app.py

import gradio as gr
import torch
import cv2
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import io
from ultralytics import FastSAM
from ultralytics.models.fastsam import FastSAMPrompt

# Set up device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load FastSAM model
model = FastSAM("FastSAM-s.pt")  # or FastSAM-x.pt

def fig2img(fig):
    buf = io.BytesIO()
    fig.savefig(buf)
    buf.seek(0)
    img = Image.open(buf)
    return img

def plot_masks(annotations, output_shape):
    fig, ax = plt.subplots(figsize=(10, 10))
    ax.imshow(annotations[0].orig_img)
    
    for ann in annotations:
        for mask in ann.masks.data:
            mask = cv2.resize(mask.cpu().numpy().astype('uint8'), output_shape[::-1])
            masked = np.ma.masked_where(mask == 0, mask)
            ax.imshow(masked, alpha=0.5, cmap=plt.cm.get_cmap('jet'))

    ax.axis('off')
    plt.close()
    return fig2img(fig)

def segment_everything(input_image):
    try:
        if input_image is None:
            return None, "Please upload an image before submitting."
        
        input_image = Image.fromarray(input_image).convert("RGB")
        
        # Run FastSAM model in "everything" mode
        everything_results = model(input_image, device=device, retina_masks=True, imgsz=1024, conf=0.25, iou=0.9, agnostic_nms=True)
        
        # Prepare a Prompt Process object
        prompt_process = FastSAMPrompt(input_image, everything_results, device=device)
        
        # Get everything segmentation
        ann = prompt_process.everything_prompt()
        
        # Plot the results
        result_image = plot_masks(ann, input_image.size)
        
        return result_image, f"Segmented everything in the image. Found {len(ann[0].masks)} objects."
    
    except Exception as e:
        return None, f"An error occurred: {str(e)}"

# Create Gradio interface
iface = gr.Interface(
    fn=segment_everything,
    inputs=[
        gr.Image(type="numpy", label="Upload an image")
    ],
    outputs=[
        gr.Image(type="pil", label="Segmented Image"),
        gr.Textbox(label="Status")
    ],
    title="FastSAM Everything Segmentation",
    description="Upload an image to segment all objects using FastSAM."
)

# Launch the interface
iface.launch()