app.py

import sys

# small hack to import from src
sys.path.append("src")

import spaces
import gradio as gr

import time
import numpy as np

import omniglue
from omniglue import utils

HEADER = """
<div align="center">
    <p>
        <span style="font-size: 30px; vertical-align: bottom;"> OmniGlue: Generalizable Feature Matching with Foundation Model Guidance</span>
    </p>
    
    <p>
        Upload two images 🖼️ of the object and identify matches between them 🚀
    </p>
</div>

"""

ABSTRACT = """
The image matching field has been witnessing a continuous emergence of novel learnable feature matching techniques, with ever-improving performance on conventional benchmarks. However, our investigation shows that despite these gains, their potential for real-world applications is restricted by their limited generalization capabilities to novel image domains. In this paper, we introduce OmniGlue, the first learnable image matcher that is designed with generalization as a core principle. OmniGlue leverages broad knowledge from a vision foundation model to guide the feature matching process, boosting generalization to domains not seen at training time. Additionally, we propose a novel keypoint position-guided attention mechanism which disentangles spatial and appearance information, leading to enhanced matching descriptors. We perform comprehensive experiments on a suite of 6 datasets with varied image domains, including scene-level, object-centric and aerial images. OmniGlue’s novel components lead to relative gains on unseen domains of 18.8% with respect to a directly comparable reference model, while also outperforming the recent LightGlue method by 10.1% relatively.
"""

@spaces.GPU
def find_matches(image0, image1):

    # Load models.
    print("> Loading OmniGlue (and its submodules: SuperPoint & DINOv2)...")
    start = time.time()
    og = omniglue.OmniGlue(
        og_export="./models/og_export",
        sp_export="./models/sp_v6",
        dino_export="./models/dinov2_vitb14_pretrain.pth",
    )
    print(f"> \tTook {time.time() - start} seconds.")

    # Perform inference.
    print("> Finding matches...")
    start = time.time()
    match_kp0, match_kp1, match_confidences = og.FindMatches(image0, image1)
    num_matches = match_kp0.shape[0]
    print(f"> \tFound {num_matches} matches.")
    print(f"> \tTook {time.time() - start} seconds.")

    # Filter by confidence (0.02).
    print("> Filtering matches...")
    match_threshold = 0.02  # Choose any value [0.0, 1.0).
    keep_idx = []
    for i in range(match_kp0.shape[0]):
        if match_confidences[i] > match_threshold:
            keep_idx.append(i)
    num_filtered_matches = len(keep_idx)
    match_kp0 = match_kp0[keep_idx]
    match_kp1 = match_kp1[keep_idx]
    match_confidences = match_confidences[keep_idx]
    print(f"> \tFound {num_filtered_matches}/{num_matches} above threshold {match_threshold}")

    # Visualize.
    print("> Visualizing matches...")
    viz = utils.visualize_matches(
        image0,
        image1,
        match_kp0,
        match_kp1,
        np.eye(num_filtered_matches),
        show_keypoints=True,
        highlight_unmatched=True,
        title=f"{num_filtered_matches} matches",
        line_width=2,
    )
    return viz


with gr.Blocks() as demo:

    gr.Markdown(HEADER)
    with gr.Accordion("Abstract (click to open)", open=False):
        gr.Image("res/og_diagram.png")
        gr.Markdown(ABSTRACT)

    with gr.Row():
        image_1 = gr.Image()
        image_2 = gr.Image()
    button = gr.Button(value="Find Matches")
    output = gr.Image()
    button.click(find_matches, [image_1, image_2], output)

    gr.Examples(
        examples=[
            ["res/demo1.jpg", "res/demo2.jpg"],
            ["res/tower-1.webp", "res/tower-2.jpeg"]
        ],
        inputs=[image_1, image_2],
        outputs=[output],
        fn=find_matches,
        cache_examples="lazy",
    )

if __name__ == "__main__":
    demo.launch()