im1 = cv2.imread('../data/im1.png')[:, :, ::-1]
im2 = cv2.imread('../data/im2.png')[:, :, ::-1]
corresp = np.load('../data/some_corresp.npz')
pts1 = corresp['pts1']
pts2 = corresp['pts2']
# select 7 points
N = pts1.shape[0]
select_ids = [53, 17, 43, 46, 27, 56, 77]
pts1 = pts1[select_ids, :].copy()
pts2 = pts2[select_ids, :].copy()
M = np.max(im1.shape)
Farray = sevenpoint(pts1, pts2, M)
for idx, F in enumerate(Farray):
print('visualizing F%d' % idx)
print(F)
displayEpipolarF(im1, im2, F)
print('Enter the id of F which is correct:')
correct_id = int(input())
assert 0 <= correct_id < len(Farray)
F_correct = Farray[correct_id]
np.savez('q2_2.npz', F=F_correct, M=M, pts1=pts1, pts2=pts2)
'''
visualizing F0
[[ 1.37155686e-08 -8.61003099e-08 -8.27427609e-04]
[ 2.16183531e-07 1.90452142e-09 -2.92701083e-05]
K1 = intr['K1']
K2 = intr['K2']
N = data['pts1'].shape[0]
M = 640
# 2.1
F8 = sub.eightpoint(data['pts1'], data['pts2'], M)
assert F8.shape == (3, 3), 'eightpoint returns 3x3 matrix'
print('F8')
print(F8)
#helper.displayEpipolarF(im1, im2, F8)
# 2.2
#F7 = sub.sevenpoint(data['pts1'][:7, :], data['pts2'][:7, :], M)
F7 = sub.sevenpoint(data['pts1'][70:77, :], data['pts2'][70:77, :], M)
assert (len(F7) == 1) | (len(F7) == 3), 'sevenpoint returns length-1/3 list'
for f7 in F7:
assert f7.shape == (3, 3), 'seven returns list of 3x3 matrix'
#print(F7.shape)
print('F7')
print(F7)
#helper.displayEpipolarF(im1, im2, F7[0])
# 3.1
print('E')
print(sub.essentialMatrix(F8, K1, K2))
C1 = np.concatenate([np.random.rand(3, 3), np.ones([3, 1])], axis=1)
image1 = cv2.imread(
'/home/geekerlink/Desktop/Computer Vision/Homeworks/hw4/hw4/data/im1.png'
)
image2 = cv2.imread(
'/home/geekerlink/Desktop/Computer Vision/Homeworks/hw4/hw4/data/im2.png'
)
im_width = image1.shape[1]
im_height = image1.shape[0]
#calculating the scaling factor
M = max(im_height, im_width)
M = float(M)
#calculating the fundamental matrix
F = submission.eightpoint(pts1, pts2, M)
helper.displayEpipolarF(image1, image2, F)
#seven point algorithm
F_seven_temp = submission.sevenpoint(pts1, pts2, M)
errorvalue = []
for i in range(0, 3):
least_error = np.sum(np.square(np.subtract(F, F_seven_temp[i])))
errorvalue.append(least_error)
error_index = np.argmin(errorvalue)
F_seven = F_seven_temp[error_index]
helper.displayEpipolarF(image1, image2, F_seven)
#intrinsic parameters
intrinsic = np.load(
'/home/geekerlink/Desktop/Computer Vision/Homeworks/hw4/hw4/data/intrinsics.npz'
)
b = intrinsic.files
k1 = intrinsic[b[0]]
k2 = intrinsic[b[1]]
#calculating essential matrix
import helper
import numpy as np
import submission
import matplotlib.pyplot as plt
img1 = plt.imread("../data/im1.png")
img2 = plt.imread("../data/im2.png")
h, w, c = img1.shape
points = np.load("../data/some_corresp.npz")
p1 = points['pts1']
p2 = points['pts2']
M = max(h, w)
F = submission.sevenpoint(p1, p2, M)
print(F.shape)
helper.displayEpipolarF(img1, img2, F)
def ransacF(pts1, pts2, M):
num_iter=5000
tol=10e-3
#create a set of random index to match points
randIdx=np.random.randint(0,pts1.shape[0]-1,size=num_iter*8,dtype=int)
randIdx=randIdx.reshape(num_iter,8)
max_inliers=0
for i in range(num_iter):
index=randIdx[i,:]
P1=pts1[index,:]
P2=pts2[index,:]
F=sub.eightpoint(P1,P2,M)
num_inliers=0
inliers_points=np.repeat(False,len(pts1))
for m in range(len(pts1)):
p2=np.vstack([pts2[m,0],pts2[m,1],1])
epipolar_line = np.dot(F,p2)
# a = epipolar_line[0]
# b = epipolar_line[1]
# c = epipolar_line[2]
p1=np.hstack([pts1[m,0],pts1[m,1],1])
diff= abs(np.dot(p1,epipolar_line))
if diff<tol:
num_inliers=num_inliers+1
inliers_points[m]=True
if num_inliers>max_inliers:
max_inliers=num_inliers
inliers=inliers_points
F_best=F
print('times:',i)
print('max num of inlier points',max_inliers)
num_iter=5000
tol=3*10e-4
#create a set of random index to match points
randIdx=np.random.randint(0,pts1.shape[0]-1,size=num_iter*7,dtype=int)
randIdx=randIdx.reshape(num_iter,7)
max_inliers=0
for i in range(num_iter):
print('times:',i)
index=randIdx[i,:]
P1=pts1[index,:]
P2=pts2[index,:]
Farray=sub.sevenpoint(P1,P2,M)
inliers_points=np.repeat(False,len(pts1))
for j in range(Farray.shape[2]):
num_inliers=0
F=Farray[:,:,j]
for m in range(len(pts1)):
p2=np.hstack([pts2[m,0],pts2[m,1],1])
# a = epipolar_line[0]
# b = epipolar_line[1]
# c = epipolar_line[2]
p1=np.vstack([pts1[m,0],pts1[m,1],1])
epipolar_line = np.dot(F,p1)
diff= abs(np.dot(p2,epipolar_line))
if diff<tol:
num_inliers=num_inliers+1
inliers_points[m]=True
if num_inliers>max_inliers:
max_inliers=num_inliers
inliers=inliers_points
F_best=F
print('max num of inlier points',max_inliers)
return F_best,inliers
F8 = sub.eightpoint(data['pts1'], data['pts2'], M)
assert F8.shape == (3, 3), 'eightpoint returns 3x3 matrix'
# hlpr.displayEpipolarF(im1, im2, F8)
# np.savez('q2_1.npz', F=F8, M=M)
print("q2-1:\n", F8)
# 2.2
# select 7 best corresponding pair
aug_pts1 = np.hstack((data['pts1'], np.ones((N, 1))))
aug_pts2 = np.hstack((data['pts2'], np.ones((N, 1))))
xFx = np.abs(np.diag(aug_pts2 @ F8 @ np.transpose(aug_pts1)))
best_idx = xFx.argsort()[:7]
pts1 = data['pts1'][best_idx]
pts2 = data['pts2'][best_idx]
F7 = sub.sevenpoint(pts1, pts2, M)
assert (len(F7) == 1) | (len(F7)
== 3), 'sevenpoint returns length-1/3 list'
for f7 in F7:
assert f7.shape == (3, 3), 'seven returns list of 3x3 matrix'
# hlpr.displayEpipolarF(im1, im2, f7)
# np.savez('q2_2.npz', F=F7, M=M, pts1=pts1, pts2=pts2)
print("q2-2:\n", F7)
# 3.1
intrinsics_data = np.load('../data/intrinsics.npz')
# print(intrinsics_data.files)
# [print(i) for i in intrinsics_data]
E = sub.essentialMatrix(F8, intrinsics_data["K1"], intrinsics_data["K2"])
print("q3-1:\n", E)
import numpy as np
import matplotlib.pyplot as plt
import submission as sub
data = np.load('../data/some_corresp.npz')
im1 = plt.imread('../data/im1.png')
im2 = plt.imread('../data/im2.png')
N = data['pts1'].shape[0]
M = 640
# 2.1
F8 = sub.eightpoint(data['pts1'], data['pts2'], M)
assert F8.shape == (3, 3), 'eightpoint returns 3x3 matrix'
# 2.2
F7 = sub.sevenpoint(data['pts1'][:7, :], data['pts2'][:7, :], M);
assert len(F7) == 1 | len(F7) == 3, 'sevenpoint returns length-1/3 list'
for f7 in F7:
assert f7.shape == (3, 3), 'seven returns list of 3x3 matrix'
# 3.1
C1 = np.concatenate([np.random.rand(3, 3), np.ones([3, 1])], axis=1)
C2 = np.concatenate([np.random.rand(3, 3), np.ones([3, 1])], axis=1)
P, err = sub.triangulate(C1, data['pts1'], C2, data['pts2']);
assert P.shape == (N, 3), 'triangulate returns Nx3 matrix P'
assert np.isscalar(err), 'triangulate returns scalar err'
# 4.1
x2, y2 = sub.epipolarCorrespondence(im1, im2, F8, data['pts1'][0, 0], data['pts1'][0, 1])
# roots=np.roots([a0,a1,a2,a3])
# roots=np.real(roots)
# print('roots=',roots)
#
# T = np.array([[1/M, 0, 0],
# [0, 1/M, 0],
# [0, 0, 1]])
# Farray=np.zeros([3,3,len(roots)])
# for i in range(len(roots)):
# F=roots[i] * F1 + (1 - roots[i]) * F2
# # [U1, S1, V1] = np.linalg.svd(F)
# # S1[2]=0
# # F = np.dot(U1,np.diag(S1)).dot(V1)
# F=np.dot(np.transpose(T), F).dot(T)
# Farray[:,:,i]=F
# np.savez('../data/q2_2.npz', F=F, M=M, pts1=pts1,pts2=pts2)
# return Farray
if __name__ == '__main__':
data = np.load('../data/some_corresp.npz')
im1 = plt.imread('../data/im1.png')
im2 = plt.imread('../data/im2.png')
N = data['pts1'].shape[0] # N=110
M = 640
pts1 = data['pts1']
pts2 = data['pts2']
Farray = sub.sevenpoint(pts1[7:14, :], pts2[7:14, :], M)
print('F=', Farray[:, :, 2])
helper.displayEpipolarF(im1, im2, Farray[:, :, 2])
import numpy as np
import matplotlib.pyplot as plt
import submission as sub
import helper as hp
data = np.load('../data/some_corresp.npz')
im1 = plt.imread('../data/im1.png')
im2 = plt.imread('../data/im2.png')
K_matrices = np.load('../data/intrinsics.npz')
N = data['pts1'].shape[0]
M = 640
# 2.1
#F8 = sub.eightpoint(data['pts1'], data['pts2'], M)
#hp.displayEpipolarF(im1,im2,F8)
#F = F8
#2.2
F7 = sub.sevenpoint(data['pts1'][21:28, :], data['pts2'][21:28, :], M)
hp.displayEpipolarF(im1, im2, F7)
E = sub.essentialMatrix(F, K_matrices['K1'], K_matrices['K2'])
