Face Recognition实现人脸识别

Face Recognition

Recognize and manipulate(操纵) faces from Python or from the command line with the world's simplest face recognition library.

Built using dlib's state-of-the-art face recognition built with deep learning. The model has an accuracy of 99.38% on theLabeled Faces in the Wild benchmark.
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。

安装

熟悉python的直接ＧitHub上搜索，安装即可

基本使用

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

把源码examples下载下来，有一个demo是从视频里面读取通过cv2,然后输出一个标注好的视频。

import face_recognition
import cv2

# This is a demo of running face recognition on a video file and saving the results to a new video file.
#
# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.

# Open the input movie file
input_movie = cv2.VideoCapture("hamilton_clip.mp4")
length = int(input_movie.get(cv2.CAP_PROP_FRAME_COUNT))

# Create an output movie file (make sure resolution/frame rate matches input video!)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
output_movie = cv2.VideoWriter('output.avi', fourcc, 29.97, (640, 360))

# Load some sample pictures and learn how to recognize them.
lmm_image = face_recognition.load_image_file("lin-manuel-miranda.png")
lmm_face_encoding = face_recognition.face_encodings(lmm_image)[0]

al_image = face_recognition.load_image_file("alex-lacamoire.png")
al_face_encoding = face_recognition.face_encodings(al_image)[0]

known_faces = [
    lmm_face_encoding,
    al_face_encoding
]

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
frame_number = 0

while True:
    # Grab a single frame of video
    ret, frame = input_movie.read()
    frame_number += 1

    # Quit when the input video file ends
    if not ret:
        break

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_frame = frame[:, :, ::-1]

    # Find all the faces and face encodings in the current frame of video
    face_locations = face_recognition.face_locations(rgb_frame)
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

    face_names = []
    for face_encoding in face_encodings:
        # See if the face is a match for the known face(s)
        match = face_recognition.compare_faces(known_faces, face_encoding, tolerance=0.50)

        # If you had more than 2 faces, you could make this logic a lot prettier
        # but I kept it simple for the demo
        name = None
        if match[0]:
            name = "Lin-Manuel Miranda"
        elif match[1]:
            name = "Alex Lacamoire"

        face_names.append(name)

    # Label the results
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        if not name:
            continue

        # Draw a box around the face
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        # Draw a label with a name below the face
        cv2.rectangle(frame, (left, bottom - 25), (right, bottom), (0, 0, 255), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.5, (255, 255, 255), 1)

    # Write the resulting image to the output video file
    print("Writing frame {} / {}".format(frame_number, length))
    output_movie.write(frame)

# All done!
input_movie.release()
cv2.destroyAllWindows()

先看官网的入门教程，再看这个。注意输出的视频格式要和输入的格式一样，建议视频帧数30。

再看一个标注图片的例子

import face_recognition 
import cv2
import signal

image = face_recognition.load_image_file("/home/urb/documents/girl.jpg")

face_locations = face_recognition.face_locations(image)

print(face_locations)
a, b, c, d = face_locations[0]

image2 = cv2.imread("/home/urb/documents/girl2.jpg")

print(type(image2))  # 读取的数据是ndarry

image1=image2*1
image1[:,:,0]=image[:,:,2]
image1[:,:,2]=image[:,:,0]


cv2.rectangle(image1, (d, a), (b, c), (255, 0, 0), 2)
cv2.imshow("image2", image1)

signal.signal(signal.SIGINT, signal.SIG_DFL)

cv2.waitKey (0)  
cv2.destroyAllWindows()  

你要准备两张带人脸的一模一样的图片。
注意opencv的图片读取的格式还要变化一下才能给face_recognition使用。
执行之后就能看到人脸有个蓝色的框框。