forked from qfgaohao/pytorch-ssd
-
Notifications
You must be signed in to change notification settings - Fork 0
/
run_ssd_example.py
71 lines (64 loc) · 3.19 KB
/
run_ssd_example.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
from vision.ssd.vgg_ssd import create_vgg_ssd, create_vgg_ssd_predictor
from vision.ssd.mobilenetv1_ssd import create_mobilenetv1_ssd, create_mobilenetv1_ssd_predictor
from vision.ssd.mobilenetv1_ssd_lite import create_mobilenetv1_ssd_lite, create_mobilenetv1_ssd_lite_predictor
from vision.ssd.squeezenet_ssd_lite import create_squeezenet_ssd_lite, create_squeezenet_ssd_lite_predictor
from vision.ssd.mobilenet_v2_ssd_lite import create_mobilenetv2_ssd_lite, create_mobilenetv2_ssd_lite_predictor
from vision.ssd.mobilenetv3_ssd_lite import create_mobilenetv3_large_ssd_lite, create_mobilenetv3_small_ssd_lite
from vision.utils.misc import Timer
import cv2
import sys
if len(sys.argv) < 5:
print('Usage: python run_ssd_example.py <net type> <model path> <label path> <image path>')
sys.exit(0)
net_type = sys.argv[1]
model_path = sys.argv[2]
label_path = sys.argv[3]
image_path = sys.argv[4]
class_names = [name.strip() for name in open(label_path).readlines()]
if net_type == 'vgg16-ssd':
net = create_vgg_ssd(len(class_names), is_test=True)
elif net_type == 'mb1-ssd':
net = create_mobilenetv1_ssd(len(class_names), is_test=True)
elif net_type == 'mb1-ssd-lite':
net = create_mobilenetv1_ssd_lite(len(class_names), is_test=True)
elif net_type == 'mb2-ssd-lite':
net = create_mobilenetv2_ssd_lite(len(class_names), is_test=True)
elif net_type == 'mb3-large-ssd-lite':
net = create_mobilenetv3_large_ssd_lite(len(class_names), is_test=True)
elif net_type == 'mb3-small-ssd-lite':
net = create_mobilenetv3_small_ssd_lite(len(class_names), is_test=True)
elif net_type == 'sq-ssd-lite':
net = create_squeezenet_ssd_lite(len(class_names), is_test=True)
else:
print("The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite.")
sys.exit(1)
net.load(model_path)
if net_type == 'vgg16-ssd':
predictor = create_vgg_ssd_predictor(net, candidate_size=200)
elif net_type == 'mb1-ssd':
predictor = create_mobilenetv1_ssd_predictor(net, candidate_size=200)
elif net_type == 'mb1-ssd-lite':
predictor = create_mobilenetv1_ssd_lite_predictor(net, candidate_size=200)
elif net_type == 'mb2-ssd-lite' or net_type == "mb3-large-ssd-lite" or net_type == "mb3-small-ssd-lite":
predictor = create_mobilenetv2_ssd_lite_predictor(net, candidate_size=200)
elif net_type == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(net, candidate_size=200)
else:
predictor = create_vgg_ssd_predictor(net, candidate_size=200)
orig_image = cv2.imread(image_path)
image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)
boxes, labels, probs = predictor.predict(image, 10, 0.4)
for i in range(boxes.size(0)):
box = boxes[i, :]
cv2.rectangle(orig_image, (box[0], box[1]), (box[2], box[3]), (255, 255, 0), 4)
#label = f"""{voc_dataset.class_names[labels[i]]}: {probs[i]:.2f}"""
label = f"{class_names[labels[i]]}: {probs[i]:.2f}"
cv2.putText(orig_image, label,
(box[0] + 20, box[1] + 40),
cv2.FONT_HERSHEY_SIMPLEX,
1, # font scale
(255, 0, 255),
2) # line type
path = "run_ssd_example_output.jpg"
cv2.imwrite(path, orig_image)
print(f"Found {len(probs)} objects. The output image is {path}")