-
Notifications
You must be signed in to change notification settings - Fork 0
/
receiver.py
109 lines (95 loc) · 4.24 KB
/
receiver.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
#!/usr/bin/env python
from os import system
from os.path import join, dirname, abspath
ROOT = dirname(abspath(__file__))
import pika
import sys
#sys.path.insert(0, join(ROOT, '..'))
#from detect_image import *
sys.path.insert(0,join(ROOT, '../../py-faster-rcnn/tools'))
from chaitu_detect_objects import *
from create_xml import *
#import Image
import numpy as np
from PIL import Image
#from pylab import *
#matplotlib.use('Agg')
from image_processing import *
#from caffe_IS.detect_image import *
from jaweson import json
class Receiver:
def listen(self):
'''
This functions listens to the client rabbitmq(130.245.168.168) on port 5672 with queue name _argus_queue
'''
credentials = pika.PlainCredentials('guest', 'guest')
connection = pika.BlockingConnection(pika.ConnectionParameters('130.245.168.168', 5672, '/',credentials))
channel = connection.channel()
channel.queue_declare(queue='_argus_queue')
channel.basic_qos(prefetch_count=1)
channel.basic_consume(self.on_request, queue='_argus_queue')
channel.start_consuming()
def on_request(self, ch, method, props, body):
'''
This function serves three api calls 1) identify_objects 2) save_image 3) learn_features
'''
image = json.loads(body)
#gets the api call
if "operation" in image:
operation = image["operation"]
ROOT = dirname(abspath(__file__))
image_store_dir = join(ROOT, '../examples/images')
caffe_input_file = join(ROOT, '../_temp/det_input.txt')
if "image_path" in image:
#retrive image_name from rabbitmq and generate string(path) to save the image
image_name = image["image_path"]
image_file_path = image_store_dir + '/' + image_name.split('/')[-1]+'.jpg'
if "image" in image:
#retrive image and store in temporary location
im = image["image"]
[height, width, depth] = im.shape
img = Image.fromarray(im)
img.save(image_file_path)
#dummy response
#response = deep_learning("I")
#Using RCNN detector
#ROOT = dirname(abspath(__file__))
#image_store_dir = join(ROOT, '../examples/images')
#caffe_input_file = join(ROOT, '../_temp/det_input.txt')
#cmd1 = 'echo ' + image_file_path + ' > ' + caffe_input_file
#system(cmd1)
#caffe_output_file = join(ROOT, '../_temp/_output.h5')
#response = detect(caffe_input_file , caffe_output_file)
#Using Faster RCNN Detector
if operation == "identify_objects":
response = detect_objects(image_file_path)
print response
elif operation == "save":
data = image["data"]
image_id = data["image_id"]
labels = data["labels"]
image_shape = [height, width, depth]
save_path = "/var/services/homes/kchakka/py-faster-rcnn/VOCdevkit/VOC2007/Annotations"
response = create_xml(image_id, labels, image_shape, save_path)
fast_rcnn_imagedb = "/var/services/homes/kchakka/py-faster-rcnn/VOCdevkit/VOC2007/JPEGImages/" + response+".jpg"
img.save(fast_rcnn_imagedb)
rcnn_image_file_path = "/var/services/homes/kchakka/py-faster-rcnn/VOCdevkit/VOC2007/ImageSets/Main/train.txt"
cmd1 = 'echo ' + response + '>>' + rcnn_image_file_path
system(cmd1)
print response
elif operation == "learn_features":
#cmd = "cd /var/services/homes/kchakka/py-faster-rcnn"
#system(cmd)
print "Operation is learn_features"
net_name = "VGG16"
weights = "/var/services/homes/kchakka/py-faster-rcnn/data/faster_rcnn_models/VGG16_faster_rcnn_final.caffemodel"
imdb = "voc_2007_train"
train_script = "/var/services/homes/kchakka/py-faster-rcnn/tools/train_faster_rcnn_alt_opt.py"
cmd1 = "python " + train_script + " --net_name=" + net_name + " --weights=" + weights + " --imdb=" + imdb
system(cmd1)
print "Training the network"
#cmd = "cd /var/services/homes/kchakka/caffe/argus"
#system(cmd)
response = "Training done"
ch.basic_publish(exchange='', routing_key=props.reply_to, properties=pika.BasicProperties(correlation_id = props.correlation_id), body=str(response))
ch.basic_ack(delivery_tag=method.delivery_tag)