SIFT feature extraction for drone imageries

SIFT, or Scale-Invariant Feature Transform, is a powerful algorithm used in computer vision for detecting, describing, and matching local features in images. SIFT is designed to identify features that remain consistent across changes in scale, rotation, and illumination. It is applied to drone imageries to compute keypoints in each video frame. A keypoint is a tuple of a pixel position and a feature descriptor that describes the image in a patch around that pixel - a vector representation of the local image region. SIFT matches features between images by comparing their descriptors using metrics like Euclidean distance. For every video frame, SIFT yields a set of keypoints.

The implementation to get sift features is as follows:

import cv2

sift = cv2.xfeatures2d.SIFT_create()

def compute_one(im):

        return sift.detectAndCompute(im, None)

def compute_sift(frames):

        print('get sift features')

        sift_features = [(None, None) for _ in frames]

        for frame_idx, im in enumerate(frames):

            if im is None or frame_idx % 3 != 0:

                continue

            print('... sift {}/{}'.format(frame_idx, len(frames)))

            keypoints, descs = compute_one(im)

            sift_features[frame_idx] = (keypoints, descs)

        return sift_features