I'm trying to apply contrast and brightness to a bitmap in memory and I'm completely lost. Currently I'm trying to use Magick++ to do it, but if one of the other APIs would work better I'm all ears. I managed to find Magick::Image::sigmoidalContrast() for applying the contrast, but I can't figure out how to get it to work. I'm creating an image, passing it the buffer pointer, then calling that function, but it doesn't seem like it's changing anything so my first though was that it's making a copy and modifying that. Even so, I have no idea how to get the data out of the Magick::Image object.
Here's what I got so far.
Magick::Image image(fBitmapData->mGetTextureWidth(), fBitmapData->mGetTextureHeight(), "RGBA", MagickCore::CharPixel, pixels);
image.sigmoidalContrast(1, 20.0);
The documentation is useless and after searching I could only find hints that the first parameter is actually a boolean, even though it takes a size_t, that specifies whether to add or subtract the contrast, and the second value is something I have no idea what to pass so I'm just using 20.0 to test.
So does anyone know if this will work for contrast, and if not, then how do you apply contrast? And likewise I still have no idea how to apply brightness either and can't find any functions that look like they would work.
Figured it out; The function for contrast I was using was correct, and for brightness I ended up using image.modulate(brightness, 100.0, 100.0);. To get the data out of the image object you can grab the pixels of the entire image by doing
const MagickCore::PixelPacket * magickPixels = image.getConstPixels(0, 0, image.columns(), image.rows());
And then copy the magickPixels data back into the original pixels that were passed into the image constructor. An important thing to note is that the member MagickCore::PixelPacket::opacity is not what you would think it would be. If the pixel is completely transparent you'd think the value would be 0, right? Well for some reason ImageMagick is doing it opposite. So for full transparency the value would be 255. This means you need to do 255 - opacity to get the correct value.
Also be careful of the MAGICKCORE_QUANTUM_DEPTH that ImageMagick was compiled with, as this will change the values drastically. For my code MAGICKCORE_QUANTUM_DEPTH just happened to be defined as 16 so all of the values were a range of 0 to 65535, which I just fixed by doing realValue = magickValue >> 8 when copying the data back over since the texture data is unsigned char values.
Just for clarification on how to use these functions, since the documentation is horrible and completely wrong, the first parameter to signmoidalContrast() is actually a boolean, even though the type is a size_t, that specifies whether to increase the contrast (true) or reduce it (false), and the second is a range from 0.00001 to 20.0. I say 0.00001 because 0.0 is an invalid value so it just needs to be some decimal that is close to but not exactly 0.0.
For modulate() the documentation says that each value should be specified as 1.0 for no change, which is completely wrong. The values are actually a percentage so for no change you would specify 100.0.
I hope that helps someone because it took me all damn day to figure this stuff out.
According to the Imagemagick website - for the command line but may be the same?
-sigmoidal-contrast contrastxmid-point
increase the contrast without saturating highlights or shadows.
Increase the contrast of the image using a sigmoidal transfer function without saturating highlights or shadows. Contrast indicates how much to increase the contrast. For example, near 0 is none, 3 is typical and 20 is a lot. Note that exactly zero is invalid, but 0.0001 is negligibly different from no change in contrast. mid-point indicates where midtones fall in the resultant image (0 is white; 50% is middle-gray; 100% is black). By default the image contrast is increased, use +sigmoidal-contrast to decrease the contrast.
To achieve the equivalent of a sigmoidal brightness change, use -sigmoidal-contrast brightnessx0% to increase brightness and class="arg">+sigmoidal-contrast brightnessx0% to decrease brightness.
On the command line there is a new brightness contrast setting that may be in later versions of magic++?
-brightness-contrast brightness{xcontrast}{%}}
Adjust the brightness and/or contrast of the image.
Brightness and Contrast values apply changes to the input image. They are not absolute settings. A brightness or contrast value of zero means no change. The range of values is -100 to +100 on each. Positive values increase the brightness or contrast and negative values decrease the brightness or contrast. To control only contrast, set the brightness=0. To control only brightness, set contrast=0 or just leave it off.
You may also use -channel to control which channels to apply the brightness and/or contrast change. The default is to apply the same transformation to all channels.
Brightness and Contrast arguments are converted to offset and slope of a linear transform and applied using -function polynomial "slope,offset".
The slope varies from 0 at contrast=-100 to almost vertical at contrast=+100. For brightness=0 and contrast=-100, the result are totally midgray. For brightness=0 and contrast=+100, the result will approach but not quite reach a threshold at midgray; that is the linear transformation is a very steep vertical line at mid gray.
Negative slopes, i.e. negating the image, are not possible with this function. All achievable slopes are zero or positive.
The offset varies from -0.5 at brightness=-100 to 0 at brightness=0 to +0.5 at brightness=+100. Thus, when contrast=0 and brightness=100, the result is totally white. Similarly, when contrast=0 and brightness=-100, the result is totally black.
As the range of values for the arguments are -100 to +100, adding the '%' symbol is no different than leaving it off.
If magick++ is like Imagick it may be lagging a long way behind the Imagemagick options
Related
I am running the tutorial found here: https://software.intel.com/en-us/articles/using-librealsense-and-opencv-to-stream-rgb-and-depth-data
It gets the depth values from the r200 using the following lines:
cv::Mat depth16( _depth_intrin.height, _depth_intrin.width, CV_16U,(uchar *)_rs_camera.get_frame_data( rs::stream::depth ) );
cv::Mat depth8u = depth16;
depth8u.convertTo( depth8u, CV_8UC1, 255.0/1000 );
imshow( WINDOW_DEPTH, depth8u );
And the output image steam is:
https://imgur.com/EmdhFNk
You can see the color image as well. I've also put a tape measure across the bottom that goes as far as 3.5m (the range for the r200 is supposed to be up to 3.5m)
Why on earth is the color binary? I've tried adding different color images but it seems to not be depth values at all. Also it makes no sense that the floor is consistently black even though it spans from 1m to 5m away. Why are all objects white? The table and couch are obviously different distances away.
How can I improve this? I know you can get good depth values from the r200 as I get them in the examples. See (http://docs.ros.org/kinetic/api/librealsense/html/cpp-capture_8cpp_source.html) but these use glfw as opposed to OpenCV. I'm wondering why the depth values are so odd once theyve been converted.
Ideally i would like to generate depth values and filter any outside the range of 1m to 2m away. Thanks!
Edit: As #MSalters pointed out, the first half of my answer was erroneous and due to my misreading of the OP's code. The second half contains the right answer.
If your depth range is 1-3.5m, measured in millimetres (1000mm-3500mm); dividing the result by 1000 will give you data in the range 1.0-3.5. However, your source data is a 16-bit unsigned type, which can't represent decimal or floating point values, only integers, so your values get truncated to one of {0,1,2,3}. You might get away with this in convertTo, as it may marshal the types internally, but it's a potential source of error.
There is a second problem though... CV_8U is an 8-bit unsigned char, which can also only represent integer values, this time in the range from 0-255. Since your data can be in the range 0...3500, by multiplying by 0.255 as you do in your example, anything over 1000mm depth results in a value over 255 and so gets truncated there.
Instead of converting the raw depth image as you are above, you could use the cv::normalize function, with the NORM_MINMAX normalisation-type to normalise your data down to the 0...255 range. You can set the destination image format to CV_8U too.
This is probably only suitable for visualisation though, as it'll be affected by the source data input range. Instead, if you know your max value is 3500, and your min is 0, divide the source image by 3500 and multiply by 255. That said, where possible, it's probably best to keep it in the 16-bit format for the sake of depth resolution.
I have Problem understanding all Parameter of backgroundsubtractormog2.
I looked in the code (located in bfgf_gaussmix2.cpp), but don't see the connection to the mentioned paper. For exmaple is Tb = varThreshold, but what is the name of Tb in the paper?
I am especially interested in the fat marked parameter.
Let's start with the easy parameter [my remarks]:
int nmixtures
Maximum allowed number of mixture components. Actual number is determined dynamically per pixel.
[set 0 for GMG]
uchar nShadowDetection
The value for marking shadow pixels in the output foreground mask. Default value is 127.
float fTau
Shadow threshold. The shadow is detected if the pixel is a darker version of the background. Tau is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel is more than twice darker then it is not shadow.
Now to the ones i don't understand:
float backgroundRatio
Threshold defining whether the component is significant enough to be included into the background model ( corresponds to TB=1-cf from the paper??which paper??). cf=0.1 => TB=0.9 is default. For alpha=0.001, it means that the mode should exist for approximately 105 frames before it is considered foreground.
float varThresholdGen
Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the existing components (corresponds to Tg). If it is not close to any component, a new component is generated. 3 sigma => Tg=3*3=9 is default. A smaller Tg value generates more components. A higher Tg value may result in a small number of components but they can grow too large. [i don't understand a word of this]
In the Constructor the variable varThreshold is used. Is it the same as varThresholdGen?
Threshold on the squared Mahalanobis distance to decide whether it is well described by the background model (see Cthr??). This parameter does not affect the background update. A typical value could be 4 sigma, that is, varThreshold=4*4=16; (see Tb??).
float fVarInit
Initial variance for the newly generated components. It affects the speed of adaptation. The parameter value is based on your estimate of the typical standard deviation from the images. OpenCV uses 15 as a reasonable value.
float fVarMin
Parameter used to further control the variance.
float fVarMax
Parameter used to further control the variance.
float fCT
Complexity reduction parameter. This parameter defines the number of samples needed to accept to prove the component exists. CT=0.05 is a default value for all the samples. By setting CT=0 you get an algorithm very similar to the standard Stauffer&Grimson algorithm.
Someone asked pretty much the same question on the OpenCV website, but without an answer.
Well, I don't think anyone could tell you which parameter is what if you don't know the details of the algorithm that you are using. Besides, you should not need anyone to tell you which parameter is what if you know the details of the algorithm. I'm telling this for detailed parameters (fCT, fVarMax, etc.) not for straightforward ones (nmixtures, nShadowDetection, etc.).
So, I think you should read the papers referenced in the documentation. Here are the links for the papers 1, 2, 3.
And also you should read this paper as well, which is the beginning of background estimation.
After reading these papers and checking out the code with, I'm sure you will understand what those parameters are.
Good luck!
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've run into some nasty problem with my recorder. Some people are still using it with analog tuners, and analog tuners have a tendency to spit out 'snow' if there is no signal present.
The Problem is that when noise is fed into the encoder, it goes completely crazy and first consumes all CPU then ultimately freezes. Since main point od the recorder is to stay up and running no matter what, I have to figure out how to proceed with this, so encoder won't be exposed to the data it can't handle.
So, idea is to create 'entropy detector' - a simple and small routine that will go through the frame buffer data and calculate entropy index i.e. how the data in the picture is actually random.
Result from the routine would be a number, that will be 0 for completely back picture, and 1 for completely random picture - snow, that is.
Routine in itself should be forward scanning only, with few local variables that would fit into registers nicely.
I could use zlib or 7z api for such task, but I would really want to cook something on my own.
Any ideas?
PNG works this way (approximately): For each pixel, replace its value by the value that it had minus the value of the pixel left to it. Do this from right to left.
Then you can calculate the entropy (bits per character) by making a table of how often which value appears now, making relative values out of these absolute ones and adding the results of log2(n)*n for each element.
Oh, and you have to do this for each color channel (r, g, b) seperately.
For the result, take the average of the bits per character for the channels and divide it by 2^8 (assuming that you have 8 bit per color).
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.