/
webcam.py
198 lines (165 loc) · 7.23 KB
/
webcam.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
import argparse
import time
import os
import cv2
import numpy as np
from camera import Camera
from loftr.utils.cvpr_ds_config import default_cfg
from utils import make_query_image, get_coarse_match, make_student_config
def main():
parser = argparse.ArgumentParser(description='LoFTR demo.')
parser.add_argument('--weights', type=str, default='weights/LoFTR_teacher.pt', # 'weights/outdoor_ds.ckpt',
help='Path to network weights.')
parser.add_argument('--original', action='store_true',
help='If specified the original LoFTR model will be used.')
parser.add_argument('--camid', type=int, default=0,
help='OpenCV webcam video capture ID, usually 0 or 1.')
parser.add_argument('--device', type=str, default='cuda',
help='cpu or cuda')
parser.add_argument('--trt', type=str, help='TensorRT model engine path')
parser.add_argument('--onnx', type=str, help='ONNX model path')
opt = parser.parse_args()
print(opt)
if opt.original:
model_cfg = default_cfg
else:
model_cfg = make_student_config(default_cfg)
print('Loading pre-trained network...')
use_trt = False
use_onnx = False
if opt.trt and os.path.exists(opt.trt):
from trtmodel import TRTModel
matcher = TRTModel(opt.trt)
print('Successfully loaded TensorRT model.')
use_trt = True
elif opt.onnx and os.path.exists(opt.onnx):
import onnxruntime
if opt.device == 'cuda':
onnx_providers = ['CUDAExecutionProvider']
else:
onnx_providers = ['CPUExecutionProvider']
matcher = onnxruntime.InferenceSession(opt.onnx, providers=onnx_providers)
print(f'ONNX runtime device {onnxruntime.get_device()}')
use_onnx = True
else:
# import torch only if it's required because it occupies too much memory
from loftr import LoFTR
import torch
import torch.nn.functional
matcher = LoFTR(config=model_cfg)
checkpoint = torch.load(opt.weights)
if checkpoint is not None:
if 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
else:
state_dict = checkpoint['model_state_dict']
missed_keys, unexpected_keys = matcher.load_state_dict(state_dict, strict=False)
if len(missed_keys) > 0:
print('Checkpoint is broken')
return 1
if not use_trt:
device = torch.device(opt.device)
matcher = matcher.eval().to(device=device)
print('Successfully loaded pre-trained weights.')
else:
print('Failed to load checkpoint')
return 1
print('Opening camera...')
camera = Camera(opt.camid)
win_name = 'LoFTR features'
cv2.namedWindow(win_name)
prev_frame_time = 0
stop_img = None
stop_frame = None
do_blur = False
do_pause = False
img_size = (model_cfg['input_width'], model_cfg['input_height'])
loftr_coarse_resolution = model_cfg['resolution'][0]
while True:
frame, ret = camera.get_frame()
if ret and frame is not None:
if not do_pause:
if do_blur:
frame = cv2.blur(frame, (3, 3))
new_img = frame.copy()
# make batch
if stop_frame is None:
frame0 = make_query_image(frame, img_size)
stop_img = new_img.copy()
else:
frame0 = stop_frame
frame1 = make_query_image(frame, img_size)
if use_trt or use_onnx:
img0 = frame0[None][None] / 255.0
img1 = frame1[None][None] / 255.0
else:
img0 = torch.from_numpy(frame0)[None][None].to(device=device) / 255.0
img1 = torch.from_numpy(frame1)[None][None].to(device=device) / 255.0
# Inference with LoFTR and get prediction
start = time.perf_counter()
if use_trt:
conf_matrix = matcher(img0, img1)
if opt.original:
conf_matrix = conf_matrix.reshape((1, 4800, 4800))
else:
conf_matrix = conf_matrix.reshape((1, 1200, 1200))
elif use_onnx:
onnx_inputs = {matcher.get_inputs()[0].name: img0.astype(np.float32),
matcher.get_inputs()[1].name: img1.astype(np.float32)}
onnx_outputs = matcher.run(None, onnx_inputs)
conf_matrix = onnx_outputs[0]
else:
with torch.no_grad():
conf_matrix, _ = matcher(img0, img1)
conf_matrix = conf_matrix.cpu().numpy()
mkpts0, mkpts1, mconf = get_coarse_match(conf_matrix, img_size[1], img_size[0], loftr_coarse_resolution)
infer_time = time.perf_counter() - start
# filter only the most confident features
n_top = 20
indices = np.argsort(mconf)[::-1]
indices = indices[:n_top]
mkpts0 = mkpts0[indices, :]
mkpts1 = mkpts1[indices, :]
left_image = stop_img.copy()
draw_features(left_image, mkpts0, img_size, color=(0, 255, 0))
draw_features(new_img, mkpts1, img_size, color=(0, 255, 0))
# combine images
res_img = np.hstack((left_image, new_img))
# draw FPS
new_frame_time = time.perf_counter()
time_diff = new_frame_time - prev_frame_time
prev_frame_time = new_frame_time
draw_fps(time_diff, res_img)
draw_inference(infer_time, res_img)
cv2.imshow(win_name, res_img)
key = cv2.waitKey(delay=1)
if key == ord('q'):
print('Quitting, \'q\' pressed.')
break
if key == ord('s'):
stop_img = frame.copy()
stop_frame = make_query_image(stop_img, img_size)
if key == ord('b'):
do_blur = not do_blur
if key == ord('p'):
do_pause = not do_pause
camera.close()
cv2.destroyAllWindows()
def draw_fps(time_diff, image):
fps = 1. / time_diff
fps_str = 'FPS: ' + str(int(fps))
cv2.putText(image, fps_str, (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (200, 200, 200), 2, cv2.LINE_AA)
def draw_inference(time_diff, image):
fps_str = f'Inference: {time_diff:.2} s'
cv2.putText(image, fps_str, (10, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (200, 200, 200), 2, cv2.LINE_AA)
def draw_features(image, features, img_size, color, draw_text=True):
indices = range(len(features))
sx = image.shape[1] / img_size[0]
sy = image.shape[0] / img_size[1]
for i, point in zip(indices, features):
point_int = (int(round(point[0] * sx)), int(round(point[1] * sy)))
cv2.circle(image, point_int, 2, color, -1, lineType=16)
if draw_text:
cv2.putText(image, str(i), point_int, cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1, cv2.LINE_AA)
if __name__ == "__main__":
main()