File size: 3,308 Bytes
7a3f743 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
# Technical Specification: White Box Cartoonization
## Architectural Overview
**White-Box Cartoonization** is a deep learning-based image transformation system that utilizes a white-box representation framework to convert real-world photographs into high-quality cartoon images. Unlike standard "black-box" models, this architecture decomposes images into three distinct components—**Surface**, **Structure**, and **Texture**—to maintain artistic control and structural integrity throughout the GAN-based generation process.
### Processing Pipeline Diagram
```mermaid
graph TD
User((End User)) -->|Uploads Image| WebApp["Frontend Layer (HTML5/CSS3)"]
WebApp -->|XHR POST Request| Flask["Backend Gateway (Flask)"]
Flask -->|Image Tensor| Inference["Inference Engine (TensorFlow)"]
subgraph GAN_Inference ["Core AI Processing (White-Box Logic)"]
Inference -->|Representation Extraction| Generator["U-Net Generator Network"]
Generator -->|"Surface/Structure/Texture Processing"| Refinement["Guided Filter (Mathematical Layer)"]
end
Refinement -->|Cartoonized Output| Flask
Flask -->|JSON Response| WebApp
WebApp -->|"DOM Update (Result Display)"| User
```
---
## Technical Implementations
### 1. Neural Engine: TensorFlow & GAN Framework
The core of the system is built on an extended **Generative Adversarial Network (GAN)** framework.
- **White-Box Representation**: The model explicitly processes the surface representation (smooth textures), structure representation (global shapes), and texture representation (detailed lines) separately to achieve superior artistic results.
- **Generator Architecture**: Utilizes a **U-Net** based generator (`network.py`) with residual blocks to preserve feature resolutions during the upsampling/downsampling stages.
- **Output Refinement**: A **Guided Filter** implementation (`guided_filter.py`) is used as a post-processing layer to ensure edges remain sharp and the final image preserves semantic information from the input.
### 2. Application Layer: Flask Web Gateway
The backend serves as an orchestration layer between the user interface and the AI model.
- **Inference Liaison**: `backend.py` manages the pre-computation and model loading, ensuring that the heavy TensorFlow graph is initialized once and reused for multiple user requests.
- **RESTful Endpoint**: `app.py` exposes a `/cartoonize` endpoint that handles standard HTTP requests, image normalization, and data serialization.
### 3. Presentation Layer: Vanilla Web Stack
The frontend is designed for high performance and responsiveness without heavy framework overhead.
- **Dynamic Styling**: Implements a custom CSS3 theme system (`theme.css`, `style.css`) that adapts to both desktop and mobile viewports.
- **Asynchronous Orchestration**: `main.js` manages the image upload lifecycle, camera integration, and state-based UI updates using modern `fetch` APIs and async/await patterns.
---
## Technical Prerequisites
- **Runtime**: Python 3.8 or higher
- **Neural Framework**: TensorFlow 2.x
- **Server Engine**: Flask 3.1.2
- **Core Libraries**: `OpenCV-Python` (Image processing), `NumPy` (Tensor manipulation), `tf-slim`.
---
*Technical Specification | Computer Engineering Project | Version 1.0*
|