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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 2025-05-08 11:00:47 +08:00
parent c2660c6983
commit 6c6ceeebea
3 changed files with 175 additions and 37 deletions
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2
app/utils/qwenvl_analyzer.py
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@ -30,7 +30,7 @@ class QwenAnalyzer:
self.model_name = model_name self.model_name = model_name
self.api_key = api_key self.api_key = api_key
self.base_url = base_url or "https://dashscope.aliyuncs.com/compatible-mode/v1" self.base_url = base_url
# 配置API客户端 # 配置API客户端
self._configure_client() self._configure_client()

22
app/utils/utils.py
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@ -197,6 +197,28 @@ def time_convert_seconds_to_hmsm(seconds) -> str:
return "{:02d}:{:02d}:{:02d},{:03d}".format(hours, minutes, seconds, milliseconds) return "{:02d}:{:02d}:{:02d},{:03d}".format(hours, minutes, seconds, milliseconds)
def format_time(seconds: float) -> str:
"""
将秒数转换为格式化的时间字符串 (HH:MM:SS,mmm)
参数:
seconds: 需要转换的秒数，可以是整数或浮点数
返回:
格式化的时间字符串，格式为 HH:MM:SS,mmm
"""
# 计算小时、分钟、秒和毫秒
hours = int(seconds // 3600)
remaining_seconds = seconds % 3600
minutes = int(remaining_seconds // 60)
remaining_seconds = remaining_seconds % 60
secs = int(remaining_seconds)
milliseconds = int((remaining_seconds - secs) * 1000)
# 格式化为时间字符串
return "{:02d}:{:02d}:{:02d},{:03d}".format(hours, minutes, secs, milliseconds)
def text_to_srt(idx: int, msg: str, start_time: float, end_time: float) -> str: def text_to_srt(idx: int, msg: str, start_time: float, end_time: float) -> str:
start_time = time_convert_seconds_to_hmsm(start_time) start_time = time_convert_seconds_to_hmsm(start_time)
end_time = time_convert_seconds_to_hmsm(end_time) end_time = time_convert_seconds_to_hmsm(end_time)

188
webui/tools/generate_script_docu.py
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@ -96,7 +96,7 @@ def generate_script_docu(params):
raise Exception(f"关键帧提取失败: {str(e)}") raise Exception(f"关键帧提取失败: {str(e)}")
""" """
2. 视觉分析 2. 视觉分析(批量分析每一帧)
""" """
vision_llm_provider = st.session_state.get('vision_llm_providers').lower() vision_llm_provider = st.session_state.get('vision_llm_providers').lower()
logger.debug(f"VLM 视觉大模型提供商: {vision_llm_provider}") logger.debug(f"VLM 视觉大模型提供商: {vision_llm_provider}")
@ -167,37 +167,138 @@ def generate_script_docu(params):
# ===================处理分析结果=================== # ===================处理分析结果===================
update_progress(60, "正在整理分析结果...") update_progress(60, "正在整理分析结果...")
# 合并所有批次的析结果 # 合并所有批次的分析结果
frame_analysis = "" frame_analysis = ""
merged_frame_observations = [] # 合并所有批次的帧观察
overall_activity_summaries = [] # 合并所有批次的整体总结
prev_batch_files = None prev_batch_files = None
frame_counter = 1 # 初始化帧计数器，用于给所有帧分配连续的序号
logger.debug(json.dumps(results, indent=4, ensure_ascii=False))
for result in results: for result in results:
if 'error' in result: if 'error' in result:
logger.warning(f"批次 {result['batch_index']} 处理出现警告: {result['error']}") 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 keyframe_001136_000045.jpg 将 000045 精度提升到 毫秒
# 获取当前批次的文件列表
batch_files = get_batch_files(keyframe_files, result, vision_batch_size) 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(f"批次 {result['batch_index']} 处理完成，共 {len(batch_files)} 张图片")
# logger.debug(batch_files)
# 获取批次的时间戳范围
first_timestamp, last_timestamp, _ = get_batch_timestamps(batch_files, prev_batch_files) 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}") logger.debug(f"处理时间戳: {first_timestamp}-{last_timestamp}")
# 添加带时间戳的分析结果 # 解析响应中的JSON数据
frame_analysis += f"\n=== {first_timestamp}-{last_timestamp} ===\n" response_text = result['response']
frame_analysis += result['response'] try:
frame_analysis += "\n" # 处理可能包含```json```格式的响应
if "```json" in response_text:
json_content = response_text.split("```json")[1].split("```")[0].strip()
elif "```" in response_text:
json_content = response_text.split("```")[1].split("```")[0].strip()
else:
json_content = response_text.strip()
response_data = json.loads(json_content)
# 提取frame_observations和overall_activity_summary
if "frame_observations" in response_data:
frame_obs = response_data["frame_observations"]
overall_summary = response_data.get("overall_activity_summary", "")
# 添加时间戳信息到每个帧观察
for i, obs in enumerate(frame_obs):
if i < len(batch_files):
# 从文件名中提取时间戳
file_path = batch_files[i]
file_name = os.path.basename(file_path)
# 提取时间戳字符串 (格式如: keyframe_000675_000027000.jpg)
# 格式解析: keyframe_帧序号_毫秒时间戳.jpg
timestamp_parts = file_name.split('_')
if len(timestamp_parts) >= 3:
timestamp_str = timestamp_parts[-1].split('.')[0]
try:
timestamp_seconds = int(timestamp_str) / 1000 # 转换为秒
formatted_time = utils.format_time(timestamp_seconds) # 格式化时间戳
except ValueError:
logger.warning(f"无法解析时间戳: {timestamp_str}")
timestamp_seconds = 0
formatted_time = "00:00:00,000"
else:
logger.warning(f"文件名格式不符合预期: {file_name}")
timestamp_seconds = 0
formatted_time = "00:00:00,000"
# 添加额外信息到帧观察
obs["frame_path"] = file_path
obs["timestamp"] = formatted_time
obs["timestamp_seconds"] = timestamp_seconds
# 使用全局递增的帧计数器替换原始的frame_number
if "frame_number" in obs:
obs["original_frame_number"] = obs["frame_number"] # 保留原始编号作为参考
obs["frame_number"] = frame_counter # 赋值连续的帧编号
frame_counter += 1 # 增加帧计数器
# 添加到合并列表
merged_frame_observations.append(obs)
# 添加批次整体总结信息
if overall_summary:
# 从文件名中提取时间戳数值
first_time_str = first_timestamp.split('_')[-1].split('.')[0]
last_time_str = last_timestamp.split('_')[-1].split('.')[0]
# 转换为毫秒并计算持续时间（秒）
try:
first_time_ms = int(first_time_str)
last_time_ms = int(last_time_str)
batch_duration = (last_time_ms - first_time_ms) / 1000
except ValueError:
# 使用 utils.time_to_seconds 函数处理格式化的时间戳
first_time_seconds = utils.time_to_seconds(first_time_str.replace('_', ':').replace('-', ','))
last_time_seconds = utils.time_to_seconds(last_time_str.replace('_', ':').replace('-', ','))
batch_duration = last_time_seconds - first_time_seconds
overall_activity_summaries.append({
"batch_index": result['batch_index'],
"time_range": f"{first_timestamp}-{last_timestamp}",
"duration_seconds": batch_duration,
"summary": overall_summary
})
except Exception as e:
logger.error(f"解析批次 {result['batch_index']} 的响应数据失败: {str(e)}")
# 添加原始响应作为回退
frame_analysis += f"\n=== {first_timestamp}-{last_timestamp} ===\n"
frame_analysis += response_text
frame_analysis += "\n"
# 更新上一个批次的文件 # 更新上一个批次的文件
prev_batch_files = batch_files prev_batch_files = batch_files
# 将合并后的结果转为JSON字符串
merged_results = {
"frame_observations": merged_frame_observations,
"overall_activity_summaries": overall_activity_summaries
}
# 保存完整的分析结果为JSON
analysis_json_path = os.path.join(utils.task_dir(), "frame_analysis.json")
with open(analysis_json_path, 'w', encoding='utf-8') as f:
json.dump(merged_results, f, ensure_ascii=False, indent=2)
# 同时保存原始文本格式的分析结果（兼容性）
if not frame_analysis.strip() and merged_frame_observations:
# 如果没有原始文本但有合并结果，则从合并结果生成文本
frame_analysis = json.dumps(merged_results, ensure_ascii=False, indent=2)
if not frame_analysis.strip(): if not frame_analysis.strip():
raise Exception("未能生成有效的帧分析结果") raise Exception("未能生成有效的帧分析结果")
# 保存分析结果 # # 保存文本格式分析结果
analysis_path = os.path.join(utils.temp_dir(), "frame_analysis.txt") # analysis_path = os.path.join(utils.temp_dir(), "frame_analysis.txt")
with open(analysis_path, 'w', encoding='utf-8') as f: # with open(analysis_path, 'w', encoding='utf-8') as f:
f.write(frame_analysis) # f.write(frame_analysis)
update_progress(70, "正在生成脚本...") update_progress(70, "正在生成脚本...")
@ -211,26 +312,41 @@ def generate_script_docu(params):
frame_content_list = [] frame_content_list = []
prev_batch_files = None prev_batch_files = None
for i, result in enumerate(results): # 使用合并后的观察结果构建帧内容列表
if 'error' in result: if merged_frame_observations:
continue for obs in merged_frame_observations:
frame_content = {
"_id": obs.get("frame_number", 0), # 使用全局连续的帧编号作为ID
"timestamp": obs.get("timestamp", ""),
"picture": obs.get("observation", ""),
"narration": "",
"OST": 2,
"timestamp_seconds": obs.get("timestamp_seconds", 0)
}
frame_content_list.append(frame_content)
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', ''))}")
else:
# 兼容旧的处理方式，如果没有合并后的观察结果
for i, result in enumerate(results):
if 'error' in result:
continue
batch_files = get_batch_files(keyframe_files, result, vision_batch_size) batch_files = get_batch_files(keyframe_files, result, vision_batch_size)
_, _, timestamp_range = get_batch_timestamps(batch_files, prev_batch_files) _, _, timestamp_range = get_batch_timestamps(batch_files, prev_batch_files)
frame_content = { frame_content = {
"_id": i + 1, "_id": i + 1,
"timestamp": timestamp_range, "timestamp": timestamp_range,
"picture": result['response'], "picture": result['response'],
"narration": "", "narration": "",
"OST": 2 "OST": 2
} }
frame_content_list.append(frame_content) frame_content_list.append(frame_content)
logger.debug(f"添加帧内容: 时间范围={timestamp_range}, 分析结果长度={len(result['response'])}") logger.debug(f"添加帧内容: 时间范围={timestamp_range}, 分析结果长度={len(result['response'])}")
# 更新上一个批次的文件 # 更新上一个批次的文件
prev_batch_files = batch_files prev_batch_files = batch_files
if not frame_content_list: if not frame_content_list:
raise Exception("没有有效的帧内容可以处理") raise Exception("没有有效的帧内容可以处理")