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Fixed the affine transformation error. #5

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Fixed the affine transformation error. #5

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4 changes: 2 additions & 2 deletions quick_start/coarseAlignFeatMatch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,8 +19,8 @@
sys.path.append('../utils/')
import outil

from scipy.misc import imresize
from scipy import signal
# from scipy.misc import imresize
# from scipy import signal
## resize image according to the minsize, at the same time resize the x,y coordinate


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57 changes: 41 additions & 16 deletions utils/outil.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,12 +46,31 @@ def mutualMatching(featA, featB) :


def Affine(X, Y):
H21 = np.linalg.lstsq(Y, X[:, :2])[0]
H21 = H21.T
H21 = np.array([[H21[0, 0], H21[0, 1], H21[0, 2]],
[H21[1, 0], H21[1, 1], H21[1, 2]],
[0, 0, 1]])

N = X.shape[0]
device = X.device
A = np.zeros((N, 6, 7))
for i in range(3):
u, v, u_, v_ = Y[:, i, 0].cpu().numpy(), Y[:, i, 1].cpu().numpy(),\
X[:, i, 0].cpu().numpy(), X[:, i, 1].cpu().numpy()
A[:, 2 * i] = np.stack([
np.zeros(N), np.zeros(N), np.zeros(N),
-u, -v, -np.ones(N), v_
], axis=1)
A[:, 2 * i + 1] = np.stack([
u, v, np.ones(N), np.zeros(N), np.zeros(N),
np.zeros(N), -u_
], axis=1)

# svd composition
u, s, v = np.linalg.svd(A)
# reshape the min singular value into a 2 by 3 matrix
v = v[:, -1] # smallest eigenvalue
H21 = np.zeros((N, 3, 3))
H21[:, :2, :] = np.reshape(v[:, :-1], (N, 2, 3))
for i in range(N):
H21[i] /= v[i, -1]
H21[:, -1, -1] = 1
H21 = torch.tensor(H21, device=device).float().cuda()
return H21

def Hough(X, Y) :
Expand Down Expand Up @@ -80,7 +99,7 @@ def Homography(X, Y):
np.zeros(N), -u_ * u, -u_ * v, -u_
], axis=1)

#svd compositionq
#svd composition
u, s, v = np.linalg.svd(A)
#reshape the min singular value into a 3 by 3 matrix
H21 = torch.tensor(np.reshape(v[:, 8], (N, 3, 3))).float().cuda()
Expand Down Expand Up @@ -119,15 +138,21 @@ def RANSAC(nbIter, match1, match2, tolerance, nbPoint, Transform) :

samples = torch.randint(nbMatch, (nbIter, nbPoint), device=match1.device)

# HARDCODED FOR HOMOGRPAHIES FOR NOW
conditions = torch.stack([
samples[:, 0] == samples[:, 1],
samples[:, 0] == samples[:, 2],
samples[:, 0] == samples[:, 3],
samples[:, 1] == samples[:, 2],
samples[:, 1] == samples[:, 3],
samples[:, 2] == samples[:, 3]
], dim=1) # N * nb_cond
if nbPoint == 4:
conditions = torch.stack([
samples[:, 0] == samples[:, 1],
samples[:, 0] == samples[:, 2],
samples[:, 0] == samples[:, 3],
samples[:, 1] == samples[:, 2],
samples[:, 1] == samples[:, 3],
samples[:, 2] == samples[:, 3]
], dim=1) # N * nb_cond
elif nbPoint == 3:
conditions = torch.stack([
samples[:, 0] == samples[:, 1],
samples[:, 0] == samples[:, 2],
samples[:, 1] == samples[:, 2],
], dim=1) # N * nb_cond

duplicated_samples = torch.any(conditions, dim=1)
unique_samples = samples[~duplicated_samples] # N * nbPoint
Expand Down