Step counter test1

app/src/main/java/com/example/jnicpp/bowling/PoseLandmarkSmoother.kt

@@ -1,37 +1,57 @@
+/**
+ * @file PoseLandmarkSmoother.kt
+ * @brief Per-frame exponential-moving-average smoothing of ML Kit pose landmarks.
+ */
 package com.example.jnicpp.bowling
 
 import com.google.mlkit.vision.pose.Pose
 
 /**
- * A single pose landmark's position and detection confidence, smoothed
- * across frames by [PoseLandmarkSmoother]. Deliberately independent of ML
- * Kit's own `PoseLandmark`/`PointF3D` so downstream consumers (angle math,
- * skeleton drawing) don't need any ML Kit types.
+ * @brief A single pose landmark's position and detection confidence,
+ *        smoothed across frames by [PoseLandmarkSmoother].
+ *
+ * Deliberately independent of ML Kit's own `PoseLandmark`/`PointF3D` so
+ * downstream consumers (angle math, skeleton drawing) don't need any ML Kit
+ * types.
+ *
+ * @param x Smoothed x coordinate, in analysis-image pixel space.
+ * @param y Smoothed y coordinate, in analysis-image pixel space.
+ * @param inFrameLikelihood Smoothed detection confidence in [0, 1].
  */
 data class SmoothedLandmark(val x: Float, val y: Float, val inFrameLikelihood: Float)
 
 /**
- * Low-pass-filters ML Kit's per-frame [Pose] landmarks with an exponential
- * moving average, so the drawn skeleton doesn't visibly jitter/flicker from
- * frame-to-frame detector noise. This smooths `inFrameLikelihood` too, not
- * just position -- without that, a landmark hovering right around
- * [PoseSkeletonRenderer.MIN_LIKELIHOOD] makes whole bones repeatedly pop in
- * and out, which reads as flicker just as much as position jitter does.
+ * @brief Low-pass-filters ML Kit's per-frame [Pose] landmarks with an
+ *        exponential moving average, so the drawn skeleton doesn't visibly
+ *        jitter/flicker from frame-to-frame detector noise.
+ *
+ * This smooths `inFrameLikelihood` too, not just position -- without that,
+ * a landmark hovering right around [PoseSkeletonRenderer.MIN_LIKELIHOOD]
+ * makes whole bones repeatedly pop in and out, which reads as flicker just
+ * as much as position jitter does.
  *
  * State is per-landmark-type and carries across calls to [smooth], so this
  * is meant as one instance per detection stream (i.e. per [PoseAnalyzer]) --
  * create a new one whenever the stream restarts rather than reusing one
  * across unrelated streams, or the first frame of the new stream will lerp
  * in from the old stream's last pose.
+ *
+ * @param smoothingFactor Weight given to each new sample; lower = smoother
+ *        but more lag behind the true position. 0.4 noticeably cuts jitter
+ *        while still keeping up with a fast bowling arm swing.
  */
 class PoseLandmarkSmoother(
-    // Weight given to each new sample; lower = smoother but more lag behind
-    // the true position. 0.4 noticeably cuts jitter while still keeping up
-    // with a fast bowling arm swing.
     private val smoothingFactor: Float = 0.4f
 ) {
     private val previous = mutableMapOf<Int, SmoothedLandmark>()
 
+    /**
+     * @brief Applies one frame of exponential smoothing to every landmark
+     *        in [pose] and returns the updated smoothed state.
+     * @param pose The raw ML Kit detection result for the current frame.
+     * @return The smoothed landmarks seen so far, keyed by ML Kit's
+     *         `PoseLandmark` type constant (e.g. `PoseLandmark.LEFT_ELBOW`).
+     */
     fun smooth(pose: Pose): Map<Int, SmoothedLandmark> {
         for (landmark in pose.allPoseLandmarks) {
             val prev = previous[landmark.landmarkType]
@@ -49,4 +69,4 @@ class PoseLandmarkSmoother(
         }
         return previous.toMap()
     }
-}
+}