mirror of
https://github.com/linyqh/NarratoAI.git
synced 2025-12-12 03:02:48 +00:00
f6ba1824e9
- 将关键帧时间戳精确到毫秒，格式为 HHMMSSmmm - 优化场景检测算法，增加帧数检查和未检测到边界时的处理 -调整帧差计算方式，使用浮点数提高精度 - 修改文件名格式匹配规则，支持毫秒级时间戳
383 lines
14 KiB
Python
383 lines
14 KiB
Python
import cv2
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import numpy as np
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from sklearn.cluster import KMeans
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import os
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import re
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from typing import List, Tuple, Generator
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from loguru import logger
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import subprocess
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from tqdm import tqdm
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class VideoProcessor:
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def __init__(self, video_path: str):
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"""
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初始化视频处理器
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Args:
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video_path: 视频文件路径
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"""
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if not os.path.exists(video_path):
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raise FileNotFoundError(f"视频文件不存在: {video_path}")
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self.video_path = video_path
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self.cap = cv2.VideoCapture(video_path)
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if not self.cap.isOpened():
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raise RuntimeError(f"无法打开视频文件: {video_path}")
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self.total_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
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self.fps = int(self.cap.get(cv2.CAP_PROP_FPS))
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def __del__(self):
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"""析构函数，确保视频资源被释放"""
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if hasattr(self, 'cap'):
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self.cap.release()
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def preprocess_video(self) -> Generator[np.ndarray, None, None]:
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"""
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使用生成器方式读取视频帧
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Yields:
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np.ndarray: 视频帧
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"""
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self.cap.set(cv2.CAP_PROP_POS_FRAMES, 0) # 重置到视频开始
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while self.cap.isOpened():
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ret, frame = self.cap.read()
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if not ret:
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break
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yield frame
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def detect_shot_boundaries(self, frames: List[np.ndarray], threshold: int = 30) -> List[int]:
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"""
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使用帧差法检测镜头边界
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Args:
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frames: 视频帧列表
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threshold: 差异阈值，默认值调低为30
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Returns:
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List[int]: 镜头边界帧的索引列表
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"""
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shot_boundaries = []
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if len(frames) < 2: # 添加帧数检查
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logger.warning("视频帧数过少，无法检测场景边界")
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return [len(frames) - 1] # 返回最后一帧作为边界
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for i in range(1, len(frames)):
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prev_frame = cv2.cvtColor(frames[i - 1], cv2.COLOR_BGR2GRAY)
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curr_frame = cv2.cvtColor(frames[i], cv2.COLOR_BGR2GRAY)
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# 计算帧差
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diff = np.mean(np.abs(curr_frame.astype(float) - prev_frame.astype(float)))
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if diff > threshold:
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shot_boundaries.append(i)
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# 如果没有检测到任何边界，至少返回最后一帧
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if not shot_boundaries:
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logger.warning("未检测到场景边界，将视频作为单个场景处理")
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shot_boundaries.append(len(frames) - 1)
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return shot_boundaries
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def extract_keyframes(self, frames: List[np.ndarray], shot_boundaries: List[int]) -> Tuple[
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List[np.ndarray], List[int]]:
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"""
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从每个镜头中提取关键帧
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Args:
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frames: 视频帧列表
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shot_boundaries: 镜头边界列表
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Returns:
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Tuple[List[np.ndarray], List[int]]: 关键帧列表和对应的帧索引
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"""
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keyframes = []
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keyframe_indices = []
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for i in tqdm(range(len(shot_boundaries)), desc="提取关键帧"):
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start = shot_boundaries[i - 1] if i > 0 else 0
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end = shot_boundaries[i]
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shot_frames = frames[start:end]
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if not shot_frames:
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continue
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# 将每一帧转换为灰度图并展平为一维数组
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frame_features = np.array([cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY).flatten()
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for frame in shot_frames])
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try:
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# 尝试使用 KMeans
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kmeans = KMeans(n_clusters=1, random_state=0).fit(frame_features)
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center_idx = np.argmin(np.sum((frame_features - kmeans.cluster_centers_[0]) ** 2, axis=1))
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except Exception as e:
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logger.warning(f"KMeans 聚类失败，使用备选方案: {str(e)}")
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# 备选方案：选择镜头中间的帧作为关键帧
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center_idx = len(shot_frames) // 2
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keyframes.append(shot_frames[center_idx])
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keyframe_indices.append(start + center_idx)
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return keyframes, keyframe_indices
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def save_keyframes(self, keyframes: List[np.ndarray], keyframe_indices: List[int],
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output_dir: str, desc: str = "保存关键帧") -> None:
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"""
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保存关键帧到指定目录，文件名格式为：keyframe_帧序号_时间戳.jpg
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时间戳精确到毫秒，格式为：HHMMSSmmm
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"""
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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for keyframe, frame_idx in tqdm(zip(keyframes, keyframe_indices),
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total=len(keyframes),
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desc=desc):
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# 计算精确到毫秒的时间戳
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timestamp = frame_idx / self.fps
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hours = int(timestamp // 3600)
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minutes = int((timestamp % 3600) // 60)
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seconds = int(timestamp % 60)
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milliseconds = int((timestamp % 1) * 1000) # 计算毫秒部分
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time_str = f"{hours:02d}{minutes:02d}{seconds:02d}{milliseconds:03d}"
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output_path = os.path.join(output_dir,
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f'keyframe_{frame_idx:06d}_{time_str}.jpg')
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cv2.imwrite(output_path, keyframe)
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def extract_frames_by_numbers(self, frame_numbers: List[int], output_folder: str) -> None:
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"""
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根据指定的帧号提取帧，如果多个帧在同一毫秒内，只保留一个
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"""
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if not frame_numbers:
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raise ValueError("未提供帧号列表")
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if any(fn >= self.total_frames or fn < 0 for fn in frame_numbers):
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raise ValueError("存在无效的帧号")
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if not os.path.exists(output_folder):
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os.makedirs(output_folder)
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# 用于记录已处理的时间戳（毫秒）
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processed_timestamps = set()
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for frame_number in tqdm(frame_numbers, desc="提取高清帧"):
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# 计算精确到毫秒的时间戳
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timestamp = frame_number / self.fps
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timestamp_ms = int(timestamp * 1000) # 转换为毫秒
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# 如果这一毫秒已经处理过，跳过
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if timestamp_ms in processed_timestamps:
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continue
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self.cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
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ret, frame = self.cap.read()
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if ret:
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# 记录这一毫秒已经处理
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processed_timestamps.add(timestamp_ms)
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# 计算时间戳字符串
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hours = int(timestamp // 3600)
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minutes = int((timestamp % 3600) // 60)
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seconds = int(timestamp % 60)
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milliseconds = int((timestamp % 1) * 1000) # 计算毫秒部分
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time_str = f"{hours:02d}{minutes:02d}{seconds:02d}{milliseconds:03d}"
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output_path = os.path.join(output_folder,
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f"keyframe_{frame_number:06d}_{time_str}.jpg")
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cv2.imwrite(output_path, frame)
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else:
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logger.info(f"无法读取帧 {frame_number}")
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logger.info(f"共提取了 {len(processed_timestamps)} 个不同时间戳的帧")
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@staticmethod
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def extract_numbers_from_folder(folder_path: str) -> List[int]:
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"""
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从文件夹中提取帧号
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Args:
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folder_path: 关键帧文件夹路径
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Returns:
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List[int]: 排序后的帧号列表
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"""
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files = [f for f in os.listdir(folder_path) if f.endswith('.jpg')]
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# 更新正则表达式以匹配新的文件名格式：keyframe_000123_010534123.jpg
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pattern = re.compile(r'keyframe_(\d+)_\d{9}\.jpg$')
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numbers = []
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for f in files:
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match = pattern.search(f)
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if match:
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numbers.append(int(match.group(1)))
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else:
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logger.warning(f"文件名格式不匹配: {f}")
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if not numbers:
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logger.error(f"在目录 {folder_path} 中未找到有效的关键帧文件")
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return sorted(numbers)
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def process_video(self, output_dir: str, skip_seconds: float = 0, threshold: int = 30) -> None:
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"""
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处理视频并提取关键帧
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Args:
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output_dir: 输出目录
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skip_seconds: 跳过视频开头的秒数
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"""
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skip_frames = int(skip_seconds * self.fps)
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logger.info("读取视频帧...")
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frames = []
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for frame in tqdm(self.preprocess_video(),
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total=self.total_frames,
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desc="读取视频"):
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frames.append(frame)
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frames = frames[skip_frames:]
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if not frames:
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raise ValueError(f"跳过 {skip_seconds} 秒后没有剩余帧可以处理")
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logger.info("检测场景边界...")
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shot_boundaries = self.detect_shot_boundaries(frames, threshold)
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logger.info(f"检测到 {len(shot_boundaries)} 个场景边界")
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keyframes, keyframe_indices = self.extract_keyframes(frames, shot_boundaries)
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adjusted_indices = [idx + skip_frames for idx in keyframe_indices]
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self.save_keyframes(keyframes, adjusted_indices, output_dir, desc="保存压缩关键帧")
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def process_video_pipeline(self,
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output_dir: str,
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skip_seconds: float = 0,
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threshold: int = 20, # 降低默认阈值
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compressed_width: int = 320,
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keep_temp: bool = False) -> None:
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"""
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执行完整的视频处理流程
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Args:
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threshold: 降低默认阈值为20，使场景检测更敏感
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"""
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os.makedirs(output_dir, exist_ok=True)
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temp_dir = os.path.join(output_dir, 'temp')
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compressed_dir = os.path.join(temp_dir, 'compressed')
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mini_frames_dir = os.path.join(temp_dir, 'mini_frames')
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hd_frames_dir = output_dir
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os.makedirs(temp_dir, exist_ok=True)
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os.makedirs(compressed_dir, exist_ok=True)
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os.makedirs(mini_frames_dir, exist_ok=True)
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os.makedirs(hd_frames_dir, exist_ok=True)
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mini_processor = None
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compressed_video = None
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try:
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# 1. 压缩视频
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video_name = os.path.splitext(os.path.basename(self.video_path))[0]
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compressed_video = os.path.join(compressed_dir, f"{video_name}_compressed.mp4")
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# 获取原始视频的宽度和高度
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original_width = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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original_height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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logger.info("步骤1: 压缩视频...")
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if original_width > original_height:
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# 横版视频
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scale_filter = f'scale={compressed_width}:-1'
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else:
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# 竖版视频
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scale_filter = f'scale=-1:{compressed_width}'
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ffmpeg_cmd = [
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'ffmpeg', '-i', self.video_path,
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'-vf', scale_filter,
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'-y',
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compressed_video
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]
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try:
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subprocess.run(ffmpeg_cmd, check=True, capture_output=True, text=True)
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except subprocess.CalledProcessError as e:
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logger.error(f"FFmpeg 错误输出: {e.stderr}")
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raise
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# 2. 从压缩视频中提取关键帧
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logger.info("\n步骤2: 从压缩视频提取关键帧...")
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mini_processor = VideoProcessor(compressed_video)
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mini_processor.process_video(mini_frames_dir, skip_seconds, threshold)
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# 3. 从原始视频提取高清关键帧
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logger.info("\n步骤3: 提取高清关键帧...")
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frame_numbers = self.extract_numbers_from_folder(mini_frames_dir)
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if not frame_numbers:
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raise ValueError("未能从压缩视频中提取到有效的关键帧")
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self.extract_frames_by_numbers(frame_numbers, hd_frames_dir)
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logger.info(f"处理完成！高清关键帧保存在: {hd_frames_dir}")
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except Exception as e:
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import traceback
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logger.error(f"视频处理失败: \n{traceback.format_exc()}")
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raise
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finally:
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# 释放资源
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if mini_processor:
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mini_processor.cap.release()
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del mini_processor
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# 确保视频文件句柄被释放
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if hasattr(self, 'cap'):
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self.cap.release()
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# 等待资源释放
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import time
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time.sleep(0.5)
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if not keep_temp:
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try:
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# 先删除压缩视频文件
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if compressed_video and os.path.exists(compressed_video):
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try:
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os.remove(compressed_video)
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except Exception as e:
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logger.warning(f"删除压缩视频失败: {e}")
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# 再删除临时目录
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import shutil
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if os.path.exists(temp_dir):
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max_retries = 3
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for i in range(max_retries):
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try:
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shutil.rmtree(temp_dir)
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break
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except Exception as e:
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if i == max_retries - 1:
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logger.warning(f"清理临时文件失败: {e}")
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else:
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time.sleep(1) # 等待1秒后重试
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continue
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logger.info("临时文件已清理")
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except Exception as e:
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logger.warning(f"清理临时文件时出错: {e}")
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if __name__ == "__main__":
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import time
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start_time = time.time()
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processor = VideoProcessor("E:\\projects\\NarratoAI\\resource\\videos\\test.mp4")
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processor.process_video_pipeline(output_dir="output")
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end_time = time.time()
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print(f"处理完成！总耗时: {end_time - start_time:.2f} 秒")
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