I wish to give an effect to images, where the resultant image would appear as if it is painted on a rough cemented background, and the cemented background customizes itself near the edges to highlight them... Please help me in writing an algorithm to generate such an effect.
The first image is the original image
and the second image is the output im looking for.
please note the edges are detected and the mask changes near the edges to indicate the edges clearly
You need to read up on Bump Mapping. There are plenty of bump mapping algorithms.
The basic algorithm is:
for each pixel
Look up the position on the bump map texture that corresponds to the position on the bumped image.
Calculate the surface normal of the bump map
Add the surface normal from step 2 to the geometric surface normal (in case of an image it's a vector pointing up) so that the normal points in a new direction.
Calculate the interaction of the new 'bumpy' surface with lights in the scene using, for example, Phong shading -- light placement is up to you, and decides where will the shadows lie.
Finally, here's a plain C implementation for 2D images.
Starting with
1) the input image as R, G, B, and
2) a texture image, grayscale.
The images are likely in bytes, 0 to 255. Divide it by 255.0 so we have them as being from 0.0 to 1.0. This makes the math easier. For performance, you wouldn't actually do this but instead use clever fixed-point math, an implementation matter I leave to you.
First, to get the edge effects between different colored areas, add or subtract some fraction of the R, G, and B channels to the texture image:
texture_mod = texture - 0.2*R - 0.3*B
You could get fancier with with nonlinear forumulas, e.g. thresholding the R, G and B channels, or computing some mathematical expression involving them. This is always fun to experiment with; I'm not sure what would work best to recreate your example.
Next, compute an embossed version of texture_mod to create the lighting effect. This is the difference of the texture slid up and right one pixel (or however much you like), and the same texture slid. This give the 3D lighting effect.
emboss = shift(texture_mod, 1,1) - shift(texture_mod, -1, -1)
(Should you use texture_mod or the original texture data in this formula? Experiment and see.)
Here's the power step. Convert the input image to HSV space. (LAB or other colorspaces may work better, or not - experiment and see.) Note that in your desired final image, the cracks between the "mesas" are darker, so we will use the original texture_mod and the emboss difference to alter the V channel, with coefficients to control the strength of the effect:
Vmod = V * ( 1.0 + C_depth * texture_mod + C_light * emboss)
Both C_depth and C_light should be between 0 and 1, probably smaller fractions like 0.2 to 0.5 or so. You will need a fudge factor to keep Vmod from overflowing or clamping at its maximum - divide by (1+C_depth+C_light). Some clamping at the bright end may help the highlights look brighter. As always experiment and see...
As fine point, you could also modify the Saturation channel in some way, perhaps decreasing it where texture_mod is lower.
Finally, convert (H, S, Vmod) back to RGB color space.
If memory is tight or performance critical, you could skip the HSV conversion, and apply the Vmod formula instead to the individual R,G, B channels, but this will cause shifts in hue and saturation. It's a tradeoff between speed and good looks.
This is called bump mapping. It is used to give a non flat appearance to a surface.
Related
I noticed that of the two methods below for scaling an image N halfs that the first produced a more smooth image, looking more appealing to the eye.
while (lod-- > Payload->MaxZoom)
{
cv::resize(img, img, cv::Size(), 0.5, 0.5, cv::INTER_LINEAR);
}
vs
double scale = 1.0 / (1<< (lod - Payload->MaxZoom));
cv::resize(img, img, cv::Size(), scale, scale, cv::INTER_LINEAR);
I am interested in knowing if there is a interpolation that would produce similar result as the first resize but not having to loop over it N times.
Any mathematical insight into why doing the resize in multiply steps can result in a better result is also interesting.
The latter method above gives a very pixelated result (for N=5) where the first is very smooth (it makes sense since its the average of 4 pixel over N steps)
This happens because OpenCV's implementation of linear interpolation is rather simplistic.
A simple implementation of linear interpolation takes the values of four pixels closest to the interpolated point and interpolates between them. This is all right for upscaling, but for downscaling, this will ignore the values of many pixels - if there are N pixels in the output image, then it depends on at most 4N pixels of the input. This cannot give good results when the product of scaling factors is lower than 0.25.
The correct thing to do is to consider all input pixels that correspond to an output pixel after the transformation, and compute an average over them (or more generally, compute a convolution with a suitable resampling filter).
OpenCV seems to have an interpolation mode called cv::INTER_AREA, which should do the thing you want.
I am looking for a general algorithm to smoothly transition between two colors.
For example, this image is taken from Wikipedia and shows a transition from orange to blue.
When I try to do the same using my code (C++), first idea that came to mind is using the HSV color space, but the annoying in-between colors show-up.
What is the good way to achieve this ? Seems to be related to diminution of contrast or maybe use a different color space ?
I have done tons of these in the past. The smoothing can be performed many different ways, but the way they are probably doing here is a simple linear approach. This is to say that for each R, G, and B component, they simply figure out the "y = m*x + b" equation that connects the two points, and use that to figure out the components in between.
m[RED] = (ColorRight[RED] - ColorLeft[RED]) / PixelsWidthAttemptingToFillIn
m[GREEN] = (ColorRight[GREEN] - ColorLeft[GREEN]) / PixelsWidthAttemptingToFillIn
m[BLUE] = (ColorRight[BLUE] - ColorLeft[BLUE]) / PixelsWidthAttemptingToFillIn
b[RED] = ColorLeft[RED]
b[GREEN] = ColorLeft[GREEN]
b[BLUE] = ColorLeft[BLUE]
Any new color in between is now:
NewCol[pixelXFromLeft][RED] = m[RED] * pixelXFromLeft + ColorLeft[RED]
NewCol[pixelXFromLeft][GREEN] = m[GREEN] * pixelXFromLeft + ColorLeft[GREEN]
NewCol[pixelXFromLeft][BLUE] = m[BLUE] * pixelXFromLeft + ColorLeft[BLUE]
There are many mathematical ways to create a transition, what we really want to do is understand what transition you really want to see. If you want to see the exact transition from the above image, it is worth looking at the color values of that image. I wrote a program way back in time to look at such images and output there values graphically. Here is the output of my program for the above pseudocolor scale.
Based upon looking at the graph, it IS more complex than a linear as I stated above. The blue component looks mostly linear, the red could be emulated to linear, the green however looks to have a more rounded shape. We could perform mathematical analysis of the green to better understand its mathematical function, and use that instead. You may find that a linear interpolation with an increasing slope between 0 and ~70 pixels with a linear decreasing slope after pixel 70 is good enough.
If you look at the bottom of the screen, this program gives some statistical measures of each color component, such as min, max, and average, as well as how many pixels wide the image read was.
A simple linear interpolation of the R,G,B values will do it.
trumpetlicks has shown that the image you used is not a pure linear interpolation. But I think an interpolation gives you the effect you're looking for. Below I show an image with a linear interpolation on top and your original image on the bottom.
And here's the (Python) code that produced it:
for y in range(height/2):
for x in range(width):
p = x / float(width - 1)
r = int((1.0-p) * r1 + p * r2 + 0.5)
g = int((1.0-p) * g1 + p * g2 + 0.5)
b = int((1.0-p) * b1 + p * b2 + 0.5)
pix[x,y] = (r,g,b)
The HSV color space is not a very good color space to use for smooth transitions. This is because the h value, hue, is just used to arbitrarily define different colors around the 'color wheel'. That means if you go between two colors far apart on the wheel, you'll have to dip through a bunch of other colors. Not smooth at all.
It would make a lot more sense to use RGB (or CMYK). These 'component' color spaces are better defined to make smooth transitions because they represent how much of each 'component' a color needs.
A linear transition (see #trumpetlicks answer) for each component value, R, G and B should look 'pretty good'. Anything more than 'pretty good' is going to require an actual human to tweak the values because there are differences and asymmetries to how our eyes perceive color values in different color groups that aren't represented in either RBG or CMYK (or any standard).
The wikipedia image is using the algorithm that Photoshop uses. Unfortunately, that algorithm is not publicly available.
I've been researching into this to build an algorithm that takes a grayscale image as input and colorises it artificially according to a color palette:
■■■■ Grayscale input ■■■■ Output ■■■■■■■■■■■■■■■
Just like many of the other solutions, the algorithm uses linear interpolation to make the transition between colours. With your example, smooth_color_transition() should be invoked with the following arguments:
QImage input("gradient.jpg");
QVector<QColor> colors;
colors.push_back(QColor(242, 177, 103)); // orange
colors.push_back(QColor(124, 162, 248)); // blue-ish
QImage output = smooth_color_transition(input, colors);
output.save("output.jpg");
A comparison of the original image VS output from the algorithm can be seen below:
(output)
(original)
The visual artefacts that can be observed in the output are already present in the input (grayscale). The input image got these artefacts when it was resized to 189x51.
Here's another example that was created with a more complex color palette:
■■■■ Grayscale input ■■■■ Output ■■■■■■■■■■■■■■■
Seems to me like it would be easier to create the gradient using RGB values. You should first calculate the change in color for each value based on the width of the gradient. The following pseudocode would need to be done for R, G, and B values.
redDifference = (redValue2 - redValue1) / widthOfGradient
You can then render each pixel with these values like so:
for (int i = 0; i < widthOfGradient; i++) {
int r = round(redValue1 + i * redDifference)
// ...repeat for green and blue
drawLine(i, r, g, b)
}
I know you specified that you're using C++, but I created a JSFiddle demonstrating this working with your first gradient as an example: http://jsfiddle.net/eumf7/
I'm developing a software that detects boxers punching motion. At the moment i used color based segmentation using inRange function and set it to detect blue Minimum value and Blue Maximum value. The problem is that the range is quite wide and my cam at times picks out noise and segments objects of no interest. To improve the software i though of scanning image of a boxing glove and establishing exact Blue color Value before further processing.
It would make sens to me to store that value in a Vector and call it in inRange fiction
// My current function which takes the Minimum and Maximum values of Blue Color
Mat range_out;
inRange(blur_out, Scalar(100, 100, 100), Scalar(120, 255, 255), range_out);
So i would image the vector to go somewhere here.
Scan this above image compute the Blue value
Store this value in an array
recall the array in a inRange function
Could someone suggest a solution to this problem or direct me to a source of information where I can look for answers ?
since you are detecting the boxer gloves in motion so first use motion to separate it from other elements in the scene...use frame differentiation or optical flow to separate the glove and other moving areas from non moving areas...now in those moving area try for some colour detection...
Separe luminosity and cromaticity - your fixed range will not work very well in different light conditions. Your range is wide probably because you are trying to see "blue" in dark and on light at the same time. Convert your image to HSV (or La*b*) and discard V (or L), keeping H and S (or a* and b*).
Learn a color distribution instead a simple range - take some samples and compute a 2D
color histogram on H and S (a* or b*) for pixels on the glove. This histogram will be a model for the color distribution of your object. Then, use c2.calcBackProjection to detect the pixels of interest in your scene.
Clean the result using morphological close operation
Important: on step 2, play a little with different quantization values (ie, different numbers of bins).
I have a set of images of the same scene but shot with different exposures. These images have no EXIF data so there is no way to extract useful info like f-stop, shutter speed etc.
What I'm trying to do is to determine the difference in stops between the images i.e. Image1 is +1.3 stops of Image0.
My current approach is to first calculate luminance from the image's RGB values using the equation
L = 0.2126 * R + 0.7152 * G + 0.0722 * B
I've seen different numbers being used in the equation but generally it should not affect the end result L too much.
After that I derive the log-average luminance of the image.
exp(avg of log(luminance of image))
But somehow the log-avg luminance doesn't seem to give much indication on exposure difference btw the images.
Any ideas on how to determine exposure difference?
edit: on c/c++
You have to generally solve two problems:
1. Linearize your image data
(In case it's not obvious what is meant: two times more light collected by your pixel shall result in two times the intensity value in your linearized image.)
Your image input might be (sufficiently) linearized already -> you may skip to part 2. If your content came from a camera and it's a JPEG, then this will most certainly not be the case.
The real 'solution' to this problem is finding the camera response function, which you want to invert and apply to your image data to get linear intensity values. This is by no means a trivial task. The EMoR model is widely used in all sorts of software (Photoshop, PTGui, Photomatix, etc.) to describe camera response functions. Some open source software solving this problem (but using a different model iirc) is PFScalibrate.
Having that said, you may get away with a simple inverse gamma application. A rough 'gestimation' for the right gamma value might be found by doing this:
capture an evenly lit, static scene with two exposure times e and e/2
apply a couple of inverse gamma transforms (e.g. for 1.8 to 2.4 in 0.1 steps) on both images
multiply all the short exposure images with 2.0 and subtract them from the respective long exposure images
pick the gamma that lead to the smallest overall difference
2. Find the actual difference of irradiation in stops, i.e. log2(scale factor)
Presuming the scene was static (no moving objects or camera), this is relatively easy:
sum1 = sum2 = 0
foreach pixel pair (p1,p2) from the two images:
if p1 or p2 is close to 0 or 255:
skip this pair
sum1 += p1 and sum2 += p2
return log2(sum1 / sum2)
On large images this will certainly work just as well and a lot faster if you sub-sample the images.
If the camera was static but the scene was not (moving objects), this starts to work less well. I produced acceptable results in this case by simply repeating the above procedure several times and use the output of the previous run as an estimate for the correct scale factor and then discard pixel pairs who's quotient is too far away from the current estimate. So basically replacing the above if line with the following:
if <see above> or if abs(log2(p1/p2) - estimate) > 0.5:
I'd stop the repetition after a fixed number of iterations or if two consecutive estimates are sufficiently close to each other.
EDIT: A note about conversion to luminance
You don't need to do that at all (as Tony D mentioned already) and if you insist, then do it after the linearization step (as Mark Ransom noted). In a perfect setting (static scene, no noise, no de-mosaicing, no quantization) every channel of every pixel would have the same ratio p1/p2 (if neither is saturated). Therefore the relative weighting of the different channels is irrelevant. You may sum over all pixels/channels (weighing R, G and B equally) or maybe only use the green channel.
I was studying Perlin's Noise through some examples # http://dindinx.net/OpenGL/index.php?menu=exemples&submenu=shaders and couldn't help to notice that his make3DNoiseTexture() in perlin.c uses noise3(ni) instead of PerlinNoise3D(...)
Now why is that? Isn't Perlin's Noise supposed to be a summation of different noise frequencies and amplitudes?
Qestion 2 is what does ni, inci, incj, inck stand for? Why use ni instead of x,y coordinates? Why is ni incremented with
ni[0]+=inci;
inci = 1.0 / (Noise3DTexSize / frequency);
I see Hugo Elias created his Perlin2D with x,y coordinates, and so does PerlinNoise3D(...).
Thanks in advance :)
I now understand why and am going to answer my own question in hopes that it helps other people.
Perlin's Noise is actually a synthesis of gradient noises. In its production process, we must compute the dot product of a vector pointing from one of the corners flooring the input point to the input point itself with the random-generated gradient vector.
Now if the input point were a whole number, such as the xyz coordinates of a texture you want to create, the dot product would always return 0, which would give you a flat noise. So instead, we use inci, incj, inck as an alternative index. Yep, just an index, nothing else.
Now returning to question 1, there are two methods to implement Perlin's Noise:
1.Calculate the noise values separately and store them in the RGBA slots in the texture
2.Synthesize the noises up before-hand and store them in one of the RGBA slots in the texture
noise3(ni) is the actual implementation of method 1, while PerlinNoise3D(...) suggests the latter.
In my personal opinion, method 1 is much better because you have much more flexibility over how you use each octave in your shaders.
My guess on the reason for using noise3(ni) in make3DNoiseTexture() instead if PerlinNoise3D(...) is that when you use that noise texture in your shader you want to be able to replicate and modify the functionality of PerlinNoise3D(...) directly in the shader.
My guess for the reasoning behind ni, inci, incj, inck is that using x,y,z of the volume directly don't give a good result so by scaling the the noise with the frequency instead it is possible to adjust the resolution of the noise independently from the volume size.