I'm extracting frames (5 frames) from a video (car1.mp4). After that displaying the num of rows (and columns).
And then calculating the sum of pixel values of rows and columns of each frame. Finally plotting the graph between the number of columns (or rows) vs magnitude. Below is my code:
clear all
close all
clc
video=VideoReader('car1.mp4');
numFrames = video.NumberOfFrames;
for i = 1:1:5
frames = read(video,i);
imwrite(frames,['image' int2str(i), '.jpg'])
frames = imresize(frames,[100 150]);
sz = size(frames);
numRows = sz(1);
disp(numRows)
numCols = sz(2);
disp(numCols)
rm = numRows*255;
for j = 1:numRows
sumr(j)=0;
for k = 1:numCols
pixels1 = impixel(frames,k,j);
sumr(j)=sumr(j)+pixels1(1);
end
end
s1 = rm./sumr;
plot(s1)
xlabel({'Horizontal Projection','(no. of columns)'})
ylabel('Magnitude')
rc = numCols*255;
for u = 1:numCols
sumc(u)=0;
for v = 1:numRows
pixels2 = impixel(frames,v,u);
sumc(u)=sumc(u)+pixels2(1);
end
end
s2 = rc./sumc;
plot(s2)
xlabel({'Vertical Projection','(no. of rows)'})
ylabel('Magnitude')
end
The main problem is, there are five frames extracting from the video but it shows output (plot between projection vs magnitude) of only one frame. I'm using for loop so that it shows each and every frame's output (plot between projection vs magnitude), but not showing. And the other problem is why it shows only one projection either Horizontal projection or Vertical projection?
Thanks in advance:)
Related
I have had zero luck finding this elsewhere on the site, so here's my problem. I loop through about a thousand mat files, each with about 10,000 points of data. I'm trying to create an overall histogram of this data, but it's not very feasible to concatenate all this data to give to hist.
I was hoping to be able to create an N and Bin variable each loop using hist (y), then N and Bin would be recalculated on the next loop iteration by using hist(y_new). And so on and so on. That way the source data doesn't grow and when the loop finally ends, I can just use bar(). If this method wouldn't work, then I am very open-minded to other solutions.
Also, it is probably not safe to assume that the x data will remain constant throughout each iteration. I'm using 2012a.
Thanks for any help!!
I think the best solution here is to loop through your files twice: once to set the bins and once to do the histogram. But, if this is impossible in your case, here's a one shot solution that requires you to set the bin width beforehand.
clear; close all;
rng('default') % for reproducibility
% make example data
N = 10; % number of data files
M = 5; % length of data files
xs = cell(1,N);
for i = 1:N
xs{i} = trnd(1,1,M);
end
% parameters
width = 2;
% main
for i = 1:length(xs)
x = xs{i}; % "load data"
range = [min(x) max(x)];
binsPos = 0:width:range(2)+width;
binsNeg = fliplr( 0:-width:range(1)-width );
newBins = [binsNeg(1:end-1) binsPos];
newCounts = histc(x, newBins);
newCounts(end) = []; % last bin should always be zero, see help histc
if i == 1
counts = newCounts;
bins = newBins;
else
% combine new and old counts
allBins = min(bins(1), newBins(1)) : width : max(bins(end), newBins(end));
allCounts = zeros(1,length(allBins)-1);
allCounts(find(allBins==bins(1)) : find(allBins==bins(end-1))) = counts;
allCounts(find(allBins==newBins(1)) : find(allBins==newBins(end-1))) = ...
allCounts(find(allBins==newBins(1)) : find(allBins==newBins(end-1))) + newCounts;
bins = allBins;
counts = allCounts;
end
end
% check
figure
bar(bins(1:end-1) + width/2, counts)
xFull = [xs{:}];
[fullCounts] = histc(xFull, bins);
fullCounts(end) = [];
figure
bar(bins(1:end-1) + width/2, fullCounts)
I need to find the blobs from the below image.
The major problem is background. background doesn't have uniform intensity. I tried couple of things like thresholding and edge detection in MATLAB but couldn't able to find a better ways to segment out all spheroids. I need to extract the blobs and I need to find the area of each blobs. Does anyone know how to work-around with this kind of background?
Edit (07/02/17):
As suggested by Spektre I tried Following things in MATLAB.
Method 1:
img_original = imread('~/my_image.jpg'); %Read image
img_ch = single(img_original(:,:,2)); %Pick one channel( here its green)
g = fspecial('gaussian',200,100); %Kernel matrix to make the img blurr
con_img = conv2(img_ch,g,'same'); %2D convolution, this wil make the img blurr
sub_img = (con_img - img_ch); %Simple matrix subtraction
sub_img(sub_img <= 10) = 0; %Thresholding
sub_img(sub_img ~= 0) = 1;
fil_sub = imfill(sub_img,'holes'); %Fill the regions
imgfilt = imfilter(fil_sub, ones(3)); %run filter using 3by3 matrx
imgfilt(imgfilt < 8) = 0; %Reduce noisy pixels by thresholding
mfilt_img = (medfilt2(imgfilt)); %reduce unwanted pixels
img = img_ch;
img(mfilt_img ~= 0) = 255;
img2 = img_ch;
img2(img2 < 70) = 0; %Threshold for darker pixels which are left out from above methode.
img(img2 ==0) = 255;
disp_img = img_original(:,:,1);
disp_img(img ==255) = 255;
img_original(:,:,1) = disp_img;
figure, imshow(img_original)
I got the segments but still not good enough I think. This method gave good segments in the high intensity background, Even if I reduce the threshold value segments are not clear in the darker background and brightest pixels in the blobs are excluded.
Method 2:
img_original = imread('~/cancer_cells/Snap-10234.jpg'); %Read image
img_ch = single(img_original(:,:,2)); %Pick one channel( here its green)
clear new_matcel cur_img matcel avg_matrx
s=3; % Set size of the square window
mat_img = img_ch; % Working image channel
% resize the working matrix so that the dimensions matches
resize_img = resizem(mat_img,round(size(mat_img)/s)*s);
% convert matrix into small s x s matrix and save each in cells
window_c = ones(1,size(resize_img,1)/s) * s;
window_r = ones(1,size(resize_img,2)/s) * s;
mat_cel = mat2cell(resize_img,window_c,window_r);
new_matcel = cell(size(mat_cel)); % initialize new variable
% get the average value for each window and replace the actual by avg value
for i = 1:size(mat_cel,1)
for j = 1:size(mat_cel,2)
cur_img = mat_cel{i,j};
avg_value = mean(mean(cur_img));
new_matcel{i,j} = ones(s) * avg_value;
end
end
avg_matrx = cell2mat(new_matcel); % convert cells to matrix
image_sub = (abs(resize_img - avg_matrx)); % take the absolute difference
image_sub(image_sub < 7) = 0; % thresholding
image_sub(image_sub~=0) = 1;
image_sub = bwmorph(image_sub,'bridge');% fill gaps
image_sub = imfill(image_sub,'holes'); % fill the bounded regioons
% image_sub(image_sub == 1) = 255;
image_sub = resizem(image_sub,size(img_ch)); % resize to original size
disp_img = img_original(:,:,1);
disp_img(image_sub == 1) = 255;
img_original(:,:,1) = disp_img;
figure, imshow(img_original)
Much better segmented image:
even brighter pixels are included in the segment. Thanks to Spektre.
Is there a way to improve the above code? or any other idea to get more precise segments?
Thanks.
Just a quick question. I have generated a piece of code whereby I select the point of interest in every frame. However, the coordinates of the last selected point is the only one that is saved. Does anyone know how to set up the code such that all the points for each frame are saved for example in a text file with x position in column 1 and y position in column 2? Here is the code I have developed so far;
clear;
clc;
%% Video file information
obj = VideoReader('T9_720p_60p_60mm_f5.MOV');
%% Sampling rate
fps = get(obj, 'FrameRate');
dt = 1/fps;
%% Image Information
file_info = get(obj);
image_width = file_info.Width;
image_height = file_info.Height;
%%Desired image size
x_range = 1:image_height;
y_range = 1:image_width;
szx = length(x_range);
szy = length(y_range);
%%Image processing - Point selection
for n = 33:115
frame = read(obj,n);
imshow(frame);
hpoint = impoint(gca, []);
Position = getPosition(hpoint);
end
I just realized all I need was to add the following before the end of the loop
%%Save data
n = n-32;
data(n,:) = [Position];
end
Regards
I have the following code, pasted below. I would like to change it to only average the 10 most recently filtered images and not the entire group of filtered images. The line I think I need to change is: Yout(k,p,q) = (Yout(k,p,q) + (y.^2))/2;, but how do I do it?
j=1;
K = 1:3600;
window = zeros(1,10);
Yout = zeros(10,column,row);
figure;
y = 0; %# Preallocate memory for output
%Load one image
for i = 1:length(K)
disp(i)
str = int2str(i);
str1 = strcat(str,'.mat');
load(str1);
D{i}(:,:) = A(:,:);
%Go through the columns and rows
for p = 1:column
for q = 1:row
if(mean2(D{i}(p,q))==0)
x = 0;
else
if(i == 1)
meanvalue = mean2(D{i}(p,q));
end
%Calculate the temporal mean value based on previous ones.
meanvalue = (meanvalue+D{i}(p,q))/2;
x = double(D{i}(p,q)/meanvalue);
end
%Filtering for 10 bands, based on the previous state
for k = 1:10
[y, ZState{k}] = filter(bCoeff{k},aCoeff{k},x,ZState{k});
Yout(k,p,q) = (Yout(k,p,q) + (y.^2))/2;
end
end
end
% for k = 2:10
% subplot(5,2,k)
% subimage(Yout(k)*5000, [0 100]);
% colormap jet
% end
% pause(0.01);
end
disp('Done Loading...')
The best way to do this (in my opinion) would be to use a circular-buffer to store your images. In a circular-, or ring-buffer, the oldest data element in the array is overwritten by the newest element pushed in to the array. The basics of making such a structure are described in the short Mathworks video Implementing a simple circular buffer.
For each iteration of you main loop that deals with a single image, just load a new image into the circular-buffer and then use MATLAB's built in mean function to take the average efficiently.
If you need to apply a window function to the data, then make a temporary copy of the frames multiplied by the window function and take the average of the copy at each iteration of the loop.
The line
Yout(k,p,q) = (Yout(k,p,q) + (y.^2))/2;
calculates a kind of Moving Average for each of the 10 bands over all your images.
This line calculates a moving average of meanvalue over your images:
meanvalue=(meanvalue+D{i}(p,q))/2;
For both you will want to add a buffer structure that keeps only the last 10 images.
To simplify it, you can also just keep all in memory. Here is an example for Yout:
Change this line: (Add one dimension)
Yout = zeros(3600,10,column,row);
And change this:
for q = 1:row
[...]
%filtering for 10 bands, based on the previous state
for k = 1:10
[y, ZState{k}] = filter(bCoeff{k},aCoeff{k},x,ZState{k});
Yout(i,k,p,q) = y.^2;
end
YoutAvg = zeros(10,column,row);
start = max(0, i-10+1);
for avgImg = start:i
YoutAvg(k,p,q) = (YoutAvg(k,p,q) + Yout(avgImg,k,p,q))/2;
end
end
Then to display use
subimage(Yout(k)*5000, [0 100]);
You would do sth. similar for meanvalue
I am on a project thumb recognition system on matlab. I implemented Kmean Algorithm and I got results as well. Actually now I want to plot the results like here they done. I am trying but couldn't be able to do so. I am using the following code.
load training.mat; % loaded just to get trainingData variable
labelData = zeros(200,1);
labelData(1:100,:) = 0;
labelData(101:200,:) = 1;
k=2;
[trainCtr, traina] = kmeans(trainingData,k);
trainingResult1=[];
for i=1:k
trainingResult1 = [trainingResult1 sum(trainCtr(1:100)==i)];
end
trainingResult2=[];
for i=1:k
trainingResult2 = [trainingResult2 sum(trainCtr(101:200)==i)];
end
load testing.mat; % loaded just to get testingData variable
c1 = zeros(k,1054);
c1 = traina;
cluster = zeros(200,1);
for j=1:200
testTemp = repmat(testingData(j,1:1054),k,1);
difference = sum((c1 - testTemp).^2, 2);
[value index] = min(difference);
cluster(j,1) = index;
end
testingResult1 = [];
for i=1:k
testingResult1 = [testingResult1 sum(cluster(1:100)==i)];
end
testingResult2 = [];
for i=1:k
testingResult2 = [testingResult2 sum(cluster(101:200)==i)];
end
in above code trainingData is matrix of 200 X 1054 in which 200 are images of thumbs and 1054 are columns. actually each image is of 25 X 42. I reshaped each image in to row matrix (1 X 1050) and 4 other (some features) columns so total of 1054 columns are in each image. Similarly testingData I made it in the similar manner as I made testingData It is also the order of 200 X 1054. Now my Problem is just to plot the results as they did in here.
After selecting 2 features, you can just follow the example. Start a figure, use hold on, and use plot or scatter to plot the centroids and the data points. E.g.
selectedFeatures = [42,43];
plot(trainingData(trainCtr==1,selectedFeatures(1)),
trainingData(trainCtr==1,selectedFeatures(2)),
'r.','MarkerSize',12)
Would plot the selected feature values of the data points in cluster 1.