图片序列怎么提取关键帧，代码是什么

关键帧提取可以使用视频处理领域的算法，例如基于聚类、运动估计、特征点匹配等。具体实现可以使用OpenCV等图像处理库实现。以下是一个简单的基于聚类方法的Python示例代码：

import cv2
import numpy as np

def extract_key_frames(video_path, num_frames):
    cap = cv2.VideoCapture(video_path)
    if not cap.isOpened():
        print("Error opening video file")

    # 计算视频长度和帧率
    num_frames_total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    fps = cap.get(cv2.CAP_PROP_FPS)

    # 随机选择num_frames帧进行关键帧提取
    rand_frames = np.linspace(0, num_frames_total-1, num=num_frames, dtype=np.int)
    key_frames = []

    while len(key_frames) < num_frames:
        ret, frame = cap.read()
        if not ret:
            break

        # 提取每一帧的特征
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        keypoints = cv2.goodFeaturesToTrack(gray, 100, 0.01, 10)

        if keypoints is not None:
            # 根据特征点进行聚类
            kmeans = cv2.KMeans(n_clusters=1)
            kmeans.fit(keypoints)
            centroids = kmeans.cluster_centers_

            # 将聚类的质心作为关键帧
            for centroid in centroids:
                frame_idx = int(cap.get(cv2.CAP_PROP_POS_FRAMES)-1)
                time_ms = cap.get(cv2.CAP_PROP_POS_MSEC)
                key_frames.append((frame_idx, time_ms, centroid))
                if len(key_frames) >= num_frames:
                    break

    cap.release()
    return key_frames

# 从测试视频中提取5个关键帧
key_frames = extract_key_frames("test_video.mp4", 5)
for i, (frame_idx, time_ms, centroid) in enumerate(key_frames):
    print(f"Key frame {i+1}: {frame_idx}, {time_ms:.2f}, {centroid}")