Image segmentation API

This is a simple API for performing image object segmentation using YOLO segmentation model. The API takes an input image and returns processed image with predicted classes.

Installation

1. Clone the repository:

git clone https://github.com/Shazy021/Image-Segmentation-API.git

2. You can change the IP address or port in docker-compose and Dokerfile files:
   • In docker-compose.yml you can change port ports:- "8080:80"
   • In Dockerfile yo can change uvicorn parameters:

CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "80", "--workers","4"]

3. Docker compose:

docker-compose up

API will be accessible at http://localhost:8080

Usage

Request

Send a POST request to http://localhost:8080/process_image with following parameters:

• file: The input image file to be processed

Response

The API responds with a JSON object containing following fields:

• processed_image: Processed image in array format
• classes: Predicted classes in an array format

Interacting with API

Below is an example of how to interact with API using Python:

import requests
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import json

# Define path to image you want to process
image_path = "./data/test.png"

# Create request parameters
files = {"file": open(image_path, "rb")}

# Send a POST request to API endpoint
response = requests.post("http://localhost:8080/process_image", files=files)

# Check response status code
if response.status_code == 200:
    # Get processed image
    processed_image = json.loads(response.content)

    # Display processed image
    plt.imshow(processed_image['processed_image'])
    plt.show()

    # Print predicted classes
    print(processed_image['classes'])
else:
    # Handle error if request fails
    print(f"Error {response.status_code}: {response.text}")

Example

Original:

Processed imgs:

Contributing

Contributions are welcome! If you find any bugs or have any suggestions, please feel free to open an issue.

License

This project is licensed under the MIT License.