288 lines
11 KiB
Kotlin
288 lines
11 KiB
Kotlin
package com.sw.inbound.objbox
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import android.content.Context
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import android.graphics.Bitmap
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import android.net.Uri
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import android.renderscript.Element.DataType
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import androidx.core.graphics.scale
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import com.google.gson.Gson
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import com.google.gson.reflect.TypeToken
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import com.sw.inbound.MyApp
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import com.sw.inbound.utils.AssetsTool
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import com.sw.inbound.utils.ImageUtil
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import com.sw.inbound.utils.ext.toJsonString
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import io.objectbox.Box
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import io.objectbox.kotlin.boxFor
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import io.objectbox.query.IdWithScore
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import io.objectbox.query.Query
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import org.pytorch.IValue
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import org.pytorch.Module
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import org.pytorch.torchvision.TensorImageUtils
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import timber.log.Timber
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import java.io.File
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import java.io.FileOutputStream
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import java.io.IOException
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import java.io.InputStream
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object FoodModule {
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private lateinit var module_mobile: Module
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private lateinit var box: Box<Food>
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private lateinit var embeddingsList: List<List<Float>>
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private lateinit var labelsList: IntArray
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private lateinit var classInfo: FoodClassInfo
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private val NO_MEAN_RGB = floatArrayOf(0.0f, 0.0f, 0.0f)
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private val NO_STD_RGB = floatArrayOf(1.0f, 1.0f, 1.0f)
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// 1. 定义你的模型固定输入尺寸 (根据你的tflite模型修改，比如224x224)
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private const val MODEL_INPUT_WIDTH = 300
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private const val MODEL_INPUT_HEIGHT = 300
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const val DEFAULT_FOOD_INDEX = -1
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const val BAG_RATE = 0.05
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fun init(context: Context) {
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Thread {
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module_mobile = Module.load(copyAssetToCache(context, "best_embedding_model_mobile.pt"))
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box = ObjectBox.boxStore.boxFor(Food::class)
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//if (box.all.isNotEmpty()) {
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// box.removeAll()
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//}
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if (box.all.isEmpty()) {
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initDefFoodData(context)
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}
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}.start()
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}
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// fun uri2FloatArray(uri: Uri): FloatArray? {
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// return MyApp.instance?.let { context ->
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// ImageUtil.uriToBitmap(context, uri)?.let {
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// bitmap2FloatArray(it)
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// }
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// }
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// }
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fun bitmap2FloatArray(originBitmap: Bitmap, isRecycle: Boolean): FloatArray? {
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var rgb565Bitmap: Bitmap?=null
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try {
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val scaledBitmap = originBitmap.scale(MODEL_INPUT_WIDTH, MODEL_INPUT_HEIGHT)
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if (isRecycle) {
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originBitmap.recycle()
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}
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rgb565Bitmap = scaledBitmap.copy(Bitmap.Config.RGB_565, false)
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scaledBitmap.recycle()
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val inputTensor = TensorImageUtils.bitmapToFloat32Tensor(
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rgb565Bitmap,
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NO_MEAN_RGB, // [0.485, 0.456, 0.406] TORCHVISION_NORM_MEAN_RGB
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NO_STD_RGB // [0.229, 0.224, 0.225] TORCHVISION_NORM_STD_RGB
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)
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val outputTensor = module_mobile.forward(IValue.from(inputTensor)).toTensor()
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return outputTensor.dataAsFloatArray
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} catch (e: OutOfMemoryError) {
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e.printStackTrace()
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} finally {
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if (rgb565Bitmap != null && rgb565Bitmap.isRecycled.not()) {
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rgb565Bitmap.recycle()
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}
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//System.gc()
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//System.runFinalization()
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}
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return null
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}
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// fun queryFood(uri: Uri, queryCount: Int = 15): List<String>? {
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// return uri2FloatArray(uri)?.let {
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// queryFood(it, queryCount)
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// }
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// }
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// /**
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// * 返回识别物品名称列表
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// */
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// fun queryFood(bitmap: Bitmap, queryCount: Int = 15): List<String> {
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// val floatArray = bitmap2FloatArray(bitmap)
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// return queryFood(floatArray, queryCount)
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// }
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// /**
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// * 返回识别物品IdNameScore对象列表
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// */
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// fun queryFoodNameScore(bitmap: Bitmap, queryCount: Int = 15): List<IdNameScore> {
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// val floatArray = bitmap2FloatArray(bitmap)
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// return queryFoodNameScore(floatArray, queryCount)
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// }
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fun queryFoodNameScore(floatArray: FloatArray?, queryCount: Int = 15): List<IdNameScore> {
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if (floatArray == null) return emptyList()
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val query: Query<Food> =
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box.query(Food_.foodVector.nearestNeighbors(floatArray, queryCount)).build()
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//查询比较分数
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// val tempList = query.findWithScores().sortedBy { it.score }.map { "${it.get().name}|${it.get().foodIdx}|${it.score}" }
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var idScoreList: List<IdWithScore>
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try {
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idScoreList = query.findIdsWithScores();
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} finally {
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// 先关闭Query，释放Cursor
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query.close()
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}
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val nameScoreList = mutableListOf<IdNameScore>()
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idScoreList.forEach {
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nameScoreList.add(IdNameScore(id = it.id, name = box.get(it.id).name?:"", score = it.score))
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}
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Timber.tag("FoodModule").d("queryFood数据：${nameScoreList.toJsonString()}")
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return nameScoreList
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}
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fun getFoodScoreList(bitmap: Bitmap, queryCount: Int = 15): List<IdNameScore> {
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val floatArray = bitmap2FloatArray(bitmap, false)
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if (floatArray == null) return emptyList()
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val nameScoreList = queryFoodNameScore(floatArray, queryCount)
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if (nameScoreList.isEmpty()) {
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return emptyList()
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}
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val maxScoreList = nameScoreList
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.filter { it.score < 0.15 }
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.groupBy { it.name }
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.map { (_, value) -> value.minByOrNull { it.score }!! }
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.toMutableList()
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val map = mutableMapOf<String, Int>()
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nameScoreList.forEach {
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val key = it.name
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val count = map[key] ?: 0
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map[key] = count + 1
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}
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val orderList = map.entries.sortedByDescending { it.value }.map { it.key }.toMutableList()
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val firstFood = nameScoreList[0].name
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orderList.remove(firstFood)
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orderList.add(0, firstFood)
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val sortedScoreList = maxScoreList.sortedWith(compareBy {
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orderList.indexOf(it.name)
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})
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Timber.tag("FoodModule").d("getFoodScoreList数据：${sortedScoreList.toJsonString()}")
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return sortedScoreList
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}
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fun queryFood(floatArray: FloatArray, queryCount: Int = 15): List<String> {
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val map = mutableMapOf<String, Int>()
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val nameScoreList = queryFoodNameScore(floatArray, queryCount)
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nameScoreList.filter { it.score < 0.05 }.forEach {
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val count = map[it.name] ?: 0
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map[it.name] = count + 1
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}
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val list = map.entries.sortedByDescending { it.value }.map { it.key }
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return list
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}
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data class IdNameScore(
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val id:Long,
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var name:String,
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val score: Double
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)
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fun initDefFoodData(context: Context, action:()-> Unit={}) {
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val count = box.all.count { it.foodIdx == DEFAULT_FOOD_INDEX }
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if (count > 0) {
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return
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}
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val embeddingsJson = AssetsTool.readAssetsFile(context, "data/embeddings.json")
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val labelsJson = AssetsTool.readAssetsFile(context, "data/labels.json")
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val classInfoJson = AssetsTool.readAssetsFile(context, "data/class_info.json")
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embeddingsList =
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Gson().fromJson(embeddingsJson, object : TypeToken<List<List<Float>>>() {}.type)
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labelsList = Gson().fromJson(labelsJson, IntArray::class.java)
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classInfo =
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Gson().fromJson(classInfoJson, FoodClassInfo::class.java)
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val foodMap = classInfo.idx_to_class
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embeddingsList.forEachIndexed { index, floatList ->
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val classIdx = labelsList[index]
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val foodName = foodMap["$classIdx"]
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val array = floatList.toFloatArray()
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ObjectBox.boxStore.runInTx {
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box.put(Food(name = foodName, foodVector = array, foodIdx = DEFAULT_FOOD_INDEX))
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}
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}
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action()
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}
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/**
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* ，此方法的主要目的是：从assets 拷贝到 app的cache目录
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* @param context
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* @param fileName
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* @return 例如是这样：/data/user/0/com.frizzle.pluginhookandroid9/cache/plugin-debug.apk
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*
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* 不可能反正SD
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*/
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// fun copyAssetToCache(context: Context, fileName: String): String? {
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// // 此app的缓存目录 --> 会默认在 cache目录...，可以自己去看看哦
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// val cacheDir = context.getCacheDir()
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// if (!cacheDir.exists()) {
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// cacheDir.mkdirs() // TODO 如果没有缓存目录，就创建
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// }
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// val outPath = File(cacheDir, fileName) // TODO 创建输出的文件位置
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// if (outPath.exists()) {
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// outPath.delete() // TODO 如果该文件已经存在，就删掉
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// }
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// var `is`: InputStream? = null // 读取
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// var fos: FileOutputStream? = null // 写入
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// try {
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// // 创建文件，如果创建成功，就返回true
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// val res = outPath.createNewFile()
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// if (res) {
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// `is` = context.getAssets().open(fileName) // 拿到main/assets目录的输入流，用于读取字节
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// fos = FileOutputStream(outPath) // 读取出来的字节最终写到outPath
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// val buf = ByteArray(`is`.available()) // 缓存区
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// var byteCount: Int
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//
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// // 开始循环读取
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// while ((`is`.read(buf).also { byteCount = it }) != -1) {
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// fos.write(buf, 0, byteCount)
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// }
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// return outPath.getAbsolutePath()
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// }
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// } catch (e: IOException) {
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// e.printStackTrace()
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// } finally {
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// try {
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// // TODO 一定要记得关闭资源，为了不去性能的磨损
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// fos?.flush()
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// `is`?.close()
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// fos?.close()
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// } catch (e: IOException) {
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// e.printStackTrace()
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// }
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// }
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// return null
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// }
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fun copyAssetToCache(context: Context, fileName: String): String? {
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val cacheFile = File(context.cacheDir, fileName)
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val buffer = ByteArray(8 * 1024)
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var inputStream: InputStream? = null
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var outputStream: FileOutputStream? = null
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try {
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inputStream = context.assets.open(fileName)
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outputStream = FileOutputStream(cacheFile)
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var byteCount: Int
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while (inputStream.read(buffer).also { byteCount = it } != -1) {
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outputStream.write(buffer, 0, byteCount)
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}
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outputStream.channel.force(true) // 强制物理落盘，比flush更彻底
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return cacheFile.absolutePath
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} catch (e: IOException) {
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e.printStackTrace()
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Timber.tag("FoodModule").d("文件拷贝失败 fileName=$fileName, error=${e.message}")
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// 拷贝失败时删除残缺文件，避免下次读取到损坏文件
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if (cacheFile.exists()) {
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cacheFile.delete()
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}
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} finally {
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outputStream?.close()
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inputStream?.close()
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}
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return null
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}
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} |