import os

import cv2

import numpy as np

import mediapipe as mp

class VisualFeatureExtractor:

def __init__(self):

self.mp_face_mesh = mp.solutions.face_mesh

self.face_mesh = self.mp_face_mesh.FaceMesh(

static_image_mode=False,

max_num_faces=1,

refine_landmarks=True,

min_detection_confidence=0.5

)

def extract_video_features(self, video_path):

"""Extract visual features from a video without emotion analysis"""

try:

if not os.path.exists(video_path):

return np.zeros(300)

cap = cv2.VideoCapture(video_path)

face_landmarks_data = []

frame_count = 0

while True:

ret, frame = cap.read()

if not ret:

break

frame_count += 1

if frame_count % 5 != 0: # 每隔几帧处理一次

continue

# 提取面部关键点特征

rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

results = self.face_mesh.process(rgb_frame)

if results.multi_face_landmarks:

landmarks = results.multi_face_landmarks[0]

landmark_coords = []

for landmark in landmarks.landmark:

landmark_coords.extend([landmark.x, landmark.y])

face_landmarks_data.append(landmark_coords)

cap.release()

# 处理地标特征

if face_landmarks_data:

landmarks_array = np.array(face_landmarks_data)

# 统计特征：均值、标准差、极差

face_features = [

np.mean(landmarks_array, axis=0),

np.std(landmarks_array, axis=0),

np.max(landmarks_array, axis=0) - np.min(landmarks_array, axis=0)

]

combined_features = np.concatenate([

np.concatenate(face_features)

])[:300]

return combined_features

else:

return np.zeros(300)

except Exception as e:

print(f"Error processing video {video_path}: {e}")

return np.zeros(300)