🖼️ The Next Star - Facial Image Search System

📋 Features

1. Annotation Data Embedding Generation - Converts annotation data into vectors using the Sentence Transformer model (distiluse-base-multilingual-cased-v2 HUGGING FACE).
2. Search and Save Image Results - Searches for the top 3 annotations most similar to the input query and saves the corresponding images in the result/ directory.
3. Similarity-Based Search - The search is based on semantic similarity, allowing for relevant results even if the query does not exactly match any annotations.

⚙️ Requirements

The following Python packages are required to run this project:

• Python 3.7 or higher
• numpy
• faiss
• sentence-transformers
• scikit-learn

You can install the necessary packages with the following command:

pip install -r requirements.txt

🚀 How to Run

1. Prepare Data and Generate Embeddings

   Load the annotation data and generate sentence embeddings. This step will create the embeddings.npy file.

   python data_preparation.py

2. Run the Main Program

   The main program takes a query input from the terminal, performs the search, and outputs the results.

   python main.py

   Example:

   Enter your search query: 고양이상 눈매, 날렵한 턱선, 오똑한 코

   This command searches for the 3 most similar annotations to the query, copies the corresponding images to the result/ directory, and prints the full details of the matched annotations to the terminal.

📂 Results

The search results are saved in the result/ directory. The directory will contain the image files corresponding to the matched annotations, and the detailed information of the matched annotations will be printed in the terminal.

Example output:

{
  "id": 1,
  "category": "best",
  "image": "result/image02.png",
  "annotation": "강아지상 눈매, 도회적 이미지, 차가운 이미지, 도톰한 애교살, 계란형, 뭉툭한 턱 끝, 도톰한 콧망울, 적당한 콧볼, 선명한 쌍커풀, 도톰한 아랫입술, 하얀 피부, 자기주도적성향 이미지, 여성미가 있는 얼굴, 숙녀같은 이미지",
  "name": "best_female_image02"
}

⚠️ Notes

• Be sure to run data_preparation.py first to generate the embeddings.npy file.
• Verify that the image paths and annotation data are correctly specified.
• Run main.py from the project’s root directory to ensure correct file paths.