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• How to Read DJI H264 FPV Feed as OpenCV Mat Object ?

  29 mai 2019, par Walter Morawa

  TDLR : All DJI developers would benefit from decoding raw H264 video stream byte arrays to a format compatible with OpenCV.

  I’ve spent a lot of time looking for a solution to reading DJI’s FPV feed as an OpenCV Mat object. I am probably overlooking something fundamental, since I am not too familiar with Image Encoding/Decoding.

  Future developers who come across it will likely run into a bunch of the same issues I had. It would be great if DJI developers could use opencv directly without needing a 3rd party library.

  I’m willing to use ffmpeg or JavaCV if necessary, but that’s quite the hurdle for most Android developers as we’re going to have to use cpp, ndk, terminal for testing, etc. That seems like overkill. Both options seem quite time consuming. This JavaCV H264 conversion seems unnecessarily complex. I found it from this relevant question.

  I believe the issue lies in the fact that we need to decode both the byte array of length 6 (info array) and the byte array with current frame info simultaneously.

  Basically, DJI’s FPV feed comes in a number of formats.

  1. Raw H264 (MPEG4) in VideoFeeder.VideoDataListener

      // The callback for receiving the raw H264 video data for camera live view
  
      mReceivedVideoDataListener = new VideoFeeder.VideoDataListener() {
  
          @Override
  
          public void onReceive(byte[] videoBuffer, int size) {
  
              //Log.d("BytesReceived", Integer.toString(videoStreamFrameNumber));
  
              if (videoStreamFrameNumber++%30 == 0){
  
                  //convert video buffer to opencv array
  
                  OpenCvAndModelAsync openCvAndModelAsync = new OpenCvAndModelAsync();
  
                  openCvAndModelAsync.execute(videoBuffer);
  
              }
  
              if (mCodecManager != null) {
  
                  mCodecManager.sendDataToDecoder(videoBuffer, size);
  
              }
  
          }
  
      };

  2. DJI also has it’s own Android decoder sample with FFMPEG to convert to YUV format.

      @Override
  
      public void onYuvDataReceived(final ByteBuffer yuvFrame, int dataSize, final int width, final int height) {
  
          //In this demo, we test the YUV data by saving it into JPG files.
  
          //DJILog.d(TAG, "onYuvDataReceived " + dataSize);
  
          if (count++ % 30 == 0 && yuvFrame != null) {
  
              final byte[] bytes = new byte[dataSize];
  
              yuvFrame.get(bytes);
  
              AsyncTask.execute(new Runnable() {
  
                  @Override
  
                  public void run() {
  
                      if (bytes.length >= width * height) {
  
                          Log.d("MatWidth", "Made it");
  
                          YuvImage yuvImage = saveYuvDataToJPEG(bytes, width, height);
  
                          Bitmap rgbYuvConvert = convertYuvImageToRgb(yuvImage, width, height);
  
  
  
                          Mat yuvMat = new Mat(height, width, CvType.CV_8UC1);
  
                          yuvMat.put(0, 0, bytes);
  
                          //OpenCv Stuff
  
                      }
  
                  }
  
              });
  
          }
  
      }

  Edit : For those who want to see DJI’s YUV to JPEG function, here it is from the sample application :

  private YuvImage saveYuvDataToJPEG(byte[] yuvFrame, int width, int height){
  
          byte[] y = new byte[width * height];
  
          byte[] u = new byte[width * height / 4];
  
          byte[] v = new byte[width * height / 4];
  
          byte[] nu = new byte[width * height / 4]; //
  
          byte[] nv = new byte[width * height / 4];
  
  
  
          System.arraycopy(yuvFrame, 0, y, 0, y.length);
  
          Log.d("MatY", y.toString());
  
          for (int i = 0; i < u.length; i++) {
  
              v[i] = yuvFrame[y.length + 2 * i];
  
              u[i] = yuvFrame[y.length + 2 * i + 1];
  
          }
  
          int uvWidth = width / 2;
  
          int uvHeight = height / 2;
  
          for (int j = 0; j < uvWidth / 2; j++) {
  
              for (int i = 0; i < uvHeight / 2; i++) {
  
                  byte uSample1 = u[i * uvWidth + j];
  
                  byte uSample2 = u[i * uvWidth + j + uvWidth / 2];
  
                  byte vSample1 = v[(i + uvHeight / 2) * uvWidth + j];
  
                  byte vSample2 = v[(i + uvHeight / 2) * uvWidth + j + uvWidth / 2];
  
                  nu[2 * (i * uvWidth + j)] = uSample1;
  
                  nu[2 * (i * uvWidth + j) + 1] = uSample1;
  
                  nu[2 * (i * uvWidth + j) + uvWidth] = uSample2;
  
                  nu[2 * (i * uvWidth + j) + 1 + uvWidth] = uSample2;
  
                  nv[2 * (i * uvWidth + j)] = vSample1;
  
                  nv[2 * (i * uvWidth + j) + 1] = vSample1;
  
                  nv[2 * (i * uvWidth + j) + uvWidth] = vSample2;
  
                  nv[2 * (i * uvWidth + j) + 1 + uvWidth] = vSample2;
  
              }
  
          }
  
          //nv21test
  
          byte[] bytes = new byte[yuvFrame.length];
  
          System.arraycopy(y, 0, bytes, 0, y.length);
  
          for (int i = 0; i < u.length; i++) {
  
              bytes[y.length + (i * 2)] = nv[i];
  
              bytes[y.length + (i * 2) + 1] = nu[i];
  
          }
  
          Log.d(TAG,
  
                "onYuvDataReceived: frame index: "
  
                    + DJIVideoStreamDecoder.getInstance().frameIndex
  
                    + ",array length: "
  
                    + bytes.length);
  
          YuvImage yuver = screenShot(bytes,Environment.getExternalStorageDirectory() + "/DJI_ScreenShot", width, height);
  
          return yuver;
  
      }
  
  
  
      /**
  
       * Save the buffered data into a JPG image file
  
       */
  
      private YuvImage screenShot(byte[] buf, String shotDir, int width, int height) {
  
          File dir = new File(shotDir);
  
          if (!dir.exists() || !dir.isDirectory()) {
  
              dir.mkdirs();
  
          }
  
          YuvImage yuvImage = new YuvImage(buf,
  
                  ImageFormat.NV21,
  
                  width,
  
                  height,
  
                  null);
  
  
  
          OutputStream outputFile = null;
  
  
  
          final String path = dir + "/ScreenShot_" + System.currentTimeMillis() + ".jpg";
  
  
  
          try {
  
              outputFile = new FileOutputStream(new File(path));
  
          } catch (FileNotFoundException e) {
  
              Log.e(TAG, "test screenShot: new bitmap output file error: " + e);
  
              //return;
  
          }
  
          if (outputFile != null) {
  
              yuvImage.compressToJpeg(new Rect(0,
  
                      0,
  
                      width,
  
                      height), 100, outputFile);
  
          }
  
          try {
  
              outputFile.close();
  
          } catch (IOException e) {
  
              Log.e(TAG, "test screenShot: compress yuv image error: " + e);
  
              e.printStackTrace();
  
          }
  
  
  
          runOnUiThread(new Runnable() {
  
              @Override
  
              public void run() {
  
                  displayPath(path);
  
              }
  
          });
  
          return yuvImage;
  
      }

  3. DJI also appears to have a "getRgbaData" function, but there is literally not a single example online or by DJI. Go ahead and Google "DJI getRgbaData"... There’s only the reference to the api documentation that explains the self explanatory parameters and return values but nothing else. I couldn’t figure out where to call this and there doesn’t appear to be a callback function as there is with YUV. You can’t call it from the h264b byte array directly, but perhaps you can get it from the yuv data.

  Option 1 is much more preferable to option 2, since YUV format has quality issues. Option 3 would also likely involve a decoder.

  Here’s a screenshot that DJI’s own YUV conversion produces.

  I’ve looked at a bunch of things about how to improve the YUV, remove green and yellow colors and whatnot, but at this point if DJI can’t do it right, I don’t want to invest resources there.

  Regarding Option 1, I know there’s FFMPEG and JavaCV that seem like good options if I have to go the video decoding route.

  Moreover, from what I understand, OpenCV can’t handle reading and writing video files without FFMPEG, but I’m not trying to read a video file, I am trying to read an H264/MPEG4 byte[] array. The following code seems to get positive results.

      /* Async OpenCV Code */
  
      private class OpenCvAndModelAsync extends AsyncTask {
  
          @Override
  
          protected double[] doInBackground(byte[]... params) {//Background Code Executing. Don't touch any UI components
  
              //get fpv feed and convert bytes to mat array
  
              Mat videoBufMat = new Mat(4, params[0].length, CvType.CV_8UC4);
  
              videoBufMat.put(0,0, params[0]);
  
              //if I add this in it says the bytes are empty.
  
              //Mat videoBufMat = Imgcodecs.imdecode(encodeVideoBuf, Imgcodecs.IMREAD_ANYCOLOR);
  
              //encodeVideoBuf.release();
  
              Log.d("MatRgba", videoBufMat.toString());
  
              for (int i = 0; i< videoBufMat.rows(); i++){
  
                  for (int j=0; j< videoBufMat.cols(); j++){
  
                      double[] rgb = videoBufMat.get(i, j);
  
                      Log.i("Matrix", "red: "+rgb[0]+" green: "+rgb[1]+" blue: "+rgb[2]+" alpha: "
  
                              + rgb[3] + " Length: " + rgb.length + " Rows: "
  
                              + videoBufMat.rows() + " Columns: " + videoBufMat.cols());
  
                  }
  
              }
  
              double[] center = openCVThingy(videoBufMat);
  
              return center;
  
          }
  
          protected void onPostExecute(double[] center) {
  
              //handle ui or another async task if necessary
  
          }
  
      }

  Rows = 4, Columns > 30k. I get lots of RGB values that seem valid, such as red = 113, green=75, blue=90, alpha=220 as a made up example ; however, I get a ton of 0,0,0,0 values. That should be somewhat okay, since Black is 0,0,0 (although I would have thought the alpha would be higher) and I have a black object in my image. I also don’t seem to get any white values 255, 255, 255, even though there is also plenty of white area. I’m not logging the entire byte so it could be there, but I have yet to see it.

  However, when I try to compute the contours from this image, I almost always get that the moments (center x, y) are exactly in the center of the image. This error has nothing to do with my color filter or contours algorithm, as I wrote a script in python and tested that I implemented it correctly in Android by reading a still image and getting the exact same number of contours, position, etc in both Python and Android.

  I noticed it has something to do with the videoBuffer byte size (bonus points if you can explain why every other length is 6)

  2019-05-23 21:14:29.601 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 2425
  
  2019-05-23 21:14:29.802 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 2659
  
  2019-05-23 21:14:30.004 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6
  
  2019-05-23 21:14:30.263 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6015
  
  2019-05-23 21:14:30.507 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6
  
  2019-05-23 21:14:30.766 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 4682
  
  2019-05-23 21:14:31.005 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6
  
  2019-05-23 21:14:31.234 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 2840
  
  2019-05-23 21:14:31.433 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 4482
  
  2019-05-23 21:14:31.664 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6
  
  2019-05-23 21:14:31.927 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 4768
  
  2019-05-23 21:14:32.174 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6
  
  2019-05-23 21:14:32.433 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 4700
  
  2019-05-23 21:14:32.668 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6
  
  2019-05-23 21:14:32.864 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 4740
  
  2019-05-23 21:14:33.102 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6
  
  2019-05-23 21:14:33.365 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 4640

  My questions :

  I. Is this the correct format to read an h264 byte as mat ?
  Assuming the format is RGBA, that means row = 4 and columns = byte[].length, and CvType.CV_8UC4. Do I have height and width correct ? Something tells me YUV height and width is off. I was getting some meaningful results, but the contours were exactly in the center, just like with the H264.

  II. Does OpenCV handle MP4 in android like this ? If not, do we need to use FFMPEG or JavaCV ?

  III. Does the int size have something to do with it ? Why is the int size occassionally 6, and other times 2400 to 6000 ? I’ve heard about the difference between this frames information and information about the next frame, but I’m simply not knowledgeable enough to know how to apply that here.

  I’m starting to think this is where the issue lies. Since I need to get the 6 byte array for info about next frame, perhaps my modulo 30 is incorrect. So should I pass the 29th or 31st frame as a format byte for each frame ? How is that done in opencv or are we doomed to use the complicated ffmpeg ? How would I go about joining the neighboring frames/ byte arrays ?

  IV. Can I fix this using Imcodecs ? I was hoping opencv would natively handle whether a frame was color from this frame or info about next frame. I added the below code, but I am getting an empty array :

  Mat videoBufMat = Imgcodecs.imdecode(new MatOfByte(params[0]), Imgcodecs.IMREAD_UNCHANGED);

  This also is empty :

  Mat encodeVideoBuf = new Mat(4, params[0].length, CvType.CV_8UC4);
  
  encodeVideoBuf.put(0,0, params[0]);
  
  Mat videoBufMat = Imgcodecs.imdecode(encodeVideoBuf, Imgcodecs.IMREAD_UNCHANGED);

  V. Should I try converting the bytes into Android jpeg and then import it ? Why is djis yuv decoder so complicated looking ? It makes me cautious from wanting to try ffmpeg or Javacv and just stick to Android decoder or opencv decoder.

  VI. At what stage should I resize the frames to speed up calculations ?

  Edit : DJI support got back to me and confirmed they don’t have any samples for doing what I’ve described. This is a time for we the community to make this available for everyone !

  Upon further research, I don’t think opencv will be able to handle this as opencv’s android sdk has no functionality for video files/url’s (apart from a homegrown MJPEG codec).

  So is there a way in Android to convert to mjpeg or similar in order to read ? In my application, I only need 1 or 2 frames per second, so perhaps I can save the image as jpeg.

  But for real time applications we will likely need to write our own decoder. Please help so that we can make this available to everyone ! This question seems promising :

• Revision 112006 : Accélérer un peu le calcul du javascript du porte plume : on utilise ...

  16 octobre 2018, par marcimat@… — Log

  Accélérer un peu le calcul du javascript du porte plume : on utilise json_encode natif de PHP, modulo un échappement pour les appels de fonctions.

• What is Google Analytics data sampling and what’s so bad about it ?

  16 août 2019, par Joselyn Khor — Analytics Tips, Development
  What is Google Analytics data sampling, and what’s so bad about it ?

  Google (2019) explains what data sampling is :

  “In data analysis, sampling is the practice of analysing a subset of all data in order to uncover the meaningful information in the larger data set.”[1]

  This is basically saying instead of analysing all of the data, there’s a threshold on how much data is analysed and any data after that will be an assumption based on patterns.

  Google’s (2019) data sampling thresholds :

  Ad-hoc queries of your data are subject to the following general thresholds for sampling :
  [Google] Analytics Standard : 500k sessions at the property level for the date range you are using
  [Google] Analytics 360 : 100M sessions at the view level for the date range you are using (para. 3) [2]

  This threshold is limiting because your data in GA may become more inaccurate as the traffic to your website increases.

  Say you’re looking through all your traffic data from the last year and find you have 5 million page views. Only 500K of that 5 million is accurate ! The data for the remaining 4.5 million (90%) is an assumption based on the 500K sample size.

  This is a key weapon Google uses to sell to large businesses. In order to increase that threshold for more accurate reporting, upgrading to premium Google Analytics 360 for approximately US$150,000 per year seems to be the only choice.

  What’s so bad about data sampling ?

  It’s unfair to say sampled data is to be disregarded completely. There is a calculation ensuring it is representative and can allow you to get good enough insights. However, we don’t encourage it as we don’t just want “good enough” data. We want the actual facts.

  In a recent survey sent to Matomo customers, we found a large proportion of users switched from GA to Matomo due to the data sampling issue.

  The two reasons why data sampling isn’t preferable : 

  1. If the selected sample size is too small, you won’t get a good representative of all the data. 
  2. The bigger your website grows, the more inaccurate your reports will become.

  An example of why we don’t fully trust sampled data is, say you have an ecommerce store and see your GA revenue reports aren’t matching the actual sales data, due to data sampling. In GA you may be seeing revenue for the month as $1 million, instead of actual sales of $800K.

  The sampling here has caused an inaccuracy that could have negative financial implications. What you get in the GA report is an estimated dollar figure rather than the actual sales. Making decisions based on inaccurate data can be costly in this case. 

  Another disadvantage to sampled data is that you might be missing out on opportunities you would’ve noticed if you were given a view of the whole. E.g. not being able to see real patterns occurring due to the data already being predicted. 

  By not getting a chance to see things as they are and only being able to jump to the conclusions and assumptions made by GA is risky. The bigger your business grows, the less you can risk making business decisions based on assumptions that could be inaccurate. 

  If you feel you could be missing out on opportunities because your GA data is sampled data, get 100% accurately reported data. 

  The benefits of 100% accurate data

  Matomo doesn’t use data sampling on any of our products or plans. You get to see all of your data and not a sampled data set.

  Data quality is necessary for high impact decision-making. It’s hard to make strategic changes if you don’t have confidence that your data is reliable and accurate.

  Learn about how Matomo is a serious contender to Google Analytics 360. 

  Now you can import your Google Analytics data directly into your Matomo

  If you’re wanting to make the switch to Matomo but worried about losing all your historic Google Analytics data, you can now import this directly into your Matomo with the Google Analytics Importer tool.

  
  Take the challenge !

  Compare your Google Analytics data (sampled data) against your Matomo data, or if you don’t have Matomo data yet, sign up to our 30-day free trial and start tracking !

  References :

  [1 & 2] About data sampling. (2019). In Analytics Help About data sampling. Retrieved August 14, 2019, from https://support.google.com/analytics/answer/2637192