I have this MATLab code to count the number of objects in the image. There are two objects in the image I am choosing (a car and a cyclist). However, the program is returning a wrong output saying there are 0 objects. Can someone find the error in the code? Thanks.
The logic behind the code is:
1. Take two input images are given, one without objects and one with objects.
2. Convert the input images from RGB to Gray scale.
3. Compare the two images and find the difference.
4. Convert the image obtained to binary.
5. In the image, only open the blobs whose area is greater than 4000.
6. Display the count and density.
clc;
MV = imread('car.png'); %To read image
MV1 = imread('backgnd.png');
A = double(rgb2gray(MV)); %convert to gray
B= double(rgb2gray(MV1)); %convert 2nd image to gray
[height, width] = size(A); %image size?
h1 = figure(1);
%Foreground Detection
thresh=11;
fr_diff = abs(A-B);
for j = 1:width
for k = 1:height
if (fr_diff(k,j)>thresh)
fg(k,j) = A(k,j);
else
fg(k,j) = 0;
end
end
end
subplot(2,2,1) , imagesc(MV), title ({'Orignal Frame'});
subplot(2,2,2) , imshow(mat2gray(A)), title ('converted Frame');
subplot(2,2,3) , imshow(mat2gray(B)), title ('BACKGND Frame ');
sd=imadjust(fg); % adjust the image intensity values to the color map
level=graythresh(sd);
m=imnoise(sd,'gaussian',0,0.025); % apply Gaussian noise
k=wiener2(m,[5,5]); %filtering using Weiner filter
bw=im2bw(k,level);
bw2=imfill(bw,'holes');
bw3 = bwareaopen(bw2,5000);
labeled = bwlabel(bw3,8);
cc=bwconncomp(bw3);
Densityoftraffic = cc.NumObjects/(size(bw3,1)*size(bw3,2));
blobMeasurements = regionprops(labeled,'all');
numberofcars = size(blobMeasurements, 1);
subplot(2,2,4) , imagesc(labeled), title ({'Foreground'});
hold off;
disp(numberofcars); % display number of cars
disp(Densityoftraffic); %display number of vehicles
An empty image(of a road) with no objects(vehicles) in it
An image of the same road but with 2 objects(car and cyclist) in it
Try This it will help you in an optimize manner
clc
clear all
close all
im1 = imread('image1.png');
im2 = imread('image2.png');
gray1 = double(rgb2gray(im1));
gray2 = double(rgb2gray(im2));
absDif = mat2gray(abs(gray1 - gray2));
figure,imshow(absDif,[])
absDfbw = im2bw(absDif,0.9*graythresh(absDif));
figure,imshow(absDfbw,[])
absDfbw = bwareaopen(absDfbw,25);
absDfbw = imclose(absDfbw,strel('disk',5));
figure,imshow(absDfbw,[])
Results are:
Thank You
Related
I'm trying to do a vertical histogram of a binary image. I don't want to use MATLAB's functions. How to do it?
I have tried this code but I don't know if it's correct or not:
function H = histogram_binary(image)
[m,n] = size(image);
H = zeros(1,256);
for i = 1:m
for j = 1:n
H(1,image(i,j)) = H(1,image(i,j))+1;
end
end
The image is:
The result:
Why can't I see the value of black pixels in the histogram?
% Read the binary image...
img = imread('66He7.png');
% Count total, white and black pixels...
img_vec = img(:);
total = numel(img_vec);
white = sum(img_vec);
black = total - white;
% Plot the result in the form of an histogram...
bar([black white]);
set(gca(),'XTickLabel',{'Black' 'White'});
set(gca(),'YLim',[0 total]);
Output:
For what concerns your code, it is not counting black pixels since they have a value of 0 and your loop start from 1... rewrite it as follows:
function H = histogram_binary(img)
img_vec = img(:);
H = zeros(1,256);
for i = 0:255
H(i+1) = sum(img_vec == i);
end
end
But keep in mind that counting all the byte occurrences on a binary image (that can only contain 0 or 1 values) is kinda pointless and will make your histogram lack readability. On a side note, avoid using image as a variable name, since this would override an existing function.
As mentioned by #beaker in the comments above, vertical histogram in such cases generally refers to a vertical projection. Here is a way to do this :
I = imread('YcP1o.png'); % Read input image
I1 = rgb2gray(I); % convert image to grayscale
I2 = im2bw(I1); % convert grayscale to binary
I3 = ~I2; % invert the binary image
I4 = sum(I3,1); % project the inverted binary image vertically
I5 = (I4>1); % ceil the vector
plot([1:1:size(I,2)],I5); ylim([0 2])
You can further check for 0->1 transitions to count the number of characters using sum(diff(I5)>0) which gives 13 as answer in this case.
To be exact I need the four end points of the road in the image below.
I used find[x y]. It does not provide satisfying result in real time.
I'm assuming the images are already annotated. In this case we just find the marked points and extract coordinates (if you need to find the red points dynamically through code, this won't work at all)
The first thing you have to do is find a good feature to use for segmentation. See my SO answer here what-should-i-use-hsv-hsb-or-rgb-and-why for code and details. That produces the following image:
we can see that saturation (and a few others) are good candidate colors spaces. So now you must transfer your image to the new color space and do thresholding to find your points.
Points are obtained using matlab's region properties looking specifically for the centroid. At that point you are done.
Here is complete code and results
im = imread('http://i.stack.imgur.com/eajRb.jpg');
HUE = 1;
SATURATION = 2;
BRIGHTNESS = 3;
%see https://stackoverflow.com/questions/30022377/what-should-i-use-hsv-hsb-or-rgb-and-why/30036455#30036455
ViewColoredSpaces(im)
%convert image to hsv
him = rgb2hsv(im);
%threshold, all rows, all columns,
my_threshold = 0.8; %determined empirically
thresh_sat = him(:,:,SATURATION) > my_threshold;
%remove small blobs using a 3 pixel disk
se = strel('disk',3');
cleaned_sat = imopen(thresh_sat, se);% imopen = imdilate(imerode(im,se),se)
%find the centroids of the remaining blobs
s = regionprops(cleaned_sat, 'centroid');
centroids = cat(1, s.Centroid);
%plot the results
figure();
subplot(2,2,1) ;imshow(thresh_sat) ;title('Thresholded saturation channel')
subplot(2,2,2) ;imshow(cleaned_sat);title('After morpphological opening')
subplot(2,2,3:4);imshow(im) ;title('Annotated img')
hold on
for (curr_centroid = 1:1:size(centroids,1))
%prints coordinate
x = round(centroids(curr_centroid,1));
y = round(centroids(curr_centroid,2));
text(x,y,sprintf('[%d,%d]',x,y),'Color','y');
end
%plots centroids
scatter(centroids(:,1),centroids(:,2),[],'y')
hold off
%prints out centroids
centroids
centroids =
7.4593 143.0000
383.0000 87.9911
435.3106 355.9255
494.6491 91.1491
Some sample code would make it much easier to tailor a specific solution to your problem.
One solution to this general problem is using impoint.
Something like
h = figure();
ax = gca;
% ... drawing your image
points = {};
points = [points; impoint(ax,initialX,initialY)];
% ... generate more points
indx = 1 % or whatever point you care about
[currentX,currentY] = getPosition(points{indx});
should do the trick.
Edit: First argument of impoint is an axis object, not a figure object.
I have a gray scale image add1, however there are only two pixel intensities in it (0 for Black and 255 for White). I am able to track the coordinate of my pixel of consideration i.e. add1(i,j). now I want to display the connected component of which this pixel is part of. I have tried it with the regionprop using 'PixelIdxList' and 'PixelList' unsuccesfully.
Can someone help please.Thanks in advance.
As much i understand you want this:
clc
clear all
close all
im = imread('labelProb.png');
im = im2bw(im);
labelIm = bwlabel(im);
rg = regionprops(im,'PixelIdxList','Centroid');
figure,imshow(labelIm,[]),hold on
for i = 1:length(rg)
cc = rg(i).Centroid;
text(cc(1),cc(2),['label: ',num2str(i)],'Color','b','FontSize',9)
end
f = getframe();
lab = frame2im(f);
hold off
% suppose you want label number 3 only.
cc = rg(3).Centroid; % this is your pixel index;
% Extract label number through this index.
cc = round(cc);
labelNumber = labelIm(cc(2),cc(1));
% create a new blank image.
blankImage = false(size(im));
for i = 1:length(rg)
if i == labelNumber
blankImage(rg(i).PixelIdxList) = true;
end
end
figure,imshow(blankImage,[])
And result of above execution are:
If I understand your question, what you want is: given a specific coordinates (i,j) what is the label, and mask of the connected component that (i,j) is part of.
add = bwlabel( add1 ); %// convert to label mask
lij = add(i,j); %// get the label to which i,j belongs to
figure;
imshow( add == lij, [] ); %// select only the relevant label
I'm using the code on the MatLab website, "Color-Based Segmentation Using the Lab* Color Space":
http://www.mathworks.com/help/images/examples/color-based-segmentation-using-the-l-a-b-color-space.html
So I'm trying to select some areas myself instead of using the "load region_coordinates", using roipoly(fabric), but i get stuck. How do I save coordinates of the polygon I just drew? I'm actually following advice from lennon310 at Solution II, bottom of page:
A few questions about color segmentation with L*a*b*
I'm not sure when to save region_coordinates and do size(region_coordinates,1)
I made the following changes (Step 1):
1) Removed "load region_coordinates"
2) Added "region_coordinates = roipoly(fabric);"
Here's the code:
`
%% Step 1
fabric = imread(file);
figure(1); %Create figure window. "If h is not the handle and is not the Number property value of an existing figure, but is an integer, then figure(h) creates a figure object and assigns its Number property the value h."
imshow(fabric)
title('fabric')
%load regioncoordinates; % 6 marices(?) labelled val(:,:,1-6), 5x2 (row x column)
region_coordinates = roipoly(fabric);
nColors = 6;
sample_regions = false([size(fabric,1) size(fabric,2) nColors]); %Initializing an Image Dimension, 3x3 (:,:,:) to zero? Zeros() for arrays only I guess.
%Size one is column, size two is row?
for count = 1:nColors
sample_regions(:,:,count) = roipoly(fabric,region_coordinates(:,1,count),region_coordinates(:,2,count));
end
figure, imshow(sample_regions(:,:,2)),title('sample region for red');
%%Step 2
% Convert your fabric RGB image into an L*a*b* image using rgb2lab .
lab_fabric = rgb2lab(fabric);
%Calculate the mean a* and b* value for each area that you extracted with roipoly. These values serve as your color markers in a*b* space.
a = lab_fabric(:,:,2);
b = lab_fabric(:,:,3);
color_markers = zeros([nColors, 2]);%... I think this is initializing a 6x2 blank(0) array for colour storage. 6 for colours, 2 for a&b colourspace.
for count = 1:nColors
color_markers(count,1) = mean2(a(sample_regions(:,:,count))); %Label for repmat, Marker for
color_markers(count,2) = mean2(b(sample_regions(:,:,count)));
end
%For example, the average color of the red sample region in a*b* space is
fprintf('[%0.3f,%0.3f] \n',color_markers(2,1),color_markers(2,2));
%% Step 3: Classify Each Pixel Using the Nearest Neighbor Rule
%
color_labels = 0:nColors-1;
% Initialize matrices to be used in the nearest neighbor classification.
a = double(a);
b = double(b);
distance = zeros([size(a), nColors]);
%Perform classification, Elucidean Distance.
for count = 1:nColors
distance(:,:,count) = ( (a - color_markers(count,1)).^2 + (b - color_markers(count,2)).^2 ).^0.5;
end
[~, label] = min(distance,[],3);
label = color_labels(label);
clear distance;
%% Step 4: Display Results of Nearest Neighbor Classification
%
% The label matrix contains a color label for each pixel in the fabric image.
% Use the label matrix to separate objects in the original fabric image by color.
rgb_label = repmat(label,[1 1 3]);
segmented_images = zeros([size(fabric), nColors],'uint8');
for count = 1:nColors
color = fabric;
color(rgb_label ~= color_labels(count)) = 0;
segmented_images(:,:,:,count) = color;
end
%figure, imshow(segmented_images(:,:,:,1)), title('Background of Fabric');
%Looks different somehow.
figure, imshow(segmented_images(:,:,:,2)), title('red objects');
figure, imshow(segmented_images(:,:,:,3)), title('green objects');
figure, imshow(segmented_images(:,:,:,4)), title('purple objects');
figure, imshow(segmented_images(:,:,:,5)), title('magenta objects');
figure, imshow(segmented_images(:,:,:,6)), title('yellow objects');
`
You can retrieve the coordinates of the polygon using output arguments in the call to roipoly. You can then get a binary mask of the polygon, as well as vertices coordinates if you want.
Simple example demonstrating:
clear
clc
close all
A = imread('cameraman.tif');
figure;
imshow(A)
%// The vertices of the polygon are stored in xi and yi;
%// PolyMask is a binary image where pixels == 1 are white.
[polyMask, xi, yi] = roipoly(A);
This looks like this:
And if you wish to see the vertices with the binary mask:
%// display polymask
imshow(polyMask)
hold on
%// Highlight vertices in red
scatter(xi,yi,60,'r')
hold off
Which gives the following:
So to summarize:
1) The polygon's vertices are stored in xi and yi.
2) You can plot the binaryMask of the polygon using imshow(polyMask).
3) If you need the coordinates of the white pixels, you can use something like this:
[row_white,col_white] = find(polyMask == 1);
You're then good to go. Hope that helps!
I have some shapes on an image that i've attempted to label according to their Area using a solution provided to me:
stats = regionprops(BW,'Area')
stats2 = regionprops(BW,'Centroid')
figure,imshow(BW)
for k = 1:numel(stats)
xy = stats2(k).Centroid
if (stats(k).Area>TH)
text(xy(1),xy(2),'L') %// Large Shape
else
text(xy(1),xy(2),'S') %// Small Shape
end
end
But it turns out the shapes are too small for the letters ( which would be too small even if I changed the font), I'm wondering if there's a way to do the thresholding to produce a colour code i.e change the filling of the shapes based on their area?
See if this is inspiring enough for you -
%// Input image. This one is chosen as it is available in MATLAB image library
img = imread('coins.png');
%// Convert to binary image
BW = im2bw(img,0.4); %// 0.4 as binary thresehold worked for this specific image
%// Get area and pixel-list stats
stats = regionprops(BW,'Area');
stats2 = regionprops(BW,'PixelIdxList');
s1 = struct2array(stats);
[v1,v2,v3] = unique(s1);
num_colors = numel(v1);
%// Pixel values per channel for creating color codes
pix_per_ch = linspace(0,255,ceil(power(num_colors,1/3)));
%// Unique 3 color codes
all_color_codes = allcomb(pix_per_ch,pix_per_ch,pix_per_ch);
%// allcomb is a MATLAB File-exchange tool avaiialble at -
%// http://www.mathworks.in/matlabcentral/fileexchange/10064-allcomb
%// Unique 3 color codes for the number of shapes available
color_codes = all_color_codes(randi(size(all_color_codes,1),num_colors,1),:);
%// Sort these uniques colors based on their grayscale intensities
[~,ind]=sort(rgb2gray(uint8(permute(color_codes,[1 3 2]))));
sorted_color_codes = color_codes(ind,:);
%// Pre-allocate for the ouput image
out = uint8(255.*BW(:,:,ones(1,3)));
%// Assign each shape a unique color based on their areas
for k = 1:numel(stats)
ind1 = stats2(k).PixelIdxList;
indx = bsxfun(#plus,ind1,[0:2].*size(img,1)*size(img,2));
color_code = sorted_color_codes(v3(k),:);
color_code_ext = color_code(ones(1,numel(ind1)),:);
out(indx) = color_code_ext;
end
%// Display input, output results
figure,
subplot(211),imshow(img),
title('Input Image')
subplot(212),imshow(out),
title('Output Image (Brighter colors represent larger shapes)')
Output -