app.py

import matplotlib.pyplot as plt
import cv2
import kornia as K
import kornia.feature as KF
import numpy as np
import torch
from kornia_moons.feature import *
from kornia_moons.viz import *
import gradio as gr

def load_torch_image(img):
    if isinstance(img, np.ndarray):
        # If the input is already a numpy array, convert it to a tensor
        img_tensor = K.image_to_tensor(img).float() / 255.0
    else:
        # If it's a file path, load it using kornia
        img_tensor = K.io.load_image(img, K.io.ImageLoadType.RGB32)
    
    img_tensor = img_tensor.unsqueeze(0)  # Add batch dimension: 1xCxHxW
    img_tensor = K.geometry.resize(img_tensor, (700, 700))
    return img_tensor
 
def inference(img1, img2):
    img1_tensor = load_torch_image(img1)
    img2_tensor = load_torch_image(img2)
    
    matcher = KF.LoFTR(pretrained='outdoor')
    input_dict = {
        "image0": K.color.rgb_to_grayscale(img1_tensor),  # LoFTR works on grayscale images only 
        "image1": K.color.rgb_to_grayscale(img2_tensor)
    }
    
    with torch.no_grad():
        correspondences = matcher(input_dict)
    
    mkpts0 = correspondences['keypoints0'].cpu().numpy()
    mkpts1 = correspondences['keypoints1'].cpu().numpy()
    H, inliers = cv2.findFundamentalMat(mkpts0, mkpts1, cv2.USAC_MAGSAC, 0.5, 0.999, 100000)
    inliers = inliers > 0
    
    fig, ax = plt.subplots()
    draw_LAF_matches(
        KF.laf_from_center_scale_ori(torch.from_numpy(mkpts0).view(1,-1, 2),
                                    torch.ones(mkpts0.shape[0]).view(1,-1, 1, 1),
                                    torch.ones(mkpts0.shape[0]).view(1,-1, 1)),
        KF.laf_from_center_scale_ori(torch.from_numpy(mkpts1).view(1,-1, 2),
                                    torch.ones(mkpts1.shape[0]).view(1,-1, 1, 1),
                                    torch.ones(mkpts1.shape[0]).view(1,-1, 1)),
        torch.arange(mkpts0.shape[0]).view(-1,1).repeat(1,2),
        K.tensor_to_image(img1_tensor.squeeze()),
        K.tensor_to_image(img2_tensor.squeeze()),
        inliers,
        draw_dict={'inlier_color': (0.2, 1, 0.2),
                'tentative_color': None, 
                'feature_color': (0.2, 0.5, 1), 'vertical': False},
        ax=ax
    )
    plt.axis('off')
    return fig


title = "Kornia-Loftr"
description = "Gradio demo for Kornia-Loftr: Detector-Free Local Feature Matching with Transformers. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://kornia.readthedocs.io/en/latest/' target='_blank'>Open Source Differentiable Computer Vision Library</a> | <a href='https://github.com/kornia/kornia' target='_blank'>Kornia Github Repo</a> | <a href='https://github.com/zju3dv/LoFTR' target='_blank'>LoFTR Github</a> | <a href='https://arxiv.org/abs/2104.00680' target='_blank'>LoFTR: Detector-Free Local Feature Matching with Transformers</a></p>"
css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}"
examples = [['kn_church-2.jpg','kn_church-8.jpg']]

iface = gr.Interface(
    inference, 
    [
        gr.Image(type="numpy", label="Input1"),
        gr.Image(type="numpy", label="Input2")],
    gr.Plot(label="Feature Matches"),
    title=title,
    description=description,
    article=article,
    examples=examples,
    css=css
    )

iface.launch(debug=True)