forked from nianticlabs/monodepth2
-
Notifications
You must be signed in to change notification settings - Fork 0
/
test_simple.py
173 lines (144 loc) · 6.57 KB
/
test_simple.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
# Copyright Niantic 2019. Patent Pending. All rights reserved.
#
# This software is licensed under the terms of the Monodepth2 licence
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
from __future__ import absolute_import, division, print_function
import os
import sys
import glob
import argparse
import numpy as np
import PIL.Image as pil
import matplotlib as mpl
import matplotlib.cm as cm
import torch
from torchvision import transforms, datasets
from networks import factory
from layers import disp_to_depth
from utils import download_model_if_doesnt_exist, create_dir
import cv2
STEREO_SCALE_FACTOR = 5.4
def parse_args():
parser = argparse.ArgumentParser(
description='Simple testing funtion for Monodepthv2 models.')
parser.add_argument('--image_path', type=str,
help='path to a test image or folder of images', required=True)
parser.add_argument('--model_name', type=str,
help='name of a pretrained model to use',
choices=[
"mono_640x192",
"stereo_640x192",
"mono+stereo_640x192",
"mono_no_pt_640x192",
"stereo_no_pt_640x192",
"mono+stereo_no_pt_640x192",
"mono_1024x320",
"stereo_1024x320",
"mono+stereo_1024x320"],
default="mono+stereo_1024x320")
parser.add_argument('--ext', type=str,
help='image extension to search for in folder', default="jpg")
parser.add_argument("--no_cuda",
help='if set, disables CUDA',
action='store_true')
parser.add_argument('--dest', type=str,
help='destination folder', default="KITTI_odometry")
return parser.parse_args()
def test_simple(args):
"""Function to predict for a single image or folder of images
"""
assert args.model_name is not None, \
"You must specify the --model_name parameter; see README.md for an example"
if torch.cuda.is_available() and not args.no_cuda:
device = torch.device("cuda")
else:
device = torch.device("cpu")
download_model_if_doesnt_exist(args.model_name)
model_path = os.path.join("models", args.model_name)
print("-> Loading model from ", model_path)
encoder_path = os.path.join(model_path, "encoder.pth")
depth_decoder_path = os.path.join(model_path, "depth.pth")
# LOADING PRETRAINED MODEL
print(" Loading pretrained encoder")
encoder_params = {
'num_layers': 18,
'pretrained': False
}
encoder = factory.get_encoder('resnet')(encoder_params)
loaded_dict_enc = torch.load(encoder_path, map_location=device)
# extract the height and width of image that this model was trained with
feed_height = loaded_dict_enc['height']
feed_width = loaded_dict_enc['width']
filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in encoder.state_dict()}
encoder.load_state_dict(filtered_dict_enc)
encoder.to(device)
encoder.eval()
print(" Loading pretrained decoder")
decoder_params = {
'num_ch_enc': encoder.num_ch_enc,
'supervised': False
}
depth_decoder = factory.get_decoder('resnet')(decoder_params)
loaded_dict = torch.load(depth_decoder_path, map_location=device)
depth_decoder.load_state_dict(loaded_dict)
depth_decoder.to(device)
depth_decoder.eval()
# FINDING INPUT IMAGES
if os.path.isfile(args.image_path):
# Only testing on a single image
paths = [args.image_path]
output_directory = os.path.dirname(args.image_path)
elif os.path.isdir(args.image_path):
# Searching folder for images
paths = glob.glob(os.path.join(args.image_path, '*.{}'.format(args.ext)))
output_directory = args.image_path
else:
raise Exception("Can not find args.image_path: {}".format(args.image_path))
print("-> Predicting on {:d} test images".format(len(paths)))
# PREDICTING ON EACH IMAGE IN TURN
create_dir(args.dest)
with torch.no_grad():
for idx, image_path in enumerate(paths):
if image_path.endswith("_disp.jpg"):
# don't try to predict disparity for a disparity image!
continue
# Load image and preprocess
input_image = pil.open(image_path).convert('RGB')
original_width, original_height = input_image.size
input_image = input_image.resize((feed_width, feed_height), pil.LANCZOS)
input_image = transforms.ToTensor()(input_image).unsqueeze(0)
# PREDICTION
input_image = input_image.to(device)
features = encoder(input_image)
outputs = depth_decoder(features)
disp = outputs[("disp", 0)]
disp_resized = torch.nn.functional.interpolate(
disp, (original_height, original_width), mode="bilinear", align_corners=False)
# Saving numpy file
output_name = os.path.splitext(os.path.basename(image_path))[0]
name_dest_npy = os.path.join(output_directory, "{}_disp.npy".format(output_name))
scaled_disp, _ = disp_to_depth(disp, 0.1, 100)
np.save(name_dest_npy, scaled_disp.cpu().numpy())
# Saving colormapped depth image
disp_resized_np = disp_resized.squeeze().cpu().numpy()
pred_depth = 1 / disp_resized_np
pred_depth *= STEREO_SCALE_FACTOR
name_dest_im = os.path.join(args.dest, "{}_depth.png".format(output_name))
pred_depth = np.clip(pred_depth, 0.1, 100.0)
cv2.imwrite(name_dest_im, (pred_depth * 256).astype(np.uint16))
"""
vmax = np.percentile(disp_resized_np, 95)
normalizer = mpl.colors.Normalize(vmin=disp_resized_np.min(), vmax=vmax)
mapper = cm.ScalarMappable(norm=normalizer, cmap='magma')
colormapped_im = (mapper.to_rgba(disp_resized_np)[:, :, :3] * 255).astype(np.uint8)
im = pil.fromarray(colormapped_im)
name_dest_im = os.path.join(args.dest, "{}_disp.png".format(output_name))
im.save(name_dest_im)
"""
print(" Processed {:d} of {:d} images - saved prediction to {}".format(
idx + 1, len(paths), name_dest_im))
print('-> Done!')
if __name__ == '__main__':
args = parse_args()
test_simple(args)