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mikrise2
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Jan 4, 2024
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52 changes: 26 additions & 26 deletions
52
ij-plugin/src/main/kotlin/org/jetbrains/research/tasktracker/modelInference/ImageUtils.kt
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,28 +1,28 @@ | ||
| package org.jetbrains.research.tasktracker.modelInference | ||
|
|
||
| import org.opencv.core.Mat | ||
| import org.opencv.core.Size | ||
| import org.opencv.imgproc.Imgproc | ||
|
|
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| fun resizeImage(image: Mat, pixels: Double = 64.0): Mat { | ||
| val resizedImage = Mat() | ||
| Imgproc.resize(image, resizedImage, Size(pixels, pixels)) | ||
|
|
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| return resizedImage | ||
| } | ||
|
|
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| fun Mat.prepare(): Mat { | ||
| val gImage = this.toGrayImage() | ||
| return resizeImage(gImage) | ||
| } | ||
|
|
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| fun Mat.toGrayImage(): Mat { | ||
| val grayImage = Mat() | ||
| Imgproc.cvtColor(this, grayImage, Imgproc.COLOR_RGB2GRAY) | ||
|
|
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| return grayImage | ||
| } | ||
|
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| fun getPixel(tensor: IntArray, image: Mat, i: Int = 2, j: Int = 3): Float { | ||
| return image.get(tensor[i], tensor[j])[0].toFloat() | ||
| } | ||
| //import org.opencv.core.Mat | ||
| //import org.opencv.core.Size | ||
| //import org.opencv.imgproc.Imgproc | ||
| // | ||
| //fun resizeImage(image: Mat, pixels: Double = 64.0): Mat { | ||
| // val resizedImage = Mat() | ||
| // Imgproc.resize(image, resizedImage, Size(pixels, pixels)) | ||
| // | ||
| // return resizedImage | ||
| //} | ||
| // | ||
| //fun Mat.prepare(): Mat { | ||
| // val gImage = this.toGrayImage() | ||
| // return resizeImage(gImage) | ||
| //} | ||
| // | ||
| //fun Mat.toGrayImage(): Mat { | ||
| // val grayImage = Mat() | ||
| // Imgproc.cvtColor(this, grayImage, Imgproc.COLOR_RGB2GRAY) | ||
| // | ||
| // return grayImage | ||
| //} | ||
| // | ||
| //fun getPixel(tensor: IntArray, image: Mat, i: Int = 2, j: Int = 3): Float { | ||
| // return image.get(tensor[i], tensor[j])[0].toFloat() | ||
| //} |
104 changes: 52 additions & 52 deletions
104
...lugin/src/main/kotlin/org/jetbrains/research/tasktracker/modelInference/model/EmoModel.kt
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,52 +1,52 @@ | ||
| package org.jetbrains.research.tasktracker.modelInference.model | ||
|
|
||
| import io.kinference.core.KIEngine | ||
| import io.kinference.core.data.tensor.KITensor | ||
| import io.kinference.core.data.tensor.asTensor | ||
| import io.kinference.core.model.KIModel | ||
| import io.kinference.ndarray.arrays.FloatNDArray | ||
| import kotlinx.coroutines.runBlocking | ||
| import org.jetbrains.research.tasktracker.config.emotion.EmotionConfig | ||
| import org.jetbrains.research.tasktracker.modelInference.EmoPrediction | ||
| import org.jetbrains.research.tasktracker.modelInference.EmoPredictor | ||
| import org.jetbrains.research.tasktracker.modelInference.getPixel | ||
| import org.jetbrains.research.tasktracker.modelInference.prepare | ||
| import org.opencv.core.Mat | ||
|
|
||
| class EmoModel(override val emotionConfig: EmotionConfig) : EmoPredictor { | ||
|
|
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| init { | ||
| runBlocking { | ||
| loadModel() | ||
| } | ||
| } | ||
|
|
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| private lateinit var model: KIModel | ||
| private suspend fun loadModel() { | ||
| model = KIEngine.loadModel( | ||
| EmoModel::class.java | ||
| .getResource(emotionConfig.modelFilename)?.readBytes() | ||
| ?: error("${emotionConfig.modelFilename} must exist") | ||
| ) | ||
| } | ||
|
|
||
| override suspend fun predict(image: Mat): EmoPrediction { | ||
| val prepImage = image.prepare() | ||
| val tensor = FloatNDArray(INPUT_SHAPE) { idx: IntArray -> | ||
| getPixel(idx, prepImage) | ||
| } | ||
|
|
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| val outputs = model.predict(listOf(tensor.asTensor(emotionConfig.modelInputGate))) | ||
| val output = outputs[emotionConfig.modelOutputGate] | ||
| val softmaxOutput = ((output as KITensor).data as FloatNDArray).softmax() | ||
| val outputArray = softmaxOutput.array.toArray() | ||
|
|
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| val probabilities = outputArray.mapIndexed { index: Int, prob: Float -> index to prob.toDouble() }.toMap() | ||
| return EmoPrediction(probabilities, emotionConfig.modelPositionToThreshold) | ||
| } | ||
|
|
||
| // TODO maybe we need to find a better solution for face detection? | ||
| companion object { | ||
| private val INPUT_SHAPE = intArrayOf(1, 1, 64, 64) | ||
| } | ||
| } | ||
| //package org.jetbrains.research.tasktracker.modelInference.model | ||
| // | ||
| //import io.kinference.core.KIEngine | ||
| //import io.kinference.core.data.tensor.KITensor | ||
| //import io.kinference.core.data.tensor.asTensor | ||
| //import io.kinference.core.model.KIModel | ||
| //import io.kinference.ndarray.arrays.FloatNDArray | ||
| //import kotlinx.coroutines.runBlocking | ||
| //import org.jetbrains.research.tasktracker.config.emotion.EmotionConfig | ||
| //import org.jetbrains.research.tasktracker.modelInference.EmoPrediction | ||
| //import org.jetbrains.research.tasktracker.modelInference.EmoPredictor | ||
| //import org.jetbrains.research.tasktracker.modelInference.getPixel | ||
| //import org.jetbrains.research.tasktracker.modelInference.prepare | ||
| //import org.opencv.core.Mat | ||
| // | ||
| //class EmoModel(override val emotionConfig: EmotionConfig) : EmoPredictor { | ||
| // | ||
| // init { | ||
| // runBlocking { | ||
| // loadModel() | ||
| // } | ||
| // } | ||
| // | ||
| // private lateinit var model: KIModel | ||
| // private suspend fun loadModel() { | ||
| // model = KIEngine.loadModel( | ||
| // EmoModel::class.java | ||
| // .getResource(emotionConfig.modelFilename)?.readBytes() | ||
| // ?: error("${emotionConfig.modelFilename} must exist") | ||
| // ) | ||
| // } | ||
| // | ||
| // override suspend fun predict(image: Mat): EmoPrediction { | ||
| // val prepImage = image.prepare() | ||
| // val tensor = FloatNDArray(INPUT_SHAPE) { idx: IntArray -> | ||
| // getPixel(idx, prepImage) | ||
| // } | ||
| // | ||
| // val outputs = model.predict(listOf(tensor.asTensor(emotionConfig.modelInputGate))) | ||
| // val output = outputs[emotionConfig.modelOutputGate] | ||
| // val softmaxOutput = ((output as KITensor).data as FloatNDArray).softmax() | ||
| // val outputArray = softmaxOutput.array.toArray() | ||
| // | ||
| // val probabilities = outputArray.mapIndexed { index: Int, prob: Float -> index to prob.toDouble() }.toMap() | ||
| // return EmoPrediction(probabilities, emotionConfig.modelPositionToThreshold) | ||
| // } | ||
| // | ||
| // // TODO maybe we need to find a better solution for face detection? | ||
| // companion object { | ||
| // private val INPUT_SHAPE = intArrayOf(1, 1, 64, 64) | ||
| // } | ||
| //} |
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