app.py

import gradio as gr
import torch
import cv2
import numpy as np
from fastsam import FastSAM, FastSAMPrompt

# Load the FastSAM model
model = FastSAM('FastSAM-x.pt')

def segment_image(input_image, points):
    # Prepare the image
    input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
    
    # Run the model
    everything_results = model(input_image, device='cpu', retina_masks=True, imgsz=1024, conf=0.4, iou=0.9)
    
    # Prepare prompts
    prompt_process = FastSAMPrompt(input_image, everything_results, device='cpu')
    
    # Generate mask based on points
    ann = prompt_process.point_prompt(points=points, pointlabel=[1] * len(points))
    
    # Overlay the mask on the original image
    result_image = input_image.copy()
    mask = ann[0].astype(bool)
    result_image[mask] = result_image[mask] * 0.5 + np.array([255, 0, 0]) * 0.5
    
    return result_image

# Create Gradio interface
iface = gr.Interface(
    fn=segment_image,
    inputs=[
        gr.Image(type="numpy"),
        gr.Image(type="numpy", tool="sketch", brush_radius=5, label="Click on objects to segment")
    ],
    outputs=gr.Image(type="numpy"),
    title="FastSAM Image Segmentation",
    description="Click on objects in the image to segment them using FastSAM."
)

# Launch the interface
iface.launch()