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.
Related
A solution to this problem seems to not exist but I find it hard to believe it is not possible.
Imagine you have an image with a semi-transparent overlay (color=black, transparency=50%), whether over the whole image or just a portion, doesn't matter. How could one convert the pixels underneath to their original color, in essence removing the black overlay.
Just like a simple algebra equation we should be able to rearrange the variables to solve for the "original pixels" under the overlay. Something along the lines of -
original pixels * semi-transparent overlay = new pixelsoriginal pixels = semi-transparent overlay / new pixels
Obviously such an equation over simplifies the problem but I think that gets my point across. Since we know the color and percent transparency, why couldn't we "retrieve" the colors of the underlying pixels?
EDIT: Mark Ransom in the comments is correct, if you know the transparency is 50% then simply multiplying by 2 gets you to the original color. Any recommendations on how to apply this to a whole region in Photoshop or GIMP? Certainly doing it pixel by pixel is out of the question.
Thank you!
The "divide" layer mode will do what you want. In the case of semi-transparent black, use a gray with the value equal to the opacity value of the overlayed layer.
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.
I was making a circular icon with semi-transparency, so I started with a large filled-in circle with a black border, then I did white->alpha, and resized the image to my required size. Would it have made a difference if I resized first, and then did white->alpha?
Thanks.
Yes.
In general, whenever you are re-sampling, this will have an impact if you are using any anti-aliasing, or the resampling algorithm is something other than nearest-neighbor.
Try the following exercise for a visual example:
In both cases, create your circular icon.
Case 1:
Change white-center of the circle to alpha (0%, fully transparent).
Re-sample (ie: down-sample to 25%) the entire image using something other than nearest neighbour (ie: actually use antialiasing of some sort)
Paste a copy of the result over a red background.
You should only see black and red colors inside the circle when you zoom in, with a smooth transition from black-to-red.
Case 2:
Re-sample (ie: down-sample to 25%) the entire image using something other than nearest neighbour (ie: actually use antialiasing of some sort)
Change white-center of the circle to alpha (0%, fully transparent).
Paste a copy of the result over a red background.
You should see a black outer circle, with a bit of a white halo inside of it, then the red center, with a smooth black-to-white transition, and a sharp white-to-red transition. This will depend on the aggressiveness factor you set with the magic-wand tool you are likely using to auto-select the region you want to modify the alpha properties of.
Now repeat case 2, but disable any sort of anti-aliasing, and enforce the use of a nearest neighbour algorithm rather than bi-cubic spline, Hermite, Gaussian, etc. Your results will look very similar to case 1, except you won't see the smooth transition from black-to-red when you zoom in, you will just see a sharp black-to-red transition.
In general, you will get the best subjective quality when working on your images first, then re-sampling later. If you paste it as its own layer, then you still have all the image data available any none is lost, the image is just rendered smaller.
I'm trying to create a graphic in Sketch (a vector-based graphic design application). I export to PDF and this is what my original graphic looks like:
But when I set it as the image of an NSButton, it gets drawn like this:
Why does this occur? The right and bottom edges in particular are altered a lot. I'm not sure if this is a Cocoa drawing issue or an issue with my original graphic.
The problem is with (mis)alignment with the pixel grid and anti-aliasing. It looks like you've scaled the image so that the borders on the left, right, and bottom are roughly one pixel in thickness. However, the right and bottom borders are straddling the boundary between pixels. The result is that they contribute half their "darkness" to the pixel on one side of the boundary and the other half to the pixel on the other side of the boundary.
You should tweak either the proportions of the image or the size at which you're drawing it to avoid that particular alignment. It looks as though it's being rendered as roughly 10.5 pixels wide. You want it to be either 10 pixels or 11 pixels wide, so the right edge corresponds more closely to a pixel column.
Using images of articles of clothing taken against a consistent background, I would like to make all pixels in the image transparent except for the clothing. What is the best way to go about this? I have researched the algorithms that are common for this and the open source library opencv. Aside from rolling my own or using opencv is there an easy way to do this? I am open to any language or platform.
Thanks
If your background is consistend in an image but inconsistent across images it could get tricky, but here is what I would do:
Separate the image into some intensity/colour form such as YUV or Lab.
Make a histogram over the colour part. Find the most occuring colour, this is (most likely) your background (update) maybe a better trick here would be to find the most occuring colour of all pixels within one or two pixels from the edge of the image.
Starting from the eddges of the image, set all pixels that have that colour and are connected to the edge through pixels of that colour to transparent.
The edge of the piece of clothing is now going to look a bit ugly because it consist of pixels that gain their colour from both the background and the piece of clothing. To combat this you need to do a bit more work:
Find the edge of the piece of clothing through some edge detection mechanism.
Replace the colour of the edge pixels with a blend of the colour just "inside" the edge pixel (i.e. the colour of the clothing in that region) and transparent (if your output image format supports that).
If you want to get really fancy, you increase the transparency depending on how much "like" the background colour the colour of that pixel is.
Basically, find the color of the background and subtract it, but I guess you knew this. It's a little tricky to do this all automatically, but it seems possible.
First, take a look at blob detection with OpenCV and see if this is basically done for you.
To do it yourself:
find the background: There are several options. Probably easiest is to histogram the image, and the large number of pixels with similar values are the background, and if there are two large collections, the background will be the one with a big hole in the middle. Another approach is to take a band around the perimeter as the background color, but this seems inferior as, for example, reflection from a flash could dramatically brighten more centrally located background pixels.
remove the background: a first take at this would be to threshold the image based on the background color, and then run the "open" or "close" algorithms on this, and then use this as a mask to select your clothing article. (The point of open/close is to not remove small background colored items on the clothing, like black buttons on a white blouse, or, say, bright reflections on black clothing.)
OpenCV is a good tool for this.
The trickiest part of this will probably be at the shadow around the object (e.g. a black jacket on a white background will have a continuous gray shadow at some of the edges and where to make this cut?), but if you get this far, post another question.
if you know the exact color intensity of the background and it will never change and the articles of clothing will never coincide with this color, then this is a simple application of background subtraction, that is everything that is not a particular color intensity is considered an "on" pixel, one of interest. You can then use connected component labeling (http://en.wikipedia.org/wiki/Connected_Component_Labeling) to figure out seperate groupings of objects.
for a color image, with the same background on every pictures:
convert your image to HSV or HSL
determine the Hue value of the background (+/-10): do this step once, using photoshop for example, then use the same value on all your pictures.
perform a color threshold: on the hue channel exclude the hue of the background ([0,hue[ + ]hue, 255] typically), for all other channels include the whole value range (0 to 255 typically). this will select pixels which are NOT the background.
perform a "fill holes" operation (normally found along blob analysis or labelling functions) to complete the part of the clothes which may have been of the same color than the background.
now you have an image which is a "mask" of the clothes: non-zero pixels represents the clothes, 0 pixels represents the background.
this step of the processing depends on how you want to make pixels transparent: typically, if you save your image as PNG with an alpha (transparency) channel, use a logical AND (also called "masking") operation between the alpha channel of the original image and the mask build in the previous step.
voilà, the background disappeared, save the resulting image.