commit db3413a8dc8688e554d787e1ea3e965d3b4e9fe3 Author: yujie_jiang <2621675592@qq.com> Date: Wed May 28 13:52:34 2025 +0800 初始提交：添加热门视频分析项目代码 diff --git a/base_line/prompt_manager.py b/base_line/prompt_manager.py new file mode 100644 index 0000000..8782bec --- /dev/null +++ b/base_line/prompt_manager.py @@ -0,0 +1,771 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Manages the construction of prompts for different AI generation tasks. +""" + +import os +import traceback +import logging # Add logging +import re # 添加正则表达式支持 +import random # 添加随机模块支持 +from .resource_loader import ResourceLoader # Use relative import within the same package +import json + +class PromptManager: + """Handles the loading and construction of prompts.""" + + def __init__(self, + topic_system_prompt_path: str, + topic_user_prompt_path: str, + content_system_prompt_path: str, + prompts_dir: str = None, # 兼容旧配置 + prompts_config: list = None, # 新的配置方式 + resource_dir_config: list = None, + topic_gen_num: int = 1, # Default values if needed + topic_gen_date: str = "", + content_judger_system_prompt_path: str = None # 添加内容审核系统提示词路径参数 + ): + self.topic_system_prompt_path = topic_system_prompt_path + self.topic_user_prompt_path = topic_user_prompt_path + self.content_system_prompt_path = content_system_prompt_path + self.content_judger_system_prompt_path = content_judger_system_prompt_path # 添加成员变量 + self.prompts_dir = prompts_dir # 保留兼容旧配置 + self.prompts_config = prompts_config or [] # 新的配置方式 + self.resource_dir_config = resource_dir_config or [] + self.topic_gen_num = topic_gen_num + self.topic_gen_date = topic_gen_date + + # 缓存加载的文件内容 + self._style_cache = {} + self._demand_cache = {} + self._refer_cache = {} + self._system_prompt_cache = {} # 新增：系统提示词缓存 + self._user_prompt_cache = {} # 新增：用户提示词缓存 + self._dateline_cache = None # 新增：日期线缓存 + + self._sample_rate = 0.1 # 提高随机抽样率 + # 初始化时预加载配置的文件 + self._preload_prompt_files() + + def _preload_prompt_files(self): + """预加载配置中的提示文件到缓存""" + # 预加载系统提示词和用户提示词文件 + if self.topic_system_prompt_path and os.path.exists(self.topic_system_prompt_path): + content = ResourceLoader.load_file_content(self.topic_system_prompt_path) + if content: + self._system_prompt_cache["topic"] = content + logging.info(f"预加载系统提示词: {self.topic_system_prompt_path}") + + if self.topic_user_prompt_path and os.path.exists(self.topic_user_prompt_path): + content = ResourceLoader.load_file_content(self.topic_user_prompt_path) + if content: + self._user_prompt_cache["topic"] = content + logging.info(f"预加载用户提示词: {self.topic_user_prompt_path}") + + if self.content_system_prompt_path and os.path.exists(self.content_system_prompt_path): + content = ResourceLoader.load_file_content(self.content_system_prompt_path) + if content: + self._system_prompt_cache["content"] = content + logging.info(f"预加载内容系统提示词: {self.content_system_prompt_path}") + + # 预加载内容审核系统提示词 + if self.content_judger_system_prompt_path and os.path.exists(self.content_judger_system_prompt_path): + content = ResourceLoader.load_file_content(self.content_judger_system_prompt_path) + if content: + self._system_prompt_cache["judger_system_prompt"] = content + logging.info(f"预加载内容审核系统提示词: {self.content_judger_system_prompt_path}") + + # 预加载日期线文件 + if self.topic_user_prompt_path: + user_prompt_dir = os.path.dirname(self.topic_user_prompt_path) + dateline_path = os.path.join(user_prompt_dir, "2025各月节日宣传节点时间表.md") + if os.path.exists(dateline_path): + self._dateline_cache = ResourceLoader.load_file_content(dateline_path) + logging.info(f"预加载日期线文件: {dateline_path}") + + + # 加载prompts_config配置的文件 + if not self.prompts_config: + return + + for config_item in self.prompts_config: + prompt_type = config_item.get("type", "").lower() + file_paths = config_item.get("file_path", []) + + if prompt_type == "style": + for path in file_paths: + if os.path.exists(path): + filename = os.path.basename(path) + content = ResourceLoader.load_file_content(path) + if content: + self._style_cache[filename] = content + name_without_ext = os.path.splitext(filename)[0] + self._style_cache[name_without_ext] = content # 同时缓存不带扩展名的版本 + + elif prompt_type == "demand": + for path in file_paths: + # print(path) + if os.path.exists(path): + filename = os.path.basename(path) + # print(filename) + content = ResourceLoader.load_file_content(path) + # print(content) + if content: + self._demand_cache[filename] = content + name_without_ext = os.path.splitext(filename)[0] + self._demand_cache[name_without_ext] = content # 同时缓存不带扩展名的版本 + + elif prompt_type == "refer": + for path in file_paths: + # print(path) + if os.path.exists(path): + filename = os.path.basename(path) + # print(filename) + # 检测是否为JSON文件 + if filename.lower().endswith('.json'): + # 直接加载和解析JSON文件 + try: + with open(path, 'r', encoding='utf-8') as f: + content = f.read() + # 移除可能的BOM头 + if content.startswith('\ufeff'): + content = content[1:] + # 解析JSON + json_data = json.loads(content) + # 存储解析后的对象 + self._refer_cache[filename] = json_data + logging.info(f"预加载并解析JSON参考文件: {filename}") + except Exception as e: + logging.error(f"预加载JSON文件{filename}失败: {str(e)}") + # 失败时尝试以普通文本加载 + content = ResourceLoader.load_file_content(path) + if content: + self._refer_cache[filename] = content + else: + # 非JSON文件使用普通加载方式 + content = ResourceLoader.load_all_refer_files(path, 1) + if content: + self._refer_cache[filename] = content + logging.info(f"预加载普通参考文件: {filename}") + + def find_directory_fuzzy_match(self, name, directory=None, files=None): + """ + 对文件名进行模糊匹配，查找最匹配目标名称的文件 + + Args: + name: 目标名称 + directory: 目录路径，如果提供则从目录中读取文件列表 + files: 文件名列表，如果提供则直接使用 + + Returns: + tuple: (最佳匹配文件名, 匹配分数) 如果没有匹配则返回 (None, 0) + """ + logging.info(f"尝试对文件名进行模糊匹配: {name}") + + try: + # 准备文件列表 + all_files = [] + if files: + all_files = files + elif directory and os.path.isdir(directory): + all_files = [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))] + + if not all_files: + logging.warning(f"没有可用于匹配的文件") + return None, 0 + + logging.info(f"找到 {len(all_files)} 个文件可用于模糊匹配") + + # 从名称中提取关键词 + # 通过常见分隔符分割（+、空格、_、-等） + parts = re.split(r'[+\s_\-]', name) + keywords = [] + for part in parts: + # 只保留长度大于1的有意义关键词 + if len(part) > 1: + keywords.append(part) + + # 尝试匹配更短的语义单元（例如中文的2-3个字的词语） + for i in range(len(name) - 1): + keyword = name[i:i+2] # 提取2个字符 + if len(keyword) == 2 and all('\u4e00' <= c <= '\u9fff' for c in keyword): + keywords.append(keyword) + + logging.info(f"用于文件模糊匹配的关键词: {keywords}") + + # 对每个文件进行评分 + file_scores = {} + for filename in all_files: + score = 0 + file_lower = filename.lower() + + # 精确匹配，去掉扩展名比较 + name_without_ext = os.path.splitext(name)[0].lower() + file_without_ext = os.path.splitext(filename)[0].lower() + + if name_without_ext == file_without_ext: + # 精确匹配给高分 + score += 10 + else: + # 为每个匹配的关键词增加分数 + for keyword in keywords: + if keyword.lower() in file_lower: + score += 1 + + # 如果得分大于0（至少有匹配），记录该文件 + if score > 0: + file_scores[filename] = score + + # 选择得分最高的文件 + if file_scores: + best_match = max(file_scores.items(), key=lambda x: x[1]) + found_file = best_match[0] + score = best_match[1] + logging.info(f"模糊匹配成功！匹配文件: {found_file}，匹配分数: {score}") + return found_file, score + else: + logging.warning(f"模糊匹配未找到任何包含关键词的文件") + return None, 0 + + except Exception as e: + logging.exception(f"文件模糊匹配过程中出错: {e}") + return None, 0 + + def _get_style_content(self, style_name): + """获取Style文件内容，优先从缓存获取，如果不存在则尝试从目录加载""" + # 首先检查缓存 + if style_name in self._style_cache: + return self._style_cache[style_name] + + # 确保有扩展名 + if not style_name.lower().endswith('.txt'): + style_file = f"{style_name}.txt" + else: + style_file = style_name + style_name = os.path.splitext(style_name)[0] # 移除扩展名 + + # 尝试模糊匹配缓存中的文件名 + cache_files = list(self._style_cache.keys()) + matched_key, score = self.find_directory_fuzzy_match(style_name, files=cache_files) + + if matched_key and score > 0: + return self._style_cache[matched_key] + + # 如果没有在缓存中找到模糊匹配，尝试从prompts_dir加载 + if self.prompts_dir: + style_dir = os.path.join(self.prompts_dir, "Style") + style_path = os.path.join(style_dir, style_file) + + # 精确路径匹配 + if os.path.exists(style_path): + content = ResourceLoader.load_file_content(style_path) + if content: + # 保存到缓存 + self._style_cache[style_name] = content + self._style_cache[style_file] = content + return content + + # 如果精确匹配失败，尝试目录中的模糊匹配 + if os.path.isdir(style_dir): + matched_file, score = self.find_directory_fuzzy_match(style_name, directory=style_dir) + if matched_file and score > 0: + matched_path = os.path.join(style_dir, matched_file) + content = ResourceLoader.load_file_content(matched_path) + if content: + # 保存到缓存 + self._style_cache[style_name] = content + self._style_cache[matched_file] = content + file_without_ext = os.path.splitext(matched_file)[0] + self._style_cache[file_without_ext] = content # 同时缓存不带扩展名的版本 + return content + + logging.warning(f"未能找到Style文件: '{style_name}'，尝试过以下位置: 缓存, {self.prompts_dir}/Style/") + return None + + def _get_demand_content(self, demand_name): + """获取Demand文件内容，优先从缓存获取，如果不存在则尝试从目录加载""" + # 首先检查缓存 + if demand_name in self._demand_cache: + return self._demand_cache[demand_name] + + # 确保有扩展名 + if not demand_name.lower().endswith('.txt'): + demand_file = f"{demand_name}.txt" + else: + demand_file = demand_name + demand_name = os.path.splitext(demand_name)[0] # 移除扩展名 + + # 尝试模糊匹配缓存中的文件名 + cache_files = list(self._demand_cache.keys()) + matched_key, score = self.find_directory_fuzzy_match(demand_name, files=cache_files) + + if matched_key and score > 0: + return self._demand_cache[matched_key] + + # 如果没有在缓存中找到模糊匹配，尝试从prompts_dir加载（向后兼容） + if self.prompts_dir: + demand_dir = os.path.join(self.prompts_dir, "Demand") + demand_path = os.path.join(demand_dir, demand_file) + + # 精确路径匹配 + if os.path.exists(demand_path): + content = ResourceLoader.load_file_content(demand_path) + if content: + # 保存到缓存 + self._demand_cache[demand_name] = content + self._demand_cache[demand_file] = content + return content + + # 如果精确匹配失败，尝试目录中的模糊匹配 + if os.path.isdir(demand_dir): + matched_file, score = self.find_directory_fuzzy_match(demand_name, directory=demand_dir) + if matched_file and score > 0: + matched_path = os.path.join(demand_dir, matched_file) + content = ResourceLoader.load_file_content(matched_path) + if content: + # 保存到缓存 + self._demand_cache[demand_name] = content + self._demand_cache[matched_file] = content + file_without_ext = os.path.splitext(matched_file)[0] + self._demand_cache[file_without_ext] = content # 同时缓存不带扩展名的版本 + return content + + # 如果所有尝试都失败 + logging.warning(f"未能找到Demand文件: '{demand_name}'，尝试过以下位置: 缓存, {self.prompts_dir}/Demand/") + return None + + def _get_refer_content(self, refer_dir): + """从Refer目录加载JSON和文本参考文件 + + Args: + refer_dir: Refer目录路径 + + Returns: + dict: 文件名到内容的映射 + """ + results = {} + if not os.path.isdir(refer_dir): + return results + + # 列出所有文件 + refer_files = [f for f in os.listdir(refer_dir) if os.path.isfile(os.path.join(refer_dir, f))] + + for refer_file in refer_files: + refer_path = os.path.join(refer_dir, refer_file) + content = None + + # 对JSON文件特殊处理 + if refer_file.lower().endswith('.json'): + try: + with open(refer_path, 'r', encoding='utf-8') as f: + content = f.read() + # 移除可能的BOM头 + if content.startswith('\ufeff'): + content = content[1:] + + # 验证是否为有效的JSON + try: + json.loads(content) # 只验证，不存储结果 + logging.info(f"成功验证JSON文件: {refer_file}") + except json.JSONDecodeError as e: + logging.warning(f"文件{refer_file}内容不是有效的JSON: {str(e)}") + except Exception as e: + logging.error(f"读取JSON文件{refer_file}时出错: {str(e)}") + content = None + else: + # 使用ResourceLoader加载非JSON文件 + content = ResourceLoader.load_file_content(refer_path) + + if content: + results[refer_file] = content + + return results + + def _load_and_parse_json(self, file_path): + """安全地加载和解析JSON文件 + + Args: + file_path: JSON文件路径 + + Returns: + dict or None: 解析后的JSON对象，解析失败则返回None + """ + try: + with open(file_path, 'r', encoding='utf-8') as f: + content = f.read() + # 移除可能的BOM头 + if content.startswith('\ufeff'): + content = content[1:] + return json.loads(content) + except Exception as e: + logging.error(f"加载和解析JSON文件{file_path}时出错: {str(e)}") + return None + + def _get_all_refer_contents(self, random_sample=True): + """获取所有Refer文件内容，可选择随机抽样文件内容 + + Args: + random_sample: 是否对文件内容进行随机抽样，默认为True + + Returns: + str: 组合后的refer内容 + """ + import json + + # 初始化结果字符串 + refer_content_all = "" + + # 如果缓存为空，尝试加载文件 + if not self._refer_cache and self.prompts_dir: + refer_dir = os.path.join(self.prompts_dir, "Refer") + if os.path.isdir(refer_dir): + refer_files = [f for f in os.listdir(refer_dir) if os.path.isfile(os.path.join(refer_dir, f))] + for refer_file in refer_files: + file_path = os.path.join(refer_dir, refer_file) + + # 对JSON文件特殊处理 + if refer_file.lower().endswith('.json'): + try: + with open(file_path, 'r', encoding='utf-8') as f: + content = f.read() + # 移除可能的BOM头 + if content.startswith('\ufeff'): + content = content[1:] + # 解析并存储JSON对象 + json_data = json.loads(content) + self._refer_cache[refer_file] = json_data + logging.info(f"加载并解析JSON参考文件: {refer_file}") + except Exception as e: + logging.error(f"加载JSON文件{refer_file}失败: {str(e)}") + # 失败时尝试以普通文本加载 + content = ResourceLoader.load_file_content(file_path) + if content: + self._refer_cache[refer_file] = content + else: + # 非JSON文件使用普通加载方式 + content = ResourceLoader.load_file_content(file_path) + if content: + self._refer_cache[refer_file] = content + + if not self._refer_cache: + logging.warning("没有找到任何Refer文件") + return refer_content_all + + logging.info(f"找到{len(self._refer_cache)}个Refer文件") + + # 处理所有文件 + for filename, content in self._refer_cache.items(): + # 添加文件头部信息 + refer_content_all += f"--- Refer File: {filename} ---\n" + + # 检查内容类型 + if isinstance(content, dict) and 'title' in content and 'examples' in content: + # 已解析的JSON对象 + title = content.get("title", "未命名参考资料") + description = content.get("description", "") + examples = content.get("examples", []) + + refer_content_all += f"标题: {title}\n" + refer_content_all += f"描述: {description}\n\n" + + if examples: + # 处理examples数组 + if random_sample and len(examples) > 10: + # 对examples进行随机抽样 + sample_size = max(10, int(len(examples) * self._sample_rate)) + sampled_examples = random.sample(examples, sample_size) + logging.info(f"从文件{filename}的JSON中随机抽样了{sample_size}/{len(examples)}个示例") + + refer_content_all += "示例:\n" + for idx, example in enumerate(sampled_examples, 1): + content_text = example.get("content", "") + refer_content_all += f"{idx}. {content_text}\n" + else: + # 不进行抽样或examples数量较少，使用全部 + refer_content_all += "示例:\n" + for idx, example in enumerate(examples, 1): + content_text = example.get("content", "") + refer_content_all += f"{idx}. {content_text}\n" + elif isinstance(content, str): + # 文本内容 + if random_sample: + lines = content.split('\n') + if len(lines) > 10: # 只对较长的内容进行抽样 + sample_size = max(10, int(len(lines) * self._sample_rate)) + sampled_lines = random.sample(lines, sample_size) + # 保持原有顺序 + sampled_lines.sort(key=lambda line: lines.index(line)) + sampled_content = '\n'.join(sampled_lines) + logging.info(f"从文件{filename}中随机抽样了{sample_size}/{len(lines)}行内容") + refer_content_all += f"{sampled_content}\n" + else: + # 内容较短，不进行抽样 + refer_content_all += f"{content}\n" + else: + # 不进行抽样 + refer_content_all += f"{content}\n" + else: + # 内容是其他类型 + refer_content_all += f"未知内容类型: {type(content)}\n" + + # 添加文件之间的分隔 + refer_content_all += "\n" + + return refer_content_all + + def get_topic_prompts(self): + """Constructs the system and user prompts for topic generation.""" + logging.info("Constructing prompts for topic generation...") + try: + # --- System Prompt --- + system_prompt = self._system_prompt_cache.get("topic") + if not system_prompt: + if not self.topic_system_prompt_path: + logging.error("Topic system prompt path not provided during PromptManager initialization.") + return None, None + system_prompt = ResourceLoader.load_file_content(self.topic_system_prompt_path) + if system_prompt: + self._system_prompt_cache["topic"] = system_prompt + else: + logging.error(f"Failed to load topic system prompt from '{self.topic_system_prompt_path}'.") + return None, None + + # --- User Prompt --- + base_user_prompt = self._user_prompt_cache.get("topic") + if not base_user_prompt: + if not self.topic_user_prompt_path: + logging.error("Topic user prompt path not provided during PromptManager initialization.") + return None, None + base_user_prompt = ResourceLoader.load_file_content(self.topic_user_prompt_path) + if base_user_prompt: + self._user_prompt_cache["topic"] = base_user_prompt + else: + logging.error(f"Failed to load base topic user prompt from '{self.topic_user_prompt_path}'.") + return None, None + + # --- Build the dynamic part of the user prompt --- + user_prompt_dynamic = "你拥有的创作资料如下：\n" + + # 添加prompts_config配置的文件信息 + if self.prompts_config: + for config_item in self.prompts_config: + prompt_type = config_item.get("type", "").lower() + file_paths = config_item.get("file_path", []) + + if file_paths: + user_prompt_dynamic += f"{prompt_type.capitalize()}文件列表:\n" + for path in file_paths: + filename = os.path.basename(path) + user_prompt_dynamic += f"- {filename}\n" + user_prompt_dynamic += "\n" + + # 兼容旧配置：Add genPrompts directory structure + elif self.prompts_dir and os.path.isdir(self.prompts_dir): + try: + gen_prompts_list = os.listdir(self.prompts_dir) + for gen_prompt_folder in gen_prompts_list: + folder_path = os.path.join(self.prompts_dir, gen_prompt_folder) + if os.path.isdir(folder_path): + try: + # List files, filter out subdirs if needed + gen_prompts_files = [f for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))] + user_prompt_dynamic += f"{gen_prompt_folder}\n{gen_prompts_files}\n" + except OSError as e: + logging.warning(f"Could not list directory {folder_path}: {e}") + except OSError as e: + logging.warning(f"Could not list base prompts directory {self.prompts_dir}: {e}") + else: + logging.warning(f"Neither prompts_config nor prompts_dir provided or valid.") + + # Add resource directory contents + for dir_info in self.resource_dir_config: + source_type = dir_info.get("type", "UnknownType") + source_file_paths = dir_info.get("file_path", []) + for file_path in source_file_paths: + # Use ResourceLoader's static method + file_content = ResourceLoader.load_file_content(file_path) + if file_content: + user_prompt_dynamic += f"{source_type}信息:\n{os.path.basename(file_path)}\n{file_content}\n\n" + else: + logging.warning(f"Could not load resource file {file_path}") + + # Add dateline information (optional) + if self._dateline_cache: + user_prompt_dynamic += f"\n{self._dateline_cache}" + else: + user_prompt_dir = os.path.dirname(self.topic_user_prompt_path) + dateline_path = os.path.join(user_prompt_dir, "2025各月节日宣传节点时间表.md") # Consider making this configurable + if os.path.exists(dateline_path): + dateline_content = ResourceLoader.load_file_content(dateline_path) + if dateline_content: + self._dateline_cache = dateline_content + user_prompt_dynamic += f"\n{dateline_content}" + + # Combine dynamic part, base template, and final parameters + user_prompt = user_prompt_dynamic + base_user_prompt + + user_prompt += f"\n选题数量：{self.topic_gen_num}\n选题日期：{self.topic_gen_date}\n" + + logging.info(f"Topic prompts constructed. System: {len(system_prompt)} chars, User: {len(user_prompt)} chars.") + return system_prompt, user_prompt + + except Exception as e: + logging.exception("Error constructing topic prompts:") + return None, None + + def get_content_prompts(self, topic_item): + """Constructs the system and user prompts for content generation.""" + logging.info("Constructing prompts for content generation...") + + try: + # --- System Prompt --- + system_prompt = self._system_prompt_cache.get("content") + if not system_prompt: + if not self.content_system_prompt_path: + logging.error("Content system prompt path not provided.") + return None, None + + system_prompt = ResourceLoader.load_file_content(self.content_system_prompt_path) + if system_prompt: + self._system_prompt_cache["content"] = system_prompt + else: + logging.error(f"Failed to load content system prompt from '{self.content_system_prompt_path}'.") + return None, None + + # --- User Prompt --- + style = "" + demand = "" + refers = "" + object_info = "" + + # Extract style from topic + if "style" in topic_item and topic_item["style"]: + style_name = topic_item["style"] + style_content = self._get_style_content(style_name) + if style_content: + style = f"Style: {style_name}\n{style_content}\n\n" + else: + logging.warning(f"Style content for '{style_name}' not found.") + + # Extract demand from topic + if "target_audience" in topic_item and topic_item["target_audience"]: + demand_name = topic_item["target_audience"] + demand_content = self._get_demand_content(demand_name) + if demand_content: + demand = f"Demand: {demand_name}\n{demand_content}\n\n" + else: + logging.warning(f"Demand content for '{demand_name}' not found.") + + # Add refer contents - 现在使用随机抽样 + refers_content = self._get_all_refer_contents(random_sample=True) + if refers_content: + refers = f"Reference:\n{refers_content}\n\n" + + # Get object information + object_name = topic_item.get("object", "") + if object_name: + # 优化：遍历resource_dir_config查找对象描述 + found_object_info = False + + # 1. 搜集所有可能的资源文件 + all_description_files = [] + for dir_info in self.resource_dir_config: + if dir_info.get("type") in ["Object", "Description"]: + all_description_files.extend(dir_info.get("file_path", [])) + + # 2. 尝试精确匹配 + for file_path in all_description_files: + if object_name in os.path.basename(file_path): + info = ResourceLoader.load_file_content(file_path) + if info: + object_info = f"Object: {object_name}\n{info}\n\n" + logging.info(f"找到对象'{object_name}'的精确匹配资源文件: {file_path}") + found_object_info = True + break + + # 3. 如果精确匹配失败，尝试模糊匹配 + if not found_object_info and all_description_files: + logging.info(f"尝试模糊匹配对象'{object_name}'的资源文件") + + # 提取所有文件名 + file_names = [os.path.basename(f) for f in all_description_files] + # 模糊匹配 + matched_filename, score = self.find_directory_fuzzy_match(object_name, files=file_names) + + if matched_filename and score > 0: + # 找到匹配的完整路径 + for file_path in all_description_files: + if os.path.basename(file_path) == matched_filename: + info = ResourceLoader.load_file_content(file_path) + if info: + object_info = f"Object: {object_name}\n{info}\n\n" + logging.info(f"模糊匹配找到对象'{object_name}'的资源文件: {file_path}，匹配分数: {score}") + found_object_info = True + break + + if not found_object_info: + logging.warning(f"未找到对象'{object_name}'的任何匹配资源文件") + + # Get product information if any + product_name = topic_item.get("product", "") + if product_name: + product_info = "" + # 优化：遍历resource_dir_config查找产品描述 + found_product_info = False + + # 搜集所有可能的产品资源文件 + all_product_files = [] + for dir_info in self.resource_dir_config: + if dir_info.get("type") == "Product": + all_product_files.extend(dir_info.get("file_path", [])) + + # 尝试精确匹配 + for file_path in all_product_files: + if product_name in os.path.basename(file_path): + info = ResourceLoader.load_file_content(file_path) + if info: + product_info = f"Product: {product_name}\n{info}\n\n" + logging.info(f"找到产品'{product_name}'的精确匹配资源文件: {file_path}") + found_product_info = True + break + + # 如果精确匹配失败，尝试模糊匹配 + if not found_product_info and all_product_files: + logging.info(f"尝试模糊匹配产品'{product_name}'的资源文件") + + # 提取所有文件名 + file_names = [os.path.basename(f) for f in all_product_files] + # 模糊匹配 + matched_filename, score = self.find_directory_fuzzy_match(product_name, files=file_names) + + if matched_filename and score > 0: + # 找到匹配的完整路径 + for file_path in all_product_files: + if os.path.basename(file_path) == matched_filename: + info = ResourceLoader.load_file_content(file_path) + if info: + product_info = f"Product: {product_name}\n{info}\n\n" + logging.info(f"模糊匹配找到产品'{product_name}'的资源文件: {file_path}，匹配分数: {score}") + found_product_info = True + break + + if not found_product_info: + logging.warning(f"未找到产品'{product_name}'的任何匹配资源文件") + + # 添加产品信息到对象信息中 + if product_info: + object_info += product_info + + # Construct final user prompt + user_prompt = f"""请为我创建一个旅游文案。 + +{style}{demand}{refers}{object_info} + +请考虑以上所有信息，创作一篇{topic_item.get('target_audience','')}文旅内容。""" + + return system_prompt, user_prompt + except Exception as e: + traceback.print_exc() + logging.error(f"Error creating content prompts: {str(e)}") + return None, None \ No newline at end of file diff --git a/base_line/tweet_prompt.txt b/base_line/tweet_prompt.txt new file mode 100644 index 0000000..04dc1b4 --- /dev/null +++ b/base_line/tweet_prompt.txt @@ -0,0 +1,509 @@ +请为我创建一个旅游文案。 + +Style: 攻略风文案提示词 +你是景区小红书爆款文案策划，你将根据要求创作爆款文案，根据以下规则一步步执行： +一、标题创作 +步骤：先速览景区资料内容，找到与用户需求适配的景点亮点，然后通读用户给的爆款标题素材，挑选一个在用户画像+节点节日+标题吸睛三个方面最契合的进行改写，创作出全新的爆款标题。在此期间以下几点需要你遵循： +1.必加1个emoji，标题字数18字以内 +2.有网感，爆破式标题，吸睛，结合所在地 +3.标题结合<标题参考格式>进行仿写 + +二、正文创作 +注意：正文以<我><你>这种有人味的人称代词视角创作输出，不要出现<宝子们><姐妹们>这些很假的称呼 +1. 通过了解产品和当前节点需求，如情人节要约会、儿童节要带孩子玩、临近周末会考虑周边游、传统节日偏好民俗活动等以此类推，直击用户痛点。 +2. 正文围绕所选定的风格提示词进行创作，有场景感，让人感觉是你的真是体验 +3. 开头引入部分可以参考我给你的<正文开头引入段落参考>文档，结合所给景区/民宿资料进行改写创作 +4. 分段+分点论述，开头引入+优势亮点（根据所给材料选择角度：交通优势、住宿优势、周边资源、必打卡项、路线推荐等等...）+你认为的最大优势 +5. 如果用户给出范文案例，请参考范文案例的写作风格和内容 +6. 一定要有一种和目标用户对话的感觉，语言平实靠谱，娓娓道来 + +三、TAG标签创作 +每一篇笔记需要携带13个TAG标签，以下举例的加号（+）这个符号不要体现，仅代表内容连接的意思 +1.前三个TAG标签的内容围绕<#产品所在地区+旅游><#产品所在省份+旅游><#产品所在地区+周边游><#产品周边一线城市+周边游> +2.接着三个TAG标签的内容围绕<#景区/酒店名称><#景区/酒店名称+攻略><#景区/酒店名称+推荐> +3.再来三个TAG标签的内容围绕<#对应节日+出游><#对应节日+反向旅行><#对应人群+好去处> +4.最后三个TAG标签的内容围绕<#周末去哪儿玩><#小众旅游地><##产品所在地区+周末好去处> +注意：以上TAG直接按要求直出内容，不需要在直出的时候进行分类 + +输出注意点： + 1.按要求输出标签+正文+TAG标签 + 2.正文直白清楚围绕产品，不要出现比喻和联想 + 3.千万不要虚构景区信息没有的设施设备和活动信息，如果材料中没有指明，不要提及价格信息；如果没有明确的产品价格或者优惠活动信息，不要提及价格和优惠 + 4.输出内容中，要符合社会道德规范和法律规范 + 5.TAG标签按要求顺序在正文最后直出进行罗列 + 6.所有的价格（如XXX元）改成XXXr，即用代替<元>，如：200元变成200r + 7.关于所有节假日补差价都不要在文中提及 + + 具体输出格式参照以下格式： + { + "content": "（正文内容）", + "tag": "（TAG内容, 如#地点 #节日 #需求人群旅游 #周末去哪儿玩）", + "title": "（标题内容）" + } + +Demand: 亲子向文旅需求 +亲子向用户画像： +1. 基本属性 +- 年龄：25-45岁（家长群体），孩子年龄集中在3-12岁. +- 性别：女性主导型，约70%为妈妈群体. + +2. 平台行为 +- 互动习惯：偏好收藏实用攻略. +- 发布内容：分享带娃旅行的温馨瞬间 + +3.中国旅游出游做攻略高峰点： +1. 春节假期：1-2月 +2. 暑假：6-8月 +3. 国庆黄金周：9-10月初 +4. 五一假期：4-5月初 +5. 元旦：12月中下旬 +6.清明：4月初 +7.端午：5-6月 +8.中秋：8月底-9月 +9.寒假：12-2月 + + +Reference: +--- Refer File: 标题参考格式.json --- +标题: 标题参考格式 +描述: 你创作标题的时候可以直接按照以下参考标题，结合景区/酒店资料进行仿写。 + +示例: +1. 呼伦贝尔亲子游！娃哭着不愿回来的攻略～ +2. 打工人首选🙋广州出发周末两日游 +3. 2-3月广东赏花季🌸6个绝美赏花地必收藏！ +4. 去了8次扬州❗这条高质量遛娃路线藏不住了 +5. 东莞小洱海🌊来吹吹蓝色的风 +6. 🦒佛山长鹿遛娃一日游，节日氛围已经拉满 +7. 南京2天1晚亲子游旅游攻略! 不踩雷！ +8. 周末去哪玩｜电玩🎮天河客运站 +9. 广州欢乐模拟经营密室🧑🏻‍🍳2人可开‼️ +10. 广东周边亲子游｜适合6岁以下娃踏春好去处❗️ +11. 假期打开新方式🥳一天玩转古镇+春日赏花🌸 +12. 广东亲子游ᵕ̈过年还要再来赏樱花🌸 +13. 天呐🤣这里已经颠覆了我的想象‼️实在太好玩 + +--- Refer File: 正文范文参考.json --- +标题: 正文范文参考 +描述: 你需要用这些内容作为参考，创作正文笔记。你可以整合多段获取材料，创作出更丰富的内容。参考写法和视角 + +示例: +1. 夏天和假期准备来啦！ +我又发现一个新的玩水避暑胜地！ +这是全新的一个网红夏威夷泳道啦～ +- +🏨阳江 DS 温泉酒店 +超级推荐的是夏威夷亲水别墅 +无限次免费游玩的夏威夷网红泳道 +踏出房门就能跳进泳池夏天太爽了！ +- +💰599🉐套餐👇 +【住】酒店别墅星空双床房1间1晚; +【吃】价值168元两大一小自助早餐 +【泡】赠送2池阳台私家泡池温泉水 +【泡】无限次爽泡高热偏硅酸公共温泉 +【玩】畅玩网红超长泳道、水上乐园 +【玩】打卡儿童狮子星空中儿童乐园 +【赏】天香大草坪、花森林婚庆广场 +【赠】公区温泉泳道免费水果、茶饮、清食等 +【赠】管家服务，一次洗漱用品，酒店免费停车 +--------------- +💥怎么订：一定要先关.🐖后下方留言咨询：999，稍后会一一回复哈 +2. 来广州外下午三点多的时候外面太热了 实在不知道去哪 突然决定去长隆水上乐园 +临时起意 即刻出发！ +目的地：地铁三号线汉溪长隆 + +1⃣️什么时候去？票价多少？ +建议工作日去人少些 +下午4点后进场 美团69r/人 ！！！很便宜！ +我觉得这个时间点太棒了 +太阳已经不是很毒辣 也不是太热了 不用担心晒伤 + +2⃣️没带泳衣怎么办？园内买泳衣太贵怎么办？ +不用担心！万能的小🍠 +我直接搜“广州长隆水上乐园泳衣”几个关键字 +果然出现了很多5分钟内送泳衣的帖子 +随便找了一家直接➕了联系方式 +在地铁上就看商家发的图 +选好了泳衣款式➕一个男士泳裤➕防水袋 +三个东西一共才105！ +力省几百！！ +而且店家还给我把东西送到地铁口，还顺便把我人免费送到水上乐园园区门口 + +当然也可以直接把泳衣送到园区门口 （切记先不要进园！进园他就送不了了！问了好几家都是这样说！只能在没进园之前送到地铁口或者园区门口！） + +没带泳衣的姐妹千万不要在园区里买！！ + +3⃣️进去玩什么？ +我们是五点多才进园 +但是到八点半已经玩完了六七个项目 +所有大项目几乎都玩了 +介绍一下时间紧任务重的 我们的玩法 ： +离心滑道—摇滚巨轮-合家大滑板-竞速赛道-巨兽碗-巨洪峡 +-上面全部玩完之后也才七点五十多 +然后八点电音节开场觉得没什么意思 +就去刷之前排队人太多没玩过的项目 +当然是直接冲大喇叭！！ +这个时候的大喇叭直接没人！上去就玩没排队！！！ +迅速玩完 又去玩了一些小项目比如 垂直极限这种 都没人排队了 直接随便玩 + +点击左下角查看更多， +听说现在力度超级给力呢！ +3. ☀️暑假来了，同学们想去广州长隆水上乐园玩吗？如果想去，那首先要做攻略，下面是阿毅替你们写的攻略。 +· +🧳出发前要做哪些准备？ +1、订票🎫： +门票分为全天票09:30-22:30、夜场票17:00-22:30 +可在欢乐长隆gzh、某携、某哪、某评上订 +2、做交通攻略🚘： +自驾导航长隆水上乐园，景区有停车场，收20 +打车用顺风车比较划算，目的地填长隆水上乐园 +坐地铁3号线到汉溪长隆站，从E口出来，步行1.2公里可到景区门口 +3、收拾行李🪪： +身份证、毛巾、手机防水袋、游泳衣、防晒霜、拖鞋、手机、塑料袋、零食、水 +· +🗺️进园步骤？ +1、刷身份证进园 +2、先去租个柜子储存东西，小柜40，大柜60，另外还要给20押金，等还了手环会返回 +3、去更衣室换衣服 +4、可以去玩了 +· +🤹‍♀️必看演出： +1、长隆水上电音派对🪩 +地点：造浪池舞台 +时间：20:00-21:00 +2、玩水激乐大巡游💃 +地点：园区 +时间：17:00-17:35 +3、水战派对🔫 +地点：沙滩区舞台 +时间：17:00-17:35 +4、泡泡派对🫧 +地点：亲子市集 +时间：16:00-16:20 +· +🎢必玩项目： +1、超级大喇叭📢 +坐在四叶草浮圈里，从六层高的平台出发，滑进一个巨大的喇叭中，在快速滑行中尽情欢叫 +2、摇滚巨轮🪇 +竖立自转滑道，整体外型犹如一座水上摩天轮，高达26米，每个转角都十分圆滑 +3、巨洪峡🌊 +在狭长的峡谷中只有滔天巨浪与你相伴，在跌宕起伏中挑战山洪暴发的澎湃，领略被抛向浪尖的快感 +4、超级巨兽碗👾 +首先在紫水晶通道俯冲穿越，来到一个巨碗之中。当你稍觉平稳，突然出现一个黑洞将你吸入，让你措手不及 +5、垂直极限↕️ +有3条滑道，黄色滑道滑下时速度能达到近40公里，蓝色滑道为旋转滑道，体验离心冲击，橙色滑道垂直度高，体验水花四溅的感觉 +6、巨蟒滑道🐍 +从弯形盘旋滑道划出后会进入长达6米的全封闭滑道，集合了扭转，螺旋和振动的感觉 +7、大滑板滑道🛹 +6人一组坐在浮圈上，从高处快速滑到底部，然后继续向上滑行到呈 90度的滑道上，然后再从垂直的滑道上滑落到平缓的地段 +· +❤️温馨提示： +建议9点半刚开园就去玩，那会人很多，如果下午去人会很多，玩一个项目要等一两个小时。 +4. 🏨广州森林海温泉度假酒店 +♨️室内水乐园，水温30度以上，温泉泡池全面！ +😭千万别错过这个森林海美食专属套餐 +- +现在只要1399就可以享受3大3小的的Chao值套餐哦~真的是太划算啦! +🔥3大3小自助早餐+3大3小自助晚餐+2天无限次嬉水乐园十亲子房 +现在还有限时活动，每个成人住客可以免费带两个1.5以下的小朋友一起畅玩嬉水乐园💦 +- +限时特价房型是森林海爆款房型 +🔹海洋亲子/嬉水海洋亲子/星空亲子 +🎈自助早餐 +🎈私家露台温泉泡池 +🎈无限次威尼斯泳道 +🎈嬉水乐园【天阳湖区、神秘海岛、古海森林、森林温泉、抓鱼摸虾] +- +怎么订❓先关㊗️，然后下方留言【999】看到会一一回复！ +5. 深圳海边酒店已经住过洲际和万豪 +这两家酒店海滩都很漂亮 +没想到这次住桔钓沙莱华直接来了个玻璃海震撼 +庆幸遇上大晴天☀️ +才能看到这美到窒息的景色 +唯一缺点是有围网也不能下海游泳🏊… + +🏨酒店很大不过偏老旧，公区维护一般。房间正海景，躺在床上就能看到海滩，阳台有大浴缸可以一边看海一边泡澡🛀，度假感拉满！ + +🎡入住每间房都有赠送一次免费小火车体验+儿童乐园100个币+帆船/皮划艇项目，赠送的游戏币可以在儿童乐园电玩区使用，室内乐园还有攀爬区、蹦床区、波波球… 足够小朋友玩很久了 + +🍽️酒店自助早餐一般，不太好吃。晚餐在🍠找的友友农庄，家常菜，偏贵，味道还行吧，窑鸡不好吃，感觉附近就没什么好吃的东西。 + +💗莱华酒店虽然没有很完美，比起洲际不够豪华，比起万豪不够漂亮，但是它游玩项目足够多，性价比也很高，而且为了这片玻璃海景色我一定会再刷的！！🏖️ +6. 位于大鹏新区的桔钓沙,这里沙子超白超好干净还很细腻 脚踩下去都软乎乎的 ! +不出深圳，也能实现海滨度假🏖 +. +推门见大海🌊下楼即沙滩🏖 +【深圳桔钓沙莱华度假酒店🏨】 +作为深圳为数不多拥有独.立沙滩的度假酒店，私密性ji高 +而且酒店附近的可玩项目好多 +杨梅坑踩单车🚴‍♀、看美人鱼拍摄地🧜‍♀、徒步七娘山⛰、观国家地质博物馆等，都离酒店不到2公里路程！妥妥的遛娃好去处！ +· +🏊‍♂还有超大的私人泳池，沙滩就在泳池旁边，随时可以下海畅游！ +☀️沙滩边还有各种娱乐设施，小朋友可以挖沙踏浪，大人则可以玩滑皮划艇 +👧儿童乐园也是一大亮点！室内室外都有各种儿童游乐设施 +室内攀岩、积木乐园、、4D动感影院、音乐厅和电子游戏等 +爸妈们可以省心省力地遛娃，让孩子们尽情享受快乐的时光！🎉 +· +🏨酒店拥有246间客房，间间都带有宽阔独.立景观阳台 +有多种房型可供选择，房间设施齐全，有超大浴缸. +· +【双人下午茶套票】 +💰1299 +🔹豪华园景房一间一晚 +🔹双人自助早餐 +🔹双人下午茶1套(大堂吧/池畔吧 二选一) +- +想要购买的宝子们，戳我即可获取，酒店住宿套餐 +7. 发现了一家神仙度假酒店！ +不在三亚，也不在东南亚 +而是在深圳⛱️ +这片果冻海真的美到心颤💕 +- +🌟桔钓沙莱华度假酒店 +✅封神级沙滩体验🏝️ +有深圳一绝的牛奶沙+玻璃海，光脚踩在沙滩上，就像踩在热奶粉上一样巨舒服。海水呈渐变色，如果冻一般 +- +✅东南亚秘境园林🌴 +15万㎡东南亚热带花园，秒穿普吉岛 +- +✅100%阳台景观房⛱️ +所有房型都有超大阳台，山海园林尽享眼底 +- +✅亲子友好天花板🛝 +拥有1600㎡的儿童奇妙世界，含欢乐海洋泡泡池、室内攀岩、积木乐园、知识海洋、DIY手工坊等，玩到不想走 +- +✅海上玩乐不停⛵️ +游艇出海、摩托艇、香蕉船、帆船、皮划艇等超多水上活动 +- +🌟现在这个「小马代」错峰大促，人均最低只要400+，即享： +✔️海景豪华房1间1晚 +✔️双人自助早餐 +✔️双人小帆船或皮划艇套餐（2选1） +✔️客房迷你吧冰箱饮品 +✔️奇妙世界项目：鲸鱼大滑梯、潜艇通道、海马秋千、波波球池、儿童攀岩、儿童蹦床、手工制作等 +✔️沙滩排球、沙滩足球 +✔️赠送双人精美旅拍1份 +✔️每间房赠送一张免费小火车票 +- +*使用有效期至2025年4月29日 +- +今年第一场海边度假，准备好开始了吗？ +在牛奶沙上踩出脚印👣 +看着波光粼粼的海发呆🏝️ +等一场橘子汽水味的日落🌅 +这才是度假啊～ +-！ +8. +伙计们，都给我冲‼️ +北洛秘境是真的有点东西 +🚗出行方式：自驾（酒店本身没有停车场） + +🏨住宿day1：北洛秘境度假酒店1栋 +（1）属于外部干干净净，内部缝缝补补…… +（2）30层天际泳池🉑去，大海带来的疗愈…… +（3）包含早晚餐，附赠的悬崖泳池、山体公园、临海栈道套票（整体还是相当划算❤️） +🏨住宿day2：铂悦度假公寓 + +属于民宿类，大落地窗➕独立阳台，三面看海😍，晚上看沙滩的烟花都达不到我住的高度 + +🌟打卡推荐🌟 + +1️⃣首先需要知道：山体公园、悬崖泳池、悬崖咖啡、临海栈道都是在一块（只能单次进出‼️，一条道走到底） + +2️⃣山体公园（8:00-18:30），栈道式爬山，建议早上11:00前或者下午15点后完成打卡，它主要包括： + +（1）🌈彩虹步道（比较脏，顶上损坏待维修，在入口打卡即可✅）； + +（2）不走回头路🚏路标➕鸟巢🪹（分布在道路两边，可以拍到海和马路上的“阳江”logo）； + +（3）不上班行不行🚏路标➕露天小房子（主打一个社畜的精神支柱，房子目前有点像半竣工状态）； + +（4）破破烂烂的粉色车尾巴（我合理怀疑它是报废状态，就跟我的精神状态蛮契合）； + +（5）悬崖咖啡馆（😐比较有个性，但饮品看个人口味，拍照的话，我想说手残党上午大概率是会废片）； + +（6）粉色观景台（圆形⭕️设计，可以上二楼看海，但太晒了，我没去……）可以在外围把建筑做成背景，进去拍的话，一整个粉掉…… + +（7）网红天际秋千（两根大铁链子⛓️‍💥垂下来的，是景点摄影付费装置，平时秋千的板板都是收起来的😳）； + +3️⃣悬崖泳池（8:30-18:30），这里需要二次检票（游泳需要泳装➕泳帽，拍照的话检票进去即可），可以付费寄存（20rmb，真的没必要😐）…… + +❤️很出片是一回事，很晒是另一回事❤️ + +4️⃣海边栈道：去‼️都给我去这里‼️真的好好看😻，果冻海➕岩石➕远处的船➕脚下的石头路，一整个波妞附体 + +‼️建议 +真的要做好防晒😳，感觉已经黑一圈了 +穿衣是自由的，但姐妹儿，穿鞋一定是自律的 +防蚊虫是必须的，拍照是要大胆尝试的 +旅游要自己开心的😃 +9. ▶️清远新世界酒店 +还没退房就已经想要二刷的酒店 +真的太适合躺平溜娃了！ + +🌟酒店环境服务 +很干净整洁的酒店，整体装修风格很舒服，房间也很大。入住登记后有工作人员帮忙拿行李带路，管家服务也很周到，大晚上有需求也回复得很及时，遇到的工作人员都很热情友好～ +入住的时候尽量让安排到3座，玩的地方基本都在这边，我们住2座，每次都要经过主楼走很远。 + +🎪儿童乐园（嗨玩） +儿童乐园区域看似不大，但有很多东西玩，三个不同类型的室内游戏房，户外区域有滑梯、蹦床、沙池、小水池、蔬菜园，还有平衡车可以骑，适合不同年龄段的小朋友。我们工作日去几乎包场，完全解放双手，阿乐自己玩得很开心。 + +⛳️娱乐项目（活馆） +在3座1楼，射箭、迷你高尔夫、地上桌球在一个房间进去，室内球类在对面房。有些项目需要提前预约，但我们去的时候没什么人，都是去了就能玩。迷你高尔夫和地上桌球很适合小朋友，尤其地上桌球真的太好玩了！我觉得每个宝宝都会喜欢的哈哈哈。 + +♨️温泉（9：00-22：30） +🏊🏻‍♂️泳池 （9：00-20：00） +室外游泳池不是恒温的，我们去的时候天气有点凉，就没去了。带阿乐去泡了温泉，室内有一个泡池一个汗蒸房，室外区域不大，但环境很舒服，泡池不大，有五六个可以泡，建议人少的时候去，体验会好很多。 + +💡以上的酒店活动项目，入住前退房后也是可以玩的，这点很人性化，毕竟2天1夜真的安排不过来哈哈～ + +🍱餐饮 +酒店的自助早餐在主楼的1楼，环境很好，我们在室外用餐很惬意，早餐种类挺多，出品我觉得比很多酒店早餐都要好吃很多哈哈～ +正餐我们没有在酒店吃，附近有很多农庄，当晚我们选了【来来农庄】，开车5min就到，老板很热情，菜品也很不错；第二天退房后去了【三禾稻里】，10min路程，我们到店太晚了只有西餐，价格偏贵，但味道挺好的。不得不说这里真的好美啊，如果不带娃的话我可能会选这个民宿哈哈，很适合躺平发呆～ + +🌟总结：是一家非常不错的亲子酒店，很适合亲子家庭入住，必二刷！ +10. 一开始看朋友去了觉得不错，还心想清远酒店应该很便宜吧，一查居然要1300+当时觉得有点奢侈，但是去了之后觉得好好玩，好舒服，还想再来！ + +👨‍👩‍👦 酒店设施——超多亲子项目 +有三个主题的儿童屋，小厨房、钢琴、玩具等，户外可以玩沙子、滑滑梯，还有小农场可以浇花，有大型桌球台、小型高尔夫场，还可以射箭。我儿子玩得很开心，我自己也觉得很有趣！ + +♨️ 酒店房间&温泉—— +房间挺新，可以加装围栏，房间有个独立的小池子，我们晚上把娃带睡之后，在露台吃宵夜泡温泉，带娃人幸福时刻。公域的温泉环境也不错，不过池子有点小 + +🥣 酒店自助早餐——很丰盛 选择很多 +餐厅环境很好，吃早餐的时候看窗外简直心旷神怡。选择也很多，光是那碟绿色肠粉就拿了好几盘！早餐吃得很满足，也挺好吃 + +🌴 酒店户外环境——很大 绿化很好 +超级适合我崽骑车！吃完早餐之后，在酒店户外绕了一圈，有种像逛公园的感觉，光合作用拉满，真的很舒服！ + +⭐️⭐️⭐️⭐️⭐️ +已经是两个月前去的了，还是忍不住mark篇帖子记录。当时选了工作日来的，人少体验好，真的觉得很适合两岁起的娃，我们应该还会再去玩滴！ + + + +Object: 北洛秘境盛季酒店 +{ + "主体名称": "北洛秘境盛季酒店", + "产品有效期": "2025年5月13日-6月30日", + “产品售卖期”:"2025年5月13日-6月30日", + "地址": "闸坡镇北洛大道199号碧桂园北洛明珠花园3栋1层", + "交通指南": "【交通建议】导航酒店地址自驾前往或高铁到站后导航前往,广州到阳江站1.5H，到了高铁站可以搭顺风车1H到酒店，人均30。人多也可以直接打顺风车。", + "使用规则、加价说明、预约规则、退改政策、优惠内容": "【使用规则】 +① 本产品购买成功未预约支持随时退，预约成功不改不退 +② 本产品具体节假日加收最终以国家法定节假日为准，如漏补或少补会有客服联系补回，介意勿拍 +③ 本产品为特价秒杀，如突遇特殊情况无法正常安排入住，不做任何赔偿，介意者勿拍（订单特殊情况无法安排会有客服联系改期或退款） +【加收说明】 +①5月6月平日不加收，周六+50元，端午节5月31日-6月1日加收150元 +②指定海景房，在基础加收上叠加30元/间 +③升级2房1厅海景房，5月加收80元，周六加收230，6月6月加收130元，周六加收280元，端午节5月31日-6月1日加收330元， +【预约规则】 +库存有限，先约先得，加收均为线上加收 +【注意事项】 +①床型大小：双床1.2M*2 +②入住时间：15点后    退房时间：12点前 +③本套餐不包含门票，周边游玩景点需自费 +④注：仅供参考，实际以现场为准 +", + "产品最突出优势":"①低于某程、某团价格 +②可以升级海景2房1厅 +③节假日：端午节可用", + "产品亮点":"周边游玩： +酒店位置超优越，能畅游马尾岛，那里的海水清澈见底，沙滩细腻柔软 ，漫步海边，吹着海风，真的好惬意。还能去北洛东边免费沙滩，玩沙踏浪、晒太阳。 + +游玩路线：山体公园→悬崖无边泳池→海边栈道→椰林沙滩 + +①靠近海陵岛北洛湾沙滩，有12处风景各异的天然海滩，异彩纷呈地点缀，私人海滩、悬崖栈道、天际泳池、山体公园的山顶秋千、粉红观景台，超多景点打卡 +-悬崖临海栈道 + +沿着礁石悬崖蜿蜒的橙色步道，俯拍海浪拍岸的瞬间，仿佛走在世界尽头！（穿防滑鞋！） + + -珍珠白沙滩 +沙子细软得像面粉，一点都不硌脚。椰林树影，水清，还有海边夜市～ + +-山体公园 +登顶10分钟就能get俯瞰全岛的视角，记得和“海边秋千”合影！ + +-彩虹灯塔 +上山的路有一段彩虹楼梯，楼梯的尽头是彩色灯塔，在这里可以俯瞰整个白沙滩。 + +-临海栈道 +一边是悬崖，一边是大海，临海栈道依山而建，海浪不断拍打着脚下的礁石，浪花飞溅，场面太壮观了 + +②酒店楼下小区免费使用儿童游客设施 + +③自费打卡北洛湾沙滩泳池、秘境悬崖泳池（悬崖泳池自东向西方向全线视野可以将整片蔚蓝的大海一览无遗，泳池建于马尾岛山顶的悬崖边，108米高空俯瞰大海，Tiffany蓝的池水）等 + +美食TIP： +▪️ 闸坡渔港的海鲜大排档！皮皮虾/海胆炒饭 +▪️ 本地糖水铺的“椰子冻”清爽解暑 + +④游玩建议：2天1晚，酒店自身处处是网红拍照打卡点，周边景点也多，值得去逛逛，比如天麓山景区、月光殿堂、风车山等 +" +} + + +Product: 北洛秘境盛季酒店-海景或山景双床房 +{ + "产品名称":"北洛秘境盛季酒店-海景或山景双床房", + "产品门市价":199, + "产品实际售价":99, + "套票详情": " 【阳江·北洛秘境盛季酒店】可升级海景2房，99元抢全新海景/山景双床房（随机安排），毗邻悬崖泳池，打卡“东方夏威夷”海陵岛！感受天空海阔融为一体的奇妙体验～ ", + "产品售卖期":"2025年5月13日-6月30日", + "产品库存":100, + "产品最突出优势":"①低于某程、某团价格 +②可以升级海景2房1厅 +③节假日：端午节可用", + "产品亮点":"周边游玩： +酒店位置超优越，能畅游马尾岛，那里的海水清澈见底，沙滩细腻柔软 ，漫步海边，吹着海风，真的好惬意。还能去北洛东边免费沙滩，玩沙踏浪、晒太阳。 + +游玩路线：山体公园→悬崖无边泳池→海边栈道→椰林沙滩 + +①靠近海陵岛北洛湾沙滩，有12处风景各异的天然海滩，异彩纷呈地点缀，私人海滩、悬崖栈道、天际泳池、山体公园的山顶秋千、粉红观景台，超多景点打卡 +-悬崖临海栈道 + +沿着礁石悬崖蜿蜒的橙色步道，俯拍海浪拍岸的瞬间，仿佛走在世界尽头！（穿防滑鞋！） + + -珍珠白沙滩 +沙子细软得像面粉，一点都不硌脚。椰林树影，水清，还有海边夜市～ + +-山体公园 +登顶10分钟就能get俯瞰全岛的视角，记得和“海边秋千”合影！ + +-彩虹灯塔 +上山的路有一段彩虹楼梯，楼梯的尽头是彩色灯塔，在这里可以俯瞰整个白沙滩。 + +-临海栈道 +一边是悬崖，一边是大海，临海栈道依山而建，海浪不断拍打着脚下的礁石，浪花飞溅，场面太壮观了 + +②酒店楼下小区免费使用儿童游客设施 + +③自费打卡北洛湾沙滩泳池、秘境悬崖泳池（悬崖泳池自东向西方向全线视野可以将整片蔚蓝的大海一览无遗，泳池建于马尾岛山顶的悬崖边，108米高空俯瞰大海，Tiffany蓝的池水）等 + +美食TIP： +▪️ 闸坡渔港的海鲜大排档！皮皮虾/海胆炒饭 +▪️ 本地糖水铺的“椰子冻”清爽解暑 + +④游玩建议：2天1晚，酒店自身处处是网红拍照打卡点，周边景点也多，值得去逛逛，比如天麓山景区、月光殿堂、风车山等 +", + "产品须知": "【使用规则】 +① 本产品购买成功未预约支持随时退，预约成功不改不退 +② 本产品具体节假日加收最终以国家法定节假日为准，如漏补或少补会有客服联系补回，介意勿拍 +③ 本产品为特价秒杀，如突遇特殊情况无法正常安排入住，不做任何赔偿，介意者勿拍（订单特殊情况无法安排会有客服联系改期或退款） +【加收说明】 +①5月6月平日不加收，周六+50元，端午节5月31日-6月1日加收150元 +②指定海景房，在基础加收上叠加30元/间 +③升级2房1厅海景房，5月加收80元，周六加收230，6月6月加收130元，周六加收280元，端午节5月31日-6月1日加收330元， +【预约规则】 +库存有限，先约先得，加收均为线上加收 +【注意事项】 +①床型大小：双床1.2M*2 +②入住时间：15点后    退房时间：12点前 +③本套餐不包含门票，周边游玩景点需自费 +④注：仅供参考，实际以现场为准 +" +} + + + +请考虑以上所有信息，创作一篇亲子向文旅需求文旅内容。 \ No newline at end of file diff --git a/base_line/标题参考格式.json b/base_line/标题参考格式.json new file mode 100644 index 0000000..efee3a3 --- /dev/null +++ b/base_line/标题参考格式.json @@ -0,0 +1,420 @@ +{ + "title": "标题参考格式", + "description": "你创作标题的时候可以直接按照以下参考标题，结合景区/酒店资料进行仿写。", + "examples": [ + { + "content": "没有人敢说惠州希尔顿酒店的事吗？" + }, + { + "content": "暑假遛娃来青甘亲子游，听听这5条良心建议" + }, + { + "content": "湖州🦒亲子圣地｜5天4晚行程分享" + }, + { + "content": "🐼成都也太适合亲子游了吧！3天2晚攻略来喽" + }, + { + "content": "带娃去大理后，我后悔了！ 😭后悔没早来！" + }, + { + "content": "🦒长隆二天一夜❸园🏨亲子游经验🐘" + }, + { + "content": "2024旅行结算｜超🐔大型亲子乐园平躺攻略" + }, + { + "content": "国庆广州亲子游❄️冰雪游乐园室内遛娃好去处" + }, + { + "content": "去了8次扬州❗这条高质量遛娃路线藏不住了" + }, + { + "content": "追鲸🐳桂西南带娃已回🔥跟风最成功的一次🔥" + }, + { + "content": "济南春季亲子游‼️3天2晚看这一篇就够了💯" + }, + { + "content": "广州亲子游🚗1.5h到温泉乐园宫崎骏树屋遛娃" + }, + { + "content": "三刷乌村‼️2天1晚保姆级吃住玩攻略‼️" + }, + { + "content": "低幼友好！京郊2h直达懒人遛娃地，解放爸妈！" + }, + { + "content": "清远新世界酒店｜遛娃真的太爽太舒服了♨️" + }, + { + "content": "2月18陵水穷游已回，说点掏心窝子的话" + }, + { + "content": "带娃避寒首选，广西崇左+北海+涠洲岛攻略！" + }, + { + "content": "嘉兴三天两夜亲子游｜杭州🚗1.5h达！" + }, + { + "content": "江浙沪亲子酒店排名，还有漏的吗？" + }, + { + "content": "崇左带娃已回...说点难听的实话" + }, + { + "content": "江浙沪遛娃❤️徐州欧乐堡两天一晚亲子游" + }, + { + "content": "成都带娃已回，亲子游真实体验(不删)" + }, + { + "content": "周末游 | 江门市赤坎古镇，是谁说不推荐的？" + }, + { + "content": "亲子游天花板❗️小众不挤还省钱✨抄作业啦" + }, + { + "content": "三亚家庭游｜5天四晚这样安排，人均1800" + }, + { + "content": "两天一夜！宁波亲子游攻略" + }, + { + "content": "西双版纳 5 天 4 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a/base_line/正文范文参考.json b/base_line/正文范文参考.json new file mode 100644 index 0000000..ab66c33 --- /dev/null +++ b/base_line/正文范文参考.json @@ -0,0 +1,35 @@ +{ "title": "正文范文参考", + "description": "你需要用这些内容作为参考，创作正文笔记。你可以整合多段获取材料，创作出更丰富的内容。参考写法和视角", + "examples":[ + { + "content": "夏天和假期准备来啦！\n我又发现一个新的玩水避暑胜地！\n这是全新的一个网红夏威夷泳道啦～\n-\n🏨阳江 DS 温泉酒店\n超级推荐的是夏威夷亲水别墅\n无限次免费游玩的夏威夷网红泳道\n踏出房门就能跳进泳池夏天太爽了！\n-\n💰599🉐套餐👇\n【住】酒店别墅星空双床房1间1晚;\n【吃】价值168元两大一小自助早餐\n【泡】赠送2池阳台私家泡池温泉水\n【泡】无限次爽泡高热偏硅酸公共温泉\n【玩】畅玩网红超长泳道、水上乐园\n【玩】打卡儿童狮子星空中儿童乐园\n【赏】天香大草坪、花森林婚庆广场\n【赠】公区温泉泳道免费水果、茶饮、清食等\n【赠】管家服务，一次洗漱用品，酒店免费停车\n---------------\n💥怎么订：一定要先关.🐖后下方留言咨询：999，稍后会一一回复哈" + }, + { + "content": "来广州外下午三点多的时候外面太热了 实在不知道去哪 突然决定去长隆水上乐园\n临时起意 即刻出发！\n目的地：地铁三号线汉溪长隆\n\n1⃣️什么时候去？票价多少？\n建议工作日去人少些\n下午4点后进场 美团69r/人 ！！！很便宜！\n我觉得这个时间点太棒了\n太阳已经不是很毒辣 也不是太热了 不用担心晒伤\n\n2⃣️没带泳衣怎么办？园内买泳衣太贵怎么办？\n不用担心！万能的小🍠\n我直接搜“广州长隆水上乐园泳衣”几个关键字\n果然出现了很多5分钟内送泳衣的帖子\n随便找了一家直接➕了联系方式\n在地铁上就看商家发的图\n选好了泳衣款式➕一个男士泳裤➕防水袋\n三个东西一共才105！\n力省几百！！\n而且店家还给我把东西送到地铁口，还顺便把我人免费送到水上乐园园区门口\n\n当然也可以直接把泳衣送到园区门口 （切记先不要进园！进园他就送不了了！问了好几家都是这样说！只能在没进园之前送到地铁口或者园区门口！）\n\n没带泳衣的姐妹千万不要在园区里买！！\n\n3⃣️进去玩什么？\n我们是五点多才进园\n但是到八点半已经玩完了六七个项目\n所有大项目几乎都玩了\n介绍一下时间紧任务重的 我们的玩法 ：\n离心滑道—摇滚巨轮-合家大滑板-竞速赛道-巨兽碗-巨洪峡\n-上面全部玩完之后也才七点五十多\n然后八点电音节开场觉得没什么意思\n就去刷之前排队人太多没玩过的项目\n当然是直接冲大喇叭！！\n这个时候的大喇叭直接没人！上去就玩没排队！！！\n迅速玩完 又去玩了一些小项目比如 垂直极限这种 都没人排队了 直接随便玩\n\n点击左下角查看更多，\n听说现在力度超级给力呢！" + }, + { + "content": "☀️暑假来了，同学们想去广州长隆水上乐园玩吗？如果想去，那首先要做攻略，下面是阿毅替你们写的攻略。\n·\n🧳出发前要做哪些准备？\n1、订票🎫：\n门票分为全天票09:30-22:30、夜场票17:00-22:30\n可在欢乐长隆gzh、某携、某哪、某评上订\n2、做交通攻略🚘：\n自驾导航长隆水上乐园，景区有停车场，收20\n打车用顺风车比较划算，目的地填长隆水上乐园\n坐地铁3号线到汉溪长隆站，从E口出来，步行1.2公里可到景区门口\n3、收拾行李🪪：\n身份证、毛巾、手机防水袋、游泳衣、防晒霜、拖鞋、手机、塑料袋、零食、水\n·\n🗺️进园步骤？\n1、刷身份证进园\n2、先去租个柜子储存东西，小柜40，大柜60，另外还要给20押金，等还了手环会返回\n3、去更衣室换衣服\n4、可以去玩了\n·\n🤹‍♀️必看演出：\n1、长隆水上电音派对🪩\n地点：造浪池舞台\n时间：20:00-21:00\n2、玩水激乐大巡游💃\n地点：园区\n时间：17:00-17:35\n3、水战派对🔫\n地点：沙滩区舞台\n时间：17:00-17:35\n4、泡泡派对🫧\n地点：亲子市集\n时间：16:00-16:20\n·\n🎢必玩项目：\n1、超级大喇叭📢\n坐在四叶草浮圈里，从六层高的平台出发，滑进一个巨大的喇叭中，在快速滑行中尽情欢叫\n2、摇滚巨轮🪇\n竖立自转滑道，整体外型犹如一座水上摩天轮，高达26米，每个转角都十分圆滑\n3、巨洪峡🌊\n在狭长的峡谷中只有滔天巨浪与你相伴，在跌宕起伏中挑战山洪暴发的澎湃，领略被抛向浪尖的快感\n4、超级巨兽碗👾\n首先在紫水晶通道俯冲穿越，来到一个巨碗之中。当你稍觉平稳，突然出现一个黑洞将你吸入，让你措手不及\n5、垂直极限↕️\n有3条滑道，黄色滑道滑下时速度能达到近40公里，蓝色滑道为旋转滑道，体验离心冲击，橙色滑道垂直度高，体验水花四溅的感觉\n6、巨蟒滑道🐍\n从弯形盘旋滑道划出后会进入长达6米的全封闭滑道，集合了扭转，螺旋和振动的感觉\n7、大滑板滑道🛹\n6人一组坐在浮圈上，从高处快速滑到底部，然后继续向上滑行到呈 90度的滑道上，然后再从垂直的滑道上滑落到平缓的地段\n·\n❤️温馨提示：\n建议9点半刚开园就去玩，那会人很多，如果下午去人会很多，玩一个项目要等一两个小时。" + }, + { + "content": "🏨广州森林海温泉度假酒店\n♨️室内水乐园，水温30度以上，温泉泡池全面！\n😭千万别错过这个森林海美食专属套餐\n-\n现在只要1399就可以享受3大3小的的Chao值套餐哦~真的是太划算啦!\n🔥3大3小自助早餐+3大3小自助晚餐+2天无限次嬉水乐园十亲子房\n现在还有限时活动，每个成人住客可以免费带两个1.5以下的小朋友一起畅玩嬉水乐园💦\n-\n限时特价房型是森林海爆款房型\n🔹海洋亲子/嬉水海洋亲子/星空亲子\n🎈自助早餐\n🎈私家露台温泉泡池\n🎈无限次威尼斯泳道\n🎈嬉水乐园【天阳湖区、神秘海岛、古海森林、森林温泉、抓鱼摸虾]\n-\n怎么订❓先关㊗️，然后下方留言【999】看到会一一回复！" + }, + { + "content": "深圳海边酒店已经住过洲际和万豪\n这两家酒店海滩都很漂亮\n没想到这次住桔钓沙莱华直接来了个玻璃海震撼\n庆幸遇上大晴天☀️\n才能看到这美到窒息的景色\n唯一缺点是有围网也不能下海游泳🏊…\n\n🏨酒店很大不过偏老旧，公区维护一般。房间正海景，躺在床上就能看到海滩，阳台有大浴缸可以一边看海一边泡澡🛀，度假感拉满！\n\n🎡入住每间房都有赠送一次免费小火车体验+儿童乐园100个币+帆船/皮划艇项目，赠送的游戏币可以在儿童乐园电玩区使用，室内乐园还有攀爬区、蹦床区、波波球… 足够小朋友玩很久了\n\n🍽️酒店自助早餐一般，不太好吃。晚餐在🍠找的友友农庄，家常菜，偏贵，味道还行吧，窑鸡不好吃，感觉附近就没什么好吃的东西。\n\n💗莱华酒店虽然没有很完美，比起洲际不够豪华，比起万豪不够漂亮，但是它游玩项目足够多，性价比也很高，而且为了这片玻璃海景色我一定会再刷的！！🏖️" + }, + { + "content": "位于大鹏新区的桔钓沙,这里沙子超白超好干净还很细腻 脚踩下去都软乎乎的 !\n不出深圳，也能实现海滨度假🏖\n.\n推门见大海🌊下楼即沙滩🏖\n【深圳桔钓沙莱华度假酒店🏨】\n作为深圳为数不多拥有独.立沙滩的度假酒店，私密性ji高\n而且酒店附近的可玩项目好多\n杨梅坑踩单车🚴‍♀、看美人鱼拍摄地🧜‍♀、徒步七娘山⛰、观国家地质博物馆等，都离酒店不到2公里路程！妥妥的遛娃好去处！\n·\n🏊‍♂还有超大的私人泳池，沙滩就在泳池旁边，随时可以下海畅游！\n☀️沙滩边还有各种娱乐设施，小朋友可以挖沙踏浪，大人则可以玩滑皮划艇\n👧儿童乐园也是一大亮点！室内室外都有各种儿童游乐设施\n室内攀岩、积木乐园、、4D动感影院、音乐厅和电子游戏等\n爸妈们可以省心省力地遛娃，让孩子们尽情享受快乐的时光！🎉\n·\n🏨酒店拥有246间客房，间间都带有宽阔独.立景观阳台\n有多种房型可供选择，房间设施齐全，有超大浴缸.\n·\n【双人下午茶套票】\n💰1299\n🔹豪华园景房一间一晚\n🔹双人自助早餐\n🔹双人下午茶1套(大堂吧/池畔吧 二选一)\n-\n想要购买的宝子们，戳我即可获取，酒店住宿套餐" + }, + { + "content": "发现了一家神仙度假酒店！\n不在三亚，也不在东南亚\n而是在深圳⛱️\n这片果冻海真的美到心颤💕\n-\n🌟桔钓沙莱华度假酒店\n✅封神级沙滩体验🏝️\n有深圳一绝的牛奶沙+玻璃海，光脚踩在沙滩上，就像踩在热奶粉上一样巨舒服。海水呈渐变色，如果冻一般\n-\n✅东南亚秘境园林🌴\n15万㎡东南亚热带花园，秒穿普吉岛\n-\n✅100%阳台景观房⛱️\n所有房型都有超大阳台，山海园林尽享眼底\n-\n✅亲子友好天花板🛝\n拥有1600㎡的儿童奇妙世界，含欢乐海洋泡泡池、室内攀岩、积木乐园、知识海洋、DIY手工坊等，玩到不想走\n-\n✅海上玩乐不停⛵️\n游艇出海、摩托艇、香蕉船、帆船、皮划艇等超多水上活动\n-\n🌟现在这个「小马代」错峰大促，人均最低只要400+，即享：\n✔️海景豪华房1间1晚\n✔️双人自助早餐\n✔️双人小帆船或皮划艇套餐（2选1）\n✔️客房迷你吧冰箱饮品\n✔️奇妙世界项目：鲸鱼大滑梯、潜艇通道、海马秋千、波波球池、儿童攀岩、儿童蹦床、手工制作等\n✔️沙滩排球、沙滩足球\n✔️赠送双人精美旅拍1份\n✔️每间房赠送一张免费小火车票\n-\n*使用有效期至2025年4月29日\n-\n今年第一场海边度假，准备好开始了吗？\n在牛奶沙上踩出脚印👣\n看着波光粼粼的海发呆🏝️\n等一场橘子汽水味的日落🌅\n这才是度假啊～\n-！" + }, + { + "content": "\n伙计们，都给我冲‼️\n北洛秘境是真的有点东西\n🚗出行方式：自驾（酒店本身没有停车场）\n\n🏨住宿day1：北洛秘境度假酒店1栋\n（1）属于外部干干净净，内部缝缝补补……\n（2）30层天际泳池🉑去，大海带来的疗愈……\n（3）包含早晚餐，附赠的悬崖泳池、山体公园、临海栈道套票（整体还是相当划算❤️）\n🏨住宿day2：铂悦度假公寓\n\n属于民宿类，大落地窗➕独立阳台，三面看海😍，晚上看沙滩的烟花都达不到我住的高度\n\n🌟打卡推荐🌟\n\n1️⃣首先需要知道：山体公园、悬崖泳池、悬崖咖啡、临海栈道都是在一块（只能单次进出‼️，一条道走到底）\n\n2️⃣山体公园（8:00-18:30），栈道式爬山，建议早上11:00前或者下午15点后完成打卡，它主要包括：\n\n（1）🌈彩虹步道（比较脏，顶上损坏待维修，在入口打卡即可✅）；\n\n（2）不走回头路🚏路标➕鸟巢🪹（分布在道路两边，可以拍到海和马路上的“阳江”logo）；\n\n（3）不上班行不行🚏路标➕露天小房子（主打一个社畜的精神支柱，房子目前有点像半竣工状态）；\n\n（4）破破烂烂的粉色车尾巴（我合理怀疑它是报废状态，就跟我的精神状态蛮契合）；\n\n（5）悬崖咖啡馆（😐比较有个性，但饮品看个人口味，拍照的话，我想说手残党上午大概率是会废片）；\n\n（6）粉色观景台（圆形⭕️设计，可以上二楼看海，但太晒了，我没去……）可以在外围把建筑做成背景，进去拍的话，一整个粉掉……\n\n（7）网红天际秋千（两根大铁链子⛓️‍💥垂下来的，是景点摄影付费装置，平时秋千的板板都是收起来的😳）；\n\n3️⃣悬崖泳池（8:30-18:30），这里需要二次检票（游泳需要泳装➕泳帽，拍照的话检票进去即可），可以付费寄存（20rmb，真的没必要😐）……\n\n❤️很出片是一回事，很晒是另一回事❤️\n\n4️⃣海边栈道：去‼️都给我去这里‼️真的好好看😻，果冻海➕岩石➕远处的船➕脚下的石头路，一整个波妞附体\n\n‼️建议\n真的要做好防晒😳，感觉已经黑一圈了\n穿衣是自由的，但姐妹儿，穿鞋一定是自律的\n防蚊虫是必须的，拍照是要大胆尝试的\n旅游要自己开心的😃" + }, + { + "content": "▶️清远新世界酒店\n还没退房就已经想要二刷的酒店\n真的太适合躺平溜娃了！\n\n🌟酒店环境服务\n很干净整洁的酒店，整体装修风格很舒服，房间也很大。入住登记后有工作人员帮忙拿行李带路，管家服务也很周到，大晚上有需求也回复得很及时，遇到的工作人员都很热情友好～\n入住的时候尽量让安排到3座，玩的地方基本都在这边，我们住2座，每次都要经过主楼走很远。\n\n🎪儿童乐园（嗨玩）\n儿童乐园区域看似不大，但有很多东西玩，三个不同类型的室内游戏房，户外区域有滑梯、蹦床、沙池、小水池、蔬菜园，还有平衡车可以骑，适合不同年龄段的小朋友。我们工作日去几乎包场，完全解放双手，阿乐自己玩得很开心。\n\n⛳️娱乐项目（活馆）\n在3座1楼，射箭、迷你高尔夫、地上桌球在一个房间进去，室内球类在对面房。有些项目需要提前预约，但我们去的时候没什么人，都是去了就能玩。迷你高尔夫和地上桌球很适合小朋友，尤其地上桌球真的太好玩了！我觉得每个宝宝都会喜欢的哈哈哈。\n\n♨️温泉（9：00-22：30）\n🏊🏻‍♂️泳池 （9：00-20：00）\n室外游泳池不是恒温的，我们去的时候天气有点凉，就没去了。带阿乐去泡了温泉，室内有一个泡池一个汗蒸房，室外区域不大，但环境很舒服，泡池不大，有五六个可以泡，建议人少的时候去，体验会好很多。\n\n💡以上的酒店活动项目，入住前退房后也是可以玩的，这点很人性化，毕竟2天1夜真的安排不过来哈哈～\n\n🍱餐饮\n酒店的自助早餐在主楼的1楼，环境很好，我们在室外用餐很惬意，早餐种类挺多，出品我觉得比很多酒店早餐都要好吃很多哈哈～\n正餐我们没有在酒店吃，附近有很多农庄，当晚我们选了【来来农庄】，开车5min就到，老板很热情，菜品也很不错；第二天退房后去了【三禾稻里】，10min路程，我们到店太晚了只有西餐，价格偏贵，但味道挺好的。不得不说这里真的好美啊，如果不带娃的话我可能会选这个民宿哈哈，很适合躺平发呆～\n\n🌟总结：是一家非常不错的亲子酒店，很适合亲子家庭入住，必二刷！" + }, + { + "content": "一开始看朋友去了觉得不错，还心想清远酒店应该很便宜吧，一查居然要1300+当时觉得有点奢侈，但是去了之后觉得好好玩，好舒服，还想再来！\n\n👨‍👩‍👦 酒店设施——超多亲子项目\n有三个主题的儿童屋，小厨房、钢琴、玩具等，户外可以玩沙子、滑滑梯，还有小农场可以浇花，有大型桌球台、小型高尔夫场，还可以射箭。我儿子玩得很开心，我自己也觉得很有趣！\n\n♨️ 酒店房间&温泉——\n房间挺新，可以加装围栏，房间有个独立的小池子，我们晚上把娃带睡之后，在露台吃宵夜泡温泉，带娃人幸福时刻。公域的温泉环境也不错，不过池子有点小\n\n🥣 酒店自助早餐——很丰盛 选择很多\n餐厅环境很好，吃早餐的时候看窗外简直心旷神怡。选择也很多，光是那碟绿色肠粉就拿了好几盘！早餐吃得很满足，也挺好吃\n\n🌴 酒店户外环境——很大 绿化很好\n超级适合我崽骑车！吃完早餐之后，在酒店户外绕了一圈，有种像逛公园的感觉，光合作用拉满，真的很舒服！\n\n⭐️⭐️⭐️⭐️⭐️\n已经是两个月前去的了，还是忍不住mark篇帖子记录。当时选了工作日来的，人少体验好，真的觉得很适合两岁起的娃，我们应该还会再去玩滴！" + } +] +} \ No newline at end of file diff --git a/code/VideoSplitter.py b/code/VideoSplitter.py new file mode 100644 index 0000000..ff70af6 --- /dev/null +++ b/code/VideoSplitter.py @@ -0,0 +1,524 @@ +import cv2 +import numpy as np +import os +import subprocess +import shutil +from datetime import timedelta +import argparse +from sklearn.metrics.pairwise import cosine_similarity +from skimage.metrics import structural_similarity as ssim + +# 设置固定的输入输出路径 +INPUT_VIDEO_PATH = "/root/autodl-tmp/hot_video_analyse/source/sample_demo_1.mp4" # 修改为视频文件夹路径 +OUTPUT_DIR = "/root/autodl-tmp/hot_video_analyse/source/Splitter" # 请修改为您的实际输出目录路径 + +# 支持的视频格式 +VIDEO_EXTENSIONS = ['.mp4', '.avi', '.mov', '.mkv', '.flv', '.wmv'] + +# 设置参数 +SAMPLE_RATE = 1 # 帧采样率 +METHOD = "ssim" # 比较方法，可选 "ssim" 或 "cosine" +## 有分镜0.2 无分镜0.7 +THRESHOLD = 0.8 # 相似度阈值 + +# 启用详细日志输出 +VERBOSE = True + +# FFMPEG可能的路径 +FFMPEG_PATHS = [ + 'ffmpeg', # 系统路径 + '/usr/bin/ffmpeg', # 常见Linux路径 + '/usr/local/bin/ffmpeg', # 常见macOS路径 + 'C:\\ffmpeg\\bin\\ffmpeg.exe', # 常见Windows路径 +] + +def find_ffmpeg(): + """查找系统中可用的ffmpeg路径""" + # 首先尝试使用which/where命令 + try: + if os.name == 'nt': # Windows + result = subprocess.run(['where', 'ffmpeg'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) + if result.returncode == 0: + return result.stdout.strip().split('\n')[0] + else: # Linux/Mac + result = subprocess.run(['which', 'ffmpeg'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) + if result.returncode == 0: + return result.stdout.strip() + except Exception: + pass + + # 然后检查预定义的路径 + for path in FFMPEG_PATHS: + if shutil.which(path): + return path + + return None + +def extract_frames(video_path, output_dir, sample_rate=1): + """ + 从视频中提取帧并保存到指定目录 + + Args: + video_path: 视频文件路径 + output_dir: 输出帧的目录 + sample_rate: 采样率（每N帧提取一帧） + + Returns: + frames_info: 包含帧信息的列表 [(frame_number, timestamp, frame_path), ...] + """ + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + # 打开视频 + cap = cv2.VideoCapture(video_path) + + # 获取视频属性 + fps = cap.get(cv2.CAP_PROP_FPS) + frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + duration = frame_count / fps + + print(f"视频信息：{frame_count}帧, {fps}fps, 时长:{timedelta(seconds=duration)}") + + frames_info = [] + frame_number = 0 + saved_count = 0 + + while True: + ret, frame = cap.read() + if not ret: + break + + if frame_number % sample_rate == 0: + # 计算时间戳（秒） + timestamp = frame_number / fps + # 保存帧 + frame_path = os.path.join(output_dir, f"frame_{saved_count:05d}.jpg") + cv2.imwrite(frame_path, frame) + # 记录帧信息 + frames_info.append((frame_number, timestamp, frame_path)) + saved_count += 1 + + frame_number += 1 + + # 显示进度 + if frame_number % 100 == 0: + print(f"处理进度: {frame_number}/{frame_count} ({frame_number/frame_count*100:.2f}%)") + + cap.release() + print(f"共提取了 {saved_count} 帧") + return frames_info + +def compare_frames(frame1_path, frame2_path, method='ssim', threshold=0.85): + """ + 比较两帧的相似度 + + Args: + frame1_path: 第一帧路径 + frame2_path: 第二帧路径 + method: 比较方法，'ssim'或'cosine' + threshold: 相似度阈值 + + Returns: + is_similar: 是否相似 + similarity: 相似度值 + """ + # 读取帧 + frame1 = cv2.imread(frame1_path) + frame2 = cv2.imread(frame2_path) + + # 调整大小以加快处理 + frame1_resized = cv2.resize(frame1, (320, 180)) + frame2_resized = cv2.resize(frame2, (320, 180)) + + # 转换为灰度图 + gray1 = cv2.cvtColor(frame1_resized, cv2.COLOR_BGR2GRAY) + gray2 = cv2.cvtColor(frame2_resized, cv2.COLOR_BGR2GRAY) + + if method == 'ssim': + # 使用结构相似性指数 + similarity, _ = ssim(gray1, gray2, full=True) + else: # cosine + # 使用余弦相似度 + flat1 = gray1.flatten().reshape(1, -1) + flat2 = gray2.flatten().reshape(1, -1) + similarity = cosine_similarity(flat1, flat2)[0][0] + + is_similar = similarity >= threshold + return is_similar, similarity + +def detect_scene_changes(frames_info, method='ssim', threshold=0.85): + """ + 检测场景变化 + + Args: + frames_info: 帧信息列表 + method: 比较方法 + threshold: 相似度阈值 + + Returns: + scenes: 场景信息列表 [(start_frame, end_frame, start_time, end_time), ...] + """ + global SCENE_START_FRAMES # 声明使用全局变量 + + if len(frames_info) < 2: + return [] + + scenes = [] + clips = [] + scene_start = frames_info[0] + + for i in range(1, len(frames_info)): + prev_frame_path = frames_info[i-1][2] + curr_frame_path = frames_info[i][2] + + is_similar, similarity = compare_frames( + prev_frame_path, curr_frame_path, method, threshold + ) + + if not is_similar: + # 场景变化，记录上一个场景 + scene_end = frames_info[i-1] + scene_duration = scene_end[1] - scene_start[1] + clips.append(scene_start[0]) + # 只记录持续时间超过1秒的场景 + if scene_duration >= 0.2: + scenes.append(( + scene_start[0], # 开始帧号 + scene_end[0], # 结束帧号 + scene_start[1], # 开始时间 + scene_end[1], # 结束时间 + scene_duration # 持续时间 + )) + + # 开始新场景 + scene_start = frames_info[i] + + if VERBOSE: + print(f"检测到场景变化点: 帧 {scene_end[0]}, 时间 {timedelta(seconds=scene_end[1])}, 相似度: {similarity:.4f}") + + # 添加最后一个场景 + scene_end = frames_info[-1] + scene_duration = scene_end[1] - scene_start[1] + if scene_duration >= 0.2: + scenes.append(( + scene_start[0], + scene_end[0], + scene_start[1], + scene_end[1], + scene_duration + )) + + print(f"\n场景变化统计:") + print(f"检测到 {len(scenes)} 个场景, 平均时长: {sum(s[4] for s in scenes)/max(1, len(scenes)):.2f}秒") + return scenes , clips + +def extract_video_clips(video_path, scenes, output_dir, ffmpeg_path=None): + """ + 根据场景信息提取视频片段 + + Args: + video_path: 视频路径 + scenes: 场景信息列表 + output_dir: 输出目录 + ffmpeg_path: ffmpeg可执行文件路径 + """ + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + # 检查ffmpeg是否可用 + if ffmpeg_path is None: + ffmpeg_path = find_ffmpeg() + if ffmpeg_path is None: + print("错误: 找不到ffmpeg。请安装ffmpeg并确保它在系统路径中。") + print("您可以从 https://ffmpeg.org/download.html 下载ffmpeg") + print("或使用包管理器安装: apt-get install ffmpeg / brew install ffmpeg 等") + return [] + + print(f"\n开始切割视频: {video_path}") + print(f"输出目录: {output_dir}") + print(f"使用ffmpeg: {ffmpeg_path}") + print("-" * 60) + + clips_info = [] + + for i, scene in enumerate(scenes): + start_time = scene[2] + end_time = scene[3] + duration = scene[4] + + output_file = os.path.join(output_dir, f"clip_{i:03d}_{duration:.2f}s.mp4") + + # 构建ffmpeg命令 + cmd = [ + ffmpeg_path, + '-i', video_path, + '-ss', f"{start_time:.2f}", + '-to', f"{end_time:.2f}", + '-c:v', 'libx264', + '-c:a', 'aac', + '-y', # 覆盖已存在的文件 + output_file + ] + + try: + print(f"\n切割片段 {i+1}/{len(scenes)}:") + print(f" 开始时间: {timedelta(seconds=start_time)}") + print(f" 结束时间: {timedelta(seconds=end_time)}") + print(f" 时长: {duration:.2f}秒") + print(f" 输出文件: {os.path.basename(output_file)}") + + if VERBOSE: + print(f" 执行命令: {' '.join(cmd)}") + + # 使用subprocess执行命令，可以获取输出和错误 + result = subprocess.run( + cmd, + stdout=subprocess.PIPE if not VERBOSE else None, + stderr=subprocess.PIPE, + text=True + ) + + if result.returncode == 0: + # 获取输出文件信息 + file_size = os.path.getsize(output_file) / (1024 * 1024) # MB + print(f" ✓ 切割成功: {os.path.basename(output_file)} ({file_size:.2f} MB)") + + clips_info.append({ + 'index': i, + 'file': output_file, + 'start': start_time, + 'end': end_time, + 'duration': duration, + 'size_mb': file_size + }) + else: + print(f" ✗ 切割失败: {result.stderr}") + except Exception as e: + print(f" ✗ 切割失败: {str(e)}") + + # 汇总信息 + if clips_info: + total_size = sum(clip['size_mb'] for clip in clips_info) + total_duration = sum(clip['duration'] for clip in clips_info) + + print("\n切割完成汇总:") + print(f" 成功切割片段数: {len(clips_info)}/{len(scenes)}") + print(f" 总时长: {timedelta(seconds=total_duration)}") + print(f" 总文件大小: {total_size:.2f} MB") + + # 列出所有文件 + print("\n输出文件列表:") + for clip in clips_info: + print(f" {os.path.basename(clip['file'])} - {clip['duration']:.2f}秒 ({clip['size_mb']:.2f} MB)") + else: + print("\n没有成功切割任何片段") + + return clips_info + +def get_video_files(directory): + """ + 获取目录中所有视频文件 + + Args: + directory: 目录路径 + + Returns: + 视频文件路径列表 + """ + video_files = [] + + # 检查是否是单个文件 + if os.path.isfile(directory): + ext = os.path.splitext(directory)[1].lower() + if ext in VIDEO_EXTENSIONS: + return [directory] + + # 遍历目录 + for root, _, files in os.walk(directory): + for file in files: + # 检查文件扩展名 + ext = os.path.splitext(file)[1].lower() + if ext in VIDEO_EXTENSIONS: + video_files.append(os.path.join(root, file)) + + return video_files + +def process_video(video_path, output_base_dir, sample_rate, method, threshold, ffmpeg_path): + """ + 处理单个视频文件 + + Args: + video_path: 视频文件路径 + output_base_dir: 基础输出目录 + sample_rate: 帧采样率 + method: 比较方法 + threshold: 相似度阈值 + ffmpeg_path: ffmpeg路径 + + Returns: + 处理是否成功 + """ + # 获取视频文件名（不含扩展名） + video_filename = os.path.splitext(os.path.basename(video_path))[0] + + # 为当前视频创建输出目录 + video_output_dir = os.path.join(output_base_dir, video_filename) + if not os.path.exists(video_output_dir): + os.makedirs(video_output_dir) + + # 创建输出子目录 + frames_dir = os.path.join(video_output_dir, 'frames') + clips_dir = os.path.join(video_output_dir, 'clips') + + if not os.path.exists(frames_dir): + os.makedirs(frames_dir) + if not os.path.exists(clips_dir): + os.makedirs(clips_dir) + + print("\n处理参数:") + print(f"输入视频: {os.path.abspath(video_path)}") + print(f"输出目录: {os.path.abspath(video_output_dir)}") + print(f"帧采样率: 每{sample_rate}帧") + print(f"比较方法: {method}") + print(f"相似度阈值: {threshold}") + print("-" * 60) + + try: + # 步骤1: 提取帧 + print("\n步骤1: 正在提取视频帧...") + frames_info = extract_frames(video_path, frames_dir, sample_rate) + + # 步骤2: 检测场景变化 + print("\n步骤2: 正在检测场景变化...") + scenes = detect_scene_changes(frames_info, method, threshold) + + # 输出场景信息 + print(f"\n检测到 {len(scenes)} 个场景:") + for i, scene in enumerate(scenes): + start_time = timedelta(seconds=scene[2]) + end_time = timedelta(seconds=scene[3]) + duration = scene[4] + print(f"场景 {i+1}: {start_time} - {end_time} (时长: {duration:.2f}s)") + + # 如果没有ffmpeg，提前报错 + if not ffmpeg_path: + print("\n错误: 缺少ffmpeg，无法继续视频切割步骤。") + print("请安装ffmpeg后重试。") + return False + + # 步骤3: 提取视频片段 + print("\n步骤3: 正在提取视频片段...") + clips_info = extract_video_clips(video_path, scenes, clips_dir, ffmpeg_path) + + # 创建结果摘要文件 + summary_file = os.path.join(video_output_dir, 'summary.txt') + try: + with open(summary_file, 'w', encoding='utf-8') as f: + f.write("智能视频切割结果摘要\n") + f.write("=" * 40 + "\n\n") + f.write(f"输入视频: {os.path.abspath(video_path)}\n") + f.write(f"处理时间: {os.path.getmtime(summary_file)}\n\n") + f.write(f"检测到场景数: {len(scenes)}\n") + f.write(f"生成片段数: {len(clips_info)}\n\n") + + f.write("片段详情:\n") + for i, clip in enumerate(clips_info): + f.write(f"{i+1}. {os.path.basename(clip['file'])}\n") + f.write(f" 开始: {timedelta(seconds=clip['start'])}\n") + f.write(f" 结束: {timedelta(seconds=clip['end'])}\n") + f.write(f" 时长: {clip['duration']:.2f}秒\n") + f.write(f" 大小: {clip['size_mb']:.2f} MB\n\n") + + print(f"\n已保存处理摘要到: {summary_file}") + except Exception as e: + print(f"保存摘要文件失败: {str(e)}") + + print("\n处理完成!") + print(f"帧提取目录: {os.path.abspath(frames_dir)}") + print(f"视频片段目录: {os.path.abspath(clips_dir)}") + + return True + + except Exception as e: + print(f"\n处理视频 {video_path} 时发生错误: {str(e)}") + return False + +def get_parent_folder_name(path): + """ + 获取路径中 'video' 上一级文件夹的名字 + """ + abs_path = os.path.abspath(path) + # 如果是文件夹，直接用 + if os.path.isdir(abs_path): + parent = os.path.dirname(abs_path.rstrip('/')) + folder_name = os.path.basename(parent) + else: + # 如果是文件，取其父目录的父目录 + parent = os.path.dirname(os.path.dirname(abs_path)) + folder_name = os.path.basename(parent) + return folder_name + +def main(): + # 欢迎信息 + print("=" * 60) + print("智能视频切割工具 - 批量处理版") + print("=" * 60) + + # 查找ffmpeg + ffmpeg_path = find_ffmpeg() + if ffmpeg_path: + print(f"已找到ffmpeg: {ffmpeg_path}") + else: + print("警告: 未找到ffmpeg，视频切割功能将不可用") + print("请安装ffmpeg并确保它在系统路径中") + + # 获取输入目录中的所有视频文件 + video_files = get_video_files(INPUT_VIDEO_PATH) + + if not video_files: + print(f"错误: 在 '{INPUT_VIDEO_PATH}' 中没有找到视频文件") + print(f"支持的视频格式: {', '.join(VIDEO_EXTENSIONS)}") + return + + # 自动获取video上一级文件夹名 + parent_folder_name = get_parent_folder_name(INPUT_VIDEO_PATH) + output_dir = os.path.join(OUTPUT_DIR, parent_folder_name) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + print(f"\n输出目录: {output_dir}") + + # 处理每个视频文件 + successful = 0 + failed = 0 + + for i, video_path in enumerate(video_files): + print("\n" + "=" * 60) + print(f"正在处理视频 [{i+1}/{len(video_files)}]: {os.path.basename(video_path)}") + print("=" * 60) + + success = process_video( + video_path=video_path, + output_base_dir=output_dir, + sample_rate=SAMPLE_RATE, + method=METHOD, + threshold=THRESHOLD, + ffmpeg_path=ffmpeg_path + ) + + if success: + successful += 1 + else: + failed += 1 + + # 打印批量处理总结 + print("\n" + "=" * 60) + print("批量处理完成!") + print("=" * 60) + print(f"总共处理: {len(video_files)} 个视频文件") + print(f"成功: {successful} 个") + print(f"失败: {failed} 个") + print(f"输出目录: {os.path.abspath(output_dir)}") + +if __name__ == "__main__": + main() diff --git a/code/__pycache__/VideoSplitter.cpython-312.pyc b/code/__pycache__/VideoSplitter.cpython-312.pyc new file mode 100644 index 0000000..7c8df37 Binary files /dev/null and b/code/__pycache__/VideoSplitter.cpython-312.pyc differ diff --git a/code/client.py b/code/client.py new file mode 100644 index 0000000..36023c0 --- /dev/null +++ b/code/client.py @@ -0,0 +1,242 @@ +from vllm import LLM, SamplingParams +from transformers import AutoTokenizer +from vllm.assets.image import ImageAsset +from vllm.assets.video import VideoAsset +from openai import OpenAI +from dataclasses import dataclass, field +from typing import Optional, ClassVar +import numpy as np +from numpy.typing import NDArray +from PIL import Image +import librosa +import librosa.util +import os +import cv2 +# import ray # 注释掉Ray导入 + +# # 使用本地模式初始化Ray，避免分布式通信问题 +# ray.init(local_mode=True, ignore_reinit_error=True) + +# 设置环境变量，禁用在线检查 +os.environ["HF_DATASETS_OFFLINE"] = "1" +os.environ["TRANSFORMERS_OFFLINE"] = "1" +os.environ["HF_HUB_OFFLINE"] = "1" + +@dataclass(frozen=True) +class VideoAsset: + name: str + num_frames: int = -1 + + _NAME_TO_FILE: ClassVar[dict[str, str]] = { + "baby_reading": "sample_demo_1.mp4", + } + + @property + def filename(self) -> str: + return self._NAME_TO_FILE[self.name] + + @property + def pil_images(self) -> list[Image.Image]: + video_path = download_video_asset(self.filename) + return video_to_pil_images_list(video_path, self.num_frames) + + @property + def np_ndarrays(self) -> NDArray: + video_path = download_video_asset(self.filename) + return video_to_ndarrays(video_path, self.num_frames) + + def get_audio(self, sampling_rate: Optional[float] = None) -> NDArray: + video_path = download_video_asset(self.filename) + return librosa.load(video_path, sr=sampling_rate)[0] + +@dataclass(frozen=True) +class LocalVideoAsset: + local_path: str + name: str = "local_video" + num_frames: int = -1 + + @property + def filename(self) -> str: + return self.local_path + + @property + def pil_images(self) -> list[Image.Image]: + return video_to_pil_images_list(self.filename, self.num_frames) + + @property + def np_ndarrays(self) -> NDArray: + return video_to_ndarrays(self.filename, self.num_frames) + + def get_audio(self, sampling_rate: Optional[float] = None) -> NDArray: + try: + if not os.path.exists(self.filename): + print(f"音频文件不存在: {self.filename}") + return np.zeros(1) # 返回空数组 + return librosa.load(self.filename, sr=sampling_rate)[0] + except Exception as e: + print(f"加载音频时出错: {e}") + return np.zeros(1) # 出错时返回空数组 + +# 辅助函数实现 +def download_video_asset(filename: str) -> str: + # 如果路径是绝对路径或相对路径，直接返回 + if filename.startswith("/") or filename.startswith("./"): + return filename + # 否则执行下载逻辑（原实现） + return f"/path/to/downloaded/{filename}" + +def video_to_pil_images_list(video_path: str, num_frames: int) -> list[Image.Image]: + """将视频转换为PIL图像列表""" + if not os.path.exists(video_path): + print(f"视频文件不存在: {video_path}") + return [] + + cap = cv2.VideoCapture(video_path) + if not cap.isOpened(): + print(f"无法打开视频: {video_path}") + return [] + + # 获取视频帧数 + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + fps = cap.get(cv2.CAP_PROP_FPS) + duration = total_frames / fps if fps > 0 else 0 + + print(f"视频信息: 总帧数={total_frames}, FPS={fps:.2f}, 时长={duration:.2f}秒") + + # 如果指定了帧数，设置采样间隔；否则读取所有帧 + if num_frames > 0 and num_frames < total_frames: + frame_interval = total_frames / num_frames + print(f"将提取 {num_frames} 帧，采样间隔为每 {frame_interval:.2f} 帧") + else: + frame_interval = 1 + num_frames = total_frames + print(f"将提取所有 {total_frames} 帧") + + pil_images = [] + frame_count = 0 + success = True + last_progress = -1 + + while success and len(pil_images) < num_frames: + # 读取下一帧 + success, frame = cap.read() + if not success: + break + + # 按间隔采样帧 + if frame_count % max(1, int(frame_interval)) == 0: + # OpenCV使用BGR，转为RGB + rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + # 转为PIL图像 + pil_image = Image.fromarray(rgb_frame) + pil_images.append(pil_image) + + # 显示进度（每10%显示一次） + progress = int(len(pil_images) / num_frames * 10) + if progress > last_progress: + print(f"提取进度: {len(pil_images)}/{num_frames} ({len(pil_images)/num_frames*100:.1f}%)") + last_progress = progress + + frame_count += 1 + + cap.release() + print(f"从视频中共提取了 {len(pil_images)} 帧") + return pil_images + +def video_to_ndarrays(video_path: str, num_frames: int) -> NDArray: + """将视频转换为NumPy数组""" + pil_images = video_to_pil_images_list(video_path, num_frames) + if not pil_images: + print(f"未能从视频中提取帧: {video_path}") + return np.zeros((1, 224, 224, 3)) + + # 将PIL图像列表转换为NumPy数组 + arrays = [] + for img in pil_images: + # 调整图像大小为统一尺寸 + img_resized = img.resize((224, 224)) + # 转换为NumPy数组 + arr = np.array(img_resized) + arrays.append(arr) + + # 堆叠为单个NumPy数组，形状为[num_frames, height, width, channels] + stacked_array = np.stack(arrays, axis=0) + print(f"NumPy数组形状: {stacked_array.shape}") + return stacked_array + +# 使用示例 +if __name__ == "__main__": + # 使用本地视频资源 + local_video = LocalVideoAsset( + local_path="/root/autodl-tmp/hot_vedio_analyse/source/sample_demo_1.mp4", + num_frames= 1 + ) + print("本地资源:", local_video.filename) + + # 获取PIL图像列表（实际会调用download_video_asset和转换函数） + pil_images = local_video.pil_images + #print("PIL图像数量:", len(pil_images)) + + # 获取NumPy数组 + #np_arrays = local_video.np_ndarrays + + # 获取音频数据 + #audio = local_video.get_audio(sampling_rate=16000) + #print("音频数据形状:", audio.shape) + + try: + print("尝试加载模型...") + + # 模型和分词器路径 + model_path = "/root/autodl-tmp/llm/Qwen2.5-VL" + + # 使用离线模式加载分词器 + tokenizer = AutoTokenizer.from_pretrained( + model_path, + local_files_only=True, + trust_remote_code=True + ) + + # 采样参数 + sampling_params = SamplingParams( + temperature=0.6, + top_p=0.95, + top_k=20, + max_tokens=1024 + ) + + openai_api_key = "EMPTY" + openai_api_base = "http://localhost:8000/v1" + client = OpenAI( + api_key=openai_api_key, + base_url=openai_api_base, + ) + completion = client.completions.create(model="Qwen/Qwen2.5-1.5B-Instruct", + prompt="San Francisco is a") + print("Completion result:", completion) + + prompt = "这个视频展示了什么内容？详细描述一下。" + + # 使用generate而不是generate_videos (如果不存在generate_videos方法) + try: + # 尝试使用generate_videos + outputs = client.completions(prompt, videos=[pil_images], sampling_params=sampling_params) + print(outputs[0].outputs[0].text) # 打印模型输出 + except AttributeError: + print("generate_videos方法不可用，尝试使用普通generate方法...") + # 如果不支持generate_videos，使用普通的generate + outputs = llm.generate([prompt], sampling_params=sampling_params) + print(outputs[0].outputs[0].text) # 打印模型输出 + + except Exception as e: + print(f"模型加载或推理过程中出错: {e}") + import traceback + traceback.print_exc() + + + + + + + + diff --git a/code/clip.py b/code/clip.py new file mode 100644 index 0000000..c938b30 --- /dev/null +++ b/code/clip.py @@ -0,0 +1,82 @@ +from VideoSplitter import * +import cv2 +from PIL import Image +import matplotlib.pyplot as plt + +def get_pil_images_from_frame_numbers(video_path, frame_numbers): + """ + 从视频中读取指定帧号的帧，并转换为PIL图像 + + Args: + video_path: 视频文件路径 + frame_numbers: 要读取的帧号列表 + + Returns: + pil_images: PIL图像列表 + """ + cap = cv2.VideoCapture(video_path) + fps = cap.get(cv2.CAP_PROP_FPS) + + pil_images = [] + + print(f"开始读取 {len(frame_numbers)} 个指定帧并转换为PIL图像...") + + for i, frame_number in enumerate(frame_numbers): + # 设置视频位置到指定帧 + cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number) + + ret, frame = cap.read() + if ret: + # 将OpenCV的BGR格式转换为RGB格式 + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + # 转换为PIL图像 + pil_image = Image.fromarray(frame_rgb) + + pil_images.append(pil_image) + + print(f"已读取并转换帧 {frame_number} ({i+1}/{len(frame_numbers)})") + else: + print(f"无法读取帧 {frame_number}") + + cap.release() + print(f"完成! 共获得了 {len(pil_images)} 个PIL图像") + return pil_images + +video_path = "/root/autodl-tmp/hot_video_analyse/source/sample_demo_1.mp4" +frames_dir = "/root/autodl-tmp/hot_video_analyse/source/Splitter/hot_video_analyse/sample_demo_1/frames" +output_dir = "/root/autodl-tmp/hot_video_analyse/source/pil_images" +sample_rate = 1 +method = "ssim" +threshold = 0.8 + +# 步骤1: 提取帧 +print("\n步骤1: 正在提取视频帧...") +frames_info = extract_frames(video_path, frames_dir, sample_rate) + +# 步骤2: 检测场景变化 +print("\n步骤2: 正在检测场景变化...") +scenes, a = detect_scene_changes(frames_info, method, threshold) + +print(f"场景开始帧号: {a}") + +# 步骤3: 将场景开始帧转换为PIL图像 +print("\n步骤3: 转换场景开始帧为PIL图像...") +pil_images = get_pil_images_from_frame_numbers(video_path, a) + +# 显示结果 +print(f"\n成功获得 {len(pil_images)} 个PIL图像") + +# 查看第一个PIL图像的信息 +if pil_images: + first_pil = pil_images[0] + pil_output_path = os.path.join(output_dir, f"first_scene_frame_{a[0]}.jpg") + first_pil.save(pil_output_path, quality=95) + print(f"第一个PIL图像信息:") + print(f" 尺寸: {first_pil.size}") + print(f" 模式: {first_pil.mode}") +""" +视频帧 +音频转文字 +全视频 +""" diff --git a/code/npy2jpg.py b/code/npy2jpg.py new file mode 100644 index 0000000..c1b8ec7 --- /dev/null +++ b/code/npy2jpg.py @@ -0,0 +1,107 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import numpy as np +from PIL import Image +import os +import sys + +def npy_to_images(npy_file): + """ + 将NumPy文件转换为图片 + + Args: + npy_file: .npy文件路径 + """ + + # 检查文件是否存在 + if not os.path.exists(npy_file): + print(f"错误: 文件不存在 - {npy_file}") + return + + # 加载NumPy数组 + print(f"加载文件: {npy_file}") + arrays = np.load(npy_file) + print(f"数组形状: {arrays.shape}") + print(f"数据类型: {arrays.dtype}") + + # 创建输出目录 + output_dir = f"images_{os.path.splitext(os.path.basename(npy_file))[0]}" + os.makedirs(output_dir, exist_ok=True) + + # 数据类型转换 + if arrays.dtype == np.float32 or arrays.dtype == np.float64: + if arrays.max() <= 1.0: + # 范围 [0, 1]，转换为 [0, 255] + arrays = (arrays * 255).astype(np.uint8) + else: + # 范围可能是 [0, 255] 的浮点数 + arrays = np.clip(arrays, 0, 255).astype(np.uint8) + else: + arrays = arrays.astype(np.uint8) + + # 根据数组维度处理 + if len(arrays.shape) == 4: + # 4D数组: 多张图片 + print(f"检测到4D数组，转换 {arrays.shape[0]} 张图片") + + for i in range(arrays.shape[0]): + frame = arrays[i] + + # 处理通道顺序 (CHW -> HWC) + if frame.shape[0] == 3 or frame.shape[0] == 1: + frame = np.transpose(frame, (1, 2, 0)) + + # 保存图片 + if len(frame.shape) == 3 and frame.shape[2] == 1: + frame = frame.squeeze(axis=2) # 移除单通道维度 + + img = Image.fromarray(frame) + filename = f"{output_dir}/image_{i+1:04d}.jpg" + img.save(filename) + + if (i + 1) % 10 == 0 or i == 0: + print(f"已保存: {filename}") + + elif len(arrays.shape) == 3: + # 3D数组: 单张图片或多张灰度图 + if arrays.shape[2] == 3 or arrays.shape[2] == 1: + # 单张彩色或灰度图片 (H, W, C) + print("检测到3D数组，转换为单张图片") + if arrays.shape[2] == 1: + arrays = arrays.squeeze(axis=2) + img = Image.fromarray(arrays) + filename = f"{output_dir}/image.jpg" + img.save(filename) + print(f"已保存: {filename}") + else: + # 多张灰度图片 (N, H, W) + print(f"检测到3D数组，转换 {arrays.shape[0]} 张灰度图片") + for i in range(arrays.shape[0]): + img = Image.fromarray(arrays[i]) + filename = f"{output_dir}/image_{i+1:04d}.jpg" + img.save(filename) + if (i + 1) % 10 == 0 or i == 0: + print(f"已保存: {filename}") + + elif len(arrays.shape) == 2: + # 2D数组: 单张灰度图片 + print("检测到2D数组，转换为灰度图片") + img = Image.fromarray(arrays) + filename = f"{output_dir}/image.jpg" + img.save(filename) + print(f"已保存: {filename}") + + else: + print(f"不支持的数组维度: {arrays.shape}") + return + + print(f"所有图片已保存到目录: {output_dir}") + +def main(): + """主函数""" + npy_file = "/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_arrays.npy" + npy_to_images(npy_file) + +if __name__ == "__main__": + main() diff --git a/code/qwen.py b/code/qwen.py new file mode 100644 index 0000000..c4acf04 --- /dev/null +++ b/code/qwen.py @@ -0,0 +1,280 @@ +from vllm import LLM, SamplingParams +from transformers import AutoTokenizer +from vllm.assets.image import ImageAsset +from vllm.assets.video import VideoAsset +from dataclasses import dataclass, field +from typing import Optional, ClassVar, List, Tuple +import numpy as np +from numpy.typing import NDArray +from PIL import Image +import librosa +import librosa.util +import cv2 +import os +from openai import OpenAI +import base64 +# import ray # 注释掉Ray导入 + +# # 使用本地模式初始化Ray，避免分布式通信问题 +# ray.init(local_mode=True, ignore_reinit_error=True) + +# 设置环境变量，禁用在线检查 +os.environ["HF_DATASETS_OFFLINE"] = "1" +os.environ["TRANSFORMERS_OFFLINE"] = "1" +os.environ["HF_HUB_OFFLINE"] = "1" + +@dataclass(frozen=True) +class VideoAsset: + name: str + num_frames: int = -1 + + _NAME_TO_FILE: ClassVar[dict[str, str]] = { + "baby_reading": "sample_demo_1.mp4", + } + + @property + def filename(self) -> str: + return self._NAME_TO_FILE[self.name] + + @property + def pil_images(self) -> list[Image.Image]: + video_path = download_video_asset(self.filename) + return video_to_pil_images_list(video_path, self.num_frames) + + @property + def np_ndarrays(self) -> NDArray: + video_path = download_video_asset(self.filename) + return video_to_ndarrays(video_path, self.num_frames) + + def get_audio(self, sampling_rate: Optional[float] = None) -> NDArray: + video_path = download_video_asset(self.filename) + return librosa.load(video_path, sr=sampling_rate)[0] + +@dataclass(frozen=True) +class LocalVideoAsset: + local_path: str + name: str = "local_video" + num_frames: int = -1 + + @property + def filename(self) -> str: + return self.local_path + + @property + def pil_images(self) -> list[Image.Image]: + return video_to_pil_images_list(self.filename, self.num_frames) + + @property + def np_ndarrays(self) -> NDArray: + return video_to_ndarrays(self.filename, self.num_frames) + + def get_audio(self, sampling_rate: Optional[float] = None) -> NDArray: + try: + if not os.path.exists(self.filename): + print(f"音频文件不存在: {self.filename}") + return np.zeros(1) # 返回空数组 + return librosa.load(self.filename, sr=sampling_rate)[0] + except Exception as e: + print(f"加载音频时出错: {e}") + return np.zeros(1) # 出错时返回空数组 + +# 辅助函数实现 +def download_video_asset(filename: str) -> str: + # 如果路径是绝对路径或相对路径，直接返回 + if filename.startswith("/") or filename.startswith("./"): + return filename + # 否则执行下载逻辑（原实现） + return f"/path/to/downloaded/{filename}" + +def video_to_pil_images_list(video_path: str, num_frames: int) -> list[Image.Image]: + """将视频转换为PIL图像列表""" + if not os.path.exists(video_path): + print(f"视频文件不存在: {video_path}") + return [] + + cap = cv2.VideoCapture(video_path) + if not cap.isOpened(): + print(f"无法打开视频: {video_path}") + return [] + + # 获取视频帧数 + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + fps = cap.get(cv2.CAP_PROP_FPS) + duration = total_frames / fps if fps > 0 else 0 + + print(f"视频信息: 总帧数={total_frames}, FPS={fps:.2f}, 时长={duration:.2f}秒") + + # 如果指定了帧数，设置采样间隔；否则读取所有帧 + if num_frames > 0 and num_frames < total_frames: + frame_interval = total_frames / num_frames + print(f"将提取 {num_frames} 帧，采样间隔为每 {frame_interval:.2f} 帧") + else: + frame_interval = 1 + num_frames = total_frames + print(f"将提取所有 {total_frames} 帧") + + pil_images = [] + frame_count = 0 + success = True + last_progress = -1 + + while success and len(pil_images) < num_frames: + # 读取下一帧 + success, frame = cap.read() + if not success: + break + + # 按间隔采样帧 + if frame_count % max(1, int(frame_interval)) == 0: + # OpenCV使用BGR，转为RGB + rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + # 转为PIL图像 + pil_image = Image.fromarray(rgb_frame) + pil_images.append(pil_image) + + # 显示进度（每10%显示一次） + progress = int(len(pil_images) / num_frames * 10) + if progress > last_progress: + print(f"提取进度: {len(pil_images)}/{num_frames} ({len(pil_images)/num_frames*100:.1f}%)") + last_progress = progress + + frame_count += 1 + + cap.release() + print(f"从视频中共提取了 {len(pil_images)} 帧") + return pil_images + +def video_to_ndarrays(video_path: str, num_frames: int) -> NDArray: + """将视频转换为NumPy数组""" + pil_images = video_to_pil_images_list(video_path, num_frames) + if not pil_images: + print(f"未能从视频中提取帧: {video_path}") + return np.zeros((1, 224, 224, 3)) + + # 将PIL图像列表转换为NumPy数组 + arrays = [] + for img in pil_images: + # 调整图像大小为统一尺寸 + img_resized = img.resize((224, 224)) + # 转换为NumPy数组 + arr = np.array(img_resized) + arrays.append(arr) + + # 堆叠为单个NumPy数组，形状为[num_frames, height, width, channels] + stacked_array = np.stack(arrays, axis=0) + print(f"NumPy数组形状: {stacked_array.shape}") + return stacked_array + +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode("utf-8") + +def encode_audio(audio_path): + with open(audio_path, "rb") as audio_file: + return base64.b64encode(audio_file.read()).decode("utf-8") + +# 使用示例 +if __name__ == "__main__": + # 使用本地视频资源 + # local_video = LocalVideoAsset( + # local_path="/root/autodl-tmp/hot_video_analyse/source/sample_demo_1.mp4", + # num_frames = -1 # 限制帧数以加快测试速度 + # ) + # print("本地资源:", local_video.filename) + + # 获取PIL图像列表（实际会调用download_video_asset和转换函数） + # pil_images = local_video.pil_images + # print("PIL图像数量:", len(pil_images)) + # print(pil_images[0]) + + # 获取NumPy数组 + # print("\n=== 加载视频帧 ===") + # np_arrays = local_video.np_ndarrays + # print(f"视频数组形状: {np_arrays.shape}") + # scene_change_arrays = np.load('/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_arrays.npy') + # print(f"场景变化帧数组形状: {scene_change_arrays.shape}") + + # 获取音频数据 + # audio = local_video.get_audio(sampling_rate=16000) + # print("音频数据形状:", audio.shape) + # print(type(audio)) + base64_audio = encode_audio("/root/autodl-tmp/hot_video_analyse/source/transcription/sample_demo_1_audio.wav") + client = OpenAI( + # 若没有配置环境变量，请用阿里云百炼API Key将下行替换为：api_key="sk-xxx", + api_key=os.getenv("DASHSCOPE_API_KEY"), + base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", + ) + + base64_image_0 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000034_t1.13s_ssim0.5673.jpg") + base64_image_1 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000117_t3.90s_ssim0.1989.jpg") + base64_image_2 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000119_t3.97s_ssim0.2138.jpg") + base64_image_3 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000140_t4.67s_ssim0.5160.jpg") + base64_image_4 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000160_t5.33s_ssim0.4934.jpg") + base64_image_5 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000180_t6.00s_ssim0.3577.jpg") + base64_image_6 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000201_t6.70s_ssim0.3738.jpg") + base64_image_7 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000222_t7.40s_ssim0.6104.jpg") + base64_image_8 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000243_t8.10s_ssim0.5099.jpg") + base64_image_9 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000261_t8.70s_ssim0.4735.jpg") + base64_image_10 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000281_t9.37s_ssim0.2703.jpg") + base64_image_11 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000301_t10.03s_ssim0.2772.jpg") + base64_image_12 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000321_t10.70s_ssim0.3721.jpg") + base64_image_13 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000341_t11.37s_ssim0.3700.jpg") + base64_image_14 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000361_t12.03s_ssim0.3494.jpg") + base64_image_15 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000382_t12.73s_ssim0.3423.jpg") + base64_image_16 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000402_t13.40s_ssim0.3252.jpg") + base64_image_17 = encode_image("/root/autodl-tmp/hot_video_analyse/source/scene_change/scene_change_frames/scene_change_000421_t14.03s_ssim0.2086.jpg") + + + completion = client.chat.completions.create( + model="/root/autodl-tmp/llm/Qwen2.5-Omni", + messages=[ + { + "role": "user", + "content": [ + { + "type": "video", + "video": [ + f"data:image/jpeg;base64,{base64_image_1}", + f"data:image/jpeg;base64,{base64_image_2}", + f"data:image/jpeg;base64,{base64_image_3}", + f"data:image/jpeg;base64,{base64_image_4}", + f"data:image/jpeg;base64,{base64_image_5}", + f"data:image/jpeg;base64,{base64_image_6}", + f"data:image/jpeg;base64,{base64_image_7}", + f"data:image/jpeg;base64,{base64_image_8}", + f"data:image/jpeg;base64,{base64_image_9}", + f"data:image/jpeg;base64,{base64_image_10}", + f"data:image/jpeg;base64,{base64_image_11}", + f"data:image/jpeg;base64,{base64_image_12}", + f"data:image/jpeg;base64,{base64_image_13}", + f"data:image/jpeg;base64,{base64_image_14}", + f"data:image/jpeg;base64,{base64_image_15}", + f"data:image/jpeg;base64,{base64_image_16}", + f"data:image/jpeg;base64,{base64_image_17}", + ], + }, + { + "type": "audio", + "audio": f"data:audio/mpeg;base64,{base64_audio}", + "format": "wav", + }, + {"type": "text", "text": "描述这个视频的具体过程"}, + ], + } + ], + # 设置输出数据的模态，当前支持两种：["text","audio"]、["text"] + modalities=["text"], + #audio={"voice": "Cherry", "format": "wav"}, + # stream 必须设置为 True，否则会报错 + stream=True, + stream_options={"include_usage": True}, + ) + + for chunk in completion: + if chunk.choices: + print(chunk.choices[0].delta) + else: + print(chunk.usage) + + + diff --git a/code/video_split.py b/code/video_split.py new file mode 100644 index 0000000..767c1c9 --- /dev/null +++ b/code/video_split.py @@ -0,0 +1,540 @@ +from vllm import LLM, SamplingParams +from transformers import AutoTokenizer +from vllm.assets.image import ImageAsset +from vllm.assets.video import VideoAsset +from dataclasses import dataclass, field +from typing import Optional, ClassVar, List, Tuple +import numpy as np +from numpy.typing import NDArray +from PIL import Image +import librosa +import librosa.util +import os +import cv2 +from skimage.metrics import structural_similarity as ssim +# import ray # 注释掉Ray导入 + +# # 使用本地模式初始化Ray，避免分布式通信问题 +# ray.init(local_mode=True, ignore_reinit_error=True) + +# 设置环境变量，禁用在线检查 +os.environ["HF_DATASETS_OFFLINE"] = "1" +os.environ["TRANSFORMERS_OFFLINE"] = "1" +os.environ["HF_HUB_OFFLINE"] = "1" + +@dataclass(frozen=True) +class VideoAsset: + name: str + num_frames: int = -1 + + _NAME_TO_FILE: ClassVar[dict[str, str]] = { + "baby_reading": "sample_demo_1.mp4", + } + + @property + def filename(self) -> str: + return self._NAME_TO_FILE[self.name] + + @property + def pil_images(self) -> list[Image.Image]: + video_path = download_video_asset(self.filename) + return video_to_pil_images_list(video_path, self.num_frames) + + @property + def np_ndarrays(self) -> NDArray: + video_path = download_video_asset(self.filename) + return video_to_ndarrays(video_path, self.num_frames) + + def get_audio(self, sampling_rate: Optional[float] = None) -> NDArray: + video_path = download_video_asset(self.filename) + return librosa.load(video_path, sr=sampling_rate)[0] + +@dataclass(frozen=True) +class LocalVideoAsset: + local_path: str + name: str = "local_video" + num_frames: int = -1 + + @property + def filename(self) -> str: + return self.local_path + + @property + def pil_images(self) -> list[Image.Image]: + return video_to_pil_images_list(self.filename, self.num_frames) + + @property + def np_ndarrays(self) -> NDArray: + return video_to_ndarrays(self.filename, self.num_frames) + + def get_audio(self, sampling_rate: Optional[float] = None) -> NDArray: + try: + if not os.path.exists(self.filename): + print(f"音频文件不存在: {self.filename}") + return np.zeros(1) # 返回空数组 + return librosa.load(self.filename, sr=sampling_rate)[0] + except Exception as e: + print(f"加载音频时出错: {e}") + return np.zeros(1) # 出错时返回空数组 + +# 辅助函数实现 +def download_video_asset(filename: str) -> str: + # 如果路径是绝对路径或相对路径，直接返回 + if filename.startswith("/") or filename.startswith("./"): + return filename + # 否则执行下载逻辑（原实现） + return f"/path/to/downloaded/{filename}" + +def video_to_pil_images_list(video_path: str, num_frames: int) -> list[Image.Image]: + """将视频转换为PIL图像列表""" + if not os.path.exists(video_path): + print(f"视频文件不存在: {video_path}") + return [] + + cap = cv2.VideoCapture(video_path) + if not cap.isOpened(): + print(f"无法打开视频: {video_path}") + return [] + + # 获取视频帧数 + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + fps = cap.get(cv2.CAP_PROP_FPS) + duration = total_frames / fps if fps > 0 else 0 + + print(f"视频信息: 总帧数={total_frames}, FPS={fps:.2f}, 时长={duration:.2f}秒") + + # 如果指定了帧数，设置采样间隔；否则读取所有帧 + if num_frames > 0 and num_frames < total_frames: + frame_interval = total_frames / num_frames + print(f"将提取 {num_frames} 帧，采样间隔为每 {frame_interval:.2f} 帧") + else: + frame_interval = 1 + num_frames = total_frames + print(f"将提取所有 {total_frames} 帧") + + pil_images = [] + frame_count = 0 + success = True + last_progress = -1 + + while success and len(pil_images) < num_frames: + # 读取下一帧 + success, frame = cap.read() + if not success: + break + + # 按间隔采样帧 + if frame_count % max(1, int(frame_interval)) == 0: + # OpenCV使用BGR，转为RGB + rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + # 转为PIL图像 + pil_image = Image.fromarray(rgb_frame) + pil_images.append(pil_image) + + # 显示进度（每10%显示一次） + progress = int(len(pil_images) / num_frames * 10) + if progress > last_progress: + print(f"提取进度: {len(pil_images)}/{num_frames} ({len(pil_images)/num_frames*100:.1f}%)") + last_progress = progress + + frame_count += 1 + + cap.release() + print(f"从视频中共提取了 {len(pil_images)} 帧") + return pil_images + +def video_to_ndarrays(video_path: str, num_frames: int) -> NDArray: + """将视频转换为NumPy数组""" + pil_images = video_to_pil_images_list(video_path, num_frames) + if not pil_images: + print(f"未能从视频中提取帧: {video_path}") + return np.zeros((1, 224, 224, 3)) + + # 将PIL图像列表转换为NumPy数组 + arrays = [] + for img in pil_images: + # 调整图像大小为统一尺寸 + img_resized = img.resize((224, 224)) + # 转换为NumPy数组 + arr = np.array(img_resized) + arrays.append(arr) + + # 堆叠为单个NumPy数组，形状为[num_frames, height, width, channels] + stacked_array = np.stack(arrays, axis=0) + print(f"NumPy数组形状: {stacked_array.shape}") + return stacked_array + +@dataclass +class SceneChangeFrame: + """场景变化帧信息""" + frame_index: int + timestamp: float + ssim_score: float + is_scene_change: bool + +def calculate_ssim_between_frames(frame1: np.ndarray, frame2: np.ndarray) -> float: + """ + 计算两帧之间的SSIM相似度 + + Args: + frame1: 第一帧图像 (H, W, C) + frame2: 第二帧图像 (H, W, C) + + Returns: + SSIM相似度分数 (0-1之间) + """ + try: + # 转换为灰度图像以提高计算效率 + if len(frame1.shape) == 3: + gray1 = cv2.cvtColor(frame1, cv2.COLOR_RGB2GRAY) + else: + gray1 = frame1 + + if len(frame2.shape) == 3: + gray2 = cv2.cvtColor(frame2, cv2.COLOR_RGB2GRAY) + else: + gray2 = frame2 + + # 确保两个图像尺寸相同 + if gray1.shape != gray2.shape: + gray2 = cv2.resize(gray2, (gray1.shape[1], gray1.shape[0])) + + # 计算SSIM + score = ssim(gray1, gray2, data_range=255) + return score + except Exception as e: + print(f"计算SSIM时出错: {e}") + return 1.0 # 出错时返回高相似度 + +def detect_scene_changes_from_arrays(video_arrays: np.ndarray, + similarity_threshold: float = 0.8, + fps: float = 30.0) -> List[SceneChangeFrame]: + """ + 从视频NumPy数组中检测场景变化 + + Args: + video_arrays: 视频帧数组，形状为 (num_frames, height, width, channels) + similarity_threshold: SSIM相似度阈值，低于此值认为是场景变化 + fps: 视频帧率，用于计算时间戳 + + Returns: + 场景变化帧列表 + """ + if len(video_arrays) < 2: + print("视频帧数不足，无法进行场景变化检测") + return [] + + print(f"开始检测场景变化...") + print(f"视频帧数: {len(video_arrays)}") + print(f"帧尺寸: {video_arrays.shape[1:3]}") + print(f"SSIM相似度阈值: {similarity_threshold}") + print(f"视频帧率: {fps} FPS") + + scene_changes = [] + + # 逐帧比较相似度 + for i in range(1, len(video_arrays)): + # 获取前后两帧 + prev_frame = video_arrays[i-1] + curr_frame = video_arrays[i] + + # 计算SSIM相似度 + ssim_score = calculate_ssim_between_frames(prev_frame, curr_frame) + + # 计算时间戳 + timestamp = i / fps + + # 判断是否为场景变化 + is_scene_change = ssim_score < similarity_threshold + + # 创建场景变化信息 + scene_change = SceneChangeFrame( + frame_index=i, + timestamp=timestamp, + ssim_score=ssim_score, + is_scene_change=is_scene_change + ) + + scene_changes.append(scene_change) + + # 输出场景变化信息 + if is_scene_change: + print(f"场景变化检测到! 帧号: {i}, 时间: {timestamp:.2f}s, SSIM: {ssim_score:.4f}") + + # 显示进度 + if i % 100 == 0: + progress = i / len(video_arrays) * 100 + print(f"检测进度: {i}/{len(video_arrays)} ({progress:.1f}%)") + + # 统计结果 + scene_change_count = sum(1 for sc in scene_changes if sc.is_scene_change) + print(f"\n检测完成!") + print(f"总比较帧数: {len(scene_changes)}") + print(f"场景变化数量: {scene_change_count}") + + return scene_changes + +def get_scene_change_frames(scene_changes: List[SceneChangeFrame]) -> List[SceneChangeFrame]: + """ + 获取所有场景变化帧（相似度低于阈值的帧） + + Args: + scene_changes: 场景变化检测结果列表 + + Returns: + 场景变化帧列表 + """ + return [sc for sc in scene_changes if sc.is_scene_change] + +def save_scene_change_frames_from_arrays(video_arrays: np.ndarray, + scene_change_frames: List[SceneChangeFrame], + output_dir: str = "scene_change_frames"): + """ + 保存场景变化帧为图像文件 + + Args: + video_arrays: 视频帧数组 + scene_change_frames: 场景变化帧列表 + output_dir: 输出目录 + """ + if not scene_change_frames: + print("没有检测到场景变化帧") + return + + # 创建输出目录 + os.makedirs(output_dir, exist_ok=True) + + print(f"保存 {len(scene_change_frames)} 个场景变化帧到 {output_dir}") + + for i, scene_change in enumerate(scene_change_frames): + frame_index = scene_change.frame_index + + if frame_index < len(video_arrays): + # 获取帧图像 + frame = video_arrays[frame_index] + + # 转换为PIL图像并保存 + pil_image = Image.fromarray(frame.astype(np.uint8)) + filename = f"scene_change_{frame_index:06d}_t{scene_change.timestamp:.2f}s_ssim{scene_change.ssim_score:.4f}.jpg" + filepath = os.path.join(output_dir, filename) + pil_image.save(filepath) + + if (i + 1) % 10 == 0: + print(f"已保存 {i + 1}/{len(scene_change_frames)} 帧") + + print(f"所有场景变化帧已保存到: {output_dir}") + +def export_scene_change_results(scene_change_frames: List[SceneChangeFrame], + similarity_threshold: float, + output_file: str = "scene_change_results.txt"): + """ + 导出场景变化检测结果到文件 + + Args: + scene_change_frames: 场景变化帧列表 + similarity_threshold: 使用的相似度阈值 + output_file: 输出文件路径 + """ + with open(output_file, 'w', encoding='utf-8') as f: + f.write(f"场景变化检测结果\n") + f.write(f"相似度阈值: {similarity_threshold}\n") + f.write(f"场景变化数量: {len(scene_change_frames)}\n") + f.write("-" * 50 + "\n") + + for sc in scene_change_frames: + f.write(f"帧号: {sc.frame_index:6d}, " + f"时间: {sc.timestamp:8.2f}s, " + f"SSIM: {sc.ssim_score:.6f}\n") + + print(f"检测结果已导出到: {output_file}") + +def get_scene_change_frames_arrays(video_arrays: np.ndarray, + scene_change_frames: List[SceneChangeFrame]) -> np.ndarray: + """ + 获取场景变化帧对应的NumPy数组集合 + + Args: + video_arrays: 完整的视频帧数组，形状为 (num_frames, height, width, channels) + scene_change_frames: 场景变化帧列表 + + Returns: + 场景变化帧的NumPy数组，形状为 (num_scene_changes, height, width, channels) + """ + if not scene_change_frames: + print("没有场景变化帧") + return np.array([]) + + # 提取场景变化帧的索引 + scene_change_indices = [sc.frame_index for sc in scene_change_frames] + + # 确保索引在有效范围内 + valid_indices = [idx for idx in scene_change_indices if 0 <= idx < len(video_arrays)] + + if not valid_indices: + print("没有有效的场景变化帧索引") + return np.array([]) + + # 提取对应的帧数组 + scene_change_arrays = video_arrays[valid_indices] + + print(f"提取了 {len(valid_indices)} 个场景变化帧的NumPy数组") + print(f"场景变化帧数组形状: {scene_change_arrays.shape}") + + return scene_change_arrays + +# 使用示例 +if __name__ == "__main__": + # 使用本地视频资源 + local_video = LocalVideoAsset( + local_path="/root/autodl-tmp/hot_video_analyse/source/sample_demo_1.mp4", + num_frames = -1 # 限制帧数以加快测试速度 + ) + print("本地资源:", local_video.filename) + + # 获取PIL图像列表（实际会调用download_video_asset和转换函数） + # pil_images = local_video.pil_images + # print("PIL图像数量:", len(pil_images)) + # print(pil_images[0]) + + # 获取NumPy数组 + print("\n=== 加载视频帧 ===") + np_arrays = local_video.np_ndarrays + print(f"视频数组形状: {np_arrays.shape}") + + # 设置检测参数 + similarity_threshold = 0.7 # SSIM相似度阈值 + fps = 30.0 # 假设帧率为30fps，可以根据实际视频调整 + + print(f"\n=== 开始场景变化检测 ===") + print(f"相似度阈值: {similarity_threshold}") + + # 检测场景变化 + scene_changes = detect_scene_changes_from_arrays( + video_arrays=np_arrays, + similarity_threshold=similarity_threshold, + fps=fps + ) + + # 获取场景变化帧（相似度低于阈值的帧） + scene_change_frames = get_scene_change_frames(scene_changes) + + # 获取场景变化帧对应的NumPy数组集合 + scene_change_arrays = get_scene_change_frames_arrays(np_arrays, scene_change_frames) + + print(f"\n=== 场景变化帧NumPy数组信息 ===") + if len(scene_change_arrays) > 0: + print(f"场景变化帧数组形状: {scene_change_arrays.shape}") + print(f"数据类型: {scene_change_arrays.dtype}") + print(f"数组大小: {scene_change_arrays.nbytes / (1024*1024):.2f} MB") + + # 获取详细信息（包含帧信息和数组） + print(f"\n=== 检测结果摘要 ===") + print(f"低于阈值 {similarity_threshold} 的帧数: {len(scene_change_frames)}") + + if scene_change_frames: + print(f"\n所有场景变化帧:") + for i, sc in enumerate(scene_change_frames): + print(f"{i+1:3d}. 帧号: {sc.frame_index:6d}, " + f"时间: {sc.timestamp:7.2f}s, " + f"SSIM: {sc.ssim_score:.6f}") + + # 保存场景变化帧图像 + print(f"\n=== 保存场景变化帧 ===") + save_scene_change_frames_from_arrays( + video_arrays=np_arrays, + scene_change_frames=scene_change_frames, + output_dir="scene_change_frames" + ) + + # 导出结果到文件 + export_scene_change_results( + scene_change_frames=scene_change_frames, + similarity_threshold=similarity_threshold, + output_file="scene_change_results.txt" + ) + + # 演示如何使用场景变化帧的NumPy数组 + print(f"\n=== 场景变化帧NumPy数组使用示例 ===") + print(f"场景变化帧数组变量名: scene_change_arrays") + print(f"可以直接使用这个数组进行进一步处理，例如:") + print(f"- 输入到深度学习模型") + print(f"- 进行图像处理操作") + print(f"- 保存为其他格式") + + # 保存场景变化帧数组到文件（可选） + print(f"\n=== 保存场景变化帧数组 ===") + if len(scene_change_arrays) > 0: + np.save("scene_change_arrays.npy", scene_change_arrays) + print(f"场景变化帧NumPy数组已保存到: scene_change_arrays.npy") + print(f"可以使用 np.load('scene_change_arrays.npy') 重新加载") + else: + print("没有检测到场景变化帧") + print("建议:") + print("1. 降低相似度阈值（如0.7或0.6）") + print("2. 检查视频是否包含场景变化") + print("3. 增加视频帧数进行更全面的检测") + + # 获取音频数据 + audio = local_video.get_audio(sampling_rate=16000) + print("音频数据形状:", audio.shape) + print(type(audio)) + """ + try: + print("尝试加载模型...") + + # 模型和分词器路径 + model_path = "/root/autodl-tmp/llm/Qwen2.5-VL" + + # 使用离线模式加载分词器 + tokenizer = AutoTokenizer.from_pretrained( + model_path, + local_files_only=True, + trust_remote_code=True + ) + + # 采样参数 + sampling_params = SamplingParams( + temperature=0.6, + top_p=0.95, + top_k=20, + max_tokens=1024 + ) + + # 离线模式初始化模型 + llm = LLM( + model=model_path, + tokenizer=model_path, # 使用相同的路径作为分词器路径 + max_model_len=4096, + tensor_parallel_size=1, # 减少为1，避免多GPU通信问题 + gpu_memory_utilization=0.8, # 稍微降低内存使用率 + trust_remote_code=True, + enable_lora=False, # 禁用LoRA + ) + + # 使用提取的PIL图像 + print("生成视频描述...") + prompt = "这个视频展示了什么内容？详细描述一下。" + + # 使用generate而不是generate_videos (如果不存在generate_videos方法) + try: + # 尝试使用generate_videos + outputs = llm.generate_videos(prompt, videos=[pil_images], sampling_params=sampling_params) + print(outputs[0].outputs[0].text) # 打印模型输出 + except AttributeError: + print("generate_videos方法不可用，尝试使用普通generate方法...") + # 如果不支持generate_videos，使用普通的generate + outputs = llm.generate([prompt], sampling_params=sampling_params) + print(outputs[0].outputs[0].text) # 打印模型输出 + + except Exception as e: + print(f"模型加载或推理过程中出错: {e}") + import traceback + traceback.print_exc() + """ + + + + + + + diff --git a/code/video_to_text.py b/code/video_to_text.py new file mode 100644 index 0000000..3dd215b --- /dev/null +++ b/code/video_to_text.py @@ -0,0 +1,267 @@ +import os +import subprocess +import whisper +import torch +from moviepy import VideoFileClip +import warnings +warnings.filterwarnings("ignore") + +class VideoToTextConverter: + def __init__(self, model_size="base"): + """ + 初始化视频转文字转换器 + + Args: + model_size: Whisper模型大小 ("tiny", "base", "small", "medium", "large") + """ + self.model_size = model_size + self.model = None + self.load_model() + + def load_model(self): + """加载Whisper模型""" + try: + print(f"正在加载Whisper {self.model_size} 模型...") + self.model = whisper.load_model(self.model_size) + print("模型加载成功!") + except Exception as e: + print(f"模型加载失败: {e}") + print("尝试使用CPU模式...") + self.model = whisper.load_model(self.model_size, device="cpu") + + def extract_audio_moviepy(self, video_path, audio_path): + """ + 使用moviepy提取音频 + + Args: + video_path: 视频文件路径 + audio_path: 输出音频文件路径 + """ + try: + print("正在使用moviepy提取音频...") + video = VideoFileClip(video_path) + audio = video.audio + audio.write_audiofile(audio_path, verbose=False, logger=None) + audio.close() + video.close() + print(f"音频提取成功: {audio_path}") + return True + except Exception as e: + print(f"moviepy提取音频失败: {e}") + return False + + def extract_audio_ffmpeg(self, video_path, audio_path): + """ + 使用ffmpeg提取音频 + + Args: + video_path: 视频文件路径 + audio_path: 输出音频文件路径 + """ + try: + print("正在使用ffmpeg提取音频...") + cmd = [ + 'ffmpeg', '-i', video_path, + '-vn', # 不包含视频 + '-acodec', 'pcm_s16le', # 音频编码 + '-ar', '16000', # 采样率 + '-ac', '1', # 单声道 + '-y', # 覆盖输出文件 + audio_path + ] + + result = subprocess.run(cmd, capture_output=True, text=True) + if result.returncode == 0: + print(f"音频提取成功: {audio_path}") + return True + else: + print(f"ffmpeg错误: {result.stderr}") + return False + except Exception as e: + print(f"ffmpeg提取音频失败: {e}") + return False + + def extract_audio(self, video_path, audio_path=None): + """ + 提取视频中的音频 + + Args: + video_path: 视频文件路径 + audio_path: 输出音频文件路径，如果为None则自动生成 + + Returns: + audio_path: 成功提取的音频文件路径，失败返回None + """ + if audio_path is None: + # 自动生成音频文件路径 + video_dir = os.path.dirname(video_path) + video_name = os.path.splitext(os.path.basename(video_path))[0] + audio_path = os.path.join(video_dir, f"{video_name}_audio.wav") + + # 确保输出目录存在 + os.makedirs(os.path.dirname(audio_path), exist_ok=True) + + # 首先尝试使用moviepy + if self.extract_audio_moviepy(video_path, audio_path): + return audio_path + + # 如果moviepy失败，尝试使用ffmpeg + if self.extract_audio_ffmpeg(video_path, audio_path): + return audio_path + + print("所有音频提取方法都失败了") + return None + + def transcribe_audio(self, audio_path, language="zh"): + """ + 将音频转换为文字 + + Args: + audio_path: 音频文件路径 + language: 语言代码 ("zh"中文, "en"英文, None自动检测) + + Returns: + dict: 包含转录结果的字典 + """ + if self.model is None: + print("模型未加载，无法进行转录") + return None + + try: + print("正在进行语音识别...") + + # 设置转录选项 + options = { + "language": language if language != "auto" else None, + "task": "transcribe", + "fp16": torch.cuda.is_available() # 如果有GPU则使用半精度 + } + + result = self.model.transcribe(audio_path, **options) + + print("语音识别完成!") + return result + + except Exception as e: + print(f"语音识别失败: {e}") + return None + + def video_to_text(self, video_path, output_dir=None, language="zh", save_audio=True): + """ + 完整的视频转文字流程 + + Args: + video_path: 视频文件路径 + output_dir: 输出目录，如果为None则使用视频所在目录 + language: 语言代码 + save_audio: 是否保存提取的音频文件 + + Returns: + dict: 包含转录结果和文件路径的字典 + """ + if output_dir is None: + output_dir = os.path.dirname(video_path) + + video_name = os.path.splitext(os.path.basename(video_path))[0] + + # 提取音频 + audio_path = os.path.join(output_dir, f"{video_name}_audio.wav") + extracted_audio = self.extract_audio(video_path, audio_path) + + if extracted_audio is None: + return {"success": False, "error": "音频提取失败"} + + # 转录音频 + transcription = self.transcribe_audio(extracted_audio, language) + + if transcription is None: + return {"success": False, "error": "语音识别失败"} + + # 保存转录结果 + text_path = os.path.join(output_dir, f"{video_name}_transcription.txt") + with open(text_path, 'w', encoding='utf-8') as f: + f.write(transcription["text"]) + + # 保存详细结果（包含时间戳） + detailed_path = os.path.join(output_dir, f"{video_name}_detailed.txt") + with open(detailed_path, 'w', encoding='utf-8') as f: + for segment in transcription["segments"]: + start_time = segment["start"] + end_time = segment["end"] + text = segment["text"] + f.write(f"[{start_time:.2f}s - {end_time:.2f}s]: {text}\n") + + # 如果不需要保存音频文件，则删除 + if not save_audio and os.path.exists(extracted_audio): + os.remove(extracted_audio) + extracted_audio = None + + result = { + "success": True, + "text": transcription["text"], + "segments": transcription["segments"], + "language": transcription["language"], + "audio_path": extracted_audio, + "text_path": text_path, + "detailed_path": detailed_path + } + + return result + + def print_result(self, result): + """打印转录结果""" + if not result["success"]: + print(f"转录失败: {result['error']}") + return + + print("\n" + "="*50) + print("视频转文字结果") + print("="*50) + print(f"检测到的语言: {result['language']}") + print(f"文本文件: {result['text_path']}") + print(f"详细文件: {result['detailed_path']}") + if result['audio_path']: + print(f"音频文件: {result['audio_path']}") + + print("\n完整文本:") + print("-" * 30) + print(result["text"]) + + print("\n分段文本 (前5段):") + print("-" * 30) + for i, segment in enumerate(result["segments"][:5]): + start_time = segment["start"] + end_time = segment["end"] + text = segment["text"] + print(f"[{start_time:.2f}s - {end_time:.2f}s]: {text}") + + if len(result["segments"]) > 5: + print(f"... 还有 {len(result['segments']) - 5} 个分段") + + +def main(): + """示例用法""" + # 初始化转换器 + converter = VideoToTextConverter(model_size="base") + + # 视频文件路径 + video_path = "/root/autodl-tmp/hot_video_analyse/source/sample_demo_1.mp4" + output_dir = "/root/autodl-tmp/hot_video_analyse/source/transcription" + + # 确保输出目录存在 + os.makedirs(output_dir, exist_ok=True) + + # 转换视频为文字 + result = converter.video_to_text( + video_path=video_path, + output_dir=output_dir, + language="auto", # 中文，也可以用"en"英文或"auto"自动检测 + save_audio=True + ) + + # 打印结果 + converter.print_result(result) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/source/Splitter/hot_video_analyse/sample_demo_1/clips/clip_000_1.10s.mp4 b/source/Splitter/hot_video_analyse/sample_demo_1/clips/clip_000_1.10s.mp4 new file mode 100644 index 0000000..bee13e3 Binary files 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b/source/scene_change/scene_change_frames/scene_change_000421_t14.03s_ssim0.2086.jpg new file mode 100644 index 0000000..2f0e033 Binary files /dev/null and b/source/scene_change/scene_change_frames/scene_change_000421_t14.03s_ssim0.2086.jpg differ diff --git a/source/scene_change/scene_change_results.txt b/source/scene_change/scene_change_results.txt new file mode 100644 index 0000000..b664946 --- /dev/null +++ b/source/scene_change/scene_change_results.txt @@ -0,0 +1,22 @@ +场景变化检测结果 +相似度阈值: 0.7 +场景变化数量: 18 +-------------------------------------------------- +帧号: 34, 时间: 1.13s, SSIM: 0.567270 +帧号: 117, 时间: 3.90s, SSIM: 0.198907 +帧号: 119, 时间: 3.97s, SSIM: 0.213831 +帧号: 140, 时间: 4.67s, SSIM: 0.516040 +帧号: 160, 时间: 5.33s, SSIM: 0.493418 +帧号: 180, 时间: 6.00s, SSIM: 0.357658 +帧号: 201, 时间: 6.70s, SSIM: 0.373841 +帧号: 222, 时间: 7.40s, SSIM: 0.610406 +帧号: 243, 时间: 8.10s, SSIM: 0.509904 +帧号: 261, 时间: 8.70s, SSIM: 0.473469 +帧号: 281, 时间: 9.37s, SSIM: 0.270296 +帧号: 301, 时间: 10.03s, SSIM: 0.277190 +帧号: 321, 时间: 10.70s, SSIM: 0.372099 +帧号: 341, 时间: 11.37s, SSIM: 0.370019 +帧号: 361, 时间: 12.03s, SSIM: 0.349443 +帧号: 382, 时间: 12.73s, SSIM: 0.342284 +帧号: 402, 时间: 13.40s, SSIM: 0.325209 +帧号: 421, 时间: 14.03s, SSIM: 0.208583 diff --git a/source/transcription/sample_demo_1_audio.wav b/source/transcription/sample_demo_1_audio.wav new file mode 100644 index 0000000..de8f0cf Binary files /dev/null and b/source/transcription/sample_demo_1_audio.wav differ diff --git a/source/transcription/sample_demo_1_detailed.txt b/source/transcription/sample_demo_1_detailed.txt new file mode 100644 index 0000000..f152e6f --- /dev/null +++ b/source/transcription/sample_demo_1_detailed.txt @@ -0,0 +1 @@ +[0.00s - 2.00s]: I diff --git a/source/transcription/sample_demo_1_transcription.txt b/source/transcription/sample_demo_1_transcription.txt new file mode 100644 index 0000000..09bffbd --- /dev/null +++ b/source/transcription/sample_demo_1_transcription.txt @@ -0,0 +1 @@ + I \ No newline at end of file