使用Jupyter实现人脸识别的图像分类代码

好的，以下是使用Jupyter实现人脸识别的图像分类代码：

首先，我们需要导入必要的库和模块：

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import cv2
import os
import random
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout
from keras.utils import to_categorical

接下来，我们需要加载人脸数据集：

DATADIR = 'path/to/dataset'
CATEGORIES = ['person_1', 'person_2', 'person_3'] # 人脸分类的类别

data = []
for category in CATEGORIES:
    path = os.path.join(DATADIR, category)
    label = CATEGORIES.index(category)
    for img in os.listdir(path):
        img_path = os.path.join(path, img)
        face_img = cv2.imread(img_path)
        face_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2GRAY) # 转换为灰度图像
        face_img = cv2.resize(face_img, (50, 50)) # 将图像大小调整为50x50像素
        data.append([face_img, label])
        
random.shuffle(data) # 打乱数据集

现在，我们需要将数据集分成训练集和测试集：

X = []
y = []

for face_img, label in data:
    X.append(face_img)
    y.append(label)

X = np.array(X).reshape(-1, 50, 50, 1) # 调整数据形状
y = to_categorical(y)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

接下来，我们需要构建卷积神经网络模型：

model = Sequential()

model.add(Conv2D(32, (3, 3), activation='relu', input_shape=X_train.shape[1:]))
model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))

model.add(Flatten())

model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))

model.add(Dense(3, activation='softmax'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

最后，我们需要训练模型并评估其性能：

history = model.fit(X_train, y_train, epochs=20, validation_data=(X_test, y_test))

y_pred = model.predict(X_test)
y_pred = np.argmax(y_pred, axis=1)
y_test = np.argmax(y_test, axis=1)

print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))

这就是使用Jupyter实现人脸识别的图像分类代码了。