fix: 添加工作流
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279
examples/jsonrpc_client_demo.py
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279
examples/jsonrpc_client_demo.py
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#!/usr/bin/env python3
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"""
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JSON-RPC Client Demo
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JSON-RPC 客户端演示
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演示如何使用Python客户端调用场景检测JSON-RPC API
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"""
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import json
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import requests
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import time
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from typing import Dict, Any, Optional
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class SceneDetectionClient:
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"""场景检测JSON-RPC客户端"""
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def __init__(self, server_url: str = "http://localhost:8080"):
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self.server_url = server_url
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self.request_id = 0
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def _call_method(self, method: str, params: Dict[str, Any]) -> Dict[str, Any]:
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"""调用JSON-RPC方法"""
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self.request_id += 1
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payload = {
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"jsonrpc": "2.0",
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"method": method,
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"params": params,
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"id": self.request_id
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}
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try:
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response = requests.post(
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self.server_url,
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json=payload,
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headers={'Content-Type': 'application/json'},
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timeout=60
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)
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if response.status_code == 200:
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return response.json()
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else:
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return {
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"error": {
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"code": response.status_code,
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"message": f"HTTP Error: {response.text}"
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}
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}
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except requests.exceptions.RequestException as e:
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return {
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"error": {
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"code": -1,
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"message": f"Request failed: {str(e)}"
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}
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}
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def detect_scenes(self, video_path: str, detector_type: str = "content",
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threshold: float = 30.0, min_scene_length: float = 1.0) -> Dict[str, Any]:
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"""基础场景检测"""
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params = {
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"video_path": video_path,
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"detector_type": detector_type,
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"threshold": threshold,
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"min_scene_length": min_scene_length
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}
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return self._call_method("scene.detect", params)
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def detect_scenes_workflow(self, video_path: str, detector_type: str = "content",
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threshold: float = 30.0, min_scene_length: float = 1.0,
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output_path: Optional[str] = None, output_format: str = "json",
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enable_ai_analysis: bool = True) -> Dict[str, Any]:
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"""LangGraph工作流场景检测"""
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params = {
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"video_path": video_path,
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"detector_type": detector_type,
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"threshold": threshold,
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"min_scene_length": min_scene_length,
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"enable_ai_analysis": enable_ai_analysis
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}
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if output_path:
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params["output_path"] = output_path
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params["output_format"] = output_format
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return self._call_method("scene.detect_workflow", params)
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def get_video_info(self, video_path: str) -> Dict[str, Any]:
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"""获取视频信息"""
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params = {"video_path": video_path}
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return self._call_method("scene.get_video_info", params)
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def batch_detect(self, directory: str, detector_type: str = "content",
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threshold: float = 30.0, min_scene_length: float = 1.0,
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output_dir: Optional[str] = None, output_format: str = "json") -> Dict[str, Any]:
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"""批量场景检测"""
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params = {
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"directory": directory,
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"detector_type": detector_type,
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"threshold": threshold,
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"min_scene_length": min_scene_length,
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"output_format": output_format
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}
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if output_dir:
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params["output_dir"] = output_dir
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return self._call_method("scene.batch_detect", params)
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def demo_basic_detection():
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"""演示基础场景检测"""
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print("🎯 基础场景检测演示")
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print("=" * 50)
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client = SceneDetectionClient("http://localhost:8081")
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# 测试视频路径
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video_path = "assets/1/1752032011698.mp4"
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print(f"📹 检测视频: {video_path}")
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# 调用基础检测
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start_time = time.time()
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result = client.detect_scenes(video_path, threshold=15.0)
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end_time = time.time()
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if "error" in result:
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print(f"❌ 检测失败: {result['error']}")
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return
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detection_result = result.get("result", {})
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if detection_result.get("success"):
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print(f"✅ 检测成功!")
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print(f" 场景数量: {detection_result['total_scenes']}")
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print(f" 视频时长: {detection_result['total_duration']:.2f}秒")
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print(f" 检测时间: {detection_result['detection_time']:.2f}秒")
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print(f" API调用时间: {end_time - start_time:.2f}秒")
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# 显示场景详情
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scenes = detection_result.get("scenes", [])
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print(f"\n🎬 场景详情:")
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for scene in scenes[:5]: # 只显示前5个
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print(f" 场景 {scene['index']}: {scene['start_time']:.2f}s - {scene['end_time']:.2f}s")
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if len(scenes) > 5:
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print(f" ... 还有 {len(scenes) - 5} 个场景")
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else:
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print(f"❌ 检测失败: {detection_result.get('error', '未知错误')}")
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def demo_workflow_detection():
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"""演示工作流场景检测"""
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print("\n🔄 LangGraph工作流检测演示")
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print("=" * 50)
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client = SceneDetectionClient("http://localhost:8081")
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# 测试视频路径
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video_path = "assets/1/1752032011698.mp4"
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print(f"📹 检测视频: {video_path}")
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# 调用工作流检测
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start_time = time.time()
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result = client.detect_scenes_workflow(
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video_path,
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threshold=15.0,
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enable_ai_analysis=False # 禁用AI分析以避免API密钥问题
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)
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end_time = time.time()
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if "error" in result:
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print(f"❌ 工作流检测失败: {result['error']}")
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return
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workflow_result = result.get("result", {})
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detection_result = workflow_result.get("detection_result", {})
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video_info = workflow_result.get("video_info", {})
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ai_analysis = workflow_result.get("ai_analysis")
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workflow_state = workflow_result.get("workflow_state")
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if detection_result.get("success"):
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print(f"✅ 工作流检测成功!")
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print(f" 工作流状态: {workflow_state}")
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print(f" 场景数量: {detection_result['total_scenes']}")
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print(f" 检测时间: {detection_result['detection_time']:.2f}秒")
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print(f" API调用时间: {end_time - start_time:.2f}秒")
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# 显示视频信息
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if video_info:
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print(f"\n📹 视频信息:")
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print(f" 分辨率: {video_info.get('resolution')}")
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print(f" 帧率: {video_info.get('fps'):.2f} fps")
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print(f" 时长: {video_info.get('duration'):.2f}秒")
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# 显示AI分析结果
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if ai_analysis:
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print(f"\n🧠 AI分析: {ai_analysis}")
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else:
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print(f"❌ 工作流检测失败: {detection_result.get('error', '未知错误')}")
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def demo_video_info():
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"""演示获取视频信息"""
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print("\n📊 视频信息获取演示")
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print("=" * 50)
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client = SceneDetectionClient("http://localhost:8081")
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# 测试视频路径
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video_path = "assets/1/1752032011698.mp4"
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print(f"📹 获取视频信息: {video_path}")
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# 获取视频信息
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start_time = time.time()
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result = client.get_video_info(video_path)
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end_time = time.time()
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if "error" in result:
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print(f"❌ 获取失败: {result['error']}")
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return
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info_result = result.get("result", {})
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if info_result.get("success"):
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info = info_result.get("info", {})
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print(f"✅ 获取成功!")
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print(f" 文件名: {info.get('filename')}")
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print(f" 分辨率: {info.get('resolution')}")
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print(f" 帧率: {info.get('fps'):.2f} fps")
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print(f" 总帧数: {info.get('frame_count'):,}")
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print(f" 时长: {info.get('duration'):.2f}秒")
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print(f" 文件大小: {info.get('file_size'):,} 字节")
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print(f" API调用时间: {end_time - start_time:.2f}秒")
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else:
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print(f"❌ 获取失败: {info_result.get('error', '未知错误')}")
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def main():
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"""主演示函数"""
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print("🚀 JSON-RPC 场景检测客户端演示")
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print("=" * 60)
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# 检查服务器连接
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client = SceneDetectionClient("http://localhost:8081")
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try:
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# 简单的连接测试
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test_result = client.get_video_info("nonexistent.mp4")
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if "error" in test_result and "Request failed" in str(test_result["error"]):
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print("❌ 无法连接到JSON-RPC服务器")
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print("💡 请先启动服务器: python3 -m python_core.cli jsonrpc start --port 8081")
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return
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except Exception as e:
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print(f"❌ 连接测试失败: {e}")
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return
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print("✅ 服务器连接正常")
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# 运行演示
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demo_video_info()
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demo_basic_detection()
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demo_workflow_detection()
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print("\n🎉 演示完成!")
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print("\n💡 更多用法:")
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print(" • 调整检测阈值以获得不同的场景分割效果")
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print(" • 使用不同的检测器类型 (content/threshold/adaptive)")
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print(" • 启用AI分析获得智能建议 (需要配置API密钥)")
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print(" • 批量处理多个视频文件")
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if __name__ == "__main__":
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main()
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