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perf: parallelize pose + object detection and reduce throttle frequency
Run pose detection on a dedicated thread concurrently with object
detection so per-frame latency is max(pose, object) instead of the
sum. Also bump detection throttle from 20ms (~50 FPS) to 33ms (~30 FPS)
to reduce CPU contention with minimal perceptual difference.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
author
virtualintern
co-author
Claude Opus 4.6 (1M context)
date
(Apr 1, 2026, 9:43 PM +0300)
commit
32e7cc50
32e7cc50f5d3b86798a45d86447a9120bbab844e
parent
7a88017f
7a88017fcb7213ccf3d26436a798e7daeefc3468
+37
-22
+4
-3
posedetection/src/androidMain/kotlin/com.performancecoachlab/posedetection/camera/CameraView.android.kt
+4
-3
posedetection/src/androidMain/kotlin/com.performancecoachlab/posedetection/camera/CameraView.android.kt
···
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var latestObjects by remember { mutableStateOf<List<AnalysisObject>>(emptyList()) }
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// Throttle detectors to avoid doing heavy work every frame.
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// Tune these to balance smoothness vs CPU usage.
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val objectIntervalMs = 20L
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val poseIntervalMs = 20L
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// 33ms ≈ 30 FPS target — sufficient for smooth real-time feedback
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// while leaving CPU headroom for recording and other work.
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val objectIntervalMs = 33L
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val poseIntervalMs = 33L
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val lastObjectRunAtMs = remember { AtomicLong(0L) }
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val lastPoseRunAtMs = remember { AtomicLong(0L) }
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+33
-19
posedetection/src/androidMain/kotlin/com/performancecoachlab/posedetection/camera/Utils.android.kt
+33
-19
posedetection/src/androidMain/kotlin/com/performancecoachlab/posedetection/camera/Utils.android.kt
···
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import org.tensorflow.lite.support.image.TensorImage
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import org.tensorflow.lite.support.tensorbuffer.TensorBuffer
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import java.nio.ByteBuffer
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import java.util.concurrent.Executors
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import kotlin.math.absoluteValue
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import kotlin.math.max
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import kotlin.math.min
···
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)
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}
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// Shared executor for running pose detection in parallel with object detection.
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private val poseExecutor = Executors.newSingleThreadExecutor()
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// Core processing used by both live camera (ImageProxy) and offline frames (FrameAnalyser).
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fun process(
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tensorImage: TensorImage?,
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mlKitScaleX: Float = 1f,
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mlKitScaleY: Float = 1f,
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) {
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// Launch pose detection on a separate thread so it runs in parallel with object detection.
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val poseFuture = if (poseDetector != null && mlKitImage != null) {
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poseExecutor.submit<Skeleton?> {
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runCatching {
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val pose = Tasks.await(poseDetector.process(mlKitImage))
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skeletonFromPoseScaled(
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pose = pose,
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timestamp = timestamp,
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width = width,
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height = height,
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scaleX = mlKitScaleX,
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scaleY = mlKitScaleY,
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)
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}.onFailure { t ->
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Logger.e(t) { "MLKit poseDetector.process failed" }
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}.getOrNull()
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}
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} else null
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+
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// Run object detection on the current thread (concurrent with pose above).
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val objectsDetected = if (objectDetector != null && tensorImage != null) {
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val outputShape = objectDetector.modelInfo.outputShape
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val output = TensorBuffer.createFixedSize(outputShape, DataType.FLOAT32)
···
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isElementsFirst = false
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}
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else -> return onComplete(
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AnalysisResult(skeleton = null, objects = emptyList()),
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bitmap
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)
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else -> {
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val skeleton = poseFuture?.get()
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return onComplete(
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AnalysisResult(skeleton = skeleton, objects = emptyList()),
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bitmap
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)
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}
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}
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fun valueAt(elementIndex: Int, channelIndex: Int): Float {
···
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}
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} else emptyList()
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val skeleton: Skeleton? = if (poseDetector != null && mlKitImage != null) {
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runCatching {
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val pose = Tasks.await(poseDetector.process(mlKitImage))
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skeletonFromPoseScaled(
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pose = pose,
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timestamp = timestamp,
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width = width,
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height = height,
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scaleX = mlKitScaleX,
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scaleY = mlKitScaleY,
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)
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}.onFailure { t ->
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Logger.e(t) { "MLKit poseDetector.process failed" }
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}.getOrNull()
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} else null
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// Wait for pose result (already running or already done).
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val skeleton = poseFuture?.get()
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onComplete(
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AnalysisResult(