deepflow-engine 0.1.7

Automated frame-by-frame rendering and video synthesis for physics simulations and generative game shorts.

DeepFlow-Engine

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🚀 What is DeepFlow?

DeepFlow is a frame-by-frame simulation engine designed for games and physics systems that lets you:

• 🎮 Run games interactively (like normal pygame) with optional playback recording
• ⚙️ Simulate them headlessly (no window) for batch processing
• 🎞️ Generate frames automatically for video synthesis
• 🔊 Log events (like collisions) and trigger audio events
• 🎬 Render videos with perfectly synced audio (ready for reels/shorts)
• 📤 Publish outputs directly (Telegram, Discord coming soon)

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🧠 Core Idea

Write your game once, and use it in multiple modes:

Game Logic → Engine → Frames → Video → Publish

This separation of concerns allows you to:

• Develop interactively with real-time feedback
• Generate content programmatically for automation
• Test deterministically in headless mode
• Scale to batch video generation

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📦 Installation

pip install deepflow-engine

Requirements: Python 3.12+

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📚 Documentation & Examples

For complete working examples, check out the example/ directory:

• 🎮 Full game implementation with collision detection
• 🎬 Interactive and headless modes
• 📹 Video rendering with audio
• 🎯 Best practices and patterns

Run examples:

cd example
python main.py --help

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⚡ Quick Start

1. Create a Game

Extend DeepFlowGame with your game logic:

import deepflow_engine as dfe
import pygame


class MyGame(dfe.DeepFlowGame):
    def start(self):
        """Initialize game state"""
        self.x = 100

    def update(self):
        """Update game logic (called every frame)"""
        self.x += 100 * self.dt  # Always use dt for frame-independent movement!

    def render(self, canvas):
        """Render game state to canvas"""
        canvas.fill((255, 255, 255))
        pygame.draw.circle(canvas, (0, 0, 255), (int(self.x), 200), 20)

    def get_audio_map(self):
        """Map event types to audio files"""
        return {}

2. Run the Engine

Interactive Mode (Preview & Play)

game = MyGame()
engine = dfe.DeepFlowEngine(game, interactive=True)
dfe.run_pipeline(engine)

Headless Mode (Generate Video)

game = MyGame()
engine = dfe.DeepFlowEngine(
    game,
    interactive=False,
    frames_dir="frames",
    video_length_seconds=10,
)

output = dfe.run_pipeline(engine)
print(f"Video saved: {output}")

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🎯 Complete Example

For a complete, production-ready example with collision detection, event handling, and video generation, see the example/ directory:

# Run interactive mode
python example/main.py --interactive

# Generate video headlessly
python example/main.py --headless --duration 10

The example demonstrates best practices for building games with DeepFlow.

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🎥 Output

DeepFlow automatically:

1. Simulates your game frame-by-frame
2. Saves frames to disk
3. Logs events (collisions, audio triggers, etc.)
4. Renders video with synced audio

Output structure:

frames/                    # Individual frames
collisions_log.json       # Event log
deepflow_output.mp4       # Final video

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🔊 Audio System

Define Audio Assets

In your game class:

def get_audio_map(self):
    return {
        "collision": "assets/crash.wav",
        "score": "assets/point.wav",
    }

Trigger Events

During gameplay:

def update(self):
    if self.collision_detected():
        self.play_audio("collision")

Behavior

• Interactive mode: Plays sound instantly
• Headless mode: Logs event for final video rendering

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🎮 Input Handling

Use get_input() abstraction:

def get_input(self):
    if self._engine.interactive:
        return pygame.key.get_pressed()
    return None

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⏱️ Time-Based Movement (IMPORTANT)

Always use dt:

self.x += speed * self.dt

❌ Don’t do:

self.x += 5

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🧩 Engine Modes

Mode	Use Case	Interactive
Interactive	Play/preview the game in real-time	Yes
Interactive + Record	Play while recording frames for video	Yes
Headless	Batch generate deterministic simulations	No
Pipeline	Full automated video generation	No

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📤 Publishing (Optional)

Telegram

Send generated videos directly to Telegram:

from deepflow_engine.publisher import TelegramPublisher

publisher = TelegramPublisher(bot_token="YOUR_BOT_TOKEN", chat_id="YOUR_CHAT_ID")
publisher.send_video("deepflow_output.mp4")

Discord (Open for Contributions 🚀)

A Discord publisher is in the roadmap.

Interested in implementing it?

• Implement DiscordPublisher extending BasePublisher
• Follow the existing TelegramPublisher pattern
• Open a PR with tests

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🧠 Design Philosophy

DeepFlow follows clean architecture principles:

Game     → Pure game logic (independent of engine)
Engine   → Execution engine (runs game at any speed)
Renderer → Video output (handles frame->video conversion)
Publisher→ Distribution (sends to external services)

This separation ensures:

• ✅ Games are testable and reusable
• ✅ Easy to add new modes (headless, interactive, streaming)
• ✅ Simple to integrate with other tools

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🔥 Use Cases

• 🎮 Game Automation: Auto-play games and record gameplay
• 🎬 Content Creation: Generate Instagram Reels/YouTube Shorts automatically
• 🧪 Simulation & Visualization: Physics simulations with video output
• 🤖 AI/RL Training: Gym-style environments with video logging (coming soon)
• 🧠 Generative Content: Batch create variations of games for viral content

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🛠️ Roadmap

• Discord publisher
• CLI tool (deepflow run game.py)
• Gymnasium/Gym integration for RL
• Multi-event timeline system
• Streaming output support
• WebGL renderer for browser playback

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🤝 Contributing

We welcome contributions! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes and write tests
4. Commit with clear messages (git commit -m 'Add amazing feature')
5. Push and open a Pull Request

Guidelines:

• Keep the API clean and intuitive
• Avoid tight coupling between game and engine
• Prefer composition over inheritance
• Add tests for new features
• Update documentation

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📜 License

Apache 2.0 License - see LICENSE for details

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👀 Final Note

DeepFlow is not just a game engine.

It's a content engine - designed to transform game logic into automated, scalable content production.

Use it to build interactive experiences, and let it generate the reels.

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Made with ❤️ by deependujha