Miłosz Orzeł

OVERVIEW

Part 1 introduced you to OpenCV and its Emgu CV wrapper library plus showed the easiest way to create Emgu project in Visual Studio 2017. Part 2 was all about grabbing frames from video file. The third (and last) episode focuses on image transformations and contour detection...

If case you forgot: here's the complete code sample on GitHub (focus on Program.cs file as it contains all image acquisition and processing code). This is the app in action:

STEP 4: Difference detection and noise removal

In previous post you've seen that VideoProcessingLoop method invoked ProcessFrame for each frame grabbed from video, here's the method:

// Determines boundary of brightness while turning grayscale image to binary (black-white) image
private const int Threshold = 5;

// Erosion to remove noise (reduce white pixel zones)
private const int ErodeIterations = 3;

// Dilation to enhance erosion survivors (enlarge white pixel zones)
private const int DilateIterations = 3;

// ...

// Containers for images demonstrating different phases of frame processing 
private static Mat rawFrame = new Mat(); // Frame as obtained from video
private static Mat backgroundFrame = new Mat(); // Frame used as base for change detection
private static Mat diffFrame = new Mat(); // Image showing differences between background and raw frame
private static Mat grayscaleDiffFrame = new Mat(); // Image showing differences in 8-bit color depth
private static Mat binaryDiffFrame = new Mat(); // Image showing changed areas in white and unchanged in black
private static Mat denoisedDiffFrame = new Mat(); // Image with irrelevant changes removed with opening operation
private static Mat finalFrame = new Mat(); // Video frame with detected object marked

// ...

private static void ProcessFrame(Mat backgroundFrame, int threshold, int erodeIterations, int dilateIterations)
{
    // Find difference between background (first) frame and current frame
    CvInvoke.AbsDiff(backgroundFrame, rawFrame, diffFrame);

    // Apply binary threshold to grayscale image (white pixel will mark difference)
    CvInvoke.CvtColor(diffFrame, grayscaleDiffFrame, ColorConversion.Bgr2Gray);
    CvInvoke.Threshold(grayscaleDiffFrame, binaryDiffFrame, threshold, 255, ThresholdType.Binary);

    // Remove noise with opening operation (erosion followed by dilation)
    CvInvoke.Erode(binaryDiffFrame, denoisedDiffFrame, null, new Point(-1, -1), erodeIterations, BorderType.Default, new MCvScalar(1));
    CvInvoke.Dilate(denoisedDiffFrame, denoisedDiffFrame, null, new Point(-1, -1), dilateIterations, BorderType.Default, new MCvScalar(1));

    rawFrame.CopyTo(finalFrame);
    DetectObject(denoisedDiffFrame, finalFrame);
}

AbsDiff and CvtColor

First the current frame (kept in rawFrame) is compared to background with CvInvoke.AbsDiff. In other words: current frame is subtracted from background frame pixel by pixel (absolute value is used to avoid negative results). After that the difference image is converted into grayscale with CvInvoke.CvtColor call. We care only about overall pixel difference (not it's individual blue-green-red color components). The whiter the pixel is the more it color has changed... Take a look at below picture showing background frame, current frame and the grayscale difference:

Threshold

Grayscale image is changed into and image with only black and white (binary) pixels with the use of CvInvoke.Threshold. Our intention is to mark changed pixels as white. Threshold value allows as to control change detection sensitivity. Below you can see how different thresholds produce various binary results:

First image (left) was produced with Threshold = 1, so even the slightest change got marked - such image is not a suitable input for contour detection. Image in the middle used Threshold = 5. Drone position is clearly marked and smaller white pixel zones can be removed with erosion... Last image (right) is the result of setting Threshold to = 200. This time sensitivity was too low and we got just a couple of white pixels. 

Erode and Dilate

It's hard to find a threshold that gives desired difference detection for each video frame. If threshold is too low then too many pixels are white, if it's too high then the drone might not be detected at all... The best is to pick a threshold which marks the change we need even if we get a bit of undesired white spots as these can be safely removed with erosion followed by dilation. When combined, these two operations create so called opening operation which can be treated as a noise removal method. Opening is a type of morphology transformation (read this article to learn more about them). These operations work by probing pixel neighborhood with a structuring element (aka kernel) and deciding pixel value based on the values found in the neighborhood...

CvInvoke.Erode is meant to simulate a physical process in which object area is reduced due to destructive effects of its surroundings. Detailed behavior depends on the parameters passed (structuring element, anchor, border type - never mind, this is a beginners guide, right?) but the general idea is like this: if pixel is white but has a black pixel around it then it should become black too. The more erode iterations are run the more white pixel zones get eaten away. Here's an example of image erosion:

On the left is the input image and on the right we have the result of erosion which used structuring element in the shape of a 3x3 square (this is the default value used when null is passes for element parameter in Erode invocation). The value of output pixel was decided by probing all neighboring pixels and checking for their minimal value. If a pixel was white in the input image but had at least one black pixel in its immediate surroundings then it became black in the output image.

If erosion is used wisely we can get rid of irrelevant white pixels. But there's a catch: the white pixel zone that marks the drone is reduced too. Don't worry: dilation is going to help us! Just like pupils in your eyes are enlarged (dilated) when it gets dark the white pixel zones that survived erosion can get enlarged too... Again details vary by CvInvoke.Dilate parameters but generally speaking: if pixel is black but has while neighbor it gets white too. The more iterations are run the more white zones are enlarged. This is an example of dilation:

On the left we have the same input image as used in erosion example and on the right we can see the result of single call to Dilate method (again with 3 by 3 kernel matrix). Notice how pixel obtains the maximal value of its surroundings (if any neighboring pixel is white it becomes white too)...

Erosion followed by dilation is such a common transformation that OpenCV has methods that combine the two into one opening operation but using separate Erode and Dilate calls gives you a bit more control. Below you can see how opening cleared the noise and enhanced white spot that marks drone position:

STEP 5: Contour detection

Once all above image preparation steps are done we have a binary image which is suitable input for contour detection provided by DetectObject method:

private static void DetectObject(Mat detectionFrame, Mat displayFrame)
{
    using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
    {
        // Build list of contours
        CvInvoke.FindContours(detectionFrame, contours, null, RetrType.List, ChainApproxMethod.ChainApproxSimple);

        // Selecting largest contour
        if (contours.Size > 0)
        {
            double maxArea = 0;
            int chosen = 0;
            for (int i = 0; i < contours.Size; i++)
            {
                VectorOfPoint contour = contours[i];

                double area = CvInvoke.ContourArea(contour);
                if (area > maxArea)
                {
                    maxArea = area;
                    chosen = i;
                }
            }

            // Draw on a frame
            MarkDetectedObject(displayFrame, contours[chosen], maxArea);
        }
    }
}

The method takes binary difference image (detectionFrame) on which contours will be detected and another Mat instance (displayFrame) on which detected object will be marked (it's a copy of unprocessed frame)...

CvInvoke.FindContours takes the image and runs contour detection algorithm to find boundaries between black (zero) and white (non-zero) pixels on 8-bit single channel image - our Mat instance with the result of AbsDiff->CvtColor->Threshold->Erode->Dilate suites it just fine! 

A contour is a VectorOfPoint, because image might have many contours we keep them inside VectorOfVectorOfPoint. In case many contours get detected we want to pick the largest of them. This is easy thanks to a CvInvoke.ContourArea method...

Read the docs about contour hierarchy and approximation methods if you are curious about RetrType.List, ChainApproxMethod.ChainApproxSimple enum values seen in CvInvoke.FindContours call. This is a good read too...

STEP 6: Drawing and writing on a frame

Once we've found the drone (that is we have a contour that marks its position) it would be good to present this information to the user. This is done by MarkDetectedObject method:

private static void MarkDetectedObject(Mat frame, VectorOfPoint contour, double area)
{
    // Getting minimal rectangle which contains the contour
    Rectangle box = CvInvoke.BoundingRectangle(contour);

    // Drawing contour and box around it
    CvInvoke.Polylines(frame, contour, true, drawingColor);
    CvInvoke.Rectangle(frame, box, drawingColor);

    // Write information next to marked object
    Point center = new Point(box.X + box.Width / 2, box.Y + box.Height / 2);

    var info = new string[] {
        $"Area: {area}",
        $"Position: {center.X}, {center.Y}"
    };

    WriteMultilineText(frame, info, new Point(box.Right + 5, center.Y));
}

The method uses CvInvoke.BoundingRectangle to find minimal box (Rectangle) that surrounds the entire contour. Box is later drawn with a call to CvInvoke.Rectangle. The contour itself is plotted by CvInvoke.Polylines method which takes a list of points that describe the line. You can notice that both drawing methods receive drawingColor parameter, it is an instance of MCvScalar defined this way:

private static MCvScalar drawingColor = new Bgr(Color.Red).MCvScalar;

Bgr structure constructor can take 3 values that define its individual color components or it can take a Color structure like in my example.

Important: Point, Rectangle and Color structures come from System.Drawing assembly which by default is not included in new console application template so you need to add reference to System.Drawing.dll yourself.

Information about detected object location is written by WriteMultilineText helper method (the same method is used to print info about frame number and processing time). This is the code:

private static void WriteMultilineText(Mat frame, string[] lines, Point origin)
{
    for (int i = 0; i < lines.Length; i++)
    {
        int y = i * 10 + origin.Y; // Moving down on each line
        CvInvoke.PutText(frame, lines[i], new Point(origin.X, y), FontFace.HersheyPlain, 0.8, drawingColor);
    }
}

In each invocation of CvInvoke.PutText method y coordinate of the line is increased so lines are not colliding with each other...

This is how frame captured from video looks like after drawing and writing is applied:

STEP 7: Showing it all

In part 1 you've seen that CvInvoke.Imshow method can be used to present a window with an image (Mat instance). Below method is called for every video frame so the user has a chance to see various stages of image processing and the final result:

private static void ShowWindowsWithImageProcessingStages()
{
    CvInvoke.Imshow(RawFrameWindowName, rawFrame);
    CvInvoke.Imshow(GrayscaleDiffFrameWindowName, grayscaleDiffFrame);
    CvInvoke.Imshow(BinaryDiffFrameWindowName, binaryDiffFrame);
    CvInvoke.Imshow(DenoisedDiffFrameWindowName, denoisedDiffFrame);
    CvInvoke.Imshow(FinalFrameWindowName, finalFrame);
}

Displaying intermediate steps is a great debugging aid for any image processing application (I didn't show separate windows for erosion and dilation because only 6 windows of my test video fit on full HD screen):

SUMMARY

This three part series assumed that you were completely new to image processing with OpenCV/Emgu CV. Now you have some idea what these libraries are and how to use them in Visual Studio 2017 project while following a coding approach recommended for version 3 of the libs...

You've learned how to grab frames from video and how to prepare them for contour detection using fundamental image processing operations (difference, color space conversion, thresholding and morphological transformations). You also know how to draw shapes and text on an image. Good job!

Computer vision is a complex yet very interesting topic (its importance is constantly increasing), you've just made a first step in this field - who knows, maybe one day I will ride in autonomous vehicle powered by your software? :) 

OVERVIEW

In Part 1 you have learned what OpenCV is, what is the role of Emgu CV wrapper and how to create a Visual Studio 2017 C# project that utilizes the two libraries. In this part I will show you how to loop through frames captured from video file. Check the first part to watch demo video and find information about sample project (all the interesting stuff is inside Program.cs  - keep this file opened in separate tab as its fragments will be shown in this post)...

STEP 1: Capturing video from file

Before any processing can happen we need to obtain a frame from video file. This can be easily done by using VideoCapture class (many tutorials mention Capture class instead but it is not available in recent Emgu versions).

Check the Main method from our sample project:

private const string BackgroundFrameWindowName = "Background Frame";
// ...
private static Mat backgroundFrame = new Mat(); // Frame used as base for change detection
// ...

static void Main(string[] args)
{
    string videoFile = @"PUT A PATH TO VIDEO FILE HERE!";

    using (var capture = new VideoCapture(videoFile)) // Loading video from file
    {
        if (capture.IsOpened)
        {
            // ...

            // Obtaining and showing first frame of loaded video (used as the base for difference detection)
            backgroundFrame = capture.QueryFrame();
            CvInvoke.Imshow(BackgroundFrameWindowName, backgroundFrame);

            // Handling video frames (image processing and contour detection)
            VideoProcessingLoop(capture, backgroundFrame);
        }
        else
        {
            Console.WriteLine($"Unable to open {videoFile}");
        }
    }
}

VideoCapture has four constructor versions. The overload we are using takes string parameter that is a path to video file or video stream. Other versions allow us to connect to cameras. If you design your program right, switching from file input to a webcam might be as easy as changing new VideoCapture call!

Once VideoCapture instance is created we can confirm if opening went fine by accessing IsOpened property (maybe path is wrong or codecs are missing?).

VideoCapture offers few ways of acquiring frames but the one I find most convenient is by call to QueryFrame method. This method returns Mat class instance (you know it already from part 1) and moves to next frame. If next frame cannot be found then null is returned. We can use this fact to easily loop through video. 

STEP 2: Loading and presenting background frame

Our drone detection project is based on finding the difference between background frame and other frames. The assumption is that we can treat the first frame obtained from the video as the background, hence the call to QueryFrame right after creating VideoCapture object:

 backgroundFrame = capture.QueryFrame();

After background is loaded we can check how it looks with a call to Imshow method (you know it from part 1 too):

CvInvoke.Imshow(BackgroundFrameWindowName, backgroundFrame);

Is finding a (meaningful!) difference in a video always as easy as subtracting frames? No, it isn't. First of all the background might not be static (imagine that drone was flying in front of threes moved by wind or if lighting in a room was changing significantly). The second challenge might come from movements of the camera. Having a fixed background and camera position keeps our drone detection task simple enough for beginner's OpenCV tutorial plus it's not completely unrealistic. Video detection/recognition is often used in fully controlled environment such as part of factory... OpenCV is capable of handling more complex scenarios - you can read about background subtraction techniques and optical flow to get a hint...

STEP 3: Looping through video frames

We know that we can use QueryFrame to get single frame image (Mat instance) and progress to next frame and we know that QueryFrame returns null if it can't go any further. Let's use this knowledge to build a method that goes through frames in a loop:

private static void VideoProcessingLoop(VideoCapture capture, Mat backgroundFrame)
{
    var stopwatch = new Stopwatch(); // Used for measuring video processing performance

    int frameNumber = 1;
    while (true) // Loop video
    {
        rawFrame = capture.QueryFrame(); // Getting next frame (null is returned if no further frame exists)

        if (rawFrame != null) 
        {
            frameNumber++;

            stopwatch.Restart();
            ProcessFrame(backgroundFrame, Threshold, ErodeIterations, DilateIterations);
            stopwatch.Stop();

            WriteFrameInfo(stopwatch.ElapsedMilliseconds, frameNumber);
            ShowWindowsWithImageProcessingStages();

            int key = CvInvoke.WaitKey(0); // Wait indefinitely until key is pressed

            // Close program if Esc key was pressed (any other key moves to next frame)
            if (key == 27)
                Environment.Exit(0);
        }
        else
        {
            capture.SetCaptureProperty(CapProp.PosFrames, 0); // Move to first frame
            frameNumber = 0;
        }
    }
}

In each loop iteration a frame is grabbed from video file. It is then passed to ProcessFrame method which does image difference, noise removal, contour detection and drawing (it will be discussed in detail in the next post)... Call to ProcessFrame is surrounded with System.Diagnostics.Stopwatch usage - this way we can measure video processing performance. It took my laptop only about 1.5ms to fully handle each frame - I've told you OpenCV is fast! :)

If QueryFrame returns null then program moves back to first frame by calling SetCaptureProperty method on VideoCapture instance (video will be processed again).

WriteFrameInfo puts a text in the frame's upper-left corner with information about it's number and how long it took to process it. ShowWindowsWithImageProcessingStages ensures that we can see current (raw) frame, background frame, intermediate frames and final frame in separate windows... Both methods will be shown in next post.

The while loop is going to spin forever unless program execution is stopped by Escape key being pressed in any of the windows that show frames (not the console window!). If 0 is passed as WaitKey argument then program waits until some key is pressed. This let's you look at each frame as long as you want. If you pass other number to WaitKey then the program will wait until key is pressed or a delay elapses. You might use it to automatically play video at specified frame rate:

int fps = (int)capture.GetCaptureProperty(CapProp.Fps);
int key = CvInvoke.WaitKey(1000 / fps); // 40ms delay

Warning: One thing you might notice while processing videos is that moving through a file is not always as easy as setting CapProp.PosFrame to desired number. Your experience might vary from format to format. This is because video files are optimized for playing forward at natural speed and frames might not be simply kept as sequence of images. Full HD (1920x1080) movie has over 2 million pixels in each frame. Now let's say we have an hour of video at 30 FPS ->  3600 * 30 * 2,073,600 = 223,948,800,000. Independent frame compression is not enough to crush that number! No wonder some people need to dedicate their scientific/sofware careers to video compression...

Ok, enough for now - next part coming soon!

Update: Part 3 is ready!