out_inliers = torch.zeros(log_probs.size()) # inlier mask of estimated model
out_gradients = torch.zeros(
log_probs.size()) # gradient tensor (only used during training)
rand_seed = 0 # random seed to by used in C++
# run NG-RANSAC
if opt.fmat:
# === CASE FUNDAMENTAL MATRIX =========================================
# undo normalization of x and y image coordinates
util.denormalize_pts(correspondences[0:2], img1.shape)
util.denormalize_pts(correspondences[2:4], img2.shape)
incount = ngransac.find_fundamental_mat(correspondences, probs, rand_seed,
opt.hyps, opt.threshold,
opt.refine, out_model, out_inliers,
out_gradients)
else:
# === CASE ESSENTIAL MATRIX =========================================
incount = ngransac.find_essential_mat(correspondences, probs, rand_seed,
opt.hyps, opt.threshold, out_model,
out_inliers, out_gradients)
print("\n=== Model found by NG-RANSAC: =======\n")
print(out_model.numpy())
print("\nNG-RANSAC Inliers: ", int(incount))
# create a visualization of the matching, comparing results of RANSAC and NG-RANSAC
0, 10000) # provide a random seed for C++
if opt.fmat:
# === CASE FUNDAMENTAL MATRIX =========================================
# restore pixel coordinates
util.denormalize_pts(correspondences[b, 0:2], im_size1[b])
util.denormalize_pts(correspondences[b, 2:4], im_size2[b])
F = torch.zeros((3, 3))
#run NG-RANSAC
start_time = time.time()
incount = ngransac.find_fundamental_mat(
correspondences[b], probs[b], rand_seed, opt.hyps,
opt.threshold, opt.refine, F, inliers, gradients)
ransac_time += time.time() - start_time
# essential matrix from fundamental matrix (for evaluation via relative pose)
E = K2[b].transpose(0, 1).mm(F.mm(K1[b]))
pts1 = correspondences[b, 0:2].numpy()
pts2 = correspondences[b, 2:4].numpy()
# evaluation of F matrix via correspondences
valid, F1, epi_inliers, epi_error = util.f_error(
pts1, pts2, F.numpy(), gt_F[b].numpy(), opt.threshold)
if valid:
avg_F1 += F1