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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 *
import gradio as gr
def load_torch_image(fname):
img: Tensor = K.io.load_image(fname, K.io.ImageLoadType.RGB32)
img = img[None] # 1xCxHxW / fp32 / [0, 1]
img = K.geometry.resize(img, (700, 700))
return img
def inference(file1,file2):
fname1 = file1.name
fname2 = file2.name
img1 = load_torch_image(fname1)
img2 = load_torch_image(fname2)
matcher = KF.LoFTR(pretrained='outdoor')
input_dict = {"image0": K.color.rgb_to_grayscale(img1), # LofTR works on grayscale images only
"image1": K.color.rgb_to_grayscale(img2)}
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),
K.tensor_to_image(img2),
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')
fig.savefig('example.jpg',dpi=110,bbox_inches='tight')
return 'example.jpg'
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']]
gr.Interface(
inference,
[gr.inputs.Image(type="file", label="Input1"),gr.inputs.Image(type="file", label="Input2")],
gr.outputs.Image(type="file", label="Output"),
title=title,
description=description,
article=article,
enable_queue=True,
examples=examples,
css=css
).launch(debug=True) |