I'm trying to automatically extract the contour of all the puzzle pieces from a photo of a puzzle. Here's the grayscale input image:
So far, I've been able to get to a more helpful image which varies less with the lighting conditions by taking the local standard deviation (standard deviation within an 11px box), and applying a 5px box blur to the result using opencv. That gives me this:
From here, I'm not sure what to do to get down to a single pixel line between the pieces. Having approximate contours for each individual piece isn't quite good enough, because I want to be able to cut up the image into the pieces and be able to move those pieces independently while the pieces still fit together perfectly.
The results of findContours are nowhere near good enough for this.
My ideal output here is to have an image that overlays the original and has a 1px black line between every adjacent pair of pieces.
You can do the following:
Use local threshold for the image thresholding.
Remove areas with a small area.
Inverse the image (255 - ThreholdImage)
Find connected components using findContours.
The result is:
It is far from prefect but i think this is because i used the image that you gave in the post. I think if you post the original image i will get better results.
Related
I'm working on an algorithm that counts patterns (bars) in a specific image. It seemed to me very simple at the first look, but I realized the complexity quickly.
I have tried simple thresholding, template matching (small sliding windows), edge detection...
I have just few images like this one. so I think that a machine learning algorithm can't give better results! but I still need suggestions.
I think you have enough data from your images. You need to crop from your images only the bars. You would get several dozens of small images for each image. After that you can resize all the images to some predefined size (for example 24X24 pixels) use a descriptor like HOG and SVM for the learning. For the false just use any other areas from your images.
This may not work in all cases, but since these are round bars, you can also try using circle detection. Both matlab(find circles) and opencv(hough circle transform) support this hough circle transformation. One issue is that you have to play with the parameters a bit (matlab is more simplistic than open cv) but that is true of almost any method.
These methods work better with larger images so I resized yours. You also need to know the radius of the circles to look for. If your camera position is constant, this shouldn't change much. This code is taken from the matlab documentation page I linked. It doensn't find all the circles, but some tuning may help
im = imread('http://i.stack.imgur.com/NRwUq.jpg');
%find circles doesn't work well on small images, I made the image
%three times larger, if you have larger images you should use those for
%better results
bim = imresize(im, 3*size(im));
%find and display circles
[centers, radii] = imfindcircles(bim,[8 20],'ObjectPolarity','bright',...
'Sensitivity',0.9);
imshow(bim);
h = viscircles(centers,radii);
number_of_bars = numel(centers)
I added green dots to circles the detector missed and blue X's over incorrect detection. I did these by hand, but the red circles were located by matlab.
I've been trying to work on an image processing script /OCR that will allow me to extract the letters (using tesseract) from the boxes found in the image below.
Following alot of processing, I was able to get the picture to look like this
In order to remove the noise I inverted the image followed by floodfilling and gaussian blurring to remove noise. This is what I ended up with next.
After running it through some threholding and erosion to remove the noise (erosion being the step that distorted the text) I was able to get the image to look like this before running it through tesseract
This, while a pretty good rendering, allows for fairly accurate results through tesseract. Though it sometimes fails because it reads the hash (#) as a H or W. This leads me to my question!
Is there a way using opencv, skimage, PIL (opencv preferably) I can sharpen this image in order to increase my chances of tesseract properly reading my image? OR Is there a way I can get from the third to final image WITHOUT having to use erosion which ultimately distorted the text in the image.
Any help would be greatly appreciated!
OpenCV does has functions like filter2D that convolves arbitrary kernel with given image. In particular you can use kernels that are used for image sharpening. The main question is whether this will improve the results of your OCR library or not. The image is already pretty sharp and the noise in the image is not a result of blur. I never worked with teseract myself, but I am fairly sure that it already does all the noise reduction it could. And 'helping' him in this process may actually have opposite effect. For example any sharpening process tends to amplify noise (as opposite to noise reduction processes that usually are blurring images). Most of computer vision libraries give better results when provided with raw (unprocessed) images.
Edit (after question update):
There multiple ways to do so. The first one that I would test is this: Your first binary image is pretty clean and sharp. Instead of of using morphological operations that reduce quality of letters switch to filtering contours. Use findContours function to find all contours in the image and store their hierarchy (i.e. which contour is inside which). From all the found contours you actually need only the contours on first and second levels, i.e. outer and inner contours of each letter (contours at zero level are the outermost contours). Other contours can be discarded. Among the contours that do belong to first level you can discard those whose bounding box is too small to be a real letter. After those two discarding procedures I would expect that most of the remaining contours are the ones that are parts of the letters. Draw them on white image and run OCR. (If you want white letters on black background you will need to invert the order of vertices in the contours).
I am doing something similar to this problem:
Matching a curve pattern to the edges of an image
Basically, I have the same curve in two images, but with some affine transform between the two. Here is an example of two images:
Image1
Image2
So in order to get to Image2, you can apply some translation, rotation, scale, etc. to Image1.
Does anyone know how to solve for this transform?
Phase correlation doesn't work because it's not a translation only. Optical flow doesn't work since there's not enough detail to resolve translation, rotation, scale (It's pretty much a binary image). I'm not sure if Hough Transforms will give me good data.
I think some sort of keypoint matching algorithm like sift or surf would work with this kind of data as well.
The basic idea would be to find a limited number of "interesting" keypoints in each image, then match these keypoints pairwise.
Here is a quick test of your image with an online ASIFT demo:
http://demo.ipol.im/demo/my_affine_sift/result?key=BF9F4E4E006AB5168497709836C39C74#
It is probably more suited for normal greyscale images, but nevertheless it seems to work for this data. It looks like the lines connect roughly the same points around both of the curves; plugging all these pairs into something like the FindHomography function in OpenCv, the small discrepancies should even themselves out and you get the affine transformation matrix between the two images.
For your particular data you might be able to come up with better keypoint descriptors; perhaps something to detect the line ends, line crossings and sharp corners.
Or how about this: It is a little more work, but if you can vectorize your paths into a bezier or b-spline, you can get some natural keypoints from the spline descriptors.
I do not know any vectorisation library, but Inkscape has a basic implementation with which you could test the approach.
Once you have a small set of descriptors instead of a large 2d bitmap, you only need to match these descriptors between the two images, as per FindHomography.
answer to comment:
The points of interest are merely small areas that have certain properties. So the center of those areas might be black or white; the algorithm does not specifically look for white pixels or large-scale shapes such as the curve. What matter is that the lines connect roughly the same points on both curves, at least at first glance.
following up on my other question, do you guys know a good example in OpenCV, with a simple Black/White-Calibration Picture and appropriate detection-algorithms?
I just want to show some B&W-image on a screen, take a picture of that image from afar and calculate the size of the shown image, to calculate the distance to said screen.
Before I invent the wheel again, I recon this is so easy that it could be achieved through many different ways in OpenCV, yet I thought I'd ask if there's a preferred way around, possibly with some sample code.
(I got some face-detection code running using haarcascade-xml files already)
PS: I already have the resolution/dpi-part of my screen covered, so I know how big a picture would be in cm on my screen.
EDIT:
I'll make it real simple, I need:
A pattern, that is easily recognizable in an Image. Right now I'm experimenting with a checkerboard. The people who made ARDefender used this.
An appropriate algorithm to tell me the exact pixel coordinates of pattern 1) in a picture using OpenCV.
Well, it's hard to say which image is the best for recognition - in different illumination any color could be interpret as another color. Simple example:
As you can see both traffic signs have red color border but even on one image upper sign border is obviously not red.
So in my opinion you should use image with many different colors (like a rainbow). And also you said that it should be easy recognizable in different angles. That's why circle shape is the best for it.
That's why your image should look like this:
So idea of detection such object is the following:
Make different color segmentation (blue, red, green etc). For this use HSV color space.
Detect circles of specific color on image.
That area which has the biggest count of circles seems to be your object.
you just have to take pictures of your B&W object from several known distances (1m, 2m, 3m, ...) and then for each distance check the size of your object in the corresponding image.
From those datas, you will be able to create a linear function giving you the distance from the size in pixels (y = ax + b should do ;) ), translate it into your code and you're done.
Cheers
for example I have a binaryzation image like this
alt text http://www.iebayer.com/forum/attachments/month_1001/100127142364234cbfc9c9c793.jpg
I want get histogram like this!
alt text http://www.iebayer.com/forum/attachments/month_1001/100127142301dc4f49420b2389.jpg
how to do it use opencv.
While OpenCV does have Histogram functions, I am not sure I would bother using it in this case.
It seems like all you do is split the image in a number of sections and then calculate the amount of white in it. Some kind of funky spatial histogram. I am pretty sure that no OpenCV function to do that directly exists.
So I suggest running through every pixel in each section and count the number of white pixels (and, if the regions are of different sizes, count the black pixels as well). Then it is simply a matter of drawing it which can be done easily using the rectangle drawing function. If you read the documentation you'll find it's really quite informative.