app.py

from typing import Optional

import gradio as gr
import numpy as np
import supervision as sv
import torch
from PIL import Image
from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator

from utils.models import load_models, CHECKPOINT_NAMES

MARKDOWN = """
# Segment Anything Model 2 🔥
<div>
    <a href="https://github.com/facebookresearch/segment-anything-2">
        <img src="https://badges.aleen42.com/src/github.svg" alt="GitHub" style="display:inline-block;">
    </a>
    <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-segment-images-with-sam-2.ipynb">
        <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab" style="display:inline-block;">
    </a>
    <a href="https://blog.roboflow.com/what-is-segment-anything-2/">
        <img src="https://raw.githubusercontent.com/roboflow-ai/notebooks/main/assets/badges/roboflow-blogpost.svg" alt="Roboflow" style="display:inline-block;">
    </a>
    <a href="https://www.youtube.com/watch?v=Dv003fTyO-Y">
        <img src="https://badges.aleen42.com/src/youtube.svg" alt="YouTube" style="display:inline-block;">
    </a>
</div>

Segment Anything Model 2 (SAM 2) is a foundation model designed to address promptable 
visual segmentation in both images and videos. The model extends its functionality to 
video by treating images as single-frame videos. Its design, a simple transformer 
architecture with streaming memory, enables real-time video processing. A 
model-in-the-loop data engine, which enhances the model and data through user 
interaction, was built to collect the SA-V dataset, the largest video segmentation 
dataset to date. SAM 2, trained on this extensive dataset, delivers robust performance 
across diverse tasks and visual domains.
"""
EXAMPLES = [
    ["tiny", "https://media.roboflow.com/notebooks/examples/dog-2.jpeg"],
    ["small", "https://media.roboflow.com/notebooks/examples/dog-3.jpeg"],
    ["large", "https://media.roboflow.com/notebooks/examples/dog-3.jpeg"],
]

DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
MASK_ANNOTATOR = sv.MaskAnnotator(color_lookup=sv.ColorLookup.INDEX)
MODELS = load_models(device=DEVICE)


def process(checkpoint_dropdown, image_input) -> Optional[Image.Image]:
    sam2_model = MODELS[checkpoint_dropdown]
    mask_generator = SAM2AutomaticMaskGenerator(sam2_model)
    image = np.array(image_input.convert("RGB"))
    sam_result = mask_generator.generate(image)
    detections = sv.Detections.from_sam(sam_result=sam_result)
    return MASK_ANNOTATOR.annotate(scene=image_input, detections=detections)


with gr.Blocks() as demo:
    gr.Markdown(MARKDOWN)
    with gr.Row():
        checkpoint_dropdown_component = gr.Dropdown(
            choices=CHECKPOINT_NAMES,
            value=CHECKPOINT_NAMES[0],
            label="Checkpoint", info="Select a SAM2 checkpoint to use.",
            interactive=True
        )
    with gr.Row():
        with gr.Column():
            image_input_component = gr.Image(type='pil', label='Upload image')
            submit_button_component = gr.Button(value='Submit', variant='primary')
        with gr.Column():
            image_output_component = gr.Image(type='pil', label='Image Output')
    with gr.Row():
        gr.Examples(
            fn=process,
            examples=EXAMPLES,
            inputs=[checkpoint_dropdown_component, image_input_component],
            outputs=[image_output_component],
            run_on_click=True
        )

    submit_button_component.click(
        fn=process,
        inputs=[checkpoint_dropdown_component, image_input_component],
        outputs=[image_output_component]
    )

demo.launch(debug=False, show_error=True, max_threads=1)