I have an image as shown in the inset. I sampled it in Adobe Photoshop using the blue color as the image shows. The sampled image is shown in gray-scale on the left.
I know that openCV provides a similar method to sample images that is the inRange() function. How can I find out the range of HSV values that Adobe checked for to sample my image. Since the resultant image is pretty much what I want and I am not able to determine the range myself It would be a great help if some one could guide me for the same.
You can convert your image in HSV with cv::cvtColor(...) here the documentation
Then accordingly to Wikipedia the blue is near to 240° of the HUE channel of your image.
You can set something like maxHue = 270 and a minHue = 180 or other values to scan your image.
Maybe you should set a minSaturation and a minValue to avoid the black and white.
To find the best ranges you can link them with some sliders in a Qt GUI and change them until you get the same result as photoshop...
Related
I have a black area around my image and I want to create a mask using OpenCV C++ that selects just this black area so that I can paint it later. How can i do that without affecting the image itself?
I tried to convert the image to grayscale and then using threshold to convert it to binary, but it affects my image since the result contains black pixels from inside the image.
Another Question : if i want to crop the image instead of paint it, how can i do it??
Thanks in advance,
I would solve the problem like this:
Inverse-binarize the image with a threshold of 1 (i.e. all pixels with the value 0 are set to 1, all others to 0)
use cv::findContours to find white segments
remove segments that don't touch image borders
use cv::drawContours to draw the remaining segments to a mask.
There is probably a more efficient solution in terms of runtime efficiency, but you should be able to prototype my solution quite quickly.
I read dicom images with ITK using itk::ImageSeriesReader and itk::GDCMImageIO after reading i flip the images with itk::FlipImageFilter (to get right orientation of the images) and convert the itkImageData to vtkImageData using itk::ImageToVTKImageFilter. I visualization images with VTK using vtkResliceImageViewer in QVTKWidget2.
I set:
(vtkResliceImageViewer)m_imageViewer[i]->SetColorWindow(windowWidthTAGvalue[0028|1051]);
(vtkResliceImageViewer)m_imageViewer[i]->SetColorLevel(windowCenterTAGvalue[0028|1050]);
and i set following blac&white LookUpTable:
vtkLookupTable* lutbw = vtkLookupTable::New();
lutbw->SetTableRange(0,1000);
lutbw->SetSaturationRange(0,0);
lutbw->SetHueRange(0,0);
lutbw->SetValueRange(0,1);
lutbw->Build();
And images shown into my software compared with the same images shown into other software are much darker, i can not get the same effect as other DICOM viewers
My software images are right other software image is left also when i use some other LookUpTable in this example Flow i can not get the same effect (2nd row images) my image on right is much darker then other.
What i am missing why my images are darker what can i do? i was research a lot into dicom and ikt/vtk can not find good solution any help is appreciate.
Please check the values for Rescale Slope (0028,1053) and Rescale Intercept(0028,1052) and apply the Modality LUT transformation before applying the Window level.
Your dataset may have VOI LUT Function (0028,1056) attribute value of "SIGMOID" instead of "LINEAR".
I extracted the image data from one of your DICOM file (brain_009.dcm) and looked at the histogram of the image data. It looks like, the minimum value stored in the image is 0 and maximum value is 960 regardless of interpreting the data is signed or unsigned. Also, the Window Width (0028:1051) has an invalid value of “0” and you cannot use that for displaying the image.
So your default display could set the Window Width to 960 and Window Center to half the window width plus the minimum value.
there are plenty of tutorials showing how to blend two images in opencv:
http://opencv.itseez.com/doc/tutorials/core/adding_images/adding_images.html
http://aishack.in/tutorials/transparent-image-overlays-in-opencv/
But all of them are based on this equation:
opencv blending http://opencv.itseez.com/_images/math/afeb868ed1632ace1fe886b5bfbb6fd933b742b8.png
which means that I will be combining two images by averaging them and consequently I'll be loosing intensity on both images.
For instance, let alpha = 0.5, f0(x) = 255, and f1(x) = 0. After applying this equation, the result image g(x) = 127. That is not what I need. The first image should remain unchanged. And the transparency must be applied in the second one.
My problem is:
the first image f0(x) should not be changed and an alpha should be applied to the second image f1(x) when it overlays the first image f0(x).
I cannot figure out how to do this. Any help?
Unfortunately, alpha channels are not supported by OpenCV. From the imread documentation:
Note that in the current implementation the alpha channel, if any, is stripped from the output image. For example, a 4-channel RGBA image is loaded as RGB if flags > 0.
See this SO post for a possible work around using imagemagick.
Hope that is helpful!
Could you please tell me how to what are ranges for Hue, Saturation and Value indices for intense red?
I try to use this values for color tracking and I couldn't find a specific answer via Google.
you can map any color to OpenCV HSV. Actually opencv use 1800 hue cylinder while ideally it is 360, on the orher hand MS paint use 2400 cyllinder.
So to get OpenCV HSV value, simply open MS paint, open mixer, and read the value of HSV, now to map this value into OpenCV HSV multiply it with 180/240.
the range to value for saturation and value is 00-1800
You are the only one who can answer this question, since we don't know your criteria for "intense red". Collect as many samples as you can, some of which you consider intense red and some which are close but just miss the cut. Convert them all to HSL. Study the pattern.
You might put together a small app that has sliders for the H, S, and L parameters and displays a block of color corresponding to the settings. That will tell you your limits very quickly.
I want to develop a program which recolors the input image based on the given theme the same way as ms-powerpoint application does.
I am giving following link that shows what exactly i want to do.
I want to generate images same as images in below link under the Dark Variations and light Variations title based on the current theme.
http://blogs.msdn.com/powerpoint/archive/2006/07/06/658238.aspx
Can anybody give me idea,info regarding how to achieve it efficiently ??
You can give a look to the HSL colorspace to be able to have the same result. HSL means Hue, Saturation, Lightness.
You can keep the lightness of each pixel of your image and change only the hue. I think this will allow you to achieve what you want. You can find the RGB to HSL conversion on the wiki page.
Hope that helps.
Step 1: Choose the colors you want to represent black and white. For the dark variations, choose black and a light color; for the light variations, choose a dark color and white.
Step 2: Convert a pixel to gray. A common formula for this is L = R*0.3 + G*0.59 + B*0.11.
Step 3: Interpolate between the colors using the gray value. output.R = (L/255)*light.R + (1-(L/255))*dark.R and likewise for green and blue.
You can use a library like CxImage and convert the image to grayscale, then use the mix command with another image that you have made that is the same size as the original, and mix the two with the Mix command, using the filters. You can do mix-screen, and this should tint the pixels the color of the second image in the resultant image. Try playing with CxImage a bit, see if it will do what you want it to do. This is all coming off the top of my head, and its been a while since I have tried to do anything like this. YMMV, but this would be the simplest implementation. You could always look at how CxImage does the blend, and apply it to the image yourself.
I must say thanks to Mark and Patrice for ur guidance which helped me achieved it.
For light variation, I have done it by converting the theme colors to HSV colorspace and found relation between output color and theme color for black color (input) .
The relation was found to be linear for saturation and value and hue was almost constant.
I have used interpolation formula to make it generic for any given theme.
I have also make use of color matrix to achieve desired result.
Similarly for dark variation i have used white color as input and apply the same technique.