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优化视频帧提取功能，新增超级兼容性方案以提高提取成功率，增强错误处理和用户反馈。在generate_script_docu.py中更新进度显示和错误提示，提升用户体验。
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linyqh
parent
6270224d45
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c61462d706
@ -129,7 +129,8 @@ class VideoProcessor:
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logger.info(f"开始提取 {len(extraction_times)} 个关键帧，使用 {hwaccel_type} 加速")
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with tqdm(total=len(extraction_times), desc="提取视频帧", unit="帧") as pbar:
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with tqdm(total=len(extraction_times), desc="🎬 提取视频帧", unit="帧",
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bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]") as pbar:
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for i, timestamp in enumerate(extraction_times):
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frame_number = int(timestamp * self.fps)
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frame_numbers.append(frame_number)
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@ -151,16 +152,16 @@ class VideoProcessor:
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if success:
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successful_extractions += 1
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pbar.set_postfix({
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"成功": successful_extractions,
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"失败": failed_extractions,
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"当前": f"{timestamp:.1f}s"
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"✅": successful_extractions,
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"❌": failed_extractions,
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"时间": f"{timestamp:.1f}s"
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})
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else:
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failed_extractions += 1
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pbar.set_postfix({
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"成功": successful_extractions,
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"失败": failed_extractions,
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"当前": f"{timestamp:.1f}s (失败)"
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"✅": successful_extractions,
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"❌": failed_extractions,
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"时间": f"{timestamp:.1f}s"
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})
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pbar.update(1)
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@ -203,19 +204,16 @@ class VideoProcessor:
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# 对于 NVIDIA 显卡，优先使用纯软件解码 + NVENC 编码
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if self._try_extract_with_software_decode(timestamp, output_path):
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return True
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logger.debug(f"纯软件解码方案失败，尝试其他方案")
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# 策略2: 尝试标准硬件加速
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if use_hw_accel and ffmpeg_utils.is_ffmpeg_hwaccel_available():
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hw_accel = ffmpeg_utils.get_ffmpeg_hwaccel_args()
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if self._try_extract_with_hwaccel(timestamp, output_path, hw_accel):
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return True
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logger.debug(f"硬件加速方案失败，回退到软件方案")
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# 策略3: 软件方案
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if self._try_extract_with_software(timestamp, output_path):
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return True
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logger.debug(f"软件方案失败，尝试超级兼容性方案")
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# 策略4: 超级兼容性方案（Windows 特殊处理）
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return self._try_extract_with_ultra_compatibility(timestamp, output_path)
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@ -434,37 +432,21 @@ class VideoProcessor:
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if is_windows:
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process_kwargs["encoding"] = 'utf-8'
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logger.debug(f"执行命令: {' '.join(cmd)}")
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result = subprocess.run(cmd, **process_kwargs)
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# 验证输出文件
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output_path = cmd[-1]
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if os.path.exists(output_path) and os.path.getsize(output_path) > 0:
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logger.debug(f"{description} - 成功")
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return True
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else:
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logger.debug(f"{description} - 输出文件无效: {output_path}")
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return False
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except subprocess.CalledProcessError as e:
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error_msg = e.stderr if hasattr(e, 'stderr') and e.stderr else str(e)
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# 分析错误类型，提供更好的调试信息
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if "mjpeg" in error_msg.lower() and "non full-range yuv" in error_msg.lower():
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logger.debug(f"{description} - MJPEG YUV 格式问题: {error_msg[:200]}")
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elif "codec avOption" in error_msg.lower():
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logger.debug(f"{description} - 编码器参数问题: {error_msg[:200]}")
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elif "filter" in error_msg.lower():
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logger.debug(f"{description} - 滤镜链问题: {error_msg[:200]}")
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else:
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logger.debug(f"{description} - 命令执行失败: {error_msg[:200]}")
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# 简化错误日志，仅记录关键信息
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return False
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except subprocess.TimeoutExpired:
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logger.debug(f"{description} - 命令执行超时")
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return False
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except Exception as e:
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logger.debug(f"{description} - 未知错误: {str(e)}")
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return False
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def _detect_hw_accelerator(self) -> List[str]:
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@ -510,6 +492,163 @@ class VideoProcessor:
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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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def extract_frames_by_interval_ultra_compatible(self, output_dir: str, interval_seconds: float = 5.0) -> List[int]:
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"""
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使用超级兼容性方案按指定时间间隔提取视频帧
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直接使用PNG格式提取，避免MJPEG编码问题，确保最高兼容性
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Args:
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output_dir: 输出目录
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interval_seconds: 帧提取间隔（秒）
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Returns:
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List[int]: 提取的帧号列表
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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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# 计算起始时间和帧提取点
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start_time = 0
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end_time = self.duration
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extraction_times = []
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current_time = start_time
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while current_time < end_time:
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extraction_times.append(current_time)
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current_time += interval_seconds
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if not extraction_times:
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logger.warning("未找到需要提取的帧")
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return []
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# 提取帧 - 使用美化的进度条
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frame_numbers = []
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successful_extractions = 0
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failed_extractions = 0
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logger.info(f"开始提取 {len(extraction_times)} 个关键帧，使用超级兼容性方案")
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with tqdm(total=len(extraction_times), desc="🎬 提取关键帧", unit="帧",
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bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]") as pbar:
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for i, timestamp in enumerate(extraction_times):
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frame_number = int(timestamp * self.fps)
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frame_numbers.append(frame_number)
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# 格式化时间戳字符串 (HHMMSSmmm)
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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)
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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, f"keyframe_{frame_number:06d}_{time_str}.jpg")
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# 直接使用超级兼容性方案
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success = self._extract_frame_ultra_compatible(timestamp, output_path)
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if success:
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successful_extractions += 1
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pbar.set_postfix({
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"✅": successful_extractions,
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"❌": failed_extractions,
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"时间": f"{timestamp:.1f}s"
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})
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else:
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failed_extractions += 1
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pbar.set_postfix({
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"✅": successful_extractions,
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"❌": failed_extractions,
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"时间": f"{timestamp:.1f}s"
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})
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pbar.update(1)
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# 统计结果
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total_attempts = len(extraction_times)
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success_rate = (successful_extractions / total_attempts) * 100 if total_attempts > 0 else 0
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logger.info(f"关键帧提取完成: 成功 {successful_extractions}/{total_attempts} 帧 ({success_rate:.1f}%)")
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if failed_extractions > 0:
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logger.warning(f"有 {failed_extractions} 帧提取失败")
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# 验证实际生成的文件
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actual_files = [f for f in os.listdir(output_dir) if f.endswith('.jpg')]
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logger.info(f"实际生成文件数量: {len(actual_files)} 个")
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if len(actual_files) == 0:
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logger.error("未生成任何关键帧文件")
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raise Exception("关键帧提取完全失败，请检查视频文件")
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return frame_numbers
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def _extract_frame_ultra_compatible(self, timestamp: float, output_path: str) -> bool:
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"""
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超级兼容性方案提取单帧
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Args:
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timestamp: 时间戳（秒）
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output_path: 输出文件路径
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Returns:
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bool: 是否成功提取
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"""
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# 使用 PNG 格式避免 MJPEG 问题
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png_output = output_path.replace('.jpg', '.png')
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cmd = [
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"ffmpeg",
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"-hide_banner",
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"-loglevel", "error",
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"-ss", str(timestamp),
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"-i", self.video_path,
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"-vframes", "1",
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"-f", "image2", # 明确指定图片格式
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"-y",
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png_output
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]
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try:
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# 执行FFmpeg命令
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result = subprocess.run(cmd, capture_output=True, text=True, check=True, timeout=30)
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# 验证PNG文件是否成功生成
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if os.path.exists(png_output) and os.path.getsize(png_output) > 0:
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# 转换PNG为JPG
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try:
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from PIL import Image
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with Image.open(png_output) as img:
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# 转换为 RGB 模式（去除 alpha 通道）
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if img.mode in ('RGBA', 'LA'):
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background = Image.new('RGB', img.size, (255, 255, 255))
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background.paste(img, mask=img.split()[-1] if img.mode == 'RGBA' else None)
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img = background
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img.save(output_path, 'JPEG', quality=90)
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# 删除临时 PNG 文件
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os.remove(png_output)
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return True
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except Exception as e:
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logger.warning(f"PNG 转 JPG 失败: {e}")
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# 如果转换失败，直接重命名 PNG 为 JPG
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try:
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os.rename(png_output, output_path)
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return True
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except Exception:
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return False
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else:
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return False
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except subprocess.CalledProcessError as e:
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logger.warning(f"超级兼容性方案提取帧 {timestamp:.1f}s 失败: {e}")
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return False
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except subprocess.TimeoutExpired:
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logger.warning(f"超级兼容性方案提取帧 {timestamp:.1f}s 超时")
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return False
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except Exception as e:
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logger.warning(f"超级兼容性方案提取帧 {timestamp:.1f}s 异常: {e}")
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return False
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if __name__ == "__main__":
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import time
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@ -25,9 +25,9 @@ def generate_script_docu(params):
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def update_progress(progress: float, message: str = ""):
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progress_bar.progress(progress)
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if message:
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status_text.text(f"{progress}% - {message}")
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status_text.text(f"🎬 {message}")
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else:
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status_text.text(f"进度: {progress}%")
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status_text.text(f"📊 进度: {progress}%")
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try:
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with st.spinner("正在生成脚本..."):
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@ -54,7 +54,7 @@ def generate_script_docu(params):
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if keyframe_files:
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logger.info(f"使用已缓存的关键帧: {video_keyframes_dir}")
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st.info(f"使用已缓存的关键帧，如需重新提取请删除目录: {video_keyframes_dir}")
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st.info(f"✅ 使用已缓存关键帧，共 {len(keyframe_files)} 帧")
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update_progress(20, f"使用已缓存关键帧，共 {len(keyframe_files)} 帧")
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# 如果没有缓存的关键帧，则进行提取
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@ -67,30 +67,30 @@ def generate_script_docu(params):
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processor = video_processor.VideoProcessor(params.video_origin_path)
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# 显示视频信息
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st.info(f"视频信息: {processor.width}x{processor.height}, {processor.fps:.1f}fps, {processor.duration:.1f}秒")
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st.info(f"📹 视频信息: {processor.width}x{processor.height}, {processor.fps:.1f}fps, {processor.duration:.1f}秒")
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# 处理视频并提取关键帧
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update_progress(15, "正在提取关键帧...")
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# 处理视频并提取关键帧 - 直接使用超级兼容性方案
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update_progress(15, "正在提取关键帧（使用超级兼容性方案）...")
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try:
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processor.process_video_pipeline(
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# 使用优化的关键帧提取方法
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processor.extract_frames_by_interval_ultra_compatible(
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output_dir=video_keyframes_dir,
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interval_seconds=st.session_state.get('frame_interval_input'),
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)
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except Exception as extract_error:
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# 如果硬件加速失败，尝试强制使用软件方案
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logger.warning(f"硬件加速提取失败: {extract_error}")
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st.warning("硬件加速提取失败，正在尝试软件方案...")
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logger.error(f"关键帧提取失败: {extract_error}")
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# 提供详细的错误信息和解决建议
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error_msg = str(extract_error)
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if "权限" in error_msg or "permission" in error_msg.lower():
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suggestion = "建议：检查输出目录权限，或更换输出位置"
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elif "空间" in error_msg or "space" in error_msg.lower():
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suggestion = "建议：检查磁盘空间是否足够"
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else:
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suggestion = "建议：检查视频文件是否损坏，或尝试转换为标准格式"
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# 强制使用软件编码重试
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from app.utils import ffmpeg_utils
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ffmpeg_utils.force_software_encoding()
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processor.process_video_pipeline(
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output_dir=video_keyframes_dir,
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interval_seconds=st.session_state.get('frame_interval_input'),
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use_hw_accel=False # 明确禁用硬件加速
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)
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raise Exception(f"关键帧提取失败: {error_msg}\n{suggestion}")
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# 获取所有关键文件路径
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for filename in sorted(os.listdir(video_keyframes_dir)):
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@ -101,7 +101,7 @@ def generate_script_docu(params):
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# 检查目录中是否有其他文件
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all_files = os.listdir(video_keyframes_dir)
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logger.error(f"关键帧目录内容: {all_files}")
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raise Exception("未提取到任何关键帧文件，可能是 FFmpeg 兼容性问题")
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raise Exception("未提取到任何关键帧文件，请检查视频文件格式")
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update_progress(20, f"关键帧提取完成，共 {len(keyframe_files)} 帧")
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st.success(f"✅ 成功提取 {len(keyframe_files)} 个关键帧")
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@ -115,23 +115,14 @@ def generate_script_docu(params):
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except Exception as cleanup_err:
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logger.error(f"清理失败的关键帧目录时出错: {cleanup_err}")
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# 提供更详细的错误信息和解决建议
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error_msg = str(e)
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if "滤镜链" in error_msg or "filter" in error_msg.lower():
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suggestion = "建议：这可能是硬件加速兼容性问题，请尝试在设置中禁用硬件加速"
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elif "cuda" in error_msg.lower() or "nvenc" in error_msg.lower():
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suggestion = "建议：NVIDIA 显卡驱动可能需要更新，或尝试禁用硬件加速"
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else:
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suggestion = "建议：检查视频文件是否损坏，或尝试转换为标准格式"
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raise Exception(f"关键帧提取失败: {error_msg}\n{suggestion}")
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raise Exception(f"关键帧提取失败: {str(e)}")
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"""
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2. 视觉分析(批量分析每一帧)
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"""
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vision_llm_provider = st.session_state.get('vision_llm_providers').lower()
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llm_params = dict()
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logger.debug(f"VLM 视觉大模型提供商: {vision_llm_provider}")
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logger.info(f"使用 {vision_llm_provider.upper()} 进行视觉分析")
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try:
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# ===================初始化视觉分析器===================
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@ -212,7 +203,7 @@ def generate_script_docu(params):
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overall_activity_summaries = [] # 合并所有批次的整体总结
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prev_batch_files = None
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frame_counter = 1 # 初始化帧计数器，用于给所有帧分配连续的序号
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# logger.debug(json.dumps(results, indent=4, ensure_ascii=False))
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# 确保分析目录存在
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analysis_dir = os.path.join(utils.storage_dir(), "temp", "analysis")
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os.makedirs(analysis_dir, exist_ok=True)
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@ -228,11 +219,9 @@ def generate_script_docu(params):
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# 获取当前批次的文件列表
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||||
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")
|
||||
|
||||
# 获取批次的时间戳范围
|
||||
first_timestamp, last_timestamp, timestamp_range = get_batch_timestamps(batch_files, prev_batch_files)
|
||||
logger.debug(f"处理时间戳: {first_timestamp}-{last_timestamp}")
|
||||
|
||||
# 解析响应中的JSON数据
|
||||
response_text = result['response']
|
||||
@ -377,8 +366,8 @@ def generate_script_docu(params):
|
||||
"""
|
||||
4. 生成文案
|
||||
"""
|
||||
logger.info("开始准备生成解说文案")
|
||||
update_progress(80, "正在生成文案...")
|
||||
logger.info("开始生成解说文案")
|
||||
update_progress(80, "正在生成解说文案...")
|
||||
from app.services.generate_narration_script import parse_frame_analysis_to_markdown, generate_narration
|
||||
# 从配置中获取文本生成相关配置
|
||||
text_provider = config.app.get('text_llm_provider', 'gemini').lower()
|
||||
@ -414,7 +403,7 @@ def generate_script_docu(params):
|
||||
narration_dict = narration_data['items']
|
||||
# 为 narration_dict 中每个 item 新增一个 OST: 2 的字段, 代表保留原声和配音
|
||||
narration_dict = [{**item, "OST": 2} for item in narration_dict]
|
||||
logger.debug(f"解说文案创作完成:\n{"\n".join([item['narration'] for item in narration_dict])}")
|
||||
logger.info(f"解说文案生成完成，共 {len(narration_dict)} 个片段")
|
||||
# 结果转换为JSON字符串
|
||||
script = json.dumps(narration_dict, ensure_ascii=False, indent=2)
|
||||
|
||||
@ -425,20 +414,20 @@ def generate_script_docu(params):
|
||||
if script is None:
|
||||
st.error("生成脚本失败，请检查日志")
|
||||
st.stop()
|
||||
logger.success(f"剪辑脚本生成完成")
|
||||
logger.info(f"纪录片解说脚本生成完成")
|
||||
if isinstance(script, list):
|
||||
st.session_state['video_clip_json'] = script
|
||||
elif isinstance(script, str):
|
||||
st.session_state['video_clip_json'] = json.loads(script)
|
||||
update_progress(80, "脚本生成完成")
|
||||
update_progress(100, "脚本生成完成")
|
||||
|
||||
time.sleep(0.1)
|
||||
progress_bar.progress(100)
|
||||
status_text.text("脚本生成完成！")
|
||||
st.success("视频脚本生成成功！")
|
||||
status_text.text("🎉 脚本生成完成！")
|
||||
st.success("✅ 视频脚本生成成功！")
|
||||
|
||||
except Exception as err:
|
||||
st.error(f"生成过程中发生错误: {str(err)}")
|
||||
st.error(f"❌ 生成过程中发生错误: {str(err)}")
|
||||
logger.exception(f"生成脚本时发生错误\n{traceback.format_exc()}")
|
||||
finally:
|
||||
time.sleep(2)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user