hotdog_recognition_example.py
7.31 KB
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
#*****************************************************
# *
# Copyright 2018 Amazon.com, Inc. or its affiliates. *
# All Rights Reserved. *
# *
#*****************************************************
""" A sample lambda for hotdog detection"""
from threading import Thread, Event
import os
import json
import numpy as np
import awscam
import cv2
import greengrasssdk
class LocalDisplay(Thread):
""" Class for facilitating the local display of inference results
(as images). The class is designed to run on its own thread. In
particular the class dumps the inference results into a FIFO
located in the tmp directory (which lambda has access to). The
results can be rendered using mplayer by typing:
mplayer -demuxer lavf -lavfdopts format=mjpeg:probesize=32 /tmp/results.mjpeg
"""
def __init__(self, resolution):
""" resolution - Desired resolution of the project stream """
# Initialize the base class, so that the object can run on its own
# thread.
super(LocalDisplay, self).__init__()
# List of valid resolutions
RESOLUTION = {'1080p' : (1920, 1080), '720p' : (1280, 720), '480p' : (858, 480)}
if resolution not in RESOLUTION:
raise Exception("Invalid resolution")
self.resolution = RESOLUTION[resolution]
# Initialize the default image to be a white canvas. Clients
# will update the image when ready.
self.frame = cv2.imencode('.jpg', 255*np.ones([640, 480, 3]))[1]
self.stop_request = Event()
def run(self):
""" Overridden method that continually dumps images to the desired
FIFO file.
"""
# Path to the FIFO file. The lambda only has permissions to the tmp
# directory. Pointing to a FIFO file in another directory
# will cause the lambda to crash.
result_path = '/tmp/results.mjpeg'
# Create the FIFO file if it doesn't exist.
if not os.path.exists(result_path):
os.mkfifo(result_path)
# This call will block until a consumer is available
with open(result_path, 'w') as fifo_file:
while not self.stop_request.isSet():
try:
# Write the data to the FIFO file. This call will block
# meaning the code will come to a halt here until a consumer
# is available.
fifo_file.write(self.frame.tobytes())
except IOError:
continue
def set_frame_data(self, frame):
""" Method updates the image data. This currently encodes the
numpy array to jpg but can be modified to support other encodings.
frame - Numpy array containing the image data of the next frame
in the project stream.
"""
ret, jpeg = cv2.imencode('.jpg', cv2.resize(frame, self.resolution))
if not ret:
raise Exception('Failed to set frame data')
self.frame = jpeg
def join(self):
self.stop_request.set()
def greengrass_infinite_infer_run():
""" Entry point of the lambda function"""
try:
# Use a squeezenet model and pick out the hotdog label. The model type
# is classification.
model_type = 'classification'
# Create an IoT client for sending to messages to the cloud.
client = greengrasssdk.client('iot-data')
iot_topic = '$aws/things/{}/infer'.format(os.environ['AWS_IOT_THING_NAME'])
# Create a local display instance that will dump the image bytes to a FIFO
# file that the image can be rendered locally.
local_display = LocalDisplay('480p')
local_display.start()
# The sample projects come with optimized artifacts, hence only the artifact
# path is required.
model_path = '/opt/awscam/artifacts/mxnet_squeezenet.xml'
# Load the model onto the GPU.
client.publish(topic=iot_topic, payload='Loading hotdog model')
model = awscam.Model(model_path, {'GPU': 1})
client.publish(topic=iot_topic, payload='Hotdog model loaded')
# Since this is a binary classifier only retrieve 2 classes.
num_top_k = 2
# The height and width of the training set images
input_height = 224
input_width = 224
# Do inference until the lambda is killed.
while True:
# Get a frame from the video stream
ret, frame = awscam.getLastFrame()
if not ret:
raise Exception('Failed to get frame from the stream')
# Resize frame to the same size as the training set.
frame_resize = cv2.resize(frame, (input_height, input_width))
# Run the images through the inference engine and parse the results using
# the parser API, note it is possible to get the output of doInference
# and do the parsing manually, but since it is a classification model,
# a simple API is provided.
parsed_inference_results = model.parseResult(model_type,
model.doInference(frame_resize))
# Get top k results with highest probabilities
top_k = parsed_inference_results[model_type][0:num_top_k-1]
# Get the probability of 'hotdog' label, which corresponds to label 934 in SqueezeNet.
prob_hotdog = 0.0
for obj in top_k:
if obj['label'] == 934:
prob_hotdog = obj['prob']
break
# Compute the probability of a hotdog not being in the image.
prob_not_hotdog = 1.0 - prob_hotdog
# Add two bars to indicate the probability of a hotdog being present and
# the probability of a hotdog not being present.
# See https://docs.opencv.org/3.4.1/d6/d6e/group__imgproc__draw.html
# for more information about the cv2.rectangle method.
# Method signature: image, point1, point2, color, and tickness.
cv2.rectangle(frame, (0, 0), (int(frame.shape[1] * 0.2 * prob_not_hotdog), 80),
(0, 0, 255), -1)
cv2.rectangle(frame, (0, 90), (int(frame.shape[1] * 0.2 * prob_hotdog), 170), (0, 255, 0), -1)
font = cv2.FONT_HERSHEY_SIMPLEX
# See https://docs.opencv.org/3.4.1/d6/d6e/group__imgproc__draw.html
# for more information about the cv2.putText method.
# Method signature: image, text, origin, font face, font scale, color,
# and tickness
cv2.putText(frame, 'Not hotdog', (10, 70), font, 3, (225, 225, 225), 8)
cv2.putText(frame, 'Hotdog', (10, 160), font, 3, (225, 225, 225), 8)
# Send the top k results to the IoT console via MQTT
client.publish(topic=iot_topic, payload=json.dumps({'Hotdog': prob_hotdog,
'Not hotdog': prob_not_hotdog}))
# Set the next frame in the local display stream.
local_display.set_frame_data(frame)
except Exception as ex:
client.publish(topic=iot_topic, payload='Error in hotdog lambda: {}'.format(ex))
# Execute the function above
greengrass_infinite_infer_run()