def read_tum_seq(tum_rgbd_base_dir, seq_name):
"""
Read the SUN3D sequence to the frames collection
:param sun3d_seq_dir: input sun3d sequence directory
:return: uniform frames collection.
"""
frames = FrameSeqData()
abs_seq_dir = os.path.join(tum_rgbd_base_dir, seq_name)
# Read intrinsic mat
fx = 525.0 # focal length x
fy = 525.0 # focal length y
cx = 319.5 # optical center x
cy = 239.5 # optical center y
K_param = np.asarray([fx, fy, cx, cy, 0.0, 0.0], dtype=np.float32)
default_img_dim = (480, 640)
if os.path.exists(os.path.join(abs_seq_dir, 'rdpose_associate.txt')):
gt_file = 'rdpose_associate.txt'
else:
gt_file = 'rd_associate.txt'
frame_idx = 0
with open(os.path.join(abs_seq_dir, gt_file), 'r') as f:
for line in f:
# Load frame data
if gt_file.startswith('rdpose_associate'):
timestamp, img_file_name, _, depth_file_name, _, tx, ty, tz, qx, qy, qz, qw = line.strip(
).split(' ')
tx = float(tx)
ty = float(ty)
tz = float(tz)
qx = float(qx)
qy = float(qy)
qz = float(qz)
qw = float(qw)
R_mat = trans.quaternion_matrix([qw, qx, qy,
qz]).astype(np.float32)
t = np.array([tx, ty, tz]).astype(np.float32)
Twc_mat = R_mat
Twc_mat[:3, 3] = t
Tcw = np.linalg.inv(Twc_mat)[:3, :]
else:
timestamp, img_file_name, _, depth_file_name = line.strip(
).split(' ')
Tcw = np.eye(4)[:3, :]
frames.append_frame(
frame_idx=frame_idx,
img_file_name=os.path.join(seq_name, img_file_name),
Tcw=Tcw[:3, :],
camera_intrinsic=K_param,
frame_dim=default_img_dim,
time_stamp=float(timestamp),
depth_file_name=os.path.join(seq_name, depth_file_name))
frame_idx += 1
return frames
np.float32) / 255.0
next_img = cv2.imread(os.path.join(base_dir, next_name)).astype(
np.float32) / 255.0
cur_depth = load_depth_from_png(os.path.join(base_dir, cur_depth_name))
h, w, c = cur_img.shape
rel_T = cam_opt.relateive_pose(cur_Tcw[:3, :3], cur_Tcw[:3, 3],
next_Tcw[:3, :3], next_Tcw[:3, 3])
# Translation
Cb = cam_opt.camera_center_from_Tcw(rel_T[:3, :3], rel_T[:3, 3])
baseline = np.linalg.norm(Cb)
# View angle
q = trans.quaternion_from_matrix(rel_T)
R = trans.quaternion_matrix(q)
rel_rad, rel_axis, _ = trans.rotation_from_matrix(R)
rel_deg = np.rad2deg(rel_rad)
next2cur, _ = cam_opt.wrapping(cur_img, next_img, cur_depth, K,
rel_T[:3, :3], rel_T[:3, 3])
show_multiple_img([{
'img': cur_img,
'title': 'a'
}, {
'img': next2cur,
'title': 'wrap_b2a'
}, {
'img': next_img,
'title': 'b'
}, {
def preprocess(sample_dict, pre_x2d, out_dim, rescale_dist=0.0):
rand_angle = np.random.random_sample() * 2.0 * np.pi
rand_R = quaternion_matrix(quaternion_about_axis(rand_angle, (0.0, 1.0, 0.0)))[:3, :3]
rand_R = torch.FloatTensor(rand_R).unsqueeze(0)
scene_rgb = sample_dict['frames_img'][:, :5, ...].cuda()
scene_depth = sample_dict['frames_depth'][:, :5, ...].cuda()
scene_K = sample_dict['frames_K'][:, :5, ...].cuda()
scene_Tcw = sample_dict['frames_Tcw'][:, :5, ...]
scene_ori_rgb = sample_dict['frames_ori_img'][:, :5, ...].cuda()
scene_neg_tags = sample_dict['frames_neg_tags'][:, :5, ...].cuda()
N, L, C, H, W = scene_rgb.shape
# scene_rgb = scene_rgb.view(N, L, C, H, W)
scene_depth = scene_depth.view(N * L, 1, H, W)
scene_K = scene_K.view(N * L, 3, 3)
scene_Tcw = scene_Tcw.view(N * L, 3, 4)
# generate 3D world position of scene
d = scene_depth.view(N * L, H * W, 1) # dim (N*L, H*W, 1)
X_3d = batched_pi_inv(scene_K, pre_x2d, d) # dim (N*L, H*W, 3)
Rwc, twc = batched_inv_pose(R=scene_Tcw[:, :3, :3],
t=scene_Tcw[:, :3, 3].squeeze(-1)) # dim (N*L, 3, 3), （N, 3)
X_world = batched_transpose(Rwc.cuda(), twc.cuda(), X_3d) # dim (N*L, H*W, 3)
X_world = X_world.contiguous().view(N, L * H * W, 3) # dim (N, L*H*W, 3)
scene_center = torch.mean(X_world, dim=1) # dim (N, 3)
X_world -= scene_center.view(N, 1, 3)
X_world = batched_transpose(rand_R.cuda().expand(N, 3, 3),
torch.zeros(1, 3, 1).cuda().expand(N, 3, 1),
X_world) # dim (N, L*H*W, 3), data augmentation
X_world = X_world.view(N, L, H, W, 3).permute(0, 1, 4, 2, 3).contiguous() # dim (N, L, 3, H, W)
# query image:
query_img = sample_dict['img']
query_ori_img = sample_dict['ori_img']
# compute multiscale ground truth query_X_worlds & valid_masks
query_X_worlds = []
valid_masks = []
out_H, out_W = out_dim
query_depth = sample_dict['depth'].cuda()
ori_query_depth = query_depth.clone()
N, C, H, W = query_depth.shape
for i in range(4):
query_depth_patch = F.unfold(
query_depth,
kernel_size=(H // out_H, W // out_W),
stride=(H // out_H, W // out_W)
).view(N, -1, out_H, out_W)
mask = torch.gt(query_depth_patch, 1e-5)
count = torch.sum(mask.float(), dim=1)
query_depth_down = torch.sum(query_depth_patch * mask.float(), dim=1) / \
torch.where(torch.le(count, 1e-5),
torch.full(count.shape, 1e6).to(count.device),
count) # (N, 1, out_H, out_W)
query_Tcw = sample_dict['Tcw']
query_K = sample_dict['K'].clone().cuda()
query_K[:, 0, 0] *= out_W / W
query_K[:, 0, 2] *= out_W / W
query_K[:, 1, 1] *= out_H / H
query_K[:, 1, 2] *= out_H / H
query_d = query_depth_down.view(N, out_H * out_W, 1) # dim (N, H*W, 1)
out_x_2d = x_2d_coords_torch(N, out_H, out_W).cuda().view(N, -1, 2)
query_X_3d = batched_pi_inv(query_K, out_x_2d, query_d) # dim (N, H*W, 3)
query_Rwc, query_twc = batched_inv_pose(R=query_Tcw[:, :3, :3],
t=query_Tcw[:, :3, 3].squeeze(-1)) # dim (N, 3, 3), （N, 3)
query_X_world = batched_transpose(query_Rwc.cuda(), query_twc.cuda(), query_X_3d) # dim (N, H*W, 3)
query_X_world -= scene_center.view(N, 1, 3)
query_X_world = batched_transpose(rand_R.cuda().expand(N, 3, 3),
torch.zeros(1, 3, 1).cuda().expand(N, 3, 1),
query_X_world) # dim (N, H*W, 3), data augmentation
query_X_world = query_X_world.permute(0, 2, 1).view(N, 3, out_H, out_W).contiguous() # dim (N, 3, H, W)
query_X_worlds.append(query_X_world.cuda())
valid_masks.append(torch.gt(query_depth_down, 1e-5).cuda().view(N, out_H, out_W))
if i == 3:
query_X_worlds.append(query_X_world.cuda())
valid_masks.append(torch.gt(query_depth_down, 1e-5).cuda().view(N, out_H, out_W))
out_H //= 2
out_W //= 2
# compute norm_query_Tcw for normalized scene coordinate
query_twc = query_twc.cuda() - scene_center.view(N, 3, 1)
norm_query_Twc = torch.cat([query_Rwc.cuda(), query_twc], dim=-1) # dim (N, 3, 4)
norm_query_Twc = torch.bmm(rand_R.cuda().expand(N, 3, 3), norm_query_Twc) # dim (N, 3, 4)
query_Rcw, query_tcw = batched_inv_pose(R=norm_query_Twc[:, :3, :3],
t=norm_query_Twc[:, :3, 3].squeeze(-1)) # dim (N, 3, 3), （N, 3)
norm_query_Tcw = torch.cat([query_Rcw, query_tcw.view(N, 3, 1)], dim=-1) # dim (N, 3, 4)
# compute down sampled query K
out_H, out_W = out_dim
query_K = sample_dict['K'].clone().cuda()
query_K[:, 0, 0] *= out_W / W
query_K[:, 0, 2] *= out_W / W
query_K[:, 1, 1] *= out_H / H
query_K[:, 1, 2] *= out_H / H
if rescale_dist > 0:
query_X_worlds, X_world, rescale_factor = rescale_scene_coords(query_X_worlds, X_world, scene_neg_tags, rescale_dist)
else:
rescale_factor = torch.ones(N)
scene_input = torch.cat((scene_rgb, X_world), dim=2)
return scene_input.cuda(), query_img.cuda(), query_X_worlds[::-1], valid_masks[::-1], \
scene_ori_rgb.cuda(), query_ori_img.cuda(), X_world.cuda(), \
torch.gt(scene_depth, 1e-5).cuda().view(N, L, H, W), norm_query_Tcw, query_K, scene_neg_tags, rescale_factor.cuda()