Develop Reference

OpenCV on Android: net.forward yields "215 Assertion failed"

Following this tutorial from openCV, and it should be straight forward. However, it crashes with an assertion fail on the net.forward, that I cannot resolve/find anywhere else.
Thought this problem seemed similar and tried to go through the fix/problem finding. However, restarting the discussion and trials showed it is likely not the same. I used initially 3.4.3, which did not support the same Mat type somehow. Updated to 3.4.7 now, and can confirm the blob size is okay (generated from image). Tried also various other prototxt and caffemodels, but doubt by now that the problem lies there (works if the files are okay, otherwise the net loading fails). The key code should be this:
// Load a network.
public void onCameraViewStarted(int width, int height) {
String proto = getPath("deploy.prototxt", this);
String weights = getPath("MobileNetSSD_deploy.caffemodel", this);
net = Dnn.readNetFromCaffe(proto, weights);
Log.i(TAG, "Network loaded successfully");
}
public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
// Get a new frame
Mat frame = inputFrame.rgba();
Imgproc.cvtColor(frame, frame, Imgproc.COLOR_RGBA2RGB);
// Forward image through network.
Mat blob = Dnn.blobFromImage(frame, 0.007843,
new Size(300, 300),
new Scalar(127.5, 127.5, 127.5));
net.setInput(blob);
Mat detections = net.forward(); //***215 ASSERTION FAILED occurs***
int cols = frame.cols();
int rows = frame.rows();
detections = detections.reshape(1, (int)detections.total() / 7);
for (int i = 0; i < detections.rows(); ++i) {
double confidence = detections.get(i, 2)[0];
if (confidence > 0.2) {
int classId = (int)detections.get(i, 1)[0];
int left = (int)(detections.get(i, 3)[0] * cols);
int top = (int)(detections.get(i, 4)[0] * rows);
int right = (int)(detections.get(i, 5)[0] * cols);
int bottom = (int)(detections.get(i, 6)[0] * rows);
// Draw rectangle around detected object.
Imgproc.rectangle(frame, new Point(left, top), new Point(right, bottom),
new Scalar(0, 255, 0));
String label = classNames[classId] + ": " + confidence;
int[] baseLine = new int[1];
Size labelSize = Imgproc.getTextSize(label, Core.FONT_HERSHEY_SIMPLEX, 0.5, 1, baseLine);
// Draw background for label.
Imgproc.rectangle(frame, new Point(left, top - labelSize.height),
new Point(left + labelSize.width, top + baseLine[0]),
new Scalar(255, 255, 255), Core.FILLED);
// Write class name and confidence.
Imgproc.putText(frame, label, new Point(left, top),
Core.FONT_HERSHEY_SIMPLEX, 0.5, new Scalar(0, 0, 0));
}
}
return frame;
}
public void onCameraViewStopped() {}
// Upload file to storage and return a path.
private static String getPath(String file, Context context) {
AssetManager assetManager = context.getAssets();
BufferedInputStream inputStream = null;
try {
// Read data from assets.
inputStream = new BufferedInputStream(assetManager.open(file));
byte[] data = new byte[inputStream.available()];
inputStream.read(data);
inputStream.close();
// Create copy file in storage.
File outFile = new File(context.getFilesDir(), file);
FileOutputStream os = new FileOutputStream(outFile);
os.write(data);
os.close();
// Return a path to file which may be read in common way.
return outFile.getAbsolutePath();
} catch (IOException ex) {
Log.i(TAG, "Failed to upload a file");
}
return "";
}
The full error message is
cv::Exception: OpenCV(3.4.7) /build/3_4_pack-android/opencv/modules/dnn/src/layers/batch_norm_layer.cpp:39: error: (-215:Assertion failed) blobs.size() >= 2 in function 'cv::dnn::BatchNormLayerImpl::BatchNormLayerImpl(const cv::dnn::experimental_dnn_34_v13::LayerParams&)'
I expect it to not crash. The frame should be okay (image loaded), the net is not empty, and the layers in the net seem fine too (checked since there are some differences using caffe in java). Any help is appreciated!

After some days of research in different directions, I found the problem: the frame format should be BGR, not RGB! That means
Imgproc.cvtColor(frame, frame, Imgproc.COLOR_RGBA2BGR);

Java char recognition with OpenCV

i am trying to build an application that with a camera can recognize some numbers and letters from a board in front of the camera.
At moment i can detect faces, counters but i want to use ROI and Tess4j to recognize this live video.
Do you know any example of something like this?
My idea is that i have to analyze frame by frame and when i detect a char like '*' i make the full verification of the values
public class Demo {
public static void main(String[] args) throws Exception {
String classifierName = null;
if (args.length > 0) {
classifierName = args[0];
} else {
URL url = new URL("https://raw.github.com/Itseez/opencv/2.4.0/data/haarcascades/haarcascade_frontalface_alt.xml");
File file = Loader.extractResource(url, null, "classifier", ".xml");
file.deleteOnExit();
classifierName = file.getAbsolutePath();
}
// Preload the opencv_objdetect module to work around a known bug.
Loader.load(opencv_objdetect.class);
// We can "cast" Pointer objects by instantiating a new object of the desired class.
CvHaarClassifierCascade classifier = new CvHaarClassifierCascade(cvLoad(classifierName));
if (classifier.isNull()) {
System.err.println("Error loading classifier file \"" + classifierName + "\".");
System.exit(1);
}
// The available FrameGrabber classes include OpenCVFrameGrabber (opencv_videoio),
// DC1394FrameGrabber, FlyCaptureFrameGrabber, OpenKinectFrameGrabber, OpenKinect2FrameGrabber,
// RealSenseFrameGrabber, PS3EyeFrameGrabber, VideoInputFrameGrabber, and FFmpegFrameGrabber.
FrameGrabber grabber = FrameGrabber.createDefault(0);
grabber.start();
// CanvasFrame, FrameGrabber, and FrameRecorder use Frame objects to communicate image data.
// We need a FrameConverter to interface with other APIs (Android, Java 2D, or OpenCV).
OpenCVFrameConverter.ToIplImage converter = new OpenCVFrameConverter.ToIplImage();
// FAQ about IplImage and Mat objects from OpenCV:
// - For custom raw processing of data, createBuffer() returns an NIO direct
// buffer wrapped around the memory pointed by imageData, and under Android we can
// also use that Buffer with Bitmap.copyPixelsFromBuffer() and copyPixelsToBuffer().
// - To get a BufferedImage from an IplImage, or vice versa, we can chain calls to
// Java2DFrameConverter and OpenCVFrameConverter, one after the other.
// - Java2DFrameConverter also has static copy() methods that we can use to transfer
// data more directly between BufferedImage and IplImage or Mat via Frame objects.
IplImage grabbedImage = converter.convert(grabber.grab());
int width = grabbedImage.width();
int height = grabbedImage.height();
IplImage grayImage = IplImage.create(width, height, IPL_DEPTH_8U, 1);
IplImage rotatedImage = grabbedImage.clone();
// Objects allocated with a create*() or clone() factory method are automatically released
// by the garbage collector, but may still be explicitly released by calling release().
// You shall NOT call cvReleaseImage(), cvReleaseMemStorage(), etc. on objects allocated this way.
CvMemStorage storage = CvMemStorage.create();
// The OpenCVFrameRecorder class simply uses the CvVideoWriter of opencv_videoio,
// but FFmpegFrameRecorder also exists as a more versatile alternative.
FrameRecorder recorder = FrameRecorder.createDefault("output.avi", width, height);
recorder.start();
// CanvasFrame is a JFrame containing a Canvas component, which is hardware accelerated.
// It can also switch into full-screen mode when called with a screenNumber.
// We should also specify the relative monitor/camera response for proper gamma correction.
CanvasFrame frame = new CanvasFrame("Some Title", CanvasFrame.getDefaultGamma()/grabber.getGamma());
// Let's create some random 3D rotation...
CvMat randomR = CvMat.create(3, 3), randomAxis = CvMat.create(3, 1);
// We can easily and efficiently access the elements of matrices and images
// through an Indexer object with the set of get() and put() methods.
DoubleIndexer Ridx = randomR.createIndexer(), axisIdx = randomAxis.createIndexer();
axisIdx.put(0, (Math.random()-0.5)/4, (Math.random()-0.5)/4, (Math.random()-0.5)/4);
cvRodrigues2(randomAxis, randomR, null);
double f = (width + height)/2.0; Ridx.put(0, 2, Ridx.get(0, 2)*f);
Ridx.put(1, 2, Ridx.get(1, 2)*f);
Ridx.put(2, 0, Ridx.get(2, 0)/f); Ridx.put(2, 1, Ridx.get(2, 1)/f);
System.out.println(Ridx);
// We can allocate native arrays using constructors taking an integer as argument.
CvPoint hatPoints = new CvPoint(3);
while (frame.isVisible() && (grabbedImage = converter.convert(grabber.grab())) != null) {
cvClearMemStorage(storage);
// Let's try to detect some faces! but we need a grayscale image...
cvCvtColor(grabbedImage, grayImage, CV_BGR2GRAY);
CvSeq faces = cvHaarDetectObjects(grayImage, classifier, storage,
1.1, 3, CV_HAAR_FIND_BIGGEST_OBJECT | CV_HAAR_DO_ROUGH_SEARCH);
int total = faces.total();
for (int i = 0; i < total; i++) {
CvRect r = new CvRect(cvGetSeqElem(faces, i));
int x = r.x(), y = r.y(), w = r.width(), h = r.height();
cvRectangle(grabbedImage, cvPoint(x, y), cvPoint(x+w, y+h), CvScalar.RED, 1, CV_AA, 0);
// To access or pass as argument the elements of a native array, call position() before.
hatPoints.position(0).x(x-w/10) .y(y-h/10);
hatPoints.position(1).x(x+w*11/10).y(y-h/10);
hatPoints.position(2).x(x+w/2) .y(y-h/2);
cvFillConvexPoly(grabbedImage, hatPoints.position(0), 3, CvScalar.GREEN, CV_AA, 0);
}
// Let's find some contours! but first some thresholding...
cvThreshold(grayImage, grayImage, 64, 255, CV_THRESH_BINARY);
// To check if an output argument is null we may call either isNull() or equals(null).
CvSeq contour = new CvSeq(null);
cvFindContours(grayImage, storage, contour, Loader.sizeof(CvContour.class),
CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
while (contour != null && !contour.isNull()) {
if (contour.elem_size() > 0) {
CvSeq points = cvApproxPoly(contour, Loader.sizeof(CvContour.class),
storage, CV_POLY_APPROX_DP, cvContourPerimeter(contour)*0.02, 0);
cvDrawContours(grabbedImage, points, CvScalar.BLUE, CvScalar.BLUE, -1, 1, CV_AA);
}
contour = contour.h_next();
}
//TESS4J - se ahou os contornos, analisar conteúdo
if(contour != null && !contour.isNull()) {
IplImageToBufferedImage(grayImage);
ITesseract instance = new Tesseract(); // JNA Interface Mapping
instance.setTessVariable("tessedit_char_whitelist", "0123456789");
try {
String result = instance.doOCR(IplImageToBufferedImage(grayImage));
System.out.println(result);
} catch (TesseractException e) {
System.err.println(e.getMessage());
}
}
cvWarpPerspective(grabbedImage, rotatedImage, randomR);
Frame rotatedFrame = converter.convert(rotatedImage);
frame.showImage(rotatedFrame);
recorder.record(rotatedFrame);
}
frame.dispose();
recorder.stop();
grabber.stop();
}
public static BufferedImage IplImageToBufferedImage(IplImage src) {
OpenCVFrameConverter.ToIplImage grabberConverter = new OpenCVFrameConverter.ToIplImage();
Java2DFrameConverter paintConverter = new Java2DFrameConverter();
Frame frame = grabberConverter.convert(src);
return paintConverter.getBufferedImage(frame,1);
}
}

OpenCV detecting drilled holes

I'm working on a project where I have to detect drilled holes on a surface. (the top two holes and there for orientation purposes only)
After detecting the holes the pattern will judge the placement of the holes and give results. I have created an overlay grid layout and placed it over the camera2api preview so the user can align the holes and scan (The real testing will not be of a picture from the LCD as shown in the screenshot)
Currently, I'm cropping the image based on the grid and resizing it to 1920x2560 to have a consistent frame for pattern judgement, which makes a single grid of roughly about 300px. I am unable to detect the blobs can someone suggest what sort of filtering I should choose for this work and if there is a better approach for doing this rather than using a grid layout as the placement of the holes in regard to the orientation holes matter for final results (both x and y axis)
Here is my code:
Mat srcMat = resizeAndCropMatToGrid(mats[0]);
if (srcMat == null) {
exception = new Exception("Cropping Failed");
errorMessage = "Unable to crop image based on grid";
return null;
}
matProgressTask = srcMat;
Mat processedMat = new Mat();
Imgproc.cvtColor(srcMat, processedMat, Imgproc.COLOR_BGR2GRAY);
Imgproc.GaussianBlur(processedMat, processedMat, new org.opencv.core.Size(5, 5), 5);
Imgproc.threshold(processedMat, processedMat, 115, 255, Imgproc.THRESH_BINARY);
matProgressTask = processedMat;
FeatureDetector featureDetector = FeatureDetector.create(FeatureDetector.SIMPLEBLOB);
featureDetector.read(Environment.getExternalStorageDirectory() + "/Android/blob.xml");
MatOfKeyPoint matOfKeyPoint = new MatOfKeyPoint();
featureDetector.detect(processedMat, matOfKeyPoint);
KeyPoint[] keyPointsArray = matOfKeyPoint.toArray();
Log.e("keypoints", "" + Arrays.toString(keyPointsArray));
if (keyPointsArray.length < 1) {
exception = new Exception("Blobs Missing");
errorMessage = "Error: Unable to filter blobs";
} else {
try {
MatOfKeyPoint matOfKeyPointFilteredBlobs = new MatOfKeyPoint(keyPointsArray);
Features2d.drawKeypoints(srcMat, matOfKeyPointFilteredBlobs, srcMat, new Scalar(255, 0, 0), Features2d.DRAW_OVER_OUTIMG);
} catch (Exception e) {
e.printStackTrace();
exception = e;
errorMessage = "Error: Unable to draw Blobs";
return null;
}
matProgressTask = srcMat;
onProgressUpdate();
patterData = pinpointBlobsToGetData(keyPointsArray);
if (patterData == null) {
exception = new Exception("Unable to establish pattern");
errorMessage = "Error: Key points array is null";
}
}
And here is the blobby file configuration that I'm using:
<?xml version="1.0"?>
<opencv_storage>
<format>3</format>
<thresholdStep>10.</thresholdStep>
<minThreshold>50.</minThreshold>
<maxThreshold>120.</maxThreshold>
<minRepeatability>2</minRepeatability>
<minDistBetweenBlobs>20.</minDistBetweenBlobs>
<filterByColor>1</filterByColor>
<blobColor>0</blobColor>
<filterByArea>1</filterByArea>
<minArea>2300.</minArea>
<maxArea>4500.</maxArea>
<filterByCircularity>1</filterByCircularity>
<minCircularity>0.2</minCircularity>
<maxCircularity>1.0</maxCircularity>
<filterByInertia>1</filterByInertia>
<minInertiaRatio>0.2</minInertiaRatio>
<maxInertiaRatio>1.0</maxInertiaRatio>
<filterByConvexity>1</filterByConvexity>
<minConvexity>0.2</minConvexity>
<maxConvexity>1.0</maxConvexity>
</opencv_storage>

I am using Python.
For the second image you provided I successfully detected the holes...
...using this code...
import cv2
import numpy as np
img = cv2.imread("C:\\Users\\Link\\Desktop\\2.jpg")
# cv2.imshow("original", img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# cv2.imshow("gray", gray)
blur = cv2.medianBlur(gray, 31)
# cv2.imshow("blur", blur)
ret, thresh = cv2.threshold(blur, 127, 255, cv2.THRESH_OTSU)
# cv2.imshow("thresh", thresh)
canny = cv2.Canny(thresh, 75, 200)
# cv2.imshow('canny', canny)
im2, contours, hierarchy = cv2.findContours(canny, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
contour_list = []
for contour in contours:
approx = cv2.approxPolyDP(contour, 0.01 * cv2.arcLength(contour, True), True)
area = cv2.contourArea(contour)
if 5000 < area < 15000:
contour_list.append(contour)
msg = "Total holes: {}".format(len(approx)//2)
cv2.putText(img, msg, (20, 40), cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), 2, cv2.LINE_AA)
cv2.drawContours(img, contour_list, -1, (0, 255, 0), 2)
cv2.imshow('Objects Detected', img)
cv2.imwrite("detected_holes.png", img)
cv2.waitKey(0)
Now, the first is a bit different. The same code will not work in detecting the right amount of holes. The program keep detecting also what is clearly not a hole (crack in left bottom angle..) with missing some main holes.
Here is an example of what I am talking about:
Not only the counter in that case is wrong but also, the main problem, is that the hole at right bottom can't be detected.

So, I have managed to figure it by passing the mat directly to FeatureDetector class without any prior processing...
Mat srcMat = mats[0];
if (srcMat == null) {
exception = new Exception("Cropping Failed");
errorMessage = "Unable to crop image based on grid";
return null;
}
matProgressTask = srcMat;
FeatureDetector featureDetector = FeatureDetector.create(FeatureDetector.SIMPLEBLOB);
featureDetector.read(Environment.getExternalStorageDirectory() + "/Android/blob.xml");
Log.e("LoadingBlob", "wqfqfwq");
MatOfKeyPoint matOfKeyPoint = new MatOfKeyPoint();
featureDetector.detect(srcMat, matOfKeyPoint);
KeyPoint[] keyPointsArray = matOfKeyPoint.toArray();
Log.e("keypoints", "" + Arrays.toString(keyPointsArray));
if (keyPointsArray.length < 1) {
exception = new Exception("Blobs Missing");
errorMessage = "Error: Unable to filter blobs";
} else {
try {
MatOfKeyPoint matOfKeyPointFilteredBlobs = new MatOfKeyPoint(keyPointsArray);
Features2d.drawKeypoints(srcMat, matOfKeyPointFilteredBlobs, srcMat, new Scalar(0, 255, 0), Features2d.DRAW_OVER_OUTIMG);
} catch (Exception e) {
e.printStackTrace();
exception = e;
errorMessage = "Error: Unable to draw Blobs";
return null;
}
matProgressTask = srcMat;
onProgressUpdate();
patterData = pinpointBlobsToGetData(keyPointsArray);
if (patterData == null) {
exception = new Exception("Unable to establish pattern");
errorMessage = "Error: Key points array is null";
}
}
And my feature detector parameters file is:
<?xml version="1.0"?>
<opencv_storage>
<format>3</format>
<thresholdStep>10.</thresholdStep>
<minThreshold>50.</minThreshold>
<maxThreshold>120.</maxThreshold>
<minRepeatability>2</minRepeatability>
<minDistBetweenBlobs>20.</minDistBetweenBlobs>
<filterByColor>0</filterByColor>
<blobColor>0</blobColor>
<filterByArea>1</filterByArea>
<minArea>3000.</minArea>
<maxArea>10000.</maxArea>
<filterByCircularity>1</filterByCircularity>
<minCircularity>0.3</minCircularity>
<maxCircularity>1.0</maxCircularity>
<filterByInertia>1</filterByInertia>
<minInertiaRatio>0.3</minInertiaRatio>
<maxInertiaRatio>1.0</maxInertiaRatio>
<filterByConvexity>1</filterByConvexity>
<minConvexity>0.3</minConvexity>
<maxConvexity>1.0</maxConvexity>
</opencv_storage>
The result images:

Reading DataMatrix/QR code zxing java

The below data matrix is being read well using Barcode Scanner, zxing mobile app. However, the same is not being read by zxing java library.
I have some image transformation code commented. Even transforming the image, rotation or scaling doesn't help.
Ideally, I would like to perform all possible image pre-processing programatically until decoded.
What is the logic the mobile app using, since am scanning the same image from the computer screen and it is working.
Please find below, the code am using for decoding.
public class BarcodeReader {
private static Map<DecodeHintType,Object> hintsMap;
public static void main(String...args){
BufferedImage before = null;
hintsMap = new EnumMap<DecodeHintType, Object>(DecodeHintType.class);
hintsMap.put(DecodeHintType.TRY_HARDER, Boolean.TRUE);
hintsMap.put(DecodeHintType.POSSIBLE_FORMATS, EnumSet.allOf(BarcodeFormat.class));
//hintsMap.put(DecodeHintType.PURE_BARCODE, Boolean.FALSE);
try
{
before = ImageIO.read(new File("C:/ocr.jpg"));
decode(before);
/* for(int i=1; i < 1000;i++){
AffineTransform transform = new AffineTransform();
double rad = (double)i/100;
double scale = (double)i/100;
System.out.println("rad "+scale);
//transform.rotate(rad, before.getWidth()/2, before.getHeight()/2);
transform.scale(scale, scale);
BufferedImage after = new BufferedImage(before.getWidth(), before.getHeight(), BufferedImage.TYPE_INT_ARGB);
AffineTransformOp op = new AffineTransformOp(transform, AffineTransformOp.TYPE_BILINEAR);
after = op.filter(before, after);
decode(after);
}*/
//tmpBfrImage = tmpBfrImage.getSubimage(200, 100, 800, 800);
}
catch (IOException tmpIoe)
{
tmpIoe.printStackTrace();
}
}
public static void decode(BufferedImage tmpBfrImage){
if (tmpBfrImage == null)
throw new IllegalArgumentException("Could not decode image.");
LuminanceSource tmpSource = new BufferedImageLuminanceSource(tmpBfrImage);
BinaryBitmap tmpBitmap = new BinaryBitmap(new HybridBinarizer(tmpSource));
MultiFormatReader tmpBarcodeReader = new MultiFormatReader();
Result tmpResult;
String tmpFinalResult;
try
{
if (hintsMap != null && ! hintsMap.isEmpty())
tmpResult = tmpBarcodeReader.decode(tmpBitmap, hintsMap);
else
tmpResult = tmpBarcodeReader.decode(tmpBitmap);
// setting results.
tmpFinalResult = String.valueOf(tmpResult.getText());
System.out.println(tmpFinalResult);
System.exit(0);;
}
catch (Exception tmpExcpt)
{
tmpExcpt.printStackTrace();
}
}
}

I had problems at multiple levels. I downloaded zxing source from github and debugged it.
The first problem was adding the below line as hints screws up the recognition hintsMap.put(DecodeHintType.PURE_BARCODE, Boolean.FALSE);
Looking at their source code for DataMatrixReader, there was a line doing this
if (hints != null && hints.containsKey(DecodeHintType.PURE_BARCODE))
So, irrespective of setting PURE_BARCODE true or false, it considers it as true. Ideally hints should not contain the key.
The second problem was with the way the detector for DataMatrix works.
The detector was identifying the 'L' by looking at the number of black and white transitions from each vertices. Ideally, the transitions from Top-Left to Bottom-Left and Bottom-Left to Bottom-Right should have 0 transitions.
However, since the line was drawn closer towards the outer edge of the box, the transitions were not becoming 0. I made changes to move it closer to the center of the Left and Bottom Black Lines. This means moving the vertical red line to the right and the bottom red line a bit upwards. I added a new method Correct Points, that makes the necessary correction. This correction works for me, ideally one should be making the correction a bit more smarter.
ResultPoint pointA = correctPoints(cornerPoints[0], Vertices.TOPLEFT);
ResultPoint pointB = correctPoints(cornerPoints[1], Vertices.BOTTOMLEFT);
ResultPoint pointC = correctPoints(cornerPoints[2], Vertices.TOPRIGHT);
ResultPoint pointD = correctPoints(cornerPoints[3], Vertices.BOTTOMRIGHT);
---
---
private ResultPoint correctPoints(ResultPoint point, Vertices vertice){
if(vertice.equals(Vertices.TOPLEFT))
return new ResultPoint(point.getX()+10, point.getY()+5);
else if(vertice.equals(Vertices.BOTTOMLEFT)){
return new ResultPoint(point.getX()+10, point.getY()-5);
}else if(vertice.equals(Vertices.TOPRIGHT)){
return new ResultPoint(point.getX(), point.getY()+10);
}else{
return new ResultPoint(point.getX()-10, point.getY()-5);
}
}
After making these changes, data matrix detection was working for images that were as bad as or even poorer than these.

I was having similar problems using ZXing to decode DataMatrix barcodes. From what I can see, ZXing doesn't traverse the entire image you send it, but rather starts from the middle and expands out until it has found a barcode. So, if the DataMatrix barcode isn't centered in the image, ZXing will not be able to reliably find it. I implemented (a rather slow) workaround that fixes this problem, by creating different cropped versions of the image:
My core decode method is similar to that of the original post. My image traversal logic is as follows:
// Read the original image
final BufferedImage image = ImageIO.read(...);
final int width = image.getWidth();
final int height = image.getHeight();
// Try detect codes using different sections of the image.
//
// +------+------+
// | ##|## |
// | ##|## |
// | ##|## |
// +------+------+
// | | |
// | | |
// | | |
// +------+------+
//
// We create 9 cropped versions of the image, with each cropped
// version being 1/4 of the original image. We traverse the
// original image from left-to-right, top-to-bottom, and create
// 9 sub-images that we try to decode in turn.
for (int i=0; i<3; i++) {
for (int j=0; j<3; j++) {
final int x = i * width / 4;
final int y = j * height / 4;
final BufferedImage crop = image.getSubimage(x, y, width / 2, height / 2);
decoded(crop);
}
}

OpenCV: VideoCapture::get(CV_CAP_PROP_FPS) returns 0 FPS direct video input without using camera in java

VideoWriter videoWriter = new VideoWriter(outputFile, fourCC.toInt(), videoCapture.get(Videoio.CAP_PROP_FPS),
frameSize, true);
this return framesize as 0

Solution At first you try to open the video and check if opened successfully as below:
VideoCapture capture = new VideoCapture(filePath);
if(!capture.isOpened()) {
System.out.println("Cannot open the video.");
return;
}
If it not returned, then it opened successfully, now you read the frame and display or at least check the height and width of the frame as below:
Mat frame = new Frame();
capture.read(frame);
int width = (int) capture.get(Videoio.CAP_PROP_FRAME_WIDTH);
int height = (int) capture.get(Videoio.CAP_PROP_FRAME_HEIGHT);
System.out.println("width = " + width );
System.out.println("height = " + height);
If everything goes well then, it is OK, you can use capture and read the frames, if not working, go to your OpenCV extracted folder and copy this line
C:\opencv\build\x64\vc14\bin
to your Environment Variable and restart the your PC and try again.