Question

我有用于两个图像的SIFT / SURF代码，但是我一次需要4000张图像。首先是否可以一次处理2000张图像？如果是的话，请告诉我如何？我需要一次比较所有图像。例如，我在图像1中有一辆车，我想检查该车仍在那儿多少张图像。

import sys # For debugging only
import numpy as np
import cv2
from matplotlib import pyplot as plt

MIN_MATCH_COUNT = 10

img1 = cv2.imread('image.jpg',0) # queryImage
img2 = cv2.imread('target.jpg',0) # trainImage

# Initiate SIFT detector
sift = cv2.SIFT()

# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)

FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)

flann = cv2.FlannBasedMatcher(index_params, search_params)

matches = flann.knnMatch(des1,des2,k=2)

# store all the good matches as per Lowe's ratio test.
good = []
for m,n in matches:
if m.distance MIN_MATCH_COUNT:
    src_pts = np.float32([ kp1[m.queryIdx].pt for m in good 
    ]).reshape(-1,1,2)
    dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good 
    ]).reshape(-1,1,2)

    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
    matchesMask = mask.ravel().tolist()

    h,w = img1.shape
    pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] 
    ]).reshape(-1,1,2)
    dst = cv2.perspectiveTransform(pts,M)

    img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)

else:
    print "Not enough matches are found - %d/%d" % 
    (len(good),MIN_MATCH_COUNT)
    matchesMask = None

draw_params = dict(matchColor = (0,255,0), # draw matches in green 
     color
               singlePointColor = None,
               matchesMask = matchesMask, # draw only inliers
               flags = 2)

img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)

plt.imshow(img3, 'gray'),plt.show()

如何在python中一次对3000张以上的多个图像实施SIFT或SURF匹配（提取和匹配）？

0 个答案: