인공 지능 분야에서 Python의 강력한 사용 사례 살펴보기

from textblob import TextBlob

text = "I love this movie!"
blob = TextBlob(text)
sentiment = blob.sentiment.polarity

if sentiment > 0:
    print("Positive")
elif sentiment < 0:
    print("Negative")
else:
    print("Neutral")

from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier

# 加载手写数字数据集
digits = load_digits()

# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=42)

# 构建多层感知机分类器模型
model = MLPClassifier(hidden_layer_sizes=(64, 64), activation='relu', solver='adam', max_iter=500)

# 模型训练
model.fit(X_train, y_train)

# 模型预测
predictions = model.predict(X_test)

# 打印预测结果
print(predictions)

import cv2

# 加载人脸识别器
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# 加载图像
image = cv2.imread('image.jpg')

# 转换为灰度图像
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 检测人脸
faces = face_cascade.detectMultiScale(gray, 1.3, 5)

# 绘制人脸矩形框
for (x, y, w, h) in faces:
    cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)

# 显示结果图像
cv2.imshow('image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()