python opencv-3.0 SIFT/SURF 特徵提取與匹配__python

一、環境準備

目前 Opencv 有2.x 和 3.x 版本，兩個版本之間的差異主要是一些功能函數被放置到了不同的功能模組，因此大多數情況兩個版本的代碼並不能通用。建議安裝 Anaconda，自行下載相應版本。直接命令安裝Opencv3, lake :

conda install -c menpo opencv3pip install lake

二、SIFT/SURF 特徵提取與匹配

# coding: utf-8from matplotlib import pyplot as pltfrom lake.decorator import time_costimport cv2print 'cv version: ', cv2.__version__def bgr_rgb(img):    (r, g, b) = cv2.split(img)    return cv2.merge([b, g, r])def orb_detect(image_a, image_b):    # feature match    orb = cv2.ORB_create()    kp1, des1 = orb.detectAndCompute(image_a, None)    kp2, des2 = orb.detectAndCompute(image_b, None)    # create BFMatcher object    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)    # Match descriptors.    matches = bf.match(des1, des2)    # Sort them in the order of their distance.    matches = sorted(matches, key=lambda x: x.distance)    # Draw first 10 matches.    img3 = cv2.drawMatches(image_a, kp1, image_b, kp2, matches[:100], None, flags=2)    return bgr_rgb(img3)@time_costdef sift_detect(img1, img2, detector='surf'):    if detector.startswith('si'):        print "sift detector......"        sift = cv2.xfeatures2d.SURF_create()    else:        print "surf detector......"        sift = cv2.xfeatures2d.SURF_create()    # find the keypoints and descriptors with SIFT    kp1, des1 = sift.detectAndCompute(img1, None)    kp2, des2 = sift.detectAndCompute(img2, None)    # BFMatcher with default params    bf = cv2.BFMatcher()    matches = bf.knnMatch(des1, des2, k=2)    # Apply ratio test    good = [[m] for m, n in matches if m.distance < 0.5 * n.distance]    # cv2.drawMatchesKnn expects list of lists as matches.    img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, None, flags=2)    return bgr_rgb(img3)if __name__ == "__main__":    # load image    image_a = cv2.imread('./img1.jpg')    image_b = cv2.imread('./img2.png')    # ORB    # img = orb_detect(image_a, image_b)    # SIFT or SURF    img = sift_detect(image_a, image_b)    plt.imshow(img)    plt.show()

三、輸出展示

cv version:  3.1.0surf detector......==> time-cost: 0.187422     sift_detect

Output:

img1

img2