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---
license: mit
language:
- en
base_model:
- google/deeplabv3_mobilenet_v2_1.0_513
pipeline_tag: image-to-image
tags:
- segmentation
- sticker
- deeplab
- image
- ai-sticker
---
# 🖼️ AI Sticker Generator API
Welcome to the **AI Sticker Generator API**! This API is designed to transform images into high-quality "stickers" by isolating the primary object using advanced **semantic segmentation** techniques. The stickers produced have smooth, feathered edges to ensure a polished and professional look.
## Examples
### Before and After Transformation
- **Before:** 
**After:** 
- **Before:** 
**After:** 
- **Before:** 
**After:** 
## 📋 Key Features
### 🔍 Semantic Segmentation
Automatically identifies and isolates the main subject in an image, providing precise cutouts for clear and visually appealing stickers.
### 🌟 Feathered Edges
Applies a Gaussian blur to the mask edges, creating a soft and natural transition to transparency for a more polished finish.
### ⚡ Built with FastAPI
Utilizes FastAPI for high performance, scalability, and fast response times suitable for production environments.
### 📂 Versatile Image Support
Supports **PNG** and **JPEG** formats, ensuring compatibility with widely used image types.
## 🚀 Quick Start Guide
Entire Code is on GitHub as well : ![URL]https://github.com/rajasami156/AI-Image-Sticker-Generator.git
1. **Clone the repository** and navigate to the project directory.
2. **Install dependencies** (requires Python 3.8+).
3. **Start the API server** using the provided configuration file.
4. Once the server is running, the API is accessible locally, ready to accept image uploads for sticker generation.
## 🛠️ API Endpoints
### `POST /create_sticker/`
- **Description**: Upload an image to generate a sticker with a transparent background.
- **Supported File Types**: PNG and JPEG formats.
- **Response**: Returns a PNG image of the sticker with a transparent background. In case of an unsupported file format, an error message will be returned.
## 🧩 How It Works
1. **Model & Preprocessing**: Uploaded images are preprocessed and passed through a pre-trained model for semantic segmentation.
2. **Mask Generation**: A binary mask isolates the main object in the image.
3. **Edge Feathering**: A Gaussian blur is applied to mask edges, creating a smooth transition.
4. **Sticker Creation**: The mask adds transparency, producing an image that can be directly used as a sticker.
## 📂 Directory Structure
Generated stickers are saved in a designated output directory, ensuring easy access and organization of created stickers.
## ⚙️ Configuration
Before running the application, confirm that the output directory exists. This directory is essential for storing generated stickers for easy retrieval and management.
## 📜 License
Licensed under the MIT License, allowing easy adaptation and building upon this work.
## 🙋♂️ Contributing
Contributions are welcome! To contribute, create a new issue or pull request for bug fixes, enhancements, or new features. All contributions should adhere to the project's coding standards and guidelines.
## Built with ❤️ by [SAMIULLAH]
## 📞 Support
For support or inquiries, please contact nicesami156@gmail.com. |