I want to denoise a video using OpenCV and C++. I found on the OpenCV doc site this:
fastNlMeansDenoising(contourImage,contourImage2);
Every time a new frame is loaded, my program should denoise the current frame (contourImage) and write it to contourImage2.
But if I run the code, it returns 0 and exits. What am I doing wrong or is there an alternative way to denoise an image? (It should be fast, because I am processing a video)
while you are using c++ you are not providing the full argument try this that way.
cv::fastNlMeansDenoisingColored(contourImage, contourImage2, 10, 10,7, 21);
// This is Original Function to be used.
cv::fastNlMeansDenoising(src[, dst[, h[, templateWindowSize[, searchWindowSize]]]]) → dst
Parameters:
src – Input 8-bit 1-channel, 2-channel or 3-channel image.
dst – Output image with the same size and type as src .
templateWindowSize – Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels.
searchWindowSize – Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater.
searchWindowsSize - greater denoising time. Recommended value 21 pixels.
h – Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
I am working on project what detect hematoma from skin. I am having issue with color after convertion from RGB to HSV. My algorithm detect hematoma by its color.
With some images I have good results like here:
Original img: http://imgur.com/WHiOWdj
Result img: http://imgur.com/PujbnHa
But with some images i have bad result like this:
Original img: http://imgur.com/OshB99r
Result img: http://imgur.com/CuNzAId
The same original image after convertion to HSV: http://imgur.com/lkVwtCs
Do you have any ideas how to fix it?
Thanks
Looking at your result image I think that you are only using the H channel of the original image in your algorithm. The false positive detection can inherit from that the some part of the healty skin has quite the same H value than the hematoma has. You can see on the qrey-scale image of H channel that both parts have similar values:
The difference between the two parts is the saturation value. On the following image you can see the S channel of the original image and it shows perfectly that at the hematoma the saturation is much higher than at other the part of the arm:
This was expected because the hematoma has much stronger color than the healty skin has.
So, I suggest you to use both H and S channel in your algorithm that is you have to take into account only that parts of H image where the S image contains high saturation values. A possible and simple solution to do that is that you binarize both H and S images and with an AND operation you can execute this filtering:
H image after binarisation:
S image after binarisation:
Image after H&S operation:
You can see that on the result image only the hematoma part is white (except some noise but you can eliminate easily, for example by size or by morphological filtering).
EDIT
Important to note that binarization is one of most important (and sometimes also very complicated) step in the object detection algorithms namely binarization is the first highlight of the objects to detect.
If the the external conditions (lighting, color of objects etc.) do not change significantly from image to image you can use fix binaraziation thresholds. If this constant environment can not be issured you have to use more complicated methods. There are a lot of possibilies you can use, here you can read some examples:
Wikipedia - Thresholding
Wikipedia - Balanced histogram thresholding
Several solutions are based on the histogram analysis: on the histograms with objects there are always more local maximums which positions can vary depend on the environment and if you find them you can adapt the binarization threshold easily.
For example the histogram of the H channel of the original image is the following:
The first maximum belongs to the background, the second to the skin and the last to the hematome. It can be supposed that these 3 thresholds can be found in each image only their positions vary depend on the lighting or on other conditions. To put a threshold between the 2nd and the 3rd local maximum it can be a good choice to highlight the hematome.
Finally I offer you the read the following articel about thresholding in OpenCV:
OpenCV - Thresholding
I've been under the assumption that my gamma correction pipeline should be as follows:
Use sRGB format for all textures loaded in (GL_SRGB8_ALPHA8) as all art programs pre-gamma correct their files. When sampling from a GL_SRGB8_ALPHA8 texture in a shader OpenGL will automatically convert to linear space.
Do all lighting calculations, post processing, etc. in linear space.
Convert back to sRGB space when writing final color that will be displayed on the screen.
Note that in my case the final color write involves me writing from a FBO (which is a linear RGB texture) to the back buffer.
My assumption has been challenged as if I gamma correct in the final stage my colors are brighter than they should be. I set up for a solid color to be drawn by my lights of value { 255, 106, 0 }, but when I render I get { 255, 171, 0 } (as determined by print-screening and color picking). Instead of orange I get yellow. If I don't gamma correct at the final step I get exactly the right value of { 255, 106, 0 }.
According to some resources modern LCD screens mimic CRT gamma. Do they always? If not, how can I tell if I should gamma correct? Am I going wrong somewhere else?
Edit 1
I've now noticed that even though the color I write with the light is correct, places where I use colors from textures are not correct (but rather far darker as I would expect without gamma correction). I don't know where this disparity is coming from.
Edit 2
After trying GL_RGBA8 for my textures instead of GL_SRGB8_ALPHA8, everything looks perfect, even when using the texture values in lighting computations (if I half the intensity of the light, the output color values are halfed).
My code is no longer taking gamma correction into account anywhere, and my output looks correct.
This confuses me even more, is gamma correction no longer needed/used?
Edit 3 - In response to datenwolf's answer
After some more experimenting I'm confused on a couple points here.
1 - Most image formats are stored non-linearly (in sRGB space)
I've loaded a few images (in my case both .png and .bmp images) and examined the raw binary data. It appears to me as though the images are actually in the RGB color space, as if I compare the values of pixels with an image editing program with the byte array I get in my program they match up perfectly. Since my image editor is giving me RGB values, this would indicate the image stored in RGB.
I'm using stb_image.h/.c to load my images and followed it all the way through loading a .png and did not see anywhere that it gamma corrected the image while loading. I also examined the .bmps in a hex editor and the values on disk matched up for them.
If these images are actually stored on disk in linear RGB space, how am I supposed to (programatically) know when to specify an image is in sRGB space? Is there some way to query for this that a more featured image loader might provide? Or is it up to the image creators to save their image as gamma corrected (or not) - meaning establishing a convention and following it for a given project. I've asked a couple artists and neither of them knew what gamma correction is.
If I specify my images are sRGB, they are too dark unless I gamma correct in the end (which would be understandable if the monitor output using sRGB, but see point #2).
2 - "On most computers the effective scanout LUT is linear! What does this mean though?"
I'm not sure I can find where this thought is finished in your response.
From what I can tell, having experimented, all monitors I've tested on output linear values. If I draw a full screen quad and color it with a hard-coded value in a shader with no gamma correction the monitor displays the correct value that I specified.
What the sentence I quoted above from your answer and my results would lead me to believe is that modern monitors output linear values (i.e. do not emulate CRT gamma).
The target platform for our application is the PC. For this platform (excluding people with CRTs or really old monitors), would it be reasonable to do whatever your response to #1 is, then for #2 to not gamma correct (i.e. not perform the final RGB->sRGB transformation - either manually or using GL_FRAMEBUFFER_SRGB)?
If this is so, what are the platforms on which GL_FRAMEBUFFER_SRGB is meant for (or where it would be valid to use it today), or are monitors that use linear RGB really that new (given that GL_FRAMEBUFFER_SRGB was introduced 2008)?
--
I've talked to a few other graphics devs at my school and from the sounds of it, none of them have taken gamma correction into account and they have not noticed anything incorrect (some were not even aware of it). One dev in particular said that he got incorrect results when taking gamma into account so he then decided to not worry about gamma. I'm unsure what to do in my project for my target platform given the conflicting information I'm getting online/seeing with my project.
Edit 4 - In response to datenwolf's updated answer
Yes, indeed. If somewhere in the signal chain a nonlinear transform is applied, but all the pixel values go unmodified from the image to the display, then that nonlinearity has already been pre-applied on the image's pixel values. Which means, that the image is already in a nonlinear color space.
Your response would make sense to me if I was examining the image on my display. To be sure I was clear, when I said I was examining the byte array for the image I mean I was examining the numerical value in memory for the texture, not the image output on the screen (which I did do for point #2). To me the only way I could see what you're saying to be true then is if the image editor was giving me values in sRGB space.
Also note that I did try examining the output on monitor, as well as modifying the texture color (for example, dividing by half or doubling it) and the output appeared correct (measured using the method I describe below).
How did you measure the signal response?
Unfortunately my methods of measurement are far cruder than yours. When I said I experimented on my monitors what I meant was that I output solid color full screen quad whose color was hard coded in a shader to a plain OpenGL framebuffer (which does not do any color space conversion when written to). When I output white, 75% gray, 50% gray, 25% gray and black the correct colors are displayed. Now here my interpretation of correct colors could most certainly be wrong. I take a screenshot and then use an image editing program to see what the values of the pixels are (as well as a visual appraisal to make sure the values make sense). If I understand correctly, if my monitors were non-linear I would need to perform a RGB->sRGB transformation before presenting them to the display device for them to be correct.
I'm not going to lie, I feel I'm getting a bit out of my depth here. I'm thinking the solution I might persue for my second point of confusion (the final RGB->sRGB transformation) will be a tweakable brightness setting and default it to what looks correct on my devices (no gamma correction).
First of all you must understand that the nonlinear mapping applied to the color channels is often more than just a simple power function. sRGB nonlinearity can be approximated by about x^2.4, but that's not really the real deal. Anyway your primary assumptions are more or less correct.
If your textures are stored in the more common image file formats, they will contain the values as they are presented to the graphics scanout. Now there are two common hardware scenarios:
The scanout interface outputs a linear signal and the display device will then internally apply a nonlinear mapping. Old CRT monitors were nonlinear due to their physics: The amplifiers could put only so much current into the electron beam, the phosphor saturating and so on – that's why the whole gamma thing was introduced in the first place, to model the nonlinearities of CRT displays.
Modern LCD and OLED displays either use resistor ladders in their driver amplifiers, or they have gamma ramp lookup tables in their image processors.
Some devices however are linear, and ask the image producing device to supply a proper matching LUT for the desired output color profile on the scanout.
On most computers the effective scanout LUT is linear! What does this mean though? A little detour:
For illustration I quickly hooked up my laptop's analogue display output (VGA connector) to my analogue oscilloscope: Blue channel onto scope channel 1, green channel to scope channel 2, external triggering on line synchronization signal (HSync). A quick and dirty OpenGL program, deliberately written with immediate mode was used to generate a linear color ramp:
#include <GL/glut.h>
void display()
{
GLuint win_width = glutGet(GLUT_WINDOW_WIDTH);
GLuint win_height = glutGet(GLUT_WINDOW_HEIGHT);
glViewport(0,0, win_width, win_height);
glMatrixMode(GL_PROJECTION);
glLoadIdentity();
glOrtho(0, 1, 0, 1, -1, 1);
glMatrixMode(GL_MODELVIEW);
glLoadIdentity();
glBegin(GL_QUAD_STRIP);
glColor3f(0., 0., 0.);
glVertex2f(0., 0.);
glVertex2f(0., 1.);
glColor3f(1., 1., 1.);
glVertex2f(1., 0.);
glVertex2f(1., 1.);
glEnd();
glutSwapBuffers();
}
int main(int argc, char *argv[])
{
glutInit(&argc, argv);
glutInitDisplayMode(GLUT_RGBA | GLUT_DOUBLE);
glutCreateWindow("linear");
glutFullScreen();
glutDisplayFunc(display);
glutMainLoop();
return 0;
}
The graphics output was configured with the Modeline
"1440x900_60.00" 106.50 1440 1528 1672 1904 900 903 909 934 -HSync +VSync
(because that's the same mode the flat panel runs in, and I was using cloning mode)
gamma=2 LUT on the green channel.
linear (gamma=1) LUT on the blue channel
This is how the signals of a single scanout line look like (upper curve: Ch2 = green, lower curve: Ch1 = blue):
You can clearly see the x⟼x² and x⟼x mappings (parabola and linear shapes of the curves).
Now after this little detour we know, that the pixel values that go to the main framebuffer, go there as they are: The OpenGL linear ramp underwent no further changes and only when a nonlinear scanout LUT was applied it altered the signal sent to the display.
Either way the values you present to the scanout (which means the on-screen framebuffers) will undergo a nonlinear mapping at some point in the signal chain. And for all standard consumer devices this mapping will be according to the sRGB standard, because it's the smallest common factor (i.e. images represented in the sRGB color space can be reproduced on most output devices).
Since most programs, like webbrowsers assume the output to undergo a sRGB to display color space mapping, they simply copy the pixel values of the standard image file formats to the on-screen frame as they are, without performing a color space conversion, thereby implying that the color values within those images are in sRGB color space (or they will often merely convert to sRGB, if the image color profile is not sRGB); the correct thing to do (if, and only if the color values written to the framebuffer are scanned out to the display unaltered; assuming that scanout LUT is part of the display), would be conversion to the specified color profile the display expects.
But this implies, that the on-screen framebuffer itself is in sRGB color space (I don't want to split hairs about how idiotic that is, lets just accept this fact).
How to bring this together with OpenGL? First of all, OpenGL does all it's color operations linearly. However since the scanout is expected to be in some nonlinear color space, this means, that the end result of the rendering operations of OpenGL somehow must be brougt into the on-screen framebuffer color space.
This is where the ARB_framebuffer_sRGB extension (which went core with OpenGL-3) enters the picture, which introduced new flags used for the configuration of window pixelformats:
New Tokens
Accepted by the <attribList> parameter of glXChooseVisual, and by
the <attrib> parameter of glXGetConfig:
GLX_FRAMEBUFFER_SRGB_CAPABLE_ARB 0x20B2
Accepted by the <piAttributes> parameter of
wglGetPixelFormatAttribivEXT, wglGetPixelFormatAttribfvEXT, and
the <piAttribIList> and <pfAttribIList> of wglChoosePixelFormatEXT:
WGL_FRAMEBUFFER_SRGB_CAPABLE_ARB 0x20A9
Accepted by the <cap> parameter of Enable, Disable, and IsEnabled,
and by the <pname> parameter of GetBooleanv, GetIntegerv, GetFloatv,
and GetDoublev:
FRAMEBUFFER_SRGB 0x8DB9
So if you have a window configured with such a sRGB pixelformat and enable sRGB rasterization mode in OpenGL with glEnable(GL_FRAMEBUFFER_SRGB); the result of the linear colorspace rendering operations will be transformed in sRGB color space.
Another way would be to render everything into an off-screen FBO and to the color conversion in a postprocessing shader.
But that's only the output side of rendering signal chain. You also got input signals, in the form of textures. And those are usually images, with their pixel values stored nonlinearly. So before those can be used in linear image operations, such images must be brought into a linear color space first. Lets just ignore for the time being, that mapping nonlinear color spaces into linear color spaces opens several of cans of worms upon itself – which is why the sRGB color space is so ridiculously small, namely to avoid those problems.
So to address this an extension EXT_texture_sRGB was introduced, which turned out to be so vital, that it never went through being ARB, but went straight into the OpenGL specification itself: Behold the GL_SRGB… internal texture formats.
A texture loaded with this format undergoes a sRGB to linear RGB colorspace transformation, before being used to source samples. This gives linear pixel values, suitable for linear rendering operations, and the result can then be validly transformed to sRGB when going to the main on-screen framebuffer.
A personal note on the whole issue: Presenting images on the on-screen framebuffer in the target device color space IMHO is a huge design flaw. There's no way to do everything right in such a setup without going insane.
What one really wants is to have the on-screen framebuffer in a linear, contact color space; the natural choice would be CIEXYZ. Rendering operations would naturally take place in the same contact color space. Doing all graphics operations in contact color spaces, avoids the opening of the aforementioned cans-of-worms involved with trying to push a square peg named linear RGB through a nonlinear, round hole named sRGB.
And although I don't like the design of Weston/Wayland very much, at least it offers the opportunity to actually implement such a display system, by having the clients render and the compositor operate in contact color space and apply the output device's color profiles in a last postprocessing step.
The only drawback of contact color spaces is, that there it's imperative to use deep color (i.e. > 12 bits per color channel). In fact 8 bits are completely insufficient, even with nonlinear RGB (the nonlinearity helps a bit to cover up the lack of perceptible resolution).
Update
I've loaded a few images (in my case both .png and .bmp images) and examined the raw binary data. It appears to me as though the images are actually in the RGB color space, as if I compare the values of pixels with an image editing program with the byte array I get in my program they match up perfectly. Since my image editor is giving me RGB values, this would indicate the image stored in RGB.
Yes, indeed. If somewhere in the signal chain a nonlinear transform is applied, but all the pixel values go unmodified from the image to the display, then that nonlinearity has already been pre-applied on the image's pixel values. Which means, that the image is already in a nonlinear color space.
2 - "On most computers the effective scanout LUT is linear! What does this mean though?
I'm not sure I can find where this thought is finished in your response.
This thought is elaborated in the section that immediately follows, where I show how the values you put into a plain (OpenGL) framebuffer go directly to the monitor, unmodified. The idea of sRGB is "put the values into the images exactly as they are sent to the monitor and build consumer displays to follow that sRGB color space".
From what I can tell, having experimented, all monitors I've tested on output linear values.
How did you measure the signal response? Did you use a calibrated power meter or similar device to measure the light intensity emitted from the monitor in response to the signal? You can't trust your eyes with that, because like all our senses our eyes have a logarithmic signal response.
Update 2
To me the only way I could see what you're saying to be true then is if the image editor was giving me values in sRGB space.
That's indeed the case. Because color management was added to all the widespread graphics systems as an afterthought, most image editors edit pixel values in their destination color space. Note that one particular design parameter of sRGB was, that it should merely retroactively specify the unmanaged, direct value transfer color operations as they were (and mostly still are done) done on consumer devices. Since there happens no color management at all, the values contained in the images and manipulated in editors must be in sRGB already. This works for so long, as long images are not synthetically created in a linear rendering process; in case of the later the render system has to take into account the destination color space.
I take a screenshot and then use an image editing program to see what the values of the pixels are
Which gives you of course only the raw values in the scanout buffer without the gamma LUT and the display nonlinearity applied.
I wanted to give a simple explanation of what went wrong in the initial attempt, because although the accepted answer goes in-depth on colorspace theory, it doesn't really answer that.
The setup of the pipeline was exactly right: use GL_SRGB8_ALPHA8 for textures, GL_FRAMEBUFFER_SRGB (or custom shader code) to convert back to sRGB at the end, and all your intermediate calculations will be using linear light.
The last bit is where you ran into trouble. You wanted a light with a color of (255, 106, 0) - but that's an sRGB color, and you're working with linear light. To get the color you want, you need to convert that color to the linear space, the same way that GL_SRGB8_ALPHA8 is doing for your textures. For your case, this would be a vec3 light with intensity (1, .1441, 0) - this is the value after applying gamma-compression.
I want to find the background in multiple images captured with a fixed camera. Camera detect moving objects(animal) and captured sequential Images. So I need to find a simple background model image by process 5 to 10 captured images with same background.
Can someone help me please??
Is your eventual goal to find foreground? Can you show some images?
If animals move fast enough they will create a lot of intensity changes while background pixels will remain closely correlated among most of the frames. I won’t write you real code but will give you a pseudo-code in openCV. The main idea is to average only correlated pixels:
Mat Iseq[10];// your sequence
Mat result, Iacc=0, Icnt=0; // Iacc and Icnt are float types
loop through your sequence, i=0; i<N-1; i++
matchTemplate(Iseg[i], Iseq[i+1], result, CV_TM_CCOEFF_NORMED);
mask = 1 & (result>0.9); // get correlated part, which is probably background
Iacc += Iseq[i] & mask + Iseq[i+1] & mask; // accumulate background infer
Icnt += 2*mask; // keep count
end of loop;
Mat Ibackground = Iacc.mul(1.0/Icnt); // average background (moving parts fade away)
To improve the result you may reduce mage resolution or apply blur to enhance correlation. You can also clean every mask from small connected components by erosion, for example.
If
each pixel location appears as background in more than half the frames, and
the colour of a pixel does not vary much across the subset of frames in which it is background,
then there's a very simple algorithm: for each pixel location, just take the median intensity over all frames.
How come? Suppose the image is greyscale (this makes it easier to explain, but the process will work for colour images too -- just treat each colour component separately). If a particular pixel appears as background in more than half the frames, then when you take the intensities of that pixel across all frames and sort them, a background-coloured pixel must appear at the half-way (median) position. (In the worst case, all background-coloured pixels get pushed to the very front or the very back in this order, but even then there are enough of them to cover the half-way point.)
If you only have 5 images it's going to be hard to identify background and most sophisticated techniques probably won't work. For general background identification methods, see Link
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).