API Reference

Programmatic access to Image Matching WebUI functions

API Reference

Complete programmatic access to Image Matching WebUI functionality.

Quick Start

from imcui.api import ImageMatchingAPI
from imcui.ui import get_matcher_zoo

# Get available matchers
matchers = get_matcher_zoo()

# Initialize API
api = ImageMatchingAPI(conf=matchers["superpoint-lightglue"])

# Match two images
result = api(image0, image1)

Core API

ImageMatchingAPI

Main API class for image matching operations.

from imcui.api import ImageMatchingAPI

api = ImageMatchingAPI(conf=matcher_config, device="cuda")
result = api(image0, image1)

Parameters:

Parameter	Type	Required	Default	Description
conf	dict	Yes	-	Matcher configuration
device	str	No	”cuda”	Device: “cuda”, “cpu”, “mps”

Returns:

Dictionary containing:

• keypoints0, keypoints1: Detected keypoints
• matches: Pairwise matches
• scores: Confidence scores
• H (optional): Homography matrix (if RANSAC enabled)

Matcher Utilities

Get Available Matchers

from imcui.ui import get_matcher_zoo

matchers = get_matcher_zoo()
print(f"Available: {list(matchers.keys())}")

Get Model Names

from imcui.ui import get_available_model_names

models = get_available_model_names()

Matching Functions

Run Matching

from imcui.ui import run_matching

result = run_matching(
    image0, image1,
    matcher_name="superpoint-lightglue",
    device="cuda"
)

Run RANSAC

from imcui.ui import run_ransac

filtered_matches = run_ransac(
    kp0, kp1, matches,
    ransac_method="CV2_USAC_MAGSAC",
    threshold=8.0
)

Filter Matches

from imcui.ui import filter_matches

clean_matches = filter_matches(matches, scores, threshold=0.2)

Geometry Functions

Compute Geometry

from imcui.ui import compute_geometry

H, mask = compute_geometry(
    kp0, kp1, matches,
    geometry_type="homography",
    ransac_method="CV2_USAC_MAGSAC"
)

Geometry Types:

• homography - Planar scenes
• fundamental - Calibrated cameras
• essential - Stereo vision

Visualization Functions

Display Keypoints

from imcui.ui import display_keypoints

result = display_keypoints(
    image, keypoints,
    color=(0, 255, 0)
)

Display Matches

from imcui.ui import display_matches

result = display_matches(
    image0, image1,
    kp0, kp1, matches,
    color=(0, 255, 0)
)

Plot Images

from imcui.ui import plot_images

plot_images([img0, img1], titles=["Image 0", "Image 1"])

Utility Functions

Get Configuration Path

from imcui import get_default_config_path

config_path = get_default_config_path()

Get Data Path

from imcui import get_example_data_path

data_path = get_example_data_path()

Get Version

from imcui import get_version

version = get_version()

Model Caching

ARC Size-Aware Cache

from imcui.ui import ARCSizeAwareModelCache

cache = ARCSizeAwareModelCache()
model = cache.get_model("superpoint-lightglue")

LRU Cache

from imcui.ui import LRUModelCache

cache = LRUModelCache(max_size=10)
model = cache.get_model("loftr")

Complete Example

from imcui.api import ImageMatchingAPI
from imcui.ui import (
    DEVICE,
    get_matcher_zoo,
    display_matches
)
import cv2

# Load images
img0 = cv2.imread("image0.jpg")
img1 = cv2.imread("image1.jpg")

# Get available matchers
matchers = get_matcher_zoo()

# Initialize API
api = ImageMatchingAPI(
    conf=matchers["superpoint-lightglue"],
    device=DEVICE
)

# Run matching
result = api(img0, img1)

# Visualize results
result_image = display_matches(
    img0, img1,
    result["keypoints0"],
    result["keypoints1"],
    result["matches"]
)

cv2.imwrite("matches.jpg", result_image)

API Updates: All matching algorithms are maintained in the vismatch repository. Check their documentation for the latest algorithms and features.