From f6ba1824e9fd2e40b708258cf049f4622d66cf78 Mon Sep 17 00:00:00 2001 From: linyqh Date: Tue, 3 Dec 2024 21:19:49 +0800 Subject: [PATCH] =?UTF-8?q?feat(video):=20=E6=8F=90=E5=8D=87=E5=85=B3?= =?UTF-8?q?=E9=94=AE=E5=B8=A7=E6=97=B6=E9=97=B4=E6=88=B3=E7=B2=BE=E5=BA=A6?= =?UTF-8?q?=E5=88=B0=E6=AF=AB=E7=A7=92?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 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 --- app/utils/video_processor_v2.py | 94 +++++++++++++++++------------ webui/components/script_settings.py | 2 +- 2 files changed, 57 insertions(+), 39 deletions(-) diff --git a/app/utils/video_processor_v2.py b/app/utils/video_processor_v2.py index 03bbb84..825306b 100644 --- a/app/utils/video_processor_v2.py +++ b/app/utils/video_processor_v2.py @@ -51,21 +51,34 @@ class VideoProcessor: def detect_shot_boundaries(self, frames: List[np.ndarray], threshold: int = 30) -> List[int]: """ 使用帧差法检测镜头边界 - + Args: frames: 视频帧列表 - threshold: 差异阈值 - + threshold: 差异阈值，默认值调低为30 + Returns: List[int]: 镜头边界帧的索引列表 """ shot_boundaries = [] + if len(frames) < 2: # 添加帧数检查 + logger.warning("视频帧数过少，无法检测场景边界") + return [len(frames) - 1] # 返回最后一帧作为边界 + for i in range(1, len(frames)): prev_frame = cv2.cvtColor(frames[i - 1], cv2.COLOR_BGR2GRAY) curr_frame = cv2.cvtColor(frames[i], cv2.COLOR_BGR2GRAY) - diff = np.mean(np.abs(curr_frame.astype(int) - prev_frame.astype(int))) + + # 计算帧差 + diff = np.mean(np.abs(curr_frame.astype(float) - prev_frame.astype(float))) + if diff > threshold: shot_boundaries.append(i) + + # 如果没有检测到任何边界，至少返回最后一帧 + if not shot_boundaries: + logger.warning("未检测到场景边界，将视频作为单个场景处理") + shot_boundaries.append(len(frames) - 1) + return shot_boundaries def extract_keyframes(self, frames: List[np.ndarray], shot_boundaries: List[int]) -> Tuple[ @@ -113,12 +126,7 @@ class VideoProcessor: output_dir: str, desc: str = "保存关键帧") -> None: """ 保存关键帧到指定目录，文件名格式为：keyframe_帧序号_时间戳.jpg - - Args: - keyframes: 关键帧列表 - keyframe_indices: 关键帧索引列表 - output_dir: 输出目录 - desc: 进度条描述 + 时间戳精确到毫秒，格式为：HHMMSSmmm """ if not os.path.exists(output_dir): os.makedirs(output_dir) @@ -126,11 +134,13 @@ class VideoProcessor: for keyframe, frame_idx in tqdm(zip(keyframes, keyframe_indices), total=len(keyframes), desc=desc): + # 计算精确到毫秒的时间戳 timestamp = frame_idx / self.fps hours = int(timestamp // 3600) minutes = int((timestamp % 3600) // 60) seconds = int(timestamp % 60) - time_str = f"{hours:02d}{minutes:02d}{seconds:02d}" + milliseconds = int((timestamp % 1) * 1000) # 计算毫秒部分 + time_str = f"{hours:02d}{minutes:02d}{seconds:02d}{milliseconds:03d}" output_path = os.path.join(output_dir, f'keyframe_{frame_idx:06d}_{time_str}.jpg') @@ -138,11 +148,7 @@ class VideoProcessor: def extract_frames_by_numbers(self, frame_numbers: List[int], output_folder: str) -> None: """ - 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 - - Args: - frame_numbers: 要提取的帧号列表 - output_folder: 输出文件夹路径 + 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 """ if not frame_numbers: raise ValueError("未提供帧号列表") @@ -153,29 +159,31 @@ class VideoProcessor: if not os.path.exists(output_folder): os.makedirs(output_folder) - # 用于记录已处理的时间戳（秒） - processed_seconds = set() + # 用于记录已处理的时间戳（毫秒） + processed_timestamps = set() for frame_number in tqdm(frame_numbers, desc="提取高清帧"): - # 计算时间戳（秒） - timestamp_seconds = int(frame_number / self.fps) + # 计算精确到毫秒的时间戳 + timestamp = frame_number / self.fps + timestamp_ms = int(timestamp * 1000) # 转换为毫秒 - # 如果这一秒已经处理过，跳过 - if timestamp_seconds in processed_seconds: + # 如果这一毫秒已经处理过，跳过 + if timestamp_ms in processed_timestamps: continue self.cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number) ret, frame = self.cap.read() if ret: - # 记录这一秒已经处理 - processed_seconds.add(timestamp_seconds) + # 记录这一毫秒已经处理 + processed_timestamps.add(timestamp_ms) # 计算时间戳字符串 - hours = int(timestamp_seconds // 3600) - minutes = int((timestamp_seconds % 3600) // 60) - seconds = int(timestamp_seconds % 60) - time_str = f"{hours:02d}{minutes:02d}{seconds:02d}" + hours = int(timestamp // 3600) + minutes = int((timestamp % 3600) // 60) + seconds = int(timestamp % 60) + milliseconds = int((timestamp % 1) * 1000) # 计算毫秒部分 + time_str = f"{hours:02d}{minutes:02d}{seconds:02d}{milliseconds:03d}" output_path = os.path.join(output_folder, f"keyframe_{frame_number:06d}_{time_str}.jpg") @@ -183,27 +191,34 @@ class VideoProcessor: else: logger.info(f"无法读取帧 {frame_number}") - logger.info(f"共提取了 {len(processed_seconds)} 个不同时间戳的帧") + logger.info(f"共提取了 {len(processed_timestamps)} 个不同时间戳的帧") @staticmethod def extract_numbers_from_folder(folder_path: str) -> List[int]: """ 从文件夹中提取帧号 - + Args: folder_path: 关键帧文件夹路径 - + Returns: List[int]: 排序后的帧号列表 """ files = [f for f in os.listdir(folder_path) if f.endswith('.jpg')] - # 更新正则表达式以匹配新的文件名格式：keyframe_000123_010534.jpg - pattern = re.compile(r'keyframe_(\d+)_\d+\.jpg$') + # 更新正则表达式以匹配新的文件名格式：keyframe_000123_010534123.jpg + pattern = re.compile(r'keyframe_(\d+)_\d{9}\.jpg$') numbers = [] + for f in files: match = pattern.search(f) if match: numbers.append(int(match.group(1))) + else: + logger.warning(f"文件名格式不匹配: {f}") + + if not numbers: + logger.error(f"在目录 {folder_path} 中未找到有效的关键帧文件") + return sorted(numbers) def process_video(self, output_dir: str, skip_seconds: float = 0, threshold: int = 30) -> None: @@ -212,7 +227,7 @@ class VideoProcessor: Args: output_dir: 输出目录 - skip_seconds: 跳过视���开头的秒数 + skip_seconds: 跳过视频开头的秒数 """ skip_frames = int(skip_seconds * self.fps) @@ -240,11 +255,14 @@ class VideoProcessor: def process_video_pipeline(self, output_dir: str, skip_seconds: float = 0, - threshold: int = 30, + threshold: int = 20, # 降低默认阈值 compressed_width: int = 320, keep_temp: bool = False) -> None: """ - 执行完整的视频处理流程：压缩、提取关键帧、导出高清帧 + 执行完整的视频处理流程 + + Args: + threshold: 降低默认阈值为20，使场景检测更敏感 """ os.makedirs(output_dir, exist_ok=True) temp_dir = os.path.join(output_dir, 'temp') @@ -358,7 +376,7 @@ if __name__ == "__main__": import time start_time = time.time() - processor = VideoProcessor("best.mp4") - processor.process_video_pipeline(output_dir="output4") + processor = VideoProcessor("E:\\projects\\NarratoAI\\resource\\videos\\test.mp4") + processor.process_video_pipeline(output_dir="output") end_time = time.time() print(f"处理完成！总耗时: {end_time - start_time:.2f} 秒") diff --git a/webui/components/script_settings.py b/webui/components/script_settings.py index 30c23d3..62c81b0 100644 --- a/webui/components/script_settings.py +++ b/webui/components/script_settings.py @@ -438,7 +438,7 @@ def generate_script(tr, params): if 'error' in result: logger.warning(f"批次 {result['batch_index']} 处理出现警告: {result['error']}") continue - # 获取当前批次的文件列表 + # 获取当前批次的文件列表 keyframe_001136_000045.jpg 将 000045 精度提升到 毫秒 batch_files = get_batch_files(keyframe_files, result, vision_batch_size) logger.debug(f"批次 {result['batch_index']} 处理完成，共 {len(batch_files)} 张图片") logger.debug(batch_files)