SIFT

Pipeline in brief

1. Build a scale space with Gaussian blur at multiple scales per octave.
2. Take Difference of Gaussians (DoG); find 3D extrema (x, y, scale).
3. Refine location, discard low-contrast and edge-like points.
4. Assign dominant orientation from gradient histograms.
5. Sample a canonical 16×16 neighborhood into orientation histograms → 128 floats per keypoint.

SIFT_create and detectAndCompute

import cv2

gray = cv2.imread("building.jpg", cv2.IMREAD_GRAYSCALE)
sift = cv2.SIFT_create(nfeatures=500, nOctaveLayers=3, contrastThreshold=0.04,
                       edgeThreshold=10, sigma=1.6)
kp, des = sift.detectAndCompute(gray, None)

print(len(kp), None if des is None else des.shape)

contrastThreshold ↑ → fewer weak keypoints. edgeThreshold ↑ → more points along elongated structures.

Detect only, then compute

kp = sift.detect(gray, None)
kp, des = sift.compute(gray, kp)

Brute-force L2 matching + ratio test

import cv2

im1 = cv2.imread("a.jpg", cv2.IMREAD_GRAYSCALE)
im2 = cv2.imread("b.jpg", cv2.IMREAD_GRAYSCALE)
sift = cv2.SIFT_create()
k1, d1 = sift.detectAndCompute(im1, None)
k2, d2 = sift.detectAndCompute(im2, None)

bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=False)
pairs = bf.knnMatch(d1, d2, k=2)

good = []
for pair in pairs:
    if len(pair) < 2:
        continue
    m, n = pair
    if m.distance < 0.7 * n.distance:
        good.append(m)

vis = cv2.drawMatches(im1, k1, im2, k2, good[:80], None, flags=2)

FLANN for larger descriptor sets

For thousands of keypoints, FLANN can be faster than exhaustive BF matching. Use KD-tree or k-means index parameters tuned to float descriptors.

import cv2
import numpy as np

FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)

d1f = d1.astype(np.float32)
d2f = d2.astype(np.float32)
pairs = flann.knnMatch(d1f, d2f, k=2)

Environment notes

If cv2.SIFT_create is missing, install a recent opencv-python (SIFT returned to the main build after the patent expired in many jurisdictions). Some older wheels required opencv-contrib-python. Always check your OpenCV version with print(cv2.__version__).