-
Notifications
You must be signed in to change notification settings - Fork 31
/
ssd_test_img.py
96 lines (74 loc) · 3.33 KB
/
ssd_test_img.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
import os
from click import UsageError
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.utils.misc import Timer
import matplotlib.pyplot as plt
import cv2
import sys
import numpy as np
if __name__ == "__main__":
if len(sys.argv) != 4:
raise UsageError("Usage: python ssd_test_img.py <img_name> <model_path> <net_type>")
img_path, model_path, net_type = sys.argv[1], sys.argv[2], sys.argv[3]
timer = Timer()
label_path = "models/voc-model-labels.txt"
img_name = img_path.split("/")[1]
# --------------- #
class_names = [name.strip() for name in open(label_path).readlines()]
num_classes = len(class_names)
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 == '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':
predictor = create_mobilenetv2_ssd_lite_predictor(net, candidate_size=200, nms_method="soft")
elif net_type == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(net, candidate_size=200)
else:
print("The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite.")
sys.exit(1)
print("Loading Trained Model is Done!\n")
print("Starting Detection...\n")
orig_image = cv2.imread('images/' + img_name)
image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)
color = np.random.uniform(0, 255, size = (10, 3))
timer.start()
boxes, labels, probs = predictor.predict(image, 10, 0.4)
interval = timer.end()
print('Time: {:.2f}s, Detect Objects: {:d}.'.format(interval, labels.size(0)))
fps = 1/interval
for i in range(boxes.size(0)):
box = boxes[i, :]
label = f"{class_names[labels[i]]}: {probs[i]:.2f}"
i_color = int(labels[i])
box = [round(b.item()) for b in box]
cv2.rectangle(orig_image, (box[0], box[1]), (box[2], box[3]), color[i_color], 2)
cv2.putText(orig_image, label,
(box[0] - 10, box[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX,
1, # font scale
color[i_color],
2) # line type
print(orig_image.shape)
cv2.imwrite('outputs/' + img_name, orig_image)
print("Check the result!")