def get_krt(im1, im2):
ims = [im1, im2]
sifts = []
for x in range(2):
sifts.append(ims[x][:-3] + "sift")
# compute features
#sift.process_image('../../data/book_frontal.JPG','../../data/im0.sift')
sift.process_image(ims[0], sifts[0])
l0, d0 = sift.read_features_from_file(sifts[0])
#sift.process_image('../../data/book_perspective.JPG','../../data/im1.sift')
sift.process_image(ims[1], sifts[1])
l1, d1 = sift.read_features_from_file(sifts[1])
# match features and estimate homography
matches = sift.match_twosided(d0, d1)
ndx = matches.nonzero()[0]
fp = homography.make_homog(l0[ndx, :2].T)
ndx2 = [int(matches[i]) for i in ndx]
print len(ndx2)
tp = homography.make_homog(l1[ndx2, :2].T)
model = homography.RansacModel()
H, ransac_data = homography.H_from_ransac(fp, tp, model)
# camera calibration
#K = camera.my_calibration((747,1000))
K = camera.my_calibration((Image.open(im2).size))
# 3D points at plane z=0 with sides of length 0.2
box = cube.cube_points([0, 0, 0.1], 0.1)
# project bottom square in first image
cam1 = camera.Camera(hstack((K, dot(K, array([[0], [0], [-1]])))))
# first points are the bottom square
box_cam1 = cam1.project(homography.make_homog(box[:, :5]))
# use H to transfer points to the second image
print dot(H, box_cam1)
box_trans = homography.normalize(dot(H, box_cam1))
# compute second camera matrix from cam1 and H
cam2 = camera.Camera(dot(H, cam1.P))
A = dot(linalg.inv(K), cam2.P[:, :3])
A = array([A[:, 0], A[:, 1], cross(A[:, 0], A[:, 1])]).T
cam2.P[:, :3] = dot(K, A)
# project with the second camera
box_cam2 = cam2.project(homography.make_homog(box))
# test: projecting point on z=0 should give the same
point = array([1, 1, 0, 1]).T
print homography.normalize(dot(dot(H, cam1.P), point))
print cam2.project(point)
import pickle
with open('%s.pkl' % ims[1][:-4], 'w') as f:
pickle.dump(K, f)
pickle.dump(dot(linalg.inv(K), cam2.P), f)
sys.stderr.write("K and Rt dumped to %s.pkl\n" % ims[1][:-4])
def get_H(im0_path, im1_path):
"""
Get the Homography matrix.
"""
# compute features
sift.process_image(im0_path, 'im0.sift')
l0, d0 = sift.read_features_from_file('im0.sift')
sift.process_image(im1_path, 'im1.sift')
l1, d1 = sift.read_features_from_file('im1.sift')
# match features and estimate homography
matches = sift.match_twosided(d0, d1)
ndx = matches.nonzero()[0]
fp = homography.make_homog(l0[ndx, :2].T)
ndx2 = [int(matches[i]) for i in ndx]
tp = homography.make_homog(l1[ndx2, :2].T)
model = homography.RansacModel()
H = homography.H_from_ransac(fp, tp, model)[0]
return H
def compute_homography():
# compute features
#sift.process_image('../../data/book_frontal.JPG','../../data/im0.sift')
im1 = '../../data/space_front.jpg'
im2 = '../../data/space_perspective.jpg'
im1 = '../../data/mag_front.jpg'
im2 = '../../data/mag_perspective.jpg'
ims = [im1, im2]
sifts = []
for k in range(2):
sifts.append(ims[k][:-4] + ".sift")
l0, d0 = sift.read_features_from_file(sifts[0])
l1, d1 = sift.read_features_from_file(sifts[1])
# match features and estimate homography
matches = sift.match_twosided(d0, d1)
ndx = matches.nonzero()[0]
fp = homography.make_homog(l0[ndx, :2].T)
ndx2 = [int(matches[i]) for i in ndx]
tp = homography.make_homog(l1[ndx2, :2].T)
model = homography.RansacModel()
H, ransac_data = homography.H_from_ransac(fp, tp, model)
return H
tic.k('matched')
def convert_points(j):
ndx = matches[j].nonzero()[0]
fp = homography.make_homog(l[j + 1][ndx, :2].T)
ndx2 = [int(matches[j][i]) for i in ndx]
tp = homography.make_homog(l[j][ndx2, :2].T)
return fp, tp
model = homography.RansacModel()
fp, tp = convert_points(1)
H_12 = homography.H_from_ransac(fp, tp, model)[0]
tic.k('12 homogd')
fp, tp = convert_points(0)
H_01 = homography.H_from_ransac(fp, tp, model)[0]
tic.k('01 homogd')
tp, fp = convert_points(2) # Note: Reversed.
H_32 = homography.H_from_ransac(fp, tp, model)[0]
tic.k('32 homogd')
tp, fp = convert_points(3) # Note: Reversed.
H_43 = homography.H_from_ransac(fp, tp, model)[0]
tic.k('43 homogd')
# FIXME: Consider using bundle adjustment and Levenberg-Marquardt instead of
def convert_points(j):
ndx = matches[j].nonzero()[0]
fp = homography.make_homog(l[j + 1][ndx, :2].T)
ndx2 = [int(matches[j][i]) for i in ndx]
tp = homography.make_homog(l[j][ndx2, :2].T)
# switch x and y - TODO this should move elsewhere
fp = vstack([fp[1], fp[0], fp[2]])
tp = vstack([tp[1], tp[0], tp[2]])
return fp, tp
# estimate the homographies
model = homography.RansacModel()
fp, tp = convert_points(1)
H_12 = homography.H_from_ransac(fp, tp, model)[0] #im 1 to 2
fp, tp = convert_points(0)
H_01 = homography.H_from_ransac(fp, tp, model)[0] #im 0 to 1
tp, fp = convert_points(2) #NB: reverse order
H_32 = homography.H_from_ransac(fp, tp, model)[0] #im 3 to 2
tp, fp = convert_points(3) #NB: reverse order
H_43 = homography.H_from_ransac(fp, tp, model)[0] #im 4 to 3
delta = 2000 #for padding and translation
im1 = array(Image.open(imname[1]))
im2 = array(Image.open(imname[2]))
im_12 = warp.panorama(H_12, im1, im2, delta, delta)
im1 = array(Image.open(imname[0]))
im_02 = warp.panorama(dot(H_12, H_01), im1, im_12, delta, delta)
bf = cv2.BFMatcher(crossCheck=True)
matches = bf.match(d0, d1)
#https://stackoverflow.com/questions/30716610/how-to-get-pixel-coordinates-from-feature-matching-in-opencv-python
kp0 = [l0[mat.queryIdx].pt for mat in matches]
kp1 = [l1[mat.trainIdx].pt for mat in matches]
kp0 = np.array(kp0)
kp1 = np.array(kp1)
#%%
''' Compute from and to points '''
fp = homography.make_homog(kp0[:100, :2].T)
tp = homography.make_homog(kp1[:100, :2].T)
model = homography.RansacModel()
H, _ = homography.H_from_ransac(fp, tp, model)
#%%
''' Draw cone '''
def cube_points(c, wid):
""" Creates a list of points for plotting
a cube with plot. (the first 5 points are
the bottom square, some sides repeated). """
p = []
#bottom
p.append([c[0] - wid, c[1] - wid, c[2] - wid])
p.append([c[0] - wid, c[1] + wid, c[2] - wid])
p.append([c[0] + wid, c[1] + wid, c[2] - wid])
p.append([c[0] + wid, c[1] - wid, c[2] - wid])
sift.process_image('book_frontal.JPG', 'im0.sift')
l0, d0 = sift.read_features_from_file('im0.sift')
sift.process_image('book_perspective.JPG', 'im1.sift')
l1, d1 = sift.read_features_from_file('im1.sift')
# 匹配特征，并计算单应性矩阵
# match features and estimate homography
matches = sift.match_twosided(d0, d1) #匹配
ndx = matches.nonzero()[0]
fp = homography.make_homog(l0[ndx, :2].T) #vstack
ndx2 = [int(matches[i]) for i in ndx]
tp = homography.make_homog(l1[ndx2, :2].T) #vstack
model = homography.RansacModel()
H, inliers = homography.H_from_ransac(fp, tp, model) #删除误匹配
# camera calibration
K = my_calibration((747, 1000)) #相机焦距光圈初始化
# 3D points at plane z=0 with sides of length 0.2
box = cube_points([0, 0, 0.1], 0.1)
# project bottom square in first image
cam1 = camera.Camera(hstack((K, dot(K, array([[0], [0], [-1]]))))) #相机初始化
# first points are the bottom square
box_cam1 = cam1.project(homography.make_homog(box[:, :5])) #在相机中的坐标
# use H to transfer points to the second image
box_trans = homography.normalize(dot(H, box_cam1)) #单应性矩阵的变换
fp = homography.make_homog(q_locs[:, :2].T)
model = homography.RansacModel()
rank = {}
for ndx in res[1:]:
locs, descr = sift.read_features_from_file(featlist[ndx -
1]) # res is 1-based
matches = sift.match(q_descr, descr)
ind = matches.nonzero()[0]
ind2 = [int(matches[i]) for i in ind]
tp = homography.make_homog(locs[:, :2].T)
try:
H, inliers = homography.H_from_ransac(fp[:, ind],
tp[:, ind2],
model,
match_threshold=4)
except:
inliers = []
rank[ndx] = len(inliers)
sorted_rank = sorted(rank.items(), key=lambda t: t[1], reverse=True)
res_geom = [res[0]] + [s[0] for s in sorted_rank]
print 'homography results for query %d' % query_imid, res_geom
imagesearch.plot_results(searcher, res[:8])
imagesearch.plot_results(searcher, res_geom[:8])
kp1, des1 = m_ORB.detectAndCompute(G_img1, None)
kp2, des2 = m_ORB.detectAndCompute(G_img2, None)
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1, des2)
matches = sorted(
matches,
key=lambda x: x.distance) # Sort them in the order of their distance.
obj1, obj2 = Cli.Manage_data(matches, kp1, kp2)
model = h**o.RansacModel()
H = h**o.H_from_ransac(obj1, obj2, model)
# # Draw first 60 matches.
# img3 = img2
# img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:60], img3)
# cv2.imshow('result', img3)
# cv2.imwrite('result.jpg', img3)
# cv2.waitKey()
# 位于边长为0.2， z = 0平面上底部的正方形
Box = cube_points([0, 0, 0.1], 0.1)
cam1 = Cli.Camera(
numpy.hstack((K, numpy.dot(K, numpy.array([[0], [0], [-1]])))))
box_cam1 = cam1.project(h**o.make_homog(Box[:, :5]))
# 対応点を同次座標の点に変換する関数
def convert_points(matches,kp1,kp2):
# ndx = matches.nonzero()[0]
fp=np.array([kp1[m.queryIdx].pt for m in matches])
fp = homography.make_homog(fp.T)
# ndx2 = [int(matches[i]) for i in ndx]
tp=np.array([kp2[m.trainIdx].pt for m in matches])
tp = homography.make_homog(tp.T)
return fp,tp
# +
# ホモグラフィーを推定
model = homography.RansacModel()
fp,tp = convert_points(matches,kp1,kp2)
H,inlier=homography.H_from_ransac(fp,tp,model)
inlier
# -
inl_matches=[matches[i] for i in inlier]
img3 = cv2.drawMatches(im1, kp1, im2, kp2, inl_matches, None, flags=2)
ip.show_img(img3,figsize=(20,30))
ip.imwrite('inl_match.jpg',img3)
np.vstack((fp,tp))
# +
MIN_MATCH_COUNT=10
# Initiate AKAZE detector
ndx = matches[j].nonzero()[0]
fp = homography.make_homog(l[j + 1][ndx, :2].T)
ndx2 = [int(matches[j][i]) for i in ndx]
tp = homography.make_homog(l[j][ndx2, :2].T)
# switch x and y
fp = np.vstack([fp[1], fp[0], fp[2]])
tp = np.vstack([tp[1], tp[0], tp[2]])
return fp, tp
# estimate the homographies
model = homography.RansacModel()
fp, tp = convert_points(0)
H_01 = homography.H_from_ransac(fp, tp, model)[0] # im 0 to 1
fp, tp = convert_points(1)
H_12 = homography.H_from_ransac(fp, tp, model)[0] # im 1 to 2
# warp the images
delta = 800
im0 = np.array(Image.open(imname[0]), "uint8")
im1 = np.array(Image.open(imname[1]), "uint8")
im2 = np.array(Image.open(imname[2]), "uint8")
im_01 = warp.panorama(H_01, im0, im1, delta, delta)
im_12 = warp.panorama(H_12, im1, im2, delta, delta)
im0 = np.array(Image.open(imname[0]), "f")
im_02 = warp.panorama(np.dot(H_12, H_01), im0, im_12, delta, delta)
def convert_points(j):
ndx = matches[j].nonzero()[0]
fp = homography.make_homog(l[j + 1][ndx, :2].T)
ndx2 = [int(matches[j][i]) for i in ndx]
tp = homography.make_homog(l[j][ndx2, :2].T)
return fp, tp
model = homography.RansacModel()
fp, tp = convert_points(0)
tic.k('converted')
if True:
# Ignore wrong matches.
H_12 = homography.H_from_ransac(fp, tp, model)[0]
else:
# Assume all matches are correct.
H_12 = homography.H_from_points(fp, tp)
tic.k('homogd')
# ...
delta = 600
im1 = array(Image.open(imname[0]).convert('L'))
im2 = array(Image.open(imname[1]).convert('L'))
tic.k('imloaded')
im_12 = warp.panorama(H_12, im1, im2, delta, delta, alpha=0.5)
if __name__ == "__main__":
import cv2
import ORB_Match
img1 = cv2.imread('5.jpg', cv2.IMREAD_COLOR)
img2 = cv2.imread('6.jpg', cv2.IMREAD_COLOR)
# 记录程序运行时间
e1 = cv2.getTickCount()
# your code execution
kp1, kp2, Matched = ORB_Match.ORB_match(img1, img2)
def convert_points(j, matches):
"""将匹配转换成齐次坐标点的函数"""
ndx = matches[j].nonzero()[0]
fp = homography.make_homog(kp1[ndx, :2].T)
ndx2 = [int(matches[j][i]) for i in ndx]
tp = homography.make_homog(kp2[ndx2, :2].T)
return fp, tp
model = homography.RansacModel()
fp, tp = convert_points(1)
H_12 = homography.H_from_ransac(fp, tp, model)[0] # img1到img2的单应性矩阵
img_12 = panorama(H_12, img1, img2, 2000, 2000)
# Detect_and_Draw()
e2 = cv2.getTickCount()
time = (e2 - e1) / cv2.getTickFrequency()
print "Processing time is %f s" % time
