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How I Built an AWS DeepLens Clone with the Horned Sungem AI Camera – InApps 2022

March 21, 2022 by Phu Nguyen

How I Built an AWS DeepLens Clone with the Horned Sungem AI Camera – InApps 2022

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How I Built an AWS DeepLens Clone with the Horned Sungem AI Camera – InApps is an article under the topic Software Development Many of you are most interested in today !! Today, let’s InApps.net learn How I Built an AWS DeepLens Clone with the Horned Sungem AI Camera – InApps in today’s post !

Meet Horned Sungem, the AI Camera

Horned Sungem (HS) is a Chinese company specializing in AI. The AI camera built for developers, students, hobbyists, and enthusiasts to create their own AI applications with ease.

The device has a USB-C connector that can be plugged into a Raspberry Pi or any other computing device such as a PC or Mac. It has native support for the Raspberry Pi Camera connected through the CSI interface. The camera comes with two ports — USB-C and micro-USB. The USB-C type connector is used for connecting the camera to the computing device while the micro-USB is for external power. When connected to a low-power device like a Raspberry Pi Zero, the camera doesn’t get enough power from the host. An external power source helps HS to work at its peak performance.

The best thing I love about HS is the Python SDK that runs on ARM or Intel host computing devices. The SDK dramatically simplifies implementing machine learning models for inference. Fully trained models for object detection and face recognition based on popular datasets such as CIFAR-10 and PASCAL-VOC are just a method call away.

All the developer has to do is to import the HS API and call a method by passing the model name. The output comes back with the label of the detected object.

I used the object detection model trained with the PASCAL-VOC dataset to identify bus and car. For more details on using the SDK, refer to HS SDK on Github.

import memcache from hsapi import ObjectDetector net = ObjectDetector(zoom = True, verbose = 2) video_capture = cv2.VideoCapture(0) vehicle = memcache.Client([‘127.0.0.1:11211’], debug=0) try: while True: _, img = video_capture.read() result = net.run(img) if(len(result[1])): label = net.labels[result[1][0][0]] print(label) b_state=digitalRead(button) time.sleep(.5) if (b_state == 1): print(“Button pressed”) sendMsg() setType(label) img = net.plot(result) cv2.imshow(“Toll Gate”, img) cv2.waitKey(1) finally: net.quit()

import memcache

from hsapi import ObjectDetector

net = ObjectDetector(zoom = True, verbose = 2)

video_capture = cv2.VideoCapture(0)

vehicle = memcache.Client([‘127.0.0.1:11211’], debug=0)

try:

while True:

_, img = video_capture.read()

result = net.run(img)

if(len(result[1])):

label = net.labels[result[1][0][0]]

print(label)

b_state=digitalRead(button)

time.sleep(.5)

if (b_state == 1):

print(“Button pressed”)

sendMsg()

setType(label)

img = net.plot(result)

cv2.imshow(“Toll Gate”, img)

cv2.waitKey(1)

finally:

net.quit()

Dear IoT, Meet AI

As soon as a car or a bus is detected, an LED glows providing a visual hint of the vehicle type. To achieve this, I connected a Grove Pi Hat to Raspberry Pi Zero W which gives me access to the Grove family of sensors and actuators. A Red and Green Grove LEDs are connected to the digital pins to represent the type of vehicle. A button is connected to send out a WhatsApp message.

Based on the detected vehicle type, I control the LEDs. The code is pretty straightforward.

def setType(label): if(label==”car”): digitalWrite(car_led,1) digitalWrite(bus_led,0) vehicle.set(“Prompt”,car_msg) elif(label==”bus”): digitalWrite(bus_led,1) digitalWrite(car_led,0) vehicle.set(“Prompt”,bus_msg) else: digitalWrite(bus_led,0) digitalWrite(car_led,0) vehicle.set(“Prompt”,no_msg)

def setType(label):

if(label==“car”):

digitalWrite(car_led,1)

digitalWrite(bus_led,0)

vehicle.set(“Prompt”,car_msg)

elif(label==“bus”):

digitalWrite(bus_led,1)

digitalWrite(car_led,0)

vehicle.set(“Prompt”,bus_msg)

else:

digitalWrite(bus_led,0)

digitalWrite(car_led,0)

vehicle.set(“Prompt”,no_msg)

Adding Voice to Smart Camera with Google Assistant

The fascinating part of the demo is connecting Google Home to the camera. I used Dialogflow to create an Action that talks to the camera.

To facilitate the communication between Raspberry Pi Zero and Dialogflow, I pointed the fulfillment webhook of the Intent to a ngrok endpoint running on Pi. Ngork exposed a Flask REST endpoint to Dialogflow through a tunnel. The beauty of this model is that everything runs on the Pi without the need of an external web service.

Since Raspberry Pi Zero is a constrained device, I didn’t want to install a database to store the outcome of object detection. I simply used Memcache to write the output from the object detection code that is read by Flask code when the webhook is invoked. Memcache came as a handy in-memory database to share data between two independent Python programs — object detection and webhook.

Below is the code for implementing the Dialogflow webhook.

from __future__ import print_function from future.standard_library import install_aliases install_aliases() from urllib.parse import urlparse, urlencode from urllib.request import urlopen, Request from urllib.error import HTTPError import json import os from flask import Flask from flask import request from flask import make_response import memcache shared=None app = Flask(__name__) @app.route(‘/’, methods=[‘POST’]) def webhook(): req = request.get_json(silent=True, force=True) print(“Request:”) print(json.dumps(req, indent=4)) res = processRequest(req) res = json.dumps(res, indent=4) print(res) r = make_response(res) r.headers[‘Content-Type’] = ‘application/json’ return r def processRequest(req): res = makeWebhookResult() return res def makeWebhookResult(): speech=shared.get(“Prompt”) print(“Response:”) print(speech) return { “speech”: speech, “displayText”: speech, “source”: “Smart Toll Gate demo with Horned Sungem” } if __name__ == ‘__main__’: port = int(os.getenv(‘PORT’, 8080)) print(“Starting app on port %d” % port) shared = memcache.Client([‘127.0.0.1:11211’], debug=0) app.run(debug=False, port=port, host=”0.0.0.0″, threaded=True)

Sending WhatsApp Messages through Twilio API

When one of the LEDs is lit, the push button may be pressed to send out a WhatsApp message. I tapped into the most recent feature of Twilio messaging to implement this feature. Using WhatsApp API is not very different from the classic SMS API of Twilio SDK.

The code below sends the message via Twilio:

def sendMsg(): account_sid = “” auth_token =”” client = Client(account_sid, auth_token) message = client.messages.create(to=”whatsapp:”,from_=”whatsapp:SRC_PHONE_NO”,body=vehicle.get(“Prompt”)) print(vehicle.get(“Prompt”)) print(message.sid)

def sendMsg():

account_sid = “”

auth_token =“”

client = Client(account_sid, auth_token)

message = client.messages.create(to=“whatsapp:”,from_=“whatsapp:SRC_PHONE_NO”,body=vehicle.get(“Prompt”))

print(vehicle.get(“Prompt”))

print(message.sid)

In less than 150 lines of code, I could implement an end-to-end smart camera application that emulated AWS DeepLens.

You can access the complete source code from this Github Gist.

InApps is a wholly owned subsidiary of Insight Partners, an investor in the following companies mentioned in this article: Unit.

Source: InApps.net

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Phu Nguyen

As a Senior Tech Enthusiast, I bring a decade of experience to the realm of tech writing, blending deep industry knowledge with a passion for storytelling. With expertise in software development to emerging tech trends like AI and IoT—my articles not only inform but also inspire. My journey in tech writing has been marked by a commitment to accuracy, clarity, and engaging storytelling, making me a trusted voice in the tech community.