mediapipe/docs/getting_started/android_archive_library.md

layout	title	parent	grand_parent	nav_order
default	MediaPipe Android Archive	MediaPipe on Android	Getting Started	2

MediaPipe Android Archive

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Experimental Only

The MediaPipe Android Archive (AAR) library is a convenient way to use MediaPipe with Android Studio and Gradle. MediaPipe doesn't publish a general AAR that can be used by all projects. Instead, developers need to add a mediapipe_aar() target to generate a custom AAR file for their own projects. This is necessary in order to include specific resources such as MediaPipe calculators needed for each project.

Steps to build a MediaPipe AAR

1. Create a mediapipe_aar() target.

   In the MediaPipe directory, create a new mediapipe_aar() target in a BUILD file. You need to figure out what calculators are used in the graph and provide the calculator dependencies to the mediapipe_aar(). For example, to build an AAR for MediaPipe Face Detection, you can put the following code into mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/BUILD.

   load("//mediapipe/java/com/google/mediapipe:mediapipe_aar.bzl", "mediapipe_aar")
   
   mediapipe_aar(
       name = "mp_face_detection_aar",
       calculators = ["//mediapipe/graphs/face_detection:mobile_calculators"],
   )
   

2. Run the Bazel build command to generate the AAR.

   bazel build -c opt --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
       --fat_apk_cpu=arm64-v8a,armeabi-v7a --strip=ALWAYS \
       //path/to/the/aar/build/file:aar_name
   

   For the face detection AAR target we made in the step 1, run:

   bazel build -c opt --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --fat_apk_cpu=arm64-v8a,armeabi-v7a \
       //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mp_face_detection_aar
   
   # It should print:
   # Target //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mp_face_detection_aar up-to-date:
   # bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mp_face_detection_aar.aar
   

3. (Optional) Save the AAR to your preferred location.

   cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mp_face_detection_aar.aar
   /absolute/path/to/your/preferred/location
   

Steps to use a MediaPipe AAR in Android Studio with Gradle

1. Start Android Studio and go to your project.

2. Copy the AAR into app/libs.

   cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mp_face_detection_aar.aar
   /path/to/your/app/libs/
   

   

3. Make app/src/main/assets and copy assets (graph, model, and etc) into app/src/main/assets.

   Build the MediaPipe binary graph and copy the assets into app/src/main/assets, e.g., for the face detection graph, you need to build and copy the binary graph and the face detection tflite model.

   bazel build -c opt mediapipe/mediapipe/graphs/face_detection:mobile_gpu_binary_graph
   cp bazel-bin/mediapipe/graphs/face_detection/mobile_gpu.binarypb /path/to/your/app/src/main/assets/
   cp mediapipe/modules/face_detection/face_detection_front.tflite /path/to/your/app/src/main/assets/
   

   

4. Make app/src/main/jniLibs and copy OpenCV JNI libraries into app/src/main/jniLibs.

   MediaPipe depends on OpenCV, you will need to copy the precompiled OpenCV so files into app/src/main/jniLibs. You can download the official OpenCV Android SDK from here and run:

   cp -R ~/Downloads/OpenCV-android-sdk/sdk/native/libs/arm* /path/to/your/app/src/main/jniLibs/
   

   

5. Modify app/build.gradle to add MediaPipe dependencies and MediaPipe AAR.

   dependencies {
       implementation fileTree(dir: 'libs', include: ['*.jar', '*.aar'])
       implementation 'androidx.appcompat:appcompat:1.0.2'
       implementation 'androidx.constraintlayout:constraintlayout:1.1.3'
       testImplementation 'junit:junit:4.12'
       androidTestImplementation 'androidx.test.ext:junit:1.1.0'
       androidTestImplementation 'androidx.test.espresso:espresso-core:3.1.1'
       // MediaPipe deps
       implementation 'com.google.flogger:flogger:0.3.1'
       implementation 'com.google.flogger:flogger-system-backend:0.3.1'
       implementation 'com.google.code.findbugs:jsr305:3.0.2'
       implementation 'com.google.guava:guava:27.0.1-android'
       implementation 'com.google.guava:guava:27.0.1-android'
       implementation 'com.google.protobuf:protobuf-java:3.11.4'
       // CameraX core library
       def camerax_version = "1.0.0-beta10"
       implementation "androidx.camera:camera-core:$camerax_version"
       implementation "androidx.camera:camera-camera2:$camerax_version"
       implementation "androidx.camera:camera-lifecycle:$camerax_version"
   }
   

6. Follow our Android app examples to use MediaPipe in Android Studio for your use case. If you are looking for an example, a face detection example can be found here and a multi-hand tracking example can be found here.