AI and Appium for Camera Testing

Environment
Windows 10
Tensorflow=1.1.0
python=2.7 (for running Appium)
python=3.8 (for running keras-data-augmentation)
global modules path = C:\Users\natha\AppData\Roaming\npm
appium = 1.19.1
node = 14.15.0
npm=6.14.9
test-ai-classifies=2.1.1


Architecture of Smart Home Solution

Smart home solutions include IOT powered devices such as lights, fans, water pumping motor, kitchen accessories etc. Smart assistants are voice command based systems which are used to switch on or off the IOT devices. Android app is built with voice command detector which is essentially an AI based system. This voice commands are such as ‘stop’ and ‘go’. These commands are sent to cloud where already these smart home devices subscribed to these commands. So these devices receive these commands and switch on or switch off. …


Figure1: Architecture diagram

A Smart home solution monitors the locker door status using a data pipeline that consists of an ELK stack. A Raspberry Pi (RPI) based IoT system gets the door status using a magnetic switch. It sends ‘tweets’ whenever the door status is getting changed and also periodically. An ELK stack (Elastic Search/Logstash/Kibana) processes these tweets and displays the door status in a dashboard. RPI is a node in a Blockchain network that consists of GCP VMs and laptop-based Blockchain nodes. An AI/Android face recognition app opens if the user shows the face and closes the locker door if the user…


Figure1: Architecture diagram

An Artificial Intelligence and Android based face recognition app is used to open and close a remote smart door in a smart home. Tensorflow web is used to train the user’s face images and build an AI model. This AI model is converted to mobile friendly — ‘tflite’ format. An Android application is built with this AI model. This app communicates with Blockchain nodes through JSON-RPC and Web3J interface. Two Blockchain miner nodes are running on Google cloud platform based VMs. The third node is a RPI based Blockchain node. These three nodes constitute a P2P network. A smart contract…


Figure1: Architecture diagram

Smart home solutions can control the house hold electrical and electronics equipments such as light, fan, doors, lockers, water pumping motor, TV, refridgerator etc. Android app can be used to control these equipments through Blockchain and smart contracts. Android app connects with Ethereum nodes through JSON-RPC and Web3j. Raspberry Pi is employed as an ethereum node along with two more GCP VM based miner nodes. All 3 nodes run Blockchain and form a P2P network. The Raspberry Pi node controls a light to switch on and off. A smart contract is deployed on Blockchain through Web3.0 and controls the light…


Smart home solutions enable controlling the electrical devices such as lights, fans, water heater, water pumping motor, kitchen accessories etc. Smart contracts work on Blockchain and control these devices. In this mini-project, a peer to peer (P2P) network is built with two GCP (Google Cloud Platform) based VM(Virtual Machine)s and one RPI(Raspberry) based systems. These two VMs are employed as miners and approve the transactions from the miner1 to RPI. RPI is connected with a relay which in-turn turns on and off a light. A smart contract is deployed on Blockchain through Web3.0 and controls the light by transactions such…


Architecture of Smart Home Solution using Gesture Recognition

Smart home solutions involve AI and IoT technologies to control doors in order to ensure safety and security of the home. AI is used to train images of human gestures such as palm and fist. An Android application recognizes the gestures by using this trained model. This app sends gesture info as MQTT message through MQTT server. An IoT app subscribes to these messages and receives. This IoT app controls a servo motor to open or close the door based on the received MQTT message. This project involves technologies such as AI, Data Science, IoT, Android, Cloud computing etc.

The…


An AI classifier called test-ai-classifier (detalis in Reference section) for Appium is open sourced. This post will show you how to build the classifier using Google Colab, customize them for your application, and add it to Appium.

Steps to re-train:

Step1: Create zip file
git clone https://github.com/testdotai/classifier-builder.git
Create Zip file — training_images.zip

Step2: Upload to google drive (Example: digitransolutions5@gmail.com)

Upload training_images.zip to google drive and share.

Step3: Create a sharable link to anyone
https://drive.google.com/file/d/1q9ZcSU7JtjcZPbXLtrsbpyUgsGPwj2er/view?usp=sharing
So id = 1q9ZcSU7JtjcZPbXLtrsbpyUgsGPwj2er

Step4: Copy zip file to colab
!wget — load-cookies /tmp/cookies.txt “https://docs.google.com/uc?export=download&confirm=$(wget — quiet — save-cookies /tmp/cookies.txt — keep-session-cookies — no-check-certificate ‘https://docs.google.com/uc?export=download&id=1q9ZcSU7JtjcZPbXLtrsbpyUgsGPwj2er' -O- |…

Anbunathan Ramaiah

Dr. Anbunathan R is founder & CEO of ‘DigiTran Solutions’ and making innovative products, based on Digital Transformation technologies such as AI/Blockchain/IOT

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