Firstly, many apologies if this question has been posted/asked/answered (let me know the link if that is the case).
How do I capture the HOG values displayed/plotted on the visualisation figure in Matlab?For example in this link (Matlab) https://uk.mathworks.com/help/vision/ref/extracthogfeatures.html
img = imread('cameraman.tif');
[featureVector,hogVisualization] = extractHOGFeatures(img);
%Plot HOG features on the original image
figure;
imshow(img);
hold on;
plot(hogVisualization);
What I don't understand is when I open 'hogVisualization' in matlab the values which are plotted in the image don't make sense. Where can I find the values plotted on the original image?
To be more specific, here is what I'm trying to do. In this image here > phase I'm trying to detect the lines in the red region (I can detect these lines). However, as you can see these lines are disconnected in the blue region. In my algorithm, I need to track which direction I should go (e.g. to the left, right or to a certain angle direction) when it reaches the disconnected line.
For this purpose, I used HOG to find the orientation. Visually, I can see the correct orientation when I visualise it, which can be used to connect two disconnected lines within the blue region. But the problem is I need to find the values plotted in the image. How do I get these values? I can see them visualised on the image but I can't retrieve the actual numerical values.
Thanks,
Erick
Related
I am struggling to find a good contour detection function that would count the number of contour in bw images that I have processed using some previous tools. As you can see, my profile picture is an example of such images,
,
In this image, ideally, I wish to have a function which counts four closed contour.
I don't mind if it also detects the really tiny contours in between, or the entire shape itself as extra contours. As long as it counts the medium sized ones, I can fix the rest by applying area threshold. My problem is that any function I have tried detects only one contour - the entire shape, as it cannot separate it to the su-conours which are connected to one another.
Any suggestions?
Here is my shot at this, although your question might get closed because it's off-topic, too broad or a possible duplicate. Anyhow I propose another way to count the number of contours. You could also do it using bwboundaries as was demonstrated in the link provided by #knedlsepp in the possible duplicate. Just for the sake of it here is another way.
The idea is to apply a morphological closure of your image and actually count the number of enclosed surfaces instead instead of contours. That might end up being the same thing but I think it's easier to visualize surfaces.
Since the shapes in your image look like circle (kind of...) the structuring element used to close the image is a disk. The size (here 5) is up to you but for the image you provided its fine. After that, use regionprops to locate image regions (here the blobs) and count them, which comes back to counting contours I guess. You can provide the Area parameter to filter out shapes based on their area. Here I ask the function to provide centroids to plot them.
Whole code:
clear
clc
close all
%// Read, threshold and clean up the image
Im = im2bw(imread('ImContour.png'));
Im = imclearborder(Im);
%// Apply disk structuring element to morphologically close the image.
%// Play around with the size to alter the output.
se = strel('disk',5);
Im_closed = imclose(Im,se);
%// Find centroids of circle-ish shapes. Youcan also get the area to filter
%// out those you don't want.
S = regionprops(~Im_closed,'Centroid','Area');
%// remove the outer border of the image (1st output of regioprops).
S(1) = [];
%// Make array with centroids and show them.
Centro = vertcat(S.Centroid);
imshow(Im)
hold on
scatter(Centro(:,1),Centro(:,2),40,'filled')
And the output:
So as you see the algorithm detected 5 regions, but try playing a bit with the parameters and you will see which ones to change to get the desired output of 4.
Have fun!
I am working on a project to perform automatically the landing of a quadrotor by visual recognition on a target. I have the code to detect the target through HOG features. Now the idea is to find the triangle, which is isosceles, and measure the lines so that I can determine the orientation that way. I have tried Hough, but I cannot manage to succeed.
The target is a proposed one
, and it consists of an isosceles triangle inside a circle. But if you can think of a better one, please let me know.
Please, ask any questions if anything is unclear. Thank you very much
Update 1:
#McMa 's idea works well when I deal only with the target as an image. This is the code:
clc; close all;
im=imread('target.bmp');
im=rgb2gray(im);
im2=imcrop(im,[467.51 385.51 148.98 61.98]);
im2=imcomplement(im2);
im2=imrotate(im2,0);
s=regionprops(im2,'Area','Centroid','Extrema','Orientation');
[imH,imW]=size(im2);
if imH-s(end).Centroid(2) < imH/2
state=1; % Upright
else
state=2; % Upside down
end
imshow(im2);hold on
plot(s(end).Centroid(1), s(end).Centroid(2), 'b*')
if s(end).Orientation>0
degrees=s(end).Orientation;
else
degrees=s(end).Orientation+180;
end
if (0<degrees)&&(degrees<89.99) && state==2
degrees=degrees+180;
elseif (90<degrees) && (degrees<179) && state==1
degrees=degrees+180;
end
fprintf('The orientation is %g degrees\n',degrees)
Update 2:
Now I have another problem: I need to know somehow whether the camera is seeing the whole target or only the small circle+triangle. I need this before computing the orientation.
I have tried many options. For example, I wanted to count the number of circles, so if there are 2, it is seeing the big target, and if there is 1, just the small one. But they are not well detected. Even if I play with the sensitivity, it's not going to be a robust method.
Image: https://www.dropbox.com/s/7mbpna3xfquq5n7/P0016.bmp?dl=0
Classifer: https://www.dropbox.com/s/236vm3romw56983/Cascade1Matlab.xml?dl=0
im=imread('P0016.bmp');
detector = vision.CascadeObjectDetector('Cascade1Matlab.xml');
bbox = step(detector, im); % Detect the target.
detectedImg = insertObjectAnnotation(im, 'rectangle', bbox, 'target'); % Insert bounding boxes and return marked image.
imshow(detectedImg)
BW=rgb2gray(im);
BW=imcrop(BW,bbox(1,:) +[0 0 10 10]);
[imH,imW]=size(im);
centers = imfindcircles(im,[1 round(imH)]);
figure;hold on;
imshow(im);
plot(centers(:,1),centers(:,2),'r*','LineWidth',4)
I also tried with other approaches such as the Euler number, but with no success, I can't find anything that works properly.
I think the easiest and fastest way would be finding your target and binarize the image. Afterwards use regionprops() and read the "Orientation" property to read the orientation.
If you can't use that toolbox the function is very easily implement by calculating the covariance matrix of your region. Let me know if you need some tips on this.
Edit:
I just so happend to have some nicely vectorized functions around here ;) so if speed is a top priority, you can easily write your own regionprops() trimmed to the bare minimum like this:
function M=ImMoment(Image,ii,jj)
ImSize=size(Image);
K=repmat((1:ImSize(1))',1,ImSize(2)).^ii;
J=repmat(1:ImSize(2),ImSize(1),1).^jj;
M=K.*J.*Image;
M=sum(M(:));
end
for the image moments and
function [Matrix,Centroid,Angle]=CovMat(Image)
Centroid=[ImMoment(Image,0,1)/ImMoment(Image,0,0),...
ImMoment(Image,1,0)/ImMoment(Image,0,0)];
Miu20=ImMoment(Image,0,2)/ImMoment(Image,0,0)-Centroid(1)^2;
Miu02=ImMoment(Image,2,0)/ImMoment(Image,0,0)-Centroid(2)^2;
Miu11=ImMoment(Image,1,1)/ImMoment(Image,0,0)-Centroid(1)*Centroid(2);
Matrix=[Miu20,Miu11 %Covariance Matrix in case you need it for anything...
Miu11,Miu02];
Angle=1/2*atand(2*Miu11/(Miu20-Miu02)); %Your orientation
end
for your orientation and covariance matrix. more about it here.
Image moments are very might, have fun!
I have a very blunt solution in mind. It may work. I have not actually tried it, since there is no image to work on. So if it fails, post the error.
Assumptions:- You have filtered the image and obtained the binary image that contains only triangle "OR" with uniform noise.
Now you can take a 0 degree image (image1). Filter it and obtain binary image (bw1).
So when you are trying to land your quadrotor, take image (image2), convert it binary (bw2).
Now find the correlation between these two images {corr2(bw1, bw2)}. Store this in a variable.
Rotate image with a step angle. Let angle be 5 degrees. {imrotate(bw2, 5)}
Now again find correlation between these two images.
Do this for all angles.
The orientation would the angle (no. of rotation * 5) where the correlation is maximum.
The term maximum signifies that, you may not find the correlation to be 1 as this highly depends on your filtering techniques to obtain perfect binary image.
I also accept that computing the correlation for all the angles requires high computation speed as well as long time. This would be really difficult to achieve in real time if you do not have high computation speed. (In this case you can look into Parallel Computing Toolbox) specially parfor.
Hope this was useful to you. Post a comment if you face any error.
Finally good luck. Nice project.
P.S. Pad white or black pixels depending on your binary image while rotating image.
I want to detect the contour of a ring/disc which may be rotated in 3D. I used Detect circles with various radii in grayscale image via Hough Transform by Tao Peng. The results were very close to my requirement. Except for two points:
Using Tao Peng's code I could get a neat blue line indicating the detected contour. I want to access these co-ordinates (sub-pixels) for further processing. I could not figure out where these co-ordinates of detected contour are stored. If you have any idea, please let me know.
Is there any such code to detect ellipse and not only circles? This is because a ring when rotated in 3D wouldn't necessarily be a circle (in which case Tao Peng's code fails. But this is the best I have come across till date. I do not want to binarize my image, as I'll be losing out on a lot of information). Do let me know if there's anything.
Apart from this code, if there's any other one which does a similar job (even if it is like Tao Peng's code, for circles, plus gives me the co-ordinate values), please tell me.
I would prefer MATLAB, but C would also do.
This is an example image who's contour I want to detect, with high accuracy. (The outline of silver disc)
Regards.
Edit:
Here is an example of my output using Tao Peng's code.
I have searched the internet for a solution to the question above but have had no luck up to now. I have been producing a number of 2D plots where the origin of (0,0 point) is represented by an image. I have made these by plotting the data on an image, where the image is all white apart from the desired symbol at the center point (so I can reshape it as needed). I then move the axis so they cross at the center point. This all works fine, but I now have to create a number of plots using ‘fill’ to place two shaded areas on the plot that overlap in the center. This causes the symbol to be difficult to see even using ‘alpha’.
I therefore have two options to get the desired effect, both requiring me to put an image on top of the figure after the data is plotted. These options are:
1) I place the image on top of the plot and apply alpha to it (May not look very good as it will mute the plot).
2) The better option would be to crop the image around the symbol and then position it on top of the plot so the image center is at the origin (I have no idea how to position the image this way).
Both methods need the image to be placed on top of the plot after the data is plotted. I have tried 'hold on' and calling 'figure(imagesc(Image))' neither work. I have Matlab 2012b but no toolboxes (so cannot use subimage etc.)
Thanks for any help you can give
You can set the transparency of individual pixels of an image using the 'AlphaData' option. So, you can plot the overlay as follows:
% plot your data normally
...
hold on
% assuming the overlay is a 11x11 image
image(-5:5,-5:5,image_matrix,'AlphaData',alpha_matrix);
image_matrix would obviously be the matrix with your image data stored in it, while alpha_matrix would be a matrix of the same size as image_matrix. Every pixel you want to show would have a value of 1, every pixel you want to hide (the white pixels) would be 0.
I want to get a metric of straightness of contour in my binary image (relatively faster). The image looks as follows:
Now, the contours in the red box are the ones which I would like to be removed preferably. Since they are not straight. These are the things I have tried. I am as of now implementing in MATLAB.
1.Collect row and column coordinates of each contour and then take derivative. For straight objects (such as rectangle), derivative will be mostly low with a few spikes (along the corners of the rectangle).
Problem: The coordinates collected are not in order i.e. the order in which the contour will be traversed if we imaging it as a path. Therefore, derivative gives absurdly high values sometimes. Also, the contour is not absolutely straight, its an output of edge detection algorithm, so you can imagine that there might be some discontinuity (see the rectangle at the bottom, human eye can understand that it is a rectangle though it is not absolutely straight).
2.Tried to think about polyfit, but again this contour issue comes up. Since its a rectangle I don't know how to apply polyfit to that point set.
Also, I would like to remove contours which are distributed vertically/horizontally. Basically this is a lane detection algorithm. So lanes cannot be absolutely vertical/horizontal.
Any ideas?
You should look into the features of regionprops more. To be fair I stole the script from this answer, but here it is:
BW = imread('lanes.png');
BW = im2bw(BW);
figure(1),
subplot(1,2,1);
imshow(BW);
cc = bwconncomp(BW);
l = labelmatrix(cc);
a_rp = regionprops(CC,'Area','MajorAxisLength','MinorAxislength','Orientation','PixelList','Eccentricity');
idx = ([a_rp.Eccentricity] > 0.99 & [a_rp.Area] > 100 & [a_rp.Orientation] < 70 & [a_rp.Orientation] > -90);
BW2 = ismember(l,find(idx));
subplot(1,2,2);
imshow(BW2);
You can mess around with the properties. 'Orientation', 'Eccentricity', and 'Area' are probably the parameters you want to mess with. I also messed with the ratios of the major/minor axis lengths but eccentricity basically does this (eccentricity is a measure of how "circular" an ellipse is). Here's the output:
I actually saw a good video specifically from matlab for lane detection using regionprops. I'll try to see if I can find it and link it.
You can segment your image using bwlabel, then work separately on each bwlabel connected object, using find. This should help solve your order problem.
About a metric, the only thing that come to mind at the moment is to fit to an ellipse, and set the a/b (major axis/minor axis) ratio (basically eccentricity) a parameter. For example a straight line (even if not perfect) will be fitted to an ellipse with a very big major axis and a very small minor axis. So say you set a ratio threshold of >10 etc... Fitting to an ellipse can be done using this FEX submission for example.