|
import gradio as gr |
|
import kornia as K |
|
import kornia.feature as KF |
|
import torch |
|
import matplotlib |
|
|
|
matplotlib.use("Agg") |
|
import numpy as np |
|
from plot_utils import plot_images, plot_lines, plot_color_line_matches |
|
|
|
sold2 = KF.SOLD2(pretrained=True, config=None) |
|
ransac = K.geometry.RANSAC(model_type="homography_from_linesegments", inl_th=3.0) |
|
|
|
|
|
def infer(img1, img2, line_style: str): |
|
torch_img1 = K.image_to_tensor(img1).float() / 255.0 |
|
torch_img2 = K.image_to_tensor(img2).float() / 255.0 |
|
|
|
torch_img1_gray = K.color.rgb_to_grayscale(torch_img1) |
|
torch_img2_gray = K.color.rgb_to_grayscale(torch_img2) |
|
|
|
imgs = torch.stack( |
|
[torch_img1_gray, torch_img2_gray], |
|
) |
|
|
|
with torch.inference_mode(): |
|
outputs = sold2(imgs) |
|
|
|
line_seg1 = outputs["line_segments"][0] |
|
line_seg2 = outputs["line_segments"][1] |
|
desc1 = outputs["dense_desc"][0] |
|
desc2 = outputs["dense_desc"][1] |
|
|
|
with torch.inference_mode(): |
|
matches = sold2.match(line_seg1, line_seg2, desc1[None], desc2[None]) |
|
|
|
valid_matches = matches != -1 |
|
match_indices = matches[valid_matches] |
|
|
|
matched_lines1 = line_seg1[valid_matches] |
|
matched_lines2 = line_seg2[match_indices] |
|
|
|
imgs_to_plot = [K.tensor_to_image(torch_img1), K.tensor_to_image(torch_img2)] |
|
|
|
fig = plot_images( |
|
imgs_to_plot, ["Image 1 - detected lines", "Image 2 - detected lines"] |
|
) |
|
if line_style == "Line Matches": |
|
lines_to_plot = [line_seg1.numpy(), line_seg2.numpy()] |
|
plot_lines(lines_to_plot, fig, ps=3, lw=2, indices={0, 1}) |
|
elif line_style == "Color Line Matches": |
|
plot_color_line_matches([matched_lines1, matched_lines2], fig, lw=2) |
|
elif line_style == "Line Segment Homography Warping": |
|
_, _, img1_warp_to2 = get_homography_values( |
|
matched_lines1, matched_lines2, torch_img1 |
|
) |
|
fig = plot_images( |
|
[K.tensor_to_image(torch_img2), K.tensor_to_image(img1_warp_to2)], |
|
["Image 2", "Image 1 wrapped to 2"], |
|
) |
|
elif line_style == "Matched Lines for Homography Warping": |
|
_, correspondence_mask, _ = get_homography_values( |
|
matched_lines1, matched_lines2, torch_img1 |
|
) |
|
plot_color_line_matches( |
|
[matched_lines1[correspondence_mask], matched_lines2[correspondence_mask]], |
|
fig, |
|
lw=2, |
|
) |
|
return fig |
|
|
|
|
|
def get_homography_values(matched_lines1, matched_lines2, torch_img1): |
|
H_ransac, correspondence_mask = ransac( |
|
matched_lines1.flip(dims=(2,)), matched_lines2.flip(dims=(2,)) |
|
) |
|
img1_warp_to2 = K.geometry.warp_perspective( |
|
torch_img1[None], H_ransac[None], (torch_img1.shape[1:]) |
|
) |
|
|
|
return H_ransac, correspondence_mask, img1_warp_to2 |
|
|
|
|
|
description = """In this space you can try out Line Detection and Segment Matching with the Kornia library as seen in [this tutorial](https://kornia.github.io/tutorials/#category=Line%20matching). |
|
|
|
Just upload two images of a scene with different view points, choose an option for output and run the demo. |
|
""" |
|
|
|
|
|
Iface = gr.Interface( |
|
fn=infer, |
|
inputs=[ |
|
gr.components.Image(), |
|
gr.components.Image(), |
|
gr.components.Dropdown( |
|
[ |
|
"Line Matches", |
|
"Color Line Matches", |
|
"Line Segment Homography Warping", |
|
"Matched Lines for Homography Warping", |
|
], |
|
value="Line Matches", |
|
label="Options", |
|
), |
|
], |
|
outputs=gr.components.Plot(), |
|
examples=[["terrace0.JPG", "terrace1.JPG", "Line Matches"]], |
|
title="Line Segment Matching with Kornia", |
|
description=description, |
|
).launch() |
|
|