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如何用Python实时监控

发表于：2024-11-23 作者：千家信息网编辑

千家信息网最后更新 2024年11月23日，这期内容当中小编将会给大家带来有关如何用Python实时监控，文章内容丰富且以专业的角度为大家分析和叙述，阅读完这篇文章希望大家可以有所收获。最近突然有个奇妙的想法，就是当我对着电脑屏幕的时候，电脑会

千家信息网最后更新 2024年11月23日如何用Python实时监控

这期内容当中小编将会给大家带来有关如何用Python实时监控，文章内容丰富且以专业的角度为大家分析和叙述，阅读完这篇文章希望大家可以有所收获。

最近突然有个奇妙的想法，就是当我对着电脑屏幕的时候，电脑会先识别屏幕上的人脸是否是本人，如果识别是本人的话需要回答电脑说的暗语，答对了才会解锁并且有三次机会。如果都没答对就会发送邮件给我，通知有人在动我的电脑并上传该人头像。

过程

环境是win10代码我使用的是python3所以在开始之前需要安装一些依赖包，请按顺序安装否者会报错

pip install cmake -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install dlib -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install face_recognition -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple

接下来是构建识别人脸以及对比人脸的代码

import face_recognition
import cv2
import numpy as np

video_capture = cv2.VideoCapture(0)
my_image = face_recognition.load_image_file("my.jpg")
my_face_encoding = face_recognition.face_encodings(my_image)[0]
known_face_encodings = [
    my_face_encoding
]
known_face_names = [
    "Admin"
]

face_names = []
face_locations = []
face_encodings = []
process_this_frame = True

while True:
    ret, frame = video_capture.read()
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
    rgb_small_frame = small_frame[:, :, ::-1]
    if process_this_frame:
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
        face_names = []
        for face_encoding in face_encodings:
            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
            name = "Unknown"
            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
            best_match_index = np.argmin(face_distances)
            if matches[best_match_index]:
                name = known_face_names[best_match_index]
            face_names.append(name)

    process_this_frame = not process_this_frame
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        top *= 4
        left *= 4
        right *= 4
        bottom *= 4
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

    cv2.imshow('Video', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

video_capture.release()
cv2.destroyAllWindows()

其中my.jpg需要你自己拍摄上传，运行可以发现在你脸上会出现Admin的框框，我去网上找了张图片类似这样子

识别功能已经完成了接下来就是语音识别和语音合成，这需要使用到百度AI来实现了，去登录百度AI的官网到控制台选择左边的语音技术，然后点击面板的创建应用按钮，来到创建应用界面

打造电脑版人脸屏幕解锁神器

创建后会得到AppID、API Key、Secret Key记下来，然后开始写语音合成的代码。安装百度AI提供的依赖包

pip install baidu-aip -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install playsound -i https://pypi.tuna.tsinghua.edu.cn/simple

然后是简单的语音播放代码，运行下面代码可以听到萌妹子的声音

import sys
from aip import AipSpeech
from playsound import playsound

APP_ID = ''
API_KEY = ''
SECRET_KEY = ''

client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)
result = client.synthesis('你好吖', 'zh', 1, {'vol': 5, 'per': 4, 'spd': 5, })

if not isinstance(result, dict):
    with open('auido.mp3', 'wb') as file:
        file.write(result)

filepath = eval(repr(sys.path[0]).replace('\\', '/')) + '//auido.mp3'
playsound(filepath)

有了上面的代码就完成了检测是否在电脑前（人脸识别）以及电脑念出暗语（语音合成）然后我们还需要回答暗号给电脑，所以还需要完成语音识别。

import wave
import pyaudio
from aip import AipSpeech

APP_ID = ''
API_KEY = ''
SECRET_KEY = ''

client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)
CHUNK = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 8000
RECORD_SECONDS = 3
WAVE_OUTPUT_FILENAME = "output.wav"

p = pyaudio.PyAudio()
stream = p.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK)

print("* recording")
frames = []
for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
    data = stream.read(CHUNK)
    frames.append(data)
print("* done recording")

stream.stop_stream()
stream.close()
p.terminate()
wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
wf.setnchannels(CHANNELS)
wf.setsampwidth(p.get_sample_size(FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(frames))def get_file_content():
    with open(WAVE_OUTPUT_FILENAME, 'rb') as fp:
        return fp.read()result = client.asr(get_file_content(), 'wav', 8000, {'dev_pid': 1537, })
print(result)

运行此代码之前需要安装pyaudio依赖包，由于在win10系统上安装会报错所以可以通过如下方式安装。到这个链接 https://www.lfd.uci.edu/~gohlke/pythonlibs/#pyaudio 去下载对应的安装包然后安装即可。

运行后我说了你好，可以看到识别出来了。那么我们的小模块功能就都做好了接下来就是如何去整合它们。可以发现在人脸识别代码中if matches[best_match_index]这句判断代码就是判断是否为电脑主人，所以我们把这个判断语句当作main函数的入口。

if matches[best_match_index]:
    # 在这里写识别到之后的功能
    name = known_face_names[best_match_index]

那么识别到后我们应该让电脑发出询问暗号，也就是语音合成代码，然我们将它封装成一个函数，顺便重构下人脸识别的代码。

import cv2
import time
import numpy as np
import face_recognition

video_capture = cv2.VideoCapture(0)
my_image = face_recognition.load_image_file("my.jpg")
my_face_encoding = face_recognition.face_encodings(my_image)[0]
known_face_encodings = [
    my_face_encoding
]
known_face_names = [
    "Admin"
]

face_names = []
face_locations = []
face_encodings = []
process_this_frame = Truedef speak(content):
    import sys
    from aip import AipSpeech
    from playsound import playsound
    APP_ID = ''
    API_KEY = ''
    SECRET_KEY = ''
    client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)
    result = client.synthesis(content, 'zh', 1, {'vol': 5, 'per': 0, 'spd': 5, })
    if not isinstance(result, dict):
        with open('auido.mp3', 'wb') as file:
            file.write(result)
    filepath = eval(repr(sys.path[0]).replace('\\', '/')) + '//auido.mp3'
    playsound(filepath)try:
    while True:
        ret, frame = video_capture.read()
        small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
        rgb_small_frame = small_frame[:, :, ::-1]
        if process_this_frame:
            face_locations = face_recognition.face_locations(rgb_small_frame)
            face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
            face_names = []
            for face_encoding in face_encodings:
                matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
                name = "Unknown"
                face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
                best_match_index = np.argmin(face_distances)
                if matches[best_match_index]:
                    speak("识别到人脸，开始询问暗号，请回答接下来我说的问题")
                    time.sleep(1)
                    speak("天王盖地虎")
                    error = 1 / 0
                    name = known_face_names[best_match_index]
                face_names.append(name)
        process_this_frame = not process_this_frame
        for (top, right, bottom, left), name in zip(face_locations, face_names):
            top *= 4
            left *= 4
            right *= 4
            bottom *= 4
            font = cv2.FONT_HERSHEY_DUPLEX
            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
            cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
            cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

        cv2.imshow('Video', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
except Exception as e:
    print(e)
finally:
    video_capture.release()
    cv2.destroyAllWindows()

这里有一点需要注意，由于playsound播放音乐的时候会一直占用这个资源，所以播放下一段音乐的时候会报错，解决方法是修改~\Python37\Lib\site-packages下的playsound.py文件，找到如下代码

在sleep函数下面添加winCommand('close', alias)这句代码，保存下就可以了。运行发现可以正常将两句话都说出来。那么说出来之后就要去监听了，我们还要打包一个函数。

def record():
    import wave
    import json
    import pyaudio
    from aip import AipSpeech

    APP_ID = ''
    API_KEY = ''
    SECRET_KEY = ''

    client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)
    CHUNK = 1024
    FORMAT = pyaudio.paInt16
    CHANNELS = 1
    RATE = 8000
    RECORD_SECONDS = 3
    WAVE_OUTPUT_FILENAME = "output.wav"

    p = pyaudio.PyAudio()
    stream = p.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK)

    print("* recording")
    frames = []
    for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
        data = stream.read(CHUNK)
        frames.append(data)
    print("* done recording")

    stream.stop_stream()
    stream.close()
    p.terminate()
    wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
    wf.setnchannels(CHANNELS)
    wf.setsampwidth(p.get_sample_size(FORMAT))
    wf.setframerate(RATE)
    wf.writeframes(b''.join(frames))

    def get_file_content():
        with open(WAVE_OUTPUT_FILENAME, 'rb') as fp:
            return fp.read()

    result = client.asr(get_file_content(), 'wav', 8000, {'dev_pid': 1537, })
    result = json.loads(str(result).replace("'", '"'))
    return result["result"][0]

将识别到人脸后的代码修改成如下

if matches[best_match_index]:
    speak("识别到人脸，开始询问暗号，请回答接下来我说的问题")
    time.sleep(1)
    speak("天王盖地虎")

    flag = False
    for times in range(0, 3):
        content = record()
        if "小鸡炖蘑菇" in content:
            speak("暗号通过")
            flag = True
            break
        else:
            speak("暗号不通过，再试一次")
    if flag:
        print("解锁")
    else:
        print("发送邮件并将坏人人脸图片上传！")
    error = 1 / 0
    name = known_face_names[best_match_index]