I'm not sure if I'm doing the right way.
IplImage *dog_1 = cvCreateImage(cvGetSize(oriImg), oriImg->depth, oriImg->nChannels);
IplImage *dog_2 = cvCreateImage(cvGetSize(oriImg), oriImg->depth, oriImg->nChannels);
int kernel2 = 1;
int kernel1 = 5;
cvSmooth(oriImg, dog_2, CV_GAUSSIAN, kernel2, kernel2);
cvSmooth(oriImg, dog_1, CV_GAUSSIAN, kernel1, kernel1);
cvSub(dog_2, dog_1, dst, 0);
Am I doing the right way? Is the above the correct way of doing DOG? I just tried it from the explanation from wiki. But I could not get the desired image like in the wiki page http://en.wikipedia.org/wiki/Difference_of_Gaussians
[Edited]
I quote this from wiki page
"Difference of Gaussians is a grayscale image enhancement algorithm that involves the subtraction of one blurred version of an original grayscale image from another, less blurred version of the original. The blurred images are obtained by convolving the original grayscale image with Gaussian kernels having different standard deviations."
While reading a paper, the DoG image is done by
Original image, I(x,y) -> Blurs -> I1(x,y)
I1(x,y) -> Blurs -> I2(x,y)
output = I2(x,y) - I1(x,y)
As you see it's a slightly different from what I'm doing where I get the I1 and I2 using different kernel from the original image
Which one is the correct one or I misinterpret the meaning in the wiki?
If the image you've attached is your sample output, it doesn't necessarily look wrong. The DoG operation is quite simple: blur with two Gaussians of different sizes and compute the difference image. That appears to be what your code is doing, so I'd say you have it right.
If your worries stem from looking at the Wikipedia article (where the image is predominantly white, rather than black), it is just the inversion of the image that you have. I would not worry about that.
Related
In this question I'm mostly seeking for advice and guidance on overall understanding of some concepts of drawing wth GTK+ and Cairo in C language (IMO the information on topic is rather scarce, also my experience in really modest).
I'm coding some pet application which captures frames from webcam and displays them on a GTK window.
My app is working, but there are some points which I don't feel like grasped.
Overall process:
I've got a webcam frame as an array of bytes mmaped from webcam device to my app's process memory. So when another frame is captured what I have is a 640*480*3 bytes long array which is denoted as being in a RGB24 format. After some searching it looks like for a purpose of displaying it in a GTK window I need to create an object called drawing area using gtk_drawing_area_new(), add a "draw" callback and do "drawing" there in a designated callback. So, according to Cairo "drawing" is a process of applying "source" to "destination". I assume that I already have a source - my webcam mmaped pixels, but it looks like I need to use some "source" that Cairo is able to understand. I found a candidate:
cairo_surface_t* surface = cairo_image_surface_create(CAIRO_FORMAT_RGB24, 640, 480);
As I see this call creates some Cairo acceptable object, which along the way allocates a buffer in my app's memory which I can get, using:
unsigned char* surface_data = cairo_image_surface_get_data(surface);
According to docs this is a 640x480x4 bytes long buffer, which, on a little endian archs, should be filled with BGRA formatted pixel data.
Then I should rearrange my original webcam pixels for EVERY frame captured using this :
for (size_t idx_src=0, idx_dst=0; idx_src<640*480*3; idx_dst+=4, idx_src+=3) {
surface_data[idx_dst] = image[idx_src+2]; //B [3rd pos -> 1st pos]
surface_data[idx_dst+1] = image[idx_src+1]; //G [no change]
surface_data[idx_dst+2] = image[idx_src]; //R [1st pos -> 3rd pos]
}
After this I should do "drawing" with:
cairo_set_source_surface(cr, surface, 0, 0);
cairo_paint(cr);
So questions:
Is it what is supposed to be done for task at hand or I miss
something completely here ?
What confuses me is that I should
rearrange my original webcam pixels for EVERY frame captured (this
presumably consumes some cpu time, could be a limiting factor for
capturing in HD res at high frame rates). Is there some other way ?
Let's suppose I somehow acquire pixels from webcam in a Cairo
conforming format, e.g. 640x480x4 BGRA formatted bytes. Is there a
way to "wrap" this data in some Cairo acceptable object to exclude
pixel rearranging part ?
Any other thoughts I should've consider ?
Thanks for attention.
For most of your questions: Cairo only supports some image formats. Since your data comes in another format, you will have to convert it. All this copying around will likely be too slow. To make this work with an acceptable speed, you would need some other approach. No, I do not have any helpful suggestions here.
An unhelpful one would be: Is there some example for this webcam that you could look at?
Let's suppose I somehow acquire pixels from webcam in a Cairo conforming format, e.g. 640x480x4 BGRA formatted bytes. Is there a way to "wrap" this data in some Cairo acceptable object to exclude pixel rearranging part ?
Yup. cairo_image_surface_create_for_data.
I am trying to create a image processing software.
I get some weird results trying to create an Unsharp Mask effect.
I will attach my code here and I will explain what it does and where the problems are (or at least , where I think they are):
void unsharpMask(SDL_Surface* inputSurface,SDL_Surface* outputSurface)
{
Uint32* pixels = (Uint32*)inputSurface->pixels;
Uint32* outputPixels=(Uint32*)outputSurface->pixels;
Uint32* blurredPixels=(Uint32*)blurredSurface->pixels;
meanBlur(infoSurface,blurredSurface);
for (int i=0;i<inputSurface->h;i++)
{
for(int j=0;j<inputSurface->w;j++)
{
Uint8 rOriginal,gOriginal,bOriginal;
Uint8 rBlurred,gBlurred,bBlurred;
Uint32 rMask,gMask,bMask;
Uint32 rFinal,gFinal,bFinal;
SDL_GetRGB(blurredPixels[i*blurredSurface->w+j],blurredSurface->format,&rBlurred,&gBlurred,&bBlurred);
SDL_GetRGB(pixels[i*inputSurface->w+j],inputSurface->format,&rOriginal,&gOriginal,&bOriginal);
rMask=rOriginal - rBlurred;
rFinal=rOriginal + rMask;
if(rFinal>255) rFinal=255;
if(rFinal<=0) rFinal=0;
gMask=gOriginal - gBlurred;
gFinal=gOriginal + gMask;
if(gFinal>255) gFinal=255;
if(gFinal<0) gFinal=0;
bMask=bOriginal - bBlurred;
bFinal=bOriginal + bMask;
if(bFinal>255) bFinal=255;
if(bFinal<0) bFinal=0;
Uint32 pixel =SDL_MapRGB(outputSurface->format,rFinal,gFinal,bFinal);
outputPixels[i *outputSurface->w+j]=pixel;
}
}
}
So, as you can see, my function gets 2 arguments: the image source(from which pixel data will be extracted, and a target, where the image will be projected). I blur the original image, then i subtract the RGB value of the blurred image from the source image to get "the mask" and then , i add the mask to the original image and that's it. I added some clamping to make sure everything stays in the correct range and then I draw every pixel resulted on the output surface. All these surfaces have been converted in an SDL_PIXELFORMAT_ARGB8888 . The output surface is loaded into a texture (also SDL_PIXELFORMAT_ARGB8888) and rendered on the screen.
The results are pretty good in 90% of the image, I get the effect I want, however , there are some pixels that look weird in some places.
Original:
Result:
I tried to fix this in any possible way I knew. I thought is a format problem and played with the pixel bit depth , but I couldn't get to a good result. What i found is that all the values > 255 are negative values and I tried to make them completely white. And it works for the skies ,for example, but if you can see on my examples, the dark values, on the grass are also affected, which makes this not a good solution.
I also get this kind of wrong pixels when I want to add a contrast or do sharpen using kernel convolution, and the values are really bright/dark.
In my opinion there may be a problem with the pixel format, but I'm not sure if that's true.
Is there anyone that had this kind of problem before or knows a potential solution?
Given an image (i.e. newspaper, scanned newspaper, magazine etc), how do I detect the region containing text? I only need to know the region and remove it, don't need to do text recognition.
The purpose is I want to remove these text areas so that it will speed up my feature extraction procedure as these text areas are meaningless for my application. Anyone know how to do this?
BTW, it will be good if this can be done in Matlab!
Best!
You can use Stroke Width Transform (SWT) to highlight text regions.
Using my mex implementation posted here, you can
img = imread('http://i.stack.imgur.com/Eyepc.jpg');
[swt swtcc] = SWT( img, 0, 10 );
Playing with internal parameters of the edge-map extraction and image filtering in SWT.m can help you tweak the resulting mask to your needs.
To get this result:
I used these parameters for the edge map computation in SWT.m:
edgeMap = single( edge( img, 'canny', [0.05 0.25] ) );
Text detection in natural images is an active area of research in computer vision community. U can refer to ICDAR papers. But in your case I think it should be simple enough. As you have text from newspaper or magazines, it should be of fixed size and horizontally oriented.
So, you can apply scanning window of a fixed size, say 32x32. Train it on ICDAR 2003 training dataset for positive windows having text in it. U can use a small feature set of color and gradients and train an SVM which would give a positive or negative result for a window having text or not.
For reference go to http://crypto.stanford.edu/~dwu4/ICDAR2011.pdf . For code, you can try their homepages
This example in the Computer Vision System Toolbox in Matlab shows how to detect text using MSER regions.
If your image is well binarized and you know the usual size of the text you could use the HorizontalRunLengthSmoothing and VerticalRunLengthSmoothing algorithms. They are implemented in the open source library Aforge.Net but it should be easy to reimplement them in Matlab.
The intersection of the result image from these algorithm will give you a good indication that the region contains text, it is not perfect but it is fast.
I have to do some image processing but I don't know where to start. My problem is as follows :-
I have a 2D fiber image (attached with this post), in which the fiber edges are denoted by white color and the inside of the fiber is black. I want to choose any black pixel inside the fiber, and travel from it along the length of the fiber. This will involve comparing the contrast with the surrounding pixels and then travelling in the desired direction. My main aim is to find the length of the fiber
So can someone please tell me atleast where to start? I have made a rough algorithm in my mind on how to approach my problem but I don't know even which software/library to use.
Regards
Adi
EDIT1 - Instead of OpenCV, I started using MATLAB since I found it much easier. I applied the Hough Transform and then Houghpeaks function with max no. of peaks = 100 so that all fibers are included. After that I got the following image. How do I find the length now?
EDIT2 - I found a research article on how to calculate length using Hough Transform but I'm not able to implement it in MATLAB. Someone please help
If your images are all as clean as the one you posted, it's quite an easy problem.
The very first technique I'd try is using a Hough Transform to estimate the line parameters, and there is a good implementation of the algorithm in OpenCV. After you have them, you can estimate their length any way you want, based on whatever other constraints you have.
Problem is two-fold as I see it:
1) locate start and end point from your starting position.
2) decide length between start and end points
Since I don't know your input data I assume it's pixel data with a 0..1 data on each pixel representing it's "whiteness".
In order to find end points I would do some kind of WALKER/AI that tries to walk in different locations, knowing original pos and last traversed direction then continuing along that route until "forward arc" is all white. This assumes fiber is somewhat straight (is it?).
Once you got start and end points you can input these into a a* path finding algorithm and give black pixels a low value and white very high. Then find shortest distance between start and end point, that is the length of the fiber.
Kinda hard to give more detail since I have no idea what techniques you gonna use and some example input data.
Assumptions:
-This image can be considered a binary image where there are only 0s(black) and 1s(white).
-all the fibers are straight and their starting and ending points are on borders.
-we can come up with a limit for thickness in fiber(thickness of white lines).
Under these assumptions:
start scanning the image border(start from wherever you want in whichever direction you want...just be consistent) until you encounter with the first white pixel.At this point your program will understand that this is definitely a starting point. By knowing this, you will gather all the white pixels until you reach a certain limit(or a threshold). The idea here is, if there is a fiber,you will get the angle between the fiber and the border the starting point is on...of course the more pixels you get(the inner you get)the surer you will be in the end. This is the trickiest part. after somehow ending up with a line...you need to calculate the angle(basic trigonometry). Since you know the starting point, the width/height of the image and the angle(or cos/sin of those) you will have the exact coordinate of the end point. Be advised...the exactness here is not really what you might have understood because we may(the thing is we will) have calculation errors in cos/sin values. So you need to hold the threshold as long as possible. So your end point will not be a point actually but rather an area indicating possibility that the ending point is somewhere inside that area. The rest is just simple maths.
Obviously you can put too much detail in this method like checking the both white lines that makes the fiber and deciding which one is longer or you can allow some margin for error since those lines will not be straight properly...this is where a conceptual thickness comes to the stage etc.
Programming:
C# has nice stuff and easy for you to use...I'll put some code here...
newBitmap = new Bitmap(openFileDialog1.FileName);
for (int x = 0; x < newBitmap.Width; x++)
{
for (int y = 0; y < newBitmap.Height; y++)
{
Color originalColor = newBitmap.GetPixel(x, y);//gets the pixel value...
//things go here...
}
}
you'll get the image from a openfiledialog and bitmap the image. inside the nested for loop this code scans the image left-to-right however you can change this...
Since you know C++ and C, I would recommend OpenCV
. It is open-source so if you don't trust anyone like me, you won't have a problem ;). Also if you want to use C# like #VictorS. Mentioned I would use EmguCV which is the C# equivilant of OpenCV. Tutorials for OpenCV are included and for EmguCV can be found on their website. Hope this helps!
Download and install the latest version of 3Dslicer,
Load your data and go the the package>EM segmenter without Atlas>
Choose your anatomical tree in 2 different labels, the back one which is your purpose, the white edges.
The choose the whole 2D image as your ROI and click on segment.
Here is the result, I labeled the edges in green and the black area in white
You can modify your tree and change the structures you define.
You can give more samples to your segmentation to make it more accurate.
I have completed an image processing algorithm where I extract certain features from two similar images.
I'm using OpenCV2.1 and I wish to showcase a comparison between these two similar images. I wish to combine both the images into one, where the final image will have both the images next to one another. Like in the figure below.
Also, the black dots are the similarities my algorithm has found, now I want to mark them with digits. Where, point 1 on the right is the corresponding matching point on the left.**
What OpenCV functions are useful for this work?
If you really want them in the same window, and assuming they have same width and height (if they are similar they should have same width and height). You could try to create an image with a final width twice bigger than the width of your 2 similar images. And then use ROI to copy them.
You can write a new function to encapsulate these (usefull) functions in one function in order to have a nice code.
Mat img1,img2; //They are previously declared and of the same width & height
Mat imgResult(img1.rows,2*img1.cols,img1.type()); // Your final image
Mat roiImgResult_Left = imgResult(Rect(0,0,img1.cols,img1.rows)); //Img1 will be on the left part
Mat roiImgResult_Right = imgResult(Rect(img1.cols,0,img2.cols,img2.rows)); //Img2 will be on the right part, we shift the roi of img1.cols on the right
Mat roiImg1 = img1(Rect(0,0,img1.cols,img1.rows));
Mat roiImg2 = img2(Rect(0,0,img2.cols,img2.rows));
roiImg1.copyTo(roiImgResult_Left); //Img1 will be on the left of imgResult
roiImg2.copyTo(roiImgResult_Right); //Img2 will be on the right of imgResult
Julien,
The easiest way I can think right now would be to create two windows instead of one. You can do it using cvNamedWindow(), and then position them side by side with cvMoveWindow().
After that if you now the position of the similarities on the images, you can draw your text near them. Take a look at cvInitFont(), cvPutText().