SAMI156
/

AI-Image-Sticker-Generator

@@ -1,73 +1,87 @@
-# 🖼️ 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:** ![Before](output/check3.png)
-  **After:** ![After](output/sticker_check3.png)
-- **Before:** ![Before](output/check.png)
-  **After:** ![After](output/sticker_check.png)
-- **Before:** ![Before](output/check5.png)
-  **After:** ![After](output/sticker_check5.png)
-## 📋 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
-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.

+---
+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:** ![Before](output/check3.png)
+  **After:** ![After](output/sticker_check3.png)
+- **Before:** ![Before](output/check.png)
+  **After:** ![After](output/sticker_check.png)
+- **Before:** ![Before](output/check5.png)
+  **After:** ![After](output/sticker_check5.png)
+## 📋 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
+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.