I guess there are various forms of this question peresnt here in stackoverflow. But I was unable to understand how I can solve my problem.
I have an image and I want to find the frequency content of the image.
img = imread('test.tif');
img = rgb2gray(img);
[N M] = size(img);
%% Compute power spectrum
imgf = fftshift(fft2(img));
imgfp = (abs(imgf)/(N*M)).^2;
I know I have to use the fft for this purpose. But I was wondering if I can find the maximum frequency in the images in terms of a particular value, say 'x cycles/mm' or 'x cycles/inch'.
What would be the best way to do this?
Thank you.
The FFT returns data in an array, where each array element is somewhat related to cycles per total data width (or height, etc.). So you could divide each FFT bin number by the image size in some dimensional unit (say "inches") to get cycles per unit dimension (say cycles per inch).
Note that except for some very specific narrowly specified types of images (say, constant amplitude exactly aperture periodic sinusoidal gradients), any image content will get spattered across the entire frequency spectrum and range of the FFT result. So you will likely have to set some non-zero threshold for frequency content before you can limit your "maximum" frequency finding.
Related
I am looking to find peak regions in 2D data (if you will, grayscale images or 2D landscapes, created through a Hough transform). By peak region I mean a locally maximal peak, yet NOT a single point but a part of the surrounding contributing region that goes with it. I know, this is a vague definition, but maybe the word mountain or the images below will give you an intuition of what I mean.
The peaks marked in red (1-4) are what I want, the ones in pink (5-6) examples for the "grey zone", where it would be okay if those smaller peaks are not found but also okay if they are.
Images contain between 1-20 peaked regions, different in height. The 2D data for above surf plot is shown below with a possible result (orange corresponds to Peak 1, green corresponds to Peak 2 a/b, ...). Single images for tests can be found in the description links:
Image left: input image - - - - middle: (okaish) result - - - - right: result overlayed over image.
The result above was produced using simple thresholding (MATLAB code):
% thresh_scale = 15; % parameter: how many thresholding steps
% thresh_perc = 6; % parameter: threshold at which we clip
thresh = multithresh(H,thresh_scale);
q_image = imquantize(H, thresh);
q_image(q_image <= thresh_perc) = 0; % regions under threshold are thrown away
q_image(q_image > thresh_perc) = 1; % ... while all others are preserved
q_image = imbinarize(q_image); % binarize for further processing
B = bwareaopen(q_image, nhood_minsize); % Filter really small regions
[L, L_num] = bwlabel(B); % <- result % Label connected components
Some values like these (15 and 6) often work fine if there are few similar peaks, but this isn't consistent if more peaks are present or they vary a lot. I mainly have two problems, that also can't be fixed by simply adjusting the parameters:
higher peaks can mask lower (but clearly distinguishable) peaks. Since the threshold is relative to the highest peak, other peaks may fall below.
in some cases the valley between two peaks is above the threshold, merging several peaks into one (as can be observed with Peak 2 a/b).
I also don't want a huge region for a high peak, so the peak region should probably be defined as some percentage of the mountain. I figured instead of a global thresholding, I'd rather have a method that finds peak regions relative to their immediate environment. I've looked into mean-shift and MSER segmentation, but those seem to be suited for segmenting real images, not kind of synthetic data.
Somehow I imagined filling a negative of the landscape with a certain amount of water would give me the regions I'm looking for: basins that fill and spread with how the surrounding regions are shaped. Like pouring water over below image and the resulting waterpools are the regions I'm looking for.
I thought that is what the floodfill or watershed algorithm do, but floodfill seems like something completely else and the watershed results are not at all what I had in mind, also when applying some preprocessing that I thought could help (clipped at 1/10):
Or when using the same clipping threshold as with above example (clipped at 6/15):
Produced with this code (MATLAB):
thresh = multithresh(H, 10); % set to either 10 || 15 for the examples
q_image = imquantize(H, thresh);
mask = false(size(q_image)); % create clipping mask...
mask(q_image > 1) = true; % ... to remove lowest 10% || lowest 6/15
% show with: figure, imshow(mask);
% OPTIONAL: Gaussian smoothing
H = imgaussfilt(H, 2); % apply before adding Inf values
% OPTIONAL: H-minima transform
H = imhmin(H, 10); % parameter is threshold for suppressing shallow minima
H = -H; % Complement the image
H(~mask) = Inf; % force "ground" pixels to Inf
L = watershed(D);
L(~mask) = 0; % clip "ground" from result
imshow(label2rgb(L,'lines',[.5 .5 .5])); % show result
My question now: Is there an algorithm that fills a landscape and gives me the resulting waterpools (for various amounts of water poured) to do what I've tried to achieve with above methods? Or any other suggestion is welcome. I'm implementing MATLAB (or if need be Python), but I can work with any code or pseude-code.
To distinguish this from this question, my maxima are not separated by zero-values. What I want is similar, yet none of the suggestions there are helpful (hill-climbing/simulated annealing will give you only one point...).
This question is also interesting, but it solves the problem with constraints (assume exactly 5 peaks of a certain size) that make the suggested approaches not useful for my case.
In such peak finding problems, I mostly use morphological operations. Since the Hough transform results are mostly noisy, I prefer blurring it first, then apply tophat and extended maxima transform. Then for each local maximum, find the region around it with adaptive thresholding. Here is a sample code:
im=imread('udIuy.png');
% blur
im=imgaussfilt(im,1);
% tophat transform
im2=imtophat(im,strel('disk',5));
% extended maximums
im3=imextendedmax(im2,10);
% Extract each blob
s=regionprops(im3,'Centroid','PixelIdxList');
figure,imagesc(im),axis image
for i=1:numel(s)
x=ceil(s(i).Centroid);
tmp=im*0;
tmp(s(i).PixelIdxList)=1;
tmp2=tmp.*im2;
% The maximum amplitude and location
[refV,b]=max(tmp2(:));
[x2,y2]=ind2sub(size(im),b);
% select the region around local max amplitude
tmp=bwselect(im2>refV*0.6,y2,x2,4);
[xi,yi]=find(tmp);
hold on, plot(yi,xi,'r.')
hold on, text(y2+10,x2,num2str(i),'Color','white','FontSize',16)
end
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.
UPDATE
Here is my code that is meant to add up the two matrices and using element by element addition and then divide by two.
function [ finish ] = stackAndMeanImage (initFrame, finalFrame)
cd 'C:\Users\Disc-1119\Desktop\Internships\Tracking\Octave\highway\highway (6-13-2014 11-13-41 AM)';
pkg load image;
i = initFrame;
f = finalFrame;
astr = num2str(i);
tmp = imread(astr, 'jpg');
d = f - i
for a = 1:d
a
astr = num2str(i + 1);
read_tmp = imread(astr, 'jpg');
read_tmp = rgb2gray(read_tmp);
tmp = tmp :+ read_tmp;
tmp = tmp / 2;
end
imwrite(tmp, 'meanimage.JPG');
finish = 'done';
end
Here are two example input images
http://imgur.com/5DR1ccS,AWBEI0d#1
And here is one output image
http://imgur.com/aX6b0kj
I am really confused as to what is happening. I have not implemented what the other answers have said yet though.
OLD
I am working on an image processing project where I am now manually choosing images that are 'empty' or only have the background, so that my algorithm can compute the differences and then do some more analysis, I have a simple piece of code that computes the mean of the two images, which I have converted to grayscale matrices, but this only works for two images, because when I find the mean of two, then take this mean and find the mean of this versus the next image, and do this repeatedly, I end up with a washed out white image that is absolutely useless. You can't even see anything.
I found that there is a function in Matlab called imFuse that is able to average images. I was wondering if anyone knew the process that imFuse uses to combine images, I am happy to implement this into Octave, or if anyone knew of or has already written a piece of code that achieves something similiar to this. Again, I am not asking for anyone to write code for me, just wondering what the process for this is and if there are already pre-existing functions out there, which I have not found after my research.
Thanks,
AeroVTP
You should not end up with a washed-out image. Instead, you should end up with an image, which is technically speaking temporally low-pass filtered. What this means is that half of the information content is form the last image, one quarter from the second last image, one eight from the third last image, etc.
Actually, the effect in a moving image is similar to a display with slow response time.
If you are ending up with a white image, you are doing something wrong. nkjt's guess of type challenges is a good one. Another possibility is that you have forgotten to divide by two after summing the two images.
One more thing... If you are doing linear operations (such as averaging) on images, your image intensity scale should be linear. If you just use the RGB values or some grayscale values simply calculated from them, you may get bitten by the nonlinearity of the image. This property is called the gamma correction. (Admittedly, most image processing programs just ignore the problem, as it is not always a big challenge.)
As your project calculates differences of images, you should take this into account. I suggest using linearised floating point values. Unfortunately, the linearisation depends on the source of your image data.
On the other hand, averaging often the most efficient way of reducing noise. So, there you are in the right track assuming the images are similar enough.
However, after having a look at your images, it seems that you may actually want to do something else than to average the image. If I understand your intention correctly, you would like to get rid of the cars in your road cam to give you just the carless background which you could then subtract from the image to get the cars.
If that is what you want to do, you should consider using a median filter instead of averaging. What this means is that you take for example 11 consecutive frames. Then for each pixel you have 11 different values. Now you order (sort) these values and take the middle (6th) one as the background pixel value.
If your road is empty most of the time (at least 6 frames of 11), then the 6th sample will represent the road regardless of the colour of the cars passing your camera.
If you have an empty road, the result from the median filtering is close to averaging. (Averaging is better with Gaussian white noise, but the difference is not very big.) But your averaging will be affected by white or black cars, whereas median filtering is not.
The problem with median filtering is that it is computationally intensive. I am very sorry I speak very broken and ancient Octave, so I cannot give you any useful code. In MatLab or PyLab you would stack, say, 11 images to a M x N x 11 array, and then use a single median command along the depth axis. (When I say intensive, I do not mean it couldn't be done in real time with your data. It can, but it is much more complicated than averaging.)
If you have really a lot of traffic, the road is visible behind the cars less than half of the time. Then the median trick will fail. You will need to take more samples and then find the most typical value, because it is likely to be the road (unless all cars have similar colours). There it will help a lot to use the colour image, as cars look more different from each other in RGB or HSV than in grayscale.
Unfortunately, if you need to resort to this type of processing, the path is slightly slippery and rocky. Average is very easy and fast, median is easy (but not that fast), but then things tend to get rather complicated.
Another BTW came into my mind. If you want to have a rolling average, there is a very simple and effective way to calculate it with an arbitrary length (arbitrary number of frames to average):
# N is the number of images to average
# P[i] are the input frames
# S is a sum accumulator (sum of N frames)
# calculate the sum of the first N frames
S <- 0
I <- 0
while I < N
S <- S + P[I]
I <- I + 1
# save_img() saves an averaged image
while there are images to process
save_img(S / N)
S <- -P[I-N] + S + P[I]
I <- I + 1
Of course, you'll probably want to use for-loops, and += and -= operators, but still the idea is there. For each frame you only need one subtraction, one addition, and one division by a constant (which can be modified into a multiplication or even a bitwise shift in some cases if you are in a hurry).
I may have misunderstood your problem but I think what you're trying to do is the following. Basically, read all images into a matrix and then use mean(). This is providing that you are able to put them all in memory.
function [finish] = stackAndMeanImage (ini_frame, final_frame)
pkg load image;
dir_path = 'C:\Users\Disc-1119\Desktop\Internships\Tracking\Octave\highway\highway (6-13-2014 11-13-41 AM)';
imgs = cell (1, 1, d);
## read all images into a cell array
current_frame = ini_frame;
for n = 1:(final_frame - ini_frame)
fname = fullfile (dir_path, sprintf ("%i", current_frame++));
imgs{n} = rgb2gray (imread (fname, "jpg"));
endfor
## create 3D matrix out of all frames and calculate mean across 3rd dimension
imgs = cell2mat (imgs);
avg = mean (imgs, 3);
## mean returns double precision so we cast it back to uint8 after
## rescaling it to range [0 1]. This assumes that images were all
## originally uint8, but since they are jpgs, that's a safe assumption
avg = im2uint8 (avg ./255);
imwrite (avg, fullfile (dir_path, "meanimage.jpg"));
finish = "done";
endfunction
I’m trying to calculate an average value of one color over the whole image in order to determine how color, saturation or intencity or eny other value describing this changes between frmaes of the video.
However i would like to get just one value that will describe whole frame (and sigle, chosen color in it). Calculating simple average value of color in frame gives me very small differences between video frames, just 2-3 on a 0..255 space.
Is there any other method to determine color of the image other than histogram which as i understand will give me more than one value describing single frame.
Which library are you using for image processing? If it's OpenCV (or Matlab) then the steps here will be quite easy. Otherwise you'd need to look around and experiment a bit.
Use a Mean Shift filter on RGB (or gray, whichever) to cluster the colors in the image - nearly similar colors are clustered together. This lessens the number of colors to deal with.
Change to gray-level and compute a frequency histogram with bins [0...255] of pixel values that are present in the image
The highest frequency - the median - will correspond to the bin (color) that is present the most. The frequency of each bin will give you the no. of pixels of the color that is present in the frame.
Take the median value as the single color to describe your frame - the color present in the largest amount in the frame.
The key point here is if the above steps are fast enough for realtime video. You'd have to try to find out I guess.
Worst case scenario, you could loop over all the pixels in the image and do a count. Not sure what you are using programming wise but I use Python with Numpy something similar to this. Where pb is a gtk pixbuf with my image in it.
def pull_color_out(self, pb, *args):
counter = 0
dat = pb.get_pixels_array().copy()
for y in range(0,pb.get_width()):
for x in range(0,pb.get_height()):
p = dat[x][y]
#counts pure red pixels
if p[1] = 255 and p[2] = 0 and p[3] = 0:
counter += 1
return counter
Other than that, I would normally use a histogram and get the data I need from that. Mainly, this will not be your fastest option, especially for a video, but if you have time or just a few frames then hack away :P
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.)