I'm trying to make a reliable passenger counting system using matlab, the camera will be fixed and above the door. I a able to get flow lines using Lucas Kanade optical flow, the lines represent the people's motion. I want to:
extract from these lines only the end points, if the line is long enough (the lines matrix contains all the points, even say a point on some random part of floor which will not have changed)
Cluster these 'good points' and get the centroids of the clustsers, representing people
Create bounding boxes of a fixed size on those cluster centers and send them to the multiple object KLT tracking program.
Would anyone happen to show me a good way to extract the points I want from the line matrix? My matlab syntax is atrocious and I'm running out of time to get this done, its for a uni project. Thanks in advance!
%example
videoReader = vision.VideoFileReader('5Converted.avi','ImageColorSpace','Intensity','VideoOutputDataType','uint8');
converter = vision.ImageDataTypeConverter;
opticalFlow = vision.OpticalFlow('ReferenceFrameDelay', 1);
opticalFlow.OutputValue = 'Horizontal and vertical components in complex form';
shapeInserter = vision.ShapeInserter('Shape','Lines','BorderColor','Custom', 'CustomBorderColor', 255);
videoPlayer = vision.VideoPlayer('Name','Motion Vector');
while ~isDone(videoReader)
frame = step(videoReader);
im = step(converter, frame);
of = step(opticalFlow, im);
[lines, trackedPoints] = getFlowLines(of, 20);
if ~isempty(lines)
out = step(shapeInserter, im, lines);
end
end
release(videoPlayer);
release(videoReader);
And this is the GetFLowLines Function (slightly modified version of an example):
> function [vel_Lines, allTrackedPoints] = getFlowLines(vel_Values,
> scaleFactor) %Modified function based on Matlab's
> 'videooptflowlines.m' helper function
>
> persistent first_time; persistent X; persistent Y; persistent RV;
> persistent CV; if isempty(first_time)
> first_time = 1;
> %% user may change the following three parameters
> borderOffset = 5;
> decimFactorRow = 5;
> decimFactorCol = 5;
> %%
> [R C] = size(vel_Values);
> RV = borderOffset:decimFactorRow:(R-borderOffset);
> CV = borderOffset:decimFactorCol:(C-borderOffset);
> [Y X] = meshgrid(CV,RV); end
>
> tmp = vel_Values(RV,CV); tmp = tmp.*scaleFactor; vel_Lines = [Y(:)
> X(:) Y(:)+real(tmp(:)) X(:)+imag(tmp(:))]; allTrackedPoints =
> [Y(:)+real(tmp(:)) X(:)+imag(tmp(:))];
Related
I want to calculate if a real-time signal pass some thresholds in the first step. In the first step, I want to detect if the real signal pass under those thresholds (in order to detect a peak in the signal). My Matlab code:
k=1;
t = 1;
l=1;
for i =1:length(sm) //sm my signal.
if (sm(i) > 0.25)
first(k) = i;
k = k+1;
if (sm(i) > 0.5)
second(t) = i;
t =t +1;
if (sm(i) > 0.75)
third(l) = i;
l = l+1;
end
end
end
end
Example:
![enter image description here][1]
I want to calculate the times that the signal pass over and under the three thresholds 0.25, 0.5, 0.75 and to return those windows. Basically the three main peaks that I have in my example.
Basically what am I trying to do is to use fastsmooth function and findpeaks.
signalSmoothed = fastsmooth(sm,50); plot(signalSmoothed)
[max_pk1 max_pk2] = findpeaks(signalSmoothed);
find(max_pk1 >0.5)
inversex = 1.01*max(signalSmoothed) - signalSmoothed;
[min_pk1 min_pk2] = findpeaks(inversex);
find(min_pk1 >0.5)
Which are the heuristics in order to take only the desired peaks? Moreover the depticted image is an offline example. Generally I want to perform the technique online.
EDIT: I wrongfully defined as peak my desired curve result which is the whole wave not just the max value.
Here is a solution to get the points where the signal sm passes the thresholds 0.25, 0.50 and 0.75. The points can be converted into windows inside the data-range and get stored in W. Finally we can plot them easily in the same figure. Note that we need to do some checks in the local function getwindows to handle special cases, for example when the window starts outside the data-range. The detection of windows inside another window is done in the getwindowsspecial-function.
Here is the code:
function peakwindow
% generate sample data
rng(7);
sm = 2*rand(1,25)-0.5;
sm = interp1(1:length(sm),sm,1:0.01:100*length(sm));
% get points
firstup = find(diff(sm > 0.25)==1);
secondup = find(diff(sm > 0.50)==1);
thirdup = find(diff(sm > 0.75)==1);
firstdown = find(diff(sm < 0.25)==1);
seconddown = find(diff(sm < 0.50)==1);
thirddown = find(diff(sm < 0.75)==1);
% plot the result
figure; hold on;
plot(sm,'k')
plot(firstup,sm(firstup),'*')
plot(firstdown,sm(firstdown),'*')
plot(secondup,sm(secondup),'*')
plot(seconddown,sm(seconddown),'*')
plot(thirdup,sm(thirdup),'*')
plot(thirddown,sm(thirddown),'*')
% get windows
W1 = getwindows(firstup,firstdown);
W2 = getwindows(secondup,seconddown);
W3 = getwindows(thirdup,thirddown);
% get special window
WS = getwindowsspecial(W1,W3);
% plot windows
plotwindow(W1,0.25,'r');
plotwindow(W2,0.50,'r');
plotwindow(W3,0.75,'r');
plotwindow(WS,0,'b-');
function W = getwindows(up,down)
if length(up)>1 && length(down)>1 && up(1)>down(1)
down(1)=[]; % handle case when window begins out of bounds left
end
if length(up)<1 || length(down)<1;
W = []; % handle if no complete window present
else
% concatenate and handle case when a window ends out of bounds right
W = [up(1:length(down));down]';
end
function plotwindow(W,y,lspec)
for i = 1:size(W,1)
plot(W(i,:),[y,y],lspec)
end
% get windows of U where there is a window of H inside
function W = getwindowsspecial(U,H)
W = []; % empty matrix to begin with
for i = 1:size(U,1) % for all windows in U
if any(H(:,1)>=U(i,1) & H(:,1)<=U(i,2))
W = [W;U(i,:)]; % add window
end
end
This is the result:
To see that the handling works properly, we can plot the result when initialized with rng(3):
Note that the window of 0.25 and 0.50 would start out of bounds left and therefore is not present in the plotted windows.
i am currently using bwconnomp to label each connected object and regionpropsto find area, majoraxis, minoraxis of each labeled object respectively. i am also displaying each labeled object its area,majoraxis and minoraxis. now i want to set some threshold for area,majoraxis and minoraxis and if the value of area,majoraxis and minoraxis is above specified threshold then that object has to be masked.how this can be done??
here is my code
clc
clear all
close all
Index = 1;
scrsz = get(0,'ScreenSize');
%read an image
while Index ~= 0
% Open a dialog and select an image file
[FileName,FilePath,Index] = uigetfile('*.png', 'Open Imagefile ');
if Index == 0
disp('Procedure Done')
break;
end
inimage = imread([num2str(FilePath) FileName]);
D=inimage;
A=inimage;
subplot(2,3,1);
imshow(inimage);
title('original image');
%labeling algorithm
B=im2bw(inimage);
C=imfill(B,'holes');
label=bwlabel(C);
max(max(label))
CC = bwconncomp(B);
data = regionprops(CC,'all');
for j=1:max(max(label))
[row, col] = find(label==j);
len=max(row)-min(row)+2;
breadth=max(col)-min(col)+2;
target=uint8(zeros([len breadth] ));
sy=min(col)-1;
sx=min(row)-1;
for i=1:size(row,1)
x=row(i,1)-sx;
y=col(i,1)-sy;
target(x,y)=A(row(i,1),col(i,1));
end
mytitle=strcat('Object Number:' ,num2str(j),'area:', num2str(data(j).Area),'MajorAxis: ',num2str(data(j).MajorAxisLength),'MinorAxis: ',num2str(data(j).MinorAxisLength));
figure,imshow(target);title(mytitle);
a=size(target);
ax=a(1);
ay=a(2);
pos=[1,1,ay,ax];
rectangle('Position',pos,'EdgeColo','r')
end
next = input('next image? press Enter: ');
if next == 0
channelactivity = 0;
break
else
close all
disp('==================================')
pause(0.2)
continue
end
end
Here is a way to do it. The code is commented so easy to follow; the important line is the following:
AboveAreaIndices = find(vertcat(data.Area) > SomeValue)
In which you store the indices of the objects whose area is larger than SomeValue. In the example I color them red but you can do whatever you want with them or remove them altogether from the data structure.
You can also use logical operators to combine multiple conditions for example using the MinorAxis and MajorAxis properties. Note that I used AllArea as anew variable to store the concatenated areas to make things clearer, but you can keep them as vertcat(data.Area).
AboveIndices = find(vertcat(data.Area) > SomeValue & vertcat(data. MinorAxis) > SomeValue & Bla bla bla...);
Whole code:
clear
clc
close all
%// Read and clean up sample image
A = imread('rice.png');
A = im2bw(A,.5);
A = bwareaopen(A,50);
CC = bwconncomp(A);
%// Same as you.
data = regionprops(CC,'all');
%// Concatenate all the areas into an array.
AllArea = vertcat(data.Area);
%//========================================
%//==== Apply threshold on area here \\====
AboveAreaIndices = find(AllArea > 150);
%// If you wish to remove the entries from the data structure
% data(AllArea>150) = [];
%//========================================
%// Same for centroids...for display purposes
AllCentroids = vertcat(data.Centroid);
%// Display original and thresholded objects. Use the indices calculated
%// above to "mask" large areas if you want
imshow(A);
hold on
scatter(AllCentroids(:,1),AllCentroids(:,2),40,'b','filled')
scatter(AllCentroids(AboveAreaIndices,1),AllCentroids(AboveAreaIndices,2),40,'r','filled')
And sample output:
I am developing a template matching program in MATLAB. The code runs well, and finds the closest result. My first question, in this code, I am using the function corr2(). I would like to try a version using the formula (I tried to upload a picture of but I need 10 reputations).
I understand the formula itself, but I am not sure what variables should I use to use it. For example, what is exactly the m and n mean in my images where can I get them? In another words, what does the formula take as inputs?
Second question is, when I run the code I have now, it takes long, is there any thing I can change to speed it up?
Original = imread('Red.jpg'); % Read original image
Template = imread('temp.png'); % Read template image
OriDu = im2double(Original); % convert original image
TempDu = im2double(Template); % convert template
OriH = size(Original, 1); %height of the Original image
OriW = size(Original, 2); %width of the Original image
OriD = size(Original, 3); %colour depth
TempH = size(Template, 1); %height of the Template image
TempW = size(Template, 2); %width of the Template image
TempD = size(Template, 3); %colour depth
TempDu = reshape(TempDu, TempH*TempW, 3);
corr = 0; % to check the best correlation found
%% two for loops to go through the original image.
for i = 1:OriH-TempH
for j = 1:OriW-TempW
% take a segment of the original image( same size as the template size)
segment = OriDu(i: (i - 1) + TempH, j: (j - 1) + TempW, :);
segment = reshape(segment, TempH*TempW, 3);
output = corr2(TempDu, segment);
if output > corr
corr = output;
x = i;
y = j;
end
end
end
figure;
subplot(1,2,1), imshow(Template), title('Template');
subplot(1,2,2), imshow(OriDu(x:x+TempH, y:y+TempW, :)),title('Segment of the similar part');
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.