-
Notifications
You must be signed in to change notification settings - Fork 6
/
Copy pathinference.py
240 lines (200 loc) · 9.11 KB
/
inference.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
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
"""
@Date: 2021/09/19
@description:
"""
import json
import os
import argparse
import cv2
import numpy as np
import torch
import matplotlib.pyplot as plt
import glob
from tqdm import tqdm
from PIL import Image
from config.defaults import merge_from_file, get_config
from dataset.mp3d_dataset import MP3DDataset
from dataset.zind_dataset import ZindDataset
from dataset.pano_s2d3d_dataset import PanoS2D3DDataset
from dataset.pano_s2d3d_mix_dataset import PanoS2D3DMixDataset
from models.build import build_model
from loss import GradLoss
from postprocessing.post_process import post_process
from preprocessing.pano_lsd_align import panoEdgeDetection, rotatePanorama
from utils.boundary import corners2boundaries
from utils.conversion import depth2xyz
from utils.logger import get_logger
from utils.misc import tensor2np_d, tensor2np
from evaluation.accuracy import show_grad
from models.my_layout_net import My_Layout_Net
from utils.writer import xyz2json
from visualization.boundary import draw_boundaries
from visualization.floorplan import draw_floorplan, draw_iou_floorplan
def parse_option():
parser = argparse.ArgumentParser(description='Panorama Layout Transformer training and evaluation script')
parser.add_argument('--img_glob',
type=str,
required=False,
help='image glob path')
parser.add_argument('--cfg',
type=str,
required=True,
metavar='FILE',
help='path of config file')
parser.add_argument('--post_processing',
type=str,
default='manhattan',
choices=['manhattan', 'atalanta', 'original'],
help='post-processing type')
parser.add_argument('--output_dir',
type=str,
default='src/output',
help='path of output')
parser.add_argument('--visualize_3d', action='store_true',
help='visualize_3d')
parser.add_argument('--device',
type=str,
default='cuda',
help='device')
args = parser.parse_args()
args.mode = 'test'
print("arguments:")
for arg in vars(args):
print(arg, ":", getattr(args, arg))
print("-" * 50)
return args
def visualize_2d(img, dt, show_depth=True, show_floorplan=True, show=False, save_path=None):
dt_np = tensor2np_d(dt)
dt_depth = dt_np['depth'][0]
dt_xyz = depth2xyz(np.abs(dt_depth))
dt_ratio = dt_np['ratio'][0][0]
dt_boundaries = corners2boundaries(dt_ratio, corners_xyz=dt_xyz, step=None, visible=False, length=img.shape[1])
vis_img = draw_boundaries(img, boundary_list=dt_boundaries, boundary_color=[0, 1, 0])
if 'processed_xyz' in dt:
dt_boundaries = corners2boundaries(dt_ratio, corners_xyz=dt['processed_xyz'][0], step=None, visible=False,
length=img.shape[1])
vis_img = draw_boundaries(vis_img, boundary_list=dt_boundaries, boundary_color=[1, 0, 0])
if show_depth:
dt_grad_img = show_depth_normal_grad(dt)
grad_h = dt_grad_img.shape[0]
vis_merge = [
vis_img[0:-grad_h, :, :],
dt_grad_img,
]
vis_img = np.concatenate(vis_merge, axis=0)
# vis_img = dt_grad_img.transpose(1, 2, 0)[100:]
if show_floorplan:
if 'processed_xyz' in dt:
floorplan = draw_iou_floorplan(dt['processed_xyz'][0][..., ::2], dt_xyz[..., ::2],
dt_board_color=[1, 0, 0, 1], gt_board_color=[0, 1, 0, 1])
else:
floorplan = show_alpha_floorplan(dt_xyz)
vis_img = np.concatenate([vis_img, floorplan[:, 60:-60, :]], axis=1)
if show:
plt.imshow(vis_img)
plt.show()
if save_path:
result = Image.fromarray((vis_img * 255).astype(np.uint8))
result.save(save_path)
return vis_img
def preprocess(img_ori, q_error=0.7, refine_iter=3, vp_cache_path=None):
# Align images with VP
if os.path.exists(vp_cache_path):
with open(vp_cache_path) as f:
vp = [[float(v) for v in line.rstrip().split(' ')] for line in f.readlines()]
vp = np.array(vp)
else:
# VP detection and line segment extraction
_, vp, _, _, _, _, _ = panoEdgeDetection(img_ori,
qError=q_error,
refineIter=refine_iter)
i_img = rotatePanorama(img_ori, vp[2::-1])
if vp_cache_path is not None:
with open(vp_cache_path, 'w') as f:
for i in range(3):
f.write('%.6f %.6f %.6f\n' % (vp[i, 0], vp[i, 1], vp[i, 2]))
return i_img, vp
def show_depth_normal_grad(dt):
grad_conv = GradLoss().to(dt['depth'].device).grad_conv
dt_grad_img = show_grad(dt['depth'][0], grad_conv, 50)
dt_grad_img = cv2.resize(dt_grad_img, (1024, 60), interpolation=cv2.INTER_NEAREST)
return dt_grad_img
def show_alpha_floorplan(dt_xyz, side_l=512):
fill_color = [0.2, 0.2, 0.2, 0.2]
dt_floorplan = draw_floorplan(xz=dt_xyz[..., ::2], fill_color=fill_color,
border_color=[1, 0, 0, 1], side_l=side_l, show=False, center_color=[1, 0, 0, 1])
dt_floorplan = Image.fromarray((dt_floorplan * 255).astype(np.uint8), mode='RGBA')
back = np.zeros([side_l, side_l, len(fill_color)], dtype=np.float)
back[..., :] = [0.8, 0.8, 0.8, 1]
back = Image.fromarray((back * 255).astype(np.uint8), mode='RGBA')
iou_floorplan = Image.alpha_composite(back, dt_floorplan).convert("RGB")
dt_floorplan = np.array(iou_floorplan) / 255.0
return dt_floorplan
def save_pred_json(xyz, ration, save_path):
# xyz[..., -1] = -xyz[..., -1]
json_data = xyz2json(xyz, ration)
with open(save_path, 'w') as f:
f.write(json.dumps(json_data, indent=4) + '\n')
return json_data
def inference():
if len(img_paths) == 0:
logger.error('No images found')
return
bar = tqdm(img_paths, ncols=100)
for img_path in bar:
if not os.path.isfile(img_path):
logger.error(f'The {img_path} not is file')
continue
name = os.path.basename(img_path).split('.')[0]
bar.set_description(name)
img = np.array(Image.open(img_path).resize((1024, 512), Image.BICUBIC))[..., :3]
if args.post_processing is not None and 'manhattan' in args.post_processing:
bar.set_description("Preprocessing")
img, vp = preprocess(img, vp_cache_path=os.path.join(args.output_dir, f"{name}_vp.txt"))
img = (img / 255.0).astype(np.float32)
run_one_inference(img, model, args, name)
def inference_dataset(dataset):
bar = tqdm(dataset, ncols=100)
for data in bar:
bar.set_description(data['id'])
run_one_inference(data['image'].transpose(1, 2, 0), model, args, name=data['id'])
@torch.no_grad()
def run_one_inference(img, model, args, name):
model.eval()
dt = model(torch.from_numpy(img.transpose(2, 0, 1)[None]).to(args.device))
if args.post_processing != 'original':
dt['processed_xyz'] = post_process(tensor2np(dt['depth']), type_name=args.post_processing)
visualize_2d(img, dt, show=True, save_path=os.path.join(args.output_dir, f"{name}_pred.png"))
output_xyz = dt['processed_xyz'][0] if 'processed_xyz' in dt else depth2xyz(tensor2np(dt['depth'][0]))
json_data = save_pred_json(output_xyz, tensor2np(dt['ratio'][0])[0],
save_path=os.path.join(args.output_dir, f"{name}_pred.json"))
if args.visualize_3d:
from visualization.visualizer.visualizer import visualize_3d
visualize_3d(json_data, (img * 255).astype(np.uint8))
if __name__ == '__main__':
logger = get_logger()
args = parse_option()
config = get_config(args)
if 'cuda' in args.device and not torch.cuda.is_available():
logger.info(f'The {args.device} is not available, will use cpu ...')
config.defrost()
args.device = "cpu"
config.TRAIN.DEVICE = "cpu"
config.freeze()
model, _, _, _ = build_model(config, logger)
os.makedirs(args.output_dir, exist_ok=True)
# img_paths = sorted(glob.glob(args.img_glob))
# inference()
dataset = MP3DDataset(root_dir='/opt/data/private/360Layout/Datasets/mp3d', mode='test')
#dataset = ZindDataset(root_dir='/opt/data/private/360Layout/Datasets/zind', mode='test')
#dataset = PanoS2D3DMixDataset(root_dir='/opt/data/private/360Layout/Datasets/pano_s2d3d', mode='test', subset='pano')
#dataset = PanoS2D3DMixDataset(root_dir='/opt/data/private/360Layout/Datasets/pano_s2d3d', mode='test', subset='s2d3d')
# dataset = MP3DDataset(root_dir='/data/cylin/zzs/Datasets/mp3d', mode='test', split_list=[
# ['7y3sRwLe3Va', '155fac2d50764bf09feb6c8f33e8fb76'],
# ['e9zR4mvMWw7', 'c904c55a5d0e420bbd6e4e030b9fe5b4'],
# ])
# dataset = ZindDataset(root_dir='./src/dataset/zind', mode='test', split_list=[
# '1169_pano_21',
# '0583_pano_59',
# ], vp_align=True)
inference_dataset(dataset)