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darknet19
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package org.deeplearning4j.examples.convolution.objectdetection; | |
import java.io.File; | |
import java.net.URI; | |
import java.net.URISyntaxException; | |
import java.util.List; | |
import java.util.Random; | |
import org.bytedeco.javacv.CanvasFrame; | |
import org.bytedeco.javacv.OpenCVFrameConverter; | |
import org.datavec.api.io.filters.RandomPathFilter; | |
import org.datavec.api.records.metadata.RecordMetaDataImageURI; | |
import org.datavec.api.split.FileSplit; | |
import org.datavec.api.split.InputSplit; | |
import org.datavec.image.loader.NativeImageLoader; | |
import org.datavec.image.recordreader.objdetect.ObjectDetectionRecordReader; | |
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator; | |
import org.deeplearning4j.examples.convolution.ObjectDetectionLabelProvider; | |
import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
import org.deeplearning4j.nn.conf.CacheMode; | |
import org.deeplearning4j.nn.conf.ConvolutionMode; | |
import org.deeplearning4j.nn.conf.GradientNormalization; | |
import org.deeplearning4j.nn.conf.WorkspaceMode; | |
import org.deeplearning4j.nn.conf.inputs.InputType; | |
import org.deeplearning4j.nn.conf.layers.*; | |
import org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer; | |
import org.deeplearning4j.nn.graph.ComputationGraph; | |
import org.deeplearning4j.nn.layers.objdetect.DetectedObject; | |
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; | |
import org.deeplearning4j.nn.transferlearning.FineTuneConfiguration; | |
import org.deeplearning4j.nn.transferlearning.TransferLearning; | |
import org.deeplearning4j.nn.weights.WeightInit; | |
import org.deeplearning4j.optimize.listeners.ScoreIterationListener; | |
import org.deeplearning4j.util.ModelSerializer; | |
import org.deeplearning4j.zoo.model.Darknet19; | |
import org.deeplearning4j.zoo.model.TinyYOLO; | |
import org.nd4j.linalg.activations.Activation; | |
import org.nd4j.linalg.api.ndarray.INDArray; | |
import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler; | |
import org.nd4j.linalg.factory.Nd4j; | |
import org.nd4j.linalg.learning.config.Adam; | |
import org.nd4j.linalg.learning.config.Nesterovs; | |
import org.nd4j.linalg.lossfunctions.LossFunctions; | |
import org.slf4j.Logger; | |
import org.slf4j.LoggerFactory; | |
import static org.bytedeco.javacpp.opencv_core.*; | |
import static org.bytedeco.javacpp.opencv_imgproc.*; | |
/** | |
* Example transfer learning from a Tiny YOLO model pretrained on ImageNet and Pascal VOC | |
* to perform object detection with bounding boxes on The Street View House Numbers (SVHN) Dataset. | |
* <p> | |
* References: <br> | |
* - YOLO: Real-Time Object Detection: https://pjreddie.com/darknet/yolo/ <br> | |
* - The Street View House Numbers (SVHN) Dataset: http://ufldl.stanford.edu/housenumbers/ <br> | |
* <p> | |
* Please note, cuDNN should be used to obtain reasonable performance: https://deeplearning4j.org/cudnn | |
* | |
* @author saudet | |
*/ | |
public class HouseNumberDetection { | |
private static final Logger log = LoggerFactory.getLogger(HouseNumberDetection.class); | |
public static void main(String[] args) throws Exception { | |
// parameters matching the pretrained TinyYOLO model | |
int width = 416; | |
int height = 416; | |
int nChannels = 3; | |
int gridWidth = 13; | |
int gridHeight = 13; | |
int[] inputShape = {3, 416, 416}; | |
// number classes (digits) for the SVHN datasets | |
int nClasses = 1; //10; | |
// parameters for the Yolo2OutputLayer | |
int nBoxes = 5; | |
double lambdaNoObj = 0.5; | |
double lambdaCoord = 1.0; | |
double[][] priorBoxes = {{2, 5}, {2.5, 6}, {3, 7}, {3.5, 8}, {4, 9}}; | |
double detectionThreshold = 0.5; | |
// parameters for the training phase | |
int batchSize = 8; | |
int nEpochs = 10; | |
double learningRate = 1e-2; // 0.0001 | |
double lrMomentum = 0.9; | |
int seed = 123; | |
Random rng = new Random(seed); | |
String dataDir = "/home/remote/dataset/"; | |
File imgDir = new File(dataDir, "JPEGImages"); | |
log.info("Load data..."); | |
RandomPathFilter pathFilter = new RandomPathFilter(rng) { | |
@Override | |
protected boolean accept(String name) { | |
name = name.replace("/JPEGImages/", "/Annotations/").replace(".jpg", ".xml"); | |
try { | |
return new File(new URI(name)).exists(); | |
} catch (URISyntaxException ex) { | |
throw new RuntimeException(ex); | |
} | |
} | |
}; | |
InputSplit[] data = new FileSplit(imgDir, NativeImageLoader.ALLOWED_FORMATS, rng).sample(pathFilter, 0.8, 0.2); | |
InputSplit trainData = data[0]; | |
InputSplit testData = data[1]; | |
ObjectDetectionRecordReader recordReaderTrain = new ObjectDetectionRecordReader(height, width, nChannels, | |
gridHeight, gridWidth, new ObjectDetectionLabelProvider(dataDir)); | |
recordReaderTrain.initialize(trainData); | |
ObjectDetectionRecordReader recordReaderTest = new ObjectDetectionRecordReader(height, width, nChannels, | |
gridHeight, gridWidth, new ObjectDetectionLabelProvider(dataDir)); | |
recordReaderTest.initialize(testData); | |
// ObjectDetectionRecordReader performs regression, so we need to specify it here | |
RecordReaderDataSetIterator train = new RecordReaderDataSetIterator(recordReaderTrain, batchSize, 1, 1, true); | |
train.setPreProcessor(new ImagePreProcessingScaler(0, 1)); | |
RecordReaderDataSetIterator test = new RecordReaderDataSetIterator(recordReaderTest, 1, 1, 1, true); | |
test.setPreProcessor(new ImagePreProcessingScaler(0, 1)); | |
ComputationGraph model; | |
String modelFilename = "resources/darknet19.zip"; | |
if (new File(modelFilename).exists()) { | |
log.info("Load model..."); | |
model = ModelSerializer.restoreComputationGraph(modelFilename); | |
} else { | |
log.info("Build model..."); | |
ComputationGraph pretrained = (ComputationGraph) Darknet19.builder() | |
.numClasses(30) | |
.updater(new Nesterovs(learningRate, lrMomentum)) | |
.workspaceMode(WorkspaceMode.NONE) | |
.inputShape(inputShape) | |
.build() | |
.init(); | |
INDArray priors = Nd4j.create(priorBoxes); | |
pretrained.setInputs(priors); | |
//model = new TransferLearning.GraphBuilder(pretrained).build(); | |
model = pretrained; | |
System.out.println(model.summary(InputType.convolutional(height, width, nChannels))); | |
log.info("Train model..."); | |
model.setListeners(new ScoreIterationListener(1)); | |
for (int i = 0; i < nEpochs; i++) { | |
train.reset(); | |
while (train.hasNext()) { | |
model.fit(train.next()); | |
} | |
log.info("*** Completed epoch {} ***", i); | |
} | |
// save the model | |
ModelSerializer.writeModel(model, modelFilename, true); | |
} | |
// visualize results on the test set | |
NativeImageLoader imageLoader = new NativeImageLoader(); | |
CanvasFrame frame = new CanvasFrame("HouseNumberDetection"); | |
OpenCVFrameConverter.ToMat converter = new OpenCVFrameConverter.ToMat(); | |
org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer yout = | |
(org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer)model.getOutputLayer(0); | |
List<String> labels = train.getLabels(); | |
test.setCollectMetaData(true); | |
while (test.hasNext() && frame.isVisible()) { | |
org.nd4j.linalg.dataset.DataSet ds = test.next(); | |
RecordMetaDataImageURI metadata = (RecordMetaDataImageURI)ds.getExampleMetaData().get(0); | |
INDArray features = ds.getFeatures(); | |
INDArray results = model.outputSingle(features); | |
List<DetectedObject> objs = yout.getPredictedObjects(results, detectionThreshold); | |
File file = new File(metadata.getURI()); | |
log.info(file.getName() + ": " + objs); | |
Mat mat = imageLoader.asMat(features); | |
Mat convertedMat = new Mat(); | |
mat.convertTo(convertedMat, CV_8U, 255, 0); | |
int w = metadata.getOrigW() * 2; | |
int h = metadata.getOrigH() * 2; | |
Mat image = new Mat(); | |
resize(convertedMat, image, new Size(w, h)); | |
for (DetectedObject obj : objs) { | |
double[] xy1 = obj.getTopLeftXY(); | |
double[] xy2 = obj.getBottomRightXY(); | |
String label = labels.get(obj.getPredictedClass()); | |
int x1 = (int) Math.round(w * xy1[0] / gridWidth); | |
int y1 = (int) Math.round(h * xy1[1] / gridHeight); | |
int x2 = (int) Math.round(w * xy2[0] / gridWidth); | |
int y2 = (int) Math.round(h * xy2[1] / gridHeight); | |
rectangle(image, new Point(x1, y1), new Point(x2, y2), Scalar.RED); | |
putText(image, label, new Point(x1 + 2, y2 - 2), FONT_HERSHEY_DUPLEX, 1, Scalar.GREEN); | |
} | |
frame.setTitle(new File(metadata.getURI()).getName() + " - HouseNumberDetection"); | |
frame.setCanvasSize(w, h); | |
frame.showImage(converter.convert(image)); | |
frame.waitKey(); | |
} | |
frame.dispose(); | |
} | |
} |
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