如果有人來到未來，這是一個小樣本與openCV做這個。它基於opencv sample，但是（在我看來），這有點更清楚，所以我也包括它。
測試了OpenCV的2.4.4
#!/usr/bin/env python
'''
Uses SURF to match two images.
Finds common features between two images and draws them
Based on the sample code from opencv:
samples/python2/find_obj.py
USAGE
find_obj.py <image1> <image2>
'''
import sys
import numpy
import cv2
###############################################################################
# Image Matching
###############################################################################
def match_images(img1, img2, img1_features=None, img2_features=None):
"""Given two images, returns the matches"""
detector = cv2.SURF(3200)
matcher = cv2.BFMatcher(cv2.NORM_L2)
if img1_features is None:
kp1, desc1 = detector.detectAndCompute(img1, None)
else:
kp1, desc1 = img1_features
if img2_features is None:
kp2, desc2 = detector.detectAndCompute(img2, None)
else:
kp2, desc2 = img2_features
#print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))
raw_matches = matcher.knnMatch(desc1, trainDescriptors=desc2, k=2)
kp_pairs = filter_matches(kp1, kp2, raw_matches)
return kp_pairs
def filter_matches(kp1, kp2, matches, ratio=0.75):
"""Filters features that are common to both images"""
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])
kp_pairs = zip(mkp1, mkp2)
return kp_pairs
###############################################################################
# Match Diplaying
###############################################################################
def draw_matches(window_name, kp_pairs, img1, img2):
"""Draws the matches"""
mkp1, mkp2 = zip(*kp_pairs)
H = None
status = None
if len(kp_pairs) >= 4:
p1 = numpy.float32([kp.pt for kp in mkp1])
p2 = numpy.float32([kp.pt for kp in mkp2])
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
if len(kp_pairs):
explore_match(window_name, img1, img2, kp_pairs, status, H)
def explore_match(win, img1, img2, kp_pairs, status=None, H=None):
"""Draws lines between the matched features"""
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
vis = numpy.zeros((max(h1, h2), w1 + w2), numpy.uint8)
vis[:h1, :w1] = img1
vis[:h2, w1:w1 + w2] = img2
vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
if H is not None:
corners = numpy.float32([[0, 0], [w1, 0], [w1, h1], [0, h1]])
reshaped = cv2.perspectiveTransform(corners.reshape(1, -1, 2), H)
reshaped = reshaped.reshape(-1, 2)
corners = numpy.int32(reshaped + (w1, 0))
cv2.polylines(vis, [corners], True, (255, 255, 255))
if status is None:
status = numpy.ones(len(kp_pairs), numpy.bool_)
p1 = numpy.int32([kpp[0].pt for kpp in kp_pairs])
p2 = numpy.int32([kpp[1].pt for kpp in kp_pairs]) + (w1, 0)
green = (0, 255, 0)
red = (0, 0, 255)
for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
if inlier:
col = green
cv2.circle(vis, (x1, y1), 2, col, -1)
cv2.circle(vis, (x2, y2), 2, col, -1)
else:
col = red
r = 2
thickness = 3
cv2.line(vis, (x1 - r, y1 - r), (x1 + r, y1 + r), col, thickness)
cv2.line(vis, (x1 - r, y1 + r), (x1 + r, y1 - r), col, thickness)
cv2.line(vis, (x2 - r, y2 - r), (x2 + r, y2 + r), col, thickness)
cv2.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:
cv2.line(vis, (x1, y1), (x2, y2), green)
cv2.imshow(win, vis)
###############################################################################
# Test Main
###############################################################################
if __name__ == '__main__':
if len(sys.argv) < 3:
print "No filenames specified"
print "USAGE: find_obj.py <image1> <image2>"
sys.exit(1)
fn1 = sys.argv[1]
fn2 = sys.argv[2]
img1 = cv2.imread(fn1, 0)
img2 = cv2.imread(fn2, 0)
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)
kp_pairs = match_images(img1, img2)
if kp_pairs:
draw_matches('find_obj', kp_pairs, img1, img2)
else:
print "No matches found"
cv2.waitKey()
cv2.destroyAllWindows()
謝謝，這個我指出了正確的方向。欣賞它。 –