org.deeplearning4j.nn.layers.objdetect.DetectedObject Java Examples
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org.deeplearning4j.nn.layers.objdetect.DetectedObject.
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Example #1
Source File: TransformRotatingImages.java From Java-Machine-Learning-for-Computer-Vision with MIT License | 6 votes |
private static void cropImageWithYOLOBoundingBox(ComputationGraph yolo, Speed selectedSpeed, File file) throws Exception { if (file.isDirectory()) { return; } BufferedImage bufferedImage = ImageIO.read(file); INDArray features = LOADER.asMatrix(bufferedImage); opencv_core.Mat mat = LOADER.asMat(features); PRE_PROCESSOR.transform(features); INDArray results = yolo.outputSingle(features); Yolo2OutputLayer outputLayer = (Yolo2OutputLayer) yolo.getOutputLayer(0); List<DetectedObject> predictedObjects = outputLayer.getPredictedObjects(results, 0.5); YoloUtils.nms(predictedObjects, 0.5); Optional<DetectedObject> max = predictedObjects.stream() .max((o1, o2) -> ((Double) o1.getConfidence()).compareTo(o2.getConfidence())); createCroppedImage(mat, selectedSpeed, max.get(), file); }
Example #2
Source File: YOLOApp.java From java-ml-projects with Apache License 2.0 | 6 votes |
private void drawBoxes(List<DetectedObject> predictedObjects) { ctx.setLineWidth(3); int w = yoloModel.getInputWidth(); int h = yoloModel.getInputHeight(); int gridW = yoloModel.getGridW(); int gridH = yoloModel.getGridH(); ctx.setTextAlign(TextAlignment.CENTER); for (DetectedObject obj : predictedObjects) { String cl = yoloModel.getModelClasses()[obj.getPredictedClass()]; double[] xy1 = obj.getTopLeftXY(); double[] xy2 = obj.getBottomRightXY(); int x1 = (int) Math.round(w * xy1[0] / gridW); int y1 = (int) Math.round(h * xy1[1] / gridH); int x2 = (int) Math.round(w * xy2[0] / gridW); int y2 = (int) Math.round(h * xy2[1] / gridH); int rectW = x2 - x1; int rectH = y2 - y1; ctx.setStroke(colors.get(cl)); ctx.strokeRect(x1, y1, rectW, rectH); ctx.strokeText(cl, x1 + (rectW / 2), y1 - 2); ctx.setFill(Color.WHITE); ctx.fillText(cl, x1 + (rectW / 2), y1 - 2); } }
Example #3
Source File: YOLOOutputAdapter.java From konduit-serving with Apache License 2.0 | 5 votes |
private DetectedObjectsBatch[] getPredictedObjects(INDArray[] outputs, double threshold, String[] outputNames, int originalHeight, int originalWidth) { // assuming "standard" output from TensorFlow using a "normal" YOLOv2 model //INDArray permuted = outputs[0].permute(0, 3, 1, 2); INDArray permuted = outputs[0]; INDArray activated = YoloUtils.activate(boundingBoxPriors, permuted); List<DetectedObject> predictedObjects1 = YoloUtils.getPredictedObjects(boundingBoxPriors, activated, threshold, 0.4); DetectedObjectsBatch[] detectedObjects = new DetectedObjectsBatch[predictedObjects1.size()]; int n = numLabels; // an arbitrary number of classes returned per object for (int i = 0; i < detectedObjects.length; i++) { DetectedObject detectedObject = predictedObjects1.get(i); long x = Math.round(originalWidth * predictedObjects1.get(i).getCenterX() / gridWidth); long y = Math.round(originalHeight * predictedObjects1.get(i).getCenterY() / gridHeight); long w = Math.round(originalWidth * predictedObjects1.get(i).getWidth() / gridWidth); long h = Math.round(originalHeight * predictedObjects1.get(i).getHeight() / gridHeight); detectedObjects[i] = new DetectedObjectsBatch(); detectedObjects[i].setCenterX(x); detectedObjects[i].setCenterY(y); detectedObjects[i].setWidth(w); detectedObjects[i].setHeight(h); detectedObjects[i].setPredictedClasses(new String[]{labels.getLabel(detectedObject.getPredictedClass())}); detectedObjects[i].setPredictedClassNumbers(new int[]{detectedObject.getPredictedClass()}); detectedObjects[i].setConfidences(new float[]{detectedObject.getClassPredictions().getFloat(detectedObject.getPredictedClass())}); } return detectedObjects; }
Example #4
Source File: TrainCifar10Model.java From Java-Machine-Learning-for-Computer-Vision with MIT License | 5 votes |
public INDArray getEmbeddings(opencv_core.Mat file, DetectedObject obj, Speed selectedSpeed) throws Exception { BufferedImage croppedImage = ImageUtils.cropImageWithYolo(selectedSpeed, file, obj, false); INDArray croppedContent = LOADER.asMatrix(croppedImage); IMAGE_PRE_PROCESSOR.transform(croppedContent); Map<String, INDArray> stringINDArrayMap = getCifar10Transfer() .feedForward(croppedContent, false); INDArray indArray = stringINDArrayMap.get(CONTENT_LAYER_NAME); return indArray; }
Example #5
Source File: MarkedObject.java From Java-Machine-Learning-for-Computer-Vision with MIT License | 5 votes |
public MarkedObject(DetectedObject detectedObject, INDArray l2Norm, long created, opencv_core.Mat matFrame) { this.detectedObject = detectedObject; this.l2Norm = l2Norm; this.created = created; this.matFrame = matFrame; id = "" + ID.incrementAndGet(); }
Example #6
Source File: Yolo.java From Java-Machine-Learning-for-Computer-Vision with MIT License | 5 votes |
public void predictBoundingBoxes(String windowName) throws Exception { long start = System.currentTimeMillis(); Yolo2OutputLayer outputLayer = (Yolo2OutputLayer) modelsMap.get(windowName).getOutputLayer(0); Mat matFrame = stackMap.get(windowName).pop(); INDArray indArray = prepareImage(matFrame); log.info("stack of frames size " + stackMap.get(windowName).size()); if (indArray == null) { return; } INDArray results = modelsMap.get(windowName).outputSingle(indArray); if (results == null) { return; } List<DetectedObject> predictedObjects = outputLayer.getPredictedObjects(results, YOLO_DETECTION_THRESHOLD); YoloUtils.nms(predictedObjects, 0.5); List<MarkedObject> markedObjects = predictedObjects.stream() .filter(e -> groupMap.get(map.get(e.getPredictedClass())) != null) .map(e -> { try { return new MarkedObject(e, trainCifar10Model.getEmbeddings(matFrame, e, selectedSpeed), System.currentTimeMillis(), matFrame); } catch (Exception e1) { e1.printStackTrace(); } return null; }).collect(Collectors.toCollection(() -> new ConcurrentArrayList<>())); if (outputFrames) { for (MarkedObject markedObject : markedObjects) { ImageUtils.cropImageWithYolo(selectedSpeed, markedObject.getMatFrame(), markedObject.getDetectedObject(), outputFrames); } } this.predictedObjects = markedObjects; log.info("stack of predictions size " + this.predictedObjects.size()); log.info("Prediction time " + (System.currentTimeMillis() - start) / 1000d); }
Example #7
Source File: Yolo.java From Java-Machine-Learning-for-Computer-Vision with MIT License | 5 votes |
public void drawBoundingBoxesRectangles(Frame frame, Mat matFrame) { if (invalidData(frame, matFrame)) return; ArrayList<DetectedObject> detectedObjects = new ArrayList<>(predictedObjects); YoloUtils.nms(detectedObjects, 0.5); for (DetectedObject detectedObject : detectedObjects) { createBoundingBoxRectangle(matFrame, frame.imageWidth, frame.imageHeight, detectedObject); } }
Example #8
Source File: Yolo.java From Java-Machine-Learning-for-Computer-Vision with MIT License | 5 votes |
private void createBoundingBoxRectangle(Mat file, int w, int h, DetectedObject obj) { double[] xy1 = obj.getTopLeftXY(); double[] xy2 = obj.getBottomRightXY(); int predictedClass = obj.getPredictedClass(); int x1 = (int) Math.round(w * xy1[0] / selectedSpeed.gridWidth); int y1 = (int) Math.round(h * xy1[1] / selectedSpeed.gridHeight); int x2 = (int) Math.round(w * xy2[0] / selectedSpeed.gridWidth); int y2 = (int) Math.round(h * xy2[1] / selectedSpeed.gridHeight); rectangle(file, new Point(x1, y1), new Point(x2, y2), Scalar.RED); putText(file, groupMap.get(map.get(predictedClass)), new Point(x1 + 2, y2 - 2), FONT_HERSHEY_DUPLEX, 1, Scalar.GREEN); }
Example #9
Source File: YOLOModel.java From java-ml-projects with Apache License 2.0 | 5 votes |
public List<DetectedObject> run(File input, double threshold) { INDArray img; try { img = loadImage(input); } catch (IOException e) { throw new Error("Not able to load image from: " + input.getAbsolutePath(), e); } INDArray output = yoloModel.outputSingle(img); Yolo2OutputLayer outputLayer = (Yolo2OutputLayer) yoloModel.getOutputLayer(0); return outputLayer.getPredictedObjects(output, threshold); }
Example #10
Source File: YOLOApp.java From java-ml-projects with Apache License 2.0 | 5 votes |
private void update() { AppUtils.doBlockingAsyncWork(scene, () -> { ctx.clearRect(0, 0, yoloModel.getInputWidth(), yoloModel.getInputHeight()); ctx.drawImage(currentImageFXImage, 0, 0); List<DetectedObject> detectedObjects = yoloModel.run(currentImage, sldThreshold.getValue()); drawBoxes(detectedObjects); }); }
Example #11
Source File: YoloModelAdapter.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Override public List<DetectedObject> apply(Model model, INDArray[] inputs, INDArray[] masks, INDArray[] labelsMasks) { if (model instanceof ComputationGraph) { val blindLayer = ((ComputationGraph) model).getOutputLayer(outputLayerIndex); if (blindLayer instanceof Yolo2OutputLayer) { val output = ((ComputationGraph) model).output(false, inputs, masks, labelsMasks); return ((Yolo2OutputLayer) blindLayer).getPredictedObjects(output[outputIndex], detectionThreshold); } else { throw new ND4JIllegalStateException("Output layer with index [" + outputLayerIndex + "] is NOT Yolo2OutputLayer"); } } else throw new ND4JIllegalStateException("Yolo2 model must be ComputationGraph"); }
Example #12
Source File: MarkedObject.java From Java-Machine-Learning-for-Computer-Vision with MIT License | 4 votes |
public DetectedObject getDetectedObject() { return detectedObject; }
Example #13
Source File: YOLO2_TF_Client.java From SKIL_Examples with Apache License 2.0 | 4 votes |
private void renderJavaFXStyle(GraphicsContext ctx) throws Exception { INDArray boundingBoxPriors = Nd4j.create(YOLO2.DEFAULT_PRIOR_BOXES); ctx.setLineWidth(3); ctx.setTextAlign(TextAlignment.LEFT); long start = System.nanoTime(); for (int i = 0; i < 1; i++) { INDArray permuted = networkGlobalOutput.permute(0, 3, 1, 2); INDArray activated = YoloUtils.activate(boundingBoxPriors, permuted); List<DetectedObject> predictedObjects = YoloUtils.getPredictedObjects(boundingBoxPriors, activated, 0.6, 0.4); //System.out.println( "width: " + imageWidth ); //System.out.println( "height: " + imageHeight ); for (DetectedObject o : predictedObjects) { ClassPrediction classPrediction = labels.decodePredictions(o.getClassPredictions(), 1).get(0).get(0); String label = classPrediction.getLabel(); long x = Math.round(imageWidth * o.getCenterX() / gridWidth); long y = Math.round(imageHeight * o.getCenterY() / gridHeight); long w = Math.round(imageWidth * o.getWidth() / gridWidth); long h = Math.round(imageHeight * o.getHeight() / gridHeight); System.out.println("\"" + label + "\" at [" + x + "," + y + ";" + w + "," + h + "], score = " + o.getConfidence() * classPrediction.getProbability()); double[] xy1 = o.getTopLeftXY(); double[] xy2 = o.getBottomRightXY(); int x1 = (int) Math.round(imageWidth * xy1[0] / gridWidth); int y1 = (int) Math.round(imageHeight * xy1[1] / gridHeight); int x2 = (int) Math.round(imageWidth * xy2[0] / gridWidth); int y2 = (int) Math.round(imageHeight * xy2[1] / gridHeight); int rectW = x2 - x1; int rectH = y2 - y1; //System.out.printf("%s - %d, %d, %d, %d \n", label, x1, x2, y1, y2); //System.out.println( "color: " + colors.get(label) ); ctx.setStroke(colors.get(label)); ctx.strokeRect(x1, y1, rectW, rectH); int labelWidth = label.length() * 10; int labelHeight = 14; ctx.setFill( colors.get(label) ); ctx.strokeRect(x1, y1-labelHeight, labelWidth, labelHeight); ctx.fillRect(x1, y1 - labelHeight, labelWidth, labelHeight); ctx.setFill( Color.WHITE ); ctx.fillText(label, x1 + 3, y1 - 3 ); } } globalEndTime = System.nanoTime(); long end = System.nanoTime(); System.out.println("Rendering code took: " + (end - start) / 1000000 + " ms"); System.out.println("Overall Program Time: " + (globalEndTime - globalStartTime) / 1000000 + " ms"); }
Example #14
Source File: YoloModelAdapter.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Override public List<DetectedObject> apply(INDArray... outputs) { throw new UnsupportedOperationException("Please use apply(Model, INDArray[], INDArray[]) signature"); }