def get_pt_from_img(img, depth, K, Twc):
H, W = depth.shape
x_2d = x_2d_coords(H, W).reshape((H * W, 2))
X_3d = pi_inv(K, x_2d, depth.reshape((H * W, 1)))
X_3d = transpose(Twc[:3, :3], Twc[:3, 3], X_3d)
colors = img.reshape((H * W, 3)) / 255.0
return X_3d, colors
import banet_track.ba_module as module
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.sun3d.read_util import read_sun3d_depth
''' Configuration ------------------------------------------------------------------------------------------------------
'''
base_dir = '/mnt/Exp_3/scannet'
seq_name = 'scene0105_02'
frames = FrameSeqData(os.path.join(base_dir, seq_name, 'seq.json'))
''' Script -------------------------------------------------------------------------------------------------------------
'''
x_2d = cam_opt.x_2d_coords(240, 320)
for frame_idx in range(10, len(frames), 5):
cur_frame = frames.frames[frame_idx]
cur_Tcw = cur_frame['extrinsic_Tcw']
cur_name = cur_frame['file_name']
cur_depth_name = cur_frame['depth_file_name']
next_frame = frames.frames[frame_idx + 10]
next_Tcw = next_frame['extrinsic_Tcw']
next_name = next_frame['file_name']
K = K_from_frame(cur_frame)
# Read image
cur_img = cv2.imread(os.path.join(base_dir, cur_name)).astype(
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 keyPressEvent(obj, event):
global frame_idx
global refer_T
key = obj.GetKeySym()
if key == 'Right':
# vis.clear_frame_poses()
# if frame_idx > 30:
# return
cur_frame = frames.frames[frame_idx]
cur_Tcw = cur_frame['extrinsic_Tcw']
cur_name = cur_frame['file_name']
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
from visualizer.visualizer_2d import show_multiple_img
from visualizer.visualizer_3d import Visualizer
from seq_data.sun3d.read_util import read_sun3d_depth
''' Configuration ------------------------------------------------------------------------------------------------------
'''
base_dir = '/mnt/Exp_3/scannet'
seq_name = 'scene0131_02'
frames = FrameSeqData(os.path.join(base_dir, seq_name, 'seq.json'))
''' Scripts ------------------------------------------------------------------------------------------------------------
'''
vis = Visualizer()
frame_idx = 0
x_2d = cam_opt.x_2d_coords(480, 640)
def keyPressEvent(obj, event):
global frame_idx
key = obj.GetKeySym()
if key == 'Right':
cur_frame = frames.frames[frame_idx]
cur_Tcw = cur_frame['extrinsic_Tcw']
cur_name = cur_frame['file_name']
cur_depth_name = cur_frame['depth_file_name']
next_frame = frames.frames[frame_idx + 1]
next_Tcw = next_frame['extrinsic_Tcw']
next_name = next_frame['file_name']
def get_pt_from_img(img, depth, K):
H, W = depth.shape
x_2d = x_2d_coords(H, W).reshape((H * W, 2))
X_3d = pi_inv(K, x_2d, depth.reshape((H * W, 1)))
colors = img.reshape((H * W, 3)) / 255.0
return X_3d, colors
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
def sel_triple_sun3d(base_dir, scene_frames, max_triple_num,
num_sample_per_triple, trans_thres, overlap_thres):
"""
Select triples (anchor, positive, negative) from a sun3d sequence
:param base_dir: dataset base directory
:param scene_frames: scene frames to extract triples
:param max_triple_num: maximum number of triples
:param num_sample_per_triple: number of positive/negative samples per triple
:param trans_thres: translation threshold for positive samples, based on the center of different frames
:param overlap_thres: overlap threshold for positive samples, (low, high)
:return: [{'anchor': frame_dict, 'positive': FrameSeqData, 'negative': FrameSeqData}, {...}, ...]
"""
dim = scene_frames.get_frame_dim(scene_frames.frames[0])
K = scene_frames.get_K_mat(scene_frames.frames[0])
pre_cache_x2d = cam_opt.x_2d_coords(dim[0], dim[1])
camera_centers = np.empty((len(scene_frames), 3), dtype=np.float32)
for i, frame in enumerate(scene_frames.frames):
Tcw = scene_frames.get_Tcw(frame)
center = cam_opt.camera_center_from_Tcw(Tcw[:3, :3], Tcw[:3, 3])
camera_centers[i, :] = center
kdtree = KDTree(camera_centers)
triple_list = []
anchor_idces = np.random.choice(len(scene_frames),
max_triple_num,
replace=False)
for anchor_idx in anchor_idces:
anchor_frame = scene_frames.frames[anchor_idx]
anchor_Tcw = scene_frames.get_Tcw(anchor_frame)
anchor_depth_path = scene_frames.get_depth_name(anchor_frame)
anchor_depth = read_sun3d_depth(
os.path.join(base_dir, anchor_depth_path))
anchor_depth[anchor_depth < 1e-5] = 1e-5
potential_pos_idces = kdtree.query_ball_point(
camera_centers[anchor_idx], trans_thres)
pos_idces = []
for potential_pos_idx in potential_pos_idces:
potential_pos_frame = scene_frames.frames[potential_pos_idx]
potential_pos_Tcw = scene_frames.get_Tcw(potential_pos_frame)
overlap = cam_opt.photometric_overlap(anchor_depth,
K,
Ta=anchor_Tcw,
Tb=potential_pos_Tcw,
pre_cache_x2d=pre_cache_x2d)
if overlap_thres[0] < overlap < overlap_thres[1]:
pos_idces.append(potential_pos_idx)
if len(pos_idces) < num_sample_per_triple:
continue
else:
sel_pos_idces = np.random.choice(pos_idces,
num_sample_per_triple,
replace=False)
neg_idces = list(set(range(len(scene_frames))) - set(pos_idces))
sel_neg_idces = np.random.choice(neg_idces,
num_sample_per_triple,
replace=False)
triple_list.append({
'anchor':
copy.deepcopy(anchor_frame),
'positive': [
copy.deepcopy(scene_frames.frames[idx])
for idx in sorted(sel_pos_idces)
],
'negative': [
copy.deepcopy(scene_frames.frames[idx])
for idx in sorted(sel_neg_idces)
],
})
# print(camera_centers[anchor_idx])
# print(camera_centers[pos_idces])
# print(camera_centers[neg_idces])
# print('----------------------------------------------------------')
return triple_list