python - 多幅图像的OpenCV特征匹配

Question

如何使用 FLANN 为多张图片优化 SIFT 特征匹配？

我有一个取自 Python OpenCV 文档的工作示例。然而，这是将一张图像与另一张图像进行比较，而且速度很慢。我需要它来搜索一系列图像(几千张)中匹配的特征，并且我需要它更快。

我目前的想法:

1. 遍历所有图像并保存特征。怎么样？
2. 将来自相机的图像与上述基础进行比较，并找到正确的图像。怎么样？
3. 给我结果，匹配图片什么的。

http://docs.opencv.org/trunk/doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.html

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()

---

更新

在尝试了很多事情之后，我现在可能更接近解决方案了。我希望可以建立索引，然后像这样在其中搜索:

flann_params = dict(algorithm=1, trees=4)
flann = cv2.flann_Index(npArray, flann_params)
idx, dist = flann.knnSearch(queryDes, 1, params={})

但是我仍然没有设法为 flann_Index 参数构建一个可接受的 npArray。

loop through all images as image:
  npArray.append(sift.detectAndCompute(image, None))
npArray = np.array(npArray)

Answer 1

我从来没有在 Python 中解决这个问题，但是我将环境切换到 C++，在那里您可以获得更多 OpenCV 示例，而不必使用文档较少的包装器。

可以在此处找到有关我在多个文件中进行匹配的问题的示例:https://github.com/Itseez/opencv/blob/2.4/samples/cpp/matching_to_many_images.cpp