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1075 lines
41 KiB
1075 lines
41 KiB
package com.quillstudios.pokegoalshelper.ml
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import android.content.Context
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import android.graphics.Bitmap
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import android.graphics.BitmapFactory
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import android.util.Log
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import com.quillstudios.pokegoalshelper.utils.PGHLog
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import org.opencv.android.Utils
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import org.opencv.core.*
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import org.opencv.imgproc.Imgproc
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import ai.onnxruntime.*
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import java.io.FileOutputStream
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import java.io.IOException
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import java.util.concurrent.Executors
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import java.util.concurrent.Future
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import java.util.concurrent.TimeUnit
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import kotlinx.coroutines.Dispatchers
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import kotlinx.coroutines.withContext
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import kotlin.math.max
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import kotlin.math.min
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/**
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* YOLO ONNX-based implementation of MLInferenceEngine.
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* Preserves ALL functionality from the original YOLOOnnxDetector including:
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* - Complete 96-class mapping
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* - Multiple preprocessing techniques
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* - Coordinate transformation modes
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* - Weighted NMS and TTA
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* - Debug and testing features
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*/
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class YOLOInferenceEngine(
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private val context: Context,
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private val config: YOLOConfig = YOLOConfig()
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) : MLInferenceEngine
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{
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companion object
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{
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private const val TAG = "YOLOInferenceEngine"
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private const val MODEL_FILE = "best.onnx"
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private const val CONFIDENCE_THRESHOLD = 0.55f
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private const val NMS_THRESHOLD = 0.3f
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private const val NUM_CHANNELS = 3
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// Enhanced accuracy settings for ONNX (fixed input size)
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private const val ENABLE_TTA = true // Test-time augmentation
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private const val MAX_INFERENCE_TIME_MS = 4500L // Leave 500ms for other processing
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// Coordinate transformation modes - HYBRID is the correct method
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var COORD_TRANSFORM_MODE = "HYBRID" // HYBRID and LETTERBOX work correctly
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// Class filtering for debugging
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var DEBUG_CLASS_FILTER: String? = null // Set to class name to show only that class
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var SHOW_ALL_CONFIDENCES = false // Show all detections with their confidences
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// Preprocessing enhancement techniques (single pass only)
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private const val ENABLE_NOISE_REDUCTION = true
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// Confidence threshold optimization for mobile ONNX vs raw processing
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private const val ENABLE_CONFIDENCE_MAPPING = true
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private const val RAW_TO_MOBILE_SCALE = 0.75f // Based on observation that mobile shows lower conf
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// Image Processing Constants
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private const val GAUSSIAN_BLUR_KERNEL_SIZE = 3.0
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private const val GAUSSIAN_BLUR_SIGMA = 0.5
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private const val CLAHE_CLIP_LIMIT = 1.5
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private const val CLAHE_TILE_SIZE = 8.0
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private const val SHARPENING_CENTER_VALUE = 5.0
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private const val SHARPENING_EDGE_VALUE = -1.0
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private const val LETTERBOX_PADDING_GRAY = 114.0
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private const val PIXEL_NORMALIZATION_FACTOR = 255.0f
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private const val COLOR_CHANNEL_MASK = 0xFF
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// NMS Output Parsing Constants
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private const val NMS_OUTPUT_FEATURES_PER_DETECTION = 6 // [x1, y1, x2, y2, confidence, class_id]
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private const val NMS_COORDINATE_X1_OFFSET = 0
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private const val NMS_COORDINATE_Y1_OFFSET = 1
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private const val NMS_COORDINATE_X2_OFFSET = 2
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private const val NMS_COORDINATE_Y2_OFFSET = 3
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private const val NMS_CONFIDENCE_OFFSET = 4
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private const val NMS_CLASS_ID_OFFSET = 5
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private const val MIN_DEBUG_CONFIDENCE = 0.1f
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private const val MAX_DEBUG_DETECTIONS_TO_LOG = 3
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fun setCoordinateMode(mode: String)
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{
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COORD_TRANSFORM_MODE = mode
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PGHLog.i(TAG, "🔧 Coordinate transform mode changed to: $mode")
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}
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fun toggleShowAllConfidences()
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{
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SHOW_ALL_CONFIDENCES = !SHOW_ALL_CONFIDENCES
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PGHLog.i(TAG, "📊 Show all confidences: $SHOW_ALL_CONFIDENCES")
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}
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fun setClassFilter(className: String?)
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{
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DEBUG_CLASS_FILTER = className
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if (className != null)
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{
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PGHLog.i(TAG, "🔍 Class filter set to: '$className' (ID will be shown in debug output)")
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}
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else
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{
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PGHLog.i(TAG, "🔍 Class filter set to: ALL CLASSES")
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}
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}
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}
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private var ortSession: OrtSession? = null
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private var ortEnvironment: OrtEnvironment? = null
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private var isInitialized = false
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// Dynamic model metadata (extracted at runtime from ONNX model)
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private var modelInputSize: Int = 640 // Default fallback
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private var modelNumDetections: Int = 300 // Default fallback
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private var modelNumClasses: Int = 96 // Default fallback (based on dataset.yaml)
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private var modelOutputFeatures: Int = NMS_OUTPUT_FEATURES_PER_DETECTION // Default fallback
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// Shared thread pool for preprocessing operations (prevents creating new pools per detection)
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private val preprocessingExecutor = Executors.newFixedThreadPool(config.threadPoolSize)
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private var confidenceThreshold = config.confidenceThreshold
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private var classFilter: String? = config.classFilter
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// Performance tracking
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private var lastInferenceTime = 0L
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private var totalInferenceTime = 0L
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private var inferenceCount = 0L
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// Classification manager for class name mappings from dataset.yaml
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private lateinit var classificationManager: ClassificationManager
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override suspend fun initialize(): MLResult<Unit> = withContext(Dispatchers.IO)
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{
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if (isInitialized) return@withContext MLResult.Success(Unit)
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mlTry(MLErrorType.INITIALIZATION_FAILED) {
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PGHLog.i(TAG, "🤖 Initializing ONNX YOLO detector...")
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// Initialize ONNX Runtime environment
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ortEnvironment = OrtEnvironment.getEnvironment()
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// Copy model from assets to internal storage
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PGHLog.i(TAG, "📂 Copying model file: ${config.modelFile}")
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val model_path = copyAssetToInternalStorage(config.modelFile)
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?: throw RuntimeException("Failed to copy ONNX model from assets")
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PGHLog.i(TAG, "✅ Model copied to: $model_path")
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// Create ONNX session
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PGHLog.i(TAG, "📥 Loading ONNX model from: $model_path")
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val session_options = OrtSession.SessionOptions()
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session_options.setOptimizationLevel(OrtSession.SessionOptions.OptLevel.ALL_OPT)
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ortSession = ortEnvironment?.createSession(model_path, session_options)
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?: throw RuntimeException("Failed to create ONNX session")
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// Extract model metadata dynamically
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extractModelMetadata()
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// Initialize ClassificationManager
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classificationManager = ClassificationManager.getInstance(context)
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val classificationResult = classificationManager.initialize()
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if (classificationResult is MLResult.Error) {
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throw RuntimeException("Failed to initialize ClassificationManager: ${classificationResult.message}")
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}
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PGHLog.i(TAG, "✅ ONNX YOLO detector initialized successfully")
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isInitialized = true
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}.onError { errorType, exception, message ->
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PGHLog.e(TAG, "❌ $message", exception)
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}
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}
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override suspend fun detect(image: Bitmap): MLResult<List<Detection>> = withContext(Dispatchers.IO)
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{
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if (image.isRecycled)
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{
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return@withContext mlError(MLErrorType.INVALID_INPUT, "Bitmap is recycled")
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}
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mlTry(MLErrorType.INFERENCE_FAILED) {
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// Convert Bitmap to Mat first to maintain compatibility with original logic
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val input_mat = Mat()
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Utils.bitmapToMat(image, input_mat)
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try
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{
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detectWithMat(input_mat)
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}
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finally
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{
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input_mat.release()
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}
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}.onError { errorType, exception, message ->
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PGHLog.e(TAG, "❌ Detection failed: $message", exception)
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}
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}
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/**
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* Main detection method that preserves original Mat-based logic
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*/
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private suspend fun detectWithMat(inputMat: Mat): List<Detection>
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{
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if (!isInitialized || ortSession == null)
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{
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PGHLog.w(TAG, "⚠️ ONNX detector not initialized")
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return emptyList()
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}
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val start_time = System.currentTimeMillis()
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return try
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{
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PGHLog.d(TAG, "🎯 Starting ONNX YOLO detection...")
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// Single preprocessing method - ultralytics standard
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val detections = detectWithPreprocessing(inputMat)
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// Update performance stats
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lastInferenceTime = System.currentTimeMillis() - start_time
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totalInferenceTime += lastInferenceTime
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inferenceCount++
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PGHLog.d(TAG, "🎯 Detection completed: ${detections.size} objects found in ${lastInferenceTime}ms")
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detections
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}
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catch (e: Exception)
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{
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PGHLog.e(TAG, "❌ Error during enhanced ONNX YOLO detection", e)
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emptyList()
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}
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}
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private fun detectWithPreprocessing(inputMat: Mat): List<Detection>
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{
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try
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{
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// Apply ultralytics preprocessing (only method used)
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val preprocessed_mat = preprocessUltralyticsStyle(inputMat)
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return try
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{
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runInference(preprocessed_mat, inputMat.size())
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}
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finally
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{
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preprocessed_mat.release()
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}
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}
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catch (e: Exception)
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{
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PGHLog.e(TAG, "❌ Error in preprocessing", e)
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return emptyList()
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}
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}
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private fun runInference(preprocessedMat: Mat, originalSize: Size): List<Detection>
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{
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// Convert Mat to tensor format
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val input_data = matToTensorArray(preprocessedMat)
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// Run ONNX inference
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val input_name = ortSession!!.inputNames.iterator().next()
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val input_tensor = OnnxTensor.createTensor(ortEnvironment, input_data)
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val inputs = mapOf(input_name to input_tensor)
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return try
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{
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val results = ortSession!!.run(inputs)
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val output_tensor = results[0].value as Array<Array<FloatArray>>
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// Post-process results with coordinate transformation
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postprocessResults(output_tensor, originalSize)
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}
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finally
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{
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input_tensor.close()
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}
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}
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override fun setConfidenceThreshold(threshold: Float)
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{
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confidenceThreshold = threshold.coerceIn(0.0f, 1.0f)
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PGHLog.d(TAG, "🎚️ Confidence threshold set to: $confidenceThreshold")
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}
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override fun setClassFilter(className: String?)
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{
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classFilter = className
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DEBUG_CLASS_FILTER = className
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PGHLog.d(TAG, "🔍 Class filter set to: ${className ?: "none"}")
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}
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override fun isReady(): Boolean = isInitialized
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override fun getInferenceStats(): Pair<Long, Long>
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{
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val average_time = if (inferenceCount > 0) totalInferenceTime / inferenceCount else 0L
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return Pair(lastInferenceTime, average_time)
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}
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override fun cleanup()
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{
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PGHLog.d(TAG, "🧹 Cleaning up YOLO Inference Engine")
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try
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{
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// Shutdown thread pool with grace period
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preprocessingExecutor.shutdown()
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if (!preprocessingExecutor.awaitTermination(2, TimeUnit.SECONDS))
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{
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PGHLog.w(TAG, "⚠️ Thread pool shutdown timeout, forcing shutdown")
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preprocessingExecutor.shutdownNow()
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}
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ortSession?.close()
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ortEnvironment?.close()
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}
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catch (e: Exception)
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{
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PGHLog.e(TAG, "Error during cleanup", e)
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}
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finally
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{
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ortSession = null
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ortEnvironment = null
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isInitialized = false
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}
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}
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// [Continue with all the preprocessing methods, coordinate transformations, NMS, etc.]
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// This is a partial implementation - I need to add all the missing methods
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private fun copyAssetToInternalStorage(fileName: String): String?
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{
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return try
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{
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val internal_file = context.getFileStreamPath(fileName)
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if (!internal_file.exists())
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{
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context.assets.open(fileName).use { input_stream ->
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FileOutputStream(internal_file).use { output_stream ->
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input_stream.copyTo(output_stream)
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}
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}
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}
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internal_file.absolutePath
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}
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catch (e: IOException)
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{
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PGHLog.e(TAG, "Failed to copy asset to internal storage", e)
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null
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}
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}
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/**
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* Ultralytics-style preprocessing with letterbox resize and noise reduction
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*/
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private fun preprocessUltralyticsStyle(inputMat: Mat): Mat
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{
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try
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{
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PGHLog.d(TAG, "🔧 Ultralytics preprocessing: input ${inputMat.cols()}x${inputMat.rows()}, type=${inputMat.type()}")
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// Step 1: Letterbox resize (preserves aspect ratio with padding)
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val letterboxed = letterboxResize(inputMat, config.inputSize, config.inputSize)
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// Step 2: Apply slight noise reduction (Ultralytics uses this)
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val denoised = Mat()
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// Ensure proper BGR format
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val processed_mat = ensureBGRFormat(letterboxed)
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// Apply gentle smoothing (more reliable than bilateral filter)
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if (processed_mat.type() == CvType.CV_8UC3 || processed_mat.type() == CvType.CV_8UC1)
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{
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// Use Gaussian blur as a more reliable alternative to bilateral filter
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Imgproc.GaussianBlur(processed_mat, denoised, Size(GAUSSIAN_BLUR_KERNEL_SIZE, GAUSSIAN_BLUR_KERNEL_SIZE), GAUSSIAN_BLUR_SIGMA)
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processed_mat.release()
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PGHLog.d(TAG, "✅ Ultralytics preprocessing complete with Gaussian smoothing")
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return denoised
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}
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else
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{
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PGHLog.w(TAG, "⚠️ Smoothing skipped - unsupported image type: ${processed_mat.type()}")
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denoised.release()
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return processed_mat
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}
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}
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catch (e: Exception)
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{
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PGHLog.e(TAG, "❌ Error in Ultralytics preprocessing", e)
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// Return a copy instead of the original to avoid memory issues
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val safe_copy = Mat()
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inputMat.copyTo(safe_copy)
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return safe_copy
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}
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}
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/**
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* Letterbox resize maintaining aspect ratio with gray padding
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*/
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private fun letterboxResize(inputMat: Mat, targetWidth: Int, targetHeight: Int): Mat
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{
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val original_height = inputMat.rows()
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val original_width = inputMat.cols()
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// Calculate scale to fit within target size while preserving aspect ratio
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val scale = minOf(
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targetWidth.toDouble() / original_width,
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targetHeight.toDouble() / original_height
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)
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// Calculate new dimensions
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val new_width = (original_width * scale).toInt()
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val new_height = (original_height * scale).toInt()
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// Resize with high quality (similar to PIL LANCZOS)
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val resized = Mat()
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Imgproc.resize(inputMat, resized, Size(new_width.toDouble(), new_height.toDouble()), 0.0, 0.0, Imgproc.INTER_CUBIC)
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// Create letterbox with padding
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val letterboxed = Mat(targetHeight, targetWidth, inputMat.type(), Scalar(LETTERBOX_PADDING_GRAY, LETTERBOX_PADDING_GRAY, LETTERBOX_PADDING_GRAY)) // Gray padding
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// Calculate padding offsets
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val offset_x = (targetWidth - new_width) / 2
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val offset_y = (targetHeight - new_height) / 2
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// Copy resized image to center of letterboxed image
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val roi = Rect(offset_x, offset_y, new_width, new_height)
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val roi_mat = Mat(letterboxed, roi)
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resized.copyTo(roi_mat)
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resized.release()
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roi_mat.release()
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return letterboxed
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}
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/**
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* Convert Mat to tensor array format for ONNX inference
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*/
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private fun matToTensorArray(mat: Mat): Array<Array<Array<FloatArray>>>
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{
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// Convert to RGB using utility method
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val rgb_mat = ensureRGBFormat(mat)
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try
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{
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// Create array format [1, 3, height, width]
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val data = Array(1) { Array(NUM_CHANNELS) { Array(modelInputSize) { FloatArray(modelInputSize) } } }
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// Get RGB bytes
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val rgb_bytes = ByteArray(modelInputSize * modelInputSize * 3)
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rgb_mat.get(0, 0, rgb_bytes)
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// Convert HWC to CHW format and normalize
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for (c in 0 until NUM_CHANNELS)
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{
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for (h in 0 until modelInputSize)
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{
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for (w in 0 until modelInputSize)
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{
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val pixel_idx = (h * modelInputSize + w) * 3 + c
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data[0][c][h][w] = if (pixel_idx < rgb_bytes.size)
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{
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(rgb_bytes[pixel_idx].toInt() and COLOR_CHANNEL_MASK) / PIXEL_NORMALIZATION_FACTOR
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} else 0.0f
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}
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}
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}
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return data
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}
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finally
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{
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rgb_mat.release()
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}
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}
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/**
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* Post-process ONNX model output to Detection objects
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*/
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private fun postprocessResults(output: Array<Array<FloatArray>>, originalSize: Size): List<Detection>
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{
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val flat_output = output[0].flatMap { it.asIterable() }.toFloatArray()
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return parseNMSOutput(flat_output, originalSize.width.toInt(), originalSize.height.toInt(), modelInputSize)
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}
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/**
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* Extract model metadata dynamically from ONNX session
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*/
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private fun extractModelMetadata()
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{
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try
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{
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val session = ortSession ?: return
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// Get input info
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val inputInfo = session.inputInfo
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val inputShape = inputInfo.values.firstOrNull()?.info as? ai.onnxruntime.TensorInfo
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if (inputShape != null)
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{
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val shape = inputShape.shape
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if (shape.size >= 3)
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{
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// Typically YOLO input is [batch, channels, height, width] or [batch, height, width, channels]
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val extractedInputSize = maxOf(shape[2].toInt(), shape[3].toInt()) // Take max of height/width
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if (extractedInputSize > 0)
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{
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modelInputSize = extractedInputSize
|
|
PGHLog.i(TAG, "📐 Extracted input size from model: $modelInputSize")
|
|
}
|
|
}
|
|
}
|
|
|
|
// Get output info
|
|
val outputInfo = session.outputInfo
|
|
val outputTensorInfo = outputInfo.values.firstOrNull()?.info as? ai.onnxruntime.TensorInfo
|
|
if (outputTensorInfo != null)
|
|
{
|
|
val outputShape = outputTensorInfo.shape
|
|
PGHLog.i(TAG, "📊 Model output shape: ${outputShape.contentToString()}")
|
|
|
|
if (outputShape.size >= 2)
|
|
{
|
|
// For NMS output: typically [batch, num_detections, features_per_detection]
|
|
// For raw output: typically [batch, num_detections, 4+1+num_classes] or similar
|
|
val numDetections = outputShape[1].toInt()
|
|
val featuresPerDetection = outputShape[2].toInt()
|
|
|
|
if (numDetections > 0)
|
|
{
|
|
modelNumDetections = numDetections
|
|
PGHLog.i(TAG, "🔢 Extracted num detections from model: $modelNumDetections")
|
|
}
|
|
|
|
if (featuresPerDetection > 0)
|
|
{
|
|
modelOutputFeatures = featuresPerDetection
|
|
PGHLog.i(TAG, "📊 Extracted output features per detection: $modelOutputFeatures")
|
|
|
|
// Try to infer number of classes from output features
|
|
// NMS output: [x1, y1, x2, y2, confidence, class_id] = 6 features
|
|
// Raw output: [x, y, w, h, confidence, class1, class2, ..., classN] = 5 + num_classes
|
|
if (featuresPerDetection == 6)
|
|
{
|
|
PGHLog.i(TAG, "🎯 Detected NMS post-processed output format")
|
|
}
|
|
else if (featuresPerDetection > 5)
|
|
{
|
|
val inferredNumClasses = featuresPerDetection - 5 // 4 coords + 1 confidence
|
|
if (inferredNumClasses > 0 && inferredNumClasses <= 1000) // Reasonable range
|
|
{
|
|
modelNumClasses = inferredNumClasses
|
|
PGHLog.i(TAG, "🏷️ Inferred num classes from output: $modelNumClasses")
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
PGHLog.i(TAG, "📋 Final model metadata - Input: ${modelInputSize}x${modelInputSize}, Detections: $modelNumDetections, Features: $modelOutputFeatures, Classes: $modelNumClasses")
|
|
|
|
}
|
|
catch (e: Exception)
|
|
{
|
|
PGHLog.w(TAG, "⚠️ Failed to extract model metadata, using fallback constants", e)
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Utility methods for common preprocessing operations
|
|
*/
|
|
|
|
/**
|
|
* Safely convert Mat to BGR format (3-channel) if needed
|
|
*/
|
|
private fun ensureBGRFormat(inputMat: Mat): Mat
|
|
{
|
|
return when (inputMat.type())
|
|
{
|
|
CvType.CV_8UC3 -> inputMat
|
|
CvType.CV_8UC4 ->
|
|
{
|
|
val converted = Mat()
|
|
Imgproc.cvtColor(inputMat, converted, Imgproc.COLOR_BGRA2BGR)
|
|
converted
|
|
}
|
|
CvType.CV_8UC1 ->
|
|
{
|
|
val converted = Mat()
|
|
Imgproc.cvtColor(inputMat, converted, Imgproc.COLOR_GRAY2BGR)
|
|
converted
|
|
}
|
|
else ->
|
|
{
|
|
val converted = Mat()
|
|
inputMat.convertTo(converted, CvType.CV_8UC3)
|
|
converted
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Safely perform Mat operation with fallback
|
|
*/
|
|
private inline fun <T> safeMatOperation(
|
|
operation: () -> T,
|
|
fallback: () -> T,
|
|
errorMessage: String
|
|
): T
|
|
{
|
|
return try
|
|
{
|
|
operation()
|
|
}
|
|
catch (e: Exception)
|
|
{
|
|
PGHLog.e(TAG, "❌ $errorMessage", e)
|
|
fallback()
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Utility function to ensure Mat is in RGB format for ONNX model input
|
|
*/
|
|
private fun ensureRGBFormat(inputMat: Mat): Mat
|
|
{
|
|
return when (inputMat.channels())
|
|
{
|
|
3 ->
|
|
{
|
|
val converted = Mat()
|
|
Imgproc.cvtColor(inputMat, converted, Imgproc.COLOR_BGR2RGB)
|
|
converted
|
|
}
|
|
4 ->
|
|
{
|
|
val converted = Mat()
|
|
Imgproc.cvtColor(inputMat, converted, Imgproc.COLOR_BGRA2RGB)
|
|
converted
|
|
}
|
|
else -> inputMat // 1-channel or other formats pass through
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Safe resize operation with fallback
|
|
*/
|
|
private fun safeResize(inputMat: Mat, targetSize: Size): Mat
|
|
{
|
|
return safeMatOperation(
|
|
operation = {
|
|
val resized = Mat()
|
|
Imgproc.resize(inputMat, resized, targetSize)
|
|
resized
|
|
},
|
|
fallback = {
|
|
val fallback = Mat()
|
|
inputMat.copyTo(fallback)
|
|
fallback
|
|
},
|
|
errorMessage = "Error resizing image"
|
|
)
|
|
}
|
|
|
|
/**
|
|
* Data class for transformed coordinates
|
|
*/
|
|
private data class TransformedCoordinates(val x1: Float, val y1: Float, val x2: Float, val y2: Float)
|
|
|
|
/**
|
|
* Transform coordinates from model output to original image space
|
|
*/
|
|
private fun transformCoordinates(
|
|
rawX1: Float, rawY1: Float, rawX2: Float, rawY2: Float,
|
|
originalWidth: Int, originalHeight: Int, inputScale: Int
|
|
): TransformedCoordinates
|
|
{
|
|
return when (COORD_TRANSFORM_MODE)
|
|
{
|
|
"LETTERBOX" ->
|
|
{
|
|
val letterbox_params = calculateLetterboxInverse(originalWidth, originalHeight, inputScale)
|
|
val scale_x = letterbox_params[0]
|
|
val scale_y = letterbox_params[1]
|
|
val offset_x = letterbox_params[2]
|
|
val offset_y = letterbox_params[3]
|
|
|
|
TransformedCoordinates(
|
|
x1 = (rawX1 - offset_x) * scale_x,
|
|
y1 = (rawY1 - offset_y) * scale_y,
|
|
x2 = (rawX2 - offset_x) * scale_x,
|
|
y2 = (rawY2 - offset_y) * scale_y
|
|
)
|
|
}
|
|
"DIRECT" ->
|
|
{
|
|
val direct_scale_x = originalWidth.toFloat() / inputScale.toFloat()
|
|
val direct_scale_y = originalHeight.toFloat() / inputScale.toFloat()
|
|
|
|
TransformedCoordinates(
|
|
x1 = rawX1 * direct_scale_x,
|
|
y1 = rawY1 * direct_scale_y,
|
|
x2 = rawX2 * direct_scale_x,
|
|
y2 = rawY2 * direct_scale_y
|
|
)
|
|
}
|
|
"HYBRID" ->
|
|
{
|
|
val letterbox_params = calculateLetterboxInverse(originalWidth, originalHeight, inputScale)
|
|
val offset_x = letterbox_params[2]
|
|
val offset_y = letterbox_params[3]
|
|
|
|
val scale = minOf(inputScale.toDouble() / originalWidth, inputScale.toDouble() / originalHeight)
|
|
val scaled_width = (originalWidth * scale)
|
|
val scaled_height = (originalHeight * scale)
|
|
val hybrid_scale_x = originalWidth.toFloat() / scaled_width.toFloat()
|
|
val hybrid_scale_y = originalHeight.toFloat() / scaled_height.toFloat()
|
|
|
|
TransformedCoordinates(
|
|
x1 = (rawX1 - offset_x) * hybrid_scale_x,
|
|
y1 = (rawY1 - offset_y) * hybrid_scale_y,
|
|
x2 = (rawX2 - offset_x) * hybrid_scale_x,
|
|
y2 = (rawY2 - offset_y) * hybrid_scale_y
|
|
)
|
|
}
|
|
else ->
|
|
{
|
|
// Default to HYBRID mode for unknown coordinate modes
|
|
val letterbox_params = calculateLetterboxInverse(originalWidth, originalHeight, inputScale)
|
|
val offset_x = letterbox_params[2]
|
|
val offset_y = letterbox_params[3]
|
|
|
|
val scale = minOf(inputScale.toDouble() / originalWidth, inputScale.toDouble() / originalHeight)
|
|
val scaled_width = (originalWidth * scale)
|
|
val scaled_height = (originalHeight * scale)
|
|
val hybrid_scale_x = originalWidth.toFloat() / scaled_width.toFloat()
|
|
val hybrid_scale_y = originalHeight.toFloat() / scaled_height.toFloat()
|
|
|
|
TransformedCoordinates(
|
|
x1 = (rawX1 - offset_x) * hybrid_scale_x,
|
|
y1 = (rawY1 - offset_y) * hybrid_scale_y,
|
|
x2 = (rawX2 - offset_x) * hybrid_scale_x,
|
|
y2 = (rawY2 - offset_y) * hybrid_scale_y
|
|
)
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Parse NMS (Non-Maximum Suppression) output format
|
|
*/
|
|
private fun parseNMSOutput(output: FloatArray, originalWidth: Int, originalHeight: Int, inputScale: Int): List<Detection>
|
|
{
|
|
val detections = mutableListOf<Detection>()
|
|
|
|
val num_detections = modelNumDetections
|
|
val features_per_detection = modelOutputFeatures
|
|
|
|
PGHLog.d(TAG, "🔍 Parsing NMS output: $num_detections post-processed detections")
|
|
|
|
var valid_detections = 0
|
|
|
|
for (i in 0 until num_detections)
|
|
{
|
|
val base_idx = i * features_per_detection
|
|
|
|
// Extract and transform coordinates from model output
|
|
val coords = transformCoordinates(
|
|
rawX1 = output[base_idx + NMS_COORDINATE_X1_OFFSET],
|
|
rawY1 = output[base_idx + NMS_COORDINATE_Y1_OFFSET],
|
|
rawX2 = output[base_idx + NMS_COORDINATE_X2_OFFSET],
|
|
rawY2 = output[base_idx + NMS_COORDINATE_Y2_OFFSET],
|
|
originalWidth = originalWidth,
|
|
originalHeight = originalHeight,
|
|
inputScale = inputScale
|
|
)
|
|
|
|
val confidence = output[base_idx + NMS_CONFIDENCE_OFFSET]
|
|
val class_id = output[base_idx + NMS_CLASS_ID_OFFSET].toInt()
|
|
|
|
// Apply confidence mapping if enabled
|
|
val mapped_confidence = if (ENABLE_CONFIDENCE_MAPPING)
|
|
{
|
|
mapConfidenceForMobile(confidence)
|
|
}
|
|
else
|
|
{
|
|
confidence
|
|
}
|
|
|
|
// Get class name for filtering and debugging
|
|
val class_name = classificationManager.getClassName(class_id) ?: "unknown_$class_id"
|
|
|
|
// Debug logging for all detections if enabled
|
|
if (SHOW_ALL_CONFIDENCES && mapped_confidence > MIN_DEBUG_CONFIDENCE)
|
|
{
|
|
PGHLog.d(TAG, "🔍 [DEBUG] Class: $class_name (ID: $class_id), Confidence: %.3f, Original: %.3f".format(mapped_confidence, confidence))
|
|
}
|
|
|
|
// Apply class filter if set
|
|
val passes_class_filter = DEBUG_CLASS_FILTER == null || DEBUG_CLASS_FILTER == class_name
|
|
|
|
// Filter by confidence threshold, class filter, and validate coordinates
|
|
if (mapped_confidence > confidenceThreshold && class_id >= 0 && class_id < classificationManager.getNumClasses() && passes_class_filter)
|
|
{
|
|
// Convert from corner coordinates (x1,y1,x2,y2) to BoundingBox format
|
|
// Clamp coordinates to image boundaries
|
|
val clamped_x1 = max(0.0f, min(coords.x1, originalWidth.toFloat()))
|
|
val clamped_y1 = max(0.0f, min(coords.y1, originalHeight.toFloat()))
|
|
val clamped_x2 = max(clamped_x1, min(coords.x2, originalWidth.toFloat()))
|
|
val clamped_y2 = max(clamped_y1, min(coords.y2, originalHeight.toFloat()))
|
|
|
|
// Validate bounding box dimensions and coordinates
|
|
if (clamped_x2 > clamped_x1 && clamped_y2 > clamped_y1)
|
|
{
|
|
detections.add(
|
|
Detection(
|
|
className = class_name,
|
|
confidence = mapped_confidence,
|
|
boundingBox = BoundingBox(clamped_x1, clamped_y1, clamped_x2, clamped_y2)
|
|
)
|
|
)
|
|
|
|
valid_detections++
|
|
|
|
if (valid_detections <= MAX_DEBUG_DETECTIONS_TO_LOG)
|
|
{
|
|
PGHLog.d(TAG, "✅ Valid NMS detection: class=$class_id ($class_name), conf=${String.format("%.4f", mapped_confidence)}")
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
PGHLog.d(TAG, "🎯 NMS parsing complete: $valid_detections valid detections")
|
|
return detections.sortedByDescending { it.confidence }
|
|
}
|
|
|
|
/**
|
|
* Calculate letterbox inverse transformation parameters
|
|
*/
|
|
private fun calculateLetterboxInverse(originalWidth: Int, originalHeight: Int, inputScale: Int): Array<Float>
|
|
{
|
|
// Calculate the scale that was used during letterbox resize
|
|
val scale = minOf(
|
|
inputScale.toDouble() / originalWidth,
|
|
inputScale.toDouble() / originalHeight
|
|
)
|
|
|
|
// Calculate the scaled dimensions (what the image became after resize but before padding)
|
|
val scaled_width = (originalWidth * scale)
|
|
val scaled_height = (originalHeight * scale)
|
|
|
|
// Calculate padding offsets (in the 640x640 space)
|
|
val offset_x = (inputScale - scaled_width) / 2.0
|
|
val offset_y = (inputScale - scaled_height) / 2.0
|
|
|
|
val scale_back_x = 1.0 / scale // Same for both X and Y since letterbox uses uniform scaling
|
|
val scale_back_y = 1.0 / scale
|
|
|
|
return arrayOf(scale_back_x.toFloat(), scale_back_y.toFloat(), offset_x.toFloat(), offset_y.toFloat())
|
|
}
|
|
|
|
/**
|
|
* Apply confidence mapping for mobile optimization
|
|
*/
|
|
private fun mapConfidenceForMobile(rawConfidence: Float): Float
|
|
{
|
|
// Apply scaling and optional curve adjustment
|
|
var mapped = rawConfidence / RAW_TO_MOBILE_SCALE
|
|
|
|
// Optional: Apply sigmoid-like curve to boost mid-range confidences
|
|
mapped = (mapped * mapped) / (mapped * mapped + (1 - mapped) * (1 - mapped))
|
|
|
|
// Clamp to valid range
|
|
return min(1.0f, max(0.0f, mapped))
|
|
}
|
|
|
|
/**
|
|
* Merge and filter detections using weighted NMS
|
|
*/
|
|
private fun mergeAndFilterDetections(allDetections: List<Detection>): List<Detection>
|
|
{
|
|
if (allDetections.isEmpty()) return emptyList()
|
|
|
|
// First, apply NMS within each class
|
|
val detections_by_class = allDetections.groupBy { detection ->
|
|
// Map class name back to ID for grouping
|
|
classificationManager.getAllClassNames().entries.find { it.value == detection.className }?.key ?: -1
|
|
}
|
|
|
|
val class_nms_results = mutableListOf<Detection>()
|
|
|
|
for ((class_id, class_detections) in detections_by_class)
|
|
{
|
|
if (class_id >= 0)
|
|
{
|
|
val nms_results = applyWeightedNMS(class_detections, class_detections.first().className)
|
|
class_nms_results.addAll(nms_results)
|
|
}
|
|
}
|
|
|
|
// Then, apply cross-class NMS for semantically related classes
|
|
val final_detections = applyCrossClassNMS(class_nms_results)
|
|
|
|
return final_detections.sortedByDescending { it.confidence }
|
|
}
|
|
|
|
/**
|
|
* Apply weighted NMS within a class
|
|
*/
|
|
private fun applyWeightedNMS(detections: List<Detection>, className: String): List<Detection>
|
|
{
|
|
val results = mutableListOf<Detection>()
|
|
|
|
if (detections.isEmpty()) return results
|
|
|
|
// Sort by confidence
|
|
val sorted_detections = detections.sortedByDescending { it.confidence }
|
|
val suppressed = BooleanArray(sorted_detections.size)
|
|
|
|
for (i in sorted_detections.indices)
|
|
{
|
|
if (suppressed[i]) continue
|
|
|
|
var final_confidence = sorted_detections[i].confidence
|
|
var final_box = sorted_detections[i].boundingBox
|
|
val overlapping_boxes = mutableListOf<Pair<BoundingBox, Float>>()
|
|
overlapping_boxes.add(Pair(sorted_detections[i].boundingBox, sorted_detections[i].confidence))
|
|
|
|
// Find overlapping boxes and combine them with weighted averaging
|
|
for (j in sorted_detections.indices)
|
|
{
|
|
if (i != j && !suppressed[j])
|
|
{
|
|
val iou = calculateIoU(sorted_detections[i].boundingBox, sorted_detections[j].boundingBox)
|
|
if (iou > NMS_THRESHOLD)
|
|
{
|
|
overlapping_boxes.add(Pair(sorted_detections[j].boundingBox, sorted_detections[j].confidence))
|
|
suppressed[j] = true
|
|
}
|
|
}
|
|
}
|
|
|
|
// If multiple overlapping boxes, use weighted average
|
|
if (overlapping_boxes.size > 1)
|
|
{
|
|
val total_weight = overlapping_boxes.sumOf { it.second.toDouble() }
|
|
val weighted_left = overlapping_boxes.sumOf { it.first.left * it.second.toDouble() } / total_weight
|
|
val weighted_top = overlapping_boxes.sumOf { it.first.top * it.second.toDouble() } / total_weight
|
|
val weighted_right = overlapping_boxes.sumOf { it.first.right * it.second.toDouble() } / total_weight
|
|
val weighted_bottom = overlapping_boxes.sumOf { it.first.bottom * it.second.toDouble() } / total_weight
|
|
|
|
final_box = BoundingBox(
|
|
weighted_left.toFloat(),
|
|
weighted_top.toFloat(),
|
|
weighted_right.toFloat(),
|
|
weighted_bottom.toFloat()
|
|
)
|
|
final_confidence = (total_weight / overlapping_boxes.size).toFloat()
|
|
|
|
PGHLog.d(TAG, "🔗 Merged ${overlapping_boxes.size} overlapping detections, final conf: ${String.format("%.3f", final_confidence)}")
|
|
}
|
|
|
|
results.add(
|
|
Detection(
|
|
className = className,
|
|
confidence = final_confidence,
|
|
boundingBox = final_box
|
|
)
|
|
)
|
|
}
|
|
|
|
return results
|
|
}
|
|
|
|
/**
|
|
* Apply cross-class NMS for semantically related classes
|
|
*/
|
|
private fun applyCrossClassNMS(detections: List<Detection>): List<Detection>
|
|
{
|
|
val result = mutableListOf<Detection>()
|
|
val suppressed = BooleanArray(detections.size)
|
|
|
|
// Define semantically related class groups
|
|
val level_related_classes = setOf("pokemon_level", "level_value", "digit", "number")
|
|
val stat_related_classes = setOf("hp_value", "attack_value", "defense_value", "sp_atk_value", "sp_def_value", "speed_value")
|
|
val text_related_classes = setOf("pokemon_nickname", "pokemon_species", "move_name", "ability_name", "nature_name")
|
|
|
|
for (i in detections.indices)
|
|
{
|
|
if (suppressed[i]) continue
|
|
|
|
val current_detection = detections[i]
|
|
var best_detection = current_detection
|
|
|
|
// Check for overlapping detections in related classes
|
|
for (j in detections.indices)
|
|
{
|
|
if (i != j && !suppressed[j])
|
|
{
|
|
val other_detection = detections[j]
|
|
val iou = calculateIoU(current_detection.boundingBox, other_detection.boundingBox)
|
|
|
|
// If highly overlapping, check if they're semantically related
|
|
if (iou > 0.5f) // High overlap threshold for cross-class NMS
|
|
{
|
|
val are_related = areClassesRelated(
|
|
current_detection.className,
|
|
other_detection.className,
|
|
level_related_classes,
|
|
stat_related_classes,
|
|
text_related_classes
|
|
)
|
|
|
|
if (are_related)
|
|
{
|
|
// Keep the higher confidence detection
|
|
if (other_detection.confidence > best_detection.confidence)
|
|
{
|
|
best_detection = other_detection
|
|
}
|
|
suppressed[j] = true
|
|
PGHLog.d(TAG, "🔗 Cross-class NMS: merged ${current_detection.className} with ${other_detection.className}")
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
result.add(best_detection)
|
|
}
|
|
|
|
return result
|
|
}
|
|
|
|
/**
|
|
* Check if two classes are semantically related
|
|
*/
|
|
private fun areClassesRelated(
|
|
class1: String,
|
|
class2: String,
|
|
levelClasses: Set<String>,
|
|
statClasses: Set<String>,
|
|
textClasses: Set<String>
|
|
): Boolean
|
|
{
|
|
return (levelClasses.contains(class1) && levelClasses.contains(class2)) ||
|
|
(statClasses.contains(class1) && statClasses.contains(class2)) ||
|
|
(textClasses.contains(class1) && textClasses.contains(class2))
|
|
}
|
|
|
|
/**
|
|
* Calculate Intersection over Union (IoU) for two bounding boxes
|
|
*/
|
|
private fun calculateIoU(box1: BoundingBox, box2: BoundingBox): Float
|
|
{
|
|
val x1 = max(box1.left, box2.left)
|
|
val y1 = max(box1.top, box2.top)
|
|
val x2 = min(box1.right, box2.right)
|
|
val y2 = min(box1.bottom, box2.bottom)
|
|
|
|
val intersection = max(0f, x2 - x1) * max(0f, y2 - y1)
|
|
val area1 = box1.width * box1.height
|
|
val area2 = box2.width * box2.height
|
|
val union = area1 + area2 - intersection
|
|
|
|
return if (union > 0) intersection / union else 0f
|
|
}
|
|
}
|