nswamy commented on a change in pull request #10179: [MXNET-116] object detector class added URL: https://github.com/apache/incubator-mxnet/pull/10179#discussion_r176309160
########## File path: scala-package/infer/src/main/scala/ml/dmlc/mxnet/infer/ObjectDetector.scala ########## @@ -0,0 +1,162 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package ml.dmlc.mxnet.infer +// scalastyle:off +import java.awt.image.BufferedImage +// scalastyle:on +import ml.dmlc.mxnet.NDArray +import ml.dmlc.mxnet.DataDesc +import scala.collection.mutable.ListBuffer +/** + * A class for object detection tasks + * + * @param modelPathPrefix PathPrefix from where to load the symbol, parameters and synset.txt + * Example: file://model-dir/ssd_resnet50_512 + * (will resolve both ssd_resnet50_512-symbol.json + * and ssd_resnet50_512-0000.params) + * file://model-dir/synset.txt + * @param inputDescriptors Descriptors defining the input node names, shape, + * layout and Type parameters + */ +class ObjectDetector(modelPathPrefix: String, + inputDescriptors: IndexedSeq[DataDesc]) { + + val classifier: Classifier = getClassifier(modelPathPrefix, inputDescriptors) + + val inputLayout = inputDescriptors(0).layout + + val inputShape = inputDescriptors(0).shape + + val handler = classifier.handler + + val predictor = classifier.predictor + + val synset = classifier.synset + + // Considering 'NCHW' as default layout when not provided + // Else get axis according to the layout + val batch = inputShape(if (inputLayout.indexOf('N')<0) 0 else inputLayout.indexOf('N')) + val channel = inputShape(if (inputLayout.indexOf('C')<0) 1 else inputLayout.indexOf('C')) + val height = inputShape(if (inputLayout.indexOf('H')<0) 2 else inputLayout.indexOf('H')) + val width = inputShape(if (inputLayout.indexOf('W')<0) 3 else inputLayout.indexOf('W')) + + /** + * To Detect bounding boxes and corresponding labels + * + * @param inputImage : PathPrefix of the input image + * @param topK : Get top k elements with maximum probability + * @return List of List of tuples of (class, [probability, xmin, ymin, xmax, ymax]) + */ + def imageObjectDetect(inputImage: BufferedImage, + topK: Option[Int] = None) + : IndexedSeq[IndexedSeq[(String, Array[Float])]] = { + + val scaledImage = ImageClassifier.reshapeImage(inputImage, width, height) + val pixelsNDArray = ImageClassifier.bufferedImageToPixels(scaledImage, inputShape) + val output = objectDetectWithNDArray(IndexedSeq(pixelsNDArray), topK) + handler.execute(pixelsNDArray.dispose()) + output + } + + /** + * Takes input images as NDArrays. Useful when you want to perform multiple operations on + * the input Array, or when you want to pass a batch of input images. + * @param input : Indexed Sequence of NDArrays + * @param topK : (Optional) How many top_k(sorting will be based on the last axis) + * elements to return. If not passed, returns all unsorted output. + * @return List of List of tuples of (class, [probability, xmin, ymin, xmax, ymax]) + */ + def objectDetectWithNDArray(input: IndexedSeq[NDArray], topK: Option[Int]) + : IndexedSeq[IndexedSeq[(String, Array[Float])]] = { + + val predictResult = predictor.predictWithNDArray(input)(0) + var batchResult = ListBuffer[IndexedSeq[(String, Array[Float])]]() + for (i <- 0 until predictResult.shape(0)) { + val r = predictResult.at(i) + batchResult += sortAndReformat(r, topK) + r.dispose() + } + handler.execute(predictResult.dispose()) + batchResult.toIndexedSeq + } + + private def sortAndReformat(predictResultND : NDArray, topK: Option[Int]) + : IndexedSeq[(String, Array[Float])] = { + val predictResult: ListBuffer[Array[Float]] = ListBuffer[Array[Float]]() + val accuracy : ListBuffer[Float] = ListBuffer[Float]() + + // iterating over the all the predictions + val length = predictResultND.shape(0) + + for (i <- 0 until length) { + val r = predictResultND.at(i) + val tempArr = r.toArray + if (tempArr(0) != -1.0) { + predictResult += tempArr + accuracy += tempArr(1) + } else { + // Ignore the minus 1 part + } + r.dispose() + } + var result = IndexedSeq[(String, Array[Float])]() + if(topK.isDefined) { + var sortedIndices = accuracy.zipWithIndex.sortBy(-_._1).map(_._2) + sortedIndices = sortedIndices.take(topK.get) + // takeRight(5) would provide the output as Array[Accuracy, Xmin, Ymin, Xmax, Ymax + result = sortedIndices.map(idx + => (synset(predictResult(idx)(0).toInt), + predictResult(idx).takeRight(5))).toIndexedSeq + } else { + result = predictResult.map(ele + => (synset(ele(0).toInt), ele.takeRight(5))).toIndexedSeq + } + + result + } + + /** + * To classify batch of input images according to the provided model + * @param inputBatch Input batch of Buffered images + * @param topK Get top k elements with maximum probability + * @return List of list of tuples of (class, probability) + */ + def imageBatchObjectDetect(inputBatch: Traversable[BufferedImage], topK: Option[Int] = None): + IndexedSeq[IndexedSeq[(String, Array[Float])]] = { + + val imageBatch = ListBuffer[NDArray]() + for (image <- inputBatch) { + val scaledImage = ImageClassifier.reshapeImage(image, width, height) + val pixelsNdarray = ImageClassifier.bufferedImageToPixels(scaledImage, inputShape) + imageBatch += pixelsNdarray + } + val op = NDArray.concatenate(imageBatch) + + val result = objectDetectWithNDArray(IndexedSeq(op), topK) + handler.execute(op.dispose()) + for (ele <- imageBatch) { Review comment: The beauty of Scala is you can express statements in concise format like this: `imageBatch.foreach(_.dispose())` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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