ML Task Vision!

This library helps you solve common computer vision tasks by using On-device machine learning. Such as blur NSFW images, censor faces, etc...

Benefit

• Privacy (We don't need to send sensitive user data to the server to process)
• Low latency (Because we don't need to request the server and wait for the response)
• Offline availability (We can use any feature without an internet connection)

How to use

Add it in your settings.gradle:

dependencyResolutionManagement {  
  repositoriesMode.set(RepositoriesMode.FAIL_ON_PROJECT_REPOS)  
    repositories {  
   ...  
    maven { url 'https://jitpack.io' }  
 }}

Add the dependency to app-level build.gradle:

dependencies {
    implementation 'com.github.duy-maimanh:ml_task_vision:0.0.2'
}

Setup BaseOptions.

val options = BaseOptions.builder()  
    .isUseGPU(false)  
    .setNumberThreads(4)  
    .build()

The setNumberThreads is the settings for CPU which mean it only works when the isUseGPU = false. But you should set it because some devices do not support GPU so they will use CPU instead.

1. NSFW detection

This is the very first library feature. That helps us to detect the image and know if it is NSFW or safe for work. It is useful for kind of apps like social media, chatting, or dating... The accuracy now is around 88% based on 8100 test images. And I continue collecting datasets and training this model and will be updated when I got higher accuracy.

Download model

There are two ways to download models.

1. Download manually.

Download the model and the label . And copy these files to src/main/asset folder.

2. Create Gradle task to auto download.

Open project-level build.gradle. Add de.undercouch:gradle-download-task:4.1.2

buildscript {
    dependencies {
        ...
        classpath 'de.undercouch:gradle-download-task:4.1.2'
    }
}

Create download_model.gradle at app folder. And copy the code below.

task downloadNSFWModel(type: Download) {
    src 'https://github.com/duy-maimanh/ml_task_vision/releases/download/0.0.2/nsfw_classifier.tflite'
    dest project.ext.ASSET_DIR + '/nsfw_classifier.tflite'
    overwrite false
}
task downloadNSFWLabel(type: Download) {
    src 'https://github.com/duy-maimanh/ml_task_vision/releases/download/0.0.2/nsfw_labels.txt'
    dest project.ext.ASSET_DIR + '/nsfw_labels.txt'
    overwrite false
}

preBuild.dependsOn downloadNSFWModel, downloadNSFWLabel

Move to app-level build.gradle. Apply the plugin and the download task.

plugins {
    ...
    id 'de.undercouch.download'
}

android {
   ...
}

// import DownloadModels task
project.ext.ASSET_DIR = projectDir.toString() + '/src/main/assets'

apply from:'download_model.gradle'

dependencies {
   ...
}

NsfwProcessOptions

val nsfwProcessOptions = NsfwProcessOptions.builder()  
    .setBaseOptions(options) // if you don't use BaseOptions skip this line. 
    .setFilterMode(FilterMode.BLUR, 0.0) // The larger the number, the more blurred the image. 
    .build()

Create Nsfw Processer

val nsfwProcess = NsfwProcess.create(context, nsfwProcessOptions)

Detect

val result = nsfwProcess.detect(uri)

or

val result = nsfwProcess.detect(bitmap)

The result contains the confidence of each label (safe and unsafe) and the filtered image.

NSFW Detection Benchmark

Run benchmark with image 583*640 and loop for 50 times and get the average running time:

	Samsung A52 (Snapdragon 720G)
GPU	78ms
CPU with 4 threads	425ms

2. Face censor

(Coming soon)

3. Background segmentation

(Coming soon)