it is simply: cv2.BRISK_create()
>>> help(cv2.BRISK_create)
Help on built-in function BRISK_create:
BRISK_create(...)
BRISK_create([, thresh[, octaves[, patternScale]]]) -> retval
. @brief The BRISK constructor
.
. @Param thresh AGAST detection threshold score.
. @Param octaves detection octaves. Use 0 to do single scale.
. @Param patternScale apply this scale to the pattern used for sampling the neighbourhood of a
. keypoint.
BRISK_create(radiusList, numberList[, dMax[, dMin[, indexChange]]]) -> retval
. @brief The BRISK constructor for a custom pattern
.
. @Param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
. keypoint scale 1).
. @Param numberList defines the number of sampling points on the sampling circle. Must be the same
. size as radiusList..
. @Param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
. scale 1).
. @Param dMin threshold for the long pairings used for orientation determination (in pixels for
. keypoint scale 1).
. @Param indexChange index remapping of the bits.
import numpy as np import cv2 import matplotlib.pyplot as plt
img1 = cv2.imread('book.jpg', 0) # Original image - ensure grayscale img2 = cv2.imread('book2.jpg', 0) # Rotated image - ensure grayscale
brisk = cv2.BRISK_create(1000, 2)
(kp1,des1) = brisk.detectAndCompute(img1,None) print(len(kp1)) (kp2,des2) = brisk.detectAndCompute(img2, None) print(len(kp2))
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1,des2) print(len(matches))
matches = sorted(matches, key=lambda val: val.distance) print(len(matches))
img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches ,None, flags=2)
plt.imshow(img3),plt.show()
i am trying this code but im not getting any keypoints will you help me
Asked: 2017-09-26 12:18:25 -0600
Seen: 4,531 times
Last updated: Sep 26 '17
