-
Notifications
You must be signed in to change notification settings - Fork 6
/
object_detection_socket.py
142 lines (103 loc) · 3.71 KB
/
object_detection_socket.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
import argparse
import platform
import subprocess
from edgetpu.detection.engine import DetectionEngine
import socket
import io
import time
import numpy as np
import json
from lib import read_label_file
from PIL import Image
from PIL import ImageDraw
# UDP_IP = '192.168.2.183'
UDP_IP = '127.0.0.1'
TCP_IP = UDP_IP
# TCP_IP = '10.0.0.1'
UDP_RECEIVE_PORT = 9100
# UDP_SEND_PORT = 9101
TCP_PORT = 9101
# BUFFER_SIZE = 1024
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--model', help='Path of the detection model.', required=True)
parser.add_argument(
'--draw', help='If to draw the results.', default=True)
parser.add_argument(
'--label', help='Path of the labels file.')
args = parser.parse_args()
renderer = None
# Initialize engine.
engine = DetectionEngine(args.model)
labels = read_label_file(args.label) if args.label else None
shown = False
frames = 0
start_seconds = time.time()
print('opening socket.')
# s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
receiveSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
# senderSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind((TCP_IP, TCP_PORT))
s.listen(1)
# senderSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
receiveSocket.bind((UDP_IP, UDP_RECEIVE_PORT))
# senderSocket.bind((UDP_IP, UDP_SEND_PORT))
print('listening...')
_, width, height, channels = engine.get_input_tensor_shape()
imageSize = width*height*3
print('waiting for client')
conn, addr = s.accept()
print('Connection address:', addr)
# Open image.
while 1:
print('waiting for packet')
data, addr = receiveSocket.recvfrom(66507)
# print('got packet of length', len(data))
if (len(data) > 0):
start_s = time.time()
# print('processing image')
try:
image = Image.open(io.BytesIO(data)).convert('RGB')
except OSError:
print('Could not read image')
continue
input = np.frombuffer(image.tobytes(), dtype=np.uint8)
results = engine.DetectWithInputTensor(input, threshold=0.25,
top_k=10)
print('time to process image', (time.time() - start_s) * 1000)
output = to_output(results, image.size, labels)
message = json.dumps({'results': output}) + '|'
# print('sending', message)
try:
conn.send(message.encode('utf-8'))
except ConnectionResetError:
print('Socket disconnected...waiting for client')
conn, addr = s.accept()
# receiveSocket.sendto(message.encode('utf-8'), addr)
# senderSocket.sendto(message.encode('utf-8'), (UDP_IP, UDP_SEND_PORT))
# receivedBytes=bytearray()
# start_s = time.time()
# Run inference.
# results = engine.DetectWithInputTensor(input, threshold=0.25,
# top_k=10)
# elapsed_s = time.time() - start_s
# conn.close()
def to_output(results, full_size, labels):
return list(map(lambda result: { \
'box': scale_box(result.bounding_box, full_size),\
'label': labels[result.label_id] if labels is not None else None
}, results))
def scale_boxes(results, full_size):
return list(map(lambda result: \
(scale_box(result.bounding_box, full_size)).tolist(), results))
def to_label_texts(results, labels):
if labels is None:
return None
else:
return list(map(lambda result: labels[result.label_id], results))
def scale_box(box, full_size):
return (box* (full_size[0], full_size[1])).flatten().tolist()
if __name__ == '__main__':
main()