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[image-processing] deprecate plot_keypoint_matching, make visualize_keypoint_matching as a standard (#39830)

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* fix: deprecate plot_keypoint_matching and make visualize_keypoint_matching for all Keypoint Matching models

* refactor: added copied from

* fix: make style

* fix: repo consistency

* fix: make style

* docs: added missing method in SuperGlue docs
This commit is contained in:
StevenBucaille
2025-08-01 12:29:57 -04:00
committed by GitHub
parent 7b4d9843ba
commit 1ec0feccdd
6 changed files with 225 additions and 36 deletions
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4
docs/source/en/model_doc/lightglue.md
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@@ -107,7 +107,7 @@ processed_outputs = processor.post_process_keypoint_matching(outputs, image_size
```py
# Easy visualization using the built-in plotting method
processor.plot_keypoint_matching(images, processed_outputs)
processor.visualize_keypoint_matching(images, processed_outputs)
```
<div class="flex justify-center">
@@ -128,7 +128,7 @@ processed_outputs = processor.post_process_keypoint_matching(outputs, image_size
- preprocess
- post_process_keypoint_matching
- plot_keypoint_matching
- visualize_keypoint_matching
<frameworkcontent>
<pt>

34
docs/source/en/model_doc/superglue.md
View File

@@ -103,38 +103,11 @@ processed_outputs = processor.post_process_keypoint_matching(outputs, image_size
print(f"Keypoint at {keypoint0.numpy()} matches with keypoint at {keypoint1.numpy()} with score {matching_score}")
```
- The example below demonstrates how to visualize matches between two images.
- Visualize the matches between the images using the built-in plotting functionality.
```py
import matplotlib.pyplot as plt
import numpy as np
# Create side by side image
merged_image = np.zeros((max(image1.height, image2.height), image1.width + image2.width, 3))
merged_image[: image1.height, : image1.width] = np.array(image1) / 255.0
merged_image[: image2.height, image1.width :] = np.array(image2) / 255.0
plt.imshow(merged_image)
plt.axis("off")
# Retrieve the keypoints and matches
output = processed_outputs[0]
keypoints0 = output["keypoints0"]
keypoints1 = output["keypoints1"]
matching_scores = output["matching_scores"]
# Plot the matches
for keypoint0, keypoint1, matching_score in zip(keypoints0, keypoints1, matching_scores):
plt.plot(
[keypoint0[0], keypoint1[0] + image1.width],
[keypoint0[1], keypoint1[1]],
color=plt.get_cmap("RdYlGn")(matching_score.item()),
alpha=0.9,
linewidth=0.5,
)
plt.scatter(keypoint0[0], keypoint0[1], c="black", s=2)
plt.scatter(keypoint1[0] + image1.width, keypoint1[1], c="black", s=2)
plt.savefig("matched_image.png", dpi=300, bbox_inches='tight')
# Easy visualization using the built-in plotting method
processor.visualize_keypoint_matching(images, processed_outputs)
```
<div class="flex justify-center">
@@ -155,6 +128,7 @@ processed_outputs = processor.post_process_keypoint_matching(outputs, image_size
- preprocess
- post_process_keypoint_matching
- visualize_keypoint_matching
<frameworkcontent>
<pt>