(Scale-Invariant Characteristic Remodel)
✅ Finds options at completely different scales (huge or small, no downside!).
✅ Works even when the picture is rotated or blurred!
✅ Utilized in picture stitching, object monitoring, and 3D reconstruction!
import cv2# Load picture
picture = cv2.imread("picture.jpg")
grey = cv2.cvtColor(picture, cv2.COLOR_BGR2GRAY) # Convert to grayscale
# Initialize SIFT detector
sift = cv2.SIFT_create()
# Detect keypoints & compute descriptors
keypoints, descriptors = sift.detectAndCompute(grey, None)
# Draw keypoints on picture
sift_image = cv2.drawKeypoints(picture, keypoints, None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# Present the end result
cv2.imshow("SIFT Options", sift_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
✅ Converts the picture to grayscale for higher processing.
✅ It detects keypoints- necessary spots within the picture.
✅ Then it extracts descriptors, a form of distinctive fingerprint of every keypoint.
✅ Attracts them on the picture for visualization!
- Object recognition
- Augmented actuality (AR)
Picture stitching- similar to making panoramas!
⚠️ Observe: SIFT is patented therefore not included by default in OpenCV, it is solely out there in OpenCV 4.4 and later.
