I'm trying to convert a colour pdf to a pure black and white tiff i.e. if the colour is anything other than white I want it to be pure black. At the moment I have colour lines turning speckled and faint which I believe is due to half-toning. Is there a way I can overwrite this half-toning? Examples below.
Current Output:
Desired Output:
Thanks.
Shades of gray are halftoned, if you want to avoid that you need to decide on a threshold below which gray values are white, and above which gray values are black.
Then you set a transfer function that maps the values. Note that its possible for both PDF and PostScritp files to include their own transfer functions (though its rare) and these may override your own.
Related
I have an image that is essentially a text document (black and white) but due to anti-aliasing/undersampling applied during scanning, the image contains a lot of color, light tone pixels and is thus saved as a full-color image i.e: takes a lot of space.
My goal is to be able to detect Black and White image candidates in order to convert them from full color to B&W which dramatically reduces their size.
Is there a way to detect such anti-aliased/undersampled images? Doing color pixel analysis doesn't help because the colored pixels end up being close in amount to the black pixels... Essentially I want to be able to detect that the colored pixels come from anti-aliasing/undersampling a black & white image and not from a picture type image.
Here is an example image:
As you can see there are many more colors than just black. However this image is a good candidate for Black & White / Greyscale conversion instead of full color. How can I detect such images? Please note that in this example the colors tend to be on the grey side but there are many cases where they are cyan or brown etc.
I think it is a valid question. I don't have 50 reputation to post a comment so I will post this as an answer.
Basically, in a black and white anti-aliased image the various grey colors are opacity differences of the black color. If we observe those pixels they will be like these listed below. So, if the operation is a color manipulation then apply the same opacity picked up from those grey pixels to the new color.
rgba(0,0,0,0.6)
rgba(0,0,0,0.9)
rgba(0,0,0,0.5)
rgba(0,0,0,0.9)
rgba(0,0,0,0.6)
rgba(0,0,0,0.1)
rgba(0,0,0,0.5)
In my opinion, the pixels other than grey, in this example image, cyan and brown as it appears can be safely ignored because they seemed like not part of the original text. If there were a few more example images of non grey pixels would have been good. But if we cannot ignore them just need to get the pixel opacity and apply the same color manipulation. In other words we treat them as black pixels.
I am using Tesseract to recognize some values from the photo which is taken via camera of a phone. My problem is that I cannot recognize numbers which are written in gray color. I know that tesseract performs binarization on the input image. I manually performed the binarization and I realized that all gray texts disappear after binarization. As you can see in the binarized image, there are not any values appeared infront of sollwert and pos/neg toleranz. I can manually set a high threshold to keep my gray colored text,but I need a automatic way in my app. Beside that, the high threshold returns a mostly black image which it also lost some of information. Do you have any suggestions on how to keep my gray color text after binarization?
Thanks in advance
I need to convert an image to greyscale except for a single color. For example, if there is some red in the image (like a red bus), this will remain in color, but the rest of the image will remain in black & white.
I think I should be able to do a rudimentary job of this by going over each pixel individually, such as here: http://brandontreb.com/image-manipulation-retrieving-and-updating-pixel-values-for-a-uiimage . I am assuming I would just leave certain pixels alone if their red component was above a certain amount, and green/blue was below a certain amount. Otherwise, set the pixel to grayscale. Is this a good approach?
I'm more interested in whether or not it is possible to do to the live camera input, such as with a Core Image filter, or using GPUImage, but I haven't been able to find any suitable filters. Any suggestions?
Update:
This seems to be possible using GPUImage with a GPUImageLookupFilter, as per: https://stackoverflow.com/a/19340583/334982
I've created a lookup.png file in Photoshop, by dropping the Saturation for all colours except red to 0. This works ok, but it doesn't seem to grey out all colours. For example, my skin still looks fairly skin coloured, and my brown table is still fairly brown.
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.
Does it mean to control the combination between an image and a color overlay applied to it depending on the color space used (RGB, RGBA, CMYK, Lab, Grayscale, HSL, HSLA)? Or does it mean to change the color layer used in combination with other layers to form the final image? (if so, what could be changed in what regard?).
RGB are abbreviations for three color channels (red, green and blue). They represent specific frequencies of light. Inside each color channel is a range of intensity and a level of saturation. This model of colors is commonly taught in school and is how most people understand colors and mixing them. A different way to represent colors is HSL which stands for Hue, Saturation and Level. Here the Hue is the frequency of the color, while the Saturation can be like the contrast level, and Level is the amount of black. HSL (A stands for Alpha or transparency) is actually a much more programmer centric way of working with color (although most programmers seem to learn the RGB Hex values for colors). There is a great website called Mothereffing HSL which lets you play with HSL values to better understand them. CMYK is for pigments (which mix differently than light) and is found on printers. Same basic idea as RGB just with Cyan Magenta Yellow and Black. Now because light and pigments don't mix the same way there is a lot of work devoted to converting one color system to another (so you can see on your screen what will eventually come out of your printer). These systems are not perfectly aligned however so the goal is to get acceptability close.
All of these colors when presented on a graph are called the color space.