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Aspose.Imaging  for Python
CMX

Use Python for CMX Images Cartoonifying

Create Python Apps to Cartoonify CMX Images and Photos via Server APIs

How to Cartoonify CMX Images and Photos with Python

We automatically response to cartoon images due to their ability to evoke a sense of nostalgia. Within the realm of graphic design, cartoon-style images serve as pivotal elements often seen in marketing articles. This Cartoonify effect involve converting photo portraits into hand-drawn renditions, adjusting brightness, converting to black and white, playing with color palettes, and merging various editing techniques to craft intricate visual effects. A suite of image filters, including ‘AdjustBrightness’, ‘BinarizeFixed’, ‘Filter’, ‘ReplaceColor’, and ‘ApplyMask’, empowers users to achieve these transformations. These filters can be utilized on original format images and photos that have been downloaded. Cartoon-style imagery is suitable for illustration purposes across diverse web pages, injecting vitality into scientific articles and rendering content more appealing to users, subsequently driving increased traffic to the site. To generate cartoon effects using CMX images, we will employ Aspose.Imaging for Python via .NET API which is a feature-rich, powerful and easy to use image manipulation and conversion API for Python platform. You may install it using the following command from your system command.

The system command line

>> pip install aspose-imaging-python-net

Steps to Cartoonify CMX via Python

You need the aspose-imaging-python-net to try the following workflow in your own environment.

  • Load CMX files with Image.Load method
  • Cartoonify images;
  • Save compressed image to disc in the supported by Aspose.Imaging format

System Requirements

Aspose.Imaging for Python is supported on all major operating systems. Just make sure that you have the following prerequisites.

  • Microsoft Windows / Linux with .NET Core Runtime.
  • Python and PyPi package manager.
 

Cartoonify CMX images - Python

using Aspose.Imaging;
using Aspose.Imaging.FileFormats.Png;
using Aspose.Imaging.ImageFilters.FilterOptions;
using Aspose.Imaging.ImageOptions;
using Aspose.Imaging.Masking;
using Aspose.Imaging.Masking.Options;
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
string templatesFolder = @"c:\Users\USER\Downloads";
Cartoonify();
void Cartoonify()
{
FilterImages(image =>
{
using (var processedImage = new PngImage(image))
{
image.Resize(image.Width * 2, image.Height, ResizeType.LeftTopToLeftTop);
processedImage.Cartoonify();
var gr = new Graphics(image);
gr.DrawImage(processedImage, processedImage.Width, 0);
gr.DrawLine(new Pen(Color.DarkRed, 3), processedImage.Width, 0, processedImage.Width, image.Height);
}
}, "cartoonify");
}
string RasterizeVectorImage(string formatExt, string inputFile)
{
string outputFile = Path.Combine(templatesFolder, $"rasterized.{formatExt}.png");
using (var image = Image.Load(inputFile))
{
image.Save(outputFile, new PngOptions());
}
return outputFile;
}
void FilterImages(Action<RasterImage> doFilter, string filterName)
{
List<string> rasterFormats = new List<string>() { "jpg", "png", "bmp", "apng", "dicom",
"jp2", "j2k", "tga", "webp", "tif", "gif", "ico" };
List<string> vectorFormats = new List<string>() { "svg", "otg", "odg", "eps", "wmf", "emf", "wmz", "emz", "cmx", "cdr" };
List<string> allFormats = new List<string>(rasterFormats);
allFormats.AddRange(vectorFormats);
allFormats.ForEach(
formatExt =>
{
var inputFile = Path.Combine(templatesFolder, $"template.{formatExt}");
bool isVectorFormat = vectorFormats.IndexOf(formatExt) > -1;
//Need to rasterize vector formats before background remove
if (isVectorFormat)
{
inputFile = RasterizeVectorImage(formatExt, inputFile);
}
var outputFile = Path.Combine(templatesFolder, $"{filterName}_{formatExt}.png");
Console.WriteLine($"Processing {formatExt}");
using (var image = (RasterImage)Image.Load(inputFile))
{
doFilter(image);
//If image is multipage save each page to png to demonstrate results
if (image is IMultipageImage multiPage && multiPage.PageCount > 1)
{
for (var pageIndex = 0; pageIndex < multiPage.PageCount; pageIndex++)
{
string fileName = $"{filterName}_page{pageIndex}_{formatExt}.png";
multiPage.Pages[pageIndex].Save(templatesFolder + fileName, new PngOptions());
File.Delete(templatesFolder + fileName);
}
}
else
{
image.Save(outputFile, new PngOptions());
File.Delete(outputFile);
}
}
//Remove rasterized vector image
if (isVectorFormat)
{
File.Delete(inputFile);
}
}
);
}
static class ImageFilterExtensions
{
public static void Cartoonify(this RasterImage image)
{
using var outlines = image.DetectOutlines(Color.Black);
image.AdjustBrightness(30);
image.Filter(image.Bounds, new MedianFilterOptions(7));
var gr = new Graphics(image);
gr.DrawImage(outlines, Point.Empty);
}
public static RasterImage DetectOutlines(this RasterImage image, Color outlineColor)
{
var outlines = new PngImage(image);
outlines
.GetDataContext()
.ApplyConvolutionFilter(ConvolutionFilterOptions.Blur)
.ApplyConvolutionFilter(ConvolutionFilterOptions.Outline)
.ApplyData();
outlines.BinarizeFixed(30);
ImageMasking.ApplyMask(outlines, outlines, new MaskingOptions() { BackgroundReplacementColor = Color.Transparent });
outlines.ReplaceColor(Color.FromArgb(255, 255, 255), 0, outlineColor);
outlines.ApplyConvolutionFilter(ConvolutionFilterOptions.Blur);
return outlines;
}
public static RasterImage ApplyOperationToRasterImage(this RasterImage image, Action<RasterImage> operation)
{
if (image is IMultipageImage multipage)
{
foreach (var page in multipage.Pages)
{
operation.Invoke((RasterImage)page);
}
}
else
{
operation.Invoke(image);
}
return image;
}
public static RasterImage ApplyFilter(this RasterImage image, FilterOptionsBase filterOptions)
{
return image.ApplyOperationToRasterImage(img =>
{
img.Filter(img.Bounds, filterOptions);
});
}
public static RasterImage ApplyConvolutionFilter(this RasterImage image, ConvolutionFilterOptions filterOptions)
{
return image.ApplyOperationToRasterImage(img =>
{
var pixelsLoader = new ImagePixelsLoader(img.Bounds);
img.LoadPartialArgb32Pixels(img.Bounds, pixelsLoader);
var outBuffer = new PixelBuffer(img.Bounds, new int[img.Width * img.Height]);
ConvolutionFilter.DoFiltering(pixelsLoader.PixelsBuffer, outBuffer, filterOptions);
img.SaveArgb32Pixels(outBuffer.Rectangle, outBuffer.Pixels);
});
}
public static IImageDataContext GetDataContext(this RasterImage image)
{
IPixelBuffer GetImageBuffer(RasterImage img)
{
var pixelsLoader = new ImagePixelsLoader(img.Bounds);
img.LoadPartialArgb32Pixels(img.Bounds, pixelsLoader);
return pixelsLoader.PixelsBuffer;
}
if (image is IMultipageImage multipage)
{
return new MultipageDataContext(
multipage.Pages.Select(page => new ImageDataContext((RasterImage)page)
{
Buffer = GetImageBuffer((RasterImage)page)
}));
}
return new ImageDataContext(image)
{
Buffer = GetImageBuffer(image)
};
}
public static IImageDataContext ApplyToDataContext(this IImageDataContext dataContext,
Func<IPixelBuffer, IPixelBuffer> processor)
{
if (dataContext is MultipageDataContext multipage)
{
foreach (var context in multipage)
{
context.Buffer = processor.Invoke(context.Buffer);
}
}
if (dataContext is ImageDataContext imageDataContext)
{
imageDataContext.Buffer = processor.Invoke(imageDataContext.Buffer);
}
return dataContext;
}
public static IImageDataContext ApplyConvolutionFilter(this IImageDataContext dataContext,
ConvolutionFilterOptions filterOptions)
{
return dataContext.ApplyToDataContext(buffer =>
{
var outBuffer = new PixelBuffer(buffer.Rectangle, new int[buffer.Rectangle.Width * buffer.Rectangle.Height]);
ConvolutionFilter.DoFiltering(buffer, outBuffer, filterOptions);
return outBuffer;
});
}
}
class ConvolutionFilter
{
public static void DoFiltering(
IPixelBuffer inputBuffer,
IPixelBuffer outputBuffer,
ConvolutionFilterOptions options)
{
var factor = options.Factor;
var bias = options.Bias;
var kernel = options.Kernel;
var filterWidth = kernel.GetLength(1);
var filterCenter = (filterWidth - 1) / 2;
int x, y;
int filterX, filterY, filterPx, filterPy, filterYPos, pixel;
double r, g, b, kernelValue;
int top = inputBuffer.Rectangle.Top;
int bottom = inputBuffer.Rectangle.Bottom;
int left = inputBuffer.Rectangle.Left;
int right = inputBuffer.Rectangle.Right;
for (y = top; y < bottom; y++)
{
for (x = left; x < right; x++)
{
r = 0;
g = 0;
b = 0;
for (filterY = -filterCenter; filterY <= filterCenter; filterY++)
{
filterYPos = filterY + filterCenter;
filterPy = filterY + y;
if (filterPy >= top && filterPy < bottom)
{
for (filterX = -filterCenter; filterX <= filterCenter; filterX++)
{
filterPx = filterX + x;
if (filterPx >= left && filterPx < right)
{
kernelValue = kernel[filterYPos, filterX + filterCenter];
pixel = inputBuffer[filterPx, filterPy];
r += ((pixel >> 16) & 0xFF) * kernelValue;
g += ((pixel >> 8) & 0xFF) * kernelValue;
b += (pixel & 0xFF) * kernelValue;
}
}
}
}
r = (factor * r) + bias;
g = (factor * g) + bias;
b = (factor * b) + bias;
r = r > 255 ? 255 : (r < 0 ? 0 : r);
g = g > 255 ? 255 : (g < 0 ? 0 : g);
b = b > 255 ? 255 : (b < 0 ? 0 : b);
outputBuffer[x, y] = ((inputBuffer[x, y] >> 24) << 24) | ((byte)r << 16) | ((byte)g << 8) | (byte)b;
}
}
}
}
class ConvolutionFilterOptions
{
public double Factor { get; set; } = 1.0;
public int Bias { get; set; } = 0;
public double[,] Kernel { get; set; }
public static ConvolutionFilterOptions Blur
{
get
{
return new ConvolutionFilterOptions
{
Kernel = new double[,] { { 1, 2, 1 }, { 2, 4, 2 }, { 1, 2, 1 } },
Factor = 0.25 * 0.25
};
}
}
public static ConvolutionFilterOptions Sharpen
{
get
{
return new ConvolutionFilterOptions
{
Kernel = new double[,] { { 0, -1, 0 }, { -1, 5, -1 }, { 0, -1, 0 } }
};
}
}
public static ConvolutionFilterOptions Emboss
{
get
{
return new ConvolutionFilterOptions
{
Kernel = new double[,] { { -2, -1, 0 }, { -1, 1, 1 }, { 0, 1, 2 } }
};
}
}
public static ConvolutionFilterOptions Outline
{
get
{
return new ConvolutionFilterOptions
{
Kernel = new double[,] { { -1, -1, -1 }, { -1, 8, -1 }, { -1, -1, -1 } }
};
}
}
public static ConvolutionFilterOptions BottomSobel
{
get
{
return new ConvolutionFilterOptions
{
Kernel = new double[,] { { -1, -2, -1 }, { 0, 0, 0 }, { 1, 2, 1 } }
};
}
}
public static ConvolutionFilterOptions TopSobel
{
get
{
return new ConvolutionFilterOptions
{
Kernel = new double[,] { { 1, 2, 1 }, { 0, 0, 0 }, { -1, -2, -1 } }
};
}
}
public static ConvolutionFilterOptions LeftSobel
{
get
{
return new ConvolutionFilterOptions
{
Kernel = new double[,] { { 1, 0, -1 }, { 2, 0, -2 }, { 1, 0, -1 } }
};
}
}
public static ConvolutionFilterOptions RightSobel
{
get
{
return new ConvolutionFilterOptions
{
Kernel = new double[,] { { -1, 0, 1 }, { -2, 0, 2 }, { -1, 0, 1 } }
};
}
}
}
interface IImageDataContext
{
void ApplyData();
}
class ImageDataContext : IImageDataContext
{
public ImageDataContext(RasterImage image)
{
this.Image = image;
}
public RasterImage Image { get; }
public IPixelBuffer Buffer { get; set; }
public void ApplyData()
{
this.Buffer.SaveToImage(this.Image);
}
}
class MultipageDataContext : List<ImageDataContext>, IImageDataContext
{
public MultipageDataContext(IEnumerable<ImageDataContext> enumerable) : base(enumerable)
{
}
public void ApplyData()
{
foreach (var context in this)
{
context.ApplyData();
}
}
}
class ImagePixelsLoader : IPartialArgb32PixelLoader
{
public ImagePixelsLoader(Aspose.Imaging.Rectangle rectangle)
{
this.PixelsBuffer = new CompositePixelBuffer(rectangle);
}
public CompositePixelBuffer PixelsBuffer { get; }
public void Process(Aspose.Imaging.Rectangle pixelsRectangle, int[] pixels, Point start, Point end)
{
this.PixelsBuffer.AddPixels(pixelsRectangle, pixels);
}
}
interface IPixelBuffer
{
Aspose.Imaging.Rectangle Rectangle { get; }
int this[int x, int y]
{
get;
set;
}
void SaveToImage(RasterImage image);
}
class PixelBuffer : IPixelBuffer
{
public PixelBuffer(Aspose.Imaging.Rectangle rectangle, int[] pixels)
{
this.Rectangle = rectangle;
this.Pixels = pixels;
}
public Aspose.Imaging.Rectangle Rectangle { get; }
public int[] Pixels { get; }
public int this[int x, int y]
{
get => this.Pixels[this.GetIndex(x, y)];
set => this.Pixels[this.GetIndex(x, y)] = value;
}
public void SaveToImage(RasterImage image)
{
image.SaveArgb32Pixels(this.Rectangle, this.Pixels);
}
public bool Contains(int x, int y)
{
return this.Rectangle.Contains(x, y);
}
private int GetIndex(int x, int y)
{
x -= this.Rectangle.Left;
y -= this.Rectangle.Top;
return x + y * this.Rectangle.Width;
}
}
class CompositePixelBuffer : IPixelBuffer
{
private readonly List<PixelBuffer> _buffers = new List<PixelBuffer>();
public CompositePixelBuffer(Aspose.Imaging.Rectangle rectangle)
{
this.Rectangle = rectangle;
}
public Aspose.Imaging.Rectangle Rectangle { get; }
public int this[int x, int y]
{
get => this.GetBuffer(x, y)[x, y];
set => this.GetBuffer(x, y)[x, y] = value;
}
public void SaveToImage(RasterImage image)
{
foreach (var pixelBuffer in this._buffers)
{
pixelBuffer.SaveToImage(image);
}
}
public IEnumerable<PixelBuffer> Buffers => this._buffers;
public void AddPixels(Aspose.Imaging.Rectangle rectangle, int[] pixels)
{
if (this.Rectangle.IntersectsWith(rectangle))
{
this._buffers.Add(new PixelBuffer(rectangle, pixels));
}
}
private PixelBuffer GetBuffer(int x, int y)
{
return this._buffers.First(b => b.Contains(x, y));
}
}
 
  • About Aspose.Imaging for Python API

    Aspose.Imaging API is an image processing solution to create, modify, draw or convert images (photos) within applications. It offers: cross-platform Image processing, including but not limited to conversions between various image formats (including uniform multi-page or multi-frame image processing), modifications such as drawing, working with graphic primitives, transformations (resize, crop, flip&rotate, binarization, grayscale, adjust), advanced image manipulation features (filtering, dithering, masking, deskewing), and memory optimization strategies. It’s a standalone library and does not depend on any software for image operations. One can easily add high-performance image conversion features with native APIs within projects. These are 100% private on-premise APIs and images are processed at your servers.

    Cartoonify CMX via Online App

    Cartoonify CMX documents by visiting our Live Demos website . The live demo has the following benefits

      No need to download or setup anything
      No need to write any code
      Just upload your CMX files and hit "Cartoonify now" button
      Instantly get the download link for the resultant file

    CMX What is CMX File Format

    Files with CMX extension are Corel Exchange image file format that is used as presentation by CorelSuite applications. It contains image data as vector graphics as well as metadata that describes the image. CMX files can be opened by CorelDraw, Corel Presentations, Paint Shop Pro and some versions of Adobe Illustrator.

    Read More

    Other Supported Cartoonify Formats

    Using Python, one can easily Cartoonify different formats including.

    APNG (Animated Portable Network Graphics)
    BMP (Bitmap Picture)
    ICO (Windows icon)
    JPG (Joint Photographic Experts Group)
    JPEG (Joint Photographic Experts Group)
    DIB (Device Independent Bitmap)
    DICOM (Digital Imaging & Communications)
    DJVU (Graphics Format)
    DNG (Digital Camera Image)
    EMF (Enhanced Metafile Format)
    EMZ (Windows Compressed Enhanced Metafile)
    GIF (Graphical Interchange Format)
    JP2 (JPEG 2000)
    J2K (Wavelet Compressed Image)
    PNG (Portable Network Graphics)
    TIFF (Tagged Image Format)
    TIF (Tagged Image Format)
    WEBP (Raster Web Image)
    WMF (Microsoft Windows Metafile)
    WMZ (Compressed Windows Media Player Skin)
    TGA (Targa Graphic)
    SVG (Scalable Vector Graphics)
    EPS (Encapsulated PostScript Language)
    CDR (Vector Drawing Image)
    OTG (OpenDocument Standard)
    ODG (Apache OpenOffice Draw Format)