当前位置： 首页 > news >正文

视频抽取关键帧算法

news 2025/8/16 22:00:07

可直接运行代码：
https://colab.research.google.com/drive/1iXgzIB8k-_ZpgCiGn-r9WgU7mvdRrKVB?usp=sharing

1. 计算帧间差分，取局部极大值（抽帧较少）

# -*- coding: utf-8 -*-
import cv2
import operator
import numpy as np
import matplotlib.pyplot as plt
import sys
from scipy.signal import argrelextrema
import osdef smooth(x, window_len=13, window='hanning'):print(len(x), window_len)s = np.r_[2 * x[0] - x[window_len:1:-1],x, 2 * x[-1] - x[-1:-window_len:-1]]if window == 'flat':  # moving averagew = np.ones(window_len, 'd')else:w = getattr(np, window)(window_len)y = np.convolve(w / w.sum(), s, mode='same')return y[window_len - 1:-window_len + 1]class Frame:def __init__(self, id, diff):self.id = idself.diff = diffdef __lt__(self, other):if self.id == other.id:return self.id < other.idreturn self.id < other.iddef __gt__(self, other):return other.__lt__(self)def __eq__(self, other):return self.id == other.id and self.id == other.iddef __ne__(self, other):return not self.__eq__(other)def rel_change(a, b):x = (b - a) / max(a, b)print(x)return xif __name__ == "__main__":print(sys.executable)# Setting fixed threshold criteriaUSE_THRESH = False# fixed threshold valueTHRESH = 0.6# Setting fixed threshold criteriaUSE_TOP_ORDER = False# Setting local maxima criteriaUSE_LOCAL_MAXIMA = True# Number of top sorted framesNUM_TOP_FRAMES = 50# 遍历当前目录下的所有MP4文件for filename in os.listdir("."):if filename.endswith(".mp4"):videopath = filename  # 当前目录下的MP4文件name = os.path.splitext(filename)[0]  # 文件名（不带扩展名）dir = f"./extract_result/{name}/"  # 保存关键帧的目录os.makedirs(dir, exist_ok=True)  # 创建目录len_window = int(50)  # 平滑窗口大小print("Target video :" + videopath)print("Frame save directory: " + dir)# load video and compute diff between framescap = cv2.VideoCapture(str(videopath))curr_frame = Noneprev_frame = Noneframe_diffs = []frames = []success, frame = cap.read()i = 0while success:luv = cv2.cvtColor(frame, cv2.COLOR_BGR2LUV)curr_frame = luvif curr_frame is not None and prev_frame is not None:# logic herediff = cv2.absdiff(curr_frame, prev_frame)diff_sum = np.sum(diff)diff_sum_mean = diff_sum / (diff.shape[0] * diff.shape[1])frame_diffs.append(diff_sum_mean)frame = Frame(i, diff_sum_mean)frames.append(frame)prev_frame = curr_framei = i + 1success, frame = cap.read()cap.release()# compute keyframekeyframe_id_set = set()if USE_TOP_ORDER:# sort the list in descending orderframes.sort(key=operator.attrgetter("diff"), reverse=True)for keyframe in frames[:NUM_TOP_FRAMES]:keyframe_id_set.add(keyframe.id)if USE_THRESH:print("Using Threshold")for i in range(1, len(frames)):if (rel_change(np.float(frames[i - 1].diff), np.float(frames[i].diff)) >= THRESH):keyframe_id_set.add(frames[i].id)if USE_LOCAL_MAXIMA:print("Using Local Maxima")diff_array = np.array(frame_diffs)sm_diff_array = smooth(diff_array, len_window)frame_indexes = np.asarray(argrelextrema(sm_diff_array, np.greater))[0]for i in frame_indexes:keyframe_id_set.add(frames[i - 1].id)# Plot the smoothed differencesplt.figure(figsize=(40, 20))plt.gca().xaxis.set_major_locator(plt.MaxNLocator(100))  # Set number of x-axis ticksplt.gca().yaxis.set_major_locator(plt.MaxNLocator(10))   # Optionally set number of y-axis ticksplt.stem(sm_diff_array)plt.savefig(dir + 'plot.png')# save all keyframes as imagecap = cv2.VideoCapture(str(videopath))curr_frame = Nonekeyframes = []success, frame = cap.read()idx = 0while success:if idx in keyframe_id_set:name = "keyframe_" + str(idx) + ".jpg"cv2.imwrite(dir + name, frame)keyframe_id_set.remove(idx)idx = idx + 1success, frame = cap.read()cap.release()print(f"关键帧已保存到：{dir}")

2.基于光流方法 （结果为保存帧信息的json文件）

import cv2
import json
import os
import numpy as npdef getInfo(sourcePath):cap = cv2.VideoCapture(sourcePath)info = {"framecount": int(cap.get(cv2.CAP_PROP_FRAME_COUNT)),"fps": cap.get(cv2.CAP_PROP_FPS),"width": int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),"height": int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),"codec": int(cap.get(cv2.CAP_PROP_FOURCC))}cap.release()return infodef scale(img, xScale, yScale):return cv2.resize(img, None, fx=xScale, fy=yScale, interpolation=cv2.INTER_AREA)def resize(img, width, height):return cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)def extract_cols(image, numCols):Z = image.reshape((-1, 3)).astype(np.float32)criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 20, 1.0)_, labels, centers = cv2.kmeans(Z, numCols, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)clusterCounts = [int(np.sum(labels == i)) for i in range(numCols)]rgbCenters = [center.tolist()[::-1] for center in centers]return [{"count": count, "col": col} for count, col in zip(clusterCounts, rgbCenters)]def calculateFrameStats(sourcePath, after_frame=0):cap = cv2.VideoCapture(sourcePath)data = {"frame_info": []}lastFrame = Nonewhile cap.isOpened():ret, frame = cap.read()if frame is None:breakframe_number = int(cap.get(cv2.CAP_PROP_POS_FRAMES) - 1)gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)gray = scale(gray, 0.25, 0.25)gray = cv2.GaussianBlur(gray, (9, 9), 0.0)if frame_number >= after_frame and lastFrame is not None:diff = cv2.absdiff(gray, lastFrame)diffMag = int(cv2.countNonZero(diff))  # 转为 Python intdata["frame_info"].append({"frame_number": frame_number, "diff_count": diffMag})lastFrame = graycap.release()diff_counts = [fi["diff_count"] for fi in data["frame_info"]]if diff_counts:data["stats"] = {"num": int(len(diff_counts)),"min": int(np.min(diff_counts)),"max": int(np.max(diff_counts)),"mean": float(np.mean(diff_counts)),"median": float(np.median(diff_counts)),"sd": float(np.std(diff_counts))}return datadef detectScenes(sourcePath, destPath, data):diff_threshold = data["stats"]["sd"] * 2.05 + data["stats"]["mean"]cap = cv2.VideoCapture(sourcePath)os.makedirs(destPath, exist_ok=True)for index, fi in enumerate(data["frame_info"]):if fi["diff_count"] < diff_threshold:continue# 将视频定位到关键帧并读取该帧cap.set(cv2.CAP_PROP_POS_FRAMES, fi["frame_number"])ret, frame = cap.read()if not ret:continue# 保存关键帧图像到目标文件夹frame_filename = os.path.join(destPath, f"key_frame_{fi['frame_number']}.jpg")cv2.imwrite(frame_filename, frame)cap.release()return data# 遍历当前目录下的所有MP4文件
for filename in os.listdir("."):if filename.endswith(".mp4"):source = filename  # 当前目录下的MP4文件dest = os.path.splitext(filename)[0]  # 以视频文件名创建目标文件夹name = os.path.splitext(filename)[0]  # 文件名（不带扩展名）after_frame = 0  # 起始帧print(f"处理视频: {source}")info = getInfo(source)print("视频信息: ", info)# 计算帧差数据并检测场景变换data = calculateFrameStats(source, after_frame)data = detectScenes(source, dest, data)# 保存元数据data_fp = os.path.join(dest, f"{name}-meta.json")with open(data_fp, 'w') as f:json.dump(data, f, indent=4)keyframe_info_fp = os.path.join(dest, f"{name}-keyframe-meta.json")keyframeInfo = [frame_info for frame_info in data["frame_info"] if "dominant_cols" in frame_info]with open(keyframe_info_fp, 'w') as f:json.dump(keyframeInfo, f, indent=4)print(f"关键帧数据和图片已保存到：{dest}")

3. 基于颜色直方图聚类 （抽帧较多）

import cv2
import numpy as np
import os# 遍历当前目录下的所有MP4文件
for filename in os.listdir("."):if filename.endswith(".mp4"):video_path = filename  # 当前目录下的MP4文件name = os.path.splitext(filename)[0]  # 文件名（不带扩展名）output_folder = f'key_frames/{name}'  # 保存关键帧的目录os.makedirs(output_folder, exist_ok=True)  # 创建目录print(f"处理视频: {video_path}")print(f"关键帧保存路径: {output_folder}")cap = cv2.VideoCapture(video_path)if not cap.isOpened():raise ValueError("无法打开视频文件！")# 获取视频帧数num = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))key = np.zeros(num)  # 初始化关键帧数组cluster = np.zeros(num)  # 初始化聚类数组cluster_count = np.zeros(num)  # 各聚类的帧数量count = 0  # 聚类数量threshold = 0.91  # 阈值centrodR = np.zeros((num, 256))  # 聚类质心R的直方图centrodG = np.zeros((num, 256))  # 聚类质心G的直方图centrodB = np.zeros((num, 256))  # 聚类质心B的直方图# 读取首帧，形成第一个聚类ret, frame = cap.read()if not ret:raise ValueError("无法读取第一帧！")count += 1preCountR = cv2.calcHist([frame], [0], None, [256], [0, 256]).flatten()preCountG = cv2.calcHist([frame], [1], None, [256], [0, 256]).flatten()preCountB = cv2.calcHist([frame], [2], None, [256], [0, 256]).flatten()cluster[0] = 1cluster_count[0] += 1centrodR[0] = preCountRcentrodG[0] = preCountGcentrodB[0] = preCountBvisit = 1# 遍历视频的其他帧for k in range(1, num):ret, frame = cap.read()if not ret:breaktmpCountR = cv2.calcHist([frame], [0], None, [256], [0, 256]).flatten()tmpCountG = cv2.calcHist([frame], [1], None, [256], [0, 256]).flatten()tmpCountB = cv2.calcHist([frame], [2], None, [256], [0, 256]).flatten()clusterGroupId = 1maxSimilar = 0# 计算相似度for clusterCountI in range(visit, count + 1):sR = np.sum(np.minimum(centrodR[clusterCountI - 1], tmpCountR))sG = np.sum(np.minimum(centrodG[clusterCountI - 1], tmpCountG))sB = np.sum(np.minimum(centrodB[clusterCountI - 1], tmpCountB))dR = sR / np.sum(tmpCountR)dG = sG / np.sum(tmpCountG)dB = sB / np.sum(tmpCountB)d = 0.30 * dR + 0.59 * dG + 0.11 * dBif d > maxSimilar:clusterGroupId = clusterCountImaxSimilar = d# 判断是否加入现有聚类或形成新聚类if maxSimilar > threshold:centrodR[clusterGroupId - 1] = (centrodR[clusterGroupId - 1] * cluster_count[clusterGroupId - 1] + tmpCountR) / (cluster_count[clusterGroupId - 1] + 1)centrodG[clusterGroupId - 1] = (centrodG[clusterGroupId - 1] * cluster_count[clusterGroupId - 1] + tmpCountG) / (cluster_count[clusterGroupId - 1] + 1)centrodB[clusterGroupId - 1] = (centrodB[clusterGroupId - 1] * cluster_count[clusterGroupId - 1] + tmpCountB) / (cluster_count[clusterGroupId - 1] + 1)cluster_count[clusterGroupId - 1] += 1cluster[k] = clusterGroupIdelse:count += 1visit += 1cluster_count[count - 1] += 1centrodR[count - 1] = tmpCountRcentrodG[count - 1] = tmpCountGcentrodB[count - 1] = tmpCountBcluster[k] = countcap.release()# 提取每个聚类的关键帧max_similarity = np.zeros(count)frame_indices = np.zeros(count, dtype=int)cap = cv2.VideoCapture(video_path)frame_number = 0while True:ret, frame = cap.read()if not ret:breaktmpCountR = cv2.calcHist([frame], [0], None, [256], [0, 256]).flatten()tmpCountG = cv2.calcHist([frame], [1], None, [256], [0, 256]).flatten()tmpCountB = cv2.calcHist([frame], [2], None, [256], [0, 256]).flatten()sR = np.sum(np.minimum(centrodR[int(cluster[frame_number]) - 1], tmpCountR))sG = np.sum(np.minimum(centrodG[int(cluster[frame_number]) - 1], tmpCountG))sB = np.sum(np.minimum(centrodB[int(cluster[frame_number]) - 1], tmpCountB))dR = sR / np.sum(tmpCountR)dG = sG / np.sum(tmpCountG)dB = sB / np.sum(tmpCountB)d = 0.30 * dR + 0.59 * dG + 0.11 * dBif d > max_similarity[int(cluster[frame_number]) - 1]:max_similarity[int(cluster[frame_number]) - 1] = dframe_indices[int(cluster[frame_number]) - 1] = frame_numberframe_number += 1cap.release()# 保存关键帧到文件夹cap = cv2.VideoCapture(video_path)for idx in frame_indices:cap.set(cv2.CAP_PROP_POS_FRAMES, idx)ret, img = cap.read()if ret:frame_filename = os.path.join(output_folder, f'key_frame_{int(idx)}.jpg')cv2.imwrite(frame_filename, img)cap.release()print(f"关键帧已保存到：{output_folder}")
http://www.dtcms.com/a/327404.html

相关文章：

  • MR一体机（VST）预算思路
  • Linux的pthread怎么实现的？（包括到汇编层的实现）
  • AWT 事件监听中的适配器模式：从原理到实战的完整指南
  • Photoshop软件打开WebP文件格的操作教程
  • leecode2439 最小化数组中的最大值
  • 大数据中的数据压缩原理
  • 【解决apisix问题】
  • 快速了解词向量模型
  • RIOT、RT-Thread 和 FreeRTOS 是三种主流的实时操作系统
  • SpringMVC的原理及执行流程？
  • Bugku-CTF-web-留言板1
  • Linux网络--2.2、TCP接口
  • PMBT2907A,215 Nxp安世半导体 双极性晶体管 开关电源管理芯片
  • 蚁剑--安装、使用
  • C# 基于halcon的视觉工作流-章29-边缘提取-亚像素
  • 力扣.870优势洗牌解决方法: 下标排序​编辑力扣.942增减字符串匹配最长回文子序列牛客.背包问题(最大体积)力扣.45跳跃游戏II 另一种思考
  • 数据结构——线性表（核心操作，附代码）
  • vue项目封装axios请求，支持判断当前环境及判断token是否过期等等（详细教程，可复制粘贴代码）
  • cuda排序算法--双调排序(Bitonic_Sort)
  • 【数据库】 MySQL 表的操作详解
  • 蓝桥杯手算题和杂题简易做法
  • 《Auracast广播音频技术解析及未来路线图》 —蓝牙技术联盟 市场拓展经理 吴志豪 技术与市场经理 鲁公羽
  • 基于 DiT 大模型与字体级分割的视频字幕无痕擦除方案，助力短剧出海
  • 深度学习与遥感入门（六）｜轻量化 MobileNetV2 高光谱分类
  • 4.7 GB 视频导致浏览器内存溢出（OOM）的解决方案
  • 从零部署Nacos：替代Eureka的服务注册与服务发现基础教程
  • 视频输入输出模块介绍和示例
  • Dubbo 3.x源码(33)—Dubbo Consumer接收服务调用响应
  • Python day42
  • tensorrt-llm0.20.0离线部署DeepSeek-R1-Distill-Qwen-32B