I searched a lot for finding the threshold values for the below mention methods.
methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', cv2.TM_SQDIFF_NORMED']
I also tried to figure them out by myself but I could only find thresholds for 3 methods which have max value of 1.0. The other methods values were in range of 10^5. I would like to know the bounds of these methods.
Can somebody point me in the right direction. My agenda is to loop through all the methods for template matching and get the best outcome.I went through the documentation and source code, but no luck.
These are the values I got , I could understand that *NORMED methods have values 0-1.
cv2.TM_CCOEFF -- 25349100.0
cv2.TM_CCOEFF_NORMED -- 0.31208357214927673
cv2.TM_CCORR -- 616707328.0
cv2.TM_CCORR_NORMED -- 0.9031367897987366
cv2.TM_SQDIFF -- 405656000.0
cv2.TM_SQDIFF_NORMED -- 0.737377941608429
As described in opencv documentation matchTemplate result is a sum of differences (varies with method) for each pixel, so for not normalized methods - thresholds would vary with size of template.
You can see formulas for each method and calculate thresholds for your template type considering that max difference between pixels is 255 for CV_8UC1 image.
So lets say you have 2 grayscale images and smallest one is 10x10.
In that case for TM_SQDIFF minimum distance would be 10x10x0^2=0 (images are identical) and maximum would be 10x10x255^2=6502500 (one image is completely black and other is white), which results in [0, 6502500] boundaries.
Of course it is possible to calculate that for the undefined sizes [A, B].
For TM_CCORR it would be AxBxmax(T(x',y')I(x+x',y+y')) = 65025AB
You can go on and calculate that for remaining methods, remember that if you have different from CV_8UC image types (like 32FC or 32SC) - you would need to replace 255 with corresponding values (max(float) max(int32))
Related
I am working with particle tracking in images in MATLAB and using regionprops function. On the provided resource there is an example with circles:
stats = regionprops('table',bw,'Centroid',...
'MajorAxisLength','MinorAxisLength')
centers = stats.Centroid;
diameters = mean([stats.MajorAxisLength stats.MinorAxisLength],2);
radii = diameters/2;
In my Matlab R2014b, the line centers = stats.Centroid; produces undesired result: my stats.Centroid structure has 20 elements (each element is two numbers - the coordinates of the center of the region). However, after the following command, my variable center is only 1x2 matrix, instead of desired 20x2.
Screenshot attached.
I tried to go around this with different methods. The only solution I found is to do:
t=zeros(20,2);
for i=1:20
t(i,:)=stats(i).Centroid;
end
However, as we all know loops are slow in MATLAB. Is there another method that takes advantage of MATLAB matrix operations?
Doing stats.Centroid would in fact give you a comma-separated list of centroids, so MATLAB would only give you the first centre of that matrix if you did centers = stats.Centroid. What you must do is encapsulate the centres in an array (i.e. [stats.Centroid]), then reshape when you're done.
Something like this should work for you:
centers = reshape([stats.Centroid], 2, []).';
What this will do is read in the centroids as a 1 x 2*M array where M is the total number of blobs and because MATLAB does reshaping in column-major format, you should make sure that specify the total number of rows to be 2 and let MATLAB figure out how many columns there are after by itself. You would then transpose the result when you're done to complete what you want.
Minor Note
If you look at the regionprops documentation page in their Tips section - http://www.mathworks.com/help/images/ref/regionprops.html#buorh6l-1, you will see that they surround stats.Area, which is the area of each blob with [] brackets to ensure that the comma-separated list of values is encapsulated in an array. This is not an accident and there is a purpose of having those there and I've basically told you what that was.
I have a binary image below:
it's an image of random abstract picture, and by using matlab, what I wanna do is to detect, how many peaks does it have so I'll know that there are roughly 5 objects in it.
As you can see, there are, 5 peaks in it, so it means there are 5 objects in it.
I've tried using imregionalmax(), but I don't find it usefull, since my image already in binary image. I also tried to use regionprops('Area'), but it shows wrong number since there is no exact whitespace between each object. Thanks in advance
An easy way to do this would be to simply sum across the rows for each column and find the peaks of the result using findpeaks. In the example below, I have opted to use the inverse of the image which will result in positive peaks where the columns are.
rowSum = sum(1 - image, 1);
If we plot this, it looks like the bottom plot
We can then use findpeaks to identify the peaks in this plot. We will apply a 5-point moving average to it to help eliminate false peaks.
[peaks, locations, widths, prominences] = findpeaks(smooth(rowSum));
You can then select the "true" peaks by thresholding based on any of these outputs. For this example we can use prominences and find the more prominent peaks.
isPeak = prominences > 50;
nPeaks = sum(isPeak)
5
Then we can plot the peaks locations to confirm
plot(locations(isPeak), peaks(isPeak), 'r*');
If you have some prior knowledge about the expected widths of the peaks, you could adjust the smooth span to match this expected width and obtain some cleaner peaks when using findpeaks.
Using an expected width of 40 for your image, findpeaks was able to perfectly detect all 5 peaks with no false positive.
findpeaks(smooth(rowSum, 40));
As your they are peaks, they are vertical structures. So in this particular case, you case use projection histograms (also know as histogram projection function): you make all the black pixels fall as if they were effected by gravity. Then you will find a curve of black pixels on the bottom of your image. Then you can count the number of peaks.
Here is the algorithm:
Invert the image (black is normally the absence of information)
Histogram projection
Closing and opening in order to clean the signal and get the final result.
You can add a maxima detection to get the top of the peaks.
I applied some operations on a grayscale image and now I am getting new values but the problem is the intensity values are now less than 0, between 0 and 255 and greater than 255. For values between [0-255] there is no problem but for intensity values < 0 and intensity values > 255 there is a problem as these values cannot occur in a grayscale image.
Therefore, I need to normalize the values so that all the values whether they are negative or greater than 255 or whatever other values are, comes in the range 0 to 255 so that the image can be displayed.
For that I know two methods:
Method #1
newImg = ((255-0)/(max(img(:))-min(img(:))))*(img-min(img(:)))
where min(img(:)) and max(img(:)) are the minimum and maximum values obtained after doing some operations on the input image img. The min can be less than 0 and the max can be greater than 255.
Method #2
I just make all the values less than 0 as 0 and all the values greater than 255 as 255, so:
img(img < 0) = 0;
img(img > 255) = 255;
I tried to use both the methods but I am getting good results using second method but not with the first one. Can anyone of you please tell me what the problem is?
That totally depends on the image content itself. Both of those methods are valid to ensure that the range of values is between [0,255]. However, before you decide on what method you're using, you need to ask yourself the following questions:
Question #1 - What is my image?
The first question you need to ask is what does your image represent? If this is the output of an edge detector for example, the method you choose will depend on the dynamic range of the values seen in the result (more below in Question #2). For example, it's preferable that you use the second method if there is a good distribution of pixels and a low variance. However, if the dynamic range is a bit smaller, then you'll want to use the first method to push up the contrast of your result.
If the output is an image subtraction, then it's preferable to use the first method because you want to visualize the exact differences between pixels. Truncating the result will not give you a good visualization of the differences.
Question #2 - What's the dynamic range of the values?
Another thing you need to take note of is how wide the dynamic range of the minimum and maximum values are. For example, if the minimum and maximum are not that far off from the limits of [0,255], then you can use the first or second method and you won't notice much of a difference. However, if your values are within a small range that is within [0,255], then doing the first method will increase contrast whereas the second method won't do anything. If it is your goal to also increase the contrast of your image and if the intensities are within the valid [0,255] range, then you should do the first method.
However, if you have minimum and maximum values that are quite far away from the [0,255] range, like min=-50 and max=350, then doing the first method won't bode very well - especially if the grayscale intensities have huge variance. What I mean by huge variance is that you would have values that are in the high range, values in the low range and nothing else. If you rescaled using the first method, this would mean that the minimum gets pushed to 0, the maximum gets shrunk to 255 and the rest of the intensities get scaled in between so for those values that are lower, they get scaled so that they're visualized as gray.
Question #3 - Do I have a clean or noisy image?
This is something that not many people think about. Is your image very clean, or are there a couple of spurious noisy spots? The first method is very bad when it comes to noisy pixels. If you only had a couple of pixel values that have a very large value but the other pixels are within the range of [0,255], this would make all of the other pixels get rescaled accordingly and would thus decrease the contrast of your image. You probably want to ignore the contribution made by these pixels and so the second method is preferable.
Conclusion
Therefore, there is nothing wrong with either of those methods that you have talked about. You need to be cognizant of what the image is, the dynamic range of values that you see once you examine the output and whether or not this is a clear or noisy image. You simply have to make a smart choice keeping those two factors in mind. So in your case, the first output probably didn't work because you have very large negative values and large positive values and perhaps very few of those values too. Doing a truncation is probably better for your application.
So I’m trying to find the rotational angle for stripe lines in images like the attached photo.
The only assumption is that the lines are parallel, and their orientation is about 90 degrees approximately more or less [say 5 degrees tolerance].
I have to make sure the stripe lines in the result image will be %100 vertical. The quality of the images varies as well as their histogram/greyscale values. So methods based on non-adaptive thresholding already failed for my cases [I’m not interested in thresholding based methods if I cannot make it adaptive]. Also, there are some random black clusters on top of the stripe lines sometimes.
What I did so far:
1) Of course HoughLines is the first option, but I couldn’t make it work for all my images, I had some partial success though following this great article:
http://felix.abecassis.me/2011/09/opencv-detect-skew-angle/.
The main reason of failure to my understanding was that, I needed to fine tune the parameters for different images. Parameters such as Canny/BW/Morphological edge detection (If needed) | parameters for minLinelength/maxLineGap/etc. For sure there’s a way to hack into this and make it work, but, to me this is a fragile solution!
2) What I’m working on right now, is to divide the image to a top slice and a bottom slice, then find the peaks and valleys of each slice. Then basically find the angle using the width of the image and translation of peaks. I’m currently working on finding which peak of the top slice belongs to which of the bottom slice, since there will be some false positive peaks in my computation due to existence of black/white clusters on top of the strip lines.
Example: Location of peaks for slices:
Top Slice = { 1, 33,67,90,110}
BottomSlice = { 3, 14, 35,63,90,104}
I am actually getting similar vectors when extracting peaks. So as can be seen, the length of vector might vary, any idea how can I get a group like:
{{1,3},{33,35},{67,63},{90,90},{110,104}}
I’m open to any idea about improving any of these algorithms or a completely new approach. If needed, I can upload more images.
If you can get a list of points for a single line, a linear regression will give you a formula for the straight line that best fits the points. A simple trig operation will convert the line formula to an angle.
You can probably use some line thinning operation to turn the stripes into a list of points.
You can run an accumulator of spatial derivatives along different angles. If you want a half-degree precision and a sample of 5 lines, you have a maximum 10*5*1500 = 7.5m iterations. You can safely reduce the sampling rate along the line tenfold, which will give you a sample size of 150 points per sample, reducing the number of iterations to less than a million. Somewhere around that point the operation of straightening the image ought to become the bottleneck.
For a thumbnail-engine I would like to develop an algorithm that takes x random thumbnails (crop, no resize) from an image, analyzes them for contrast and chooses the one with the highest contrast. I'm working with PHP and Imagick but I would be glad for some general tips about how to compute contrast of imagery.
It seems that many things are easier than computing contrast, for example counting colors, computing luminosity,etc.
What are your experiences with the analysis of picture material?
I'd do it that way (pseudocode):
L[256] = {0,0,0...}
loop over each pixel:
luminance = avg(R,G,B)
increment L[luminance] by 1
for i = 0 to 255:
if L[i] < C: L[i] = 0 // C = threshold of your chose
find index of first and last non-zero value of L[]
contrast = last - first
In looking for the image "with the highest contrast," you will need to be very careful in how you define contrast for the image. In the simplest way, contrast is the difference between the lowest intensity and the highest intensity in the image. That is not going to be very useful in your case.
I suggest you use a histogram approach to describe the contrast of a given image and then compare the properties of the histograms to determine the image with the highest contrast as you define it. You could use a variety of well known containers to represent the histogram in code, or construct a class to meet your specific needs. (I am not implying that you need to create a histogram in the form of a chart – just a statistical representation of the intensity values.) You could use the variance of each histogram directly as a measure of contrast, or use the standard deviation if that is easier to work with.
The key really lies in how you define the contrast of the image. In general, I would define a high contrast image as one with values present for all, or nearly all, the possible values. And I would further add that in this definition of a high contrast image, the intensity values of the image will tend to be distributed across the range of possible values in a uniform way.
Using this approach, a low contrast image would tend to have relatively few discrete intensity values and they would tend to be closely grouped together rather than uniformly distributed. (As a general rule, they will also tend to be grouped toward the center of the range.)