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.
Related
I am using pixel colour inspection to detect collisions. I know there are other ways to achieve this but this is my use case.
I draw a shape cloned from the main canvas on to a second canvas switching the fill and stroke colours to pur black. I then use getImageData() to get an array of pixel colours and inspect them - if I see black I have a collision with something.
However, some pixels are shades of grey because the second canvas is applying antialiasing to the shape. I want only black or transparent pixels.
How can I get the second canvas to be composed of either transparent or black only?
I have achieved this long in the past with Windows GDI via compositing/xor combinations etc. However, GDI did not always apply antialiasing. I guess the answer lies in globalCompositeOperation or filter but I cannot see what settings/filters or sequence to apply.
I appreciate I have not provided sample code but I am hoping that someone can throw me a bone and I'll work up a snippet here which might become a standard cut & paste for posterity from that.
I have a square image with a ragged edge: the transparent pixels outside the image "weave" in and out towards the image center, within some unknown range. This range may be different for each side.
Is there an algorithm that would crop the image to the largest size possible with no transparent pixels remaining? I can think of an iterative one: start with a small cropping square in the center. If no transparent pixels are detected, start again but enlarge the cropping square by 1 pixel. Then repeat. Once you detect transparent pixels after cropping, go back one step and save the result.
There is an obvious algorithm that comes to mind:
Find y* = min_y {(x,y) : P(x,y) is transparent }, where P(x,y) is the pixel at coord (x,y) then crop the image [0,y*] (assuming the image starts at zero at the bottom, and that transparent pixels always happen at the top of the image.)
Note that this algorithm has serious downsides, if y* happens to be very close to 0 because of an errant transparent pixel, you will end up cropping almost your entire image.
If you want a more robust solution, I believe you will have to frame this problem as an optimization problem and solve it, allowing for some errant transparent pixels to be masked instead of cropped. The algorithm that would do well for you would be an energy-based formulation which could be solved using graph-cuts. For example, see the GrabCut algorithm.
If you know your requirements and how bad your data can get you can make a judgment call for how involved you want to make your solution, but at this point I would highly recommend clarifying the requirements of your solution as well as how bad your data can get.
I have a system that removes the colour white (give or take a few shades), from an image and replaces it with an alpha channel. (The image is taken from the users phone camera, and tries to remove selected colouring)
This leaves harsh edges most of the time, and I want to know if it is possible to add some type of anti-aliasing on top.
The system works by taking in the image, and searching through each pixel data. If the pixel is white (or close), it will replace it with an alpha colour.
So I guess my question is, how do I make the edges less harsh. Thanks.
Anti aliasing is not what you are looking for. This takes care of effects caused by the limited resolution of your image. However, your problem is not related to resolution, you would still have it with infinite resolution.
What you need to do is when you find a white pixel, increase the transparency of the pixel itself and the pixels around it.
You can just include the four pixels immediately above, below, left or right of your white pixel, or you an choose any other shape, e.g. all pixels which lie inside a circle of given radius around the white pixel.
Also you can choose a function which determines how transparency is distributed over that shape. You can make everything half-transparent or you can decrease the effect towards the edges of that shape (though I don't think that this will be necessary).
Thus each pixel will receive transparency from several pixels around them. The resulting transparency must be computed from all these contributions. Simply multiplying them probably won't do, because you will have a hard time ever reaching alpha=0. You may however, interpret (255-alpha) as a measure of transparency, add all contributing transparencies and then convert back into alpha. Something like max (0, 255 - (255-a1) + (255-a2) ...).
It will be difficult to do this in-place, i.e. with just ony copy of the image. You might need an intermediate "image", where each pixel is associated with all transparency contributions from the pixels around it.
I wanna to color a sprite/icon with a transparent background and with shadows. I tried to shift the hue to all pixels but it looks not so natural and I have problems with the black and the white colors in an image. If an image tend to be black shifting the hue do not change the black in red or another color even shifting by 360 degrees.
Tried to color addicting and subtracting color and even in that case the black and the white tend to be colored or disappears at all.
Maybe should I put an image on the icon to achieve the coloring effect ?
Any suggestions on how to proceed.
I lost.
You've been asking a lot about this hue shifting thing, so I figured I'd try to work out an example: http://jsfiddle.net/EMujN/3/
Here's another that uses an actual icon: http://jsfiddle.net/EMujN/4/
There's a lot in there. There's a huge data URL which you can ignore unless you want to replace it. Here's the relevant part where we modify HSL.
//SHIFT H HERE
var hMod = .3;
hsl[0]=(hsl[0]+hMod)%1;
//MODIFY S HERE
var sMod = .6;
hsl[1]=Math.max(0,Math.min(1,
hsl[1]+sMod
));
//MODIFY L HERE
var lMod = 0;
hsl[2]=Math.max(0,Math.min(1,
hsl[2]+lMod
));
I've converted to HSL because it's a lot easier to accomplish what you want in that color space than RGB.
Without getting any more complex, you have three variables you can tune: how much to add to either Hue, Saturation, or Lightness. I have the lightness variable set to 0 because any higher and you will see some nasty JPEG artifacts (if you can find a decent .png that would be better, but I went with the first CC night image I could find).
I think the hue shift (yellow to green) looks pretty good though and I have maxed out the saturation, so even a normally white light appears bright purple. Like I said in my comment, you will need to increase the lightness and saturation if you want to colorize patches of black and white. Hopefully, you can figure out what you need from this example.
image used: http://commons.wikimedia.org/wiki/File:Amman_(Jordan)_at_night.jpg
I found a better solution by myself which can solve the problem with the black and white.
So basically the solution can be solved in multiple steps. Here I will define the steps. Later I'll provide some working code:
Get the image
Calculate the predominant color, averaging the image pixels or simply providing an input RGB value which is the predominant that your eye can catch.
If the predominant tends to be black or white, or both, the image has to be recolored with an addictive or subtractive method, addictive if black, subtractive if white. So basically all RGB pixels should be attenuated or sharpened until RED. I think that the best solution should be RED, because RED is first in the HUE scale, and this can help when we will hue-shift the pixels.
To have a unique algorithm which can work with different kind of images, not only black predominant or white, ideally the input the non-black and non-white predominant images should be pre-hueshifted manually, using photoshop or with another algorithm in a way that the new predominant color results to be RED too
After that the Hue shifting coloring is straighforward. We know that the predominant color is RED for all the images, and we'll shift the HUE values with a difference between the HSV value of the desired color and the HSV of the predominant color (RED).
Game over. We have a pretty universal way to color different images with hue shifting in a natural way.
Another question could be how to authomatically pre-shift the input images which predominant color is not black or white.
But this is another question.
Why this coloring method could be considered natural. Simply consider one thing. Generally the non dominant black or white colors are part of the shadows and light which gives a 3D feel to the images. On the other hand if my shoes are 100% black and i will tint them with some colors, they will no more be black. Color the dominant black cannot be achieved simply shifting the HSV parameters but other steps should be performed. The steps are the above described.
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.