I've seen a few libraries that pixelate images, some of them even feature non-square shapes such as The Pixelator's circle and diamond shapes.
I'm looking however to make a particular shape, I want a "pixel" that is 19x27 px. Essentially, the image would still look pixelated but it would use tallish rectangle shapes as the pixel base.
Are there any libraries out there that do this, if not, what alterations to existing algorithms/functions would I need to make to accomplish this?
Unless I am not understanding your question, the algorithm you need is quite simple!
Just break your image up into a grid of rectangles the size you want (in this case 19x27). Loop over each section of the grid and take the average color of the pixels inside (you can simply take the average of each channel in RGB independently). Then set all of the pixels contained inside to the average color.
This would give you an image that is the same size as your input. You could of course resize your image first to a more appropriate output size.
You might want to look up convolution matrices.
In a shader, you would use your current pixel location to grab a set of nearby pixels from the original image to render to a pixel in a new buffer image.
It is actually just a slight variation of the Box Blur image processing algorithm except that instead of grabbing from the nearby pixels you would grab by the divisions of the original image relative to the 19x27 divisions of the resulting image.
Related
I have video frames with elliptical objects in them. I'm trying to detect the main ellipse using regionprops and it works just fine.
However, since I want to speed up the process, I want regionprops to only look for those ellipses in a certain area of the image. I can crop the images each frame, to only have the relevant area left, but I would rather have regionprops look only in specified areas.
Is such an option possible?
Regioprops uses label matrix provided by bwlabel(bw_image) or just logical(grayscale_image) if you suppose that there is only object in the image. To make regionprops process only a part of image you should set irrelevant part of the label matrix to zero.
I'm looking for methods for histogram blurring in image processing. I found this old thread but the answers there does not solve my case.
One answer there suggest that
There is actually nothing called Histogram blurring.
so Is there any way for histogram blurring in image processing?
[edit1] some more info
image size is 3880x2592.
I want to blur with gaussian blur with radius about 15-20 (*pixels?).
I am using 256×16bit 8ea(*?) single ports memories.
I want to imlement this on FPGA
if by blur you mean smooth (removing high frequencies) then you can use any smooth filter or algorithm (most of them are based on FIR low pass filters)
if your question is what to smooth then the answer is same as in the question you linked it depends on what you need:
if you need smoothed histogram for some computation then smooth histogram directly and leave image be as is
if you need the image colors to be smoothed then smooth image and recompute histogram
sometimes is hard to smooth image to get smoothed histogram
(due to slow bleeding of colors or by too big data loss)
In that case you can smooth the histogram (remembering the original) then compute the area change for each color and statistically recolor whole image (it is not an easy task).
Pick (random) pixel of color a which area needs to be decreased and recolor it to closest color b thats area needs to be increased
update area of booth colors
loop until areas are matching ...
I got a png image like this:
The blue color is represent transparent. And all the circle is a pixel group. So, I would like to find the biggest one, and remove all the small pixel, which is not group with the biggest one. In this example, the biggest one is red colour circle, and I will retain it. But the green and yellow are to small, so I will remove them. After that, I will have something like this:
Any ideas? Thanks.
If you consider only the size of objects, use the following algorithm: labellize the connex components of the mask image of the objects (all object pixels are white, transparent ones are black). Then compute the areas of the connex components, and filter them. At this step, you have a label map and a list of authorized labels. You can read the label map and overwrite the mask image with setting every pixel to white if it has an authorized label.
OpenCV does not seem to have a labelling function, but cvFloodFill can do the same thing with several calls: for each unlabeled white pixel, call FloodFill with this pixel as marker. Then you can store the result of this step in an array (of the size of the image) by assigning each newly assigned pixel with its label. Repeat this as long as you have unlabellized pixels.
Else you can recode the connex component function for binary images, this algorithm is well known and easy to implement (maybe start with Matlab's bwlabel).
The handiest way to filter objects if you have an a priori knowledge of their size is to use morphological operators. In your case, with opencv, once you've loaded your image (OpenCV supports PNG), you have to do an "openning", that is an erosion followed by a dilation.
The small objects (smaller than the size of the structuring element you chose) will disappear with erosion, while the bigger will remain and be restored with the dilation.
(reference here, cv::morphologyEx).
The shape of the big object might be altered. If you're only doing detection, it is harmless, but if you want your object to avoid transformation you'll need to apply a "top hat" transform.
I was creating an effects library for a PhotoBooth App. I have created effects like Black/White, Vintage, Sepia, Retro etc. etc.
I wanted to create a few effects now in which I wanted to have a Dark Border at the edges which kind of form a frame for the image .. something like this -> Example Effect
How can I do this using Pixel Bender and Flash ?
The effect you are describing is called vignetting. It is basically just darkening the pixels with some weight that changes depending on distance from the center of the image. In image editing it corresponds to overlaying the image with black color and applying a circular or elliptic mask to it, for example:
(source: johnhpanos.com)
You can do this by several methods depending on how you operate with image and its pixels. For example by multiplying the pixels by a weight coefficient that is smaller when closer to the center and bigger when farther away from it. The distance can be calculated from the difference between pixel coordinates.
Basically I was trying to achieve this: impose an arbitrary image to a pre-defined uneven surface. (See examples below).
-->
I do not have a lot of experience with image processing or 3D algorithms, so here is the best method I can think of so far:
Predefine a set of coordinates (say if we have a 10x10 grid, we have 100 coordinates that starts with (0,0), (0,10), (0,20), ... etc. There will be 9x9 = 81 grids.
Record the transformations for each individual coordinate on the t-shirt image e.g. (0,0) becomes (51,31), (0, 10) becomes (51, 35), etc.
Triangulate the original image into 81x2=162 triangles (with 2 triangles for each grid). Transform each triangle of the image based on the coordinate transformations obtained in Step 2 and draw it on the t-shirt image.
Problems/questions I have:
I don't know how to smooth out each triangle so that the image on t-shirt does not look ragged.
Is there a better way to do it? I want to make sure I'm not reinventing the wheels here before I proceed with an implementation.
Thanks!
This is called digital image warping. There was a popular graphics text on it in the 1990s (probably from somebody's thesis). You can also find an article on it from Dr. Dobb's Journal.
Your process is essentially correct. If you work pixel by pixel, rather than trying to use triangles, you'll avoid some of the problems you're facing. Scan across the pixels in target bitmap, and apply the local transformation based on the cell you're in to determine the coordinate of the corresponding pixel in the source bitmap. Copy that pixel over.
For a smoother result, you do your coordinate transformations in floating point and interpolate the pixel values from the source image using something like bilinear interpolation.
It's not really a solution for the problem, it's just a workaround :
If you have the 3D model that represents the T-Shirt.
you can use directX\OpenGL and put your image as a texture of the t-shirt.
Then you can ask it to render the picture you want from any point of view.