Speeding up writing images into hard disk in OpenCV

Question

I am working with a 50 fps camera (in Ubuntu environment and Qt framework) and every 20 ms I get a frame to process.

I wrote a code to read images from camera and then store them in hard drive.

while(3.14)
{
 cv::Mat Camera_Image = Capture_Image();
 double T1 = (double)cv::getTickCount();
 cv::imwrite (STORE_ADDRESS,Camera_Image);
 T1 = (((double)cv::getTickCount() -T1)*1000)/cv::getTickFrequency();
 print(T1);
}

when I see the output the time to write a single image into hard disk is around 30 ms for a 2048*1080 image size. each image is single channel (gray scale) but I'm writing them in .jpg format in hard disk. size of each image in hard disk is approximately 500Kbytes.

Since I'm capturing a frame in around 20 ms, I'm not able to write them all into hard disk in real time. I've written my code using Qthread and created a queue to see if there's any improvement but the results were the same and it was only a memory overhead.

Is it possible to change this situation, or use some other library to write these images into hard disk much faster? I would also prefer a Qt solution if available ...

Plus I need to write every single frame into hard disk so please do not propose to use Motion compression algorithms since they don't apply to my situation ....

Code: Mainwindow.cpp

 Qlist<cv::Mat> FINAL_IM_VEC;
MainWindow::MainWindow(QWidget *parent) :
  QMainWindow(parent),
  ui(new Ui::MainWindow)
{
  ui->setupUi(this);

  IMREAD *IMR = new IMREAD(this);   // an instance of IMREAD Class which reads camera frames
  IMWRITE *IMW = new IMWRITE(this);  // an instance of IMWRITE Class which Writes camera frames into hard disk
  QThread *IMAGE_READ_Thread = new QThread(this);
  QThread *Image_Store_Thread = new QThread(this);
  connect(IMAGE_READ_Thread,SIGNAL(started()),IMR,SLOT(IMREAD_Process()));
  connect(Image_Store_Thread,SIGNAL(started()),IMW,SLOT(IMWrite_Process()));
  IMR.moveToThread(IMAGE_READ_Thread);
  IMW.moveToThread(Image_Store_Thread);
  IMAGE_READ_Thread->start();
  Image_Store_Thread->start();
}

imread.hpp

class IMREAD : public QObject
{
    Q_OBJECT
public:
    explicit IMREAD(QObject *parent = 0);

signals:

public slots:
    void IMREAD_Process();
private:
    bool Stop;
};

imread.cpp

IMREAD::IMREAD(QObject *parent) :
    QObject(parent)
{
  this->Stop = false;
}

void IMREAD::IMREAD_Process()
{

  while(!Stop)
    {
          cv::Mat Image = CAM::Campture_F(25);//wait a maximum of 25 milisecond to grab a new frame
          if(Image.data())
            {
          FINAL_IM_VEC.push_back(Image);
            }
      }
    }

}

imwrite.hpp

#ifndef IMWRITE_H
#define IMWRITE_H
#pragma once
#include <QObject>
class IMWRITE : public QObject
{
    Q_OBJECT
public:
    explicit IMWRITE(QObject *parent = 0);
signals:

public slots:
    void IMWrite_Process();
private:
    bool Stop;
};

imwrite.cpp

IMWRITE::IMWRITE(QObject *parent) :
    QObject(parent)
{
  this->Stop =false;
}
void IMWRITE::IMWrite_Process()
{
    static int counter = 0;
    while(!Stop)
      {
        for(int i = 0 ; i < FINAL_IM_VEC.size() ; i++)
            {
                QString address = "/home/Provisioner/ThreadT/Results/" + QString::number(counter++) + ".jpg";
                cv::imwrite(address.toUtf8().constData(),FINAL_IM_VEC[i]);
                FINAL_IM_VEC.erase(FINAL_IM_VEC.begin() + i);
                i--;
            }
      }

}

Since this is just part of the whole project, I've removed some of its irrelevant parts ...but it shows how I wrote my multithreaded code in a big picture... so if there's any thing wrong with please inform me.

Thanks in advance.

Answer 1

Let's see: 2048*1080*3(number of channels)*50 fps ~= 316MB/s, if you were writing the images in raw. If you're using JPEG, depending on the compression parameters you may get a substantial reduction, but if it's 1/5th, you're still writing a lot of data to the harddrive, specially if you're using a 5400rpm on a laptop.

Things you could do:

1. As David Schwartz suggests, you should use queues and multiple threads.
2. If you're effectively writing an image sequence, save a video instead. The data is compressed much more and the writing to disk is faster.
3. Check the specks of your current device and get an estimate of the maximum size of the images that you can write to it. Choose compression parameters to fit that size constraint.

Answer 2

There are multiple solutions generally possible, but you need to specify the format of your images - grayscale what? 8 bits? 12 bits? 16 bits?

Most other answers completely miss the mark by ignoring the physical reality of what you're trying to do: the bandwidth, both in terms of I/O and processing, is of primary importance.

Did you verify the storage bandwidth available on your system, in realistic conditions? It will be generally a bad idea to store this stream on the same drive your operating system lives on, because the seeks due to requirements of other applications will eat into your bandwidth. Remember that on a modern 50+Mbyte/s hard drive with 5ms seeks, one seek costs you 0.25MBytes of bandwidth, and that's rather optimistic since modern "run of the mill" hard drives read faster and seek slower, on average. I'd say 1MByte lost per each seek is a conservative estimate on yesteryear's consumer drives.

1. If you need to write raw frames and don't want to compress them even in a lossless fashion, then you need a storage system that can support the requisite bandwidth. Assuming 8 bit grayscale, you'll be dumping 2Mbytes/frame, at 50Hz that's 100Mbytes/s. A striped RAID 0 array of two contemporary off-the-shelf drives should be able to cope with it without problems.

2. If you are OK with burning some serious CPU or GPU for compression, but still want lossless storage, then JPEG2000 is the default choice. If you use a GPU implementation, it will leave your CPU alone for other things. I'd think the expected bandwidth reduction is 2x, so your RAID 0 will have plenty of bandwidth to spare. That would be the preferred way to use it - it will be very robust and you won't be losing any frames no matter what else the system is doing (within reason, of course).

3. If you are OK with lossy compression, then off-the-shelf jpeg libraries will do the trick. You'd probably want a 4x reduction in size, and the resultant 12.5Mbytes/s data stream can be handled by the hard drive the OS lives on.

As for the implementation: two threads are enough if there's no compression. One thread captures the images, another one dumps them to the drive. If you see no improvement compared to a single thread, then it's solely due to the bandwidth limitations of your drive. If you use GPU for compression, then one thread that handles compression is enough. If you use CPU for compression, then you need as many threads as there are cores.

There is no issue at all with storing image differences, in fact JPEG2k loves this and you my get an overall 2x compression improvement (for a total factor of 4x) if you're lucky. What you do is store a buch of difference frames for each reference frame stored in full. The ratio is based solely on the needs of the processing done afterwards - you're trading off resilience to data loss and interactive processing latency for decreased storage-time bandwidth.

I'd say anywhere between 1:5 and 1:50 ratio is reasonable. With the latter, the loss of the reference frame knocks out 1s worth of data, and randomly seeking anywhere in the data requires on average a read of a reference frame and 24 delta frames, plus the cost of decompressing 25 frames.