diff --git a/app/build.gradle b/app/build.gradle index ee314a4e..e231178f 100644 --- a/app/build.gradle +++ b/app/build.gradle @@ -67,6 +67,12 @@ android { configurations.all { resolutionStrategy.force 'com.google.code.findbugs:jsr305:3.0.2' } + sourceSets { + androidTest { + assets.srcDirs = ['src/main/assets', 'src/androidTest/assets', 'src/debug/assets/'] + java.srcDirs = ['src/main/java', 'src/androidTest/java', 'src/debug/java'] + } + } } dependencies { @@ -83,11 +89,11 @@ dependencies { implementation 'androidx.swiperefreshlayout:swiperefreshlayout:1.1.0' implementation 'androidx.preference:preference-ktx:1.2.0' implementation 'androidx.preference:preference-ktx:1.2.0' - implementation 'org.tensorflow:tensorflow-lite:2.7.0' - implementation 'org.tensorflow:tensorflow-lite-support:0.3.1' - implementation 'org.tensorflow:tensorflow-lite-metadata:0.3.1' - implementation 'org.tensorflow:tensorflow-lite-gpu:2.7.0' - implementation 'org.tensorflow:tensorflow-lite-select-tf-ops:2.6.0' + implementation 'org.tensorflow:tensorflow-lite:2.8.0' + implementation 'org.tensorflow:tensorflow-lite-support:0.4.0' + implementation 'org.tensorflow:tensorflow-lite-metadata:0.4.0' + implementation 'org.tensorflow:tensorflow-lite-gpu:2.8.0' + implementation 'org.tensorflow:tensorflow-lite-select-tf-ops:2.8.0' implementation 'androidx.camera:camera-lifecycle:1.0.2' implementation 'com.google.mlkit:pose-detection-common:17.0.0' diff --git a/app/src/androidTest/assets/0_orhan_1652085453257.csv b/app/src/androidTest/assets/0_orhan_1652085453257.csv new file mode 100644 index 00000000..d2301932 --- /dev/null +++ b/app/src/androidTest/assets/0_orhan_1652085453257.csv @@ -0,0 +1,33998 @@ +Quat_W_LF,Quat_X_LF,Quat_Y_LF,Quat_Z_LF,dq_W_LF,dq_X_LF,dq_Y_LF,dq_Z_LF,dv[1]_LF,dv[2]_LF,dv[3]_LF,Mag_X_LF,Mag_Y_LF,Mag_Z_LF,Quat_W_LW,Quat_X_LW,Quat_Y_LW,Quat_Z_LW,dq_W_LW,dq_X_LW,dq_Y_LW,dq_Z_LW,dv[1]_LW,dv[2]_LW,dv[3]_LW,Mag_X_LW,Mag_Y_LW,Mag_Z_LW,Quat_W_ST,Quat_X_ST,Quat_Y_ST,Quat_Z_ST,dq_W_ST,dq_X_ST,dq_Y_ST,dq_Z_ST,dv[1]_ST,dv[2]_ST,dv[3]_ST,Mag_X_ST,Mag_Y_ST,Mag_Z_ST,Quat_W_RW,Quat_X_RW,Quat_Y_RW,Quat_Z_RW,dq_W_RW,dq_X_RW,dq_Y_RW,dq_Z_RW,dv[1]_RW,dv[2]_RW,dv[3]_RW,Mag_X_RW,Mag_Y_RW,Mag_Z_RW,Quat_W_RF,Quat_X_RF,Quat_Y_RF,Quat_Z_RF,dq_W_RF,dq_X_RF,dq_Y_RF,dq_Z_RF,dv[1]_RF,dv[2]_RF,dv[3]_RF,Mag_X_RF,Mag_Y_RF,Mag_Z_RF,SampleTimeFine,activity 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diff --git a/app/src/androidTest/assets/resnet_model_22_06_2022.tflite b/app/src/androidTest/assets/resnet_model_22_06_2022.tflite new file mode 100644 index 00000000..ad85dab1 Binary files /dev/null and b/app/src/androidTest/assets/resnet_model_22_06_2022.tflite differ diff --git a/app/src/androidTest/assets/resnet_model_test.tflite b/app/src/androidTest/assets/resnet_model_test.tflite new file mode 100644 index 00000000..2909c8e1 Binary files /dev/null and b/app/src/androidTest/assets/resnet_model_test.tflite differ diff --git a/app/src/androidTest/java/sensors_in_paradise/sonar/InMemoryWindowTest.kt b/app/src/androidTest/java/sensors_in_paradise/sonar/InMemoryWindowTest.kt new file mode 100644 index 00000000..128b039b --- /dev/null +++ b/app/src/androidTest/java/sensors_in_paradise/sonar/InMemoryWindowTest.kt @@ -0,0 +1,80 @@ +package sensors_in_paradise.sonar + +import com.xsens.dot.android.sdk.events.XsensDotData +import org.junit.Assert +import org.junit.Test +import sensors_in_paradise.sonar.machine_learning.InMemoryWindow + +class InMemoryWindowTest { + @Test + fun initializationTest() { + val features = arrayOf("Quat_Z_LF", "dq_W_LF", "dv[1]_LF").map { it.uppercase() }.toTypedArray() + val window = InMemoryWindow(features, 2) + assert(features.contentEquals(window.keys.toTypedArray())) + } + @Test + fun addDataTest() { + val features = arrayOf("Quat_Z_LF", "dq_W_RW", "dv[1]_LF") + val window = InMemoryWindow(features, 2) + for (feature in features) { + assert(window.needsFeature(feature)) + } + val data = XsensDotData().apply { + quat = floatArrayOf(0f, 0f, 1f, 0f) + dq = doubleArrayOf(0.0, 0.0, 0.2, 23.0) + dv = doubleArrayOf(23.0, 0.0, 0.0) + } + + window.appendSensorData("LF", data) + Assert.assertFalse(window.hasEnoughDataToCompileWindow()) + window.appendSensorData("RW", data) + Assert.assertFalse(window.hasEnoughDataToCompileWindow()) + data.sampleTimeFine += 1L + + window.appendSensorData("LF", data) + Assert.assertFalse(window.hasEnoughDataToCompileWindow()) + data.sampleTimeFine += 1L + + window.appendSensorData("RW", data) + Assert.assertTrue(window.hasEnoughDataToCompileWindow()) + + window.compileWindow() + + } + @Test + fun forwardFillDataTest(){ + val features = arrayOf("Quat_W_LF", "Quat_X_LF", "Quat_Y_LF", "Quat_Z_LF") + val window = InMemoryWindow(features, 3) + + val data0 = XsensDotData().apply { + sampleTimeFine = 0L + quat = floatArrayOf(0f, Float.NaN, 1f, 0f) + dq = doubleArrayOf(0.0, 0.0, 0.2, 23.0) + dv = doubleArrayOf(23.0, 0.0, Double.NaN) + quat = floatArrayOf(0f, Float.NaN, 1f, 1f) + } + val data1 = XsensDotData().apply { + sampleTimeFine = 1L + quat = floatArrayOf(0f, 1f, Float.NaN, 2f) + } + val data2 = XsensDotData().apply { + sampleTimeFine = 2L + quat = floatArrayOf(0f, Float.NaN, 0f, 0f) + } + + window.appendSensorData("LF", data0) + Assert.assertFalse(windowHasNan(window)) + window.appendSensorData("LF", data1) + Assert.assertFalse(windowHasNan(window)) + window.appendSensorData("LF", data2) + Assert.assertFalse(windowHasNan(window)) + Assert.assertTrue(window["QUAT_W_LF"]!! == arrayListOf(Pair(0L, 0f), Pair(1L, 0f), Pair(2L, 0f))) + Assert.assertTrue(window["QUAT_X_LF"]!! == arrayListOf(Pair(0L, 0f), Pair(1L, 1f), Pair(2L, 1f))) + Assert.assertTrue(window["QUAT_Y_LF"]!! == arrayListOf(Pair(0L, 1f), Pair(1L, 1f), Pair(2L, 0f))) + Assert.assertTrue(window["QUAT_Z_LF"]!! == arrayListOf(Pair(0L, 1f), Pair(1L, 2f), Pair(2L, 0f))) + } + + private fun windowHasNan(window: InMemoryWindow): Boolean { + return window.values.any { arrayList -> arrayList.any { it.second.isNaN() } } + } +} diff --git a/app/src/androidTest/java/sensors_in_paradise/sonar/RecordingDataFileTest.kt b/app/src/androidTest/java/sensors_in_paradise/sonar/RecordingDataFileTest.kt new file mode 100644 index 00000000..2468134b --- /dev/null +++ b/app/src/androidTest/java/sensors_in_paradise/sonar/RecordingDataFileTest.kt @@ -0,0 +1,123 @@ +package sensors_in_paradise.sonar + +import android.util.Log +import androidx.test.platform.app.InstrumentationRegistry +import org.junit.Before +import org.junit.Test +import sensors_in_paradise.sonar.machine_learning.DataSet +import sensors_in_paradise.sonar.machine_learning.TFLiteModel +import sensors_in_paradise.sonar.screen_recording.RecordingDataFile +import sensors_in_paradise.sonar.use_cases.UseCase +import java.io.File + +class RecordingDataFileTest { + private val assetContext = InstrumentationRegistry.getInstrumentation().context + private val appContext = InstrumentationRegistry.getInstrumentation().targetContext + private val recordingFile = File(appContext.cacheDir, "recordingData.csv") + private val modelFile = File(appContext.cacheDir, "model_test.tflite") + + @Before + fun init() { + UseCase.extractFileFromAssets(assetContext, "0_orhan_1652085453257.csv", recordingFile) + UseCase.extractFileFromAssets(assetContext, "resnet_model_22_06_2022.tflite", modelFile) + } + + @Test + fun windowizeTest() { + val features = + arrayOf("Quat_Z_LF", "dq_W_LF", "dv[1]_LF").map { it.uppercase() }.toTypedArray() + val windowSize = 90 + val data = RecordingDataFile(recordingFile) + val startIndexes = data.getWindowStartIndexes(windowSize) + + assert(startIndexes.size > 0) + var i = 1 + for (startIndex in startIndexes) { + Log.d( + "RecordingDataFileTest-windowizeTest", + "Working on window $i of ${startIndexes.size}" + ) + val (window, activity) = data.getWindowAtIndex(startIndex, windowSize, features) + assert(window.size == features.size) + + // Test if compiling the window into a float buffer runs through + window.compileWindow() + i++ + } + } + + @Test + fun predictionPipelineTest() { + val model = TFLiteModel(modelFile) + + val features = model.getFeaturesToPredict().map { it.uppercase() }.toTypedArray() + + val data = RecordingDataFile(recordingFile) + val startIndexes = data.getWindowStartIndexes(model.windowSize) + var correctPredictions = 0 + assert(startIndexes.size > 0) + + var i = 1 + for (startIndex in startIndexes) { + val (window, activity) = data.getWindowAtIndex(startIndex, model.windowSize, features) + assert(window.size == features.size) + + // Test if compiling the window into a float buffer runs through + val input = window.compileWindowToArray() + val prediction = model.infer(arrayOf(input)) + val predictedLabel = model.convertPredictionToLabel(prediction[0]) + if (predictedLabel == activity) { + correctPredictions++ + } + Log.d( + "RecordingDataFileTest-predictionPipelineTest", + "Working on window $i of ${startIndexes.size}" + ) + i++ + } + val accuracy = (correctPredictions * 100 / startIndexes.size) + Log.d("RecordingDataFileTest", "Prediction accuracy on the example recording: $accuracy%") + } + + @Test + fun trainingPipelineTest() { + val model = TFLiteModel(modelFile) + val data = RecordingDataFile(recordingFile) + val dataSet = DataSet().apply { add(data) } + + val batches = dataSet.convertToBatches(7, model.windowSize, progressCallback = { progress -> + Log.d( + "RecordingDataFileTest-trainingPipelineTest", + "Batching dataset: $progress%" + ) + }) + + val accuracyBeforeTraining = model.evaluate(batches) { batch, window -> + Log.d( + "RecordingDataFileTest", + "Evaluating model before training. Batch: $batch%, Window Progress: $window%" + ) + } + val losses = model.train(batches, 5) { epoch, batch, window -> + Log.d( + "RecordingDataFileTest", + "Training model. Epoch $epoch%, batch $batch%, window $window%" + ) + } + Log.d( + "RecordingDataFileTest", + "Losses during training epochs: ${losses.joinToString()}" + ) + val accuracyAfterTraining = model.evaluate(batches) { batch, window -> + Log.d( + "RecordingDataFileTest", + "Evaluating model after training. Batch: $batch%, Window Progress: $window%" + ) + } + Log.d( + "RecordingDataFileTest", + "Prediction accuracy on the example recording before training: " + + "$accuracyBeforeTraining and after training: $accuracyAfterTraining%" + ) + } +} diff --git a/app/src/main/java/sensors_in_paradise/sonar/GlobalValues.kt b/app/src/main/java/sensors_in_paradise/sonar/GlobalValues.kt index 0f84c89c..eabb116e 100644 --- a/app/src/main/java/sensors_in_paradise/sonar/GlobalValues.kt +++ b/app/src/main/java/sensors_in_paradise/sonar/GlobalValues.kt @@ -3,8 +3,13 @@ package sensors_in_paradise.sonar import android.Manifest import android.content.Context import android.os.Build +import android.util.Log import com.xsens.dot.android.sdk.models.XsensDotPayload +import java.io.BufferedReader import java.io.File +import java.io.FileReader +import java.net.NetworkInterface +import java.net.SocketException import java.util.* import kotlin.collections.ArrayList @@ -17,10 +22,6 @@ class GlobalValues private constructor() { const val METADATA_JSON_FILENAME = "metadata.json" const val MEASUREMENT_MODE = XsensDotPayload.PAYLOAD_TYPE_CUSTOM_MODE_4 - fun getSensorRecordingsBaseDir(context: Context): File { - return context.getExternalFilesDir(null) ?: context.dataDir - } - fun getSensorRecordingsTempDir(context: Context): File { return context.dataDir.resolve("temp") } @@ -52,8 +53,6 @@ class GlobalValues private constructor() { return result } - val sensorTagPrefixes = listOf("LF", "LW", "ST", "RW", "RF") - fun formatTag(tagPrefix: String, deviceSetKey: String): String { return "$tagPrefix-$deviceSetKey" } @@ -66,6 +65,32 @@ class GlobalValues private constructor() { return minutes.toString().padStart(2, '0') + ":" + seconds.toString().padStart(2, '0') } + + fun getMacAddress(): String { + try { + val all = Collections.list(NetworkInterface.getNetworkInterfaces()) + for (nif in all) { + if (!nif.name.equals("wlan0", ignoreCase=true)) continue + + val macBytes = nif.hardwareAddress ?: return "" + + val res1 = StringBuilder() + for (b in macBytes) { + res1.append(String.format(Locale.US, "%02X:", b)) + } + + if (res1.isNotEmpty()) { + res1.deleteCharAt(res1.length - 1) + } + return res1.toString() + } + } catch (ex: SocketException) { + ex.message?.let { Log.e("GlobalValues", it) } + } + + return "02:00:00:00:00:00" + } + private val fileEmojiMap = mapOf( "mp4" to "\uD83C\uDF9E️", "json" to "\uD83D\uDCD8", @@ -80,5 +105,14 @@ class GlobalValues private constructor() { val extension = name.substring(name.lastIndexOf(".") + 1) return fileEmojiMap[extension] ?: "\uD83D\uDCC4" } + @Throws(NumberFormatException::class) + fun getCSVHeaderAwareFileReader(inputFile: File): BufferedReader { + val fileReader = BufferedReader(FileReader(inputFile)) + var line = fileReader.readLine() + while (line != "") { + line = fileReader.readLine() + } + return fileReader + } } } diff --git a/app/src/main/java/sensors_in_paradise/sonar/ObservableArrayList.kt b/app/src/main/java/sensors_in_paradise/sonar/ObservableArrayList.kt index eb34dceb..ba6b4f19 100644 --- a/app/src/main/java/sensors_in_paradise/sonar/ObservableArrayList.kt +++ b/app/src/main/java/sensors_in_paradise/sonar/ObservableArrayList.kt @@ -7,67 +7,113 @@ open class ObservableArrayList : ArrayList() { fun addOnSizeChangedListener(listener: (size: Int) -> Unit) { onSizeChangedListeners.add(listener) } + private val onItemAddedListener = ArrayList<(item: T, index: Int)-> Unit>() + fun addOnItemAddedListener(listener: (item: T, index: Int) -> Unit) { + onItemAddedListener.add(listener) + } + private val onItemRemovedListener = ArrayList<(item: T, index: Int)-> Unit>() + fun addOnItemRemovedListener(listener: (item: T, index: Int) -> Unit) { + onItemRemovedListener.add(listener) + } private fun notifyObserversOfSizeChanged() { for (listener in onSizeChangedListeners) { listener(size) } } - + private fun notifyObserversOfItemAdded(item: T, index: Int) { + for (listener in onItemAddedListener) { + listener(item, index) + } + } + private fun notifyObserversOfItemRemoved(item: T, index: Int) { + for (listener in onItemRemovedListener) { + listener(item, index) + } + } override fun add(element: T): Boolean { val result = super.add(element) notifyObserversOfSizeChanged() + notifyObserversOfItemAdded(element, size - 1) return result } override fun add(index: Int, element: T) { super.add(index, element) notifyObserversOfSizeChanged() + notifyObserversOfItemAdded(element, index) } override fun addAll(elements: Collection): Boolean { + val sizeBefore = size val result = super.addAll(elements) notifyObserversOfSizeChanged() + for ((i, element) in elements.withIndex()) { + notifyObserversOfItemAdded(element, sizeBefore + i) + } return result } override fun addAll(index: Int, elements: Collection): Boolean { val result = super.addAll(index, elements) notifyObserversOfSizeChanged() + for ((i, element) in elements.withIndex()) { + notifyObserversOfItemAdded(element, index + i) + } return result } override fun remove(element: T): Boolean { + val index = super.indexOf(element) val result = super.remove(element) notifyObserversOfSizeChanged() + notifyObserversOfItemRemoved(element, index) return result } override fun removeAt(index: Int): T { + val item = this[index] val result = super.removeAt(index) notifyObserversOfSizeChanged() + notifyObserversOfItemRemoved(item, index) return result } override fun removeAll(elements: Collection): Boolean { - val result = super.removeAll(elements) + val removedElements = elements.filter { it in this }.map { Pair(indexOf(it), it) } + val result = super.removeAll(elements.toSet()) notifyObserversOfSizeChanged() + for ((i, removedElement) in removedElements) { + notifyObserversOfItemRemoved(removedElement, i) + } return result } override fun removeIf(filter: Predicate): Boolean { + val removedElements = this.filter { filter.test(it) }.map { Pair(indexOf(it), it) } val result = super.removeIf(filter) notifyObserversOfSizeChanged() + for ((i, removedElement) in removedElements) { + notifyObserversOfItemRemoved(removedElement, i) + } return result } override fun removeRange(fromIndex: Int, toIndex: Int) { + val removedElements = this.subList(fromIndex, toIndex) super.removeRange(fromIndex, toIndex) notifyObserversOfSizeChanged() + for ((i, removedElement) in removedElements.withIndex()) { + notifyObserversOfItemRemoved(removedElement, fromIndex + i) + } } override fun clear() { + val removedElements = this.toList() super.clear() notifyObserversOfSizeChanged() + for ((i, removedElement) in removedElements.withIndex()) { + notifyObserversOfItemRemoved(removedElement, i) + } } } diff --git a/app/src/main/java/sensors_in_paradise/sonar/XSensDotDeviceWithOfflineMetadata.kt b/app/src/main/java/sensors_in_paradise/sonar/XSensDotDeviceWithOfflineMetadata.kt index 21c955a1..8e122d08 100644 --- a/app/src/main/java/sensors_in_paradise/sonar/XSensDotDeviceWithOfflineMetadata.kt +++ b/app/src/main/java/sensors_in_paradise/sonar/XSensDotDeviceWithOfflineMetadata.kt @@ -37,6 +37,13 @@ class XSensDotDeviceWithOfflineMetadata( } ) } + fun getTagPrefix(): String? { + return extractTagPrefixFromTag(tag) + } + + fun isTagValid(): Boolean { + return tag.matches(sensorTagRegex) + } fun hasSetColor(): Boolean { return when (getSet()) { @@ -56,5 +63,6 @@ class XSensDotDeviceWithOfflineMetadata( fun extractTagPrefixFromTag(tag: String): String? { return if (doesTagMatchPattern(tag)) tag.substring(0, 2) else null } + private val sensorTagRegex = Regex("^[A-Z]+-\\d+\$") } } diff --git a/app/src/main/java/sensors_in_paradise/sonar/machine_learning/Batch.kt b/app/src/main/java/sensors_in_paradise/sonar/machine_learning/Batch.kt new file mode 100644 index 00000000..b10b6295 --- /dev/null +++ b/app/src/main/java/sensors_in_paradise/sonar/machine_learning/Batch.kt @@ -0,0 +1,42 @@ +package sensors_in_paradise.sonar.machine_learning + +class Batch( + private val dataSet: DataSet, + private val windowStartIndexesPerRecording: ArrayList> +) { + init { + dataSet.addOnItemAddedListener { _, index -> + windowStartIndexesPerRecording.add(index, ArrayList()) + } + dataSet.addOnItemRemovedListener { _, index -> + windowStartIndexesPerRecording.removeAt(index) + } + } + + /**Loads the specified batch data into RAM and return a Pair of windows and one hot encoded labels*/ + fun compile( + model: TFLiteModel, + progressCallback: ((Int) -> Unit)? = null + ): Pair>, Array> { + val features = model.getFeaturesToPredict() + val numFeatures = features.size + val numWindows = windowStartIndexesPerRecording.sumOf { it.size } + val windows = Array(numWindows) { Array(model.windowSize) { FloatArray(numFeatures) } } + var windowIndex = 0 + val labelsOneHotEncoded = Array(numWindows) { FloatArray(model.getNumOutputClasses()) } + for ((recordingIndex, startIndexes) in windowStartIndexesPerRecording.withIndex()) { + for (startIndex in startIndexes) { + val (window, label) = dataSet[recordingIndex].getWindowAtIndex( + startIndex, + model.windowSize, + features + ) + window.compileWindowToArray(windows[windowIndex]) + labelsOneHotEncoded[windowIndex] = model.convertLabelToOneHotEncoding(label) + windowIndex++ + progressCallback?.let { it((windowIndex * 100) / numWindows) } + } + } + return Pair(windows, labelsOneHotEncoded) + } +} diff --git a/app/src/main/java/sensors_in_paradise/sonar/machine_learning/DataSet.kt b/app/src/main/java/sensors_in_paradise/sonar/machine_learning/DataSet.kt new file mode 100644 index 00000000..4a9732d6 --- /dev/null +++ b/app/src/main/java/sensors_in_paradise/sonar/machine_learning/DataSet.kt @@ -0,0 +1,39 @@ +package sensors_in_paradise.sonar.machine_learning + +import sensors_in_paradise.sonar.ObservableArrayList +import sensors_in_paradise.sonar.screen_recording.RecordingDataFile + +open class DataSet : ObservableArrayList() { + + fun convertToBatches( + windowsPerBatch: Int, + windowSize: Int, + shuffle: Boolean = true, + progressCallback: ((Int) -> Unit)? = null + ): ArrayList { + val windowStartIndexesPerRecording = this.map { it.getWindowStartIndexes(windowSize) } + + val indexesOfIndexes = ArrayList>() + for ((recordingIndex, startIndexes) in windowStartIndexesPerRecording.withIndex()) { + for (startIndex in startIndexes) { + indexesOfIndexes.add(Pair(recordingIndex, startIndex)) + } + } + if (shuffle) { + indexesOfIndexes.shuffle() + } + val numWindows = indexesOfIndexes.size + val numBatches = numWindows / windowsPerBatch + val result = ArrayList(numBatches) + for (b in 0 until numBatches) { + val batchStartIndexesPerRecording = ArrayList(this.map { ArrayList() }) + for (i in b * windowsPerBatch until b * windowsPerBatch + windowsPerBatch) { + val (recordingIndex, windowStartIndex) = indexesOfIndexes[i] + batchStartIndexesPerRecording[recordingIndex].add(windowStartIndex) + } + result.add(Batch(this, batchStartIndexesPerRecording)) + progressCallback?.let { it((b * 100) / numBatches) } + } + return result + } +} diff --git a/app/src/main/java/sensors_in_paradise/sonar/machine_learning/InMemoryWindow.kt b/app/src/main/java/sensors_in_paradise/sonar/machine_learning/InMemoryWindow.kt new file mode 100644 index 00000000..c91983ff --- /dev/null +++ b/app/src/main/java/sensors_in_paradise/sonar/machine_learning/InMemoryWindow.kt @@ -0,0 +1,234 @@ +package sensors_in_paradise.sonar.machine_learning + +import android.util.Log +import com.xsens.dot.android.sdk.events.XsensDotData +import java.nio.FloatBuffer +import java.util.Collections.max + +class InMemoryWindow(featuresWithSensorTagPrefix: Array, val windowSize: Int = 90) : + LinkedHashMap>>() { + class SensorsOutOfSyncException(msg: String = "Sensors are out of sync") : + IllegalStateException(msg) + + private val featuresPerSensorTagPrefix = LinkedHashMap>() + private val nansPerFeature = LinkedHashMap() + init { + for (feature in featuresWithSensorTagPrefix) { + if (feature.isNotEmpty()) { + put(feature.uppercase(), ArrayList(windowSize + cushion)) + nansPerFeature[feature.uppercase()] = 0 + } + } + for (deviceTagPrefix in extractDeviceTagPrefixes(this.keys)) { + Log.w( + "InMemoryWindow-init", + "deviceTagPrefix in features: $deviceTagPrefix" + ) + featuresPerSensorTagPrefix[deviceTagPrefix] = + extractFeatures(deviceTagPrefix, this.keys) + } + } + + fun needsFeature(featureWithSensorTagPrefix: String): Boolean { + return featureWithSensorTagPrefix.uppercase() in this + } + + fun appendSensorData(featureWithSensorTagPrefix: String, value: Float, timeStamp: Long) { + var v = value + val featureValues = this[featureWithSensorTagPrefix.uppercase()] + if (featureValues != null) { + // insert the new value at the end of the list or if the timestamp of the new value + // is lower than the largest timestamp, insert the new value at the correct position + var insertionIndex = featureValues.size + if (featureValues.isNotEmpty()) { + if (featureValues.last().first > timeStamp) { + insertionIndex = featureValues.indexOfLast { it.first < timeStamp } + 1 + } + } + if (value.isNaN()) { + val nansReceived = nansPerFeature[featureWithSensorTagPrefix] + nansPerFeature[featureWithSensorTagPrefix] = nansReceived!! + 1 + v = + if (insertionIndex == 0) 0.0f else featureValues[insertionIndex - 1].second + } + featureValues.add( + insertionIndex, Pair( + timeStamp, + v + ) + ) + } + } + + fun appendSensorData(deviceTagPrefix: String, xsensDotData: XsensDotData) { + val timeStamp: Long = xsensDotData.sampleTimeFine + if (deviceTagPrefix !in featuresPerSensorTagPrefix.keys) { + Log.w( + "InMemoryWindow-appendSensorData", + "The device tag prefix $deviceTagPrefix for the given sensor data is not " + + "mentioned in the features that should be collected for this window." + ) + return + } + for (featureTag in featuresPerSensorTagPrefix[deviceTagPrefix]!!) { + val featureKey = "${featureTag}_$deviceTagPrefix" + val featureValues = this[featureKey] + if (featureValues != null) { + // replace nan values with the forward fill method except for the first time stamp + val value = getValueFromXsensDotData(featureTag, xsensDotData) + appendSensorData(featureKey, value, timeStamp) + } + } + } + + private fun getValueFromXsensDotData(featureTag: String, xsensDotData: XsensDotData): Float { + // Features can have tags with axis chars like Mag_Y, Quat_Z or dq_X + // OR they can have index spelling like dv[1], dv[2] to determine their axis... + val hasAxisIndexSpelling = featureTag.contains(indexSpellingRegex) + val featureType = + if (hasAxisIndexSpelling) featureTag.substringBefore("[") else featureTag.split("_")[0] + val featureAxisTag = if (hasAxisIndexSpelling) featureTag.substringAfter("[") + .substringBefore("]") else featureTag.split("_")[1] + val featureAxisIndex = featureAxisTagToIndex(featureType, featureAxisTag) + return when (featureType) { + "QUAT" -> xsensDotData.quat[featureAxisIndex] + "ACC" -> xsensDotData.acc[featureAxisIndex].toFloat() + "MAG" -> xsensDotData.mag[featureAxisIndex].toFloat() + "GYR" -> xsensDotData.gyr[featureAxisIndex].toFloat() + "DQ" -> xsensDotData.dq[featureAxisIndex].toFloat() + "DV" -> xsensDotData.dv[featureAxisIndex].toFloat() + else -> throw IllegalArgumentException("Unknown feature type: $featureType") + } + } + + private fun featureAxisTagToIndex(featureType: String, featureAxisTag: String): Int { + return when (featureAxisTag) { + "W" -> 0 + "X" -> 1 + "Y" -> 2 + "Z" -> 3 + "1" -> 1 + "2" -> 2 + "3" -> 3 + else -> throw IllegalArgumentException("Invalid feature axis $featureAxisTag") + } - if (featureType == "QUAT" || featureType == "DQ") 0 else 1 + } + + private fun extractDeviceTagPrefixes(keys: MutableSet): List { + return keys.map { it.substringAfterLast("_") }.distinct() + .toList() + } + + private fun extractFeatures(deviceTagPrefix: String, keys: MutableSet): List { + return keys.filter { it.endsWith(deviceTagPrefix) }.map { it.substringBeforeLast("_") } + .toList() + } + + fun getRequiredSensorTagPrefixes(): List { + return extractDeviceTagPrefixes(this.keys) + } + + fun compileWindow(): FloatBuffer { + // find starting indices of feature vectors closest to the starting timestamp + val startingIndices = getStartIndices() + + // return byteBuffer of feature vectors starting at the starting indices + val buffer = FloatBuffer.allocate(this.keys.size * windowSize) + + for (i in 0 until windowSize) { + for ((j, key) in this.keys.withIndex()) { + val index = startingIndices[j] + i + if (index < this[key]!!.size) { + buffer.put(this[key]!![index].second) + } else { + throw IllegalStateException("Not enough data to fill window for feature: $key") + } + } + } + buffer.rewind() + return buffer + } + fun compileWindowToArray(): Array { + return Array(windowSize) { FloatArray(keys.size) }.apply { compileWindowToArray(this) } + } + fun compileWindowToArray(array: Array) { + // find starting indices of feature vectors closest to the starting timestamp + val startingIndices = getStartIndices() + + // return array of feature vectors starting at the starting indices + + for (i in 0 until windowSize) { + for ((j, key) in this.keys.withIndex()) { + val index = startingIndices[j] + i + if (index < this[key]!!.size) { + array[i][j] = this[key]!![index].second + } else { + throw IllegalStateException("Not enough data to fill window for feature: $key") + } + } + } + } + + fun clearValues() { + for (key in this.keys) { + this[key]?.clear() + } + } + + @Throws(SensorsOutOfSyncException::class) + private fun getStartIndices(): IntArray { + // find latest starting timestamp of each feature + val startingTimestamp: Long = max(this.values.map { if (it.isEmpty()) 0 else it[0].first }) + // find starting indices of feature vectors closest to the starting timestamp + return this.values.map { + val closestIndex = findClosestIndex(startingTimestamp, it) + if (closestIndex > sensorsOutOfSyncMinStartIndexDifference) { + throw SensorsOutOfSyncException("Sensors are out of synch by at least $closestIndex measurements") + } + return@map closestIndex + }.toIntArray() + } + + private fun hasEnoughDataToCompileWindow(startIndices: IntArray): Boolean { + for ((i, key) in this.keys.withIndex()) { + + if (startIndices[i] + windowSize > (this[key]?.size ?: 0)) { + return false + } + } + return true + } + + @Throws(SensorsOutOfSyncException::class) + fun hasEnoughDataToCompileWindow(): Boolean { + return hasEnoughDataToCompileWindow(getStartIndices()) + } + + private fun findClosestIndex( + startingTimestamp: Long, + valuesSorted: ArrayList> + ): Int { + var closestIndex = 0 + var closestDifference = Long.MAX_VALUE + for (i in 0 until valuesSorted.size) { + val difference = Math.abs(valuesSorted[i].first - startingTimestamp) + if (difference <= closestDifference) { + closestIndex = i + closestDifference = difference + } else { + return closestIndex + } + } + return closestIndex + } + + fun getNansReceivedPerFeature(): Map { + return nansPerFeature + } + + companion object { + private const val cushion = 10 + private val indexSpellingRegex = Regex("\\[\\d\\]") + private const val sensorsOutOfSyncMinStartIndexDifference = 100 + } +} diff --git a/app/src/main/java/sensors_in_paradise/sonar/machine_learning/TFLiteModel.kt b/app/src/main/java/sensors_in_paradise/sonar/machine_learning/TFLiteModel.kt new file mode 100644 index 00000000..61529f53 --- /dev/null +++ b/app/src/main/java/sensors_in_paradise/sonar/machine_learning/TFLiteModel.kt @@ -0,0 +1,279 @@ +package sensors_in_paradise.sonar.machine_learning + +import android.util.Log +import org.tensorflow.lite.Interpreter +import org.tensorflow.lite.support.metadata.MetadataExtractor +import java.io.File +import java.nio.ByteBuffer +import java.nio.FloatBuffer +import kotlin.Array as Array1 + +class TFLiteModel @Throws(InvalidModelMetadata::class) constructor(tfLiteModelFile: File) { + class InvalidModelMetadata(message: String) : Exception(message) + + val windowSize: Int + private val frequency = 60 + private val output: FloatBuffer + private val interpreter: Interpreter + private val extractor: MetadataExtractor + private val labels: ArrayList + private val features: Array1 + val signatureKeys: Array1 + + init { + val buffer = ByteBuffer.allocate(tfLiteModelFile.readBytes().size) + buffer.put(tfLiteModelFile.readBytes()) + buffer.rewind() + extractor = MetadataExtractor(buffer) + if (!hasMetadata()) { + throw InvalidModelMetadata("The model ${tfLiteModelFile.name} does not have any metadata") + } + val inputs = extractor.getInputTensorShape(0) + interpreter = Interpreter(tfLiteModelFile).apply { + resizeInput(0, inputs) + } + signatureKeys = interpreter.signatureKeys + features = readFeatureFile().split("\n").filter { it.isNotEmpty() }.toTypedArray() + labels = ArrayList(readLabelsFile().split("\n").filter { it.isNotEmpty() }) + output = FloatBuffer.allocate(labels.size) + windowSize = extractor.getInputTensorShape(0)[1] + } + + private fun hasMetadata(): Boolean { + return extractor.hasMetadata() + } + + fun close() { + interpreter.close() + } + /* + fun predict(sensorDataByteBuffer: ByteBuffer): FloatArray { + interpreter.run( + sensorDataByteBuffer, + output + ) + return output[0] + }*/ + + private fun readFeatureFile(): String { + val inputStream = extractor.getAssociatedFile("features.txt") + return inputStream.bufferedReader().use { it.readText() } + } + + private fun readLabelsFile(): String { + val inputStream = extractor.getAssociatedFile("labels.txt") + return inputStream.bufferedReader().use { it.readText() } + } + + fun getLabelsMap(): Map { + return labels.mapIndexed { index, label -> + index to label + }.toMap() + } + + fun getNumOutputClasses(): Int { + return labels.size + } + + fun getDeviceTags(): Array1 { + return features.map { it.substringAfterLast("_") }.distinct().filter { it != "" } + .toTypedArray() + } + + fun getNumDevices(): Int { + return getDeviceTags().size + } + + fun getFeaturesToPredict(): Array1 { + return features + } + + fun getWindowInMilliSeconds(): Long { + return 1000L * (windowSize / frequency) + } + + fun infer(windows: Array1>): Array1 { + val inputs = HashMap() + inputs["input_window"] = windows + val outputs = HashMap() + Log.d("TFLiteModel-runInfer", "Output buffer capacity: ${labels.size * windows.size}") + val output = Array1(windows.size) { FloatArray(labels.size) } + outputs["output"] = output + interpreter.runSignature(inputs, outputs, "infer") + return output + } + + fun evaluate( + batches: Collection, + progressCallback: ((Int, Int) -> Unit)? = null + ): Float { + var accuracy = 0f + for ((index, batch) in batches.withIndex()) { + val interBatchProgress = (index * 100) / batches.size + val batchAccuracy = evaluate(batch) { intraBatchProgress -> + progressCallback?.let { + it(interBatchProgress, intraBatchProgress) + } + } + accuracy += batchAccuracy / batches.size + } + return accuracy + } + + fun evaluate(batch: Batch, progressCallback: ((Int) -> Unit)? = null): Float { + val (windows, labels) = batch.compile(this, progressCallback) + val prediction = infer(windows) + return getAccuracyFromPredictions(prediction, labels) + } + + fun train(batch: Batch, progressCallback: ((Int) -> Unit)? = null): Float { + val (windows, labels) = batch.compile(this, progressCallback) + return train(windows, labels) + } + + private fun toPercentage(v: Int, max: Int, min: Int = 0): Int { + return (v * 100) / (max - min) + } + + /*Returns an array of losses with one entry per epoch*/ + fun train( + batches: Collection, + epochs: Int = 1, + progressCallback: ((Int, Int, Int) -> Unit)? = null + ): FloatArray { + val losses = FloatArray(epochs) + for (e in 0 until epochs) { + var epochLoss = 0f + for ((batchIndex, batch) in batches.withIndex()) { + val batchLoss = train(batch) { intraBatchProgress -> + progressCallback?.let { + it( + toPercentage(e, epochs), + toPercentage(batchIndex, batches.size), + intraBatchProgress + ) + } + } + epochLoss += batchLoss / batches.size + } + losses[e] = epochLoss + } + return losses + } + + fun train(windows: Array1>, labels: Array1): Float { + if (windows.isEmpty()) { + throw java.lang.IllegalArgumentException("Can't run training on an empty windows array") + } + val numFeatures = features.size + if (windows.size != labels.size) { + throw java.lang.IllegalArgumentException("Window size ${windows.size} is different to " + + "number of labels ${labels.size}") + } + if (windows[0].size != windowSize) { + throw java.lang.IllegalArgumentException("Size of provided windows ${windows[0].size} " + + "is different to expected window size: $windowSize") + } + if (windows[0][0].size != numFeatures) { + throw java.lang.IllegalArgumentException("Number of provided features " + + "${windows[0][0].size} inside the windows is different to expected" + + " number of features: $numFeatures") + } + val batchSize = windows.size + Log.d("Test-TFLiteModel - runTraining", "Running training on batch size $batchSize") + val inputs = HashMap() + inputs["input_windows"] = windows + inputs["labels"] = labels + val outputs = HashMap() + val loss = FloatBuffer.allocate(1) + loss.rewind() + outputs["loss"] = loss + interpreter.runSignature(inputs, outputs, "train") + return loss.array()[0] + } + + fun runSave(file: File) { + val inputs = HashMap() + val outputs = HashMap() + val checkpointPath = file.absolutePath + inputs["checkpoint_path"] = checkpointPath + interpreter.runSignature(inputs, outputs, "save") + } + + fun runRestore(file: File) { + val inputs = HashMap() + val outputs = HashMap() + val checkpointPath = file.absolutePath + inputs["checkpoint_path"] = checkpointPath + interpreter.runSignature(inputs, outputs, "restore") + } + + @Throws(IllegalArgumentException::class) + fun convertPredictionToLabel(prediction: FloatArray): String { + val labels = getLabelsMap() + if (labels.size != prediction.size) { + throw IllegalArgumentException( + "Number of items in prediction ${prediction.size} " + + "is different to number of labels in this model ${labels.size}" + ) + } + val index = prediction.indices.maxByOrNull { + prediction[it] + }!! + return labels[index]!! + } + + @Throws(IllegalArgumentException::class) + fun convertLabelToOneHotEncoding(label: String): FloatArray { + val labels = getLabelsMap() + if (!labels.containsValue(label)) { + throw IllegalArgumentException( + "Label $label" + + "is not in labels of model: $labels" + ) + } + val index = labels.filter { label == it.value }.keys.first() + + val result = FloatArray(labels.size) + result[index] = 1f + return result + } + + fun getAccuracyFromPredictions( + predictions: Array1, + actualLabels: Array1 + ): Float { + if (predictions.size != actualLabels.size) { + throw IllegalArgumentException("Size of predictions (${predictions.size}) is " + + "unequals to size of actualLabels (${actualLabels.size})") + } + var correctPredictions = 0f + for (i in predictions.indices) { + val prediction = predictions[i] + val label = actualLabels[i] + if (convertPredictionToLabel(prediction) == label) { + correctPredictions++ + } + } + return correctPredictions / predictions.size + } + + fun getAccuracyFromPredictions( + predictions: Array1, + actualLabels: Array1 + ): Float { + if (predictions.size != actualLabels.size) { + throw IllegalArgumentException("Size of predictions (${predictions.size}) " + + "is unequals to size of actualLabels (${actualLabels.size})") + } + var correctPredictions = 0f + for (i in predictions.indices) { + val prediction = predictions[i] + val label = actualLabels[i] + if (convertPredictionToLabel(prediction) == convertPredictionToLabel(label)) { + correctPredictions++ + } + } + return correctPredictions / predictions.size + } +} diff --git a/app/src/main/java/sensors_in_paradise/sonar/screen_connection/ConnectionScreen.kt b/app/src/main/java/sensors_in_paradise/sonar/screen_connection/ConnectionScreen.kt index fd11df42..f70e6f2c 100644 --- a/app/src/main/java/sensors_in_paradise/sonar/screen_connection/ConnectionScreen.kt +++ b/app/src/main/java/sensors_in_paradise/sonar/screen_connection/ConnectionScreen.kt @@ -203,7 +203,6 @@ class ConnectionScreen(private val scannedDevices: XSENSArrayList) : } override fun onConnectionCancelRequested(device: XsensDotDevice) { - // TODO Check if works device.disconnect() activity.runOnUiThread { diff --git a/app/src/main/java/sensors_in_paradise/sonar/screen_connection/SensorAdapter.kt b/app/src/main/java/sensors_in_paradise/sonar/screen_connection/SensorAdapter.kt index 7b3a1720..fe4b8d90 100644 --- a/app/src/main/java/sensors_in_paradise/sonar/screen_connection/SensorAdapter.kt +++ b/app/src/main/java/sensors_in_paradise/sonar/screen_connection/SensorAdapter.kt @@ -1,5 +1,6 @@ package sensors_in_paradise.sonar.screen_connection +import android.annotation.SuppressLint import android.content.Context import android.graphics.drawable.Drawable import android.view.LayoutInflater @@ -10,11 +11,13 @@ import android.widget.Button import android.widget.ProgressBar import android.widget.ViewFlipper import android.widget.ImageView +import androidx.appcompat.content.res.AppCompatResources import androidx.cardview.widget.CardView import androidx.recyclerview.widget.RecyclerView import com.xsens.dot.android.sdk.models.XsensDotDevice import sensors_in_paradise.sonar.R import sensors_in_paradise.sonar.XSENSArrayList +import sensors_in_paradise.sonar.util.UIHelper class SensorAdapter( context: Context, @@ -22,9 +25,12 @@ class SensorAdapter( private val connectionCallbackUI: UIDeviceConnectionInterface ) : RecyclerView.Adapter() { - private var disconnectedDrawable: Drawable? = context.getDrawable(R.drawable.ic_baseline_link_off_24) - private var connectedDrawable: Drawable? = context.getDrawable(R.drawable.ic_baseline_link_24) - private var syncedDrawable: Drawable? = context.getDrawable(R.drawable.ic_baseline_sync_24) + private var disconnectedDrawable: Drawable? = + AppCompatResources.getDrawable(context, R.drawable.ic_baseline_link_off_24) + private var connectedDrawable: Drawable? = + AppCompatResources.getDrawable(context, R.drawable.ic_baseline_link_24) + private var syncedDrawable: Drawable? = + AppCompatResources.getDrawable(context, R.drawable.ic_baseline_sync_24) /** * Provide a reference to the type of views that you are using @@ -40,6 +46,8 @@ class SensorAdapter( val cancelButton: Button = view.findViewById(R.id.button_cancel_connection_sensor_device) val statusIV: ImageView = view.findViewById(R.id.imageView_status_connection_fragment) val sensorSetView: View = view.findViewById(R.id.view_sensorSetColor_sensorDevice) + val tagSpellingWarningTextView: TextView = + view.findViewById(R.id.tv_sensorTagSpellingWarning_sensorDevice) } // Create new views (invoked by the layout manager) @@ -52,6 +60,7 @@ class SensorAdapter( } // Replace the contents of a view (invoked by the layout manager) + @SuppressLint("SetTextI18n") override fun onBindViewHolder(viewHolder: ViewHolder, position: Int) { // Get element from your dataset at this position and replace the @@ -87,7 +96,8 @@ class SensorAdapter( viewHolder.batteryPB.progress = if (isConnected) device.batteryPercentage else 0 viewHolder.batteryPB.visibility = if (isConnected) View.VISIBLE else View.GONE - viewHolder.cancelButton.visibility = if (connectionCallbackUI.isSyncing) View.GONE else View.VISIBLE + viewHolder.cancelButton.visibility = + if (connectionCallbackUI.isSyncing) View.GONE else View.VISIBLE val hasSetColor = device.hasSetColor() if (hasSetColor) { @@ -103,6 +113,19 @@ class SensorAdapter( } viewHolder.statusIV.setImageDrawable(statusDrawable) + + val isSensorTagCompliant = device.isTagValid() + viewHolder.tagSpellingWarningTextView.apply { + visibility = + if (isSensorTagCompliant || !isConnected) View.GONE else View.VISIBLE + setOnClickListener { + UIHelper.showAlert( + context, + context.getString(R.string.sensor_tag_prefix_pattern_explanation), + "Sensor tags not compliant" + ) + } + } } private fun getConnectionStateLabel(connectionState: Int): String { diff --git a/app/src/main/java/sensors_in_paradise/sonar/screen_prediction/PredictionScreen.kt b/app/src/main/java/sensors_in_paradise/sonar/screen_prediction/PredictionScreen.kt index 25497520..e8901bbd 100644 --- a/app/src/main/java/sensors_in_paradise/sonar/screen_prediction/PredictionScreen.kt +++ b/app/src/main/java/sensors_in_paradise/sonar/screen_prediction/PredictionScreen.kt @@ -1,6 +1,5 @@ package sensors_in_paradise.sonar.screen_prediction -import android.annotation.SuppressLint import android.app.Activity import android.content.Context import android.os.Handler @@ -16,16 +15,19 @@ import com.google.android.material.button.MaterialButton import com.xsens.dot.android.sdk.events.XsensDotData import com.xsens.dot.android.sdk.models.XsensDotPayload import sensors_in_paradise.sonar.* +import sensors_in_paradise.sonar.machine_learning.InMemoryWindow +import sensors_in_paradise.sonar.machine_learning.TFLiteModel import sensors_in_paradise.sonar.screen_connection.ConnectionInterface +import sensors_in_paradise.sonar.machine_learning.TFLiteModel.InvalidModelMetadata import sensors_in_paradise.sonar.screen_prediction.barChart.PredictionBarChart import sensors_in_paradise.sonar.screen_train.PredictionHistoryStorage import sensors_in_paradise.sonar.screen_train.PredictionHistoryStorage.Prediction import sensors_in_paradise.sonar.use_cases.UseCase -import sensors_in_paradise.sonar.util.PredictionHelper import sensors_in_paradise.sonar.util.PreferencesHelper import sensors_in_paradise.sonar.util.UIHelper import sensors_in_paradise.sonar.util.dialogs.MessageDialog -import java.nio.ByteBuffer +import java.io.File +import java.lang.IllegalStateException import kotlin.math.round class PredictionScreen( @@ -33,7 +35,6 @@ class PredictionScreen( private val devices: XSENSArrayList, private val sensorOccupationInterface: SensorOccupationInterface? ) : ScreenInterface, ConnectionInterface { - private lateinit var activity: Activity private lateinit var context: Context private lateinit var recyclerView: RecyclerView @@ -42,47 +43,33 @@ class PredictionScreen( private lateinit var progressBar: ProgressBar private lateinit var timer: Chronometer private lateinit var textView: TextView - private lateinit var predictionBarChart: PredictionBarChart + private var predictionBarChart: PredictionBarChart? = null private lateinit var toggleMotionLayout: MotionLayout private lateinit var metadataStorage: XSensDotMetadataStorage private var predictionHistoryStorage: PredictionHistoryStorage? = null - private lateinit var predictionHelper: PredictionHelper - private val rawSensorDataMap = mutableMapOf>>() private var lastPredictionTime = 0L - - private val numOutputs = 6 - val outputLabelMap = mapOf( - 0 to "Running", - 1 to "Squats", - 2 to "Stairs Down", - 3 to "1 min Pitch", - 4 to "Standing", - 5 to "Walking", - ).withDefault { "" } - - private val numDevices = 5 private var numConnectedDevices = 0 private var isRunning = false - + private var window: InMemoryWindow? = null private lateinit var mainHandler: Handler - - private val predictionInterval = 4000L + private var predictionInterval: Long? = null private val updatePredictionTask = object : Runnable { override fun run() { - processAndPredict() - mainHandler.postDelayed(this, predictionInterval) + if (isRunning) { + predict() + mainHandler.postDelayed(this, predictionInterval!!) + } } } private val updateProgressBarTask = object : Runnable { override fun run() { var progress = 0 - if (isRunning) { progress = - ((100 * (System.currentTimeMillis() - lastPredictionTime)) / predictionInterval).toInt() + ((100 * (System.currentTimeMillis() - lastPredictionTime)) / predictionInterval!!).toInt() mainHandler.postDelayed(this, 40) } progressBar.progress = progress @@ -98,64 +85,84 @@ class PredictionScreen( } } - private fun clearBuffers() { - rawSensorDataMap.clear() - } - - private fun resetSensorDataLists() { - for (key in rawSensorDataMap.keys) { - rawSensorDataMap[key]?.clear() + private fun checkPredictionPreconditions(): Boolean { + val isInvalidTagConnected = + devices.getConnectedWithOfflineMetadata().any { !it.isTagValid() } + if (isInvalidTagConnected) { + MessageDialog( + context, + context.getString(R.string.sensor_tag_prefix_pattern_explanation), + "Tags of connected sensors invalid" + ) + return false + } + val requiredButNotConnectedDevices = getRequiredButNotConnectedDevices() + if (requiredButNotConnectedDevices.isNotEmpty()) { + UIHelper.showAlert( + context = context, + message = "The model for this use case requires sensors with the following " + + "tag prefixes to be connected:\n" + + " ${model!!.getDeviceTags().joinToString("\n ")}\n" + + "These are currently not connected:\n" + + " ${requiredButNotConnectedDevices.joinToString("\n ")}", + title = "Not enough sensors connected" + ) + return false } + return true } private fun startDataCollection() { + if (!checkPredictionPreconditions()) { + return + } + if (model == null) { + throw IllegalStateException("The TFLiteModel instance can't be null when starting data collection") + } sensorOccupationInterface?.onSensorOccupationStatusChanged(true) - clearBuffers() lastPredictionTime = 0L - if (tryInitializeSensorDataMap()) { - for (device in devices.getConnected()) { - device.measurementMode = XsensDotPayload.PAYLOAD_TYPE_COMPLETE_QUATERNION - device.startMeasuring() - } - timer.base = SystemClock.elapsedRealtime() - timer.start() - textView.visibility = View.VISIBLE - textView.text = "" - - predictionBarChart.resetData() - - predictionHistoryStorage = - PredictionHistoryStorage( - currentUseCase, - System.currentTimeMillis(), - PreferencesHelper.shouldStorePrediction(context) - ) - predictionHistoryAdapter.predictionHistory = arrayListOf() - predictionHistoryAdapter.addPrediction(Prediction("", 0f), 0) - - isRunning = true - mainHandler.postDelayed(updatePredictionTask, 4000) - mainHandler.postDelayed(updateProgressBarTask, 100) - progressBar.visibility = View.VISIBLE - predictionButton.setIconResource(R.drawable.ic_baseline_stop_24) - - toggleMotionLayout.transitionToEnd() + window = model?.let { + InMemoryWindow( + windowSize = it.windowSize, + featuresWithSensorTagPrefix = it.getFeaturesToPredict() + ) } - } - - private fun tryInitializeSensorDataMap(): Boolean { - if (numConnectedDevices < numDevices) { - Toast.makeText(context, "Not enough devices connected!", Toast.LENGTH_SHORT).show() - return false + for (device in devices.getConnected()) { + device.measurementMode = + XsensDotPayload.PAYLOAD_TYPE_CUSTOM_MODE_4 + device.startMeasuring() } - val deviceSetKey = - metadataStorage.tryGetDeviceSetKey(devices.getConnectedWithOfflineMetadata()) - ?: return false + timer.base = SystemClock.elapsedRealtime() + timer.start() + textView.visibility = View.VISIBLE + textView.text = "" + val barChart: BarChart = activity.findViewById(R.id.barChart_predict_predictions) + predictionBarChart = + PredictionBarChart(context, barChart, (model!!.getLabelsMap().size)) + predictionBarChart?.resetData() + + predictionHistoryStorage = + PredictionHistoryStorage( + currentUseCase, + System.currentTimeMillis(), + PreferencesHelper.shouldStorePrediction(context) + ) + predictionHistoryAdapter.predictionHistory = arrayListOf() + predictionHistoryAdapter.addPrediction(Prediction("", 0f), 0) + isRunning = true + mainHandler.postDelayed(updatePredictionTask, predictionInterval!!) + mainHandler.postDelayed(updateProgressBarTask, 100) + progressBar.visibility = View.VISIBLE + predictionButton.setIconResource(R.drawable.ic_baseline_stop_24) + + toggleMotionLayout.transitionToEnd() + } - for (tagPrefix in GlobalValues.sensorTagPrefixes) { - rawSensorDataMap[GlobalValues.formatTag(tagPrefix, deviceSetKey)] = mutableListOf() - } - return true + private fun getRequiredButNotConnectedDevices(): List { + val connectedDeviceTagPrefixes = + devices.getConnectedWithOfflineMetadata().map { it.getTagPrefix() } + val requiredDeviceTagPrefixes = model!!.getDeviceTags() + return requiredDeviceTagPrefixes.filter { !connectedDeviceTagPrefixes.contains(it) } } private fun stopDataCollection() { @@ -176,9 +183,9 @@ class PredictionScreen( toggleMotionLayout.transitionToStart() } - @SuppressLint("NotifyDataSetChanged") private fun updatePrediction(output: FloatArray) { val predictions = ArrayList() + val outputLabelMap = model!!.getLabelsMap() for (i in output.indices) { val percentage = round(output[i] * 10000) / 100 val prediction = Prediction(outputLabelMap[i]!!, percentage) @@ -187,7 +194,7 @@ class PredictionScreen( predictions.sortByDescending { it.percentage } val highestPrediction = predictions[0] - predictionBarChart.changeData(predictions, highestPrediction.label) + predictionBarChart?.changeData(predictions, highestPrediction.label) textView.text = highestPrediction.label @@ -201,22 +208,33 @@ class PredictionScreen( } } - private fun processAndPredict() { - val buffer = predictionHelper.processSensorData(rawSensorDataMap) - if (buffer == null) { - Toast.makeText( - context, "Please measure an activity first!", - Toast.LENGTH_SHORT - ).show() - } else { - predict(buffer) - resetSensorDataLists() - } - } - - private fun predict(sensorDataByteBuffer: ByteBuffer) { + private fun predict() { lastPredictionTime = System.currentTimeMillis() - model?.predict(sensorDataByteBuffer)?.let { updatePrediction(it) } + if (window != null) { + try { + if (window!!.hasEnoughDataToCompileWindow()) { + model?.infer(arrayOf(window!!.compileWindowToArray()))?.let { updatePrediction(it[0]) } + window!!.clearValues() + } else { + Toast.makeText( + context, + "Not enough data collected for prediction.", + Toast.LENGTH_SHORT + ).show() + } + } catch (e: InMemoryWindow.SensorsOutOfSyncException) { + activity.runOnUiThread { + stopDataCollection() + MessageDialog( + context, + "The following exception occurred:\n${e.message}\n\n" + + "To avoid this, sync the connected sensors on the " + + "connection screen before starting live prediction.", + "Sensors are out of sync" + ) + } + } + } } override fun onActivityCreated(activity: Activity) { @@ -225,9 +243,6 @@ class PredictionScreen( this.context = activity metadataStorage = XSensDotMetadataStorage(context) - predictionHelper = - PredictionHelper(context, PreferencesHelper.shouldShowToastsVerbose(context)) - // Initializing prediction RV recyclerView = activity.findViewById(R.id.rv_prediction) predictionHistoryAdapter = PredictionHistoryAdapter(context, arrayListOf()) @@ -240,31 +255,45 @@ class PredictionScreen( progressBar = activity.findViewById(R.id.progressBar_nextPrediction_predictionFragment) predictionButton.setOnClickListener { - if (initModelFromCurrentUseCase()) { - val signatures = model?.signatureKeys - Log.d("PredictionScreen-onActivityCreated", signatures.toString()) - togglePrediction() - } else { - MessageDialog( - context, - message = context.getString( - R.string.missing_model_dialog_message, - currentUseCase.getModelFile().absolutePath - ), - title = context.getString(R.string.missing_model_dialog_title), - positiveButtonText = "Okay", - neutralButtonText = "Import default Model", - onNeutralButtonClickListener = { _, _ -> - currentUseCase.importDefaultModel() - initModelFromCurrentUseCase() + if (!isRunning) { + when (initModelFromCurrentUseCase()) { + ModelInitializationResult.SUCCESS -> { + val signatures = model?.signatureKeys + Log.d("PredictionScreen-onActivityCreated", signatures.toString()) togglePrediction() } - ) + ModelInitializationResult.MISSING_METADATA -> { + MessageDialog( + context, + message = context.getString( + R.string.missing_metadata_dialog_message + ), + title = context.getString(R.string.missing_metadata_dialog_title), + positiveButtonText = "Okay", + neutralButtonText = "Import default Model" + ) + } + ModelInitializationResult.FILE_NOT_EXISTING -> { + MessageDialog( + context, + message = context.getString( + R.string.missing_model_dialog_message, + currentUseCase.getModelFile().absolutePath + ), + title = context.getString(R.string.missing_model_dialog_title), + positiveButtonText = "Okay", + neutralButtonText = "Import default Model", + onNeutralButtonClickListener = { _, _ -> + currentUseCase.importDefaultModel() + } + ) + } + } + } else { + togglePrediction() } } - val barChart: BarChart = activity.findViewById(R.id.barChart_predict_predictions) - predictionBarChart = - PredictionBarChart(context, barChart, numOutputs, predictionInterval) + toggleMotionLayout = activity.findViewById(R.id.motionLayout_predictionToggling_predictionFragment) @@ -281,20 +310,27 @@ class PredictionScreen( numConnectedDevices = devices.getConnected().size - if (isRunning && numConnectedDevices < numDevices) { + if (isRunning && numConnectedDevices < model!!.getNumDevices()) { stopDataCollection() UIHelper.showAlert(context, "Connection to device(s) lost!") } } override fun onXsensDotDataChanged(deviceAddress: String, xsensDotData: XsensDotData) { - - val timeStamp: Long = xsensDotData.sampleTimeFine - val quat: FloatArray = xsensDotData.quat - val freeAcc: FloatArray = xsensDotData.freeAcc - - val deviceTag = metadataStorage.getTagForAddress(deviceAddress) - rawSensorDataMap[deviceTag]?.add(Pair(timeStamp, quat + freeAcc)) + val deviceTag = devices[deviceAddress]?.tag ?: return + val deviceTagPrefix = XSensDotDeviceWithOfflineMetadata.extractTagPrefixFromTag(deviceTag) + if (deviceTagPrefix != null) { + window?.appendSensorData(deviceTagPrefix, xsensDotData) + } else { + togglePrediction() + activity.runOnUiThread { + Toast.makeText( + context, + "Could not extract prefixe for device tag $deviceTag", + Toast.LENGTH_LONG + ).show() + } + } } override fun onXsensDotOutputRateUpdate(deviceAddress: String, outputRate: Int) { @@ -305,17 +341,27 @@ class PredictionScreen( currentUseCase = useCase } - private fun initModelFromCurrentUseCase(): Boolean { + enum class ModelInitializationResult { SUCCESS, FILE_NOT_EXISTING, MISSING_METADATA } + + private fun initModelFromCurrentUseCase(): ModelInitializationResult { if (!currentUseCase.getModelFile().exists()) { - return false + return ModelInitializationResult.FILE_NOT_EXISTING } - model = TFLiteModel( - currentUseCase.getModelFile(), intArrayOf( - 1, - predictionHelper.dataVectorSize, - predictionHelper.dataLineFloatSize - ), 6 - ) - return true + try { + initMetadataModel(currentUseCase.getModelFile()) + } catch (_: InvalidModelMetadata) { + return ModelInitializationResult.MISSING_METADATA + } + return ModelInitializationResult.SUCCESS + } + + @Throws(InvalidModelMetadata::class) + private fun initMetadataModel(modelFile: File) { + model = TFLiteModel(modelFile) + predictionInterval = model!!.getWindowInMilliSeconds() + cushion // ms + } + + companion object { + const val cushion = 1000L // ms } } diff --git a/app/src/main/java/sensors_in_paradise/sonar/screen_prediction/TFLiteModel.kt b/app/src/main/java/sensors_in_paradise/sonar/screen_prediction/TFLiteModel.kt deleted file mode 100644 index 03e9fbe5..00000000 --- a/app/src/main/java/sensors_in_paradise/sonar/screen_prediction/TFLiteModel.kt +++ /dev/null @@ -1,24 +0,0 @@ -package sensors_in_paradise.sonar.screen_prediction - -import org.tensorflow.lite.Interpreter -import java.io.File -import java.nio.ByteBuffer - -class TFLiteModel(tfLiteModelFile: File, inputShape: IntArray, outputSize: Int) { - private val outputs = arrayOf(FloatArray(outputSize)) - private val interpreter = Interpreter(tfLiteModelFile).apply { - resizeInput(0, inputShape) - } - val signatureKeys: Array = interpreter.signatureKeys - fun close() { - interpreter.close() - } - - fun predict(sensorDataByteBuffer: ByteBuffer): FloatArray { - interpreter.run( - sensorDataByteBuffer, - outputs - ) - return outputs[0] - } -} diff --git a/app/src/main/java/sensors_in_paradise/sonar/screen_prediction/barChart/PredictionBarChart.kt b/app/src/main/java/sensors_in_paradise/sonar/screen_prediction/barChart/PredictionBarChart.kt index c8c598e7..e9d802d0 100644 --- a/app/src/main/java/sensors_in_paradise/sonar/screen_prediction/barChart/PredictionBarChart.kt +++ b/app/src/main/java/sensors_in_paradise/sonar/screen_prediction/barChart/PredictionBarChart.kt @@ -18,7 +18,7 @@ class PredictionBarChart( private val context: Context, private val barChart: BarChart, numOutputs: Int, - private val predictionInterval: Long + private val animationDurationMs: Long = 450L ) { private val numBars = min(numOutputs, 6) @@ -119,7 +119,7 @@ class PredictionBarChart( } val changer = AnimateDataSetChanged( - (0.15 * predictionInterval).toInt(), + animationDurationMs.toInt(), barChart, oldValues, newValues diff --git a/app/src/main/java/sensors_in_paradise/sonar/screen_recording/Recording.kt b/app/src/main/java/sensors_in_paradise/sonar/screen_recording/Recording.kt index ea5a1857..a602349b 100644 --- a/app/src/main/java/sensors_in_paradise/sonar/screen_recording/Recording.kt +++ b/app/src/main/java/sensors_in_paradise/sonar/screen_recording/Recording.kt @@ -1,9 +1,10 @@ package sensors_in_paradise.sonar.screen_recording +import android.util.Log +import com.opencsv.CSVReaderHeaderAware import sensors_in_paradise.sonar.GlobalValues -import java.io.BufferedReader -import java.io.File -import java.io.FileReader +import sensors_in_paradise.sonar.XSensDotDeviceWithOfflineMetadata +import java.io.* import kotlin.math.floor import kotlin.random.Random @@ -25,6 +26,7 @@ open class Recording(val dir: File, var metadataStorage: RecordingMetadataStorag dir, RecordingMetadataStorage(dir.resolve(GlobalValues.METADATA_JSON_FILENAME)) ) + constructor(recording: Recording) : this( recording.dir, recording.metadataStorage @@ -57,7 +59,7 @@ open class Recording(val dir: File, var metadataStorage: RecordingMetadataStorag return true } - fun getDisplayTitle(): String { + fun getDisplayTitle(): String { val numActivities = metadataStorage.getActivities().size var result = "" if (hasVideoRecording()) { @@ -70,17 +72,17 @@ open class Recording(val dir: File, var metadataStorage: RecordingMetadataStorag return result } - fun getDirectory(): File { + fun getDirectory(): File { return dir } fun delete() { - val children = dir.listFiles() - if (children != null) { - for (child in children) { - child.delete() - } + val children = dir.listFiles() + if (children != null) { + for (child in children) { + child.delete() } + } dir.delete() } @@ -88,7 +90,7 @@ open class Recording(val dir: File, var metadataStorage: RecordingMetadataStorag return getVideoFile().exists() } - fun getVideoFile(): File { + fun getVideoFile(): File { return dir.resolve(VIDEO_CAPTURE_FILENAME) } @@ -96,7 +98,7 @@ open class Recording(val dir: File, var metadataStorage: RecordingMetadataStorag return getPoseSequenceFile().exists() } - fun getPoseSequenceFile(): File { + fun getPoseSequenceFile(): File { return dir.resolve(POSE_CAPTURE_FILENAME) } @@ -209,7 +211,9 @@ open class Recording(val dir: File, var metadataStorage: RecordingMetadataStorag } fun getRecordingFiles(): Array { - return dir.listFiles { file -> file.isFile && file.name.endsWith(".csv") } ?: emptyArray() + return dir.listFiles { file -> file.isFile && file.name.endsWith(".csv") && + file.name != MERGED_SENSOR_DATA_FILE_NAME } + ?: emptyArray() } private fun checkCache(): RecordingFileState? { @@ -251,15 +255,131 @@ open class Recording(val dir: File, var metadataStorage: RecordingMetadataStorag metadataStorage.setRecordingState(recordingState) } - fun getActivitiesSummary(): String { + fun getActivitiesSummary(): String { return metadataStorage.getActivities().joinToString("\n") { (activityStartTime, activity) -> GlobalValues.getDurationAsString(activityStartTime - metadataStorage.getTimeStarted()) + " " + activity } } + private fun getSensorTagPrefixForRecordingFile( + recordingFile: File, + sensorMacMap: Map? = null + ): String? { + val sensorAddress = recordingFile.name.substringBeforeLast(".") + + val addressToTag = sensorMacMap ?: metadataStorage.getSensorMacMap() + val sensorTag = addressToTag[sensorAddress] ?: addressToTag[sensorAddress.replace("-", ":")] + ?: return null + return XSensDotDeviceWithOfflineMetadata.extractTagPrefixFromTag(sensorTag) + } + + /** Returns a map of sensor tag prefix to CSVReader + */ + private fun getCSVReadersOfSensorRecordings(): LinkedHashMap { + val recordingSensorTagToReaderMap = LinkedHashMap() + val sensorMacMap = metadataStorage.getSensorMacMap() + for (file in getRecordingFiles()) { + val fileReader = GlobalValues.getCSVHeaderAwareFileReader(file) + val csvReader = CSVReaderHeaderAware(fileReader) + + val sensorAddress = file.name.substringBeforeLast(".") + val sensorTagPrefix = getSensorTagPrefixForRecordingFile(file, sensorMacMap) + if (sensorTagPrefix == null) { + Log.w( + "Recording-mergeSensorFiles", + "Can't include sensor data file ${file.name} of recording ${dir.name} " + + "in merge: Could not infer sensor tag from address $sensorAddress" + ) + continue + } + recordingSensorTagToReaderMap[sensorTagPrefix] = csvReader + } + return recordingSensorTagToReaderMap + } + + @Throws(InvalidRecordingException::class) + @Suppress("LongMethod", "ComplexMethod") + fun mergeSensorFiles(): File { + val outFile = File(dir, MERGED_SENSOR_DATA_FILE_NAME) + val recordingSensorTagToReaderMap = getCSVReadersOfSensorRecordings() + + val resultLineEntries = LinkedHashMap() + val mergedCSVWriter = PrintWriter(FileWriter(outFile)) + var hasWrittenColumnNames = false + var hasNewContent = true + + var startSampleTimeFine: Long? = null + do { + if (recordingSensorTagToReaderMap.isEmpty()) { + break + } + try { + var sampleTimeFine: Long? = null + for ((tagPrefix, csvReader) in recordingSensorTagToReaderMap) { + val line = csvReader.readMap() + if (line == null) { + hasNewContent = false + break + } + for ((key, value) in line.filter { it.key !in sensorDataColumnsToIgnoreInMerge }) { + // since the linked hash map is defined outside of the for loop, + // we preserve the order of its keys + resultLineEntries["${key}_$tagPrefix"] = value + } + if (sampleTimeFine == null) { + val stf = line["SampleTimeFine"] + ?: throw InvalidRecordingException( + "Could not find SampleTimeFine " + + "in sensor file of sensor with tag $tagPrefix " + ) + sampleTimeFine = stf.replace(" ", "").toLong() + resultLineEntries["SampleTimeFine"] = stf + } + if (startSampleTimeFine == null) { + startSampleTimeFine = sampleTimeFine + } + } + if (hasNewContent) { + val timeMsIntoRecording = sampleTimeFine!! - startSampleTimeFine!! + val activity = metadataStorage.getActivityAtTime(timeMsIntoRecording) + ?: throw InvalidRecordingException( + "Could not find activity for current " + + "row ($timeMsIntoRecording ms into recording)" + ) + + resultLineEntries["activity"] = activity + if (!hasWrittenColumnNames) { + mergedCSVWriter.println(resultLineEntries.keys.joinToString(",")) + hasWrittenColumnNames = true + } + mergedCSVWriter.println(resultLineEntries.values.joinToString(",")) + } + } catch (_: IOException) { + hasNewContent = false + } + } while (hasNewContent) + try { + mergedCSVWriter.close() + for ((_, csvReader) in recordingSensorTagToReaderMap) { + csvReader.close() + } + } catch (e: IOException) { + Log.w( + "Recording-mergeSensorFiles", + "IOException while closing csv reader or writer ${e.message}" + ) + } + return outFile + } + + class InvalidRecordingException(msg: String) : Exception(msg) + companion object { const val VIDEO_CAPTURE_FILENAME = "recording.mp4" const val POSE_CAPTURE_FILENAME = "poseSequence.csv" + private const val MERGED_SENSOR_DATA_FILE_NAME = "allSensorData.csv" + private val sensorDataColumnsToIgnoreInMerge = + arrayOf("PacketCounter", "Status", "SampleTimeFine") } } diff --git a/app/src/main/java/sensors_in_paradise/sonar/screen_recording/RecordingDataFile.kt b/app/src/main/java/sensors_in_paradise/sonar/screen_recording/RecordingDataFile.kt new file mode 100644 index 00000000..91e7289a --- /dev/null +++ b/app/src/main/java/sensors_in_paradise/sonar/screen_recording/RecordingDataFile.kt @@ -0,0 +1,103 @@ +package sensors_in_paradise.sonar.screen_recording + +import com.opencsv.CSVReaderHeaderAware +import sensors_in_paradise.sonar.machine_learning.InMemoryWindow +import java.io.File +import java.io.FileReader +import java.io.IOException + +class RecordingDataFile @Throws(IOException::class) constructor(private val mergedSensorDataFile: File) { + private val indexesOfActivityChanges = findIndexesOfActivityChanges() + + class WindowException(msg: String) : Exception(msg) + + @Throws(WindowException::class) + fun getWindowAtIndex( + startingIndex: Int, + windowSize: Int, + featuresWithSensorTagPrefix: Array + ): Pair { + val csvReader = getResetCsvReader() + csvReader.skip(startingIndex) + val window = InMemoryWindow(featuresWithSensorTagPrefix, windowSize) + var activity: String? = null + val errorMsgPrefix = "Window can't be filled from start line index $startingIndex: " + for (i in 0 until (windowSize)) { + val line: Map = optNextLine(csvReader) + ?: throw WindowException("$errorMsgPrefix Line is null before reaching window_size $windowSize") + val lineActivity = line["activity"] + ?: throw WindowException("$errorMsgPrefix Line without activity detected") + val stf = line["SampleTimeFine"]?.toLong() + ?: throw WindowException("$errorMsgPrefix Can't infer SampleTimeFine from line") + + if (activity == null) { + activity = lineActivity + } + if (activity != lineActivity) { + throw WindowException("$errorMsgPrefix Multiple activities " + + "within window have been detected: $lineActivity, $activity") + } + for ((feature, valueStr) in line) { + if (window.needsFeature(feature)) { + val value = valueStr.replace(" ", "").toFloat() + window.appendSensorData(feature, value, stf) + } + } + } + return Pair(window, activity!!) + } + + private fun getResetCsvReader(): CSVReaderHeaderAware { + return CSVReaderHeaderAware(FileReader(mergedSensorDataFile)) + } + + fun getWindowStartIndexes( + windowSize: Int + ): ArrayList { + val indexes = ArrayList() + + for (i in 0 until (indexesOfActivityChanges.size - 1)) { + val startIndex = indexesOfActivityChanges[i] + val endIndex = indexesOfActivityChanges[i + 1] - 1 + + val activityLength = endIndex - startIndex + if (activityLength < windowSize) { + continue + } + val numCompletelyFittingWindows = activityLength / windowSize + for (j in 0 until (numCompletelyFittingWindows)) { + val windowStartIndex = startIndex + j * windowSize + indexes.add(windowStartIndex) + // 50% overlap + val overlappingWindowStartIndex = windowStartIndex + windowSize / 2 + if (overlappingWindowStartIndex + windowSize <= endIndex) { + indexes.add(overlappingWindowStartIndex) + } + } + } + return indexes + } + + private fun findIndexesOfActivityChanges(): ArrayList { + val indexesOfActivityChanges = ArrayList() + val csvReader = getResetCsvReader() + var lastActivity: String? = null + var line: Map? = optNextLine(csvReader) + + var i = 0 + while (line != null) { + val activity = line["activity"] + if (activity != lastActivity) { + indexesOfActivityChanges.add(i) + lastActivity = activity + } + i++ + line = optNextLine(csvReader) + } + return indexesOfActivityChanges + } + + private fun optNextLine(csvReader: CSVReaderHeaderAware): Map? { + return csvReader.readMap() + } +} diff --git a/app/src/main/java/sensors_in_paradise/sonar/screen_recording/RecordingMetadataStorage.kt b/app/src/main/java/sensors_in_paradise/sonar/screen_recording/RecordingMetadataStorage.kt index faac1c5f..b9fa65ef 100644 --- a/app/src/main/java/sensors_in_paradise/sonar/screen_recording/RecordingMetadataStorage.kt +++ b/app/src/main/java/sensors_in_paradise/sonar/screen_recording/RecordingMetadataStorage.kt @@ -179,7 +179,8 @@ class RecordingMetadataStorage(file: File, initialJson: JSONObject? = null) : } json.put("sensorMapping", obj) } - + /** Returns map of Mac address of sensors to their respective tag + * */ fun getSensorMacMap(): Map { val result = mutableMapOf() val obj = json.getJSONObject("sensorMapping") @@ -194,6 +195,28 @@ class RecordingMetadataStorage(file: File, initialJson: JSONObject? = null) : return RecordingMetadataStorage(file, json) } + fun getActivityAtTime(relativeTimeMs: Long): String? { + if (relativeTimeMs <0) { + return null + } + val activities = getActivities() + if (relativeTimeMs> getDuration()) { + return activities.last().activity + } + if (activities.isEmpty()) { + return null + } + val absoluteRecStartTime = activities[0].timeStarted + for ((index, entry) in activities.withIndex()) { + val (absStartTime, _) = entry + val relativeActivityStartTime = absStartTime - absoluteRecStartTime + if (relativeTimeMs 0) (activities[index - 1].activity) else null + } + } + return activities.last().activity + } + companion object { private const val ON_DEVICE_TRAINING_METADATA_KEY = "onDeviceTraining" fun getDurationOfActivity( diff --git a/app/src/main/java/sensors_in_paradise/sonar/screen_recording/RecordingScreen.kt b/app/src/main/java/sensors_in_paradise/sonar/screen_recording/RecordingScreen.kt index ef6038fb..f8bb1f12 100644 --- a/app/src/main/java/sensors_in_paradise/sonar/screen_recording/RecordingScreen.kt +++ b/app/src/main/java/sensors_in_paradise/sonar/screen_recording/RecordingScreen.kt @@ -93,6 +93,7 @@ class RecordingScreen( activity.findViewById(R.id.surfaceView_camera_captureFragment) cameraManager = CameraManager(context, previewView, overlayView) + updateNoRecordingsTVVisibility() } @Suppress("LongMethod", "ComplexMethod") diff --git a/app/src/main/java/sensors_in_paradise/sonar/screen_recording/camera/pose_estimation/PoseEstimationStorageManager.kt b/app/src/main/java/sensors_in_paradise/sonar/screen_recording/camera/pose_estimation/PoseEstimationStorageManager.kt index 977a129d..8005f0fc 100644 --- a/app/src/main/java/sensors_in_paradise/sonar/screen_recording/camera/pose_estimation/PoseEstimationStorageManager.kt +++ b/app/src/main/java/sensors_in_paradise/sonar/screen_recording/camera/pose_estimation/PoseEstimationStorageManager.kt @@ -6,6 +6,10 @@ import android.widget.Toast import com.google.mediapipe.formats.proto.LandmarkProto.NormalizedLandmarkList import java.time.LocalDateTime import com.opencsv.CSVReaderHeaderAware +import sensors_in_paradise.sonar.GlobalValues +import sensors_in_paradise.sonar.screen_recording.camera.pose_estimation.data.PoseSequence +import sensors_in_paradise.sonar.screen_recording.camera.pose_estimation.data.KeyPoint +import sensors_in_paradise.sonar.screen_recording.camera.pose_estimation.data.Person import sensors_in_paradise.sonar.screen_recording.LoggingManager import sensors_in_paradise.sonar.screen_recording.camera.pose_estimation.data.* import java.io.BufferedReader @@ -152,28 +156,6 @@ class PoseEstimationStorageManager(var csvFile: File) { return "" } - private fun getHeaderAwareFileReader(context: Context, inputFile: String): FileReader { - val fileReader = FileReader(inputFile) - try { - var headerSize = "" - var c = fileReader.read().toChar() - while (c != '\n') { - c = fileReader.read().toChar() - if (c.isDigit()) { - headerSize += c - } - } - fileReader.skip(headerSize.toLong() + 1) - } catch (_: NumberFormatException) { - Toast.makeText( - context, - "CSV Header corrupt", - Toast.LENGTH_SHORT - ).show() - } - return fileReader - } - private fun personsFromCSVLine(line: Map): List { var personConfidence = line["Confidence"]!!.toFloat() val keyPoints = BodyPart.values().map { bp -> @@ -229,7 +211,7 @@ class PoseEstimationStorageManager(var csvFile: File) { ) Pose.HandPose else Pose.BodyPose - val fileReader = getHeaderAwareFileReader(context, inputFile) + val fileReader = GlobalValues.getCSVHeaderAwareFileReader(File(inputFile)) val csvReader = CSVReaderHeaderAware(fileReader) var line: Map? = mapOf("_" to "") diff --git a/app/src/main/java/sensors_in_paradise/sonar/use_cases/UseCase.kt b/app/src/main/java/sensors_in_paradise/sonar/use_cases/UseCase.kt index d106abec..922dcb1e 100644 --- a/app/src/main/java/sensors_in_paradise/sonar/use_cases/UseCase.kt +++ b/app/src/main/java/sensors_in_paradise/sonar/use_cases/UseCase.kt @@ -76,7 +76,7 @@ class UseCase( } fun importDefaultModel() { - extractFileFromAssets(context, "LSTMModel-1-18.tflite", getModelFile()) + extractFileFromAssets(context, "resnet_model.tflite", getModelFile()) } fun getDisplayInfo(): String { diff --git a/app/src/main/java/sensors_in_paradise/sonar/util/PredictionHelper.kt b/app/src/main/java/sensors_in_paradise/sonar/util/PredictionHelper.kt deleted file mode 100644 index 655bbe5f..00000000 --- a/app/src/main/java/sensors_in_paradise/sonar/util/PredictionHelper.kt +++ /dev/null @@ -1,178 +0,0 @@ -package sensors_in_paradise.sonar.util - -import android.content.Context -import android.util.Log -import android.widget.Toast -import sensors_in_paradise.sonar.XSensDotDeviceWithOfflineMetadata -import java.nio.ByteBuffer -import java.nio.ByteOrder -import kotlin.math.max -import kotlin.math.min - -class PredictionHelper( - private val context: Context, - private val showToasts: Boolean -) { - - private val numDevices = 5 - private val sizeOfFloat = 4 - private val numQuats = 0 - private val numFreeAccs = 3 - private var numDataLines = 0 - - private val dataLineByteSize = sizeOfFloat * (numQuats + numFreeAccs) * numDevices - val dataLineFloatSize = (numQuats + numFreeAccs) * numDevices - val dataVectorSize = 180 - - private fun fillEmptyDataLines(rawSensorDataMap: MutableMap>>) { - val frequency = 60 - val epsilon = 10 - - // '!!' i.O., because sensor data gets checked for null lists before - val startingTimestamp = rawSensorDataMap.maxOf { it.value.first().first } - val finishingTimestamp = rawSensorDataMap.minOf { it.value.last().first } - - if (finishingTimestamp <= startingTimestamp) { - if (showToasts) { - Toast.makeText(context, "Timestamps not in sync", Toast.LENGTH_LONG).show() - Toast.makeText(context, "Data may be inconsistent!", Toast.LENGTH_SHORT).show() - } - return - } - - // cut off every entry outside of 'starting-' and 'finishingTimestamp' - // use 'startingEntries' to ensure having a starting value if entry at 'startingTimestamp' is missing - val startingEntries = mutableMapOf() - for ((deviceTag, deviceDataList) in rawSensorDataMap) { - - while (deviceDataList.first().first < startingTimestamp - epsilon) { - startingEntries[deviceTag] = deviceDataList.first().second - deviceDataList.removeFirst() - } - - while (deviceDataList.last().first > finishingTimestamp + epsilon) { - deviceDataList.removeLast() - } - } - - // Fill empty data lines for all devices - val numLines = (((finishingTimestamp - startingTimestamp) * frequency) / 1000000).toInt() - val timeStep = 1000000.toDouble() / frequency.toDouble() - for ((deviceTag, oldDeviceDataList) in rawSensorDataMap) { - val newDeviceDataList = mutableListOf>() - - // ensure entry at 'startingTimestamp' - if (oldDeviceDataList.first().first > startingTimestamp + epsilon) { - val fillEntry = Pair(startingTimestamp, startingEntries[deviceTag]!!) - newDeviceDataList.add(fillEntry.copy()) - } else { - newDeviceDataList.add(oldDeviceDataList.first().copy()) - } - - // Iterating over all Timestamps from 'starting-' to 'finishingTimestamp'# - // Using last values if one is missing - // beginning at 1, because 0 entry is already filled and maybe needed for entry 1 - for (it in 1..numLines) { - var isEntryNullOrEmpty = true - if (oldDeviceDataList.getOrNull(it) != null) { - val lastTimestamp = oldDeviceDataList[it - 1].first - val currentTimestamp = oldDeviceDataList[it].first - isEntryNullOrEmpty = currentTimestamp - lastTimestamp > timeStep + epsilon - } - if (isEntryNullOrEmpty) { - val fillTimestamp = (newDeviceDataList.last().first + timeStep).toLong() - val fillValues = newDeviceDataList.last().second - val fillEntry = Pair(fillTimestamp, fillValues) - - newDeviceDataList.add(fillEntry.copy()) - } else { - newDeviceDataList.add(oldDeviceDataList[it].copy()) - } - } - rawSensorDataMap[deviceTag] = newDeviceDataList - } - } - - private fun normalizeLine(dataArray: FloatArray, minArray: DoubleArray, maxArray: DoubleArray): FloatArray { - // val numElements = numQuats + numFreeAccs - val numElements = 4 + numFreeAccs - val normalizedArray = FloatArray(numElements) - - val lowerBound = 0.0001 - val upperBound = 0.9999 - for (i in 0 until numElements) { - val rawNormalize = (dataArray[i].toDouble() - minArray[i]) / (maxArray[i] - minArray[i]) - val clippedNormalize = max(min(upperBound, rawNormalize), lowerBound) - - normalizedArray[i] = clippedNormalize.toFloat() - } - return normalizedArray - } - - @Suppress("MaxLineLength", "TooGenericExceptionCaught", "SwallowedException", "ReturnCount") - fun processSensorData(rawSensorDataMap: MutableMap>>): ByteBuffer? { - // check for sensors without data - - Log.d("PredictionHelper-processRawSensorData", rawSensorDataMap.map { "${it.key}: ${it.value.size}" }.joinToString()) - rawSensorDataMap.forEach { - val tag = it.key - val listLen = it.value.size - if (listLen == 0) { - UIHelper.showAlert(context, "\'$tag\' did not collect data!") - return null - } - } - - // fill empty data lines - try { - fillEmptyDataLines(rawSensorDataMap) - } catch (e: Exception) { - - Toast.makeText(context, "Filling of empty data failed", Toast.LENGTH_LONG).show() - Toast.makeText(context, "Data may be inconsistent!", Toast.LENGTH_SHORT).show() - } - - // check for minimal length - val recordedLinesCount = rawSensorDataMap.minOfOrNull { it.value.size }!! - if (recordedLinesCount < dataVectorSize) { - Toast.makeText(context, "Not enough data collected!", Toast.LENGTH_SHORT).show() - return null - } - - numDataLines = dataVectorSize - val predictionStartIndex = recordedLinesCount - numDataLines - - // normalize - var floatArray = FloatArray(0) - for (row in predictionStartIndex until recordedLinesCount) { - var lineFloatArray = FloatArray(0) - for ((deviceTag, deviceDataList) in rawSensorDataMap) { - - var normalizedFloatArray = FloatArray(0) - - when (XSensDotDeviceWithOfflineMetadata.extractTagPrefixFromTag(deviceTag)) { - "LF" -> normalizedFloatArray = normalizeLine((deviceDataList[row].second), doubleArrayOf(-0.8126836, -0.79424906, -0.7957623, -0.8094078, -31.278593, -32.166283, -18.486694), doubleArrayOf(0.8145418, 0.79727143, 0.81989765, 0.8027102, 28.956848, 30.199568, 22.69250)) - "LW" -> normalizedFloatArray = normalizeLine((deviceDataList[row].second), doubleArrayOf(-0.8398707, -0.8926556, -0.9343553, -0.9552342, -11.258037, -10.1190405, -8.37381), doubleArrayOf(0.7309214, 0.9186623, 0.97258735, 0.9084077, 10.640987, 11.26736, 12.94717)) - "ST" -> normalizedFloatArray = normalizeLine((deviceDataList[row].second), doubleArrayOf(-0.87042844, -0.6713179, -0.6706054, -0.80093706, -20.164385, -20.21316, -8.670398), doubleArrayOf(0.87503606, 0.686213, 0.67588365, 0.8398282, 15.221635, 13.93141, 11.75221)) - "RW" -> normalizedFloatArray = normalizeLine((deviceDataList[row].second), doubleArrayOf(-0.9208972, -0.8918428, -0.9212201, -0.9103423, -14.090326, -14.17955, -11.573973), doubleArrayOf(0.93993384, 0.888225, 0.9099328, 0.9181471, 14.901558, 11.34146, 15.649994)) - "RF" -> normalizedFloatArray = normalizeLine((deviceDataList[row].second), doubleArrayOf(-0.8756618, -0.85241073, -0.8467437, -0.8629473, -31.345306, -31.825573, -16.296654), doubleArrayOf(0.8837259, 0.98513246, 0.9278882, 0.8547427, 31.27872, 30.43604, 20.430)) - else -> { // Note the block - UIHelper.showAlert(context, "Unknown Device!") - } - } - - // Take only free acc - lineFloatArray += normalizedFloatArray.takeLast(3) - } - floatArray += lineFloatArray - } - - // create buffer - val sensorDataByteBuffer = ByteBuffer.allocate(numDataLines * dataLineByteSize).apply { - order(ByteOrder.LITTLE_ENDIAN) - } - sensorDataByteBuffer.asFloatBuffer().put(floatArray, 0, numDataLines * dataLineFloatSize) - sensorDataByteBuffer.rewind() - return sensorDataByteBuffer - } -} diff --git a/app/src/main/ml/model.tflite b/app/src/main/ml/model.tflite new file mode 100644 index 00000000..2f2f289d Binary files /dev/null and b/app/src/main/ml/model.tflite differ diff --git a/app/src/main/ml/model0.tflite b/app/src/main/ml/model0.tflite new file mode 100644 index 00000000..78feb421 Binary files /dev/null and b/app/src/main/ml/model0.tflite differ diff --git a/app/src/main/ml/model11.tflite b/app/src/main/ml/model11.tflite new file mode 100644 index 00000000..42d8b4ff Binary files /dev/null and b/app/src/main/ml/model11.tflite differ diff --git a/app/src/main/ml/model12.tflite b/app/src/main/ml/model12.tflite new file mode 100644 index 00000000..58366159 Binary files /dev/null and b/app/src/main/ml/model12.tflite differ diff --git a/app/src/main/ml/model8.tflite b/app/src/main/ml/model8.tflite new file mode 100644 index 00000000..a0565b70 Binary files /dev/null and b/app/src/main/ml/model8.tflite differ diff --git a/app/src/main/ml/model9.tflite b/app/src/main/ml/model9.tflite new file mode 100644 index 00000000..256feabf Binary files /dev/null and b/app/src/main/ml/model9.tflite differ diff --git a/app/src/main/ml/resnet_model.tflite b/app/src/main/ml/resnet_model.tflite new file mode 100644 index 00000000..75884f45 Binary files /dev/null and b/app/src/main/ml/resnet_model.tflite differ diff --git a/app/src/main/res/layout/sensor_device.xml b/app/src/main/res/layout/sensor_device.xml index fe3b33b4..887d79a5 100644 --- a/app/src/main/res/layout/sensor_device.xml +++ b/app/src/main/res/layout/sensor_device.xml @@ -50,14 +50,15 @@ android:layout_width="wrap_content" android:layout_height="match_parent" android:layout_gravity="center" - app:tint="?android:textColorPrimary" - android:layout_marginHorizontal="24dp" - app:srcCompat="@drawable/ic_baseline_link_off_24" /> + android:layout_marginHorizontal="16dp" + app:srcCompat="@drawable/ic_baseline_link_off_24" + app:tint="?android:textColorPrimary" /> + + diff --git a/app/src/main/res/values/strings.xml b/app/src/main/res/values/strings.xml index ec76910a..ee3f6af4 100644 --- a/app/src/main/res/values/strings.xml +++ b/app/src/main/res/values/strings.xml @@ -24,5 +24,8 @@ 120049010000000332523454 The current use case does not have a tflite model. You can either manually place a tflite model under %1$s via PC or import the default model. + The tflite model of the current use case does not have the required metadata. + Invalid tflite model Model missing for this use case! + In order to use the sensors for on device prediction they need to match the pattern [TAG_PREFIX]-[SENSOR_SET_NUMBER] (regex ^[A-Z]+-\d+$).\n\nExamples for valid sensor tags would be:\nLW-1 (LW for LeftWrist and 1 for sensor set 1)\nRF-3 (RF for RightFoot and 3 for sensor set 3)\nHowever you can give the sensor tag prefix your own semantic (not necessarily body position), it\'s just important that the tag matches the pattern diff --git a/build.gradle b/build.gradle index fc9ed026..aeb90d08 100644 --- a/build.gradle +++ b/build.gradle @@ -8,7 +8,7 @@ buildscript { mavenCentral() } dependencies { - classpath 'com.android.tools.build:gradle:7.2.0' + classpath 'com.android.tools.build:gradle:7.2.1' classpath "org.jetbrains.kotlin:kotlin-gradle-plugin:1.6.21" classpath 'com.google.gms:google-services:4.3.10' diff --git a/config/detekt/detekt.yml b/config/detekt/detekt.yml index 32b8ce42..62f8d908 100644 --- a/config/detekt/detekt.yml +++ b/config/detekt/detekt.yml @@ -726,7 +726,7 @@ style: SpacingBetweenPackageAndImports: active: false ThrowsCount: - active: true + active: false max: 2 excludeGuardClauses: false TrailingWhitespace: @@ -802,3 +802,5 @@ style: - 'android.graphics.*' - 'androidx.camera.video.*' - 'java.nio.file.*' + - 'sensors_in_paradise.sonar.*' + - 'java.io.*'