421 lines
16 KiB
Python
421 lines
16 KiB
Python
"""
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Video Generator Service
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End-to-end service for generating short videos from content.
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"""
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from datetime import datetime
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from pathlib import Path
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from typing import Optional, Callable, Literal
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from loguru import logger
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from reelforge.models.progress import ProgressEvent
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from reelforge.models.storyboard import (
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Storyboard,
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StoryboardFrame,
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StoryboardConfig,
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ContentMetadata,
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VideoGenerationResult
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)
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class VideoGeneratorService:
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"""
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Video generation service
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Orchestrates the complete pipeline:
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1. Generate narrations (LLM)
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2. Generate image prompts (LLM)
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3. Process each frame (TTS + Image + Compose + Video)
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4. Concatenate all segments
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5. Add BGM (optional)
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"""
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def __init__(self, reelforge_core):
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"""
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Initialize video generator service
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Args:
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reelforge_core: ReelForgeCore instance
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"""
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self.core = reelforge_core
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async def __call__(
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self,
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# === Content Source (Choose ONE, mutually exclusive) ===
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topic: Optional[str] = None,
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content: Optional[str] = None,
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# === Optional Title (works with any source) ===
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title: Optional[str] = None,
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# === Basic Config ===
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n_frames: int = 5,
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voice_id: str = "zh-CN-YunjianNeural",
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output_path: Optional[str] = None,
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# === LLM Parameters ===
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min_narration_words: int = 20,
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max_narration_words: int = 40,
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min_image_prompt_words: int = 50,
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max_image_prompt_words: int = 100,
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# === Image Parameters ===
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image_width: int = 1024,
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image_height: int = 1024,
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image_style_preset: Optional[str] = None,
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image_style_description: Optional[str] = None,
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# === Video Parameters ===
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video_width: int = 1080,
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video_height: int = 1920,
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video_fps: int = 30,
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# === Frame Template ===
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frame_template: Optional[str] = None,
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# === BGM Parameters ===
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bgm_path: Optional[str] = None,
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bgm_volume: float = 0.2,
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bgm_mode: Literal["once", "loop"] = "loop",
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# === Advanced Options ===
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content_metadata: Optional[ContentMetadata] = None,
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progress_callback: Optional[Callable[[ProgressEvent], None]] = None,
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) -> VideoGenerationResult:
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"""
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Generate short video from different content sources
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Args:
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topic: Topic/theme (e.g., "如何提高学习效率")
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content: User-provided content (any length)
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Note: Must provide exactly ONE of: topic or content
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title: Video title (optional)
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- If provided, use it as the video title
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- If not provided, auto-generate based on source:
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* topic → use topic text
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* content → LLM extracts title from content
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n_frames: Number of storyboard frames (default 5)
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voice_id: TTS voice ID (default "zh-CN-YunjianNeural")
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output_path: Output video path (auto-generated if None)
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min_narration_words: Min narration length
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max_narration_words: Max narration length
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min_image_prompt_words: Min image prompt length
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max_image_prompt_words: Max image prompt length
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image_width: Generated image width (default 1024)
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image_height: Generated image height (default 1024)
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image_style_preset: Preset style name (e.g., "minimal", "concept", "cinematic")
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image_style_description: Custom style description (overrides preset)
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video_width: Final video width (default 1080)
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video_height: Final video height (default 1920)
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video_fps: Video frame rate (default 30)
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frame_template: HTML template name or path (None = use PIL)
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e.g., "classic", "modern", "minimal", or custom path
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bgm_path: BGM path ("default", "happy", custom path, or None)
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bgm_volume: BGM volume 0.0-1.0 (default 0.2)
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bgm_mode: BGM mode "once" or "loop" (default "loop")
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content_metadata: Content metadata (optional, for display)
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progress_callback: Progress callback function(message, progress)
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Returns:
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VideoGenerationResult with video path and metadata
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Examples:
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# Generate from topic
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>>> result = await reelforge.generate_video(
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... topic="如何在信息爆炸时代保持深度思考",
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... n_frames=5,
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... bgm_path="default"
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... )
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# Generate from user content with auto-generated title
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>>> result = await reelforge.generate_video(
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... content="昨天我读了一本书，讲的是...",
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... n_frames=3
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... )
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# Generate from user content with custom title
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>>> result = await reelforge.generate_video(
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... content="买房子，第一应该看的是楼盘的整体环境...",
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... title="买房风水指南",
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... n_frames=5
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... )
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>>> print(result.video_path)
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"""
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# ========== Step 0: Validate parameters (mutually exclusive) ==========
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sources = [topic, content]
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source_count = sum(x is not None for x in sources)
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if source_count == 0:
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raise ValueError(
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"Must provide exactly ONE of: topic or content"
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)
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elif source_count > 1:
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raise ValueError(
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"Cannot provide multiple sources. Choose ONE of: topic or content"
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)
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# Determine source type
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if topic:
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source_type = "topic"
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else: # content
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source_type = "content"
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# Determine final title (priority: user-specified > auto-generated)
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if title:
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# User specified title, use it directly
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final_title = title
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logger.info(f"🚀 Starting video generation from {source_type} with title: '{title}'")
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else:
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# Auto-generate title based on source
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if source_type == "topic":
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final_title = topic
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logger.info(f"🚀 Starting video generation from topic: '{final_title}'")
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else: # content
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# Will generate title from content using LLM
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logger.info(f"🚀 Starting video generation from content ({len(content)} chars)")
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final_title = None # Will be generated later
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# Generate title from content if needed (before creating output path)
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if source_type == "content" and final_title is None:
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self._report_progress(progress_callback, "generating_title", 0.01)
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final_title = await self._generate_title_from_content(content)
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logger.info(f"✅ Generated title: {final_title}")
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# Auto-generate output path if not provided
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if output_path is None:
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timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
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# Use first 10 chars of final_title for filename
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safe_name = final_title[:10].replace('/', '_').replace(' ', '_')
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output_path = f"output/{timestamp}_{safe_name}.mp4"
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# Ensure output directory exists
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Path(output_path).parent.mkdir(parents=True, exist_ok=True)
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# Create storyboard config
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config = StoryboardConfig(
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n_storyboard=n_frames,
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min_narration_words=min_narration_words,
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max_narration_words=max_narration_words,
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min_image_prompt_words=min_image_prompt_words,
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max_image_prompt_words=max_image_prompt_words,
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video_width=video_width,
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video_height=video_height,
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video_fps=video_fps,
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voice_id=voice_id,
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image_width=image_width,
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image_height=image_height,
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frame_template=frame_template
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)
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# Create storyboard
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storyboard = Storyboard(
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topic=final_title, # Use final_title as video title
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config=config,
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content_metadata=content_metadata,
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created_at=datetime.now()
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)
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# Store storyboard in core for access in storyboard processor
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self.core._current_storyboard = storyboard
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try:
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# ========== Step 1: Generate narrations ==========
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self._report_progress(progress_callback, "generating_narrations", 0.05)
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narrations = await self.core.narration_generator.generate_narrations(
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config=config,
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source_type=source_type,
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content_metadata=None, # No metadata needed for topic/content
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topic=topic if source_type == "topic" else None,
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content=content if source_type == "content" else None
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)
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logger.info(f"✅ Generated {len(narrations)} narrations")
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# Step 2: Generate image prompts
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self._report_progress(progress_callback, "generating_image_prompts", 0.15)
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image_prompts = await self.core.image_prompt_generator.generate_image_prompts(
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narrations=narrations,
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config=config,
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image_style_preset=image_style_preset,
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image_style_description=image_style_description
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)
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logger.info(f"✅ Generated {len(image_prompts)} image prompts")
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# Step 3: Create frames
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for i, (narration, image_prompt) in enumerate(zip(narrations, image_prompts)):
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frame = StoryboardFrame(
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index=i,
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narration=narration,
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image_prompt=image_prompt,
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created_at=datetime.now()
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)
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storyboard.frames.append(frame)
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# Step 4: Process each frame
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for i, frame in enumerate(storyboard.frames):
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# Calculate fine-grained progress for this frame
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base_progress = 0.2 # Frames processing starts at 20%
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frame_range = 0.6 # Frames processing takes 60% (20%-80%)
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per_frame_progress = frame_range / len(storyboard.frames)
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# Create frame-specific progress callback
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def frame_progress_callback(event: ProgressEvent):
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"""Report sub-step progress within current frame"""
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# Calculate overall progress: base + previous frames + current frame progress
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overall_progress = base_progress + (per_frame_progress * i) + (per_frame_progress * event.progress)
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# Forward the event with adjusted overall progress
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if progress_callback:
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adjusted_event = ProgressEvent(
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event_type=event.event_type,
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progress=overall_progress,
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frame_current=event.frame_current,
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frame_total=event.frame_total,
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step=event.step,
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action=event.action
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)
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progress_callback(adjusted_event)
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# Report frame start
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self._report_progress(
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progress_callback,
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"processing_frame",
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base_progress + (per_frame_progress * i),
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frame_current=i+1,
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frame_total=len(storyboard.frames)
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)
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processed_frame = await self.core.storyboard_processor.process_frame(
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frame=frame,
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config=config,
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total_frames=len(storyboard.frames),
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progress_callback=frame_progress_callback
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)
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storyboard.total_duration += processed_frame.duration
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logger.info(f"✅ Frame {i+1} completed ({processed_frame.duration:.2f}s)")
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# Step 5: Concatenate videos
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self._report_progress(progress_callback, "concatenating", 0.85)
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segment_paths = [frame.video_segment_path for frame in storyboard.frames]
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from reelforge.services.video import VideoService
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video_service = VideoService()
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final_video_path = video_service.concat_videos(
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videos=segment_paths,
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output=output_path,
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bgm_path=bgm_path,
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bgm_volume=bgm_volume,
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bgm_mode=bgm_mode
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)
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storyboard.final_video_path = final_video_path
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storyboard.completed_at = datetime.now()
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logger.success(f"🎬 Video generation completed: {final_video_path}")
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# Step 6: Create result
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self._report_progress(progress_callback, "finalizing", 1.0)
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video_path_obj = Path(final_video_path)
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file_size = video_path_obj.stat().st_size
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result = VideoGenerationResult(
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video_path=final_video_path,
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storyboard=storyboard,
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duration=storyboard.total_duration,
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file_size=file_size
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)
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logger.info(f"✅ Generated video: {final_video_path}")
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logger.info(f" Duration: {storyboard.total_duration:.2f}s")
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logger.info(f" Size: {file_size / (1024*1024):.2f} MB")
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logger.info(f" Frames: {len(storyboard.frames)}")
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return result
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except Exception as e:
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logger.error(f"❌ Video generation failed: {e}")
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raise
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def _report_progress(
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self,
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callback: Optional[Callable[[ProgressEvent], None]],
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event_type: str,
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progress: float,
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**kwargs
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):
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"""
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Report progress via callback
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Args:
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callback: Progress callback function
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event_type: Type of progress event
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progress: Progress value (0.0-1.0)
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**kwargs: Additional event-specific parameters (frame_current, frame_total, etc.)
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"""
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if callback:
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event = ProgressEvent(event_type=event_type, progress=progress, **kwargs)
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callback(event)
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logger.debug(f"Progress: {progress*100:.0f}% - {event_type}")
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else:
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logger.debug(f"Progress: {progress*100:.0f}% - {event_type}")
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async def _generate_title_from_content(self, content: str) -> str:
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"""
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Generate a short, attractive title from user content using LLM
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Args:
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content: User-provided content
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Returns:
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Generated title (10 characters or less)
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"""
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# Take first 500 chars to avoid overly long prompts
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content_preview = content[:500]
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prompt = f"""请为以下内容生成一个简短、有吸引力的标题（10字以内）。
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内容：
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{content_preview}
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要求：
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1. 简短精炼，10字以内
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2. 准确概括核心内容
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3. 有吸引力，适合作为视频标题
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4. 只输出标题文本，不要其他内容
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标题："""
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# Call LLM to generate title
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response = await self.core.llm(
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prompt=prompt,
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temperature=0.7,
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max_tokens=50
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)
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# Clean up response
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title = response.strip()
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# Remove quotes if present
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if title.startswith('"') and title.endswith('"'):
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title = title[1:-1]
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if title.startswith("'") and title.endswith("'"):
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title = title[1:-1]
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# Limit to 20 chars max (safety)
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if len(title) > 20:
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title = title[:20]
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return title
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