Feature Detection (Harris, SIFT, ORB)

import cv2, numpy as np

gray = cv2.imread("chess.jpg", cv2.IMREAD_GRAYSCALE)
gray = np.float32(gray)

dst = cv2.cornerHarris(gray, blockSize=2, ksize=3, k=0.04)
dst = cv2.dilate(dst, None)

img = cv2.imread("chess.jpg")
img[dst > 0.01 * dst.max()] = (0,0,255)

import cv2

gray = cv2.imread("scene.jpg", cv2.IMREAD_GRAYSCALE)
corners = cv2.goodFeaturesToTrack(
    gray, maxCorners=100, qualityLevel=0.01, minDistance=10)

import cv2

img  = cv2.imread("book.jpg", cv2.IMREAD_GRAYSCALE)
sift = cv2.SIFT_create()
kps, descs = sift.detectAndCompute(img, None)

print("Keypoints:", len(kps), "Descriptor shape:", descs.shape)
out = cv2.drawKeypoints(img, kps, None,
        flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

import cv2

img = cv2.imread("book.jpg", cv2.IMREAD_GRAYSCALE)
orb = cv2.ORB_create(nfeatures=1000)
kps, descs = orb.detectAndCompute(img, None)