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feat(video): 提升关键帧时间戳精度到毫秒
- 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
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linyqh
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6118e63f20
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@ -51,21 +51,34 @@ class VideoProcessor:
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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
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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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diff = np.mean(np.abs(curr_frame.astype(int) - prev_frame.astype(int)))
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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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@ -113,12 +126,7 @@ class VideoProcessor:
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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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Args:
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keyframes: 关键帧列表
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keyframe_indices: 关键帧索引列表
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output_dir: 输出目录
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desc: 进度条描述
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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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@ -126,11 +134,13 @@ class VideoProcessor:
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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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time_str = f"{hours:02d}{minutes:02d}{seconds:02d}"
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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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@ -138,11 +148,7 @@ class VideoProcessor:
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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，如果多个帧在同一秒内，只保留一个
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Args:
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frame_numbers: 要提取的帧号列表
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output_folder: 输出文件夹路径
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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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@ -153,29 +159,31 @@ class VideoProcessor:
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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（秒）
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processed_seconds = set()
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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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# 计算时间戳（秒）
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timestamp_seconds = int(frame_number / self.fps)
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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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# 如果这一秒已经处理过，跳过
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if timestamp_seconds in processed_seconds:
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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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# 记录这一秒已经处理
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processed_seconds.add(timestamp_seconds)
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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_seconds // 3600)
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minutes = int((timestamp_seconds % 3600) // 60)
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seconds = int(timestamp_seconds % 60)
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time_str = f"{hours:02d}{minutes:02d}{seconds:02d}"
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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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@ -183,27 +191,34 @@ class VideoProcessor:
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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_seconds)} 个不同时间戳的帧")
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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_010534.jpg
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pattern = re.compile(r'keyframe_(\d+)_\d+\.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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@ -212,7 +227,7 @@ class VideoProcessor:
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Args:
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output_dir: 输出目录
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skip_seconds: 跳过视<EFBFBD><EFBFBD><EFBFBD>开头的秒数
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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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@ -240,11 +255,14 @@ class VideoProcessor:
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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 = 30,
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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：压缩、提取关键帧、导出高清帧
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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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@ -358,7 +376,7 @@ if __name__ == "__main__":
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import time
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start_time = time.time()
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processor = VideoProcessor("best.mp4")
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processor.process_video_pipeline(output_dir="output4")
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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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@ -438,7 +438,7 @@ def generate_script(tr, params):
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if 'error' in result:
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logger.warning(f"批次 {result['batch_index']} 处理出现警告: {result['error']}")
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continue
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# 获取当前批次的文件列表
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# 获取当前批次的文件列表 keyframe_001136_000045.jpg 将 000045 精度提升到 毫秒
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batch_files = get_batch_files(keyframe_files, result, vision_batch_size)
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logger.debug(f"批次 {result['batch_index']} 处理完成，共 {len(batch_files)} 张图片")
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logger.debug(batch_files)
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