Awesome
Image Segmentation Android sample.
This project is an effort to update the original segmentation android project that is demonstrated here. In this project CameraX is used instead of Camera2 class. You can find both implementations of CameraX inside the project, ImageCapture where Bitmap is used for inference (master branch) and ImageAnalysis where media.Image (from CameraX's ImageProxy) is used for inference (ImageAnalysis branch).
The used model, DeepLab [https://ai.googleblog.com/2018/03/semantic-image-segmentation-with.html] is a state-of-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g. person, dog, cat) to every pixel in the input image.
Switch between inference solutions (Task library vs TFLite Interpreter)
This image segmentation Android reference app demonstrates two implementation solutions:
(1)
lib_task_api
that leverages the out-of-box API from the
TensorFlow Lite Task Library;
(2)
lib_interpreter
that creates the custom inference pipleline using the
TensorFlow Lite Interpreter Java API.
The build.gradle inside app folder shows how to change
flavorDimensions "tfliteInference" to switch between the two solutions.
Inside Android Studio, you can change the build variant to whichever one you
want to build and run — just go to Build > Select Build Variant and select one
from the drop-down menu. See
configure product flavors in Android Studio
for more details.
To test the app, open the app called TFL Image Segmentation on your device.
Re-installing the app may require you to uninstall the previous installations.
For gradle CLI, running ./gradlew build can create APKs for both solutions
under app/build/outputs/apk.
Note: If you simply want the out-of-box API to run the app, we recommend
lib_task_api for inference. If you want to customize your own models and
control the detail of inputs and outputs, it might be easier to adapt your model
inputs and outputs by using lib_interpreter.
Resources used:
- TensorFlow Lite: https://www.tensorflow.org/lite
- ImageSegmentation model: https://www.tensorflow.org/lite/models/segmentation/overview
- TFHub segmentation models: https://tfhub.dev/s?module-type=image-segmentation