多模態情感分析中的特徵抽取問題

from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer

# 构建词袋模型
count_vectorizer = CountVectorizer()
bow_features = count_vectorizer.fit_transform(text_data)

# 构建TF-IDF特征
tfidf_vectorizer = TfidfVectorizer()
tfidf_features = tfidf_vectorizer.fit_transform(text_data)

import cv2

# 读取图像
image = cv2.imread('image.jpg')

# 提取颜色直方图特征
hist_features = cv2.calcHist([image], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])

# 提取纹理特征
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
texture_features = cv2.texture_feature(gray_image)

# 提取形状特征
contour, _ = cv2.findContours(gray_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
shape_features = cv2.approxPolyDP(contour, 0.01*cv2.arcLength(contour, True), True)

import librosa

# 读取音频
audio, sr = librosa.load('audio.wav')

# 提取MFCC特征
mfcc_features = librosa.feature.mfcc(y=audio, sr=sr)

# 提取短时能量特征
energy_features = librosa.feature.rmse(y=audio)

# 提取音调特征
pitch_features = librosa.piptrack(y=audio, sr=sr)

import cv2

# 读取视频
cap = cv2.VideoCapture('video.mp4')

# 定义帧间差分函数
def frame_difference(frame1, frame2):
    diff = cv2.absdiff(frame1, frame2)
    gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
    _, threshold = cv2.threshold(gray, 30, 255, cv2.THRESH_BINARY)
    return threshold

# 提取帧间差分特征
frames = []
ret, frame = cap.read()
while ret:
    frames.append(frame)
    ret, frame = cap.read()

frame_diff_features = []
for i in range(len(frames)-1):
    diff = frame_difference(frames[i], frames[i+1])
    frame_diff_features.append(diff)