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Image classification guide for Android

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The MediaPipe Image Classifier task lets you perform classification on images. You can use this task to identify what an image represents among a set of categories defined at training time. These instructions show you how to use the Image Classifier with Android apps. The code sample described in these instructions is available on GitHub.

You can see this task in action by viewing the Web demo. For more information about the capabilities, models, and configuration options of this task, see the Overview.

Code example

The MediaPipe Tasks example code is a simple implementation of a Image Classifier app for Android. The example uses the camera on a physical Android device to continuously classify objects, and can also use images and videos from the device gallery to statically classify objects.

You can use the app as a starting point for your own Android app, or refer to it when modifying an existing app. The Image Classifier example code is hosted on GitHub.

Download the code

The following instructions show you how to create a local copy of the example code using the git command line tool.

To download the example code:

  1. Clone the git repository using the following command:
    git clone https://github.com/googlesamples/mediapipe
    
  2. Optionally, configure your git instance to use sparse checkout, so you have only the files for the Image Classifier example app:
    cd examples
    git sparse-checkout init --cone
    git sparse-checkout set main/examples/image_classification/android
    

After creating a local version of the example code, you can import the project into Android Studio and run the app. For instructions, see the Setup Guide for Android.

Key components

The following files contain the crucial code for this image classification example application:

Setup

This section describes key steps for setting up your development environment and code projects to use Image Classifier. For general information on setting up your development environment for using MediaPipe tasks, including platform version requirements, see the Setup guide for Android.

Dependencies

Image Classifier uses the com.google.mediapipe:tasks-vision library. Add this dependency to the build.gradle file of your Android app development project. Import the required dependencies with the following code:

dependencies {
    ...
    implementation 'com.google.mediapipe:tasks-vision:latest.release'
}

Model

The MediaPipe Image Classifier task requires a trained model that is compatible with this task. For more information on available trained models for Image Classifier, see the task overview Models section.

Select and download the model, and then store it within your project directory:

<dev-project-root>/src/main/assets

Use the BaseOptions.Builder.setModelAssetPath() method to specify the path used by the model. This method is referred to in the code example in the next section.

In the Image Classifier example code, the model is defined in the ImageClassifierHelper.kt file.

Create the task

You can use the createFromOptions function to create the task. The createFromOptions function accepts configuration options including running mode, display names locale, max number of results, confidence threshold, and a category allow list or deny list. For more information on configuration options, see Configuration Overview.

The Image Classifier task supports 3 input data types: still images, video files, and live video streams. You need to specify the running mode corresponding to your input data type when creating the task. Choose the tab corresponding to your input data type to see how to create the task and run inference.

Image

ImageClassifierOptions options =
  ImageClassifierOptions.builder()
    .setBaseOptions(
      BaseOptions.builder().setModelAssetPath("model.tflite").build())
    .setRunningMode(RunningMode.IMAGE)
    .setMaxResults(5)
    .build();
imageClassifier = ImageClassifier.createFromOptions(context, options);
    

Video

ImageClassifierOptions options =
  ImageClassifierOptions.builder()
    .setBaseOptions(
      BaseOptions.builder().setModelAssetPath("model.tflite").build())
    .setRunningMode(RunningMode.VIDEO)
    .setMaxResults(5)
    .build();
imageClassifier = ImageClassifier.createFromOptions(context, options);
    

Live stream

ImageClassifierOptions options =
  ImageClassifierOptions.builder()
    .setBaseOptions(
      BaseOptions.builder().setModelAssetPath("model.tflite").build())
    .setRunningMode(RunningMode.LIVE_STREAM)
    .setMaxResults(5)
    .setResultListener((result, inputImage) -> {
         // Process the classification result here.
    })
    .setErrorListener((result, inputImage) -> {
         // Process the classification errors here.
    })
    .build()
imageClassifier = ImageClassifier.createFromOptions(context, options)
    

The Image Classifier example code implementation allows the user to switch between processing modes. The approach makes the task creation code more complicated and may not be appropriate for your use case. You can see this code in the setupImageClassifier() function of the ImageClassifierHelper.kt file.

Configuration options

This task has the following configuration options for Android apps:

Option Name Description Value Range Default Value
runningMode Sets the running mode for the task. Image Classifier has three modes:

IMAGE: The mode for performing classification on single image inputs.

VIDEO: The mode for performing classification on the decoded frames of a video.

LIVE_STREAM: The mode for performing classification on a live stream of input data, such as from a camera. In this mode, resultListener must be called to set up a listener to receive the classification results asynchronously.
{IMAGE, VIDEO, LIVE_STREAM} IMAGE
displayNamesLocale Sets the language of labels to use for display names provided in the metadata of the task's model, if available. Default is en for English. You can add localized labels to the metadata of a custom model using the TensorFlow Lite Metadata Writer API Locale code en
maxResults Sets the optional maximum number of top-scored classification results to return. If < 0, all available results will be returned. Any positive numbers -1
scoreThreshold Sets the prediction score threshold that overrides the one provided in the model metadata (if any). Results below this value are rejected. Any float Not set
categoryAllowlist Sets the optional list of allowed category names. If non-empty, classification results whose category name is not in this set will be filtered out. Duplicate or unknown category names are ignored. This option is mutually exclusive with categoryDenylist and using both results in an error. Any strings Not set
categoryDenylist Sets the optional list of category names that are not allowed. If non-empty, classification results whose category name is in this set will be filtered out. Duplicate or unknown category names are ignored. This option is mutually exclusive with categoryAllowlist and using both results in an error. Any strings Not set
resultListener Sets the result listener to receive the classification results asynchronously when the Image Classifier is in the live stream mode. Can only be used when running mode is set to LIVE_STREAM N/A Not set
errorListener Sets an optional error listener. N/A Not set

Prepare data

Image Classifier works with images, video file and live stream video. The task handles the data input preprocessing, including resizing, rotation and value normalization.

You need to convert the input image or frame to a com.google.mediapipe.framework.image.MPImage object before passing it to the Image Classifier.

Image

import com.google.mediapipe.framework.image.BitmapImageBuilder;
import com.google.mediapipe.framework.image.MPImage;

// Load an image on the user’s device as a Bitmap object using BitmapFactory.

// Convert an Android’s Bitmap object to a MediaPipe’s Image object.
Image mpImage = new BitmapImageBuilder(bitmap).build();
    

Video

import com.google.mediapipe.framework.image.BitmapImageBuilder;
import com.google.mediapipe.framework.image.MPImage;

// Load a video file on the user's device using MediaMetadataRetriever

// From the video’s metadata, load the METADATA_KEY_DURATION and
// METADATA_KEY_VIDEO_FRAME_COUNT value. You’ll need them
// to calculate the timestamp of each frame later.

// Loop through the video and load each frame as a Bitmap object.

// Convert the Android’s Bitmap object to a MediaPipe’s Image object.
Image mpImage = new BitmapImageBuilder(frame).build();
    

Live stream

import com.google.mediapipe.framework.image.MediaImageBuilder;
import com.google.mediapipe.framework.image.MPImage;

// Create a CameraX’s ImageAnalysis to continuously receive frames 
// from the device’s camera. Configure it to output frames in RGBA_8888
// format to match with what is required by the model.

// For each Android’s ImageProxy object received from the ImageAnalysis, 
// extract the encapsulated Android’s Image object and convert it to 
// a MediaPipe’s Image object.
android.media.Image mediaImage = imageProxy.getImage()
Image mpImage = new MediaImageBuilder(mediaImage).build();
    

In the Image Classifier example code, the data preparation is handled in the ImageClassifierHelper.kt file.

Run the task

You can call the classify function corresponding to your running mode to trigger inferences. The Image Classifier API returns the possible categories for the object within the input image or frame.

Image

ImageClassifierResult classifierResult = imageClassifier.classify(image);
    

Video

// Calculate the timestamp in milliseconds of the current frame.
long frame_timestamp_ms = 1000 * video_duration * frame_index / frame_count;

// Run inference on the frame.
ImageClassifierResult classifierResult =
    imageClassifier.classifyForVideo(image, frameTimestampMs);
    

Live stream


// Run inference on the frame. The classifications results will be available 
// via the `resultListener` provided in the `ImageClassifierOptions` when 
// the image classifier was created.
imageClassifier.classifyAsync(image, frameTimestampMs);
    

Note the following:

  • When running in the video mode or the live stream mode, you must also provide the timestamp of the input frame to the Image Classifier task.
  • When running in the image or the video mode, the Image Classifier task blocks the current thread until it finishes processing the input image or frame. To avoid blocking the user interface, execute the processing in a background thread.
  • When running in the live stream mode, the Image Classifier task doesn’t block the current thread but returns immediately. It will invoke its result listener with the detection result every time it has finished processing an input frame. If the classifyAsync function is called when the Image Classifier task is busy processing another frame, the task ignores the new input frame.

In the Image Classifier example code, the classify functions are defined in the ImageClassifierHelper.kt file.

Handle and display results

Upon running inference, the Image Classifier task returns an ImageClassifierResult object which contains the list of possible categories for the objects within the input image or frame.

The following shows an example of the output data from this task:

ImageClassifierResult:
 Classifications #0 (single classification head):
  head index: 0
  category #0:
   category name: "/m/01bwb9"
   display name: "Passer domesticus"
   score: 0.91406
   index: 671
  category #1:
   category name: "/m/01bwbt"
   display name: "Passer montanus"
   score: 0.00391
   index: 670

This result has been obtained by running the Bird Classifier on:

In the Image Classifier example code, the ClassificationResultsAdapter class in the ClassificationResultsAdapter.kt file handles the results:

fun updateResults(imageClassifierResult: ImageClassifierResult? = null) {
    categories = MutableList(adapterSize) { null }
    if (imageClassifierResult != null) {
        val sortedCategories = imageClassifierResult.classificationResult()
            .classifications()[0].categories().sortedBy { it.index() }
        val min = kotlin.math.min(sortedCategories.size, categories.size)
        for (i in 0 until min) {
            categories[i] = sortedCategories[i]
        }
    }
}