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C# How to: Min/Max Edge Detection

Article Purpose

This article serves as a detailed discussion on implementing image edge detection through pixel neighbourhood maximum and minimum value subtraction. Additional concepts illustrated in this article include implementing a median filter and RGB grayscale conversion.

Frog Filter 3×3 Smoothed

Sample Source Code

This article is accompanied by a sample source code Visual Studio project which is available for download here.

Using the Sample Application

This article’s accompanying sample source code includes a Windows Forms based sample application. The sample application provides an implementation of the concepts explored by this article. Concepts discussed can be easily replicated and tested by using the sample application.

Source/input image files can be specified from the local file system when clicking the Load Image button. Additionally users also have the option to save resulting filtered images by clicking the Save Image button.

The sample application user interface enables the user to specify three filter configuration values. These values serve as input parameters to the Min/Max Edge Detection Filter and can be described as follows:

Filter Size – Determines the number of surrounding pixels to consider when calculating the minimum and maximum pixel values. This value equates to the size of a pixel’s neighbourhood of pixels. When gradient edges expressed in a source image requires a higher or lower level of expression in result images the filter size value should be adjusted. Higher Filter Size values result in gradient edges being expressed at greater intensity levels in resulting images. Inversely, lower Filter Size values delivers the opposite result of gradient edges being expressed at lesser intensity levels.
Smooth Noise – Image noise when present in source images, can to varying degrees affect the Min/Max Edge Detection Filter’s accuracy in calculating gradient edges. In order to reduce the negative affects of source image noise an image smoothing filter may be implemented. Smoothing out image noise requires additional filter processing and therefore requires additional computation time. If source images reflect minor or no image noise, additional image smoothing may be excluded to reduce filter processing duration.
Grayscale – When required, result images can be expressed in grayscale through configuring this value.

The following image represents a screenshot of the Min/Max Edge Detection Sample application in action.

Min/Max Edge Detection

The method of edge detection illustrated in this article can be classified as a variation of commonly implemented edge detection methods. Image edges expressed within a source image can be determined through the presence of sudden and significant changes in gradient levels that occur within a small/limited perimeter.

As a means to determine gradient level changes the Min/Max Edge Detection algorithm performs pixel neighbourhood inspection, comparing maximum and minimum colour channel values. Should the difference between maximum and minimum colour values be significant, it would be an indication of a significant change in gradient level within the pixel neighbourhood being inspected.

Image noise represents interference in relation to regular gradient level expression. Image noise does not signal the presence of an image edge, although could potentially result in incorrectly determining image edge presence. Image noise and the negative impact thereof can be significantly reduced when applying image smoothing, also sometimes referred to as image blur. The Min/Max Edge Detection algorithm makes provision for optional image smoothing implemented in the form of a median filter.

The following sections provide more detail regarding the concepts introduced in this section, pixel neighbourhood and median filter.

Frog Filter 3×3 Smoothed

Pixel Neighbourhood

A pixel neighbourhood refers to a set of pixels, all of which are related through pixel location coordinates. The width and height of a pixel neighbourhood must be equal, in other words, a pixel neighbourhood can only be square. Additionally, the width/height of a pixel neighbourhood must be an uneven value. When inspecting a pixel’s neighbouring pixels, the pixel being inspected will always be located at the exact center of the pixel neighbourhood. Only when a pixel neighbourhood’s width/height are an uneven value can such a pixel neighbourhood have an exact center pixel. Each pixel represented in an image has a different set of neighbours, some neighbours overlap, but no two pixels have the exact same neighbours. A pixel’s neighbouring pixels can be determined when considering the pixel to be at the center of a block of pixels, extending half the neighbourhood size less one in horizontal, vertical and diagonal directions.

Median Filter

In context of this article and the Min/Max Edge Detection filter, median filtering has been implemented as a means to reduce source image noise. From the Median Wikipedia page we gain the following quote:

In statistics and probability theory, the median is the number separating the higher half of a data sample, a population, or a probability distribution, from the lower half. The median of a finite list of numbers can be found by arranging all the observations from lowest value to highest value and picking the middle one (e.g., the median of {3, 3, 5, 9, 11} is 5).

Frog Filter 3×3 Smoothed

The application of a median filter is based in the concept of pixel neighbourhood as discussed earlier. The implementation steps required when applying a median filter can be described as follows:

Iterate every pixel. The pixel neighbourhood of each pixel in a source image needs to be determined and inspected.
Order/Sort pixel neighbourhood values. Once a pixel neighbourhood for a specific pixel has been determined the values expressed by all the pixels in that neighbourhood needs to be sorted or ordered according to value.
Determine midpoint value. In relation to the sorted pixel neighbourhood values, the value positioned exactly halfway between first and last value needs to be determined. As an example, if a pixel neighbourhood contains a total of nine pixels, the midpoint would be at position number five, which is four positions from the first and last value inclusive. The midpoint value in a sorted range of neighbourhood pixel values, is the median value of that pixel neighbourhood’s values.

The median filter should not be confused with the mean filter. A median will always be a midpoint value from a sorted value range, whereas a mean value is equal to the calculated average of a value range. The median filter has the characteristic of reducing image noise whilst still preserving image edges. The mean filter will also reduce image noise, but will do so through generalized image blurring, also referred to as box blur, which does not preserve image edges.

Note that when applying a median filter to RGB colour images median values need to be determined per individual colour channel.

Frog Filter 3×3 Smoothed

Min/Max Edge Detection Algorithm

Image edge detection based in a min/max approach requires relatively few steps, which can be combined in source code implementations to be more efficient from a computational/processing perspective. A higher level logical definition of the steps required can be described as follows:

Image Noise Reduction – If image noise reduction is required apply a median filter to the source image.
Iterate through all of the pixels contained within an image.
For each pixel being iterated, determine the neighbouring pixels. The pixel neighbourhood size will be determined by the specified filter size.
Determine the Minimum and Maximum pixel value expressed within the pixel neighbourhood.
Subtract the Minimum from the Maximum value and assign the result to the pixel currently being iterated.
Apply Grayscale conversion to the pixel currently being iterated, only if grayscale conversion had been configured.

Implementing a Min/Max Edge Detection Filter

The source code implementation of the Min/Max Edge Detection Filter declares two methods, a median filter method and an edge detection method. A median filter and edge detection filter cannot be processed simultaneously. When applying a median filter, the median value of a pixel neighbourhood determined from a source image should be expressed in a separate result image. The original source image should not be altered whilst inspecting pixel neighbourhoods and calculating median values. Only once all pixel values in the result image has been set, can the result image serve as a source image to an edge detection filter method.

The following code snippet provides the source code definition of the MedianFilter method.

private static byte[] MedianFilter(this byte[] pixelBuffer,
                                    int imageWidth,
                                    int imageHeight,
                                    int filterSize)
{
    byte[] resultBuffer = new byte[pixelBuffer.Length];

    int filterOffset = (filterSize - 1) / 2;
    int calcOffset = 0;
    int stride = imageWidth * pixelByteCount;

    int byteOffset = 0;
    var neighbourCount = filterSize * filterSize;
    int medianIndex = neighbourCount / 2;

    var blueNeighbours = new byte[neighbourCount];
    var greenNeighbours = new byte[neighbourCount];
    var redNeighbours = new byte[neighbourCount];

    for (int offsetY = filterOffset; offsetY <
        imageHeight - filterOffset; offsetY++)
    {
        for (int offsetX = filterOffset; offsetX <
            imageWidth - filterOffset; offsetX++)
        {
            byteOffset = offsetY *
                            stride +
                            offsetX * pixelByteCount;

            for (int filterY = -filterOffset, neighbour = 0;
                filterY <= filterOffset; filterY++)
            {
                for (int filterX = -filterOffset;
                    filterX <= filterOffset; filterX++, neighbour++)
                {
                    calcOffset = byteOffset +
                                    (filterX * pixelByteCount) +
                                    (filterY * stride);

                    blueNeighbours[neighbour] = pixelBuffer[calcOffset];
                    greenNeighbours[neighbour] = pixelBuffer[calcOffset + greenOffset];
                    redNeighbours[neighbour] = pixelBuffer[calcOffset + redOffset];
                }
            }

            Array.Sort(blueNeighbours);
            Array.Sort(greenNeighbours);
            Array.Sort(redNeighbours);

            resultBuffer[byteOffset] = blueNeighbours[medianIndex];
            resultBuffer[byteOffset + greenOffset] = greenNeighbours[medianIndex];
            resultBuffer[byteOffset + redOffset] = redNeighbours[medianIndex];
            resultBuffer[byteOffset + alphaOffset] = maxByteValue;
        }
    }

    return resultBuffer;
}

Notice the definition of three separate byte arrays, each intended to represent a pixel neighbourhood’s pixel values related to a specific colour channel. Each neighbourhood colour channel byte array needs to be sorted according to value. The value located at the array index exactly halfway from the start and the end of the array represents the median value. When a median value has been determined, the result buffer pixel related to the source buffer pixel in terms of XY Location needs to be set.

Frog Filter 3×3 Smoothed

The sample source code defines two overloaded versions of an edge detection method. The first version is defined as an extension method targeting the Bitmap class. A FilterSize parameter is the only required parameter, intended to specify pixel neighbourhood width/height. In addition, when invoking this method optional parameters may be specified. When image noise reduction should be implemented the smoothNoise parameter should be defined as true. If resulting images are required in grayscale the last parameter, grayscale, should reflect true. The following code snippet provides the definition of the MinMaxEdgeDetection method.

public static Bitmap MinMaxEdgeDetection(this Bitmap sourceBitmap,
                                            int filterSize, 
                                            bool smoothNoise = false, 
                                            bool grayscale = false)
{
    return sourceBitmap.ToPixelBuffer()
                        .MinMaxEdgeDetection(sourceBitmap.Width, 
                                            sourceBitmap.Height, 
                                            filterSize,
                                            smoothNoise,
                                            grayscale)
                        .ToBitmap(sourceBitmap.Width, 
                                    sourceBitmap.Height);
}

The MinMaxEdgeDetection method as expressed above essentially acts as a wrapper method, invoking the overloaded version of this method, performing mapping between Bitmap objects and byte array pixel buffers.

An overloaded version of the MinMaxEdgeDetection method performs all of the tasks required in edge detection through means of minimum maximum pixel neighbourhood value subtraction. The method definition as provided by the following code snippet.

private static byte[] MinMaxEdgeDetection(this byte[] sourceBuffer,
                                          int imageWidth,
                                          int imageHeight,
                                          int filterSize,
                                          bool smoothNoise = false,
                                          bool grayscale = false)
{
    byte[] pixelBuffer = sourceBuffer;

    if (smoothNoise)
    {
        pixelBuffer = sourceBuffer.MedianFilter(imageWidth, 
                                                imageHeight, 
                                                filterSize);
    }

    byte[] resultBuffer = new byte[pixelBuffer.Length];

    int filterOffset = (filterSize - 1) / 2;
    int calcOffset = 0;
    int stride = imageWidth * pixelByteCount;

    int byteOffset = 0;

    byte minBlue = 0, minGreen = 0, minRed = 0;
    byte maxBlue = 0, maxGreen = 0, maxRed = 0;

    for (int offsetY = filterOffset; offsetY <
        imageHeight - filterOffset; offsetY++)
    {
        for (int offsetX = filterOffset; offsetX <
            imageWidth - filterOffset; offsetX++)
        {
            byteOffset = offsetY *
                            stride +
                            offsetX * pixelByteCount;

            minBlue = maxByteValue;
            minGreen = maxByteValue;
            minRed = maxByteValue;

            maxBlue = minByteValue;
            maxGreen = minByteValue;
            maxRed = minByteValue;

            for (int filterY = -filterOffset;
                filterY <= filterOffset; filterY++)
            {
                for (int filterX = -filterOffset;
                    filterX <= filterOffset; filterX++)
                {
                    calcOffset = byteOffset +
                                    (filterX * pixelByteCount) +
                                    (filterY * stride);

                    minBlue = Math.Min(pixelBuffer[calcOffset], minBlue);
                    maxBlue = Math.Max(pixelBuffer[calcOffset], maxBlue);

                    minGreen = Math.Min(pixelBuffer[calcOffset + greenOffset], minGreen);
                    maxGreen = Math.Max(pixelBuffer[calcOffset + greenOffset], maxGreen);

                    minRed = Math.Min(pixelBuffer[calcOffset + redOffset], minRed);
                    maxRed = Math.Max(pixelBuffer[calcOffset + redOffset], maxRed);
                }
            }

            if (grayscale)
            {
                resultBuffer[byteOffset] = ByteVal((maxBlue - minBlue) * 0.114 + 
                                                    (maxGreen - minGreen) * 0.587 + 
                                                    (maxRed - minRed) * 0.299);

                resultBuffer[byteOffset + greenOffset] = resultBuffer[byteOffset];
                resultBuffer[byteOffset + redOffset] = resultBuffer[byteOffset];
                resultBuffer[byteOffset + alphaOffset] = maxByteValue;
            }
            else
            {
                resultBuffer[byteOffset] = (byte)(maxBlue - minBlue);
                resultBuffer[byteOffset + greenOffset] = (byte)(maxGreen - minGreen);
                resultBuffer[byteOffset + redOffset] = (byte)(maxRed - minRed);
                resultBuffer[byteOffset + alphaOffset] = maxByteValue;
            }
        }
    }

    return resultBuffer;
}

As discussed earlier, image noise reduction if required should be the first task performed. Based on parameter value the method applies a median filter to the source image buffer.

When iterating a pixel neighbourhood a comparison is performed between the currently iterated neighbouring pixel’s value and the previously determined minimum and maximum values.

When the grayscale method parameter reflects true, a grayscale algorithm is applied to the difference between the determined maximum and minimum pixel neighbourhood values.

Should the grayscale method parameter reflect false, grayscale algorithm logic will not execute. Instead, the result obtained from subtracting the determined minimum and maximum values are assigned to the relevant pixel and colour channel on the result buffer image.

Frog Filter 3×3 Smoothed

Sample Images

This article features several sample images provided as examples. All sample images were created using the sample application. All of the original source images used in generating sample images have been licensed by their respective authors to allow for reproduction here. The following section lists each original source image and related license and copyright details.

C# How to: Image Distortion Blur

Published August 9, 2013 Augmented Reality , Blogging , C# , Code Samples , Extension Methods , Graphic Filters , Graphics , How to , Image Filters , Image Processing , Image Transform , Microsoft , New Version , Non-Photorealistic Rendering , Opensource , Tip , Update Leave a Comment
Tags: Alpha Red Green Blue, Augmented Reality, Binary Image, Bitmap ARGB, Bitmap Filters, Bitmap.LockBits, BitmapData, Blur, C#, Code Sample, Color Filters, Computer vision, Converting Images, Extension Methods, Graphic Filters, How to, Image, Image Blur, Image Distortion, Image Intensity, Image Manipulation, Image processing, Image Transform, Machine vision, Median Filter, Morphologial Filters, Non-Photorealistic Rendering, Pixel Filters

Article Purpose

This article explores the process of implementing an Image Distortion Blur filter. This image filter is classified as a non-photo realistic image filter, primarily implemented in rendering artistic effects.

Flower: Distortion Factor 15

Sample Source Code

This article is accompanied by a sample source code Visual Studio project which is available for download here.

Flower: Distortion Factor 10

Using the Sample Application

The sample source code that accompanies this article includes a Windows Forms based sample application. The concepts explored in this article have all been implemented as part of the sample application. From an end user perspective the following configurable options are available:

Load/Save Images – Clicking the Load Image button allows a user to specify a source/input image. If desired, output filtered images can be saved to the local file system by clicking the Save Image button.
Distortion Factor – The level or intensity of image distortion applied when implementing the filter can be specified when adjusting the Distortion Factor through the user interface. Lower factor values result in less distortion being evident in resulting images. Specifying higher factor values result in more intense image distortion being applied.

The following image is screenshot of the Image Distortion Blur sample application:

Flower: Distortion Factor 10

Flower: Distortion Factor 10

Image Distortion

In this article and the accompanying sample source code images are distorted through slightly adjusting each individual pixel’s coordinates. The direction and distance by which pixel coordinates are adjusted differ per pixel as a result of being randomly selected. The maximum distance offset applied depends on the user specified Distortion Factor. Once all pixel coordinates have been updated, implementing a Median Filter provides smoothing and an image blur effect.

Applying an Image Distortion Filter requires implementing the following steps:

Iterate Pixels – Each pixel forming part of the source/input image should be iterated.
Calculate new Coordinates – For every pixel being iterated generate two random values representing XY-coordinate offsets to be applied to a pixel’s current coordinates. Offset values can equate to less than zero in order to represent coordinates above or to the left of the current pixel.
Apply Median Filter – The newly offset pixels will appear somewhat speckled in the resulting image. Applying a Median Filter reduces the speckled appearance whilst retaining a distortion effect.

Flower: Distortion Factor 10

Median Filter

Applying a Median Filter is the final step required when implementing an Image Distortion Blur filter. Median Filters are often implemented in reducing image noise. The method of image distortion illustrated in this article express similarities when compared to image noise. In order to soften the appearance of image noise we implement a Median Filter.

A Median Filter can be applied through implementing the following steps:

Iterate Pixels – Each pixel forming part of the source/input image should be iterated.
Inspect Pixel Neighbourhood – Each neighbouring pixel in relation to the pixel currently being iterated should be added to a temporary collection.
Determine Neighbourhood Median – Once all neighbourhood pixels have been added to a temporary collection, sort the collection by value. The element value located at the middle of the collection represents the pixel neighbourhood’s Median value.

Flower: Distortion Factor 10

Flower: Distortion Factor 15

Implementing Image Distortion

The sample source code defines the DistortionBlurFilter method, an extension method targeting the Bitmap class. The following code snippet illustrates the implementation:

public static Bitmap DistortionBlurFilter( 
         this Bitmap sourceBitmap, int distortFactor) 
{
    byte[] pixelBuffer = sourceBitmap.GetByteArray(); 
    byte[] resultBuffer = sourceBitmap.GetByteArray(); 

 
    int imageStride = sourceBitmap.Width * 4; 
    int calcOffset = 0, filterY = 0, filterX = 0; 
    int factorMax = (distortFactor + 1) * 2; 
    Random rand = new Random(); 

 
    for (int k = 0; k + 4 < pixelBuffer.Length; k += 4) 
    {
        filterY = distortFactor - rand.Next(0, factorMax); 
        filterX = distortFactor - rand.Next(0, factorMax); 

 
        if (filterX * 4 + (k % imageStride) < imageStride  
                   && filterX * 4 + (k % imageStride) > 0) 
        { 
            calcOffset = k + filterY * imageStride +  
                         4 * filterX; 

 
            if (calcOffset >= 0 &&  
                calcOffset + 4 < resultBuffer.Length) 
            { 
                resultBuffer[calcOffset] = pixelBuffer[k]; 
                resultBuffer[calcOffset + 1] = pixelBuffer[k + 1]; 
                resultBuffer[calcOffset + 2] = pixelBuffer[k + 2]; 
            }
        }
    }

 
    return resultBuffer.GetImage(sourceBitmap.Width,  
                        sourceBitmap.Height).MedianFilter(3); 
}

Flower: Distortion Factor 15

Implementing a Median Filter

The MedianFilter extension method targets the Bitmap class. The implementation as follows:

public static Bitmap MedianFilter(this Bitmap sourceBitmap, 
                                  int matrixSize) 
{ 
    byte[] pixelBuffer = sourceBitmap.GetByteArray(); 
    byte[] resultBuffer = new byte[pixelBuffer.Length]; 
    byte[] middlePixel; 


    int imageStride = sourceBitmap.Width * 4; 
    int filterOffset = (matrixSize - 1) / 2; 
    int calcOffset = 0, filterY = 0, filterX = 0; 
    List<int> neighbourPixels = new List<int>(); 


    for (int k = 0; k + 4 < pixelBuffer.Length; k += 4) 
    { 
        filterY = -filterOffset; filterX = -filterOffset; 
        neighbourPixels.Clear(); 


        while (filterY <= filterOffset) 
        { 
            calcOffset = k + (filterX * 4) + 
            (filterY * imageStride); 


            if (calcOffset > 0 &&  
                calcOffset + 4 < pixelBuffer.Length) 
            { 
                neighbourPixels.Add(BitConverter.ToInt32( 
                                    pixelBuffer, calcOffset)); 
            } 


            filterX++; 


            if (filterX > filterOffset) 
             { filterX = -filterOffset; filterY++; }
        }


        neighbourPixels.Sort(); 
        middlePixel = BitConverter.GetBytes( 
                      neighbourPixels[filterOffset]); 


        resultBuffer[k] = middlePixel[0]; 
        resultBuffer[k + 1] = middlePixel[1]; 
        resultBuffer[k + 2] = middlePixel[2]; 
        resultBuffer[k + 3] = middlePixel[3]; 
    }


    return resultBuffer.GetImage(sourceBitmap.Width,  
                                 sourceBitmap.Height); 
}

Flower: Distortion Factor 25

Sample Images

This article features a number of sample images. All featured images have been licensed allowing for reproduction. The following images feature as sample images:

Lilium chalcedonicum in habitat at Hrisomiglia, Greece.
Attribution: Ernst Gügel. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Download from Wikipedia

Lilium ponticum in habitat near Ikizdere, Turkey.
Attribution: Ernst Gügel. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Download from Wikipedia

Lilium sargentiae flora.
Attribution: Denis Barthel. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Download from Wikipedia

A lilium longiflorum, commonly known as an Easter Lily.
Attribution: Matt H. Wade. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Download from Wikipedia

Flora Lilium bulbiferum ssp. croceum, ex coll. Monte Adone, Bologna, Italia.
Attribution: Denis Barthel. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Download from Wikipedia

Turks Cap Lily the Great Smoky Mountains National Park in western North Carolina.
Attribution: Arx Fortis. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Download from Wikipedia

Orange Lily in full bloom showing pollen covered stamens, Ontario, Canada. June 2002.
Attribution: Relic38. This file is licensed under the Creative Commons Attribution 3.0 Unported license.
Download from Wikipedia

Zantedeschia aethiopica (common names calla lily, arum lily).
Attribution: Two+two=4. This file has been released into the public domain. This applies worldwide.
Download from Wikipedia

C# How to: Image Abstract Colours Filter

Published July 28, 2013 Augmented Reality , Blogging , C# , Code Samples , Edge Detection , Extension Methods , Graphic Filters , How to , Image Arithmetic , Image Filtering , Image Filters , Image Processing , Image Transform , Learn Everyday , Microsoft , New Version , Non-Photorealistic Rendering , Opensource , Tip , Update 4 Comments
Tags: Augmented Reality, Binary Image, Bitmap ARGB, Bitmap Filters, Bitmap.LockBits, BitmapData, C#, Code Sample, Color Averaging, Color Filters, Computer vision, Converting Images, Edge Detect, Edge Detection, Feature extraction, Graphic Filters, Image, Image Intensity, Image processing, Image Transform, Machine vision, Non-Photorealistic Rendering, Pixel Filters

Article Purpose

This article explores Abstract Colour Image filters as a process of Non-photo Realistic Image Rendering. The output images produced reflects a variety of artistic effects.

Colour Values	Red, Blue	Filter Size	9
Edge Tracing	Black	Edge Threshold	55

Sample Source Code

This article is accompanied by a sample source code Visual Studio project which is available for download here.

Colour Values	Blue	Filter Size	9
Edge Tracing	Double Intensity	Edge Threshold	60

Using the Sample Application

The sample source code that accompanies this article includes a Windows Forms based sample application. The concepts discussed in this article have been illustrated through the implementation of the sample application.

When executing the sample application, through the user interface several options are made available to the user, described as follows:

Load/Save Images – Source/input images may be loaded from the local file system through clicking the Load Image button. If desired by the user, resulting output images can be saved to the local file system through clicking the Save Image button.
Colour Channels – The colour values Red, Green and Blue can be updated or remain unchanged when applying a filter, indicated through the associated Checkboxes.
Filter Size – The level of filter intensity depends on the size of the filter. Larger filter sizes result in more intense filtering being applied. Smaller filter size result in less intense filtering being applied.
Colour Shift Type – Colour intensity values can be swapped around through selecting the Colour Shift type: Shift Left and Shift Right.
Edge Tracing Type – When applying a filter the edges forming part of the original source/input image will be expressed as part of the output image. The method in which image edges are indicated/highlighted will be determined through the type of Edge Tracing specified when applying the filter. Supported types of Edge Tracing include: Black, White, Half Intensity, Double Intensity and Inverted.
Edge Threshold – Edges forming part of the source/input image are determines through means of image edge detection implementing a threshold value. Lower threshold values result in more emphasized edges expressed within resulting images. Higher threshold values reduce edge emphasis in resulting images.

Colour Values	Green	Filter Size	9
Edge Tracing	Double Intensity	Edge Threshold	60

Colour Values	Red, Blue	Filter Size	9
Edge Tracing	Double Intensity	Edge Threshold	55

The following image is a screenshot of the Image Abstract Colour Filter sample application in action:

Abstracting Image Colours

The Abstract Colour Filter explored in this article can be considered a non-photo realistic filter. As the title implies, non-photo realistic filters transforms an input image, usually a photograph, producing a result image which visibly lacks the aspects of realism expressed in the input image. In most scenarios the objective of non-photo realistic filters can be described as using photographic images in rendering images having an animated appearance.

Colour Values	Blue	Filter Size	11
Edge Tracing	Double Intensity	Edge Threshold	60

Colour Values	Red	Filter Size	11
Edge Tracing	Double Intensity	Edge Threshold	60

The Abstract Colour Filter can be broken down into two main components: Colour Averaging and Image Edge detection. Through implementing a variety of colour averaging algorithms resulting images express abstract yet uniform colours. Abstract colours result in output images no longer appearing photo realistic, instead output images appear unconventionally augmented/artistic.

Output images express a lesser degree of image definition and detail, when compared to input images. In some scenarios output images might not be easily recognisable. In order to retain some image detail, edge/boundary detail detected from input images will be emphasised in result images.

Colour Values	Green	Filter Size	11
Edge Tracing	Double Intensity	Edge Threshold	60

Colour Values	Green, Blue	Filter Size	11
Edge Tracing	Double Intensity	Edge Threshold	75

The steps required when applying an Abstract Colour Filter can be described as follows:

Perform Edge Detection – Using the source/input image perform edge detection, producing a binary image expressing image edges in the foreground/as white.
Calculate Pixel Neighbourhood Colour Averages – Iterate each pixel forming part of the input image, inspecting the pixel’s neighbouring pixels. Calculate the sum total and average of each colour channel, Red, Green and Blue. The value of the pixel currently being iterated should be set depending to neighbourhood average.
Trace Edges within Colour Averages – Simultaneously iterate the colour average image and the edge detected image. If the pixel being iterated forms part of an edge, update the corresponding pixel in the average colour image, depending on the specified method of Edge Tracing.

Colour Values	Red, Green	Filter Size	11
Edge Tracing	Double Intensity	Edge Threshold	75

Colour Values	Red	Filter Size	11
Edge Tracing	Black	Edge Threshold	60

Implementing Pixel Neighbourhood Colour Averaging

In the sample source code neighbourhood colour averaging has been implemented through the definition of the AverageColoursFilter extension method. This method creates a new image using the source image as input. The following code snippet provides the definition:

public static Bitmap AverageColoursFilter(this Bitmap sourceBitmap,  
                                          int matrixSize,   
                                          bool applyBlue = true, 
                                          bool applyGreen = true, 
                                          bool applyRed = true, 
                                          ColorShiftType shiftType = 
                                          ColorShiftType.None)  
{
    byte[] pixelBuffer = sourceBitmap.GetByteArray(); 
    byte[] resultBuffer = new byte[pixelBuffer.Length]; 

 
    int calcOffset = 0; 
    int byteOffset = 0; 
    int blue = 0; int green = 0; int red = 0; 
    int filterOffset = (matrixSize - 1) / 2; 

 
    for (int offsetY = filterOffset; offsetY <  
        sourceBitmap.Height - filterOffset; offsetY++) 
    {
        for (int offsetX = filterOffset; offsetX <  
            sourceBitmap.Width - filterOffset; offsetX++) 
        { 
            byteOffset = offsetY * sourceBitmap.Width*4 +  
                         offsetX * 4; 

 
            blue = 0; green = 0; red = 0; 

 
            for (int filterY = -filterOffset;  
                filterY <= filterOffset; filterY++) 
            { 
                for (int filterX = -filterOffset; 
                    filterX <= filterOffset; filterX++) 
                { 
                    calcOffset = byteOffset +  
                    (filterX * 4) + 
                    (filterY * sourceBitmap.Width * 4); 

 
                    blue += pixelBuffer[calcOffset]; 
                    green += pixelBuffer[calcOffset + 1]; 
                    red += pixelBuffer[calcOffset + 2]; 
                }
            }

 
            blue = blue / matrixSize; 
            green = green / matrixSize; 
            red = red / matrixSize; 

 
            if (applyBlue == false ) 
            { blue = pixelBuffer[byteOffset]; } 

 
            if (applyGreen == false ) 
            { green = pixelBuffer[byteOffset + 1]; } 

 
            if (applyRed == false ) 
            { red = pixelBuffer[byteOffset + 2]; } 

 
            if (shiftType == ColorShiftType.None) 
            {
                resultBuffer[byteOffset] = (byte)blue; 
                resultBuffer[byteOffset + 1] = (byte)green; 
                resultBuffer[byteOffset + 2] = (byte)red; 
                resultBuffer[byteOffset + 3] = 255; 
            }
            else if (shiftType == ColorShiftType.ShiftLeft) 
            {
                resultBuffer[byteOffset] = (byte)green; 
                resultBuffer[byteOffset + 1] = (byte)red; 
                resultBuffer[byteOffset + 2] = (byte)blue; 
                resultBuffer[byteOffset + 3] = 255; 
            }
            else if (shiftType == ColorShiftType.ShiftRight) 
            { 
                resultBuffer[byteOffset] = (byte)red; 
                resultBuffer[byteOffset + 1] = (byte)blue; 
                resultBuffer[byteOffset + 2] = (byte)green; 
                resultBuffer[byteOffset + 3] = 255; 
            } 
        }
    } 

 
    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width,  
                                     sourceBitmap.Height); 

 
    BitmapData resultData =  
               resultBitmap.LockBits(new Rectangle (0, 0, 
               resultBitmap.Width, resultBitmap.Height), 
               ImageLockMode.WriteOnly, 
               PixelFormat.Format32bppArgb); 

 
    Marshal.Copy(resultBuffer, 0, resultData.Scan0,  
                               resultBuffer.Length); 

 
    resultBitmap.UnlockBits(resultData); 

 
    return resultBitmap; 
}

Colour Values	Green, Blue	Filter Size	17
Edge Tracing	Black	Edge Threshold	85

Implementing Gradient Based Edge Detection

When applying an Abstract Colours Filter, one of the required steps involve image edge detection. The sample source code implements Gradient based edge detection through the definition of the GradientBasedEdgeDetectionFilter method. This method has been defined as an extension method targeting the bitmap class. The definition as follows:

public static Bitmap GradientBasedEdgeDetectionFilter( 
                        this Bitmap sourceBitmap, 
                        byte threshold = 0) 
{
    BitmapData sourceData = 
               sourceBitmap.LockBits(new Rectangle (0, 0, 
               sourceBitmap.Width, sourceBitmap.Height), 
               ImageLockMode.ReadOnly, 
               PixelFormat.Format32bppArgb); 

 
    byte[] pixelBuffer = new byte[sourceData.Stride * sourceData.Height]; 
    byte[] resultBuffer = new byte[sourceData.Stride * sourceData.Height]; 

 
    Marshal.Copy(sourceData.Scan0, pixelBuffer, 0, pixelBuffer.Length); 
    sourceBitmap.UnlockBits(sourceData); 

 
    int sourceOffset = 0, gradientValue = 0; 
    bool exceedsThreshold = false; 

 
    for (int offsetY = 1; offsetY <  
                          sourceBitmap.Height - 1; offsetY++) 
     {
        for (int offsetX = 1; offsetX <  
                              sourceBitmap.Width - 1; offsetX++) 
         { 
            sourceOffset = offsetY * sourceData.Stride + 
                           offsetX * 4; 
            gradientValue = 0; 
            exceedsThreshold = true; 

 
            // Horizontal Gradient 
            CheckThreshold(pixelBuffer, 
                           sourceOffset - 4, 
                           sourceOffset + 4, 
                           ref gradientValue, threshold, 2); 
            // Vertical Gradient 
            exceedsThreshold = 
            CheckThreshold(pixelBuffer, 
                           sourceOffset - sourceData.Stride, 
                           sourceOffset + sourceData.Stride, 
                           ref gradientValue, threshold, 2); 

 
            if (exceedsThreshold == false) 
             { 
                gradientValue = 0; 

 
                // Horizontal Gradient 
                exceedsThreshold = 
                CheckThreshold(pixelBuffer, 
                               sourceOffset - 4, 
                               sourceOffset + 4, 
                               ref gradientValue, threshold); 


                if (exceedsThreshold == false) 
                 { 
                    gradientValue = 0; 


                    // Vertical Gradient 
                    exceedsThreshold = 
                    CheckThreshold(pixelBuffer, 
                                   sourceOffset - sourceData.Stride, 
                                   sourceOffset + sourceData.Stride, 
                                   ref gradientValue, threshold); 


                    if (exceedsThreshold == false) 
                     {
                        gradientValue = 0; 


                        // Diagonal Gradient : NW-SE 
                        CheckThreshold(pixelBuffer, 
                                       sourceOffset - 4 - sourceData.Stride, 
                                       sourceOffset + 4 + sourceData.Stride, 
                                       ref gradientValue, threshold, 2); 
                        // Diagonal Gradient : NE-SW 
                        exceedsThreshold = 
                        CheckThreshold(pixelBuffer, 
                                       sourceOffset - sourceData.Stride + 4, 
                                       sourceOffset - 4 + sourceData.Stride, 
                                       ref gradientValue, threshold, 2); 


                        if (exceedsThreshold == false) 
                         {
                            gradientValue = 0; 


                            // Diagonal Gradient : NW-SE 
                            exceedsThreshold = 
                            CheckThreshold(pixelBuffer, 
                                           sourceOffset - 4 - sourceData.Stride, 
                                           sourceOffset + 4 + sourceData.Stride, 
                                           ref gradientValue, threshold); 


                            if (exceedsThreshold == false) 
                             {
                                gradientValue = 0; 


                                // Diagonal Gradient : NE-SW 
                                exceedsThreshold = 
                                CheckThreshold(pixelBuffer, 
                                               sourceOffset - sourceData.Stride + 4, 
                                               sourceOffset + sourceData.Stride - 4, 
                                               ref gradientValue, threshold); 
                             } 
                         }
                     }
                 } 
             }


            resultBuffer[sourceOffset] = (byte)(exceedsThreshold ? 255 : 0); 
            resultBuffer[sourceOffset + 1] = resultBuffer[sourceOffset]; 
            resultBuffer[sourceOffset + 2] = resultBuffer[sourceOffset]; 
            resultBuffer[sourceOffset + 3] = 255; 
         } 
     }


    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width, sourceBitmap.Height); 


    BitmapData resultData = resultBitmap.LockBits(new Rectangle (0, 0, 
                            resultBitmap.Width, resultBitmap.Height), 
                            ImageLockMode.WriteOnly, PixelFormat.Format32bppArgb); 


    Marshal.Copy(resultBuffer, 0, resultData.Scan0, resultBuffer.Length); 
    resultBitmap.UnlockBits(resultData); 


    return resultBitmap; 
}

Colour Values	Red, Green	Filter Size	5
Edge Tracing	Black	Edge Threshold	85

Implementing an Abstract Colour Filter

The AbstractColorsFilter method serves as means of combining an average colour image and an edge detected image. This extension method targets the Bitmap class. The following code snippet details the definition:

public static Bitmap AbstractColorsFilter(this Bitmap sourceBitmap, 
                                          int matrixSize, 
                                          byte edgeThreshold, 
                                          bool applyBlue = true, 
                                          bool applyGreen = true, 
                                          bool applyRed = true, 
                                          EdgeTracingType edgeType =  
                                          EdgeTracingType.Black, 
                                          ColorShiftType shiftType = 
                                          ColorShiftType.None) 
{ 
    Bitmap edgeBitmap =  
    sourceBitmap.GradientBasedEdgeDetectionFilter(edgeThreshold); 

  
    Bitmap colorBitmap =  
    sourceBitmap.AverageColoursFilter(matrixSize,  
                 applyBlue, applyGreen, applyRed, shiftType); 

  
    byte[] edgeBuffer = edgeBitmap.GetByteArray(); 
    byte[] colorBuffer = colorBitmap.GetByteArray(); 
    byte[] resultBuffer = colorBitmap.GetByteArray(); 

  
    for (int k = 0; k + 4 < edgeBuffer.Length; k += 4) 
    {
        if (edgeBuffer[k] == 255) 
        { 
            switch (edgeType) 
            {
                case EdgeTracingType.Black: 
                    resultBuffer[k] = 0; 
                    resultBuffer[k+1] = 0; 
                    resultBuffer[k+2] = 0; 
                    break; 
                case EdgeTracingType.White: 
                    resultBuffer[k] = 255; 
                    resultBuffer[k+1] = 255; 
                    resultBuffer[k+2] = 255; 
                    break; 
                case EdgeTracingType.HalfIntensity: 
                    resultBuffer[k] = ClipByte(resultBuffer[k] * 0.5); 
                    resultBuffer[k + 1] = ClipByte(resultBuffer[k + 1] * 0.5); 
                    resultBuffer[k + 2] = ClipByte(resultBuffer[k + 2] * 0.5); 
                    break; 
                case EdgeTracingType.DoubleIntensity: 
                    resultBuffer[k] = ClipByte(resultBuffer[k] * 2); 
                    resultBuffer[k + 1] = ClipByte(resultBuffer[k + 1] * 2); 
                    resultBuffer[k + 2] = ClipByte(resultBuffer[k + 2] * 2); 
                    break; 
                case EdgeTracingType.ColorInversion: 
                    resultBuffer[k] = ClipByte(255 - resultBuffer[k]); 
                    resultBuffer[k+1] = ClipByte(255 - resultBuffer[k+1]); 
                    resultBuffer[k+2] = ClipByte(255 - resultBuffer[k+2]); 
                    break; 
            }
        }
    }

  
    Bitmap resultBitmap = new Bitmap (sourceBitmap.Width, 
                                    sourceBitmap.Height); 

  
    BitmapData resultData = 
               resultBitmap.LockBits(new Rectangle (0, 0, 
               resultBitmap.Width, resultBitmap.Height), 
               ImageLockMode.WriteOnly, 
               PixelFormat.Format32bppArgb); 

  
    Marshal.Copy(resultBuffer, 0, resultData.Scan0, 
                               resultBuffer.Length); 

  
    resultBitmap.UnlockBits(resultData); 

  
    return resultBitmap; 
}

Colour Values	Red, Green	Filter Size	17
Edge Tracing	Double Intensity	Edge Threshold	85

Sample Images

This article features a number of sample images. All featured images have been licensed allowing for reproduction. The following images feature as sample images:

Fruit bodies of the agaric fungus Mycena atkinsoniana A.H. Sm. Specimens photographed in Strouds Run State Park, Athens, Ohio, USA
Attributed to: Dan Molter. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Download from Wikipedia

The "indigo Lactarius", species Lactarius indigo (Schwein.) Fr. Specimen photographed in Strouds Run State Park, Athens, Ohio, USA.
Attributed to: Dan Molter. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Download from Wikipedia

Amanita muscaria (fly agaric), Norway
Attributed to: User-MichaelMaggs. This file is licensed under the Creative Commons Attribution-Share Alike 2.5 Generic license.
Download from Wikipedia.

Lung Oyster (Pleurotus pulmonarius), Småland, Sweden.
Attributed to: Jörg Hempel. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Germany license.
Download from WikiMedia.org

Additional Filter Result Images

The following series of images represent additional filter results.

Colour Values	Red	Filter Size	9
Edge Tracing	Double Intensity	Edge Threshold	60

Colour Values	Green, Blue	Filter Size	9
Edge Tracing	Double Intensity	Edge Threshold	60

Colour Values	Green, Blue	Filter Size	9
Edge Tracing	Black	Edge Threshold	60

Colour Values	Red, Green, Blue	Filter Size	9
Edge Tracing	Double Intensity	Edge Threshold	55

Colour Values	Red, Green, Blue	Filter Size	11
Edge Tracing	Black	Edge Threshold	75

Colour Values	Red, Blue	Filter Size	11
Edge Tracing	Double Intensity	Edge Threshold	75

Colour Values	Green	Filter Size	17
Edge Tracing	Black	Edge Threshold	85

Colour Values	Red	Filter Size	17
Edge Tracing	Black	Edge Threshold	85

Colour Values	Red, Green, Blue	Filter Size	5
Edge Tracing	Half Edge	Edge Threshold	85

Colour Values	Blue	Filter Size	5
Edge Tracing	Double Intensity	Edge Threshold	75

Colour Values	Green	Filter Size	5
Edge Tracing	Double Intensity	Edge Threshold	75

Colour Values	Red	Filter Size	5
Edge Tracing	Double Intensity	Edge Threshold	75

Colour Values	Red, Green, Blue	Filter Size	5
Edge Tracing	Black	Edge Threshold	75

Colour Values	Red, Green, Blue	Filter Size	9
Edge Tracing	Black	Edge Threshold	55

Colour Values	Red, Green, Blue	Filter Size	3
Edge Tracing	Black	Edge Threshold	75

C# How to: Image Boundary Extraction

Published July 21, 2013 Augmented Reality , Blogging , C# , Code Samples , Edge Detection , Extension Methods , Graphic Filters , Graphics , How to , Image Arithmetic , Image Filters , Image Processing , Image Transform , Learn Everyday , Microsoft , Morphology Filters , New Version , Opensource , Tip , Update 5 Comments
Tags: Augmented Reality, Binary Image, Bitmap ARGB, Bitmap Filters, Bitmap.LockBits, BitmapData, Boundary Sharpen, Boundary Tracing, C#, Code Sample, Computer vision, Converting Images, Edge Detect, Edge Detection, Edge enhance, Edge Tracing, Feature extraction, Graphic Filters, Image, Image Boundary Extraction, Image Difference, Image Dilation, Image Erosion, Image Intensity, Image Morphology, Image processing, Image Sharpen, Image Transform, Machine vision, Morphologial Filters, Morphological Edge Detection, Morphological Transform, Pixel Filters, Structured Element

Article Purpose

This article explores various image processing concepts, which feature in combination when implementing Image Boundary Extraction. Concepts covered within this article include: Morphological Image Erosion and Image Dilation, Image Addition and Subtraction, Boundary Sharpening, Boundary Tracing and Boundary Extraction.

Parrot: Boundary Extraction, 3×3, Red, Green, Blue

Sample Source Code

This article is accompanied by a sample source code Visual Studio project which is available for download here.

Using the Sample Application

This article’s accompanying sample source code includes the definition of a sample application. The sample application serves as an implementation of the concepts discussed in this article. In using the sample application concepts can be easily tested and replicated.

The sample application has been defined as a Windows Forms application. The user interface enables the user to configure several options which influence the output produced from image filtering processes. The following section describes the options available to a user when executing the sample application:

Loading and Saving files – Users can specify source/input images through clicking the Load Image button. If desired, resulting filtered images can be saved to the local file system when clicking the Save Image button.
Filter Type – The types of filters implemented represent variations on Image Boundary Extraction. The supported filter types are: Conventional Boundary extraction, Boundary Sharpening and Boundary Tracing.
Filter Size – Filter intensity/strength will mostly be reliant on the filter size implemented. A Filter size represents the number of neighbouring pixels examined when applying filters.
Colours Applied – The sample source code and sample application provides functionality allowing a filter to only effect user specified colour components. Colour components are represented in the form of an RGB colour scheme. The inclusion or exclusion of the colour components Red, Green and Blue will be determined through user configuration.
Structuring Element – As mentioned, the Filter Size option determines the size of neighbourhood pixels examined. The Structuring Element’s setup determine the neighbouring pixels within the pixel neighbourhood size bounds that should be used as input when calculating filter results.

The following image is a screenshot of the Image Boundary Extraction sample application in action:

Parrot: Boundary Extraction, 3×3, Green

Morphological Boundary Extraction

Image Boundary Extraction can be considered a method of Image Edge Detection. In contrast to more commonly implemented gradient based edge detection methods, Image Boundary Extraction originates from Morphological Image Filters.

When drawing a comparison, Image Boundary Extraction and Morphological Edge Detection express strong similarities. Morphological Edge Detection results from the difference in image erosion and image dilation. Considered from a different point of view, creating one image expressing thicker edges and another image expressing thinner edges provides the means to calculate the difference in edges.

Image Boundary Extraction implements the same concept as Morphological Edge Detection. The base concept can be regarded as calculating the difference between two images which rendered the same image, but expressing a difference in image edges. Image Boundary Extraction relies on calculating the difference between either image erosion and the source image or image dilation and the source image. The difference between image erosion and image dilation in most cases result in more of difference than the difference between image erosion and the source image or image dilation and the source image. The result of Image Boundary Extraction representing less of a difference than Morphological Edge Detection can be observed in Image Boundary Extraction being expressed in finer/smaller width lines.

Difference of Gaussians is another method of edge detection which functions along the same basis. Edges are determined by calculating the difference between two images, each having been filtered from the same source image, using a Gaussian blur of differing intensity levels.

Parrot: Boundary Extraction, 3×3, Red, Green, Blue

Boundary Sharpening

The concept of Boundary Sharpening refers to enhancing or sharpening the boundaries or edges expressed in a source/input image. Boundaries can be easily determined or extracted as discussed earlier when exploring Boundary Extraction.

The steps involved in performing Boundary Sharpening can be described as follows:

Extract Boundaries – Determine boundaries by performing image dilation and calculating the difference between the dilated image and the source image.
Match Source Edges and Extracted Boundaries – The boundaries extracted in the previous step represent the difference between image dilation and the original source image. Ensure that extracted boundaries match the source image through performing image dilation on a copy of the source/input image.
Emphasise Extracted boundaries in source image – Perform image addition using the extracted boundaries image and dilated copy of the source image.

Parrot: Boundary Extraction, 3×3, Red, Green, Blue

Boundary Tracing

Boundary Tracing refers to applying image filters which result in image edges/boundaries appearing darker or more pronounced. This type of filter also relies on Boundary Extraction.

Boundary Tracing can be implemented in two steps, described as follows:

Extract Boundaries – Determine boundaries by performing image dilation and calculating the difference between the dilated image and the source image.
Emphasise Extracted boundaries in source image – Subtract the extracted boundaries from the original source image.

Parrot: Boundary Extraction, 3×3, Red, Green, Blue

Implementing Morphological Erosion and Dilation

The accompanying sample source code defines the MorphologyOperation method, defined as an extension method targeting the Bitmap class. In terms of parameters this method expects a two dimensional array representing a structuring element. The other required parameter represents an enumeration value indicating which Morphological Operation to perform, either erosion or dilation.

The following code snippet provides the definition in full:

private static Bitmap MorphologyOperation(this Bitmap sourceBitmap,
                                          bool[,] se,
                                          MorphologyOperationType morphType,
                                          bool applyBlue = true,
                                          bool applyGreen = true,
                                          bool applyRed = true)
{ 
    BitmapData sourceData =
               sourceBitmap.LockBits(new Rectangle(0, 0,
               sourceBitmap.Width, sourceBitmap.Height),
               ImageLockMode.ReadOnly,
               PixelFormat.Format32bppArgb);


    byte[] pixelBuffer = new byte[sourceData.Stride *
                                  sourceData.Height];


    byte[] resultBuffer = new byte[sourceData.Stride *
                                   sourceData.Height];


    Marshal.Copy(sourceData.Scan0, pixelBuffer, 0,
                               pixelBuffer.Length);


    sourceBitmap.UnlockBits(sourceData);


    int filterOffset = (se.GetLength(0) - 1) / 2;
    int calcOffset = 0, byteOffset = 0;
    byte blueErode = 0, greenErode = 0, redErode = 0;
    byte blueDilate = 0, greenDilate = 0, redDilate = 0;


    for (int offsetY = 0; offsetY <
        sourceBitmap.Height - filterOffset; offsetY++)
    {
        for (int offsetX = 0; offsetX <
            sourceBitmap.Width - filterOffset; offsetX++)
        {
            byteOffset = offsetY * sourceData.Stride +
                         offsetX * 4;


            blueErode = 255; greenErode = 255; redErode = 255;
            blueDilate = 0; greenDilate = 0; redDilate = 0;


            for (int filterY = -filterOffset;
                    filterY <= filterOffset; filterY++)
            {
                for (int filterX = -filterOffset;
                    filterX <= filterOffset; filterX++)
                {
                    if (se[filterY + filterOffset,  
                           filterX + filterOffset] == true)
                    {
                        calcOffset = byteOffset +
                                     (filterX * 4) +
                        (filterY * sourceData.Stride);


                        calcOffset = (calcOffset < 0 ? 0 :
                        (calcOffset >= pixelBuffer.Length + 2 ?
                        pixelBuffer.Length - 3 : calcOffset));


                        blueDilate =  
                        (pixelBuffer[calcOffset] > blueDilate ?
                        pixelBuffer[calcOffset] : blueDilate);


                        greenDilate =  
                        (pixelBuffer[calcOffset + 1] > greenDilate ?
                        pixelBuffer[calcOffset + 1] : greenDilate);


                        redDilate =  
                        (pixelBuffer[calcOffset + 2] > redDilate ?
                        pixelBuffer[calcOffset + 2] : redDilate);


                        blueErode =  
                        (pixelBuffer[calcOffset] < blueErode ?
                        pixelBuffer[calcOffset] : blueErode);


                        greenErode =  
                        (pixelBuffer[calcOffset + 1] < greenErode ?
                        pixelBuffer[calcOffset + 1] : greenErode);


                        redErode =  
                        (pixelBuffer[calcOffset + 2] < redErode ?
                        pixelBuffer[calcOffset + 2] : redErode);
                    }
                }
            }


            blueErode = (applyBlue ? blueErode : pixelBuffer[byteOffset]);
            blueDilate = (applyBlue ? blueDilate : pixelBuffer[byteOffset]);


            greenErode = (applyGreen ? greenErode : pixelBuffer[byteOffset + 1]);
            greenDilate = (applyGreen ? greenDilate : pixelBuffer[byteOffset + 1]);


            redErode = (applyRed ? redErode : pixelBuffer[byteOffset + 2]);
            redDilate = (applyRed ? redDilate : pixelBuffer[byteOffset + 2]);


            if (morphType == MorphologyOperationType.Erosion)
            {
                resultBuffer[byteOffset] = blueErode;
                resultBuffer[byteOffset + 1] = greenErode;
                resultBuffer[byteOffset + 2] = redErode;
            }
            else if (morphType == MorphologyOperationType.Dilation)
            {
                resultBuffer[byteOffset] = blueDilate;
                resultBuffer[byteOffset + 1] = greenDilate;
                resultBuffer[byteOffset + 2] = redDilate;
            }


            resultBuffer[byteOffset + 3] = 255;
        }
    }


    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width, sourceBitmap.Height);
    BitmapData resultData = resultBitmap.LockBits(new Rectangle(0, 0,
                            resultBitmap.Width, resultBitmap.Height),
                            ImageLockMode.WriteOnly,
                            PixelFormat.Format32bppArgb);


    Marshal.Copy(resultBuffer, 0, resultData.Scan0,
                               resultBuffer.Length);


    resultBitmap.UnlockBits(resultData);


    return resultBitmap;
}

Parrot: Boundary Extraction, 3×3, Red, Green

Implementing Image Addition

The sample source code encapsulates the process of combining two separate images through means of addition. The AddImage method serves as a single declaration of image addition functionality. This method has been defined as an extension method targeting the Bitmap class. Boundary Sharpen filtering implements image addition.

The following code snippet provides the definition of the AddImage extension method:

private static Bitmap AddImage(this Bitmapsource Bitmap, 
                               Bitmap addBitmap)
{
    BitmapData sourceData =
               sourceBitmap.LockBits(new Rectangle (0, 0,
               sourceBitmap.Width, sourceBitmap.Height),
               ImageLockMode.ReadOnly,
               PixelFormat.Format32bppArgb);


    byte[] resultBuffer = new byte[sourceData.Stride *
                                  sourceData.Height];


    Marshal.Copy(sourceData.Scan0, resultBuffer, 0,
                               resultBuffer.Length);


    sourceBitmap.UnlockBits(sourceData);


    BitmapData addData =
               addBitmap.LockBits(new Rectangle(0, 0,
               addBitmap.Width, addBitmap.Height),
               ImageLockMode.ReadOnly,
               PixelFormat.Format32bppArgb);


    byte[] addBuffer = new byte[addData.Stride *
                                  addData.Height];


    Marshal.Copy(addData.Scan0, addBuffer, 0,
                               addBuffer.Length);


    addBitmap.UnlockBits(addData);


    for (int k = 0; k + 4 < resultBuffer.Length &&
                    k + 4 < addBuffer.Length; k += 4)
    {
        resultBuffer[k] =
        AddColors(resultBuffer[k], addBuffer[k]);
 
        resultBuffer[k + 1] =
        AddColors(resultBuffer[k + 1], addBuffer[k + 1]);
 
        resultBuffer[k + 2] =
        AddColors(resultBuffer[k + 2], addBuffer[k + 2]);
 
        resultBuffer[k + 3] = 255;
    }


    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width,
                                     sourceBitmap.Height);


    BitmapData resultData =
               resultBitmap.LockBits(new Rectangle(0, 0,
               resultBitmap.Width, resultBitmap.Height),
               ImageLockMode.WriteOnly,
               PixelFormat.Format32bppArgb);


    Marshal.Copy(resultBuffer, 0, resultData.Scan0,
                               resultBuffer.Length);


    resultBitmap.UnlockBits(resultData);


    return resultBitmap;
}

private static byte AddColors(byte color1, byte color2) 
{
    int result = color1 + color2; 

 
    return (byte)(result < 0 ? 0 : (result > 255 ? 255 : result)); 
}

Parrot: Boundary Extraction, 3×3, Red, Green, Blue

Implementing Image Subtraction

In a similar fashion regarding the AddImage method the sample code defines the SubractImage method. By definition this method serves as an extension method targeting the Bitmap class. Image subtraction has been implemented in Boundary Extraction and Boundary Tracing.

The definition of the SubtractImage method listed as follows:

private static Bitmap SubtractImage(this Bitmap sourceBitmap,  
                                         Bitmap subtractBitmap) 
{
    BitmapData sourceData = 
               sourceBitmap.LockBits(new Rectangle(0, 0, 
               sourceBitmap.Width, sourceBitmap.Height), 
               ImageLockMode.ReadOnly, 
               PixelFormat.Format32bppArgb); 


    byte[] resultBuffer = new byte[sourceData.Stride * 
                                  sourceData.Height]; 


    Marshal.Copy(sourceData.Scan0, resultBuffer, 0, 
                               resultBuffer.Length); 


    sourceBitmap.UnlockBits(sourceData); 


    BitmapData subtractData = 
               subtractBitmap.LockBits(new Rectangle(0, 0, 
               subtractBitmap.Width, subtractBitmap.Height), 
               ImageLockMode.ReadOnly, 
               PixelFormat.Format32bppArgb); 


    byte[] subtractBuffer = new byte[subtractData.Stride * 
                                  subtractData.Height]; 


    Marshal.Copy(subtractData.Scan0, subtractBuffer, 0, 
                               subtractBuffer.Length); 


    subtractBitmap.UnlockBits(subtractData); 


    for (int  k = 0; k + 4 < resultBuffer.Length &&  
                    k + 4 < subtractBuffer.Length; k += 4) 
    { 
        resultBuffer[k] =  
        SubtractColors(resultBuffer[k], subtractBuffer[k]); 


        resultBuffer[k + 1] =  
        SubtractColors(resultBuffer[k + 1], subtractBuffer[k + 1]); 


        resultBuffer[k + 2] = 
        SubtractColors(resultBuffer[k + 2], subtractBuffer[k + 2]); 


        resultBuffer[k + 3] = 255; 
    }


    Bitmap resultBitmap = new Bitmap (sourceBitmap.Width, 
                                     sourceBitmap.Height); 


    BitmapData resultData = 
               resultBitmap.LockBits(new Rectangle (0, 0, 
               resultBitmap.Width, resultBitmap.Height), 
               ImageLockMode.WriteOnly, 
               PixelFormat.Format32bppArgb); 


    Marshal.Copy(resultBuffer, 0, resultData.Scan0, 
                               resultBuffer.Length); 


    resultBitmap.UnlockBits(resultData); 


    return resultBitmap; 
}

private static byte SubtractColors(byte color1, byte color2) 
{
    int result = (int)color1 - (int)color2; 

 
    return (byte)(result < 0 ? 0 : result); 
}

Parrot: Boundary Extraction, 3×3, Green

Implementing Image Boundary Extraction

In the sample source code processing Image Boundary Extraction can be achieved when invoking the BoundaryExtraction method. Defined as an extension method, the BoundaryExtraction method targets the Bitmap class.

As discussed earlier, this method performs Boundary Extraction through subtracting the source image from a dilated copy of the source image.

The following code snippet details the definition of the BoundaryExtraction method:

private static Bitmap
BoundaryExtraction(this Bitmap sourceBitmap, 
                   bool[,] se, bool applyBlue = true, 
                   bool applyGreen = true, bool applyRed = true) 
{
    Bitmap resultBitmap = 
           sourceBitmap.MorphologyOperation(se,  
           MorphologyOperationType.Dilation, applyBlue,  
                                  applyGreen, applyRed); 

 
    resultBitmap = resultBitmap.SubtractImage(sourceBitmap); 

  
    return resultBitmap; 
}

Parrot: Boundary Extraction, 3×3, Red, Blue

Implementing Image Boundary Sharpening

Boundary Sharpening in the sample source code has been implemented through the definition of the BoundarySharpen method. The BoundarySharpen extension method targets the Bitmap class. The following code snippet provides the definition:

private static Bitmap 
BoundarySharpen(this Bitmap sourceBitmap, 
                bool[,] se, bool applyBlue = true, 
                bool applyGreen = true, bool applyRed = true) 
{
    Bitmap resultBitmap = 
           sourceBitmap.BoundaryExtraction(se, applyBlue, 
                                           applyGreen, applyRed); 


    resultBitmap = sourceBitmap.MorphologyOperation(se,  
                   MorphologyOperationType.Dilation, applyBlue,  
                   applyGreen, applyRed).AddImage(resultBitmap); 


    return resultBitmap; 
}

Parrot: Boundary Extraction, 3×3, Green

Implementing Image Boundary Tracing

Boundary Tracing has been defined through the BoundaryTrace extension method, which targets the Bitmap class. Similar to the BoundarySharpen method this method performs Boundary Extraction, the result of which serves to be subtracted from the original source image. Subtracting image boundaries/edges result in those boundaries/edges being darkened, or traced. The definition of the BoundaryTracing extension method detailed as follows:

private static Bitmap
BoundaryTrace(this Bitmap sourceBitmap, 
              bool[,] se, bool applyBlue = true, 
              bool applyGreen = true, bool applyRed = true) 
{
    Bitmap resultBitmap =
    sourceBitmap.BoundaryExtraction(se, applyBlue,  
                                    applyGreen, applyRed); 


    resultBitmap = sourceBitmap.SubtractImage(resultBitmap); 

 
    return resultBitmap; 
}

Parrot: Boundary Extraction, 3×3, Green, Blue

Implementing a Wrapper Method

The BoundaryExtractionFilter method is the only method defined as publicly accessible. Following convention, this method’s definition signals the method as an extension method targeting the Bitmap class. This method has the intention of acting as a wrapper method, a single method capable of performing Boundary Extraction, Boundary Sharpening and Boundary Tracing, depending on method parameters.

The definition of the BoundaryExtractionFilter method detailed by the following code snippet:

public static Bitmap
BoundaryExtractionFilter(this Bitmap sourceBitmap, 
                         bool[,] se, BoundaryExtractionFilterType  
                         filterType, bool applyBlue = true, 
                         bool applyGreen = true, bool applyRed = true) 
{
    Bitmap resultBitmap = null; 

 
    if (filterType == BoundaryExtractionFilterType.BoundaryExtraction) 
    {
        resultBitmap =  
        sourceBitmap.BoundaryExtraction(se, applyBlue, 
                                        applyGreen, applyRed); 
    }
    else if (filterType == BoundaryExtractionFilterType.BoundarySharpen) 
    {
        resultBitmap =  
        sourceBitmap.BoundarySharpen(se, applyBlue,  
                                     applyGreen, applyRed); 
    }
    else if (filterType == BoundaryExtractionFilterType.BoundaryTrace) 
    {
        resultBitmap =  
        sourceBitmap.BoundaryTrace(se, applyBlue,  
                                   applyGreen, applyRed); 
    }

 
    return resultBitmap; 
}

Parrot: Boundary Extraction, 3×3, Red, Green, Blue

Sample Images

This article features a number of sample images. All featured images have been licensed allowing for reproduction. The following images feature as sample images:

Scarlet Macaw at Diergaarde Blijdorp, Rotterdam, Netherlands.
Attributed to: Jar0d. This file is licensed under the Creative Commons Attribution-Share Alike 2.0 Generic license.
Download from Wikipedia

A Scarlet Macaw flying away from the photographer.
Attributed to: Robert01. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Germany license.
Download from Wikipedia

Blue-and-yellow Macaw in flight.
Attributed to: Luc Viatour – www.lucnix.be. This file is licensed under the Creative Commons Attribution 2.0 Generic license.
Download from Wikipedia

Two Green-winged Macaws (also known as the Red-and-green Macaw) resting at Seaport Village, San Diego, USA.
Attributed to: Dave Fayram. This file is licensed under the Creative Commons Attribution 2.0 Generic license.
Download from Wikimedia.org

A Scarlet Macaw riding a small tricycle at an exhibition in Spain.
Attributed to: The Torch. This file is licensed under the Creative Commons Attribution 2.0 Generic license.
Download from Wikipedia.

Long-tailed Broadbill (Psarisomus dalhousiae), Kaeng Krachan National Park, Phetchaburi, Thailand.
Attributed to User-JJ Harrison. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Download from Wikipedia.

C# How to: Image ASCII Art

Published July 14, 2013 Augmented Reality , C# , Code Samples , Extension Methods , Graphic Filters , Graphics , How to , Image Arithmetic , Image Filters , Image Processing , Image Transform , Microsoft , New Version , Opensource , Tip , Update 7 Comments
Tags: ASCII Art, Augmented Reality, Binary Image, Bitmap ARGB, Bitmap Filters, Bitmap.LockBits, BitmapData, C#, Code Sample, Computer vision, Converting Images, Feature extraction, Graphic Filters, Image, Image Intensity, Image processing, Image Transform, Machine vision, Pixel Filters

Article Purpose

This article explores the concept of rendering ASCII Art from source images. Beyond exploring concepts this article also provides a practical implementation of all the steps required in creating an Image ASCII Filter.

Sir Tim Berners-Lee: 2 Pixels Per Character, 12 Characters, Font Size 4, Zoom 100

Sample Source Code

This article is accompanied by a sample source code Visual Studio project which is available for download here.

Using the Sample Application

The sample source code that accompanies this article includes a sample application. The concepts illustrated in this article can tested and replicated using the sample application.

The sample application user interface implements a variety of functionality which can be described as follows:

Loading/Saving Images – Users are able to load source/input images from the local file system through clicking the Load Image button. Rendered ASCII Art can be saved as an image file when clicking the Save Image button.
Pixels Per Character – This configuration option determines the number of pixels represented by a single character. Lower values result in better detail/definition and a larger proportional output. Higher values result in less detail/definition and a smaller proportional output.
Character Count – The number of unique characters to be rendered can be adjusted through updating this value. This value can be considered similar to number of shades of gray in a grayscale image.
Font Size – This option determines the Font Size related to the rendered text.
Zoom Level – Configure this value in order to apply a scaling level when rendering text.
Copy to Clipboard – Copy the current ASCII Art string to the Windows Clipboard, in Rich Text Format.

The following image is screenshot of the Image ASCII Art sample application is action:

Image ASCII Art Sample Application

Anders Hejlsberg: 1 Pixel Per Character, 24 Characters, Font Size 6, Zoom 20

Converting Pixels to Characters

ASCII Art in various forms have been part of computer culture since the pioneering days of computing. From Wikipedia we gain the following:

ASCII art is a graphic design technique that uses computers for presentation and consists of pictures pieced together from the 95 printable (from a total of 128) characters defined by the ASCII Standard from 1963 and ASCII compliant character sets with proprietary extended characters (beyond the 128 characters of standard 7-bit ASCII). The term is also loosely used to refer to text based visual art in general. ASCII art can be created with any text editor, and is often used with free-form languages. Most examples of ASCII art require a fixed-width font (non-proportional fonts, as on a traditional typewriter) such as Courier for presentation.

Among the oldest known examples of ASCII art are the creations by computer-art pioneer Kenneth Knowlton from around 1966, who was working for Bell Labs at the time.^[1] "Studies in Perception I" by Ken Knowlton and Leon Harmon from 1966 shows some examples of their early ASCII art.^[2]

One of the main reasons ASCII art was born was because early printers often lacked graphics ability and thus characters were used in place of graphic marks. Also, to mark divisions between different print jobs from different users, bulk printers often used ASCII art to print large banners, making the division easier to spot so that the results could be more easily separated by a computer operator or clerk. ASCII art was also used in early e-mail when images could not be embedded.

Bjarne Stroustrup: 1 Pixel Per Character, 12 Characters, Font Size 6, Zoom 60

This article explores the steps involved in rendering text strings representing ASCII Art, implementing source/input images in rendering ASCII Art text representations. The following sections details the steps required to render ASCII Art text strings from source/input images:

Generate Random Characters – Generate a string consisting of random characters. The number of characters will be determined through user input relating to the Character Count option. When generating the random string ensure that all characters added to the string are unique. In addition avoid adding control characters or punctuation characters. Control characters are non-visible characters such as Start of Text, Beep, New Line or Carriage Return. Most punctuation characters occupy a lot less screen space compared to regular alphabet characters.
Determine Row and Column Count – Rows and Columns in terms of the Character Count option indicate the ratio between pixels and characters. The number of rows equate to the image height in pixels divided by the Character Count. The number of columns equate to the image width in pixels divided by the Character Count.
Iterate Rows/Columns and Determine Colour Averages – Iterate image pixels in terms of a rows and columns grid strategy. Calculate the sum total of each grid region’s colour values. Calculate the average/mean colour value through dividing the colour sum total by the Character Count squared.
Map Average Colour Intensity to a Character – Using the average colour values calculate in the previous step, calculate a colour intensity value ranging between 0 and the number of randomly generate characters. The intensity value should be implemented as an array index in accessing the string of random characters. All of the pixels included in calculating an average value should be represented by the random character located at the index equating to the colour average intensity value.

Linus Torvalds: 1 Pixel Per Character, 16 Characters, Font Size 5, Zoom 60

Converting Text to an Image

When rendering high definition ASCII Art the resulting text can easily consist of several thousand characters. Attempting to display such a vast number of text in a traditional text editor in most scenarios would be futile. An alternative method of retaining a high definition whilst still being viewable can be achieved through creating an image from the rendered text and then reducing the image dimensions.

The sample code employs the following steps when converting rendered text to an image:

Determine Required Image Dimensions – Determine the image dimensions required to fit the rendered text.
Create a new Image and set the background colour – After having determined the required image dimensions create a new image consisting of those dimensions. Set every pixel in the new image to Black.
Draw Rendered Text – The rendered text should be drawn on the new image in plain White.
Resize Image – In order to ensure more manageable image dimensions resize the image with a specified factor.

Alan Turing: 1 Pixel Per Character, 16 Characters, Font Size 4, Zoom 100

Implementing an Image ASCII Filter

The sample source code implements four methods when implementing an Image ASCII Filter, the methods are:

ASCIIFilter
GenerateRandomString
RandomStringSort
GetColorCharacter

The GenerateRandomString method, as the name implies, generates a string consisting of randomly selected characters. The number of characters contained in the string will be determined by the parameter value passed to this method. The following code snippet provides the implementation of the GenerateRandomString method:

private static string GenerateRandomString(int maxSize) 
{
    StringBuilder stringBuilder = new StringBuilder(maxSize); 
    Random randomChar = new Random(); 

 
    char charValue; 

 
    for (int k = 0; k < maxSize; k++) 
    {
        charValue = (char)(Math.Floor(255 * randomChar.NextDouble() * 4)); 

 
        if (stringBuilder.ToString().IndexOf(charValue) != -1) 
        {
            charValue = (char)(Math.Floor((byte)charValue *  
                                    randomChar.NextDouble())); 
        } 

 
        if (Char.IsControl(charValue) == false &&  
            Char.IsPunctuation(charValue) == false &&  
            stringBuilder.ToString().IndexOf(charValue) == -1) 
        {
            stringBuilder.Append(charValue); 
 
            randomChar = new Random((int)((byte)charValue *  
                             (k + 1) + DateTime.Now.Ticks)); 
        } 
        else 
        {
            randomChar = new Random((int)((byte)charValue *  
                             (k + 1) + DateTime.UtcNow.Ticks)); 
            k -= 1; 
        } 
    } 

 
    return stringBuilder.ToString().RandomStringSort(); 
}

Sir Tim Berners-Lee: 4 Pixels Per Character, 16 Characters, Font Size 6, Zoom 100

The RandomStringSort method has been defined as an extension method targeting the string class. This method provides a means of sorting a string in a random manner, in essence shuffling a string’s characters. The definition as follows:

public static string RandomStringSort(this string stringValue) 
{
    char[] charArray = stringValue.ToCharArray(); 

 
    Random randomIndex = new Random((byte)charArray[0]); 
    int iterator = charArray.Length; 

 
    while(iterator > 1) 
    {
        iterator -= 1; 

 
        int nextIndex = randomIndex.Next(iterator + 1); 

 
        char nextValue = charArray[nextIndex]; 
        charArray[nextIndex] = charArray[iterator]; 
        charArray[iterator] = nextValue; 
    }

 
    return new string(charArray); 
}

Anders Hejlsberg: 3 Pixels Per Character, 12 Characters, Font Size 5, Zoom 50

The sample source code defines the GetColorCharacter method, intended to map pixels to character values. This method has been defined as an extension method targeting the string class. The definition as follows:

private static string colorCharacters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"; 

 
private static string GetColorCharacter(int blue, int green, int red) 
{
    string colorChar = String.Empty; 
    int intensity = (blue + green + red) / 3 *  
                    (colorCharacters.Length - 1) / 255; 

 
    colorChar = colorCharacters.Substring(intensity, 1).ToUpper(); 
    colorChar += colorChar.ToLower(); 

 
    return colorChar; 
}

Bjarne Stroustrup: 1 Pixel Per Character, 12 Characters, Font Size 4, Zoom 100

The ASCIIFilter method defined by the sample source code has the task of translating source/input images into text based ASCII Art. This method has been defined as an extension method targeting the Bitmap class. The following code snippet provides the definition:

public static string ASCIIFilter(this Bitmap sourceBitmap, int pixelBlockSize,  
                                                           int colorCount = 0) 
{
    BitmapData sourceData = sourceBitmap.LockBits(new Rectangle (0, 0, 
                            sourceBitmap.Width, sourceBitmap.Height), 
                                              ImageLockMode.ReadOnly, 
                                        PixelFormat.Format32bppArgb); 


    byte[] pixelBuffer = new byte[sourceData.Stride * sourceData.Height]; 


    Marshal.Copy(sourceData.Scan0, pixelBuffer, 0, pixelBuffer.Length); 
    sourceBitmap.UnlockBits(sourceData); 


    StringBuilder asciiArt = new StringBuilder(); 


    int avgBlue = 0; 
    int avgGreen = 0; 
    int avgRed = 0; 
    int offset = 0; 


    int rows = sourceBitmap.Height / pixelBlockSize; 
    int columns = sourceBitmap.Width / pixelBlockSize; 


    if (colorCount > 0) 
    {
        colorCharacters = GenerateRandomString(colorCount); 
    }


    for (int y = 0; y < rows; y++) 
    {
        for (int x = 0; x < columns; x++) 
        {
            avgBlue = 0; 
            avgGreen = 0; 
            avgRed = 0; 


            for (int pY = 0; pY < pixelBlockSize; pY++) 
            { 
                for (int pX = 0; pX < pixelBlockSize; pX++) 
                { 
                    offset = y * pixelBlockSize * sourceData.Stride +  
                             x * pixelBlockSize * 4; 


                    offset += pY * sourceData.Stride; 
                    offset += pX * 4; 


                    avgBlue += pixelBuffer[offset]; 
                    avgGreen += pixelBuffer[offset + 1]; 
                    avgRed += pixelBuffer[offset + 2]; 
                }
            } 


            avgBlue = avgBlue / (pixelBlockSize * pixelBlockSize); 
            avgGreen = avgGreen / (pixelBlockSize * pixelBlockSize); 
            avgRed = avgRed / (pixelBlockSize * pixelBlockSize); 


            asciiArt.Append(GetColorCharacter(avgBlue, avgGreen, avgRed)); 
        } 


        asciiArt.Append("\r\n" ); 
    }


    return asciiArt.ToString(); 
}

Linus Torvalds: 1 Pixel Per Character, 8 Characters, Font Size 4, Zoom 80

Implementing Text to Image Functionality

The sample source code implements the GDI+ Graphics class when drawing rendered ASCII Art text onto Bitmap images. The sample source code defines the TextToImage method, an extension method extending the string class. The definition listed as follows:

public static Bitmap TextToImage(this string text, Font font,  
                                                float factor) 
{
    Bitmap textBitmap = new Bitmap(1, 1); 


    Graphics graphics = Graphics.FromImage(textBitmap); 


    int width = (int)Math.Ceiling( 
                graphics.MeasureString(text, font).Width *  
                factor); 


    int height = (int)Math.Ceiling( 
                 graphics.MeasureString(text, font).Height *  
                 factor); 


    graphics.Dispose(); 


    textBitmap = new Bitmap(width, height,  
                            PixelFormat.Format32bppArgb); 


    graphics = Graphics.FromImage(textBitmap); 
    graphics.Clear(Color.Black); 


    graphics.CompositingQuality = CompositingQuality.HighQuality; 
    graphics.InterpolationMode = InterpolationMode.HighQualityBicubic; 
    graphics.PixelOffsetMode = PixelOffsetMode.HighQuality; 
    graphics.SmoothingMode = SmoothingMode.HighQuality; 
    graphics.TextRenderingHint = TextRenderingHint.AntiAliasGridFit; 


    graphics.ScaleTransform(factor, factor); 
    graphics.DrawString(text, font, Brushes.White, new PointF(0, 0)); 


    graphics.Flush(); 
    graphics.Dispose(); 


    return textBitmap; 
}

Sir Tim Berners-Lee: 1 Pixel Per Character, 32 Characters, Font Size 4, Zoom 100

Sample Images

This article features a number of sample images. All featured images have been licensed allowing for reproduction. The following image files feature a sample images:

Alan Turing – British Mathematician and Computer Scientist, widely considered to be the father of Computer science and artificial intelligence. Wikipedia article. In regards to the image file used in this article: The copyright is held by the National Portrait Gallery, London. http://www.npg.org.uk/collections/search/portrait/mw165875. Download from Wikipedia.
Anders Hejlsberg – Danish Software Engineer, original author of Turbo Pascal, chief architect of Delphi, lead architect of Microsoft C# and core developer of TypeScript. Wikipedia article. In regards to the image file used in this article: This file is licensed under the Creative Commons Attribution 2.0 Generic license. This image was originally posted to Flickr by DBegley at http://flickr.com/photos/9438069@N04/2985424826. It was reviewed on 7 April 2009 by the FlickreviewR robot and was confirmed to be licensed under the terms of the cc-by-2.0. Download from Wikipedia.
Bjarne Stroustrup – Danish Computer Scientist, original developer of the C++ programming language. Wikipedia article. In regards to the image file used in this article: Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled GNU Free Documentation License. Download from Wikipedia.
Linus Torvalds – Finnish American Software Engineer, chief architect and project coordinator of the Linux Kernel. Wikipedia article. In regards to the image file used in this article: This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license. The image is from an article in a December 2002 issue of Linux Magazine. Download from Wikipedia.
Sir Tim Berners-Lee – British Computer Scientist, attributed as the inventor of the World Wide Web, director of the World Wide Web Consortium (W3C). Wikipedia article. In regards to the image file used in this article: This file is licensed under the Creative Commons Attribution-Share Alike 2.0 Generic license. This image, originally posted to Flickr, was reviewed on 15 March 2013 by the administrator or reviewer Morning Sunshine, who confirmed that it was available on Flickr under the stated license on that date. Download from Wikipedia.

The following section lists the original image files that were used as source/input images in generating the ASCII Art images found throughout this article.

Alan Turing

Anders Hejlsberg

Bjarne Stroustrup

Linus Torvalds

Tim Berners-Lee

Archive for the 'Image Transform' Category

Article Purpose

Sample Source Code

Using the Sample Application

Min/Max Edge Detection

Pixel Neighbourhood

Median Filter

Min/Max Edge Detection Algorithm

Implementing a Min/Max Edge Detection Filter

Sample Images

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Sample Source Code

Using the Sample Application

Image Distortion

Median Filter

Implementing Image Distortion

Implementing a Median Filter

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Sample Source Code

Using the Sample Application

Abstracting Image Colours

Implementing Pixel Neighbourhood Colour Averaging

Implementing Gradient Based Edge Detection

Implementing an Abstract Colour Filter

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Additional Filter Result Images

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Sample Source Code

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Morphological Boundary Extraction

Boundary Sharpening

Boundary Tracing

Implementing Morphological Erosion and Dilation

Implementing Image Addition

Implementing Image Subtraction

Implementing Image Boundary Extraction

Implementing Image Boundary Sharpening

Implementing Image Boundary Tracing

Implementing a Wrapper Method

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Sample Source Code

Using the Sample Application

Converting Pixels to Characters

Converting Text to an Image

Implementing an Image ASCII Filter

Implementing Text to Image Functionality

Sample Images

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