-
Notifications
You must be signed in to change notification settings - Fork 52
/
make_trajs.py
204 lines (169 loc) · 7.64 KB
/
make_trajs.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
import time
import numpy as np
import timeit
import imageio
import matplotlib
import io
import os
import math
from PIL import Image
import sys
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import math
import torch.nn.functional as F
import utils.improc
from utils.basic import readPFM
import random
import glob
from filter_trajs import filter_trajs
from tensorboardX import SummaryWriter
flt3d_path = "../flyingthings"
dsets = ["TRAIN", "TEST"]
subsets = ["A", "B", "C"]
device = 'cuda'
min_lifespan = 8
mod = 'aa' # start export on orion
mod = 'ab' # float16 instead of float32, to save space
mod = 'ac' # req 256 trajs
mod = 'ad' # fix bug in filter_trajs, with visibility on last frame
def readImage(name):
if name.endswith('.pfm') or name.endswith('.PFM'):
data = readPFM(name)
if len(data.shape)==3:
return data[:,:,0:3]
else:
return data
return imageio.imread(name)
def helper(rgb_path, mask_path, flow_path, out_dir, folder_name, lr, start_ind, sw=None, include_vis=False):
cur_out_dir = os.path.join(out_dir, folder_name, lr)
out_f = os.path.join(cur_out_dir, 'trajs_at_%d.npz' % start_ind)
# print('out_f', out_f)
if os.path.isfile(out_f):
sys.stdout.write(':')
return
cur_rgb_path = os.path.join(rgb_path, folder_name, lr)
cur_mask_path = os.path.join(mask_path, folder_name, lr)
cur_flow_f_path = os.path.join(flow_path, folder_name, "into_future", lr)
cur_flow_b_path = os.path.join(flow_path, folder_name, "into_past", lr)
img_names = [folder.split('/')[-1].split('.')[0] for folder in glob.glob(os.path.join(cur_rgb_path, "*"))]
img_names = sorted(img_names)
# read rgbs and flows
rgbs = []
masks = []
flows_f = []
flows_b = []
# segs = []
for img_name in img_names:
rgbs.append(np.array(Image.open(os.path.join(cur_rgb_path, '{0}.webp'.format(img_name)))))
masks.append(readImage(os.path.join(cur_mask_path, '{0}.pfm'.format(img_name))))
try:
if lr == "left":
flows_f.append(readPFM(os.path.join(cur_flow_f_path, 'OpticalFlowIntoFuture_{0}_L.pfm'.format(img_name)))[:,:,:2])
flows_b.append(readPFM(os.path.join(cur_flow_b_path, 'OpticalFlowIntoPast_{0}_L.pfm'.format(img_name)))[:,:,:2])
else:
flows_f.append(readPFM(os.path.join(cur_flow_f_path, 'OpticalFlowIntoFuture_{0}_R.pfm'.format(img_name)))[:,:,:2])
flows_b.append(readPFM(os.path.join(cur_flow_b_path, 'OpticalFlowIntoPast_{0}_R.pfm'.format(img_name)))[:,:,:2])
except FileNotFoundError:
sys.stdout.write('!')
return
bak_all_rgbs = utils.improc.preprocess_color(torch.from_numpy(np.stack(rgbs, 0)).to(device)).permute(0,3,1,2).unsqueeze(0)
bak_all_masks = torch.from_numpy(np.stack(masks, 0)).to(device).unsqueeze(0).unsqueeze(2) # 1, S, 1, H, W
bak_all_flows_f = torch.from_numpy(np.stack(flows_f, 0)).to(device).permute(0,3,1,2).unsqueeze(0)
bak_all_flows_b = torch.from_numpy(np.stack(flows_b, 0)).to(device).permute(0,3,1,2).unsqueeze(0)
_, bak_S, _, H, W = bak_all_rgbs.shape
all_rgbs = bak_all_rgbs[:,start_ind:start_ind+min_lifespan]
all_masks = bak_all_masks[:,start_ind:start_ind+min_lifespan]
all_flows_f = bak_all_flows_f[:,start_ind:start_ind+min_lifespan-1]
all_flows_b = bak_all_flows_b[:,start_ind+1:start_ind+min_lifespan+1]
S = min_lifespan
all_masks = all_masks.float()
if include_vis:
flows_f_vis = [sw.summ_flow('', all_flows_f[:, idx], clip=300, only_return=True) for idx in range(S-1)]
flows_b_vis = [sw.summ_flow('', all_flows_b[:, idx], clip=300, only_return=True) for idx in range(S-1)]
sw.summ_rgbs('inputs_%d/flows_f' % start_ind, flows_f_vis)
sw.summ_rgbs('inputs_%d/flows_b' % start_ind, flows_b_vis)
sw.summ_rgbs('inputs_%d/rgbs' % start_ind, all_rgbs.unbind(1))
sw.summ_oneds('inputs_%d/masks' % start_ind, all_masks.unbind(1))
ys, xs = utils.basic.meshgrid2d(1, H, W)
xs = xs.reshape(1, -1)
ys = ys.reshape(1, -1)
coords = []
coord = torch.stack([xs, ys], dim=2) # B, N, 2
coords.append(coord)
for s in range(S-1):
delta = utils.samp.bilinear_sample2d(all_flows_f[:,s], coord[:,:,0].round(), coord[:,:,1].round()).permute(0,2,1) # B,N,2: forward flow at the discrete points
coord = coord + delta
coords.append(coord)
trajs = torch.stack(coords, dim=1) # B, S, N, 2
# N == 540*960 == 518400
trajs = filter_trajs(trajs, all_masks, all_flows_f, all_flows_b)
if include_vis:
max_show = 500
if not trajs.shape[2] < max_show:
inds = utils.geom.farthest_point_sample(trajs[:,0], max_show, deterministic=False)
trajs_vis = trajs[:,:,inds.reshape(-1)]
else:
trajs_vis = trajs.clone()
print('trajs vis', trajs_vis.shape)
pad = 0
if pad > 0:
all_rgbs_ = F.pad(all_rgbs[0,:min_lifespan], (pad, pad, pad, pad), 'constant', 0).unsqueeze(0)
trajs_vis = trajs_vis + pad
sw.summ_traj2ds_on_rgbs2('outputs_%d/trajs_on_rgbs' % start_ind, trajs_vis, torch.ones_like(trajs_vis[:,:,:,0]), all_rgbs_)
else:
sw.summ_traj2ds_on_rgbs2('outputs_%d/trajs_on_rgbs' % start_ind, trajs_vis, torch.ones_like(trajs_vis[:,:,:,0]), all_rgbs)
trajs = trajs[0].detach().cpu().numpy() # S, N, 2
trajs = trajs.astype(np.float16) # save space
# if there aren't 256, make it empty.
# this lets us discard them later, but still write in parallel jobs
N = trajs.shape[1]
if N < 256:
trajs = None
sys.stdout.write('%d ' % N)
else:
sys.stdout.write('.')
if not os.path.exists(cur_out_dir):
os.makedirs(cur_out_dir)
np.savez(out_f, trajs=trajs)#, visibles=visibles)
def go():
log_freq = 1
include_vis = False
log_dir = 'logs_make_trajs'
import datetime
exp_date = datetime.datetime.now().strftime('%H:%M:%S')
exp_name = '%s' % (exp_date)
print(exp_name)
writer = SummaryWriter(log_dir + '/' + exp_name, max_queue=10, flush_secs=60)
global_step = 0
for dset in dsets:
for subset in subsets:
rgb_path = os.path.join(flt3d_path, "frames_cleanpass_webp", dset, subset)
flow_path = os.path.join(flt3d_path, "optical_flow", dset, subset)
mask_path = os.path.join(flt3d_path, "object_index", dset, subset)
folder_names = [folder.split('/')[-1] for folder in glob.glob(os.path.join(rgb_path, "*"))]
print(flt3d_path, dset, subset, mod)
out_dir = os.path.join(flt3d_path, "trajs_%s" % mod, dset, subset)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
random.shuffle(folder_names)
for folder_name in folder_names:
for lr in ['left', 'right']:
for start_ind in [0,1,2]:
global_step += 1
if include_vis:
sw = utils.improc.Summ_writer(
writer=writer,
global_step=global_step,
log_freq=log_freq,
fps=5,
scalar_freq=100,
just_gif=True)
else:
sw = None
helper(rgb_path, mask_path, flow_path, out_dir, folder_name, lr, start_ind, sw=sw, include_vis=include_vis)
sys.stdout.flush()
print('done')
if __name__ == "__main__":
go()