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Python如何實(shí)現(xiàn)基于Dlib的人臉識(shí)別系統(tǒng)-創(chuàng)新互聯(lián)
這篇文章將為大家詳細(xì)講解有關(guān)Python如何實(shí)現(xiàn)基于Dlib的人臉識(shí)別系統(tǒng)，小編覺得挺實(shí)用的，因此分享給大家做個(gè)參考，希望大家閱讀完這篇文章后可以有所收獲。
python常用的庫(kù)：1.requesuts；2.scrapy；3.pillow；4.twisted；5.numpy；6.matplotlib；7.pygama；8.ipyhton等。
人臉識(shí)別系統(tǒng)的實(shí)現(xiàn)流程與之前是一樣的，只是這里我們借助了dlib和face_recognition這兩個(gè)庫(kù)來(lái)實(shí)現(xiàn)。face_recognition是對(duì)dlib庫(kù)的包裝，使對(duì)dlib的使用更方便。所以首先要安裝這2個(gè)庫(kù)。
pip3 install dlib pip3 install face_recognition
然后，還要安裝imutils庫(kù)
pip3 install imutils
我們看一下項(xiàng)目的目錄結(jié)構(gòu)：
. ├── 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維的人臉嵌入：
命令如下：
python3 encode_faces.py --dataset dataset --encodings encodings.pickle -d hog
記住：如果你的電腦內(nèi)存不夠大，請(qǐng)使用hog模型進(jìn)行人臉檢測(cè)，如果內(nèi)存夠大，可以使用cnn神經(jīng)網(wǎng)絡(luò)進(jìn)行人臉檢測(cè)。
看代碼：
# 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()輸出結(jié)果是每張圖片輸出一個(gè)人臉的128維的向量和對(duì)于的名字，并序列化到硬盤，供后續(xù)人臉識(shí)別使用。
識(shí)別圖像中的人臉：
這里使用KNN方法實(shí)現(xiàn)最終的人臉識(shí)別，而不是使用SVM進(jìn)行訓(xùn)練。
命令如下：
python3 recognize_faces_image.py --encodings encodings.pickle --image examples/example_01.png
看代碼：
# 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)實(shí)際效果如下：
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當(dāng)前名稱：Python如何實(shí)現(xiàn)基于Dlib的人臉識(shí)別系統(tǒng)-創(chuàng)新互聯(lián)
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