166 lines
6.7 KiB
Markdown
166 lines
6.7 KiB
Markdown
---
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layout: forward
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target: https://developers.google.com/mediapipe/framework/getting_started/android_archive_library
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title: MediaPipe Android Archive
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parent: MediaPipe on Android
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grand_parent: Getting Started
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nav_order: 3
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---
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# MediaPipe Android Archive
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{: .no_toc }
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1. TOC
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{:toc}
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---
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**Attention:** *Thanks for your interest in MediaPipe! We have moved to
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[https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
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as the primary developer documentation site for MediaPipe as of April 3, 2023.*
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----
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***Experimental Only***
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The MediaPipe Android Archive (AAR) library is a convenient way to use MediaPipe
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with Android Studio and Gradle. MediaPipe doesn't publish a general AAR that can
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be used by all projects. Instead, developers need to add a mediapipe_aar()
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target to generate a custom AAR file for their own projects. This is necessary
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in order to include specific resources such as MediaPipe calculators needed for
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each project.
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## Steps to build a MediaPipe AAR
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1. Create a mediapipe_aar() target.
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In the MediaPipe directory, create a new mediapipe_aar() target in a BUILD
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file. You need to figure out what calculators are used in the graph and
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provide the calculator dependencies to the mediapipe_aar(). For example, to
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build an AAR for [MediaPipe Face Detection](../solutions/face_detection.md),
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you can put the following code into
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mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/BUILD.
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```
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load("//mediapipe/java/com/google/mediapipe:mediapipe_aar.bzl", "mediapipe_aar")
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mediapipe_aar(
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name = "mediapipe_face_detection",
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calculators = ["//mediapipe/graphs/face_detection:mobile_calculators"],
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)
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```
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2. Run the Bazel build command to generate the AAR.
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```bash
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bazel build -c opt --strip=ALWAYS \
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--host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
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--fat_apk_cpu=arm64-v8a,armeabi-v7a \
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--legacy_whole_archive=0 \
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--features=-legacy_whole_archive \
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--copt=-fvisibility=hidden \
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--copt=-ffunction-sections \
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--copt=-fdata-sections \
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--copt=-fstack-protector \
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--copt=-Oz \
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--copt=-fomit-frame-pointer \
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--copt=-DABSL_MIN_LOG_LEVEL=2 \
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--linkopt=-Wl,--gc-sections,--strip-all \
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//path/to/the/aar/build/file:aar_name.aar
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```
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For the face detection AAR target we made in step 1, run:
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```bash
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bazel build -c opt --strip=ALWAYS \
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--host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
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--fat_apk_cpu=arm64-v8a,armeabi-v7a \
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--legacy_whole_archive=0 \
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--features=-legacy_whole_archive \
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--copt=-fvisibility=hidden \
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--copt=-ffunction-sections \
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--copt=-fdata-sections \
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--copt=-fstack-protector \
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--copt=-Oz \
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--copt=-fomit-frame-pointer \
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--copt=-DABSL_MIN_LOG_LEVEL=2 \
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--linkopt=-Wl,--gc-sections,--strip-all \
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//mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mediapipe_face_detection.aar
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# It should print:
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# Target //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mediapipe_face_detection.aar up-to-date:
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# bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar
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```
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3. (Optional) Save the AAR to your preferred location.
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```bash
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cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar
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/absolute/path/to/your/preferred/location
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```
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## Steps to use a MediaPipe AAR in Android Studio with Gradle
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1. Start Android Studio and go to your project.
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2. Copy the AAR into app/libs.
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```bash
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cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar
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/path/to/your/app/libs/
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```
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3. Make app/src/main/assets and copy assets (graph, model, and etc) into
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app/src/main/assets.
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Build the MediaPipe binary graph and copy the assets into
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app/src/main/assets, e.g., for the face detection graph, you need to build
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and copy
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[the binary graph](https://github.com/google/mediapipe/blob/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu/BUILD#L41)
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and
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[the face detection tflite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_short_range.tflite).
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```bash
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bazel build -c opt mediapipe/graphs/face_detection:face_detection_mobile_gpu_binary_graph
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cp bazel-bin/mediapipe/graphs/face_detection/face_detection_mobile_gpu.binarypb /path/to/your/app/src/main/assets/
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cp mediapipe/modules/face_detection/face_detection_short_range.tflite /path/to/your/app/src/main/assets/
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```
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4. Modify app/build.gradle to add MediaPipe dependencies and MediaPipe AAR.
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```
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dependencies {
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implementation fileTree(dir: 'libs', include: ['*.jar', '*.aar'])
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implementation 'androidx.appcompat:appcompat:1.0.2'
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implementation 'androidx.constraintlayout:constraintlayout:1.1.3'
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testImplementation 'junit:junit:4.12'
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androidTestImplementation 'androidx.test.ext:junit:1.1.0'
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androidTestImplementation 'androidx.test.espresso:espresso-core:3.1.1'
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// MediaPipe deps
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implementation 'com.google.flogger:flogger:latest.release'
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implementation 'com.google.flogger:flogger-system-backend:latest.release'
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implementation 'com.google.code.findbugs:jsr305:latest.release'
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implementation 'com.google.guava:guava:27.0.1-android'
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implementation 'com.google.protobuf:protobuf-javalite:3.19.1'
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// CameraX core library
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def camerax_version = "1.0.0-beta10"
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implementation "androidx.camera:camera-core:$camerax_version"
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implementation "androidx.camera:camera-camera2:$camerax_version"
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implementation "androidx.camera:camera-lifecycle:$camerax_version"
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// AutoValue
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def auto_value_version = "1.8.1"
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implementation "com.google.auto.value:auto-value-annotations:$auto_value_version"
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annotationProcessor "com.google.auto.value:auto-value:$auto_value_version"
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}
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```
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5. Follow our Android app examples to use MediaPipe in Android Studio for your
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use case. If you are looking for an example, a face detection example can be
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found
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[here](https://github.com/jiuqiant/mediapipe_face_detection_aar_example) and
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a multi-hand tracking example can be found
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[here](https://github.com/jiuqiant/mediapipe_multi_hands_tracking_aar_example).
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