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A vnc client dll for c#, written in c/c++ and c#, depends on TightVnc 2.7.10, so it can use Tight to push screen faster than vncsharp use…

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MLKit Sample App

This is an App made for Showcasing base apis for ML Kit. Its an Android app to understand the ready to use APIs of ML Kit. We can also detect faces and label images without internet using on-Device APIs. This app uses an API provided by https://firebase.google.com/docs/reference/android/com/google/firebase/ml/vision/package-summary. It is made with the intention of understanding how to use the ML Kit base apis in a simplified manner. It is an updated fork from https://github.com/the-dagger/MLKitAndroid

Features

  • Image Labelling using onDevice APIs
  • Image Labelling using onCloud APIs
  • Face Detection using onDevice APIs
  • Landmark Recognition using onCloud APIs

Screenshots









Steps to run the app

The app uses the onDevice and Cloud Vision API for image labelling and landmark identification. You must clone this repo and add it in Firebase Console (https://console.firebase.google.com) .

In Firebase upgrade from Spark Plan to Blaze.This is because the cloud recognition API is only available on paid plans. Blaze is a pay as you go plan

Enable the Cloud Vision Api (https://console.cloud.google.com/apis/library/vision.googleapis.com?q=vision&project=wherecaniwatch-dcfe6) for this Firebase project over in the Google API Console. The Cloud Apis are free for first 1000 requests in a month .

  1. Afer adding the project in Firebase Console , download the 'google-services.json' file.
  2. Paste this file in the app directory
  3. Build this project and Run
  4. Libraries

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A vnc client dll for c#, written in c/c++ and c#, depends on TightVnc 2.7.10, so it can use Tight to push screen faster than vncsharp use…

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