Cartoonize your photos

It was never so easy to transform your photos in beautiful detailed cartoon style drawings. I took some months studying and researching to develop the Photo Cartoon software, but the results are really worthy.

I would like to show you another example about what can be made using it. You can generate smart smooth regions controlling the gradient options and detailed strokes lines by setting the contour interface items (the contour lines can be softer if you desire).

Below you see a very detailed photo used as input and two cartoon images created using my renderer. The first one has more well defined filling regions and very delicate contours. The second example has very large smoothed regions and smartly selected contours lines. Both of them preserve the original details very well.

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Turning a photo into cartoon

It has been several months since I published the last graphic tool to generate photo effects. Recently I had been totally focused on text effects.

I have just released the best program to turn photos and complex images into cartoon style drawings. This powerful renderer lets you control how colors, gradients and contours should be used to generate the result you really want.

Below I show you an example of a photo transformed into a cartoon using the default renderer configuration. Realize the advanced use of gradients and contours e how the important details are kept. Click the image to view in full size.



This new tool is easy to use. You just need to read the tips and spend some time practicing. Try right now our Photo Cartoon software.

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Vexel drawings from photos: example 8

Here I show you the last example about Picture to People "Art Vexels" section.

I have not shown all possibilities, but I think it's enough. For sure you already realized how configurable this tool is.

Next post will be about a new subject: transforming photos in realistic pencil drawings.

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Vexel drawings from photos: example 1

Now my artistic vexel renderer is finished and very powerful.

As I desired, it's very configurable and let you get many different results. I have extended it to tackle RGB photos and generate colorful outputs.

For a while, I will post here some examples of vexel based drawings made with this renderer, one by one.

Since the idea is let you compare the several different results, I will always use the same input image.

Below you can see the input photo and the first example of vexel drawing.

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Vectorization 3

I have dealt with my vectorization library these days to make it suitable to my needs in http://www.picturetopeople.org. I want everything very direct and easy there.

I have made some key modifications, so now instead of need to adjust some no intuitive parameters, you can get kind of different results by applying the vectorization several times in a row. Of course, the output of one iteration is the input of the next one.

The first image is the original image vectorized. It's a very hard image to vectorize, since its global entropy doesn't tell much about it and it's local entropy is unstable. The next images show the successive use of the vectorization library, always having the previous image as input.

In a case like this, if some details are false positives, usually you can remove it by reapplying the vectorization.

You will need to click the images the see them in full size.

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Vectorization 2

My vectorization library is very powerful now. I made it for easy use without math knowledge, since progressive application of vectorization can give results as smooth as desired.

The priority in development was always remove aliases (false border information) and keep all true image detail.

First picture shows the original bitmap in low resolution. Second picture shows the raster representation of vectorized image using same size. The vector based image was created by direct application of vectorization model over the first image.

Of course, the better resolution of bitmap image, the better the vectorized image, but in fact it's no so simple like that. If you have interest about some intuitive explanation about how it works and how you can affect the final result, take a look at my special page about vectorization. It will get better step by step and soon it will have nice and useful information.

Vectorization

I'm developing my solution for "bitmap to vector" transformations.

The bigger challenge is a good conjugation between two conflicting goals:
1) smooth and beautiful results;
2) avoiding lost of significant details.

It's not easy to find a common point: for some images a kind of pattern can be just noise and for others it can be part of important details.

I'm considering some possible solutions dealing with topology and entropy.

Picture shows some initial tests. Typographical use is the canonical example, but of course the tool is generic.

Bitmaps to vector based images

Despite the hard work concerning the creation of a completely new renderer (new paradigms, new models, new algorithms), I started to model, project and code my tools for transforming raster images in vector based ones.

I'm still planning ... I don't know what degree of configuration I will offer for this functionality.

It is in the very beginning, but I hope I can show some screenshot next week.

Talking about vectorization

My tools for transforming raster images in vector based ones achieved a good point.

In fact, it was a funny challenge, since was not easy develop a math model to give results with a good balance between of smoothness and small loss of details.

The result's quality depends on several math properties of original raster image, like for example:
1) average curvature field;
2) topology distribution;
3) local and global entropy.

At first, let's see what kind of result we get by direct use of the vectorization tool over a common bad resolution bitmap:


(1) The original image


(2) The raster representation of same image after vectorization

Beginning from this very simple example, there already are several points to talk about:
1 - the original picture was very easy to vectorize;
2 - the result is not totally smooth;
3 - one intuitive way to see some math properties of source image;
4 - the vectorization was applied just once and without pre-processing.

I will discuss about all of that soon also including new examples.