def filter_seq3(seq_list: FrameSeqData, base_dir):
for seq in seq_list.frames:
pre_frame = seq[0]
center_frame = seq[1]
next_frame = seq[2]
pre_Tcw = seq_list.get_Tcw(pre_frame)
center_Tcw = seq_list.get_Tcw(center_frame)
next_Tcw = seq_list.get_Tcw(next_frame)
K_mat = seq_list.get_K_mat(center_frame)
# Read Image
pre_img_name = seq_list.get_image_name(pre_frame)
center_img_name = seq_list.get_image_name(center_frame)
next_img_name = seq_list.get_image_name(pre_frame)
pre_img = cv2.imread(os.path.join(base_dir, pre_img_name)).astype(
np.float32) / 255.0
center_img = cv2.imread(os.path.join(
base_dir, center_img_name)).astype(np.float32) / 255.0
next_img = cv2.imread(os.path.join(base_dir, next_img_name)).astype(
np.float32) / 255.0
# Read depth
pre_depth_name = seq_list.get_depth_name(pre_frame)
center_depth_name = seq_list.get_depth_name(center_frame)
next_depth_name = seq_list.get_depth_name(next_frame)
pre_depth = read_sun3d_depth(pre_depth_name)
center_depth = read_sun3d_depth(center_depth_name)
next_depth = read_sun3d_depth(next_depth_name)
def convert_rel_vo(seq: FrameSeqData, ref_T):
for frame_idx in range(0, len(seq)):
frame = seq.get_frame(frame_idx)
Tcw = seq.get_Tcw(frame)
rel_T = cam_opt.relateive_pose(ref_T[:3, :3], ref_T[:3, 3],
Tcw[:3, :3], Tcw[:3, 3])
frame['extrinsic_Tcw'] = rel_T
from frame_seq_data import FrameSeqData
from seq_data.sun3d.read_util import read_sun3d_depth
import core_3dv.camera_operator as cam_opt
import core_3dv.camera_operator_gpu as cam_opt_gpu
from visualizer.visualizer_2d import show_multiple_img
from core_io.depth_io import load_depth_from_png
valid_set_dir = '/home/ziqianb/Desktop/datasets/tgz_target/'
valid_seq_name = 'rgbd_dataset_freiburg1_desk'
seq = FrameSeqData(os.path.join(valid_set_dir, valid_seq_name, 'seq.json'))
frame_a = seq.frames[5]
frame_b = seq.frames[20]
Tcw_a = seq.get_Tcw(frame_a)
Tcw_b = seq.get_Tcw(frame_b)
K = seq.get_K_mat(frame_a)
img_a = cv2.imread(os.path.join(
valid_set_dir, seq.get_image_name(frame_a))).astype(np.float32) / 255.0
img_b = cv2.imread(os.path.join(
valid_set_dir, seq.get_image_name(frame_b))).astype(np.float32) / 255.0
depth_a = load_depth_from_png(os.path.join(valid_set_dir,
seq.get_depth_name(frame_a)),
div_factor=5000.0)
depth_b = load_depth_from_png(os.path.join(valid_set_dir,
seq.get_depth_name(frame_b)),
div_factor=5000.0)
rel_T = cam_opt.relateive_pose(Tcw_a[:3, :3], Tcw_a[:3, 3], Tcw_b[:3, :3],
from core_io.depth_io import load_depth_from_png import core_math.transfom as trans from visualizer.visualizer_2d import show_multiple_img from visualizer.visualizer_3d import Visualizer from seq_data.seven_scenes.read_util import read_7scenese_depth from img_proc.img_dim import crop_by_intrinsic from seq_data.plot_seq_2d import plot_frames_seq_2d from vo_core.track_preprocess import convert_rel_vo ''' Configuration ------------------------------------------------------------------------------------------------------ ''' base_dir = '/home/ziqianb/Desktop/intel' seq_name = 'apartment' frames = FrameSeqData(os.path.join(base_dir, seq_name, 'seq.json')) refer_T = frames.get_Tcw(frames.frames[0]) convert_rel_vo(frames, refer_T) in_intrinsic = np.asarray([525.0, 525.0, 320.0, 240.0], dtype=np.float32) in_K = cam_opt.K_from_intrinsic(in_intrinsic) ''' Scripts ------------------------------------------------------------------------------------------------------------ ''' plot_frames_seq_2d(frames, show_view_direction=True) plt.show() vis = Visualizer() frame_idx = 0 x_2d = cam_opt.x_2d_coords(int(in_intrinsic[3] * 2), int(in_intrinsic[2] * 2)) # x_2d = cam_opt.x_2d_coords(480, 640)
def rand_sel_subseq_sun3d(scene_frames,
max_subseq_num,
frames_per_subseq_num=10,
dataset_base_dir=None,
trans_thres=0.15,
rot_thres=15,
frames_range=(0, 0.7),
overlap_thres=0.6,
interval_skip_frames=1):
"""
Random select sub set of sequences from scene
:param scene_frames: scene frames to extract subset
:param trans_thres_range: translation threshold, based on the center of different frames
:param max_subseq_num: maximum number of sub sequences
:param frames_per_subseq_num: for each sub sequences, how many frames in the subset
:param frames_range: range of start and end within original scene sequences, from (0, 1)
:param interval_skip_frames: skip interval in original scene frames, used in iteration
:return: list of selected sub sequences
"""
assert dataset_base_dir is not None
n_frames = len(scene_frames)
if interval_skip_frames < 1:
interval_skip_frames = 2
# Simple selection based on trans threshold
if frames_per_subseq_num * interval_skip_frames > n_frames:
raise Exception('Not enough frames to be selected')
rand_start_frame = np.random.randint(int(frames_range[0] * len(scene_frames)),
int(frames_range[1] * len(scene_frames)),
size=max_subseq_num)
sub_seq_list = []
dim = scene_frames.get_frame_dim(scene_frames.frames[0])
K = scene_frames.get_K_mat(scene_frames.frames[0])
pre_cache_x2d = x_2d_coords(dim[0], dim[1])
for start_frame_idx in rand_start_frame:
# print('F:', start_frame_idx)
# Push start keyframe into frames
sub_frames = FrameSeqData()
pre_frame = scene_frames.frames[start_frame_idx]
sub_frames.frames.append(copy.deepcopy(pre_frame))
# Iterate the remaining keyframes into subset
cur_frame_idx = start_frame_idx
no_found_flag = False
while cur_frame_idx < n_frames:
pre_Tcw = sub_frames.get_Tcw(pre_frame)
pre_depth_path = sub_frames.get_depth_name(pre_frame)
pre_depth = read_sun3d_depth(os.path.join(dataset_base_dir, pre_depth_path))
# [Deprecated]
# pre_img_name = sub_frames.get_image_name(pre_frame)
# pre_img = cv2.imread(os.path.join(dataset_base_dir, pre_img_name)).astype(np.float32) / 255.0
# pre_center = camera_center_from_Tcw(pre_Tcw[:3, :3], pre_Tcw[:3, 3])
pre_search_frame = scene_frames.frames[cur_frame_idx + interval_skip_frames - 1]
for search_idx in range(cur_frame_idx + interval_skip_frames, n_frames, 1):
cur_frame = scene_frames.frames[search_idx]
cur_Tcw = sub_frames.get_Tcw(cur_frame)
# [Deprecated]
# cur_center = camera_center_from_Tcw(cur_Tcw[:3, :3], cur_Tcw[:3, 3])
# cur_img_name = sub_frames.get_image_name(cur_frame)
# cur_img = cv2.imread(os.path.join(dataset_base_dir, cur_img_name)).astype(np.float32) / 255.0
rel_angle = rel_rot_angle(pre_Tcw, cur_Tcw)
rel_dist = rel_distance(pre_Tcw, cur_Tcw)
overlap = photometric_overlap(pre_depth, K, Ta=pre_Tcw, Tb=cur_Tcw, pre_cache_x2d=pre_cache_x2d)
# [Deprecated]
# overlap_map, x_2d = cam_opt.gen_overlap_mask_img(pre_depth, K, Ta=pre_Tcw, Tb=cur_Tcw, pre_cache_x2d=pre_cache_x2d)
# rel_T = relateive_pose(pre_Tcw[:3, :3], pre_Tcw[:3, 3], cur_Tcw[:3, :3], cur_Tcw[:3, 3])
# wrap_img, _ = cam_opt.wrapping(pre_img, cur_img, pre_depth, K, rel_T[:3, :3], rel_T[:3, 3])
# img_list = [
# {'img': pre_img},
# {'img': cur_img},
# {'img': wrap_img},
# {'img': overlap_map},
# {'img': x_2d[:, :, 0], 'cmap':'gray'},
# {'img': x_2d[:, :, 1], 'cmap': 'gray'}
# ]
# show_multiple_img(img_list, num_cols=4)
# plt.show()
if rel_dist > trans_thres or overlap < overlap_thres or rel_angle > rot_thres:
# Select the new keyframe that larger than the trans threshold and add the previous frame as keyframe
sub_frames.frames.append(copy.deepcopy(pre_search_frame))
pre_frame = pre_search_frame
cur_frame_idx = search_idx + 1
break
else:
pre_search_frame = cur_frame
if search_idx == n_frames - 1:
no_found_flag = True
if no_found_flag:
break
if len(sub_frames) > frames_per_subseq_num - 1:
break
# If the subset is less than setting, ignore
if len(sub_frames) >= frames_per_subseq_num:
sub_seq_list.append(sub_frames)
print('sel: %d', len(sub_seq_list))
return sub_seq_list
def sel_pairs_with_overlap_range_7scene(scene_frames,
scene_lmdb: LMDBSeqModel,
max_subseq_num,
frames_per_subseq_num=10,
dataset_base_dir=None,
trans_thres=0.15,
rot_thres=15,
frames_range=(0, 0.7),
overlap_thres=0.5,
scene_dist_thres=(0.0, 1.0),
interval_skip_frames=1,
train_anchor_num=100,
test_anchor_num=100):
"""
Random select sub set of sequences from scene
:param scene_frames: scene frames to extract subset
:param trans_thres_range: translation threshold, based on the center of different frames
:param max_subseq_num: maximum number of sub sequences
:param frames_per_subseq_num: for each sub sequences, how many frames in the subset
:param frames_range: range of start and end within original scene sequences, from (0, 1)
:param interval_skip_frames: skip interval in original scene frames, used in iteration
:return: list of selected sub sequences
"""
use_lmdb_cache = True if scene_lmdb is not None else False
assert dataset_base_dir is not None
n_frames = len(scene_frames)
if interval_skip_frames < 1:
interval_skip_frames = 2
max_subseq_num = int(n_frames * max_subseq_num)
# Simple selection based on trans threshold
# if frames_per_subseq_num * interval_skip_frames > n_frames:
# # raise Exception('Not enough frames to be selected')
# return []
rand_start_frame = np.random.randint(
int(frames_range[0] * len(scene_frames)),
int(frames_range[1] * len(scene_frames)),
size=max_subseq_num)
sub_seq_list = []
dim = scene_frames.get_frame_dim(scene_frames.frames[0])
dim = list(dim)
dim[0] = int(dim[0] // 4)
dim[1] = int(dim[1] // 4)
K = scene_frames.get_K_mat(scene_frames.frames[0])
K /= 4.0
K[2, 2] = 1.0
pre_cache_x2d = cam_opt.x_2d_coords(dim[0], dim[1])
for start_frame_idx in rand_start_frame:
# print('F:', start_frame_idx)
# Push start keyframe into frames
sub_frames = FrameSeqData()
pre_frame = scene_frames.frames[start_frame_idx]
sub_frames.frames.append(copy.deepcopy(pre_frame))
sub_frames_idx = [start_frame_idx]
# Iterate the remaining keyframes into subset
cur_frame_idx = start_frame_idx
no_found_flag = False
while cur_frame_idx + interval_skip_frames < n_frames:
pre_Tcw = sub_frames.get_Tcw(pre_frame)
pre_depth_path = sub_frames.get_depth_name(pre_frame)
# pre_depth = read_sun3d_depth(os.path.join(dataset_base_dir, pre_depth_path))
pre_depth = scene_lmdb.read_depth(pre_depth_path) if use_lmdb_cache else \
read_7scenese_depth(os.path.join(dataset_base_dir, pre_depth_path))
pre_depth = cv2.resize(pre_depth, (dim[1], dim[0]),
interpolation=cv2.INTER_NEAREST)
# H, W = pre_depth.shape
# if float(np.sum(pre_depth <= 1e-5)) / float(H*W) > 0.2:
# continue
# pre_depth = torch.from_numpy(pre_depth).cuda()
# pre_Tcw_gpu = torch.from_numpy(pre_Tcw).cuda()
# pre_img_name = sub_frames.get_image_name(pre_frame)
# pre_img = cv2.imread(os.path.join(dataset_base_dir, pre_img_name))
# pre_depth = fill_depth_cross_bf(pre_img, pre_depth)
# [Deprecated]
# import cv2
# pre_img_name = sub_frames.get_image_name(pre_frame)
# pre_img = cv2.imread(os.path.join(dataset_base_dir, pre_img_name)).astype(np.float32) / 255.0
# pre_center = cam_opt.camera_center_from_Tcw(pre_Tcw[:3, :3], pre_Tcw[:3, 3])
pre_search_frame = scene_frames.frames[cur_frame_idx +
interval_skip_frames - 1]
for search_idx in range(cur_frame_idx + interval_skip_frames,
n_frames, 1):
cur_frame = scene_frames.frames[search_idx]
cur_Tcw = sub_frames.get_Tcw(cur_frame)
# cur_Tcw_gpu = torch.from_numpy(cur_Tcw).cuda()
# cur_depth_path = sub_frames.get_depth_name(cur_frame)
# cur_depth = read_sun3d_depth(os.path.join(dataset_base_dir, cur_depth_path))
# H, W = cur_depth.shape
# [Deprecated]
# cur_center = cam_opt.camera_center_from_Tcw(cur_Tcw[:3, :3], cur_Tcw[:3, 3])
# cur_img_name = sub_frames.get_image_name(cur_frame)
# cur_img = cv2.imread(os.path.join(dataset_base_dir, cur_img_name)).astype(np.float32) / 255.0
rel_angle = rel_rot_angle(pre_Tcw, cur_Tcw)
rel_dist = rel_distance(pre_Tcw, cur_Tcw)
overlap = cam_opt.photometric_overlap(
pre_depth,
K,
Ta=pre_Tcw,
Tb=cur_Tcw,
pre_cache_x2d=pre_cache_x2d)
# mean scene coordinate dist
# pre_Twc = cam_opt.camera_pose_inv(R=pre_Tcw[:3, :3], t=pre_Tcw[:3, 3])
# d_a = pre_depth.reshape((H * W, 1))
# x_a_2d = pre_cache_x2d.reshape((H * W, 2))
# X_3d = cam_opt.pi_inv(K, x_a_2d, d_a)
# pre_X_3d = cam_opt.transpose(pre_Twc[:3, :3], pre_Twc[:3, 3], X_3d).reshape((H, W, 3))
# pre_mean = np.empty((3,), dtype=np.float)
# pre_mean[0] = np.mean(pre_X_3d[pre_depth > 1e-5, 0])
# pre_mean[1] = np.mean(pre_X_3d[pre_depth > 1e-5, 1])
# pre_mean[2] = np.mean(pre_X_3d[pre_depth > 1e-5, 2])
#
# cur_Twc = cam_opt.camera_pose_inv(R=cur_Tcw[:3, :3], t=cur_Tcw[:3, 3])
# d_a = cur_depth.reshape((H * W, 1))
# x_a_2d = pre_cache_x2d.reshape((H * W, 2))
# X_3d = cam_opt.pi_inv(K, x_a_2d, d_a)
# cur_X_3d = cam_opt.transpose(cur_Twc[:3, :3], cur_Twc[:3, 3], X_3d).reshape((H, W, 3))
# cur_mean = np.empty((3,), dtype=np.float)
# cur_mean[0] = np.mean(cur_X_3d[cur_depth > 1e-5, 0])
# cur_mean[1] = np.mean(cur_X_3d[cur_depth > 1e-5, 1])
# cur_mean[2] = np.mean(cur_X_3d[cur_depth > 1e-5, 2])
#
# scene_dist = np.linalg.norm(pre_mean - cur_mean)
# def keyPressEvent(obj, event):
# key = obj.GetKeySym()
# if key == 'Left':
# tmp_img = pre_img
# X_3d = pre_X_3d.reshape((H * W, 3))
# vis.set_point_cloud(X_3d, tmp_img.reshape((H * W, 3)))
# # vis.add_frame_pose(cur_Tcw[:3, :3], cur_Tcw[:3, 3])
#
# if key == 'Right':
# tmp_img = cur_img
# X_3d = cur_X_3d.reshape((H * W, 3))
# vis.set_point_cloud(X_3d, tmp_img.reshape((H * W, 3)))
# # vis.add_frame_pose(cur_Tcw[:3, :3], cur_Tcw[:3, 3])
#
# if key == 'Up':
# vis.set_point_cloud(pre_mean.reshape((1, 3)), pt_size=10)
#
# if key == 'Down':
# vis.set_point_cloud(cur_mean.reshape((1, 3)), pt_size=10)
# return
# vis = Visualizer(1280, 720)
# vis.bind_keyboard_event(keyPressEvent)
# vis.show()
# vis.close()
# [Deprecated]
# overlap_map, x_2d = cam_opt.gen_overlap_mask_img(pre_depth, K, Ta=pre_Tcw, Tb=cur_Tcw, pre_cache_x2d=pre_cache_x2d)
# rel_T = relateive_pose(pre_Tcw[:3, :3], pre_Tcw[:3, 3], cur_Tcw[:3, :3], cur_Tcw[:3, 3])
# wrap_img, _ = cam_opt.wrapping(pre_img, cur_img, pre_depth, K, rel_T[:3, :3], rel_T[:3, 3])
# img_list = [
# {'img': pre_img},
# {'img': cur_img},
# {'img': wrap_img},
# {'img': overlap_map},
# {'img': x_2d[:, :, 0], 'cmap':'gray'},
# {'img': x_2d[:, :, 1], 'cmap': 'gray'}
# ]
# show_multiple_img(img_list, num_cols=4)
# plt.show()
# if rel_dist > trans_thres:
# print('exceed trans_thres')
# elif overlap < overlap_thres:
# print('exceed overlap_thres')
# elif rel_angle > rot_thres:
# print('exceed rot_thres')
# if overlap_thres[0] <= overlap <= overlap_thres[1] and \
# rot_thres[0] <= rel_angle <= rot_thres[1]: #and \
# # scene_dist_thres[0] <= scene_dist <= scene_dist_thres[1]:
# sub_frames.frames.append(copy.deepcopy(cur_frame))
if overlap < overlap_thres or rel_dist > trans_thres: #or scene_dist > scene_dist_thres[1]:
# Select the new keyframe that larger than the trans threshold and add the previous frame as keyframe
sub_frames.frames.append(copy.deepcopy(pre_search_frame))
pre_frame = pre_search_frame
cur_frame_idx = search_idx + 1
sub_frames_idx.append(search_idx - 1)
break
else:
pre_search_frame = cur_frame
if search_idx + 1 >= n_frames:
no_found_flag = True
if no_found_flag:
break
if len(sub_frames) > frames_per_subseq_num - 1:
break
# If the subset is less than setting, ignore
if len(sub_frames) >= frames_per_subseq_num:
min_idx = min(sub_frames_idx)
max_idx = max(sub_frames_idx)
print(min_idx, max_idx, n_frames)
# factor = (max_idx - min_idx) // 3
#
# min_Tcw = sub_frames.get_Tcw(sub_frames.frames[0])
# max_Tcw = sub_frames.get_Tcw(sub_frames.frames[-1])
potential_anchor_idces = []
# for i in range(min_idx + factor, max_idx - factor, 1):
# cur_frame = scene_frames.frames[i]
# cur_Tcw = scene_frames.get_Tcw(cur_frame)
# cur_depth_path = sub_frames.get_depth_name(cur_frame)
# cur_depth = scene_lmdb.read_depth(cur_depth_path)
# cur_depth = cv2.resize(cur_depth, (dim[1], dim[0]), interpolation=cv2.INTER_NEAREST)
# H, W = cur_depth.shape
# if float(np.sum(cur_depth <= 1e-5)) / float(H*W) > 0.2:
# continue
# min_overlap = cam_opt.photometric_overlap(cur_depth, K, Ta=cur_Tcw, Tb=min_Tcw,
# pre_cache_x2d=pre_cache_x2d)
# max_overlap = cam_opt.photometric_overlap(cur_depth, K, Ta=cur_Tcw, Tb=max_Tcw,
# pre_cache_x2d=pre_cache_x2d)
# min_rel_angle = rel_rot_angle(cur_Tcw, min_Tcw)
# max_rel_angle = rel_rot_angle(cur_Tcw, max_Tcw)
# if min_overlap < 0.65 and max_overlap < 0.65 and \
# ((0.5 < min_overlap and min_rel_angle < 20.0) or \
# (0.5 < max_overlap and max_rel_angle < 20.0)):
# potential_anchor_idces.append(i)
for i in range(min_idx, max_idx):
if i not in sub_frames_idx:
potential_anchor_idces.append(i)
if len(potential_anchor_idces
) >= train_anchor_num + test_anchor_num:
anchor_idces = np.random.choice(
range(len(potential_anchor_idces)),
size=train_anchor_num + test_anchor_num,
replace=False)
train_anchor_frames = []
for i in anchor_idces[:train_anchor_num]:
train_anchor_frames.append(
scene_frames.frames[potential_anchor_idces[i]])
test_anchor_frames = []
for i in anchor_idces[train_anchor_num:]:
test_anchor_frames.append(
scene_frames.frames[potential_anchor_idces[i]])
sub_seq_list.append({
'sub_frames': sub_frames,
'train_anchor_frames': train_anchor_frames,
'test_anchor_frames': test_anchor_frames
})
print('selected', len(potential_anchor_idces), len(sub_frames))
print('sel: %d', len(sub_seq_list))
return sub_seq_list
K = torch.from_numpy(seq.get_K_mat(frame_a)).unsqueeze(0)
H, W = depth_a.shape[1], depth_a.shape[2]
Twc_a = torch.from_numpy(seq.get_Twc(frame_a)).unsqueeze(0)
R_a, t_a = cam_opt.Rt(Twc_a)
x_2d = cam_opt.x_2d_coords(H, W, n=1)
X_3d_a = cam_opt.pi_inv(K=K, d=depth_a, x=x_2d)
X_3d_a = cam_opt.transpose(R_a, t_a, X_3d_a)
""" PnP By OpenCV
"""
# X_3d_a = X_3d_a.view(H, W, 3).cpu().numpy()
# x_2d = x_2d.view(H, W, 2).cpu().numpy()
# K = K.view(3, 3).cpu().numpy()
# dist = np.zeros(4)
# _, R_res, t_res, _ = cv2.solvePnPRansac(X_3d_a.reshape(1, H*W, 3), x_2d.reshape(1, H*W, 2), K, dist)
# R_res, _ = cv2.Rodrigues(R_res)
pnp_pose = solve_pnp(K, x_2d, X_3d_a)
pnp_pose = pnp_pose.cpu().numpy()
gt_pose = seq.get_Tcw(frame_a)
rel_trans = rel_distance(gt_pose, pnp_pose)
rel_angle = rel_rot_angle(gt_pose, pnp_pose)
print(rel_trans, rel_angle)
vis.add_frame_pose(pnp_pose[:3, :3], pnp_pose[:3, 3])
X_3d_a = X_3d_a.view(H, W, 3).cpu().numpy()
vis.set_point_cloud(X_3d_a.reshape((H * W, 3)),
colors=img_a.reshape((H * W, 3)))
vis.show()