This API leverages DeepLabV3+ with a ResNet50 backbone to perform binary semantic segmentation on plant-specific input images. The segmentation model implementation can be found here. Developed using FastAPI, the API accepts base64-encoded plant images, processes them through the model, and returns the resulting binary semantic segmentation mask encoded as base64.

• Python 3.9 or higher
• PIL
• FastAPI
• Uvicorn
• Gunicorn
• PyTorch 2.2.1

• Clone the repository

git clone https://github.com/mukund-ks/Backend-SSPSDLFAM

• Change directory

cd Backend-SSPSDLFAM

• Create a 'weights' directory under 'src' and place your model weights there

mkdir -p src/weights
cp /path/to/your_weights.pth src/weights/

• Install dependencies

pip install -r requirements.txt

To start the FastAPI server, run the following command:

uvicorn main:app --host 127.0.0.1 --port 8000

The API will be accessible at http://localhost:8000. Open your web browser or use tools like cURL or Postman to interact with API endpoints.

• Build the Docker Image:

  docker build -t backend-sspsdlfam .

• Run the Docker Container:

  docker run -p 8000:8080 backend-sspsdlfam

  The API will be accessible at http://localhost:8000.

• Method: POST
• Path: /segment
• Parameters:

{
    "img_base64": "base64-encoded image string"
}

Upon successfull processing, the API responds with a JSON object containing the base64-encoded binary mask.

{
    "mask":"base64-encoded mask string"
}

To test the API using Python, you can use the provided example script. The script loads an image, encodes it into base64, sends a POST request to the API's segmentation endpoint, and decodes the resulting base64-encoded segmentation mask.

import requests
import base64
from io import BytesIO
from PIL import Image

img_size = (256,256) # H,W of input image

with open("test_img.png", "rb") as image_file:
    encoded_img = base64.b64encode(image_file.read()).decode("utf-8")

response = requests.post(url="http://localhost:8000/segment", json={"img_base64": encoded_img})

json_res = response.json()

base64_mask = json_res["mask"]
mask_bytes = base64.b64decode(base64_mask)

mask_img = Image.open(BytesIO(mask_bytes))

mask_img.save("mask.png", format="PNG")

No authentication is currently required for this API.

Contributions are welcomed! Feel free to submit issues, feature requests, or pull requests.