OpenCV feature matching for multiple images

python opencv sift flann

Jafu · Mar 8, 2014 · Viewed 25.9k times · Source

How can I optimise the SIFT feature matching for many pictures using FLANN?

I have a working example taken from the Python OpenCV docs. However this is comparing one image with another and it's slow. I need it to search for features matching in a series of images (a few thousands) and I need it to be faster.

My current idea:

Run through all the images and save the features. How?
Compare an image from a camera with this above base, and find the correct one. How?
Give me the result, matching image or something.

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()

UPDATE

After trying out many things I might have come closer to the solution now. I hope it's possible to build the index and then search in it like this:

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

However I still haven't managed to build an accepted npArray to the flann_Index parameter.

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

Answer

I never solved this in Python, however I switched environment to C++ where you get more OpenCV examples and don't have to use a wrapper with less documentation.

An example on the issue I had with matching in multiple files can be found here: https://github.com/Itseez/opencv/blob/2.4/samples/cpp/matching_to_many_images.cpp

OpenCV feature matching for multiple images

Answer

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