---

name: extractors-and-features description: "Choose LightGlue-supported feature extractors and validate feature dictionaries before matching." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Extractors and feature dictionaries

Use this sub-skill when the task is to choose, configure, or sanity-check LightGlue-compatible local feature extractors, or when a user brings precomputed keypoints/descriptors and needs to format them for matching.

Start here

1. Pick the feature family and matcher pairing from references/extractor-reference.md.
2. Confirm the feature dictionary keys, shapes, descriptor dimensions, and scales/oris rules in references/feature-schema.md.
3. For a supplied image, run scripts/inspect_feature_schema.py to print the extractor output keys, shapes, dtypes, and validation notes. It defaults to OpenCV SIFT to avoid pretrained model downloads.
4. If something fails, use references/troubleshooting.md before changing model families or SIFT backends.

Router boundaries

• Complete image-pair matching, match_pair, visualization of matches, and extracting matched coordinates route to ../image-pair-matching/SKILL.md.
• LightGlue thresholds, adaptivity, filter_threshold, depth_confidence, width_confidence, FlashAttention, torch.compile, and raw matcher output interpretation route to ../matcher-configuration/SKILL.md.
• Benchmarking, latency plots, pruning plots, and visualization helper details route to ../performance-and-visualization/SKILL.md.

Critical operating facts

• The package exports SuperPoint, DISK, ALIKED, SIFT, DoGHardNet, LightGlue, and match_pair.
• Supported pretrained matcher presets are feature-specific: superpoint, disk, aliked, sift, doghardnet, plus raco-aliked for compatible 128-D precomputed features.
• SuperPoint, DISK, ALIKED, DoGHardNet, and feature-specific LightGlue matchers may download pretrained weights on first use. The default SIFT extractor path is the offline-safe choice when OpenCV exposes SIFT.
• SIFT and DoGHardNet features require scales and oris in addition to keypoints, descriptors, and image_size when used with their pretrained matcher presets.

Minimal feature workflow

from lightglue import SIFT
from lightglue.utils import load_image

image = load_image("image.jpg")
extractor = SIFT(max_num_keypoints=1024).eval()
features = extractor.extract(image, resize=1024)
print(features.keys())  # keypoints, descriptors, image_size, scales, oris, usually keypoint_scores

For precomputed descriptors, validate the exact schema in references/feature-schema.md before calling any matcher.