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优化qwenvl_analyzer.py中的base_url配置，移除默认值设置；在utils.py中新增format_time函数以支持秒数格式化为HH:MM:SS,mmm格式；在generate_script_docu.py中重构分析结果处理逻辑，合并批次分析结果并保存为JSON格式，同时兼容旧的文本格式输出。
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linyq
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@ -30,7 +30,7 @@ class QwenAnalyzer:
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self.model_name = model_name
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self.api_key = api_key
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self.base_url = base_url or "https://dashscope.aliyuncs.com/compatible-mode/v1"
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self.base_url = base_url
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# 配置API客户端
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self._configure_client()
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@ -197,6 +197,28 @@ def time_convert_seconds_to_hmsm(seconds) -> str:
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return "{:02d}:{:02d}:{:02d},{:03d}".format(hours, minutes, seconds, milliseconds)
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def format_time(seconds: float) -> str:
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"""
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将秒数转换为格式化的时间字符串 (HH:MM:SS,mmm)
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参数:
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seconds: 需要转换的秒数，可以是整数或浮点数
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返回:
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格式化的时间字符串，格式为 HH:MM:SS,mmm
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"""
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# 计算小时、分钟、秒和毫秒
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hours = int(seconds // 3600)
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remaining_seconds = seconds % 3600
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minutes = int(remaining_seconds // 60)
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remaining_seconds = remaining_seconds % 60
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secs = int(remaining_seconds)
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milliseconds = int((remaining_seconds - secs) * 1000)
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# 格式化为时间字符串
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return "{:02d}:{:02d}:{:02d},{:03d}".format(hours, minutes, secs, milliseconds)
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def text_to_srt(idx: int, msg: str, start_time: float, end_time: float) -> str:
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start_time = time_convert_seconds_to_hmsm(start_time)
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end_time = time_convert_seconds_to_hmsm(end_time)
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@ -96,7 +96,7 @@ def generate_script_docu(params):
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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
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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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logger.debug(f"VLM 视觉大模型提供商: {vision_llm_provider}")
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@ -167,37 +167,138 @@ def generate_script_docu(params):
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# ===================处理分析结果===================
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update_progress(60, "正在整理分析结果...")
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# 合并所有批次的析结果
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# 合并所有批次的分析结果
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frame_analysis = ""
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merged_frame_observations = [] # 合并所有批次的帧观察
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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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for result in results:
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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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# 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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continue
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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)
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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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first_timestamp, last_timestamp, _ = get_batch_timestamps(batch_files, prev_batch_files)
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# 获取批次的时间戳范围
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first_timestamp, last_timestamp, timestamp_range = get_batch_timestamps(batch_files, prev_batch_files)
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logger.debug(f"处理时间戳: {first_timestamp}-{last_timestamp}")
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# 添加带时间戳的分析结果
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frame_analysis += f"\n=== {first_timestamp}-{last_timestamp} ===\n"
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frame_analysis += result['response']
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frame_analysis += "\n"
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# 解析响应中的JSON数据
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response_text = result['response']
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try:
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# 处理可能包含```json```格式的响应
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if "```json" in response_text:
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json_content = response_text.split("```json")[1].split("```")[0].strip()
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elif "```" in response_text:
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json_content = response_text.split("```")[1].split("```")[0].strip()
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else:
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json_content = response_text.strip()
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response_data = json.loads(json_content)
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# 提取frame_observations和overall_activity_summary
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if "frame_observations" in response_data:
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frame_obs = response_data["frame_observations"]
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overall_summary = response_data.get("overall_activity_summary", "")
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# 添加时间戳信息到每个帧观察
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for i, obs in enumerate(frame_obs):
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if i < len(batch_files):
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# 从文件名中提取时间戳
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file_path = batch_files[i]
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file_name = os.path.basename(file_path)
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# 提取时间戳字符串 (格式如: keyframe_000675_000027000.jpg)
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# 格式解析: keyframe_帧序号_毫秒时间戳.jpg
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timestamp_parts = file_name.split('_')
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if len(timestamp_parts) >= 3:
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timestamp_str = timestamp_parts[-1].split('.')[0]
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try:
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timestamp_seconds = int(timestamp_str) / 1000 # 转换为秒
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formatted_time = utils.format_time(timestamp_seconds) # 格式化时间戳
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except ValueError:
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logger.warning(f"无法解析时间戳: {timestamp_str}")
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timestamp_seconds = 0
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formatted_time = "00:00:00,000"
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else:
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logger.warning(f"文件名格式不符合预期: {file_name}")
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timestamp_seconds = 0
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formatted_time = "00:00:00,000"
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# 添加额外信息到帧观察
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obs["frame_path"] = file_path
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obs["timestamp"] = formatted_time
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obs["timestamp_seconds"] = timestamp_seconds
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# 使用全局递增的帧计数器替换原始的frame_number
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if "frame_number" in obs:
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obs["original_frame_number"] = obs["frame_number"] # 保留原始编号作为参考
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obs["frame_number"] = frame_counter # 赋值连续的帧编号
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frame_counter += 1 # 增加帧计数器
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# 添加到合并列表
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merged_frame_observations.append(obs)
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# 添加批次整体总结信息
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if overall_summary:
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# 从文件名中提取时间戳数值
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first_time_str = first_timestamp.split('_')[-1].split('.')[0]
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last_time_str = last_timestamp.split('_')[-1].split('.')[0]
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# 转换为毫秒并计算持续时间（秒）
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try:
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first_time_ms = int(first_time_str)
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last_time_ms = int(last_time_str)
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batch_duration = (last_time_ms - first_time_ms) / 1000
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except ValueError:
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# 使用 utils.time_to_seconds 函数处理格式化的时间戳
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first_time_seconds = utils.time_to_seconds(first_time_str.replace('_', ':').replace('-', ','))
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last_time_seconds = utils.time_to_seconds(last_time_str.replace('_', ':').replace('-', ','))
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batch_duration = last_time_seconds - first_time_seconds
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overall_activity_summaries.append({
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"batch_index": result['batch_index'],
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"time_range": f"{first_timestamp}-{last_timestamp}",
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"duration_seconds": batch_duration,
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"summary": overall_summary
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})
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except Exception as e:
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logger.error(f"解析批次 {result['batch_index']} 的响应数据失败: {str(e)}")
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# 添加原始响应作为回退
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frame_analysis += f"\n=== {first_timestamp}-{last_timestamp} ===\n"
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frame_analysis += response_text
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frame_analysis += "\n"
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# 更新上一个批次的文件
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prev_batch_files = batch_files
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# 将合并后的结果转为JSON字符串
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merged_results = {
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"frame_observations": merged_frame_observations,
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"overall_activity_summaries": overall_activity_summaries
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}
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# 保存完整的分析结果为JSON
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analysis_json_path = os.path.join(utils.task_dir(), "frame_analysis.json")
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with open(analysis_json_path, 'w', encoding='utf-8') as f:
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json.dump(merged_results, f, ensure_ascii=False, indent=2)
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# 同时保存原始文本格式的分析结果（兼容性）
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if not frame_analysis.strip() and merged_frame_observations:
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# 如果没有原始文本但有合并结果，则从合并结果生成文本
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frame_analysis = json.dumps(merged_results, ensure_ascii=False, indent=2)
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if not frame_analysis.strip():
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raise Exception("未能生成有效的帧分析结果")
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# 保存分析结果
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analysis_path = os.path.join(utils.temp_dir(), "frame_analysis.txt")
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with open(analysis_path, 'w', encoding='utf-8') as f:
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f.write(frame_analysis)
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# # 保存文本格式分析结果
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# analysis_path = os.path.join(utils.temp_dir(), "frame_analysis.txt")
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# with open(analysis_path, 'w', encoding='utf-8') as f:
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# f.write(frame_analysis)
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update_progress(70, "正在生成脚本...")
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@ -211,26 +312,41 @@ def generate_script_docu(params):
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frame_content_list = []
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prev_batch_files = None
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for i, result in enumerate(results):
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if 'error' in result:
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continue
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# 使用合并后的观察结果构建帧内容列表
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if merged_frame_observations:
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for obs in merged_frame_observations:
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frame_content = {
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"_id": obs.get("frame_number", 0), # 使用全局连续的帧编号作为ID
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"timestamp": obs.get("timestamp", ""),
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"picture": obs.get("observation", ""),
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"narration": "",
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"OST": 2,
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"timestamp_seconds": obs.get("timestamp_seconds", 0)
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}
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frame_content_list.append(frame_content)
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logger.debug(f"添加帧内容: ID={obs.get('frame_number', 0)}, 时间={obs.get('timestamp', '')}, 描述长度={len(obs.get('observation', ''))}")
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else:
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# 兼容旧的处理方式，如果没有合并后的观察结果
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for i, result in enumerate(results):
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if 'error' in result:
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continue
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batch_files = get_batch_files(keyframe_files, result, vision_batch_size)
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_, _, timestamp_range = get_batch_timestamps(batch_files, prev_batch_files)
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batch_files = get_batch_files(keyframe_files, result, vision_batch_size)
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_, _, timestamp_range = get_batch_timestamps(batch_files, prev_batch_files)
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frame_content = {
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"_id": i + 1,
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"timestamp": timestamp_range,
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"picture": result['response'],
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"narration": "",
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"OST": 2
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}
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frame_content_list.append(frame_content)
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frame_content = {
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"_id": i + 1,
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"timestamp": timestamp_range,
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"picture": result['response'],
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"narration": "",
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"OST": 2
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}
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frame_content_list.append(frame_content)
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logger.debug(f"添加帧内容: 时间范围={timestamp_range}, 分析结果长度={len(result['response'])}")
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logger.debug(f"添加帧内容: 时间范围={timestamp_range}, 分析结果长度={len(result['response'])}")
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# 更新上一个批次的文件
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prev_batch_files = batch_files
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# 更新上一个批次的文件
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prev_batch_files = batch_files
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if not frame_content_list:
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raise Exception("没有有效的帧内容可以处理")
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