netguru / CarLens-Android

CarLens - Recognize and Collect Cars

Car Regonition

environment	google play	app center	google play
Android	Google Play	App Center

About

Welcome to the Car Regonition project. It's an internal application made for detecting cars and showing informations about them.

Testing

• Unit Testing using Mockito + JUnit. Tests concerns Presnters layer and partially Model layer of MVP architecture

Building

1. Clone repository:

   # over https:
   git clone https://github.com/netguru/car-recognition-android.git
   # or over SSH:
   [e-mail]:netguru/car-recognition-android.git

2. Place secrets.properties file in application root folder:

   AppCenterAppId=ACTUAL_KEY
   # (...)

3. Open the project with Android Studio.

Build types

Debug

• debuggable,
• disabled ProGuard,
• uses built-in shrinking (no obfuscation),
• disabled crash reporting.

Staging

• non-debuggable,
• uses full ProGuard configuration,
• enables zipAlign, shrinkResources,
• enabled crash reporting.

Release

• non-debuggable,
• uses full ProGuard configuration,
• enables zipAlign, shrinkResources,
• enabled crash reporting.

Build properties

Property	External property name	Environment variable
App Center App ID	AppCenterAppId	APP_CENTER_APP_ID
Sonar access token	-	SONAR_ACCESS_TOKEN

Bitrise

Bitrise is separated for workflow mentioned below. Feature, MasterPr and Master workflows are responsible for builds stagingRelease flavours of the project which is used for developing new features.

• feature - workflow triggered on Push or PR marge to develop branch;
• develop - workflow triggered on Push to develop branch;
• production - workflow triggered on Push to production branch;

AppCenter / Fabric environments

• Project use App Center for beta distribution and Crash managing and monitoring. Deploy to App Center is performed automatically by Bitrise system in develop and production step.

Supported devices

Supported devices are listed here: https://developers.google.com/ar/discover/supported-devices

Related repositories

• iOS
• Machine Learning