Related
I need a curve editor to make balancing of endless game then I tried to use AnimationCurve.
I need to set a curve to a certain range ex. [0;1] and if I want a value over 1, the result of the Evaluation have to extrapolate the curve. I want to be able to compute Y from X and X from Y.
The problem is AnimationCurve have only 3 WrapMode (Clamp, PingPong, Loop).
How to extrapolate an AnimationCurve ?
Is there a better tool to make curve with extrapolation (post and pre curve) ?
For real extrapolation I think you'd have to implement your own system based on Bézier mathematics. Me at least am not aware of unity providing it out of the box.
A work around for it could be to just define values beyond the 0 to 1 range to cover the extents, animation curves do allow this, I don't think there are to many issues with that.
Another solution, to stay in 0 to 1 range still but achieve the same effect, would be to model the curve from 0 to 1 so that it would cover extreme values within that range and remap the time for curve evaluation given by the object to a 0 to 1 range.
E.g.:
// define range extents
float rangeMin = -5f, rangeMax = 5f;
var range = 10f;
// range could be calculated at runtime if necessary:
// [to] (higher value) - [from] (lower value) = [range]
// 5f - -5f = 10f
var timeRaw = 0; // variable provided value
var time01 = (timeRaw - rangeMin) / range;
// reult by timeRaw = 0: (0 - -5) / 10 = 0.5
// reult by timeRaw = 5: (5 - -5) / 10 = 1.0
// reult by timeRaw = -5: (-5 - -5) / 10 = 0.0
Combining both solutions allow you to cover even more extreme values.
My question is if I have a Lion image just I want to change the color of the lion alone not the background color. For that I referred this SO question but it turns the color of whole image. Moreover the image is not looking great. I need the color change like photoshop. whether it is possible to do this in coregraphics or I have to use any other library.
EDIT : I need the color change to be like iQuikColor app
This took quite a while to figure out, mainly because I wanted to get it up and running in Swift using Core Image and CIColorCube.
#Miguel's explanation is spot on about the way you need to replace a "Hue angle range" with another "Hue angle range". You can read his post above for details on what a Hue Angle Range is.
I made a quick app that replaces a default blue truck below, with whatever you choose on Hue slider.
You can slide the slider to tell the app what color Hue you want to replace the blue with.
I'm hardcoding the Hue range to be 60 degrees, which typically seems to encompass most of a particular color but you can edit that if you need to.
Notice that it does not color the tires or the tail lights because that's outside of the 60 degree range of the truck's default blue hue, but it does handle shading appropriately.
First you need code to convert RGB to HSV (Hue value):
func RGBtoHSV(r : Float, g : Float, b : Float) -> (h : Float, s : Float, v : Float) {
var h : CGFloat = 0
var s : CGFloat = 0
var v : CGFloat = 0
let col = UIColor(red: CGFloat(r), green: CGFloat(g), blue: CGFloat(b), alpha: 1.0)
col.getHue(&h, saturation: &s, brightness: &v, alpha: nil)
return (Float(h), Float(s), Float(v))
}
Then you need to convert HSV to RGB. You want to use this when you discover a hue that in your desired hue range (aka, a color that's the same blue hue of the default truck) to save off any adjustments you make.
func HSVtoRGB(h : Float, s : Float, v : Float) -> (r : Float, g : Float, b : Float) {
var r : Float = 0
var g : Float = 0
var b : Float = 0
let C = s * v
let HS = h * 6.0
let X = C * (1.0 - fabsf(fmodf(HS, 2.0) - 1.0))
if (HS >= 0 && HS < 1) {
r = C
g = X
b = 0
} else if (HS >= 1 && HS < 2) {
r = X
g = C
b = 0
} else if (HS >= 2 && HS < 3) {
r = 0
g = C
b = X
} else if (HS >= 3 && HS < 4) {
r = 0
g = X
b = C
} else if (HS >= 4 && HS < 5) {
r = X
g = 0
b = C
} else if (HS >= 5 && HS < 6) {
r = C
g = 0
b = X
}
let m = v - C
r += m
g += m
b += m
return (r, g, b)
}
Now you simply loop through a full RGBA color cube and "adjust" any colors in the "default blue" hue range with those from your newly desired hue. Then use Core Image and the CIColorCube filter to apply your adjusted color cube to the image.
func render() {
let centerHueAngle: Float = 214.0/360.0 //default color of truck body blue
let destCenterHueAngle: Float = slider.value
let minHueAngle: Float = (214.0 - 60.0/2.0) / 360 //60 degree range = +30 -30
let maxHueAngle: Float = (214.0 + 60.0/2.0) / 360
var hueAdjustment = centerHueAngle - destCenterHueAngle
let size = 64
var cubeData = [Float](count: size * size * size * 4, repeatedValue: 0)
var rgb: [Float] = [0, 0, 0]
var hsv: (h : Float, s : Float, v : Float)
var newRGB: (r : Float, g : Float, b : Float)
var offset = 0
for var z = 0; z < size; z++ {
rgb[2] = Float(z) / Float(size) // blue value
for var y = 0; y < size; y++ {
rgb[1] = Float(y) / Float(size) // green value
for var x = 0; x < size; x++ {
rgb[0] = Float(x) / Float(size) // red value
hsv = RGBtoHSV(rgb[0], g: rgb[1], b: rgb[2])
if hsv.h < minHueAngle || hsv.h > maxHueAngle {
newRGB.r = rgb[0]
newRGB.g = rgb[1]
newRGB.b = rgb[2]
} else {
hsv.h = destCenterHueAngle == 1 ? 0 : hsv.h - hueAdjustment //force red if slider angle is 360
newRGB = HSVtoRGB(hsv.h, s:hsv.s, v:hsv.v)
}
cubeData[offset] = newRGB.r
cubeData[offset+1] = newRGB.g
cubeData[offset+2] = newRGB.b
cubeData[offset+3] = 1.0
offset += 4
}
}
}
let data = NSData(bytes: cubeData, length: cubeData.count * sizeof(Float))
let colorCube = CIFilter(name: "CIColorCube")!
colorCube.setValue(size, forKey: "inputCubeDimension")
colorCube.setValue(data, forKey: "inputCubeData")
colorCube.setValue(ciImage, forKey: kCIInputImageKey)
if let outImage = colorCube.outputImage {
let context = CIContext(options: nil)
let outputImageRef = context.createCGImage(outImage, fromRect: outImage.extent)
imageView.image = UIImage(CGImage: outputImageRef)
}
}
You can download the sample project here.
See answers below instead. Mine doesn't provide a complete solution.
Here is the sketch of a possible solution using OpenCV:
Convert the image from RGB to HSV using cvCvtColor (we only want to change the hue).
Isolate a color with cvThreshold specifying a certain tolerance (you want a range of colors, not one flat color).
Discard areas of color below a minimum size using a blob detection library like cvBlobsLib. This will get rid of dots of the similar color in the scene.
Mask the color with cvInRangeS and use the resulting mask to apply the new hue.
cvMerge the new image with the new hue with an image composed by the saturation and brightness channels that you saved in step one.
There are several OpenCV iOS ports in the net, eg: http://www.eosgarden.com/en/opensource/opencv-ios/overview/ I haven't tried this myself, but seems a good research direction.
I'm going to make the assumption that you know how to perform these basic operations, so these won't be included in my solution:
load an image
get the RGB value of a given pixel of the loaded image
set the RGB value of a given pixel
display a loaded image, and/or save it back to disk.
First of all, let's consider how you can describe the source and destination colors. Clearly you can't specify these as exact RGB values, since a photo will have slight variations in color. For example, the green pixels in the truck picture you posted are not all exactly the same shade of green. The RGB color model isn't very good at expressing basic color characteristics, so you will get much better results if you convert the pixels to HSL. Here are C functions to convert RGB to HSL and back.
The HSL color model describes three aspects of a color:
Hue - the main perceived color - i.e. red, green, orange, etc.
Saturation - how "full" the color is - i.e. from full color to no color at all
Lightness - how bright the color is
So for example, if you wanted to find all the green pixels in a picture, you will convert each pixel from RGB to HSL, then look for H values that correspond to green, with some tolerance for "near green" colors. Below is a Hue chart, from Wikipedia:
So in your case you will be looking at pixels that have a Hue of 120 degrees +/- some amount. The bigger the range the more colors will get selected. If you make your range too wide you will start seeing yellow and cyan pixels getting selected, so you'll have to find the right range, and you may even want to offer the user of your app controls to select this range.
In addition to selecting by Hue, you may want to allow ranges for Saturation and Lightness, so that you can optionally put more limits to the pixels that you want to select for colorization.
Finally, you may want to offer the user the ability to draw a "lasso selection" so that specific parts of the picture can be left out of the colorization. This is how you could tell the app that you want the body of the green truck, but not the green wheel.
Once you know which pixels you want to modify it's time to alter their color.
The easiest way to colorize the pixels is to just change the Hue, leaving the Saturation and Lightness from the original pixel. So for example, if you want to make green pixels magenta you will be adding 180 degrees to all the Hue values of the selected pixels (making sure you use modulo 360 math).
If you wanted to get more sophisticated, you can also apply changes to Saturation and that will give you a wider range of tones you can go to. I think the Lightness is better left alone, you may be able to make small adjustments and the image will still look good, but if you go too far away from the original you may start seeing hard edges where the process pixels border with background pixels.
Once you have the colorized HSL pixel you just convert it back to RGB and write it back to the image.
I hope this helps. A final comment I should make is that Hue values in code are typically recorded in the 0-255 range, but many applications show them as a color wheel with a range of 0 to 360 degrees. Keep that in mind!
Can I suggest you look into using OpenCV? It's an open source image manipulation library, and it's got an iOS port too. There are plenty of blog posts about how to use it and set it up.
It has a whole heap of functions that will help you do a good job of what you're attempting. You could do it just using CoreGraphics, but the end result isn't going to look nearly as good as OpenCV would.
It was developed by some folks at MIT, so as you might expect it does a pretty good job at things like edge detection and object tracking. I remember reading a blog about how to separate a certain color from a picture with OpenCV - the examples showed a pretty good result. See here for an example. From there I can't imagine it would be a massive job to actually change the separated color to something else.
I don't know of a CoreGraphics operation for this, and I don't see a suitable CoreImage filter for this. If that's correct, then here's a push in the right direction:
Assuming you have a CGImage (or a uiImage.CGImage):
Begin by creating a new CGBitmapContext
Draw the source image to the bitmap context
Get a handle to the bitmap's pixel data
Learn how the buffer is structured so you could properly populate a 2D array of pixel values which have the form:
typedef struct t_pixel {
uint8_t r, g, b, a;
} t_pixel;
Then create the color to locate:
const t_pixel ColorToLocate = { 0,0,0,255 }; // << black, opaque
And its substitution value:
const t_pixel SubstitutionColor = { 255,255,255,255 }; // << white, opaque
Iterate over the bitmap context's pixel buffer, creating t_pixels.
When you find a pixel which matches ColorToLocate, replace the source values with the values in SubstitutionColor.
Create a new CGImage from the CGBitmapContext.
That's the easy part! All that does is takes a CGImage, replace exact color matches, and produces a new CGImage.
What you want is more sophisticated. For this task, you will want a good edge detection algorithm.
I've not used this app you have linked. If it's limited to a few colors, then they may simply be swapping channel values, paired with edge detection (keep in mind that buffers may also be represented in multiple color models - not just RGBA).
If (in the app you linked) the user can choose an arbitrary colors, values, and edge thresholds, then you will have to use real blending and edge detection. If you need to see how this is accomplished, you may want to check out a package such as Gimp (it's an open image editor) - they have the algos to detect edges and choose by color.
We are working on DIP project where we found out the threshold of the gray scale image. Now we have to * find the maximum intensity of the two regions that we got, one region whose pixels are less than the threshold and the other whose pixels are greater than it. *
PS. We are not converting the image into binary image after finding the threshold. We just have to separate pixels in two regions and find the maximum intensity in each region
PS: We are working on MATLAB
Actually it is pretty simple to do.
Here is a function to do so:
function [ lowThrMaxIntns, highThrMaxIntns ] = FindMaxIntnesity ( mInputImage, thrLevel )
% Pixels which are lower than the threshold level
mLowThrImage = zeros(size(mInputImage));
% Pixels which are higher than the threshold level
mHightThrImage = zeros(size(mInputImage));
mLowThrImage(mInputImage < thrLevel) = mInputImage(mInputImage < thrLevel);
mHightThrImage(mInputImage >= thrLevel) = mInputImage(mInputImage >= thrLevel);
[lowThrMaxIntns lowThrMaxIntnsIdx] = max(mLowThrImage(:));
[highThrMaxIntns highThrMaxIntnsIdx] = max(mHightThrImage(:));
end
The output are only the intensities of the pixels.
If you need the pixel location, use the Idx variables (Use ind2sub for the sub scripts form).
Is there an algorithm to find out the wavelength of the color given the hue value (between 0 degree to 360 degree). Is there any built-in function in MATLABfor the same?
While Mark Ransom and Franco Callari are completely right that you cannot recover the spectrum of a perceptual color, nor unambiguously map hue values to wavelengths, you could definitely piece something together if you just want the corresponding monochromatic wavelength.
The part of the hue cycle between 270 and 360 is another problem. There is nothing corresponding to pink or magenta in the light spectrum, so let's assume that we only use hue values between 0 and 270 degrees.
Estimating that the usable part of the visible spectrum is 400-650nm, with wavelength L (in nm) and hue value H (in degrees), you can improvise this:
L = 650 - 250 / 270 * H
650 is the maximum wavelength, 250 is the wavelength range and 270 is the hue range.
I think this should be in the right direction but there may of course be room for improvement. You might be able to get better results comparing between input hues and corresponding colors on a visible spectrum chart, and then adjusting the values somewhat.
I cant provide simple solution, but there is something you need to consider:
The visible part of the spektrum is roughly between 380nm (UV-border) and 780nm (IR-border). But what you see (hue) depends on the cone-cells triggered. Above 660nm, the M-cone is not triggered at all, so everything between 660nm and 780nm is hue 0°.
at 580nm you have yellow with hue 60°, the purest green is at about 535nm, so that is 120°, and the purest blue (240°) is at about 457nm.
if you apply a linear function, yellow should be at 597nm - which it is not, so you'd need a more complex approach.
above blue, the red cone still gets triggered until we see violet, but we wont reach red again on higher frequencies, so you cant go above approximately 300°.
the hue range between 300° and 360° has no æquivalent in visible spektrum, it can only be simulated by mixing high frequency light (blue or violet) with red light, which results in something between magenta and red on the purple-line.
It is possible to find the dominant wavelength of a color/hue. But as said most colors arn’t monochromatic and the same color can be constructed with different “mixes” of wavelengths. I.e. metamerism.
Also, for the extra spectral magenta and violet colors only a complementary wavelength can be specified. I.e. the hue/dominant wavelength that additively mixes to white. Also white must be specified, since the is no absolute white due to adaption.
Also psychologically our perception of hues doesn’t follow dominant hue lines. Se the Munsell and NCS systems.
Here you can calulate dominant wavelength from RGB values or different CIE systems: http://www.brucelindbloom.com/index.html?Calc.html
I don’t have the formula though.
You can then transform RGB to/from HSL and similar. And to/from Munsell or NCS perceptual hues (NCS values are proprietary, so you have to pay and use their software).
Short answer: NO. A given hue can in general be produced by a triple infinity of wavelengths.
A "physical color" is a combination of pure spectral colors (in the visible range). In principle there exist infinitely many distinct spectral colors, and so the set of all physical colors may be thought of as an infinite-dimensional vector space (a Hilbert space). This space is typically notated Hcolor. More technically, the space of physical colors may be considered to be the topological cone over the simplex whose vertices are the spectral colors, with white at the centroid of the simplex, black at the apex of the cone, and the monochromatic color associated with any given vertex somewhere along the line from that vertex to the apex depending on its brightness.
. . .
This system implies that for any hue or non-spectral color not on the boundary of the chromaticity diagram, there are infinitely many distinct physical spectra that are all perceived as that hue or color. So, in general there is no such thing as the combination of spectral colors that we perceive as (say) a specific version of tan; instead there are infinitely many possibilities that produce that exact color. The boundary colors that are pure spectral colors can be perceived only in response to light that is purely at the associated wavelength, while the boundary colors on the "line of purples" can each only be generated by a specific ratio of the pure violet and the pure red at the ends of the visible spectral colors.
The CIE chromaticity diagram is horseshoe-shaped, with its curved edge corresponding to all spectral colors (the spectral locus), and the remaining straight edge corresponding to the most saturated purples, mixtures of red and violet.
(Source)
I found this site that converts a given wavelength to a hue. With a bit of work, you could actually reverse the process. It's not ideal, but I trust the guy who is a consultant in applied mathematics more than myself in solving this issue. That's that.
https://www.johndcook.com/wavelength_to_RGB.html
function convert(input) {
var w = parseFloat(input);
if (w >= 380 && w < 440) {
r = -(w - 440) / (440 - 380);
g = 0.0;
b = 1.0;
} else if (w >= 440 && w < 490) {
r = 0.0;
g = (w - 440) / (490 - 440);
b = 1.0;
} else if (w >= 490 && w < 510) {
r = 0.0;
g = 1.0;
b = -(w - 510) / (510 - 490);
} else if (w >= 510 && w < 580) {
r = (w - 510) / (580 - 510);
g = 1.0;
b = 0.0;
} else if (w >= 580 && w < 645) {
r = 1.0;
g = -(w - 645) / (645 - 580);
b = 0.0;
} else if (w >= 645 && w < 781) {
r = 1.0;
g = 0.0;
b = 0.0;
} else {
r = 0.0;
g = 0.0;
b = 0.0;
}
// Let the intensity fall off near the vision limits
if (w >= 380 && w < 420)
factor = 0.3 + 0.7 * (w - 380) / (420 - 380);
else if (w >= 420 && w < 701)
factor = 1.0;
else if (w >= 701 && w < 781)
factor = 0.3 + 0.7 * (780 - w) / (780 - 700);
else
factor = 0.0;
var gamma = 0.80;
var R = (r > 0 ? 255 * Math.pow(r * factor, gamma) : 0);
var G = (g > 0 ? 255 * Math.pow(g * factor, gamma) : 0);
var B = (b > 0 ? 255 * Math.pow(b * factor, gamma) : 0);
return [R, G, B]
}
There's no conversion because they don't overlap.
Hue moves you around an RGB colour space, usually sRGB that almost all consumer digital equipment uses. That's a subset of the colours that our visual systems recognise under normal conditions (defined by CIE 1931), and does not overlap the vibrant line of colours perceived at monochromatic wavelengths of light at all.
Though Hue from 0-120 (reddish orange to yellowish green) and near 240 (indigo) are reasonably close, sRGB is quite functional if you don't care about all the washed out greens and blues, and you can fake the violet and red ends of the full spectrum by making them darker Hue around 270 or 330 respectively, and the only place you can't really approximate is around 180, computer cyan just isn't close at all to the monochromatic vibrant blue-greens.
My question is if I have a Lion image just I want to change the color of the lion alone not the background color. For that I referred this SO question but it turns the color of whole image. Moreover the image is not looking great. I need the color change like photoshop. whether it is possible to do this in coregraphics or I have to use any other library.
EDIT : I need the color change to be like iQuikColor app
This took quite a while to figure out, mainly because I wanted to get it up and running in Swift using Core Image and CIColorCube.
#Miguel's explanation is spot on about the way you need to replace a "Hue angle range" with another "Hue angle range". You can read his post above for details on what a Hue Angle Range is.
I made a quick app that replaces a default blue truck below, with whatever you choose on Hue slider.
You can slide the slider to tell the app what color Hue you want to replace the blue with.
I'm hardcoding the Hue range to be 60 degrees, which typically seems to encompass most of a particular color but you can edit that if you need to.
Notice that it does not color the tires or the tail lights because that's outside of the 60 degree range of the truck's default blue hue, but it does handle shading appropriately.
First you need code to convert RGB to HSV (Hue value):
func RGBtoHSV(r : Float, g : Float, b : Float) -> (h : Float, s : Float, v : Float) {
var h : CGFloat = 0
var s : CGFloat = 0
var v : CGFloat = 0
let col = UIColor(red: CGFloat(r), green: CGFloat(g), blue: CGFloat(b), alpha: 1.0)
col.getHue(&h, saturation: &s, brightness: &v, alpha: nil)
return (Float(h), Float(s), Float(v))
}
Then you need to convert HSV to RGB. You want to use this when you discover a hue that in your desired hue range (aka, a color that's the same blue hue of the default truck) to save off any adjustments you make.
func HSVtoRGB(h : Float, s : Float, v : Float) -> (r : Float, g : Float, b : Float) {
var r : Float = 0
var g : Float = 0
var b : Float = 0
let C = s * v
let HS = h * 6.0
let X = C * (1.0 - fabsf(fmodf(HS, 2.0) - 1.0))
if (HS >= 0 && HS < 1) {
r = C
g = X
b = 0
} else if (HS >= 1 && HS < 2) {
r = X
g = C
b = 0
} else if (HS >= 2 && HS < 3) {
r = 0
g = C
b = X
} else if (HS >= 3 && HS < 4) {
r = 0
g = X
b = C
} else if (HS >= 4 && HS < 5) {
r = X
g = 0
b = C
} else if (HS >= 5 && HS < 6) {
r = C
g = 0
b = X
}
let m = v - C
r += m
g += m
b += m
return (r, g, b)
}
Now you simply loop through a full RGBA color cube and "adjust" any colors in the "default blue" hue range with those from your newly desired hue. Then use Core Image and the CIColorCube filter to apply your adjusted color cube to the image.
func render() {
let centerHueAngle: Float = 214.0/360.0 //default color of truck body blue
let destCenterHueAngle: Float = slider.value
let minHueAngle: Float = (214.0 - 60.0/2.0) / 360 //60 degree range = +30 -30
let maxHueAngle: Float = (214.0 + 60.0/2.0) / 360
var hueAdjustment = centerHueAngle - destCenterHueAngle
let size = 64
var cubeData = [Float](count: size * size * size * 4, repeatedValue: 0)
var rgb: [Float] = [0, 0, 0]
var hsv: (h : Float, s : Float, v : Float)
var newRGB: (r : Float, g : Float, b : Float)
var offset = 0
for var z = 0; z < size; z++ {
rgb[2] = Float(z) / Float(size) // blue value
for var y = 0; y < size; y++ {
rgb[1] = Float(y) / Float(size) // green value
for var x = 0; x < size; x++ {
rgb[0] = Float(x) / Float(size) // red value
hsv = RGBtoHSV(rgb[0], g: rgb[1], b: rgb[2])
if hsv.h < minHueAngle || hsv.h > maxHueAngle {
newRGB.r = rgb[0]
newRGB.g = rgb[1]
newRGB.b = rgb[2]
} else {
hsv.h = destCenterHueAngle == 1 ? 0 : hsv.h - hueAdjustment //force red if slider angle is 360
newRGB = HSVtoRGB(hsv.h, s:hsv.s, v:hsv.v)
}
cubeData[offset] = newRGB.r
cubeData[offset+1] = newRGB.g
cubeData[offset+2] = newRGB.b
cubeData[offset+3] = 1.0
offset += 4
}
}
}
let data = NSData(bytes: cubeData, length: cubeData.count * sizeof(Float))
let colorCube = CIFilter(name: "CIColorCube")!
colorCube.setValue(size, forKey: "inputCubeDimension")
colorCube.setValue(data, forKey: "inputCubeData")
colorCube.setValue(ciImage, forKey: kCIInputImageKey)
if let outImage = colorCube.outputImage {
let context = CIContext(options: nil)
let outputImageRef = context.createCGImage(outImage, fromRect: outImage.extent)
imageView.image = UIImage(CGImage: outputImageRef)
}
}
You can download the sample project here.
See answers below instead. Mine doesn't provide a complete solution.
Here is the sketch of a possible solution using OpenCV:
Convert the image from RGB to HSV using cvCvtColor (we only want to change the hue).
Isolate a color with cvThreshold specifying a certain tolerance (you want a range of colors, not one flat color).
Discard areas of color below a minimum size using a blob detection library like cvBlobsLib. This will get rid of dots of the similar color in the scene.
Mask the color with cvInRangeS and use the resulting mask to apply the new hue.
cvMerge the new image with the new hue with an image composed by the saturation and brightness channels that you saved in step one.
There are several OpenCV iOS ports in the net, eg: http://www.eosgarden.com/en/opensource/opencv-ios/overview/ I haven't tried this myself, but seems a good research direction.
I'm going to make the assumption that you know how to perform these basic operations, so these won't be included in my solution:
load an image
get the RGB value of a given pixel of the loaded image
set the RGB value of a given pixel
display a loaded image, and/or save it back to disk.
First of all, let's consider how you can describe the source and destination colors. Clearly you can't specify these as exact RGB values, since a photo will have slight variations in color. For example, the green pixels in the truck picture you posted are not all exactly the same shade of green. The RGB color model isn't very good at expressing basic color characteristics, so you will get much better results if you convert the pixels to HSL. Here are C functions to convert RGB to HSL and back.
The HSL color model describes three aspects of a color:
Hue - the main perceived color - i.e. red, green, orange, etc.
Saturation - how "full" the color is - i.e. from full color to no color at all
Lightness - how bright the color is
So for example, if you wanted to find all the green pixels in a picture, you will convert each pixel from RGB to HSL, then look for H values that correspond to green, with some tolerance for "near green" colors. Below is a Hue chart, from Wikipedia:
So in your case you will be looking at pixels that have a Hue of 120 degrees +/- some amount. The bigger the range the more colors will get selected. If you make your range too wide you will start seeing yellow and cyan pixels getting selected, so you'll have to find the right range, and you may even want to offer the user of your app controls to select this range.
In addition to selecting by Hue, you may want to allow ranges for Saturation and Lightness, so that you can optionally put more limits to the pixels that you want to select for colorization.
Finally, you may want to offer the user the ability to draw a "lasso selection" so that specific parts of the picture can be left out of the colorization. This is how you could tell the app that you want the body of the green truck, but not the green wheel.
Once you know which pixels you want to modify it's time to alter their color.
The easiest way to colorize the pixels is to just change the Hue, leaving the Saturation and Lightness from the original pixel. So for example, if you want to make green pixels magenta you will be adding 180 degrees to all the Hue values of the selected pixels (making sure you use modulo 360 math).
If you wanted to get more sophisticated, you can also apply changes to Saturation and that will give you a wider range of tones you can go to. I think the Lightness is better left alone, you may be able to make small adjustments and the image will still look good, but if you go too far away from the original you may start seeing hard edges where the process pixels border with background pixels.
Once you have the colorized HSL pixel you just convert it back to RGB and write it back to the image.
I hope this helps. A final comment I should make is that Hue values in code are typically recorded in the 0-255 range, but many applications show them as a color wheel with a range of 0 to 360 degrees. Keep that in mind!
Can I suggest you look into using OpenCV? It's an open source image manipulation library, and it's got an iOS port too. There are plenty of blog posts about how to use it and set it up.
It has a whole heap of functions that will help you do a good job of what you're attempting. You could do it just using CoreGraphics, but the end result isn't going to look nearly as good as OpenCV would.
It was developed by some folks at MIT, so as you might expect it does a pretty good job at things like edge detection and object tracking. I remember reading a blog about how to separate a certain color from a picture with OpenCV - the examples showed a pretty good result. See here for an example. From there I can't imagine it would be a massive job to actually change the separated color to something else.
I don't know of a CoreGraphics operation for this, and I don't see a suitable CoreImage filter for this. If that's correct, then here's a push in the right direction:
Assuming you have a CGImage (or a uiImage.CGImage):
Begin by creating a new CGBitmapContext
Draw the source image to the bitmap context
Get a handle to the bitmap's pixel data
Learn how the buffer is structured so you could properly populate a 2D array of pixel values which have the form:
typedef struct t_pixel {
uint8_t r, g, b, a;
} t_pixel;
Then create the color to locate:
const t_pixel ColorToLocate = { 0,0,0,255 }; // << black, opaque
And its substitution value:
const t_pixel SubstitutionColor = { 255,255,255,255 }; // << white, opaque
Iterate over the bitmap context's pixel buffer, creating t_pixels.
When you find a pixel which matches ColorToLocate, replace the source values with the values in SubstitutionColor.
Create a new CGImage from the CGBitmapContext.
That's the easy part! All that does is takes a CGImage, replace exact color matches, and produces a new CGImage.
What you want is more sophisticated. For this task, you will want a good edge detection algorithm.
I've not used this app you have linked. If it's limited to a few colors, then they may simply be swapping channel values, paired with edge detection (keep in mind that buffers may also be represented in multiple color models - not just RGBA).
If (in the app you linked) the user can choose an arbitrary colors, values, and edge thresholds, then you will have to use real blending and edge detection. If you need to see how this is accomplished, you may want to check out a package such as Gimp (it's an open image editor) - they have the algos to detect edges and choose by color.