- opencv3
- 今日使うソースコードの紹介
- 特徴量マッチング
opencv3とは
opencvとは、コンピュータで画像や動画処理のための機能が備わっているオープンソースのライブラリです。pythonとopencvを組み合わせることで様々な場面で活躍している。
opencv-python-tutroals.readthedocs.io
今回使うソースコードはこれを参考にしています。
OpenCV: Feature Matching（引用先）
ここのソースコードにはA-KAZEが使われている。
ホームページ
http://www.robesafe.com/personal/pablo.alcantarilla/kaze.html
ライセンス
https://github.com/pablofdezalc/akaze/blob/master/LICENSE
from __future__ import print_function
import numpy as np
import cv2 as cv
from common import anorm, getsize
FLANN_INDEX_KDTREE = 1
FLANN_INDEX_LSH = 6
def init_feature(name):
chunks = name.split('-')
if chunks[0] == 'sift':
detector = cv.xfeatures2d.SIFT_create()
norm = cv.NORM_L2
elif chunks[0] == 'surf':
detector = cv.xfeatures2d.SURF_create(800)
norm = cv.NORM_L2
elif chunks[0] == 'orb':
detector = cv.ORB_create(400)
norm = cv.NORM_HAMMING
elif chunks[0] == 'akaze':
detector = cv.AKAZE_create()
norm = cv.NORM_HAMMING
elif chunks[0] == 'brisk':
detector = cv.BRISK_create()
norm = cv.NORM_HAMMING
else:
return None, None
if 'flann' in chunks:
if norm == cv.NORM_L2:
flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
else:
flann_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6,
key_size = 12,
multi_probe_level = 1)
matcher = cv.FlannBasedMatcher(flann_params, {})
else:
matcher = cv.BFMatcher(norm)
return detector, matcher
def filter_matches(kp1, kp2, matches, ratio = 0.75):
mkp1, mkp2 = [], []
for m in matches:
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
m = m[0]
mkp1.append( kp1[m.queryIdx] )
mkp2.append( kp2[m.trainIdx] )
p1 = np.float32([kp.pt for kp in mkp1])
p2 = np.float32([kp.pt for kp in mkp2])
kp_pairs = zip(mkp1, mkp2)
return p1, p2, list(kp_pairs)
def explore_match(win, img1, img2, kp_pairs, status = None, H = None):
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
vis = np.zeros((max(h1, h2), w1+w2), np.uint8)
vis[:h1, :w1] = img1
vis[:h2, w1:w1+w2] = img2
vis = cv.cvtColor(vis, cv.COLOR_GRAY2BGR)
if H is not None:
corners = np.float32([[0, 0], [w1, 0], [w1, h1], [0, h1]])
corners = np.int32( cv.perspectiveTransform(corners.reshape(1, -1, 2), H).reshape(-1, 2) + (w1, 0) )
cv.polylines(vis, [corners], True, (255, 255, 255))
if status is None:
status = np.ones(len(kp_pairs), np.bool_)
p1, p2 = [], []
for kpp in kp_pairs:
p1.append(np.int32(kpp[0].pt))
p2.append(np.int32(np.array(kpp[1].pt) + [w1, 0]))
green = (0, 255, 0)
red = (0, 0, 255)
kp_color = (51, 103, 236)
for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
if inlier:
col = green
cv.circle(vis, (x1, y1), 2, col, -1)
cv.circle(vis, (x2, y2), 2, col, -1)
else:
col = red
r = 2
thickness = 3
cv.line(vis, (x1-r, y1-r), (x1+r, y1+r), col, thickness)
cv.line(vis, (x1-r, y1+r), (x1+r, y1-r), col, thickness)
cv.line(vis, (x2-r, y2-r), (x2+r, y2+r), col, thickness)
cv.line(vis, (x2-r, y2+r), (x2+r, y2-r), col, thickness)
vis0 = vis.copy()
for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
if inlier:
cv.line(vis, (x1, y1), (x2, y2), green)
cv.imshow(win, vis)
def onmouse(event, x, y, flags, param):
cur_vis = vis
if flags & cv.EVENT_FLAG_LBUTTON:
cur_vis = vis0.copy()
r = 8
m = (anorm(np.array(p1) - (x, y)) < r) | (anorm(np.array(p2) - (x, y)) < r)
idxs = np.where(m)[0]
kp1s, kp2s = [], []
for i in idxs:
(x1, y1), (x2, y2) = p1[i], p2[i]
col = (red, green)[status[i][0]]
cv.line(cur_vis, (x1, y1), (x2, y2), col)
kp1, kp2 = kp_pairs[i]
kp1s.append(kp1)
kp2s.append(kp2)
cur_vis = cv.drawKeypoints(cur_vis, kp1s, None, flags=4, color=kp_color)
cur_vis[:,w1:] = cv.drawKeypoints(cur_vis[:,w1:], kp2s, None, flags=4, color=kp_color)
cv.imshow(win, cur_vis)
cv.setMouseCallback(win, onmouse)
return vis
if __name__ == '__main__':
print(__doc__)
import sys, getopt
opts, args = getopt.getopt(sys.argv[1:], '', ['feature='])
opts = dict(opts)
feature_name = opts.get('--feature', 'brisk')
try:
fn1, fn2 = args
except:
fn1 = 'IMG_4998.JPG'
fn2 = 'IMG_4999.JPG'
img1 = cv.imread(cv.samples.findFile(fn1), cv.IMREAD_GRAYSCALE)
img2 = cv.imread(cv.samples.findFile(fn2), cv.IMREAD_GRAYSCALE)
detector, matcher = init_feature(feature_name)
if img1 is None:
print('Failed to load fn1:', fn1)
sys.exit(1)
if img2 is None:
print('Failed to load fn2:', fn2)
sys.exit(1)
if detector is None:
print('unknown feature:', feature_name)
sys.exit(1)
print('using', feature_name)
kp1, desc1 = detector.detectAndCompute(img1, None)
kp2, desc2 = detector.detectAndCompute(img2, None)
print('img1 - %d features, img2 - %d features' % (len(kp1), len(kp2)))
def match_and_draw(win):
print('matching...')
raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2)
p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
if len(p1) >= 4:
H, status = cv.findHomography(p1, p2, cv.RANSAC, 5.0)
print('%d / %d inliers/matched' % (np.sum(status), len(status)))
else:
H, status = None, None
print('%d matches found, not enough for homography estimation' % len(p1))
_vis = explore_match(win, img1, img2, kp_pairs, status, H)
match_and_draw('find_obj')
cv.waitKey()
cv.destroyAllWindows()
このコードの途中にfn1=,fn2=のところに読み込む画像を書き込む。
このコードの実行結果は次のようになる。
特徴量マッチングについて
このように特徴量のマッチングが表示され見やすくなっている。また、画像を見てみるとしっかりと特徴の同じ場所にマッチしていることが分かる。opencvには多くのライブラリがあり、特徴量マッチング以外にも様々なことが出来る。
