I have a few objects in a black background.I would like to threshold the image and transform the objects in 1 and the black background into 0.
I am not sure how to choose my threshold to isolate the black background.
You can do this simply by the following step.
Load your image.
Convert to gray-scale.
Apply binary threshold which will create the result as your requirement.
Here you can see a good explanation about Basic Thresholding Operations using OpenCV with example.
One of the simplest thing to do is using the OpenCV's threshold function on the whole image and let the threshold value be choosen automatically by means of the Otsu's algorithm (the type argument of the threshold function should be ORed with the constant THRESH_OTSU).
Otsu's original work is: OTSU, Nobuyuki. A threshold selection method from gray-level histograms. Automatica, 1975, 11.285-296: 23-27.
This approach may work very well or it may fail miserably... it depends, as always, on your images.
Related
I am trying to separate text from background by using Otsu's threshold mechanism. Even though the algorithm separates text from background, the resultant text has rough edges, which in turn decreases the accuracy of text recognition.
The input image and the output image after applying threshold are given below:
What can I do to remove just the background? I want to retain the text as it is in the original image with clear-cut edges and no breaks or thinning.
You would get better results using a local threshold operation instead of a global one like Otsu.
But you should not expect too much. Smooth looking edges are the result of gradient transitions between forground and background. You will most likely have pixels of the same value that you would consider foreground and others you would consider background in the same character...
If you want better results you should improve the quality of your input image.
I am using threshold in Opencv to find the contours. My input is a hand image. Sometimes the threshold is not good so I couldnt find the contours.
I have applied the below preprocessing steps
1. Grabcut
cv::grabCut(image, result,rectangle,bgModel,fgModel, 3,cv::GC_INIT_WITH_RECT);
gray Scale conversion
cvtColor(handMat, handMat, CV_BGR2GRAY);
meadianblur
medianBlur(handMat, handMat, MEDIAN_BLUR_K);
I used the below code to find threshold
threshold( handMat, handMat, 141, 255, THRESH_BINARY||CV_THRESH_OTSU );
Sometimes I get good output and sometimes the threshold output is not good. I have attached the two output images.
Is there any other way than threshold from which contours can be found?
Good threshold Output:
Bad threshold Output
Have you tried an adaptive threshold? A single value of threshold rarely works in real life application. Another truism - threshold is a non-linear operation and hence non-stable. Gradient on the other hand is linear so you may want to find a contour by tracking the gradient if your background is smooth and solid color. Gradient is also more reliable during illumination changes or shadows than thresholding.
Grab-cut, by the way, uses color information to improve segmentation on the boundary when you already found 90% or so of the segment, so it is a post processing step. Also your initialization of grab cut with rectangle lets in a lot of contamination from background colors. Instead of rectangle use a mask where you mark as GC_FGD deep inside your initial segment where you are sure the hand is; mark as GC_BGD far outside your segment where you sure background is; mark GC_PR_FGD or probably foreground everywhere else - this is what will be refined by grab cut. to sum up - your initialization of grab cut will look like a russian doll with three layers indicating foreground (gray), probably foreground (white) and background (balck). You can use dilate and erode to create these layers, see below
Overall my suggestion is to define what you want to do first. Are you looking for contours of arbitrary objects on arbitrary moving background? If you are looking for a contour of a hand to find fingers on relatively uniform background I would:
1. use connected components or MSER to segment out a hand. Possibly improve results with grab cut initialized with the conservative mask and not rectangle!
2. use convexity defects to find fingers if this is your goal;
One issue is to try to find contours without binarizing the image.
If your input is in color, you can try to change color space in order to enhance the difference between the hand and the background.
Otsu try to find an optimal threshold, you can also try to set it manually but Otsu is useful because if the illumination change, the threshold will adapt automatically.
There are also many other kind of binarization : Sauvola, Bradley, Niblack, Kasar... but Otsu is simple, and work well. I suggest you to do preprocessing or postprocessing if you want to improve the binarization result.
How to count corn trees accurately with opencv based on the following image? I have tried HSV conversion with inRange but got nothing so far.
Is there a way for counting the trees correctly? Even with noise reduction I think that it won't count it property.
I chose a template as follows...
and when I tried to run a template match I got the following match..
The match was fine as because I chose the template from that part of the image.However the result image containing the values of the extent of match at different areas of the full image when threshold-ed looked like this..
So you can see that if you count the white patches (neglecting the small noises) you almost get the possible number of crops...!!
EDIT
More precise result you can get if you try the template matching in the green plane of the RGB image
Your problem is easier to solve when implementing few simple preprocessing steps. Look at the result I obtained:
Steps:
Convert RGB to LAB image
Extract A channel (discard L, B channels)
Stretch/Maximize image contrast
Use Otsu's optimal threshold selection for binarization
Invert the image so that foreground is white, background is black
Based on this image template matching or other detection methods should work even better.
How to get rid of uneven illumination from images, that contain text data, usually printed but may be handwritten? It can have some spots of lights because the light reflected while making picture.
I've seen the Halcon program's segment_characters function that is doing this work perfectly,
but it is not open source.
I wish to convert an image to the image that has a constant illumination at background and more dark colored regions of text. So that binarization will be easy and without noise.
The text is assumed to be dark colored than it's background.
Any ideas?
Strictly speaking, assuming you have access to the image's pixels (you can search online for how to accomplish this in your programming language as the topic is abundantly available), the exercise involves going over the pixels once to determine a "darkness threshold". In order to do this you convert each pixel from RGB to HSL in order to get the lightness level component for each pixel. During this process you calculate an average lightness for the whole image which you can use as your "darkness threshold"
Once you have the image average lightness level, you can go over the image pixels once more and if a pixel is less than the darkness threshold, set it's color to full white RGB(255,255,255), otherwise, set it's color to full black RGB (0,0,0). This will give you a binary image with in which the text should be black - the rest should be white.
Of course, the key is in finding the appropriate darkness threshold - so if the average method doesn't give you good results you may have to come up with a different method to augment that step. Such a method could involve separating the image in the primary channels Red, Green, Blue and computing the darkness threshold for each channel separately and then using the aggressive threshold of the three..
And lastly, a better approach may be to compute the light levels distribution - as opposed to simply the average - and then from that, the range around the maximum is what you want to keep. Again, go over each pixel and if it's lightness fits the band make it black, otherwise, make it white.
EDIT
For further reading about HSL I recommend starting with the Wiky entry on HSL and HSV Color spaces.
Have you tried using morphological techniques? Closure-by-reconstruction (as presented in Gonzalez, Woods and Eddins) can be used to create a grayscale representation of background illumination levels. You can more-or-less standardize the effective illumination by:
1) Calculating the mean intensity of all the pixels in the image
2) Using closure-by-reconstruction to estimate background illumination levels
3) Subtract the output of (2) from the original image
4) Adding the mean intensity from (1) to every pixel in the output of (3).
Basically what closure-by-reconstruction does is remove all image features that are smaller than a certain size, erasing the "foreground" (the text you want to capture) and leaving only the "background" (illumination levels) behind. Subtracting the result from the original image leaves behind only small-scale deviations (the text). Adding the original average intensity to those deviations is simply to make the text readable, so that the resulting picture looks like a light-normalized version of the original image.
Use Local-Thresholding instead of the global thresholding algorithm.
Divide your image(grayscale) in to a grid of smaller images (say 50x50 px) and apply the thresholding algorithm on each individual image.
If the background features are generally larger than the letters, you can try to estimate and subsequently remove the background.
There are many ways to do that, a very simple one would be to run a median filter on your image. You want the filter window to be large enough that text inside the window rarely makes up more than a third of the pixels, but small enough that there are several windows that fit into the bright spots. This filter should result in an image without text, but with background only. Subtract that from the original, and you should have an image that can be segmented with a global threshold.
Note that if the bright spots are much smaller than the text, you do the inverse: choose the filter window such that it removes the light only.
The first thing you need to try and do it change the lighting, use a dome light or some other light that will give you a more diffuse and even light.
If that's not possible, you can try some of the ideas in this question or this one. You want to implement some type of "adaptive threshold", this will apply a local threshold to individual parts of the image so that the change in contrast won't be as noticable.
There is also a simple but effective method explained here. The simple outline of the alrithm is the following:
Split the image up into NxN regions or neighbourhoods
Calculate the mean or median pixel value for the neighbourhood
Threshold the region based on the value calculated in 2) or the value from 2) minus C (where C is a chosen constant)
It seems like what you're trying to do is improve local contrast while attenuating larger scale lighting variations. I'll agree with other posters that optimizing the image through better lighting should always be the first move.
After that, here are two tricks.
1) Use smooth_image() operator to convolve a gaussian on your original image. Use a relaitively large kernel, like 20-50px. Then subtract this blurred image from your original image. Apply scale and offset within sub_image() operator, or use equ_histo() to equalize histogram.
This basically subtracts the low spatial frequency information from the original, leaving the higher frequency information intact.
2) You could try highpass_image() operator, or one of the laplacian operators to extract a gradiant image.
I implemented some adaptive binarization methods, they use a small window and at each pixel the threshold value is calculated. There are problems with these methods:
If we select the window size too small we will get this effect (I think the reason is because of window size is small)
(source: piccy.info)
At the left upper corner there is an original image, right upper corner - global threshold result. Bottom left - example of dividing image to some parts (but I am talking about analyzing image's pixel small surrounding, for example window of size 10X10).
So you can see the result of such algorithms at the bottom right picture, we got a black area, but it must be white.
Does anybody know how to improve an algorithm to solve this problem?
There shpuld be quite a lot of research going on in this area, but unfortunately I have no good links to give.
An idea, which might work but I have not tested, is to try to estimate the lighting variations and then remove that before thresholding (which is a better term than "binarization").
The problem is then moved from adaptive thresholding to finding a good lighting model.
If you know anything about the light sources then you could of course build a model from that.
Otherwise a quick hack that might work is to apply a really heavy low pass filter to your image (blur it) and then use that as your lighting model. Then create a difference image between the original and the blurred version, and threshold that.
EDIT: After quick testing, it appears that my "quick hack" is not really going to work at all. After thinking about it I am not very surprised either :)
I = someImage
Ib = blur(I, 'a lot!')
Idiff = I - Idiff
It = threshold(Idiff, 'some global threshold')
EDIT 2
Got one other idea which could work depending on how your images are generated.
Try estimating the lighting model from the first few rows in the image:
Take the first N rows in the image
Create a mean row from the N collected rows. You know have one row as your background model.
For each row in the image subtract the background model row (the mean row).
Threshold the resulting image.
Unfortunately I am at home without any good tools to test this.
It looks like you're doing adaptive thresholding wrong. Your images look as if you divided your image into small blocks, calculated a threshold for each block and applied that threshold to the whole block. That would explain the "box" artifacts. Usually, adaptive thresholding means finding a threshold for each pixel separately, with a separate window centered around the pixel.
Another suggestion would be to build a global model for your lighting: In your sample image, I'm pretty sure you could fit a plane (in X/Y/Brightness space) to the image using least-squares, then separate the pixels into pixels brighter (foreground) and darker than that plane (background). You can then fit separate planes to the background and foreground pixels, threshold using the mean between these planes again and improve the segmentation iteratively. How well that would work in practice depends on how well your lightning can be modeled with a linear model.
If the actual objects you try to segment are "thinner" (you said something about barcodes in a comment), you could try a simple opening/closing operation the get a lighting model. (i.e. close the image to remove the foreground pixels, then use [closed image+X] as threshold).
Or, you could try mean-shift filtering to get the foreground and background pixels to the same brightness. (Personally, I'd try that one first)
You have very non-uniform illumination and fairly large object (thus, no universal easy way to extract the background and correct the non-uniformity). This basically means you can not use global thresholding at all, you need adaptive thresholding.
You want to try Niblack binarization. Matlab code is available here
http://www.uio.no/studier/emner/matnat/ifi/INF3300/h06/undervisningsmateriale/week-36-2006-solution.pdf (page 4).
There are two parameters you'll have to tune by hand: window size (N in the above code) and weight.
Try to apply a local adaptive threshold using this procedure:
convolve the image with a mean or median filter
subtract the original image from the convolved one
threshold the difference image
The local adaptive threshold method selects an individual threshold for each pixel.
I'm using this approach extensively and it's working fine with images having non uniform background.