def wrapping(I_a, I_b, d_a, K, R, t):
"""
Wrap image by providing depth, rotation and translation
:param I_a:
:param I_b:
:param d_a:
:param K:
:param R:
:param t:
:return:
"""
import banet_track.ba_module as module
H, W, C = I_a.shape
# I_a = torch.from_numpy(I_a.transpose((2, 0, 1))).cuda().view((1, C, H, W))
I_b = torch.from_numpy(I_b.transpose((2, 0, 1))).cuda().view((1, C, H, W))
d_a = torch.from_numpy(d_a).cuda().view((1, H * W))
K = torch.from_numpy(K).cuda().view(1, 3, 3)
R = torch.from_numpy(R).cuda().view(1, 3, 3)
t = torch.from_numpy(t).cuda().view(1, 3)
x_a = module.x_2d_coords_torch(1, H, W).view(1, H * W,
2).cuda() # dim: (N, H*W, 2)
X_a_3d = module.batched_pi_inv(K, x_a, d_a.view((1, H * W, 1)))
X_b_3d = module.batched_transpose(R, t, X_a_3d)
x_b_2d, _ = module.batched_pi(K, X_b_3d)
x_b_2d_out = x_b_2d.cpu().numpy()
x_b_2d = module.batched_x_2d_normalize(H, W,
x_b_2d).view(1, H, W,
2) # (N, H, W, 2)
wrap_img_b = module.batched_interp2d(I_b, x_b_2d)
return wrap_img_b.cpu().numpy().transpose((0, 2, 3, 1)).reshape(
(H, W, C)), x_b_2d_out.reshape(H, W, 2)
def verify_features(self, I_a, d_a, K, I_b, se3_gt, x, y, title):
"""
Extract feature pyramids f_a, f_b of I_a and I_b
Wrap f_b to f_a
Compute distances of a pixel in f_a with the neighbors of its corresponding pixels in f_b
:param I_a: Image of frame A, dim: (N, C, H, W)
:param d_a: Depth of frame A, dim: (N, 1, H, W)
:param K: intrinsic matrix at level 0: dim: (N, 3, 3)
:param I_b: Image of frame B, dim: (N, C, H, W)
:param se3_gt: Groundtruth of se3, dim: (N, 6)
:return:
"""
import banet_track.ba_debug as debug
(N, C, H, W) = I_a.shape
I_a.requires_grad_()
I_b.requires_grad_()
# Concate I_a and I_b
I = torch.cat([I_a, I_b], dim=0)
# Aggregate pyramid features
aggr_pyramid = self.aggregate_pyramid_features(self.backbone_net.forward(I))
aggr_pyramid_f_a = [f[:N, :, :, :] for f in aggr_pyramid]
aggr_pyramid_f_b = [f[N:, :, :, :] for f in aggr_pyramid]
for level in [2, 1, 0]:
(level_H, level_W) = self.level_dim_hw[level]
# Resize and Rescale the depth and the intrinsic matrix
rescale_ratio = 1.0 / math.pow(2, level)
level_K = rescale_ratio * K.detach() # dim: (N, 3, 3)
level_d_a = F.interpolate(d_a, scale_factor=rescale_ratio).detach() # dim: (N, 1, H, W)
# Cache several variables:
R, t = se3_exp(se3_gt)
x_a_2d = self.x_valid_2d[level] # dim: (N, H*W, 2)
X_a_3d = batched_pi_inv(level_K, x_a_2d,
level_d_a.view((N, level_H * level_W, 1)))
X_b_3d = batched_transpose(R, t, X_a_3d)
x_b_2d, _ = batched_pi(level_K, X_b_3d)
x_b_2d = module.batched_x_2d_normalize(float(level_H), float(level_W), x_b_2d).view(N, level_H, level_W, 2) # (N, H, W, 2)
# Wrap the feature
level_aggr_pyramid_f_b_wrap = batched_interp2d(aggr_pyramid_f_b[level], x_b_2d)
level_x = int(x * rescale_ratio)
level_y = int(y * rescale_ratio)
left = level_x - debug.similar_window_offset
left = left if left >= 0 else 0
right = level_x + debug.similar_window_offset
up = level_y - debug.similar_window_offset
up = up if up >= 0 else 0
down = level_y + debug.similar_window_offset
batch_distance = torch.norm(aggr_pyramid_f_a[level][:, :, up:down, left:right] - # (N, level_H, level_W)
level_aggr_pyramid_f_b_wrap[:, :, level_y:level_y+1, level_x:level_x+1], 2, 1)
show_multiple_img([{'img': I_a[0].detach().cpu().numpy().transpose(1, 2, 0), 'title': 'I_a'},
{'img': I_b[0].detach().cpu().numpy().transpose(1, 2, 0), 'title': 'I_b'},
{'img': batch_distance[0].detach().cpu().numpy(), 'title': 'feature distance', 'cmap':'gray'}],
title=title, num_cols=3)
def photometric_error(I_a, sel_pt_idx, I_b, x_b_2d):
N = I_a.shape[0] # number of batches
M = sel_pt_idx.shape[1] # number of samples
C = I_a.shape[1] # number of channels
H = I_a.shape[2]
W = I_a.shape[3]
# Wrap the image
Ib_wrap = batched_interp2d(I_b, x_b_2d)
# Intensity error
e = I_a - Ib_wrap
# select choosen indecs
e = e.view(N, C, H * W)
e = batched_index_select(e, 2, sel_pt_idx)
return e
def valid(self, I_a, d_a, sel_a_indices, K, I_b, se3_gt, epoch):
"""
Pre cache the variable for prediction
:param I_a: Image of frame A, dim: (N, C, H, W)
:param d_a: Depth of frame A, dim: (N, 1, H, W)
:param sel_a_indices: (N, 3, M)
:param K: intrinsic matrix at level 0: dim: (N, 3, 3)
:param I_b: Image of frame B, dim: (N, C, H, W)
:param se3_gt: ground truth Pose
"""
(N, C, H, W) = I_a.shape
I_a.detach()
I_b.detach()
# Ground-truth pose
R_gt, t_gt = se3_exp(se3_gt)
# Concate I_a and I_b
I = torch.cat([I_a, I_b], dim=0)
# Aggregate pyramid features
aggr_pyramid = self.aggregate_pyramid_features(
self.backbone_net.forward(I))
aggr_pyramid_f_a = [f[:N, :, :, :] for f in aggr_pyramid]
aggr_pyramid_f_b = [f[N:, :, :, :] for f in aggr_pyramid]
# Init a se(3) vector and mark requires_grad = True
# alpha = torch.tensor([1e-4, 1e-4, 1e-4, 0.0, 0.0, 0.0]).repeat(N).view((N, 6)) # dim: (N, 6)
# factor = 0.3
# alpha = module.gen_random_alpha(se3_gt, rot_angle_rfactor=1.25, trans_vec_rfactor=0.16).view((N, 6)).cuda()
# alpha.requires_grad_()
T = torch.eye(4).view(1, 4, 4).repeat(N, 1, 1).detach()
init_T = T
pred_SE3_list = [
] # (num_level: low_res to high_res, num_iter_per_level)
gt_f_pair_list = []
lambda_weight = []
flow_list = []
for level in [2, 1, 0]:
pred_SE3_list.append([])
lambda_weight.append([])
flow_list.append([])
(level_H, level_W) = self.level_dim_hw[level]
M = sel_a_indices.shape[2] # number of selected pts
# Features on current level
f_a = aggr_pyramid_f_a[level]
f_b = aggr_pyramid_f_b[level]
f_b_grad = batched_gradient(f_b) # dim: (N, 2*C, H, W)
# Resize and Rescale the depth and the intrinsic matrix
rescale_ratio = 1.0 / math.pow(2, level)
level_K = rescale_ratio * K.detach() # dim: (N, 3, 3)
level_d_a = F.interpolate(
d_a, scale_factor=rescale_ratio).detach() # dim: (N, 1, H, W)
sel_a_idx = sel_a_indices[:,
level, :].view(N,
M).detach() # dim: (N, M)
# Cache several variables:
x_a_2d = self.x_train_2d[level] # dim: (N, H*W, 2)
X_a_3d = batched_pi_inv(level_K, x_a_2d,
level_d_a.view((N, level_H * level_W, 1)))
X_a_3d_sel = batched_index_select(X_a_3d, 1,
sel_a_idx) # dim: (N, M, 3)
""" Ground-truth correspondence for Regularizer
"""
f_C = f_a.shape[1]
X_b_3d_gt = batched_transpose(R_gt, t_gt, X_a_3d)
x_b_2d_gt, _ = batched_pi(level_K, X_b_3d_gt)
x_b_2d_gt = module.batched_x_2d_normalize(float(level_H),
float(level_W),
x_b_2d_gt).view(
N, level_H, level_W,
2) # (N, H, W, 2)
gt_f_wrap_b = batched_interp2d(f_b, x_b_2d_gt)
f_a_select = batched_index_select(
f_a.view(N, f_C, level_H * level_W), 2, sel_a_idx)
gt_f_wrap_b_select = batched_index_select(
gt_f_wrap_b.view(N, f_C, level_H * level_W), 2, sel_a_idx)
gt_f_pair_list.append((f_a_select, gt_f_wrap_b_select))
# Run iteration 3 times
for itr in range(0, 6):
T, r, delta_norm, lamb, flow = module.dm_levenberg_marquardt_itr(
T, X_a_3d, X_a_3d_sel, f_a, sel_a_idx, level_K, f_b,
f_b_grad, self.lambda_prediction, level)
pred_SE3_list[-1].append(T)
flow_list[-1].append((flow, x_b_2d_gt.detach()))
return pred_SE3_list, gt_f_pair_list, init_T.detach(), flow_list
