refactor(video_pipeline): 重构视频处理管道

- 新增参数以支持更灵活的配置
-优化脚本保存逻辑，支持自定义脚本名称
-增加视频下载和脚本生成的条件判断，提高效率
- 异步生成最终视频，返回任务路径
- 更新示例使用新的管道配置

video_pipeline.py

@@ -1,5 +1,6 @@
 import requests
 import json
+import os
 import time
 from typing import Dict, Any
 
@@ -52,7 +53,7 @@ class VideoPipeline:
         return response.json()
     
     def generate_final_video(self, task_id: str, video_path: str, 
-                           script_path: str, script: list, subclip_videos: Dict[str, str]) -> Dict[str, Any]:
+                           script_path: str, script: list, subclip_videos: Dict[str, str], voice_name: str) -> Dict[str, Any]:
         """生成最终视频的第四步"""
         endpoint = f"{self.base_url}/api/v2/scripts/start-subclip"
         
@@ -62,7 +63,7 @@ class VideoPipeline:
             "video_origin_path": video_path,
             "video_aspect": "16:9",
             "video_language": "zh-CN",
-            "voice_name": "zh-CN-YunjianNeural",
+            "voice_name": voice_name,
             "voice_volume": 1,
             "voice_rate": 1.2,
             "voice_pitch": 1,
@@ -92,10 +93,9 @@ class VideoPipeline:
         response.raise_for_status()
         return response.json()
     
-    def save_script_to_json(self, script: list) -> str:
+    def save_script_to_json(self, script: list, script_name: str) -> str:
         """保存脚本到json文件"""
-        timestamp = time.strftime("%Y-%m%d-%H%M%S")
-        script_path = f"E:\\projects\\NarratoAI\\resource\\scripts\\{timestamp}.json"
+        script_path = f"E:\\projects\\NarratoAI\\resource\\scripts\\{script_name}.json"
         
         try:
             with open(script_path, 'w', encoding='utf-8') as f:
@@ -106,45 +106,55 @@ class VideoPipeline:
             print(f"保存脚本失败: {str(e)}")
             raise
     
-    def run_pipeline(self, youtube_url: str) -> Dict[str, Any]:
+    def run_pipeline(self, task_id: str, script_name: str, youtube_url: str, video_name: str="null", skip_seconds: int = 0, threshold: int = 30, vision_batch_size: int = 10, vision_llm_provider: str = "gemini", voice_name: str = "zh-CN-YunjianNeural") -> Dict[str, Any]:
         """运行完整的pipeline"""
         try:
+            current_path = os.path.dirname(os.path.abspath(__file__))
+            video_path = os.path.join(current_path, "resource", "videos", video_name)
+            # 判断视频是否存在
+            if not os.path.exists(video_path):
                 # 1. 下载视频
-            print("开始下载视频...")
-            download_result = self.download_video(youtube_url)
+                print(f"视频不存在, 开始下载视频: {video_path}")
+                download_result = self.download_video(youtube_url=youtube_url, resolution="1080p", output_format="mp4", rename=video_name)
                 video_path = download_result["output_path"]
+            else:
+                print(f"视频已存在: {video_path}")
             
-            # 2. 生成脚本
+            # 2. 判断script_name是否存在
+            # 2.1.1 拼接脚本路径 NarratoAI/resource/scripts
+            script_path = os.path.join(current_path, "resource", "scripts", script_name)
+            if os.path.exists(script_path):
+                script = json.load(open(script_path, "r", encoding="utf-8"))
+            else:
+                # 2.1.2 生成脚本
                 print("开始生成脚本...")
-            script_result = self.generate_script(video_path)
+                script_result = self.generate_script(video_path=video_path, skip_seconds=skip_seconds, threshold=threshold, vision_batch_size=vision_batch_size, vision_llm_provider=vision_llm_provider)
                 script = script_result["script"]
             
-            # 2.1 保存脚本到json文件
+            # 2.2 保存脚本到json文件
             print("保存脚本到json文件...")
-            script_path = self.save_script_to_json(script)
+            script_path = self.save_script_to_json(script, script_name)
             script_result["script_path"] = script_path
             
             # 3. 剪辑视频
             print("开始剪辑视频...")
-            crop_result = self.crop_video(video_path, script)
+            crop_result = self.crop_video(video_path=video_path, script=script)
             subclip_videos = crop_result["subclip_videos"]
             
             # 4. 生成最终视频
             print("开始生成最终视频...")
             final_result = self.generate_final_video(
-                crop_result["task_id"],
-                video_path,
-                script_path,
-                script,
-                subclip_videos
+                task_id=task_id,
+                video_path=video_path,
+                script_path=script_path,
+                script=script,
+                subclip_videos=subclip_videos,
+                voice_name=voice_name
             )
             
             return {
-                "status": "success",
-                "download_result": download_result,
-                "script_result": script_result,
-                "crop_result": crop_result,
-                "final_result": final_result
+                "status": "等待异步生成视频",
+                "path": os.path.join(current_path, "storage", "tasks", task_id)
             }
             
         except Exception as e:
@@ -153,10 +163,19 @@ class VideoPipeline:
                 "error": str(e)
             }
 
+
 # 使用示例
 if __name__ == "__main__":
     pipeline = VideoPipeline()
-    result = pipeline.run_pipeline("https://www.youtube.com/watch?v=Kenm35gdqtk")
-    print(json.dumps(result, indent=2, ensure_ascii=False))
-    result2 = pipeline.run_pipeline("https://www.youtube.com/watch?v=aEsHAcedzgw")
-    print(json.dumps(result2, indent=2, ensure_ascii=False))
+    result = pipeline.run_pipeline(
+        task_id="test_123",
+        script_name="test.json",
+        youtube_url="https://www.youtube.com/watch?v=Kenm35gdqtk",
+        video_name="test.mp4",
+        skip_seconds=0,
+        threshold=30,
+        vision_batch_size=10,
+        vision_llm_provider="gemini",
+        voice_name="zh-CN-YunjianNeural",
+    )
+    print(result)