app.py

import pathlib
import zipfile
from typing import Any, Dict, List

import cv2
import gradio as gr
import numpy as np
import torch
from gradio_image_annotation import image_annotator
from sam2.build_sam import build_sam2
from sam2.sam2_image_predictor import SAM2ImagePredictor

from src.plot_utils import render_masks

choice_mapping: Dict[str, List[str]] = {
    "tiny": ["sam2_hiera_t.yaml", "assets/checkpoints/sam2_hiera_tiny.pt"],
    "small": ["sam2_hiera_s.yaml", "assets/checkpoints/sam2_hiera_small.pt"],
    "base_plus": ["sam2_hiera_b+.yaml", "assets/checkpoints/sam2_hiera_base_plus.pt"],
    "large": ["sam2_hiera_l.yaml", "assets/checkpoints/sam2_hiera_large.pt"],
}


def predict(model_choice, annotations: Dict[str, Any]):
    config_file, ckpt_path = choice_mapping[str(model_choice)]
    device = "cuda" if torch.cuda.is_available() else "cpu"
    sam2_model = build_sam2(config_file, ckpt_path, device=device)
    predictor = SAM2ImagePredictor(sam2_model)
    predictor.set_image(annotations["image"])
    coordinates = []
    for i in range(len(annotations["boxes"])):
        coordinate = [
            int(annotations["boxes"][i]["xmin"]),
            int(annotations["boxes"][i]["ymin"]),
            int(annotations["boxes"][i]["xmax"]),
            int(annotations["boxes"][i]["ymax"]),
        ]
        coordinates.append(coordinate)

    masks, scores, _ = predictor.predict(
        point_coords=None,
        point_labels=None,
        box=np.array(coordinates),
        multimask_output=False,
    )
    for count, mask in enumerate(masks):
        mask = mask.transpose(1, 2, 0)  # type:ignore
        mask_image = (mask * 255).astype(np.uint8)  # Convert to uint8 format
        cv2.imwrite(f"assets/mask_{count}.png", mask_image)
        mask_dir = pathlib.Path("assets/")
        with zipfile.ZipFile("assets/masks.zip", "w") as archive:
            for mask_file in mask_dir.glob("mask_*.png"):
                archive.write(mask_file, arcname=mask_file.relative_to(mask_dir))

    return [
        render_masks(annotations["image"], masks),
        gr.DownloadButton("Download Mask(s)", value="assets/masks.zip", visible=True),
    ]


with gr.Blocks(delete_cache=(30, 30)) as demo:
    gr.Markdown(
        """
        # 1. Choose Model Checkpoint
        """
    )
    with gr.Row():
        model = gr.Dropdown(
            choices=["tiny", "small", "base_plus", "large"],
            value="tiny",
            label="Model Checkpoint",
            info="Which model checkpoint to load?",
        )

    gr.Markdown(
        """
        # 2. Upload your Image and draw bounding box(es)
        """
    )

    annotator = image_annotator(
        value={"image": cv2.imread("assets/example.png")},
        disable_edit_boxes=True,
        label="Draw a bounding box",
    )
    btn = gr.Button("Get Segmentation Mask(s)")
    download_btn = gr.DownloadButton(
        "Download Mask(s)", value="assets/masks.zip", visible=False
    )
    btn.click(fn=predict, inputs=[model, annotator], outputs=[gr.Plot(), download_btn])

demo.launch()