Examples with Rectangle ai.djl.modality.cv.output.Rectangle used on opensource projects

Example 11 with Rectangle

use of ai.djl.modality.cv.output.Rectangle in project djl by deepjavalibrary.

the class PpFaceDetectionTranslator method processOutput.

/**
 * {@inheritDoc}
 */
@Override
public DetectedObjects processOutput(TranslatorContext ctx, NDList list) {
    NDArray result = list.singletonOrThrow();
    float[] probabilities = result.get(":,1").toFloatArray();
    List<String> names = new ArrayList<>();
    List<Double> prob = new ArrayList<>();
    List<BoundingBox> boxes = new ArrayList<>();
    for (int i = 0; i < probabilities.length; i++) {
        if (probabilities[i] >= threshold) {
            float[] array = result.get(i).toFloatArray();
            names.add(className.get((int) array[0]));
            prob.add((double) probabilities[i]);
            boxes.add(new Rectangle(array[2], array[3], array[4] - array[2], array[5] - array[3]));
        }
    }
    return new DetectedObjects(names, prob, boxes);
}

Example 12 with Rectangle

use of ai.djl.modality.cv.output.Rectangle in project djl by deepjavalibrary.

the class PpWordDetectionTranslator method processOutput.

/**
 * {@inheritDoc}
 */
@Override
public DetectedObjects processOutput(TranslatorContext ctx, NDList list) throws FileNotFoundException {
    NDArray result = list.singletonOrThrow();
    ImageFactory factory = ImageFactory.getInstance();
    List<BoundingBox> boxes;
    // faster mechanism
    if (factory instanceof OpenCVImageFactory) {
        result = result.squeeze(0).toType(DataType.UINT8, true);
        Image image = factory.fromNDArray(result);
        boxes = image.findBoundingBoxes().parallelStream().filter(box -> {
            Rectangle rect = (Rectangle) box;
            return rect.getWidth() * image.getWidth() > 5 || rect.getHeight() * image.getHeight() > 5;
        }).collect(Collectors.toList());
    } else {
        result = result.squeeze().mul(255f).toType(DataType.UINT8, true).neq(0);
        boolean[] flattened = result.toBooleanArray();
        Shape shape = result.getShape();
        int w = (int) shape.get(0);
        int h = (int) shape.get(1);
        boolean[][] grid = new boolean[w][h];
        IntStream.range(0, flattened.length).parallel().forEach(i -> grid[i / h][i % h] = flattened[i]);
        boxes = new BoundFinder(grid).getBoxes();
    }
    List<String> names = new ArrayList<>();
    List<Double> probs = new ArrayList<>();
    int boxSize = boxes.size();
    for (int i = 0; i < boxSize; i++) {
        names.add("word");
        probs.add(1.0);
    }
    return new DetectedObjects(names, probs, boxes);
}

Example 13 with Rectangle

use of ai.djl.modality.cv.output.Rectangle in project djl by deepjavalibrary.

the class YoloTranslator method processOutput.

/**
 * {@inheritDoc}
 */
@Override
public DetectedObjects processOutput(TranslatorContext ctx, NDList list) {
    int[] classIndices = list.get(0).toType(DataType.INT32, true).flatten().toIntArray();
    double[] probs = list.get(1).toType(DataType.FLOAT64, true).flatten().toDoubleArray();
    NDArray boundingBoxes = list.get(2);
    int detected = Math.toIntExact(probs.length);
    NDArray xMin = boundingBoxes.get(":, 0").clip(0, imageWidth).div(imageWidth);
    NDArray yMin = boundingBoxes.get(":, 1").clip(0, imageHeight).div(imageHeight);
    NDArray xMax = boundingBoxes.get(":, 2").clip(0, imageWidth).div(imageWidth);
    NDArray yMax = boundingBoxes.get(":, 3").clip(0, imageHeight).div(imageHeight);
    float[] boxX = xMin.toFloatArray();
    float[] boxY = yMin.toFloatArray();
    float[] boxWidth = xMax.sub(xMin).toFloatArray();
    float[] boxHeight = yMax.sub(yMin).toFloatArray();
    List<String> retClasses = new ArrayList<>(detected);
    List<Double> retProbs = new ArrayList<>(detected);
    List<BoundingBox> retBB = new ArrayList<>(detected);
    for (int i = 0; i < detected; i++) {
        if (classIndices[i] < 0 || probs[i] < threshold) {
            continue;
        }
        retClasses.add(classes.get(classIndices[i]));
        retProbs.add(probs[i]);
        Rectangle rect;
        if (applyRatio) {
            rect = new Rectangle(boxX[i] / imageWidth, boxY[i] / imageHeight, boxWidth[i] / imageWidth, boxHeight[i] / imageHeight);
        } else {
            rect = new Rectangle(boxX[i], boxY[i], boxWidth[i], boxHeight[i]);
        }
        retBB.add(rect);
    }
    return new DetectedObjects(retClasses, retProbs, retBB);
}

Example 14 with Rectangle

use of ai.djl.modality.cv.output.Rectangle in project djl by deepjavalibrary.

the class BananaDetection method prepare.

/**
 * {@inheritDoc}
 */
@Override
public void prepare(Progress progress) throws IOException, TranslateException {
    if (prepared) {
        return;
    }
    Artifact artifact = mrl.getDefaultArtifact();
    mrl.prepare(artifact, progress);
    Path root = mrl.getRepository().getResourceDirectory(artifact);
    Path usagePath;
    switch(usage) {
        case TRAIN:
            usagePath = Paths.get("train");
            break;
        case TEST:
            usagePath = Paths.get("test");
            break;
        case VALIDATION:
        default:
            throw new UnsupportedOperationException("Validation data not available.");
    }
    usagePath = root.resolve(usagePath);
    Path indexFile = usagePath.resolve("index.file");
    try (Reader reader = Files.newBufferedReader(indexFile)) {
        Type mapType = new TypeToken<Map<String, List<Float>>>() {
        }.getType();
        Map<String, List<Float>> metadata = JsonUtils.GSON.fromJson(reader, mapType);
        for (Map.Entry<String, List<Float>> entry : metadata.entrySet()) {
            String imgName = entry.getKey();
            imagePaths.add(usagePath.resolve(imgName));
            List<Float> label = entry.getValue();
            long objectClass = label.get(0).longValue();
            Rectangle objectLocation = new Rectangle(new Point(label.get(1), label.get(2)), label.get(3), label.get(4));
            labels.add(objectClass, objectLocation);
        }
    }
    prepared = true;
}

Example 15 with Rectangle

use of ai.djl.modality.cv.output.Rectangle in project djl by deepjavalibrary.

the class CocoDetection method getLabels.

private PairList<Long, Rectangle> getLabels(CocoUtils coco, long imageId) {
    List<Long> annotationIds = coco.getAnnotationIdByImageId(imageId);
    if (annotationIds == null) {
        return new PairList<>();
    }
    PairList<Long, Rectangle> label = new PairList<>(annotationIds.size());
    for (long annotationId : annotationIds) {
        CocoMetadata.Annotation annotation = coco.getAnnotationById(annotationId);
        if (annotation.getArea() > 0) {
            double[] box = annotation.getBoundingBox();
            long labelClass = coco.mapCategoryId(annotation.getCategoryId());
            Rectangle objectLocation = new Rectangle(new Point(box[0], box[1]), box[2], box[3]);
            label.add(labelClass, objectLocation);
        }
    }
    return label;
}