opencv/samples/python2/feature_homography.py
Alexander Mordvintsev 028c44531f wrapped FlannBasedMatcher (and extended DescriptorMatcher wrapper)
updated feature_homography.py sample to use new features
2011-09-15 11:10:06 +00:00

96 lines
2.8 KiB
Python

'''
Feature homography
==================
Example of using features2d framework for interactive video homography matching.
Usage
-----
feature_homography.py [<video source>]
Keys
----
SPACE - set reference frame
ESC - exit
'''
import numpy as np
import cv2
import video
from common import draw_str, clock
import sys
detector = cv2.FastFeatureDetector(16, True)
detector = cv2.GridAdaptedFeatureDetector(detector)
extractor = cv2.DescriptorExtractor_create('ORB')
FLANN_INDEX_KDTREE = 1
FLANN_INDEX_LSH = 6
flann_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
green, red = (0, 255, 0), (0, 0, 255)
if __name__ == '__main__':
print __doc__
try: src = sys.argv[1]
except: src = 0
cap = video.create_capture(src)
ref_kp = None
while True:
ret, img = cap.read()
vis = img.copy()
kp = detector.detect(img)
kp, desc = extractor.compute(img, kp)
for p in kp:
x, y = np.int32(p.pt)
r = int(0.5*p.size)
cv2.circle(vis, (x, y), r, (0, 255, 0))
draw_str(vis, (20, 20), 'feature_n: %d' % len(kp))
if ref_kp is not None:
raw_matches = matcher.knnMatch(desc, 2)
matches = []
for m in raw_matches:
if len(m) == 2:
m1, m2 = m
if m1.distance < m2.distance * 0.7:
matches.append((m1.trainIdx, m1.queryIdx))
match_n = len(matches)
inlier_n = 0
if match_n > 10:
p0 = np.float32( [ref_kp[i].pt for i, j in matches] )
p1 = np.float32( [kp[j].pt for i, j in matches] )
H, status = cv2.findHomography(p0, p1, cv2.RANSAC, 10.0)
inlier_n = sum(status)
if inlier_n > 10:
for (x1, y1), (x2, y2), inlier in zip(np.int32(p0), np.int32(p1), status):
cv2.line(vis, (x1, y1), (x2, y2), (red, green)[inlier])
h, w = img.shape[:2]
overlay = cv2.warpPerspective(ref_img, H, (w, h))
vis = cv2.addWeighted(vis, 0.5, overlay, 0.5, 0.0)
draw_str(vis, (20, 40), 'matched: %d ( %d outliers )' % (match_n, match_n-inlier_n))
cv2.imshow('img', vis)
ch = cv2.waitKey(1)
if ch == ord(' '):
matcher.clear()
matcher.add([desc])
ref_kp = kp
ref_img = img.copy()
if ch == 27:
break