关于#python#的问题：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

Question

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
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Answer 1

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！