initial code change

app.py

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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
+from kornia_moons.viz import draw_LAF_matches
+
+
+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
+    fname2 = file2
+    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.Image(type="filepath", label="Input1"), gr.Image(type="filepath", label="Input2")],
+    gr.Image(type="filepath", label="Output"),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    css=css
+).launch(debug=True)

packages.txt

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+ffmpeg 
+libsm6 
+libxext6

requirements.txt

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+matplotlib
+kornia
+kornia_rs
+numpy
+torch
+kornia_moons
+opencv-python