def test_basic():
x = np.zeros((100, 100))
y = x.copy()
x[10, 10] = 50
y[60, 60] = 50
matches = correspond([(10, 10)], x, [(60, 60)], y)
assert_equal(matches[0], ((10, 10), (60, 60)))
plt.hold(True)
plt.imshow(img1, cmap=plt.cm.gray, interpolation='nearest')
for (i, j), a in zip(feat_mod_coord, feat_mod_area):
plt.plot(j, i, 'o', markersize=4)
plt.show()
print "Finding tentative correspondences..."
if win_size is None:
win_size = np.mean(feat_mod_area)
print win_size
win_size = np.clip(win_size, 5, 100)
print "Automatically determining window size...%d" % win_size
print "win_size=%.2f" % win_size
correspondences = correspond(feat_coord, img0.astype(np.uint8),
feat_mod_coord, img1.astype(np.uint8),
win_size=win_size)
if stack:
pairs = np.array(correspondences)
print '%d correspondences found' % len(pairs)
if len(pairs) <= 4:
raise RuntimeError('Not enough correspondences to do H-matrix'
'estimation.')
M, converged = supreme.register.sparse(pairs[:, 1, 0], pairs[:, 1, 1],
pairs[:, 0, 0], pairs[:, 0, 1],
mode=registration_method,
confidence=RANSAC_confidence)
# inliers_required=len(pairs)*0.8)
print np.array2string(M, separator=', ')
plt.imshow(img1, cmap=plt.cm.gray, interpolation='nearest')
for (i, j), a in zip(feat_mod_coord, feat_mod_area):
plt.plot(j, i, 'o', markersize=4)
plt.show()
print "Finding tentative correspondences..."
if win_size is None:
win_size = np.mean(feat_mod_area)
print win_size
win_size = np.clip(win_size, 5, 100)
print "Automatically determining window size...%d" % win_size
print "win_size=%.2f" % win_size
correspondences = correspond(feat_coord,
img0.astype(np.uint8),
feat_mod_coord,
img1.astype(np.uint8),
win_size=win_size)
if stack:
pairs = np.array(correspondences)
print '%d correspondences found' % len(pairs)
if len(pairs) <= 4:
raise RuntimeError('Not enough correspondences to do H-matrix'
'estimation.')
M, converged = supreme.register.sparse(pairs[:, 1, 0],
pairs[:, 1, 1],
pairs[:, 0, 0],
pairs[:, 0, 1],
mode=registration_method,
confidence=RANSAC_confidence)
