} in this face has the following points: {}".format(facial_feature, face_landmarks[facial_feature]))

# Let's trace out each facial feature in the image with a line!

for facial_feature in face_landmarks.keys():

d.line(face_landmarks[facial_feature], width=5)

# Show the picture

pil_image.show()

结果

I found 1 face(s) in this photograph.

The left_eyebrow in this face has the following points: [(305, 285), (321, 276), (340, 277), (360, 281), (377, 288)]

The right_eye in this face has the following points: [(422, 313), (432, 303), (446, 302), (459, 305), (449, 312), (435, 314)]

The nose_bridge in this face has the following points: [(394, 309), (394, 331), (395, 354), (396, 375)]

The right_eyebrow in this face has the following points: [(407, 287), (424, 278), (442, 273), (461, 272), (478, 279)]

The bottom_lip in this face has the following points: [(429, 409), (419, 421), (408, 428), (398, 430), (389, 429), (377, 424), (364, 412), (370, 413), (389, 414), (398, 415), (407, 413), (423, 411)]

The chin in this face has the following points: [(289, 295), (291, 323), (296, 351), (303, 378), (315, 403), (332, 428), (353, 448), (376, 464), (400, 467), (422, 461), (441, 444), (459, 425), (473, 403), (484, 377), (490, 351), (493, 323), (493, 296)]

The top_lip in this face has the following points: [(364, 412), (377, 407), (389, 403), (397, 406), (406, 402), (417, 405), (429, 409), (423, 411), (406, 412), (397, 414), (389, 413), (370, 413)]

The left_eye in this face has the following points: [(327, 308), (339, 304), (353, 306), (364, 314), (352, 317), (338, 316)]

The nose_tip in this face has the following points: [(375, 383), (386, 387), (396, 390), (407, 385), (416, 381)]

图片效果

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