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Portrait Mode#12
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Portrait Mode #12
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89 changes: 89 additions & 0 deletions
89 app/src/main/java/co/stonephone/stonecamera/plugins/PortraitModePlugin.kt
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,89 @@ | ||
| package co.stonephone.stonecamera.plugins | ||
| import android.annotation.SuppressLint | ||
| import android.graphics.Bitmap | ||
| import android.media.Image | ||
| import android.os.Build | ||
| import android.util.Log | ||
| import androidx.annotation.RequiresApi | ||
| import androidx.camera.core.ImageAnalysis | ||
| import androidx.camera.core.ImageProxy | ||
| import co.stonephone.stonecamera.MyApplication | ||
| import co.stonephone.stonecamera.StoneCameraViewModel | ||
| import co.stonephone.stonecamera.utils.ImageSegmentationUtils | ||
| import co.stonephone.stonecamera.utils.ImageSegmentationUtils.SegmentationListener | ||
| import kotlinx.coroutines.CompletableDeferred | ||
| import org.tensorflow.lite.task.vision.segmenter.Segmentation | ||
| class PortraitModePlugin: IPlugin, SegmentationListener { | ||
| override val id: String = "portraitModePlugin" | ||
| override val name: String = "Portrait Mode" | ||
| private lateinit var imageSegmentationUtils: ImageSegmentationUtils | ||
| private lateinit var bitmapBuffer: Bitmap | ||
| private var imageAnalyzer: ImageAnalysis? = null | ||
| override fun initialize(viewModel: StoneCameraViewModel) { | ||
| println("Test"); | ||
| imageSegmentationUtils = ImageSegmentationUtils( | ||
| context = MyApplication.getAppContext(), | ||
| imageSegmentationListener = this | ||
| ) | ||
| } | ||
| @RequiresApi(Build.VERSION_CODES.Q) | ||
| @SuppressLint("RestrictedApi") | ||
| override val onImageAnalysis: ((StoneCameraViewModel, ImageProxy, Image) -> CompletableDeferred<Unit>)? = | ||
| { _: StoneCameraViewModel, | ||
| imageProxy: ImageProxy, | ||
| image: Image | ||
| -> | ||
| println("Test") | ||
| Log.e("Test", "in here") | ||
| val deferred = CompletableDeferred<Unit>() | ||
| if (!::bitmapBuffer.isInitialized) { | ||
| // The image rotation and RGB image buffer are initialized only once | ||
| // the analyzer has started running | ||
| bitmapBuffer = Bitmap.createBitmap( | ||
| image.width, | ||
| image.height, | ||
| Bitmap.Config.ARGB_8888 | ||
| ) | ||
| } | ||
| segmentImage(imageProxy) | ||
| deferred | ||
| } | ||
| //TODO Image vs imageproxy | ||
| @RequiresApi(Build.VERSION_CODES.Q) | ||
| private fun segmentImage(image: ImageProxy) { | ||
| // Copy out RGB bits to the shared bitmap buffer | ||
| image.use { bitmapBuffer.copyPixelsFromBuffer(image.planes[0].buffer) } | ||
| val imageRotation = image.imageInfo.rotationDegrees | ||
| // Pass Bitmap and rotation to the image segmentation helper for processing and segmentation | ||
| imageSegmentationUtils.segment(bitmapBuffer, imageRotation) | ||
| } | ||
| override val settings: List<PluginSetting> = emptyList() | ||
| override fun onError(error: String) { | ||
| Log.e("Segmentation", "Failed segmentation"); | ||
| } | ||
| override fun onResults( | ||
| results: List<Segmentation>?, | ||
| inferenceTime: Long, | ||
| imageHeight: Int, | ||
| imageWidth: Int | ||
| ) { | ||
| Log.e("Segmentation", results.toString()); | ||
| TODO("Not yet implemented") | ||
| //Segmentation has been complete. Can update UI to place segments on the screen. | ||
| } | ||
| } | ||
136 changes: 136 additions & 0 deletions
136 app/src/main/java/co/stonephone/stonecamera/utils/ImageSegmentationUtils.kt
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,136 @@ | ||
| package co.stonephone.stonecamera.utils | ||
| import android.content.Context | ||
| import android.graphics.Bitmap | ||
| import android.os.Build | ||
| import android.os.SystemClock | ||
| import android.util.Log | ||
| import androidx.annotation.RequiresApi | ||
| import org.tensorflow.lite.gpu.CompatibilityList | ||
| import org.tensorflow.lite.support.image.ImageProcessor | ||
| import org.tensorflow.lite.support.image.TensorImage | ||
| import org.tensorflow.lite.support.image.ops.Rot90Op | ||
| import org.tensorflow.lite.task.core.BaseOptions | ||
| import org.tensorflow.lite.task.vision.segmenter.ImageSegmenter | ||
| import org.tensorflow.lite.task.vision.segmenter.OutputType | ||
| import org.tensorflow.lite.task.vision.segmenter.Segmentation | ||
| class ImageSegmentationUtils( | ||
| var numThreads: Int = 2, | ||
| var currentDelegate: Int = 0, | ||
| val context: Context, | ||
| val imageSegmentationListener: SegmentationListener? | ||
| ) { | ||
| private var imageSegmenter: ImageSegmenter? = null | ||
| init { | ||
| setupImageSegmenter() | ||
| } | ||
| fun clearImageSegmenter() { | ||
| imageSegmenter = null | ||
| } | ||
| private fun setupImageSegmenter() { | ||
| // Create the base options for the segment | ||
| val optionsBuilder = | ||
| ImageSegmenter.ImageSegmenterOptions.builder() | ||
| // Set general segmentation options, including number of used threads | ||
| val baseOptionsBuilder = BaseOptions.builder().setNumThreads(numThreads) | ||
| // Use the specified hardware for running the model. Default to CPU | ||
| when (currentDelegate) { | ||
| DELEGATE_CPU -> { | ||
| // Default | ||
| } | ||
| DELEGATE_GPU -> { | ||
| if (CompatibilityList().isDelegateSupportedOnThisDevice) { | ||
| baseOptionsBuilder.useGpu() | ||
| } else { | ||
| imageSegmentationListener?.onError("GPU is not supported on this device") | ||
| } | ||
| } | ||
| DELEGATE_NNAPI -> { | ||
| baseOptionsBuilder.useNnapi() | ||
| } | ||
| } | ||
| optionsBuilder.setBaseOptions(baseOptionsBuilder.build()) | ||
| /* | ||
| CATEGORY_MASK is being specifically used to predict the available objects | ||
| based on individual pixels in this sample. The other option available for | ||
| OutputType, CONFIDENCE_MAP, provides a gray scale mapping of the image | ||
| where each pixel has a confidence score applied to it from 0.0f to 1.0f | ||
| */ | ||
| optionsBuilder.setOutputType(OutputType.CATEGORY_MASK) | ||
| try { | ||
| imageSegmenter = | ||
| ImageSegmenter.createFromFileAndOptions( | ||
| context, | ||
| MODEL_DEEPLABV3, | ||
| optionsBuilder.build() | ||
| ) | ||
| } catch (e: IllegalStateException) { | ||
| imageSegmentationListener?.onError( | ||
| "Image segmentation failed to initialize. See error logs for details" | ||
| ) | ||
| Log.e(TAG, "TFLite failed to load model with error: " + e.message) | ||
| } | ||
| } | ||
| @RequiresApi(Build.VERSION_CODES.Q) | ||
| fun segment(image: Bitmap, imageRotation: Int) { | ||
| if (imageSegmenter == null) { | ||
| setupImageSegmenter() | ||
| } | ||
| // Inference time is the difference between the system time at the start and finish of the | ||
| // process | ||
| var inferenceTime = SystemClock.uptimeMillis() | ||
| // Create preprocessor for the image. | ||
| // See https://www.tensorflow.org/lite/inference_with_metadata/ | ||
| // lite_support#imageprocessor_architecture | ||
| val imageProcessor = | ||
| ImageProcessor.Builder() | ||
| .add(Rot90Op(-imageRotation / 90)) | ||
| .build() | ||
| // Preprocess the image and convert it into a TensorImage for segmentation. | ||
| val tensorImage = imageProcessor.process(TensorImage.fromBitmap(image)) | ||
| val segmentResult = imageSegmenter?.segment(tensorImage) | ||
| inferenceTime = SystemClock.uptimeMillis() - inferenceTime | ||
| imageSegmentationListener?.onResults( | ||
| segmentResult, | ||
| inferenceTime, | ||
| tensorImage.height, | ||
| tensorImage.width | ||
| ) | ||
| } | ||
| interface SegmentationListener { | ||
| fun onError(error: String) | ||
| fun onResults( | ||
| results: List<Segmentation>?, | ||
| inferenceTime: Long, | ||
| imageHeight: Int, | ||
| imageWidth: Int | ||
| ) | ||
| } | ||
| companion object { | ||
| const val DELEGATE_CPU = 0 | ||
| const val DELEGATE_GPU = 1 | ||
| const val DELEGATE_NNAPI = 2 | ||
| const val MODEL_DEEPLABV3 = "deeplabv3.tflite" | ||
| private const val TAG = "Image Segmentation Helper" | ||
| } | ||
| } |
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This reminds me, we need a plugin-level way to report errors. Added to this doc: https://docs.google.com/document/d/1Au_AgqrLhc9gncNjVfwruoPNTf7PF1eU3tfQZcwS17o/edit?tab=t.xp8jea3nq6j