(-215:Assertion failed) m.dims >= 2 in function 'cv::Mat::Mat'

asked 2019-03-18 06:26:31 -0500

Hi

I am trying to load a tin - yolo v3 model into java and do someobject recognition on video streams the following code i wrote with help of this post seems to open files and start image recognition as expected at the very beggining however i get this error after some frames have been analysed

Exception in thread "main" CvException [org.opencv.core.CvException: cv::Exception: OpenCV(4.0.0-alpha) C:\build\master_winpack-bindings-win64-vc14-static\opencv\modules\core\src\matrix.cpp:405: error: (-215:Assertion failed) m.dims >= 2 in function 'cv::Mat::Mat' ] at org.opencv.core.Mat.n_Mat(Native Method) at org.opencv.core.Mat.<init>(Mat.java:113) at org.opencv.core.MatOfFloat.<init>(MatOfFloat.java:27) at sample.yolo.main(yolo.java:112)

What might be happening here ? can anyone please help me out?

Here is the code i am trying to run :

package sample;

import org.opencv.core.*;
import org.opencv.dnn.*;
import org.opencv.utils.*;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.objdetect.CascadeClassifier;
import org.opencv.videoio.VideoCapture;

import java.util.ArrayList;
import java.util.List;


import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.awt.image.WritableRaster;
import java.io.ByteArrayInputStream;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.InputStream;

import javax.imageio.ImageIO;
import javax.swing.ImageIcon;
import javax.swing.JFrame;
import javax.swing.JLabel;




public class yolo {

    private static List<String> getOutputNames(Net net) {
        List<String> names = new ArrayList<>();

        List<Integer> outLayers = net.getUnconnectedOutLayers().toList();
        List<String> layersNames = net.getLayerNames();

        outLayers.forEach((item) -> names.add(layersNames.get(item - 1)));
        return names;
    }
    public static void main(String[] args) throws InterruptedException {
        System.load("C:\\Users\\subhatta\\Downloads\\opencv\\build\\java\\x64\\opencv_java400.dll");
         String modelWeights = "D:\\yolov3-tiny.weights";
            String modelConfiguration = "D:\\yolov3-tiny.cfg.txt";
            String filePath = "D:\\test.avi";
            VideoCapture cap = new VideoCapture(filePath);
             Mat frame = new Mat();

             //cap.read(frame);
             JFrame jframe = new JFrame("Video");
             JLabel vidpanel = new JLabel();
             jframe.setContentPane(vidpanel);
             jframe.setSize(600, 600);
             jframe.setVisible(true);

            Net net = Dnn.readNetFromDarknet(modelConfiguration, modelWeights);
            //Thread.sleep(5000);

            //Mat image = Imgcodecs.imread("D:\\yolo-object-detection\\yolo-object-detection\\images\\soccer.jpg");
            Size sz = new Size(416, 416);


            while (true) {

                if (cap.read(frame)) {

            Mat blob = Dnn.blobFromImage(frame, 0.00392, sz, new Scalar(0), true, false);
            net.setInput(blob);

            List<Mat> result = new ArrayList<>();
            List<String> outBlobNames = getOutputNames(net);

            net.forward(result, outBlobNames);

            outBlobNames.forEach(System.out::println);
            result.forEach(System.out::println);

            float confThreshold = 0.6f;
            List<Integer> clsIds = new ArrayList<>();
            List<Float> confs = new ArrayList<>();
            List<Rect> rects = new ArrayList<>();
            for (int i = 0; i < result.size(); ++i)
            {
                // each row is a candidate detection, the 1st 4 numbers are
                // [center_x, center_y, width, height], followed by (N-4) class probabilities
                Mat level = result.get(i);
                for (int j = 0; j < level.rows(); ++j)
                {
                    Mat row = level.row(j);
                    Mat scores = row.colRange(5, level.cols());
                    Core.MinMaxLocResult mm = Core.minMaxLoc(scores);
                    float confidence = (float)mm.maxVal;
                    Point classIdPoint = mm ...
(more)
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