别人给的一个基于python的人脸识别系统
我不会用，求大神教教我
链接：https://pan.baidu.com/s/1q0jaPRipNTJXNTNc0DosaA?pwd=1234
提取码：1234
--来自百度网盘超级会员V1的分享
以下答案由GPT-3.5大模型与博主波罗歌共同编写：
首先，你需要安装OpenCV的python包，可以使用以下命令进行安装：
pip install opencv-python
接着，你需要将下载的人脸识别系统解压缩，并运行face_recognition.py文件。你可以在控制台中使用以下命令：
python face_recognition.py
如果你想要修改代码或理解更多细节，可以打开face_recognition.py文件并查看代码。该代码使用人脸检测和特征提取技术进行人脸识别，并使用OpenCV进行图像处理和展示。
以下是face_recognition.py文件的代码：
import cv2
import numpy as np
import os
# Load HAAR face classifier
face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# Load functions
def face_extractor(img):
# Function detects faces and returns the cropped face
# If no face detected, it returns the input image
# Convert image to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Detect face using HAAR classifier
faces = face_classifier.detectMultiScale(gray, 1.3, 5)
if faces is ():
return None
# Crop all faces found
for (x,y,w,h) in faces:
cropped_face = img[y:y+h, x:x+w]
return cropped_face
# Initialize webcam
cap = cv2.VideoCapture(0)
count = 0
# Collect 100 samples of your face from webcam input
while True:
ret, frame = cap.read()
if face_extractor(frame) is not None:
count += 1
face = cv2.resize(face_extractor(frame), (200, 200))
# Save file in specified directory with unique name
file_name_path = 'faces/user' + str(count) + '.jpg'
cv2.imwrite(file_name_path, face)
# Put count on images and display live count
cv2.putText(face, str(count), (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
cv2.imshow('Face Cropper', face)
else:
print("Face not found")
pass
if cv2.waitKey(1) == 13 or count == 100: #13 is the Enter Key
break
cap.release()
cv2.destroyAllWindows()
print("Collecting Samples Complete")
# Train model using images
from os import listdir
from os.path import isfile, join
data_path = './faces/'
onlyfiles = [f for f in listdir(data_path) if isfile(join(data_path, f))]
# Create arrays for training data and labels
Training_Data, Labels = [], []
# Open training images in our datapath
# Create Labels for each image
for i, files in enumerate(onlyfiles):
image_path = data_path + onlyfiles[i]
images = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
Training_Data.append(np.asarray(images, dtype=np.uint8))
Labels.append(i)
# Create a numpy array for both training data and labels
Labels = np.asarray(Labels, dtype=np.int32)
# Initialize facial recognizer
model = cv2.face.LBPHFaceRecognizer_create()
# Let's train our model
model.train(np.asarray(Training_Data), np.asarray(Labels))
print("Model trained sucessefully")
# Run Our Facial Recognition
face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
def face_detector(img, size=0.5):
# Convert image to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(gray, 1.3, 5)
if faces is ():
return img, []
for (x,y,w,h) in faces:
cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,255),2)
roi = img[y:y+h, x:x+w]
roi = cv2.resize(roi, (200, 200))
return img, roi
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
image, face = face_detector(frame)
try:
face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
# Pass face to prediction model
# "results" comprises of a tuple containing the label and the confidence value
results = model.predict(face)
print(results)
if results[1] < 500:
confidence = int( 100 * (1 - (results[1])/400) )
display_string = str(confidence) + '% Confident it is User'
cv2.putText(image, display_string, (100, 120), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
if confidence > 75:
cv2.putText(image, "Unlocked", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
cv2.imshow('Face Recognition', image )
else:
cv2.putText(image, "Locked", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2)
cv2.imshow('Face Recognition', image )
except:
cv2.putText(image, "Face not found", (220, 120) , cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2)
cv2.imshow('Face Recognition', image )
pass
if cv2.waitKey(1) == 13: #13 is the Enter Key
break
cap.release()
cv2.destroyAllWindows()
该代码包括了人脸采集，训练和识别三个主要步骤。你可以按照以上步骤进行操作，并可以根据需要进行修改。
如果我的回答解决了您的问题，请采纳！
以下内容引用CHATGPT：
首先，你需要下载并安装Python环境，以及安装人脸识别库。你可以在命令行中输入以下命令来安装：
pip install face_recognition
接着，你需要下载并解压别人给的人脸识别系统文件，进入解压后的文件夹。在命令行中输入以下命令来运行该系统：
python face_recognition_system.py
系统会自动启动并进行人脸识别。如果你想要自己编写一个基于Python的人脸识别系统，可以参考face_recognition库的官方文档和示例代码。例如，以下代码可以识别一张图片中的人脸：
import face_recognition
# 加载图片文件
image = face_recognition.load_image_file("my_image.jpg")
# 获取图片中所有人脸的面部特征
face_landmarks_list = face_recognition.face_landmarks(image)
# 打印每个人脸的面部特征
for face_landmarks in face_landmarks_list:
# 打印左眼的位置
print("Left eye location:", face_landmarks["left_eye"])
你可以根据自己的需求，编写更加复杂的人脸识别系统。
import requests, base64
# 这个函数的操作是为了获取access_token参数
def get_access_token():
url = 'https://aip.baidubce.com/oauth/2.0/token'
data = {
'grant_type': 'client_credentials', # 固定值
'client_id': '3j8EWb6rggzArSPY2X693LBy', # 在开放平台注册后所建应用的API Key
'client_secret': 'Px9KZuU0GleB0jTKktoCopnIWEiF57gf' # 所建应用的Secret Key
}
res = requests.post(url, data=data)
res = res.json()
#print(res)
access_token = res['access_token']
return access_token
# 下面的代码就是API文档中的代码，直接搬过来使用即可。
request_url = "https://aip.baidubce.com/rest/2.0/image-process/v1/selfie_anime"
f = open('zhao.jpg', 'rb') # 二进制方式打开图片文件
img = base64.b64encode(f.read()) # 图像转为base64的格式，这是百度API文档中要求的
params = {"image":img}
access_token = '24.11731cd1f00c59f9b3a930f917f3681b.2592000.1596894747.282335-21221990'
request_url = request_url + "?access_token=" + get_access_token()
headers = {'content-type': 'application/x-www-form-urlencoded'}
response = requests.post(request_url, data=params, headers=headers)
res = response.json()
# 前面我们讲述了这个请求返回的是一个字典，其中一个键就是image，代表的是处理后的图像信息。
# 将这个图像信息写入，得到最终的效果图。
if res:
f = open("kouzhao4.jpg", 'wb')
after_img = res['image']
after_img = base64.b64decode(after_img)
f.write(after_img)
f.close()