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Android: How to Develop an ID Photo DIY Applet in 30 Min

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2.71 (5 votes)
23 Mar 2020CPOL
The source code will be shared and it's quite friendly to users who are fresh to Android. The whole process will take only 30 minutes.
It's an application level development and we won't go through the algorithm of image segmentation. I use Huawei Mlkit help to develop this app and it provides the capability of image segmentation. Developers will learn how to quickly develop a ID photo DIY applet using such SDK.

Background

I don't know if you have had such an experience. All of a sudden, schools or companies needed to provide one inch or two inch head photos of individuals. They needed to apply for a passport or student card which have requirements for the background color of the photos. However, many people don't have time to take photos at the photo studio. Or they have taken them before, but the background color of the photos doesn't meet the requirements. I had a similar experience. At that time, the school asked for a passport, and the school photo studio was closed again. I took photos with my mobile phone in a hurry, and then used the bedspread as the background to deal with it. As a result, I was scolded by the teacher.

Many years later, mlkit machine learning has the function of image segmentation. Using this SDK to develop a small program of certificate photo DIY could perfectly solve the embarrassment in that year.

Here is the demo for the result.

Image 1Image 2

1. Preparation

1.1 Add Huawei Maven Warehouse in Project Level Gradle

Open the Android studio project level build.gradle file.

在这里插入图片描述

Add the following Maven addresses:

buildscript {
    repositories {        
        maven {url 'http://developer.huawei.com/repo/'}
    }    
}
allprojects {
    repositories {       
        maven { url 'http://developer.huawei.com/repo/'}
    }
}

1.2 Add SDK Dependency in Application Level build.gradle

Introducing SDK and basic SDK of face recognition:

dependencies{ 
  // import base
  implementation 'com.huawei.hms:ml-computer-vision:1.0.2.300' 
  // import face-detetion sdk
  implementation 'com.huawei.hms:ml-computer-vision-image-segmentation-body-model:1.0.2.301'  
 }

1.3 Add Model in Android manifest.xml File

To enable the application to automatically update the latest machine learning model to the user's device after the user installs your application from the Huawei application market. Add the following statement to the Android manifest.xml file of the application:

<manifest    

   <application  

       <meta-data                     

           android:name="com.huawei.hms.ml.DEPENDENCY"          

           android:value= "imgseg "/>                    
   </application>
</manifest> 

1.4 Apply for Camera and Storage Permission in Android manifest.xml File

<!--aks for storage permission-->
<uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE" />

2. Two Key Steps of Code Development

2.1 Dynamic Authority Application

@Override
protected void onCreate(Bundle savedInstanceState) {
    super.onCreate(savedInstanceState);
    setContentView(R.layout.activity_main);
    if (!allPermissionsGranted()) {
        getRuntimePermissions();
    }
}

@Override
public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions,
                                       @NonNull int[] grantResults) {
    super.onRequestPermissionsResult(requestCode, permissions, grantResults);

    if (requestCode != PERMISSION_REQUESTS) {
        return;
    }
    boolean isNeedShowDiag = false;
    for (int i = 0; i < permissions.length; i++) {
        if (permissions[i].equals(Manifest.permission.READ_EXTERNAL_STORAGE) && 
                           grantResults[i] != PackageManager.PERMISSION_GRANTED) {
            // 如果相机或者存储权限没有授权,则需要弹出授权提示框
            isNeedShowDiag = true;
        }
    }
    if (isNeedShowDiag && !ActivityCompat.shouldShowRequestPermissionRationale
                          (this, Manifest.permission.CALL_PHONE)) {
        AlertDialog dialog = new AlertDialog.Builder(this)
                .setMessage(getString(R.string.camera_permission_rationale))
                .setPositiveButton(getString(R.string.settings), 
                      new DialogInterface.OnClickListener() {
                    @Override
                    public void onClick(DialogInterface dialog, int which) {
                        Intent intent = 
                             new Intent(Settings.ACTION_APPLICATION_DETAILS_SETTINGS);
                        intent.setData(Uri.parse
                             ("package:" + getPackageName())); // 根据包名打开对应的设置界面
                        startActivityForResult(intent, 200);
                        startActivity(intent);
                    }
                })
                .setNegativeButton(getString(R.string.cancel), 
                                   new DialogInterface.OnClickListener() {
                    @Override
                    public void onClick(DialogInterface dialog, int which) {
                        finish();
                    }
                }).create();
        dialog.show();
    }
}

2.2 Creating an Image Segmentation Detector

The image segmentation detector can be created through the image segmentation detection configurator "mlimagesegmentation setting".

MLImageSegmentationSetting setting = new MLImageSegmentationSetting.Factory()
                .setAnalyzerType(MLImageSegmentationSetting.BODY_SEG)
                .setExact(true)
                .create();        
this.analyzer = MLAnalyzerFactory.getInstance().getImageSegmentationAnalyzer(setting);

2.3 Create "mlframe" Object through android.graphics.bitmap for Analyzer to Detect Pictures

The image segmentation detector can be created through the image segmentation detection configurator "MLImageSegmentationSetting".

MLFrame mlFrame = new MLFrame.Creator().setBitmap(this.originBitmap).create();

2.4 Call "asyncanalyseframe" Method for Image Segmentation

// Create task,process the results returned by the image segmentation detector. 
Task<MLImageSegmentation> task = analyzer.asyncAnalyseFrame(frame); 
// 异步处理图像分割检测器返回结果 
Task<MLImageSegmentation> task = this.analyzer.asyncAnalyseFrame(mlFrame);
            task.addOnSuccessListener(new OnSuccessListener<MLImageSegmentation>() {
                @Override
                public void onSuccess(MLImageSegmentation mlImageSegmentationResults) {
                    // Transacting logic for segment success.
                    if (mlImageSegmentationResults != null) {
                        StillCutPhotoActivity.this.foreground = 
                                      mlImageSegmentationResults.getForeground();
                        StillCutPhotoActivity.this.preview.setImageBitmap
                                      (StillCutPhotoActivity.this.foreground);
                        StillCutPhotoActivity.this.processedImage = 
                              ((BitmapDrawable) ((ImageView) 
                              StillCutPhotoActivity.this.preview).getDrawable()).getBitmap();
                        StillCutPhotoActivity.this.changeBackground();
                    } else {
                        StillCutPhotoActivity.this.displayFailure();
                    }
                }
            }).addOnFailureListener(new OnFailureListener() {
                @Override
                public void onFailure(Exception e) {
                    // Transacting logic for segment failure.
                    StillCutPhotoActivity.this.displayFailure();
                    return;
                }
            });

2.5 Change the Picture Background

this.backgroundBitmap = BitmapUtils.loadFromPath
(StillCutPhotoActivity.this, id, targetedSize.first, targetedSize.second);
BitmapDrawable drawable = new BitmapDrawable(backgroundBitmap);
this.preview.setDrawingCacheEnabled(true);
this.preview.setBackground(drawable);
this.preview.setImageBitmap(this.foreground);
this.processedImage = Bitmap.createBitmap(this.preview.getDrawingCache());
this.preview.setDrawingCacheEnabled(false);

Conclusion

In this way, a small program of ID photo DIY has been made. Let's see the demo.

在这里插入图片描述

If you have strong hands-on ability, you can also add and change suits or other operations. The source code has been uploaded to GitHub. You can also improve this function on GitHub.

Please stamp the source code address of GitHub (the project directory is id-photo-diy).

Based on the ability of image segmentation, it cannot only be used to do the DIY program of ID photo, but also realize the following related functions:

  1. People's portraits in daily life can be cut out, some interesting photos can be made by changing the background, or the background can be virtualized to get more beautiful and artistic photos.
  2. Identify the sky, plants, food, cats and dogs, flowers, water surface, sand surface, buildings, mountains and other elements in the image, and make special beautification for these elements, such as making the sky bluer and the water clearer.
  3. Identify the objects in the video stream, edit the special effects of the video stream, and change the background.

For other functions, please brainstorm together!

For a more detailed development guide, please refer to the official website of Huawei developer Alliance:

History

  • 23rd March, 2020: Initial version

License

This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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Comments and Discussions

 
QuestionHuawei specific? Pin
JPhelps25-Mar-20 0:51
MemberJPhelps25-Mar-20 0:51 

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Posted 23 Mar 2020

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