Java Code Examples for org.bytedeco.javacpp.indexer.FloatIndexer#put()

private Mat getPerspectiveTransform(Mat sourceMat, Mat targetMat, int refWidth, int refHeight) {
    Mat initialTransform = opencv_imgproc.getPerspectiveTransform(sourceMat, targetMat);
    if(refWidth == -1 || refHeight == -1) { return initialTransform; }

    // Calculate where edges will end up in this case
    double[] extremes = calculateExtremes(initialTransform, refWidth, refHeight);

    FloatIndexer tIdx = targetMat.createIndexer();
    long rows = tIdx.size(0);
    for (long i = 0; i < rows; i++) {
        tIdx.put(i, 0, (float)(tIdx.get(i, 0) - extremes[0]));
        tIdx.put(i, 1, (float)(tIdx.get(i, 1) - extremes[1]));
    }

    return opencv_imgproc.getPerspectiveTransform(sourceMat, targetMat);
}
 

private void initNativeIntrinsic() {
    if (intrinsicsMat == null) {
        intrinsicsMat = new Mat(3, 3, CV_32FC1);
        FloatIndexer intrinsicIdx = intrinsicsMat.createIndexer(true);

        // init to 0
        int k = 0;
        for (int i = 0; i < 3; i++) {
            for (int j = 0; j < 3; j++) {
                intrinsicIdx.put(k++, 0);
            }
        }

        // set the values
        intrinsicIdx.put(0, 0, intrinsics.m00);
        intrinsicIdx.put(1, 1, intrinsics.m11);
        intrinsicIdx.put(0, 2, intrinsics.m02);
        intrinsicIdx.put(1, 2, intrinsics.m12);
        intrinsicIdx.put(2, 2, 1);
    }
}
 

private void fillNative(PVector[] objectPoints,
        PVector[] imagePoints,
        Mat op, Mat ip) {

    FloatIndexer opIdx = op.createIndexer();
    FloatIndexer ipIdx = ip.createIndexer();

    // Fill the object and image matrices.
    for (int i = 0; i < objectPoints.length; i++) {
        opIdx.put(i, 0, objectPoints[i].x);
        opIdx.put(i, 1, objectPoints[i].y);
        opIdx.put(i, 2, objectPoints[i].z);

        ipIdx.put(i, 0, imagePoints[i].x);
        ipIdx.put(i, 1, imagePoints[i].y);
    }
}
 

private Mat pointsToMat(List<Point> points){
    int rows = points.size();
    int cols = points.get(1).dimensions();
    Mat mat = new Mat(rows, cols, CvType.CV_32F);
    FloatIndexer idx = mat.createIndexer();
    for (int i = 0; i < rows; i++) {
        for (int j = 0; j < cols; j++) {
            idx.put(i, j, (float)points.get(i).get(j));
        }
    }
    return mat;
}
 

@Test
public void runSqueezenet(TestContext testContext) throws Exception {

    long inputTensorSize = 224 * 224 * 3;

    FloatPointer inputTensorValues = new FloatPointer(inputTensorSize);
    FloatIndexer idx = FloatIndexer.create(inputTensorValues);
    for (long i = 0; i < inputTensorSize; i++)
        idx.put(i, (float) i / (inputTensorSize + 1));

    DataBuffer buffer = Nd4j.createBuffer(inputTensorValues, DataType.FLOAT, inputTensorSize, idx);

    INDArray contents = Nd4j.create(buffer);

    byte[] npyContents = Nd4j.toNpyByteArray(contents);

    File inputFile = temporary.newFile();
    FileUtils.writeByteArrayToFile(inputFile, npyContents);

    for (int i = 0; i < 5; i++) {
        Response response = given().port(port)
                .multiPart("data_0", inputFile)
                .post("nd4j/numpy")
                .andReturn();
        //TODO: report memory leak in DNNL execution provider to ORT
        assertEquals("Response failed", 200, response.getStatusCode());

        INDArray bodyResult = Nd4j.createNpyFromByteArray(response.getBody().asByteArray());

        assertEquals(1.99018, bodyResult.getFloat(0), 1e-4);

        assertArrayEquals(new long[]{1, 1000}, bodyResult.shape());
    }
}
 

/**
 * Assign values to feature
 * <p>
 * 样本特征为水平、垂直直方图和低分辨率图像所组成的矢量
 * 
 * @param in
 * @param sizeData - 低分辨率图像size = sizeData*sizeData, 可以为0
 * @return
 */
public static Mat features(final Mat in, final int sizeData) {
    float[] vhist = projectedHistogram(in, Direction.VERTICAL);
    float[] hhist = projectedHistogram(in, Direction.HORIZONTAL);
    Mat lowData = new Mat();
    if (sizeData > 0) {
        resize(in, lowData, new Size(sizeData, sizeData));
    }

    int numCols = vhist.length + hhist.length + lowData.cols() * lowData.rows();
    Mat out = Mat.zeros(1, numCols, CV_32F).asMat();
    FloatIndexer idx = out.createIndexer();

    int j = 0;
    for (int i = 0; i < vhist.length; ++i, ++j) {
        idx.put(0, j, vhist[i]);
    }
    for (int i = 0; i < hhist.length; ++i, ++j) {
        idx.put(0, j, hhist[i]);
    }
    for (int x = 0; x < lowData.cols(); x++) {
        for (int y = 0; y < lowData.rows(); y++, ++j) {
            float val = lowData.ptr(x, y).get() & 0xFF;
            idx.put(0, j, val);
        }
    }

    return out;
}
 

/**
 * Assign values to feature
 * <p>
 * 样本特征为水平、垂直直方图和低分辨率图像所组成的矢量
 * 
 * @param in
 * @param sizeData
 *            低分辨率图像size = sizeData*sizeData, 可以为0
 * @return
 */
public static Mat features(final Mat in, final int sizeData) {

    float[] vhist = projectedHistogram(in, Direction.VERTICAL);
    float[] hhist = projectedHistogram(in, Direction.HORIZONTAL);

    Mat lowData = new Mat();
    if (sizeData > 0) {
        resize(in, lowData, new Size(sizeData, sizeData));
    }

    int numCols = vhist.length + hhist.length + lowData.cols() * lowData.rows();
    Mat out = Mat.zeros(1, numCols, CV_32F).asMat();
    FloatIndexer idx = out.createIndexer();

    int j = 0;
    for (int i = 0; i < vhist.length; ++i, ++j) {
        idx.put(0, j, vhist[i]);
    }
    for (int i = 0; i < hhist.length; ++i, ++j) {
        idx.put(0, j, hhist[i]);
    }
    for (int x = 0; x < lowData.cols(); x++) {
        for (int y = 0; y < lowData.rows(); y++, ++j) {
            float val = lowData.ptr(x, y).get() & 0xFF;
            idx.put(0, j, val);
        }
    }

    return out;
}
 

@Test
public void runAdd(TestContext testContext) throws Exception {

    long inputTensorSize = 3 * 4 * 5;

    FloatPointer inputTensorValues = new FloatPointer(inputTensorSize);
    FloatIndexer idx = FloatIndexer.create(inputTensorValues);
    for (long i = 0; i < inputTensorSize; i++)
        idx.put(i, (float) i / (inputTensorSize + 1));

    DataBuffer buffer = Nd4j.createBuffer(inputTensorValues, DataType.FLOAT, inputTensorSize, idx);

    INDArray contents = Nd4j.create(buffer);

    byte[] npyContents = Nd4j.toNpyByteArray(contents);

    File inputFile = temporary.newFile();
    FileUtils.writeByteArrayToFile(inputFile, npyContents);

    Response response = given().port(port)
            .multiPart("x", inputFile)
            .multiPart("y", inputFile)
            .post("nd4j/numpy")
            .andReturn();

    assertEquals("Response failed", 200, response.getStatusCode());

    INDArray bodyResult = Nd4j.createNpyFromByteArray(response.getBody().asByteArray());

    assertEquals(0.032786883, bodyResult.getFloat(1), 1e-6);

    assertArrayEquals(new long[]{1, 60}, bodyResult.shape());
}