mid_Lambda_yogapj.py
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import os
import greengrasssdk
from threading import Timer
import time
import awscam
import cv2
import mo
from threading import Thread
# Creating a greengrass core sdk client
client = greengrasssdk.client('iot-data')
# The information exchanged between IoT and clould has
# a topic and a message body.
# This is the topic that this code uses to send messages to cloud
iotTopic = '$aws/things/{}/infer'.format(os.environ['AWS_IOT_THING_NAME'])
jpeg = None
Write_To_FIFO = True
class FIFO_Thread(Thread):
def __init__(self):
''' Constructor. '''
Thread.__init__(self)
def run(self):
fifo_path = "/tmp/results.mjpeg"
if not os.path.exists(fifo_path):
os.mkfifo(fifo_path)
f = open(fifo_path,'w')
client.publish(topic=iotTopic, payload="Opened Pipe")
while Write_To_FIFO:
try:
f.write(jpeg.tobytes())
except IOError as e:
continue
def greengrass_infinite_infer_run():
try:
input_width = 224
input_height = 224
model_name = "image-classification"
error, model_path = mo.optimize(model_name,input_width,input_height, aux_inputs={'--epoch': 2,'--precision':'FP32'})
# The aux_inputs is equal to the number of epochs and in this case, it is 300
# Load model to GPU (use {"GPU": 0} for CPU)
mcfg = {"GPU": 1}
model = awscam.Model(model_path, mcfg)
client.publish(topic=iotTopic, payload="Model loaded")
model_type = "classification"
with open('caltech256_labels.txt', 'r') as f:
labels = [l.rstrip() for l in f]
topk = 2
results_thread = FIFO_Thread()
results_thread.start()
# Send a starting message to IoT console
client.publish(topic=iotTopic, payload="Inference is starting")
doInfer = True
while doInfer:
# Get a frame from the video stream
ret, frame = awscam.getLastFrame()
# Raise an exception if failing to get a frame
if ret == False:
raise Exception("Failed to get frame from the stream")
# Resize frame to fit model input requirement
frameResize = cv2.resize(frame, (input_width, input_height))
# Run model inference on the resized frame
inferOutput = model.doInference(frameResize)
# Output inference result to the fifo file so it can be viewed with mplayer
parsed_results = model.parseResult(model_type, inferOutput)
top_k = parsed_results[model_type][0:topk]
msg = '{'
prob_num = 0
for obj in top_k:
if prob_num == topk-1:
msg += '"{}": {:.2f}'.format(labels[obj["label"]], obj["prob"]*100)
else:
msg += '"{}": {:.2f},'.format(labels[obj["label"]], obj["prob"]*100)
prob_num += 1
msg += "}"
client.publish(topic=iotTopic, payload = msg)
if top_k[0]["prob"]*100 > 65 :
cv2.putText(frame, labels[top_k[0]["label"]] + ' '+ str(top_k[0]["prob"]*100), (0,22), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 165, 20), 4)
global jpeg
ret,jpeg = cv2.imencode('.jpg', frame)
except Exception as e:
msg = "myModel Lambda failed: " + str(e)
client.publish(topic=iotTopic, payload=msg)
# Asynchronously schedule this function to be run again in 15 seconds
Timer(15, greengrass_infinite_infer_run).start()
# Execute the function above
greengrass_infinite_infer_run()
# This is a dummy handler and will not be invoked
# Instead the code above will be executed in an infinite loop for our example
def function_handler(event, context):
return