Merge pull request #23 from smartscanapp/refactor/structure

Refactor file structure

Author: dev-diaries41

core/src/androidTest/kotlin/com/fpf/smartscansdk/core/data/images/Entity.kt

@@ -1,7 +1,7 @@
 package com.fpf.smartscansdk.core.data.images
 
 import androidx.room.*
-import com.fpf.smartscansdk.core.data.Embedding
+import com.fpf.smartscansdk.core.embeddings.Embedding
 
 @Entity(tableName = "image_embeddings")
 data class ImageEmbeddingEntity(

core/src/main/java/com/fpf/smartscansdk/core/data/Classification.kt

@@ -1,13 +1,13 @@
-package com.fpf.smartscansdk.ml.models.providers.embeddings
+package com.fpf.smartscansdk.core.embeddings
 
-import com.fpf.smartscansdk.core.data.ClassificationError
-import com.fpf.smartscansdk.core.data.ClassificationResult
-import com.fpf.smartscansdk.core.data.PrototypeEmbedding
-import com.fpf.smartscansdk.core.embeddings.getSimilarities
-import com.fpf.smartscansdk.core.embeddings.getTopN
+sealed class ClassificationResult {
+    data class Success(val classId: String, val similarity: Float ): ClassificationResult()
+    data class Failure(val error: ClassificationError ): ClassificationResult()
+}
 
+enum class ClassificationError{MINIMUM_CLASS_SIZE, THRESHOLD, CONFIDENCE_MARGIN}
 
-fun classify(embedding: FloatArray, classPrototypes: List<PrototypeEmbedding>, threshold: Float = 0.4f, confidenceMargin: Float = 0.05f ): ClassificationResult{
+fun fewShotClassify(embedding: FloatArray, classPrototypes: List<PrototypeEmbedding>, threshold: Float = 0.4f, confidenceMargin: Float = 0.05f ): ClassificationResult{
     if(classPrototypes.size < 2) return ClassificationResult.Failure(error= ClassificationError.MINIMUM_CLASS_SIZE) // Using a single class prototype leads to many false positives
 
     // No threshold filter applied here to allow confidence check by comparing top 2 matches
@@ -24,6 +24,4 @@ fun classify(embedding: FloatArray, classPrototypes: List<PrototypeEmbedding>, t
 
     val classId = classPrototypes[bestIndex].id
     return ClassificationResult.Success(classId=classId, similarity = bestSim)
-}
-
-
+}

core/src/main/java/com/fpf/smartscansdk/core/data/Processors.kt

@@ -56,3 +56,7 @@ fun unflattenEmbeddings(flattened: FloatArray, embeddingDim: Int): List<FloatArr
     }
     return embeddings
 }
+
+
+
+

ml/src/main/java/com/fpf/smartscansdk/ml/models/providers/embeddings/FewShotClassifier.kt renamed to core/src/main/java/com/fpf/smartscansdk/core/embeddings/Classification.kt

@@ -1,5 +1,4 @@
-package com.fpf.smartscansdk.core.data
-
+package com.fpf.smartscansdk.core.embeddings
 
 // `Embedding` represents a raw vector for a single media item, with `id` corresponding to its `MediaStoreId`.
 data class Embedding(
@@ -14,7 +13,3 @@ data class PrototypeEmbedding(
     val date: Long,
     val embeddings: FloatArray
 )
-
-
-
-

core/src/main/java/com/fpf/smartscansdk/core/embeddings/EmbeddingUtils.kt

@@ -1,7 +1,6 @@
 package com.fpf.smartscansdk.core.embeddings
 
 import android.util.Log
-import com.fpf.smartscansdk.core.data.Embedding
 import kotlinx.coroutines.Dispatchers
 import kotlinx.coroutines.withContext
 import java.io.File

core/src/main/java/com/fpf/smartscansdk/core/data/Embedings.kt renamed to core/src/main/java/com/fpf/smartscansdk/core/embeddings/Embeddings.kt

@@ -1,7 +1,5 @@
 package com.fpf.smartscansdk.core.embeddings
 
-import com.fpf.smartscansdk.core.data.Embedding
-
 interface IEmbeddingStore {
     val exists: Boolean
     suspend fun add(newEmbeddings: List<Embedding>)

core/src/main/java/com/fpf/smartscansdk/core/embeddings/FileEmbeddingStore.kt

@@ -3,13 +3,13 @@ package com.fpf.smartscansdk.core.indexers
 import android.content.ContentUris
 import android.content.Context
 import android.provider.MediaStore
-import com.fpf.smartscansdk.core.data.Embedding
-import com.fpf.smartscansdk.core.data.ProcessOptions
+import com.fpf.smartscansdk.core.embeddings.Embedding
 import com.fpf.smartscansdk.core.embeddings.IEmbeddingStore
 import com.fpf.smartscansdk.core.embeddings.ImageEmbeddingProvider
 import com.fpf.smartscansdk.core.media.getBitmapFromUri
 import com.fpf.smartscansdk.core.processors.BatchProcessor
 import com.fpf.smartscansdk.core.processors.IProcessorListener
+import com.fpf.smartscansdk.core.processors.MemoryOptions
 import kotlinx.coroutines.NonCancellable
 import kotlinx.coroutines.withContext
 
@@ -24,8 +24,9 @@ class ImageIndexer(
     private val maxImageSize: Int = 225,
     context: Context,
     listener: IProcessorListener<Long, Embedding>? = null,
-    options: ProcessOptions = ProcessOptions(),
-    ): BatchProcessor<Long, Embedding>(context, listener, options){
+    memoryOptions: MemoryOptions = MemoryOptions(),
+    batchSize: Int = 10,
+    ): BatchProcessor<Long, Embedding>(context, listener, memoryOptions, batchSize){
 
     companion object {
         const val INDEX_FILENAME = "image_index.bin"

core/src/main/java/com/fpf/smartscansdk/core/embeddings/IEmbeddingStore.kt

@@ -1,17 +1,16 @@
 package com.fpf.smartscansdk.core.indexers
 
-import android.app.Application
 import android.content.ContentUris
 import android.content.Context
 import android.provider.MediaStore
-import com.fpf.smartscansdk.core.data.Embedding
-import com.fpf.smartscansdk.core.data.ProcessOptions
+import com.fpf.smartscansdk.core.embeddings.Embedding
 import com.fpf.smartscansdk.core.embeddings.IEmbeddingStore
 import com.fpf.smartscansdk.core.embeddings.ImageEmbeddingProvider
 import com.fpf.smartscansdk.core.embeddings.generatePrototypeEmbedding
 import com.fpf.smartscansdk.core.processors.BatchProcessor
 import com.fpf.smartscansdk.core.media.extractFramesFromVideo
 import com.fpf.smartscansdk.core.processors.IProcessorListener
+import com.fpf.smartscansdk.core.processors.MemoryOptions
 
 // ** Design Constraint**: For on-device vector search, the full index needs to be loaded in-memory (or make an Android native VectorDB)
 // File-based EmbeddingStore is used over a Room version due to significant faster index loading
@@ -26,9 +25,10 @@ class VideoIndexer(
     private val height: Int,
     context: Context,
     listener: IProcessorListener<Long, Embedding>? = null,
-    options: ProcessOptions = ProcessOptions(),
+    batchSize: Int = 10,
+    memoryOptions: MemoryOptions = MemoryOptions(),
     private val store: IEmbeddingStore,
-    ): BatchProcessor<Long, Embedding>(context, listener, options){
+    ): BatchProcessor<Long, Embedding>(context, listener, memoryOptions, batchSize){
 
     companion object {
         const val INDEX_FILENAME = "video_index.bin"