之前已经介绍过人脸识别的基础概念，以及基于opencv的实现方式，今天，我们使用dlib来提取128维的人脸嵌入，并使用k临近值方法来实现人脸识别。
人脸识别系统的实现流程与之前是一样的，只是这里我们借助了dlib和face_recognition这两个库来实现。face_recognition是对dlib库的包装，使对dlib的使用更方便。所以首先要安装这2个库。
代码语言：javascript复制pip3 install dlib
pip3 install face_recognition然后，还要安装imutils库
代码语言：javascript复制 pip3 install imutils我们看一下项目的目录结构：
代码语言：javascript复制.
├── dataset
│ ├── alan_grant [22 entries exceeds filelimit, not opening dir]
│ ├── claire_dearing [53 entries exceeds filelimit, not opening dir]
│ ├── ellie_sattler [31 entries exceeds filelimit, not opening dir]
│ ├── ian_malcolm [41 entries exceeds filelimit, not opening dir]
│ ├── john_hammond [36 entries exceeds filelimit, not opening dir]
│ └── owen_grady [35 entries exceeds filelimit, not opening dir]
├── examples
│ ├── example_01.png
│ ├── example_02.png
│ └── example_03.png
├── output
│ ├── lunch_scene_output.avi
│ └── webcam_face_recognition_output.avi
├── videos
│ └── lunch_scene.mp4
├── encode_faces.py
├── encodings.pickle
├── recognize_faces_image.py
├── recognize_faces_video_file.py
├── recognize_faces_video.py
└── search_bing_api.py
10 directories, 12 files首先，提取128维的人脸嵌入：
命令如下：
代码语言：javascript复制python3 encode_faces.py --dataset dataset --encodings encodings.pickle -d hog记住：如果你的电脑内存不够大，请使用hog模型进行人脸检测，如果内存够大，可以使用cnn神经网络进行人脸检测。
看代码：
代码语言：javascript复制# USAGE
# python encode_faces.py --dataset dataset --encodings encodings.pickle
# import the necessary packages
from imutils import paths
import face_recognition
import argparse
import pickle
import cv2
import os
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--dataset", required=True,
help="path to input directory of faces images")
ap.add_argument("-e", "--encodings", required=True,
help="path to serialized db of facial encodings")
ap.add_argument("-d", "--detection-method", type=str, default="hog",
help="face detection model to use: either `hog` or `cnn`")
args = vars(ap.parse_args())
# grab the paths to the input images in our dataset
print("[INFO] quantifying faces...")
imagePaths = list(paths.list_images(args["dataset"]))
# initialize the list of known encodings and known names
knownEncodings = []
knownNames = []
# loop over the image paths
for (i, imagePath) in enumerate(imagePaths):
# extract the person name from the image path
print("[INFO] processing image {}/{}".format(i 1,
len(imagePaths)))
name = imagePath.split(os.path.sep)[-2]
# load the input image and convert it from RGB (OpenCV ordering)
# to dlib ordering (RGB)
image = cv2.imread(imagePath)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# detect the (x, y)-coordinates of the bounding boxes
# corresponding to each face in the input image
boxes = face_recognition.face_locations(rgb,
model=args["detection_method"])
# compute the facial embedding for the face
encodings = face_recognition.face_encodings(rgb, boxes)
# loop over the encodings
for encoding in encodings:
# add each encoding name to our set of known names and
# encodings
knownEncodings.append(encoding)
knownNames.append(name)
# dump the facial encodings names to disk
print("[INFO] serializing encodings...")
data = {"encodings": knownEncodings, "names": knownNames}
f = open(args["encodings"], "wb")
f.write(pickle.dumps(data))
f.close()输出结果是每张图片输出一个人脸的128维的向量和对于的名字，并序列化到硬盘，供后续人脸识别使用。
识别图像中的人脸：
这里使用KNN方法实现最终的人脸识别，而不是使用SVM进行训练。
命令如下：
代码语言：javascript复制python3 recognize_faces_image.py --encodings encodings.pickle --image examples/example_01.png看代码：
代码语言：javascript复制# USAGE
# python recognize_faces_image.py --encodings encodings.pickle --image examples/example_01.png
# import the necessary packages
import face_recognition
import argparse
import pickle
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-e", "--encodings", required=True,
help="path to serialized db of facial encodings")
ap.add_argument("-i", "--image", required=True,
help="path to input image")
ap.add_argument("-d", "--detection-method", type=str, default="cnn",
help="face detection model to use: either `hog` or `cnn`")
args = vars(ap.parse_args())
# load the known faces and embeddings
print("[INFO] loading encodings...")
data = pickle.loads(open(args["encodings"], "rb").read())
# load the input image and convert it from BGR to RGB
image = cv2.imread(args["image"])
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# detect the (x, y)-coordinates of the bounding boxes corresponding
# to each face in the input image, then compute the facial embeddings
# for each face
print("[INFO] recognizing faces...")
boxes = face_recognition.face_locations(rgb,
model=args["detection_method"])
encodings = face_recognition.face_encodings(rgb, boxes)
# initialize the list of names for each face detected
names = []
# loop over the facial embeddings
for encoding in encodings:
# attempt to match each face in the input image to our known
# encodings
matches = face_recognition.compare_faces(data["encodings"],
encoding)
name = "Unknown"
# check to see if we have found a match
if True in matches:
# find the indexes of all matched faces then initialize a
# dictionary to count the total number of times each face
# was matched
matchedIdxs = [i for (i, b) in enumerate(matches) if b]
counts = {}
# loop over the matched indexes and maintain a count for
# each recognized face face
for i in matchedIdxs:
name = data["names"][i]
counts[name] = counts.get(name, 0) 1
# determine the recognized face with the largest number of
# votes (note: in the event of an unlikely tie Python will
# select first entry in the dictionary)
name = max(counts, key=counts.get)
# update the list of names
names.append(name)
# loop over the recognized faces
for ((top, right, bottom, left), name) in zip(boxes, names):
# draw the predicted face name on the image
cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)
y = top - 15 if top - 15 15 else top 15
cv2.putText(image, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
0.75, (0, 255, 0), 2)
# show the output image
cv2.imshow("Image", image)
cv2.waitKey(0)实际效果如下：
如果要详细了解细节，请参考：https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/
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