Python基于Dlib的人脸识别系统的实现

脚本专栏 2024/11/8 佚名

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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。

pip3 install dlib
pip3 install face_recognition

然后，还要安装imutils库

 pip3 install imutils

我们看一下项目的目录结构：

.
├── 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：

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：

# 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：

python3 recognize_faces_image.py --encodings encodings.pickle 	--image examples/example_01.png

看代码：

# 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：