Hello, I would like to send in parallel to the cloud inference output MQTT messages locally from deeplens to my raspberry by. In the Deeplens recipes (trash sorting) there is an example of it which I took as guide to modify the Face Detection Lambda. I am struggling to get it work. Could you please help me out here. The version code posted here below does not send any messages anymore. Here the code I'm trying to use: #*****************************************************

*

Copyright 2018 Amazon.com, Inc. or its affiliates. *

All Rights Reserved. *

*

#***************************************************** """ A sample lambda for face detection""" from threading import Thread, Event import os import json import numpy as np import awscam import cv2 import greengrasssdk import time import mo

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, 'wb') 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 tof 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 infinite_infer_run(): """ Entry point of the lambda function""" try: # This face detection model is implemented as single shot detector (ssd). model_type = 'ssd' output_map = {1: 'face'} # 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']) pi_topic = 'deeplens/infer' # 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_deploy_ssd_FP16_FUSED.xml' # Load the model onto the GPU. client.publish(topic=iot_topic, payload='Loading face detection model') model = awscam.Model(model_path, {'GPU': 1}) client.publish(topic=iot_topic, payload='Face detection model loaded') # Set the threshold for detection detection_threshold = 0.25 # The height and width of the training set images input_height = 300 input_width = 300 # 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 ssd model, # a simple API is provided. parsed_inference_results = model.parseResult(model_type, model.doInference(frame_resize)) # Compute the scale in order to draw bounding boxes on the full resolution # image. yscale = float(frame.shape[0]) / float(input_height) xscale = float(frame.shape[1]) / float(input_width) # Dictionary to be filled with labels and probabilities for MQTT cloud_output = {} # Get the detected faces and probabilities for obj in parsed_inference_results[model_type]: if obj['prob'] > detection_threshold: # Add bounding boxes to full resolution frame xmin = int(xscale * obj['xmin']) ymin = int(yscale * obj['ymin']) xmax = int(xscale * obj['xmax']) ymax = int(yscale * obj['ymax']) # 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, (xmin, ymin), (xmax, ymax), (255, 165, 20), 10) # Amount to offset the label/probability text above the bounding box. text_offset = 15 # 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, '{:.2f}%'.format(obj['prob'] * 100), (xmin, ymin-text_offset), cv2.FONT_HERSHEY_SIMPLEX, 2.5, (255, 165, 20), 6) # Store label and probability to send to cloud cloud_output[output_map[obj['label']]] = obj['prob'] # Set the next frame in the local display stream. local_display.set_frame_data(frame) # Send results to the cloud client.publish(topic=iot_topic, payload=json.dumps(cloud_output)) # Send the top k results to the Raspberry Pi via MQTT pi_output = {} pi_output[output_map[obj['label']]] = obj['prob'] client.publish(topic=pi_topic, payload=json.dumps(pi_output)) except Exception as ex: client.publish(topic=iot_topic, payload='Error in face detection lambda: {}'.format(ex))

infinite_infer_run()

Essentially I added the few last lines and also setup the IoT part of it with certificates and policies.

Thank you for any help or hint