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Example of object detection with DL4J on images of red blood cells
package org.deeplearning4j.examples.convolution.objectdetection;
import java.io.File;
import java.net.URI;
import java.net.URISyntaxException;
import java.util.ArrayList;
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.InputSplit;
import org.datavec.api.split.FileSplit;
import org.datavec.image.loader.NativeImageLoader;
import org.datavec.image.recordreader.objdetect.ObjectDetectionRecordReader;
import org.datavec.image.recordreader.objdetect.impl.VocLabelProvider;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
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.ConvolutionLayer;
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.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.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.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 images of red blood cells.
* <p>
* References: <br>
* - YOLO: Real-Time Object Detection: https://pjreddie.com/darknet/yolo/ <br>
* - Images of red blood cells: https://github.com/cosmicad/dataset <br>
* <p>
* Please note, cuDNN should be used to obtain reasonable performance: https://deeplearning4j.org/cudnn
*
* @author saudet
*/
public class RedBloodCellDetection {
private static final Logger log = LoggerFactory.getLogger(RedBloodCellDetection.class);
public static void main(String[] args) throws java.lang.Exception {
// parameters matching the pretrained TinyYOLO model
int width = 416;
int height = 416;
int nChannels = 3;
int gridWidth = 13;
int gridHeight = 13;
// number classes for the red blood cells (RBC)
int nClasses = 1;
// parameters for the Yolo2OutputLayer
int nBoxes = 5;
double lambdaNoObj = 0.5;
double lambdaCoord = 5.0;
double[][] priorBoxes = {{2, 2}, {2, 2}, {2, 2}, {2, 2}, {2, 2}};
double detectionThreshold = 0.3;
// parameters for the training phase
int batchSize = 10;
int nEpochs = 50;
double learningRate = 1e-3;
double lrMomentum = 0.9;
int seed = 123;
Random rng = new Random(seed);
String dataDir = "/path/to/cosmicad/dataset/";
File imageDir = 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(imageDir, 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 VocLabelProvider(dataDir));
recordReaderTrain.initialize(trainData);
ObjectDetectionRecordReader recordReaderTest = new ObjectDetectionRecordReader(height, width, nChannels,
gridHeight, gridWidth, new VocLabelProvider(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 = "model_rbc.zip";
if (new File(modelFilename).exists()) {
log.info("Load model...");
model = ModelSerializer.restoreComputationGraph(modelFilename);
} else {
log.info("Build model...");
ComputationGraph pretrained = (ComputationGraph)new TinyYOLO().initPretrained();
INDArray priors = Nd4j.create(priorBoxes);
FineTuneConfiguration fineTuneConf = new FineTuneConfiguration.Builder()
.seed(seed)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.gradientNormalizationThreshold(1.0)
.updater(new Adam.Builder().learningRate(learningRate).build())
//.updater(new Nesterovs.Builder().learningRate(learningRate).momentum(lrMomentum).build())
.activation(Activation.IDENTITY)
.trainingWorkspaceMode(WorkspaceMode.SEPARATE)
.inferenceWorkspaceMode(WorkspaceMode.SEPARATE)
.build();
model = new TransferLearning.GraphBuilder(pretrained)
.fineTuneConfiguration(fineTuneConf)
.removeVertexKeepConnections("conv2d_9")
.addLayer("convolution2d_9",
new ConvolutionLayer.Builder(1,1)
.nIn(1024)
.nOut(nBoxes * (5 + nClasses))
.stride(1,1)
.convolutionMode(ConvolutionMode.Same)
.weightInit(WeightInit.UNIFORM)
.hasBias(false)
.activation(Activation.IDENTITY)
.build(),
"leaky_re_lu_8")
.addLayer("outputs",
new Yolo2OutputLayer.Builder()
.lambbaNoObj(lambdaNoObj)
.lambdaCoord(lambdaCoord)
.boundingBoxPriors(priors)
.build(),
"convolution2d_9")
.setOutputs("outputs")
.build();
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);
}
ModelSerializer.writeModel(model, modelFilename, true);
}
// visualize results on the test set
NativeImageLoader imageLoader = new NativeImageLoader();
CanvasFrame frame = new CanvasFrame("RedBloodCellDetection");
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() + " - RedBloodCellDetection");
frame.setCanvasSize(w, h);
frame.showImage(converter.convert(image));
frame.waitKey();
}
frame.dispose();
}
}

liweigu commented Feb 12, 2018

How many mAP can RedBloodCellDetection get?

Owner

saudet commented Feb 21, 2018

Didn't try to evaluate it, but not very high, unfortunately. This is limited by TinyYOLO at the moment: deeplearning4j/deeplearning4j#4256

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