开源软件名称（OpenSource Name）：

FirebaseExtended/mlkit-material-android

开源软件地址(OpenSource Url)：

https://github.com/FirebaseExtended/mlkit-material-android

开源编程语言(OpenSource Language)：

开源软件介绍(OpenSource Introduction)：

UPDATE: This project has been moved to https://github.com/googlesamples/mlkit/tree/master/android/material-showcase as part of the ML Kit's new standalone SDK. Learn more

ML Kit Showcase App with Material Design

This app demonstrates how to build an end-to-end user experience with Google ML Kit APIs and following the new Material for ML design guidelines.

The goal is to make it as easy as possible to integrate ML Kit into your app with an experience that has been user tested for the specific use cases that are covered:

Visual search using the Object Detection & Tracking API - a complete workflow from object detection to product search in live camera and static image
Barcode detection using the Barcode API in live camera

Status

This sample is no longer actively maintained and is left here for reference only.

Steps to run the app

Clone this repo locally

git clone https://github.com/firebase/mlkit-material-android

Create a Firebase project in the Firebase console, if you don't already have one
Add a new Android app into your Firebase project with package name com.google.firebase.ml.md
Download the config file (google-services.json) from the new added app and move it into the module folder (i.e. app/)
Build and run it on an Android device

How to use the app

This app supports two usage scenarios: Live Camera and Static Image.

Live Camera scenario

It uses the camera preview as input and contains two workflow: object detection & visual search, and barcode detection. There's also a Settings page to allow you to configure several options:

Camera
- Specify the preview size of rear camera manually (Default size is chose appropriately based on screen size)
Object detection
- Whether or not to enable multiple objects and coarse classification
Product search
- Whether or not to enable auto search: if enabled, search request will be fired automatically once object is detected and confirmed, otherwise a search button will appear to trigger search manually
- Required time that the auto-detected object needs to be focused for being regarded as user-confirmed
Barcode detection
- Barcode aiming frame size
- Barcode size check: will prompt "Move closer" if the current detected barcode size is not big enough
- Delay loading result: to simulate the case where the detected barcode requires further processing before displaying result.

Static Image scenario

It'll prompt to select an image from the Image Picker, detect objects in the picked image, and then perform visual search on them. There're well designed UI components (overlay dots, card carousel etc.) to indicate the detected objects and search results.

Note that the visual search functionality here is mock since no real search backend has set up for this repository, but it should be easy to hook up with your own search service (e.g. Product Search) by only replacing the SearchEngine class implementation.