OpenGL: Gamma corrected image doesn't appear linear - c++

I'm using OpenGL for rendering, and when I write linear values to the default framebuffer (without any gamma correction) they appear linear on my monitor. This goes against everything I thought I knew about gamma correction (as explained here: http://gamedevelopment.tutsplus.com/articles/gamma-correction-and-why-it-matters--gamedev-14466 ). Without gamma correction, I would expect to see mid-range colors darkened non-linearly by my monitor.
But here is what I actually see; first with no gamma correction on my part, then with gamma correction:
Here's my fragment shader without gamma correction (drawn on a fullscreen quad to the default framebuffer). This results in the linear image on the left:
out vec4 fsOut0;
void main( void )
{
// split the screen into 10 discrete color bands
float yResolution = 768.0;
int intVal = int(gl_FragCoord.y / yResolution * 10.0);
fsOut0.rgb = vec3( float(intVal) / 10.0 );
fsOut0.a = 1.0;
}
And here's the shader with added gamma correction (from linear space to sRGB). This results in the brighter-than-linear image on the right:
out vec4 fsOut0;
void main( void )
{
// split the screen into 10 discrete color bands
float yResolution = 768.0;
int intVal = int(gl_FragCoord.y / yResolution * 10.0);
fsOut0.rgb = vec3( float(intVal) / 10.0 );
// gamma correction
fsOut0.rgb = pow( fsOut0.rgb, vec3(1.0/2.2) );
fsOut0.a = 1.0;
}
I'm verifying whether or not the colors are linear just by looking at them, and by using the color picker in Photoshop and looking at the differences in RGB values between color bands. For the linear-looking image the difference between each color is (mostly) constant.
I have also tried requesting an sRGB-capable default framebuffer. In this case, writing linear values with no gamma correction looks like the second image (non-linear).
What am I doing wrong? Or could it be that my two monitors are both miscalibrated AND that Photoshop does not pick colors in linear space? Or is my "non-linear" image actually the correct linear result, but it just doesn't seem linear to my eyes?
My question is sort of a duplicate of this: Do I need to gamma correct the final color output on a modern computer/monitor
Unfortunately the accepted answer is extremely confusing and the parts of it I was able to follow seem contradictory, or at least not fully explained for someone less knowledgeable than the answerer.

Well, both your left and right pictures are as is to be expected. They are perfectly fine, and yes, I know my stuff.
It's just that our eyes are not very linear either, so e.g. 1/5th of linear intensity (luminous intensity) is perceived as "half as bright as white". This is what you see on the right, in the corrected image, near the bottom.
This is the reason for gamma being there in the first place - to help encoding by mimicking the eye's response. IOW, gamma makes the non-linear ramp look linear.
However, a physically linear ramp (as on the right) is therefore quite counter to being perceived as linear. Remember that the real world has a quite large dynamic range (in terms of luminous intensity), and our eyes are compensating for that. This is confusing you, but unlike many others, you actually got the numbers right.

Related

Scalable Ambient Obscurance rendering issue

I am trying to implement this SAO algorithm.
I am getting the following result :
I can't figure out why I have the nose on top of the walls, it seems to be a z-buffer issue.
Here are my input values :
const float projScale = 100.0;
const float radius = 0.9;
const float bias = 0.0005;
const float intensityDivR6 = pow(radius, 6);
I am using the original shader without modifications, except that I disable the usage of mipmaps of the depth buffer.
My depth buffer (on different scene, sorry) :
It should be an issue with the zbuffer linearization or it's not between -1 and 1.
Thank you Bruno, I finally figure out what were the issues.
The first was that I didn't transform my Z correctly, they use a specific pre-pass to make the Z linear and put it between -1 and 1. I was using an incompatible method to do it.
I also had to negate my near and far planes values directly in the projection matrix to compute correctly some uniforms.
Result :
I had a similar problem, having visual wrong occlusion, linked to the near/far, so I decided to give you what I've done to fix it.
The problem I had is discribed in a previous comment. I was getting self occlusion, when the camera was close to an object or when the radius was really too big.
If you take a closer look at the conversion from depth buffer value to camera-space value (the reconstructCSZ function from the g3d engine), you will see that replacing the depth by 0 will give you the near plane if you work with positive near/far. So, what it means is that every time you will get a tap outside the model, you will get a z component equals to near, which will give you wrong occlusion for fragments having a z close to 0.
You basically have to discard each taps that are located on the near plane, to avoid them being taken into account when comptuing the full contribution.

interpolation for smooth downscale of image in OpenCV

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.

Smooth color transition algorithm

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/

Cement Effect - Artistic Effect

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.

Optimizing a pinhole camera rendering system

I'm making a software rasterizer for school, and I'm using an unusual rendering method instead of traditional matrix calculations. It's based on a pinhole camera. I have a few points in 3D space, and I convert them to 2D screen coordinates by taking the distance between it and the camera and normalizing it
Vec3 ray_to_camera = (a_Point - plane_pos).Normalize();
This gives me a directional vector towards the camera. I then turn that direction into a ray by placing the ray's origin on the camera and performing a ray-plane intersection with a plane slightly behind the camera.
Vec3 plane_pos = m_Position + (m_Direction * m_ScreenDistance);
float dot = ray_to_camera.GetDotProduct(m_Direction);
if (dot < 0)
{
float time = (-m_ScreenDistance - plane_pos.GetDotProduct(m_Direction)) / dot;
// if time is smaller than 0 the ray is either parallel to the plane or misses it
if (time >= 0)
{
// retrieving the actual intersection point
a_Point -= (m_Direction * ((a_Point - plane_pos).GetDotProduct(m_Direction)));
// subtracting the plane origin from the intersection point
// puts the point at world origin (0, 0, 0)
Vec3 sub = a_Point - plane_pos;
// the axes are calculated by saying the directional vector of the camera
// is the new z axis
projected.x = sub.GetDotProduct(m_Axis[0]);
projected.y = sub.GetDotProduct(m_Axis[1]);
}
}
This works wonderful, but I'm wondering: can the algorithm be made any faster? Right now, for every triangle in the scene, I have to calculate three normals.
float length = 1 / sqrtf(GetSquaredLength());
x *= length;
y *= length;
z *= length;
Even with a fast reciprocal square root approximation (1 / sqrt(x)) that's going to be very demanding.
My questions are thus:
Is there a good way to approximate the three normals?
What is this rendering technique called?
Can the three vertex points be approximated using the normal of the centroid? ((v0 + v1 + v2) / 3)
Thanks in advance.
P.S. "You will build a fully functional software rasterizer in the next seven weeks with the help of an expert in this field. Begin." I ADORE my education. :)
EDIT:
Vec2 projected;
// the plane is behind the camera
Vec3 plane_pos = m_Position + (m_Direction * m_ScreenDistance);
float scale = m_ScreenDistance / (m_Position - plane_pos).GetSquaredLength();
// times -100 because of the squared length instead of the length
// (which would involve a squared root)
projected.x = a_Point.GetDotProduct(m_Axis[0]).x * scale * -100;
projected.y = a_Point.GetDotProduct(m_Axis[1]).y * scale * -100;
return projected;
This returns the correct results, however the model is now independent of the camera position. :(
It's a lot shorter and faster though!
This is called a ray-tracer - a rather typical assignment for a first computer graphics course* - and you can find a lot of interesting implementation details on the classic Foley/Van Damm textbook (Computer Graphics Principes and Practice). I strongly suggest you buy/borrow this textbook and read it carefully.
*Just wait until you get started on reflections and refraction... Now the fun begins!
It is difficult to understand exactly what your code doing, because it seems to be performing a lot of redundant operations! However, if I understand what you say you're trying to do, you are:
finding the vector from the pinhole to the point
normalizing it
projecting backwards along the normalized vector to an "image plane" (behind the pinhole, natch!)
finding the vector to this point from a central point on the image plane
doing dot products on the result with "axis" vectors to find the x and y screen coordinates
If the above description represents your intentions, then the normalization should be redundant -- you shouldn't have to do it at all! If removing the normalization gives you bad results, you are probably doing something slightly different from your stated plan... in other words, it seems likely that you have confused yourself along with me, and that the normalization step is "fixing" it to the extent that it looks good enough in your test cases, even though it probably still isn't doing quite what you want it to.
The overall problem, I think, is that your code is massively overengineered: you are writing all your high-level vector algebra as code to be executed in the inner loop. The way to optimize this is to work out all your vector algebra on paper, find the simplest expression possible for your inner loop, and precompute all the necessary constants for this at camera setup time. The pinhole camera specs would only be the inputs to the camera setup routine.
Unfortunately, unless I miss my guess, this should reduce your pinhole camera to the traditional, boring old matrix calculations. (ray tracing does make it easy to do cool nonstandard camera stuff -- but what you describe should end up perfectly standard...)
Your code is a little unclear to me (plane_pos?), but it does seem that you could cut out some unnecessary calculation.
Instead of normalizing the ray (scaling it to length 1), why not scale it so that the z component is equal to the distance from the camera to the plane-- in fact, scale x and y by this factor, you don't need z.
float scale = distance_to_plane/z;
x *= scale;
y *= scale;
This will give the x and y coordinates on the plane, no sqrt(), no dot products.
Well, off the bat, you can calculate normals for every triangle when your program starts up. Then when you're actually running, you just have to access the normals. This sort of startup calculation to save costs later tends to happen a lot in graphics. This is why we have large loading screens in a lot of our video games!