I have a video of moving hose in an experiment and I need to detect certain points in that hose and calculate the amplitude of their movements, I am using the code below and I am able to extract the required point using detectSURFFeatures, the function get many unnecessary points so I am using cuba = ref_pts.selectStrongest(5); to choose only five points, the problem is I can not get a function to put a bounding box about this 5 points and get their pixel values through the video, Kindly advice what functions can be used, thanks :)
clear;
clc;
% Image aquisition from Video and converting into gray scale
vidIn = VideoReader('ItaS.mp4');
%% Load reference image, and compute surf features
ref_img = read(vidIn, 1);
ref_img_gray = rgb2gray(ref_img);
ref_pts = detectSURFFeatures(ref_img_gray);
[ref_features, ref_validPts] = extractFeatures(ref_img_gray, ref_pts);
figure; imshow(ref_img);
hold on; plot(ref_pts.selectStrongest(5));
cuba = ref_pts.selectStrongest(5);
stats1 = round(cuba.Location);
If you want to find the bounding box which covers all the five points you selected: stats1 now contains (x, y) coordinates of the selected 5 points. Find min and max for x and y coordinates. min values of x and y gives you the starting point of the rectangle. Width and height of the bounding box is now the difference of max and min in y and x directions.
If you want to extract the part of the original image inside the bounding box: just copy that part to another variable as you want. Consider the following example.
img2 = img1(y:h, x:w, :)
Here, x and y are the x and y coordinates of the top left corner of the bounding box. w and h are the width and height of the bounding box.
Related
I have a grayscale image I want to extract the regions of interest using detectSURFFeatures(). Using this function I get a surfPoints object.
by displaying this object on the image I get circles as regions of interest.
For my case I want the rectangular areas encompassing these circles.
To be more clear i have a image 1:
I want to extract Region of Interest (ROI) using : detectSURFFeatures(), we obtain the image
if you can see we have circular region, and for my case i want the rectangular ROI that contains the circular region :
It looks like the radius is fully determined by the points.Scale parameter.
% Detection of the SURF features:
I = imread('cameraman.tif');
points = detectSURFFeatures(I);
imshow(I); hold on;
% Select and plot the 10 strongest features
p = points.selectStrongest(10)
plot(p);
% Here we add the bounding box around the circle.
c = 6; % Correction factor for the radius
for ii = 1:10
x = p.Location(ii,1); % x coordinate of the circle's center
y = p.Location(ii,2); % y coordinate of the circle's center
r = p.Scale(ii); % Scale parameter
rectangle('Position',[x-r*c y-r*c 2*r*c 2*r*c],'EdgeColor','r')
end
And we obtain the following result:
In this example the correction factor for the radius is 6. I guess that this value correspond to half of the default Scale propertie's value of a SURFPoints object (which is 12.0). But since there is no information about that in the documentation, I can be wrong. And be carreful, the scale parameter of each ROI is not the same thing as the scale propertie of a SURFPoints object.
I am struggling with some algorithm to extract the region from an image which has the maximum change in pixels. I got the following image after preprocessing.
I did following steps of pre-processing
x = imread('test2.jpg');
gray_x = rgb2gray(x);
I = medfilt2(gray_x,[3 3]);
gray_x = I;
%%
canny_x = edge(gray_x,'canny',0.3);
figure,imshow(canny_x);
%%
s = strel('disk',3);
si = imdilate(canny_x,s);
%figure5
figure; imshow(si);
se = imerode(canny_x,s);title('dilation');
%figure6
figure; imshow(se);title('Erodsion');
I = imsubtract(si,se);
%figure7
figure; imshow(I);
Basically what I am struggling for, is to make weapon detection system using Image processing. I want to localize possible area's to be weapon so that I could feed them to my classifier to identify if it is a weapon or not. Any suggestions? Thank you
A possible solution could be:
Find corner points in the image (Harris corner points, etc)
Set value of all the corner points to white while remaining image will be black
Take a rectangular window and traverse it over the whole image
sum all the white pixels in that rectangular window
select that region whose sum is maximum of all regions
I have this 3D image generated from the simple code below.
% Input Image size
imageSizeY = 200;
imageSizeX = 120;
imageSizeZ = 100;
%# create coordinates
[rowsInImage, columnsInImage, pagesInImage] = meshgrid(1:imageSizeY, 1:imageSizeX, 1:imageSizeZ);
%# get coordinate array of vertices
vertexCoords = [rowsInImage(:), columnsInImage(:), pagesInImage(:)];
centerY = imageSizeY/2;
centerX = imageSizeX/2;
centerZ = imageSizeZ/2;
radius = 28;
%# calculate distance from center of the cube
sphereVoxels = (rowsInImage - centerY).^2 + (columnsInImage - centerX).^2 + (pagesInImage - centerZ).^2 <= radius.^2;
%# Now, display it using an isosurface and a patch
fv = isosurface(sphereVoxels,0);
patch(fv,'FaceColor',[0 0 .7],'EdgeColor',[0 0 1]); title('Binary volume of a sphere');
view(45,45);
axis equal;
grid on;
xlabel('x-axis [pixels]'); ylabel('y-axis [pixels]'); zlabel('z-axis [pixels]')
I have tried plotting the image with isosurface and some other volume visualization tools, but there remains quite a few surprises for me from the plots.
The code has been written to conform to the image coordinate system (eg. see: vertexCoords) which is a left-handed coordinate system I presume. Nonetheless, the image is displayed in the Cartesian (right-handed) coordinate system. I have tried to see this displayed as the figure below, but that’s simply not happening.
I am wondering if the visualization functions have been written to display the image the way they do.
Image coordinate system:
Going forward, there are other aspects of the code I am to write for example if I have an input image sphereVoxels as in above, in addition to visualizing it, I would want to find north, south east, west, top and bottom locations in the image, as well as number and count the coordinates of the vertices, plus more.
I foresee this would likely become confusing for me if I don’t stick to one coordinate system, and considering that the visualization tools predominantly use the right-hand coordinate system, I would want to stick with that from the onset. However, I really do not know how to go about this.
Right-hand coordinate system:
Any suggestions to get through this?
When you call meshgrid, the dimensions x and y axes are switched (contrary to ndgrid). For example, in your case, it means that rowsInImage is a [120x100x200] = [x,y,z] array and not a [100x120x200] = [y,x,z] array even if meshgrid was called with arguments in the y,x,z order. I would change those two lines to be in the classical x,y,z order :
[columnsInImage, rowsInImage, pagesInImage] = meshgrid(1:imageSizeX, 1:imageSizeY, 1:imageSizeZ);
vertexCoords = [columnsInImage(:), rowsInImage(:), pagesInImage(:)];
I have a list of coordinates, which are generated from another program, and I have an image.
I'd like to load those coordinates (making circular regions of interest (ROIs) with a diameter of 3 pixels) onto my image, and extract the intensity of those pixels.
I can load/impose the coordinates on to the image by using;
imshow(file);
hold on
scatter(xCoords, yCoords, 'g')
But can not extract the intensity.
Can you guys point me in the right direction?
I am not sure what you mean by a circle with 3 pixels diameter since you are in a square grid (as mentioned by Ander Biguri). But you could use fspecial to create a disk filter and then normalize. Something like this:
r = 1.5; % for diameter = 3
h = fspecial('disk', r);
h = h/h(ceil(r),ceil(r));
You can use it as a mask to get the intensities at the given region of the image.
im = imread(file);
ROI = im(xCoord-1:xCoord+1; yCoord-1:yCoord+1);
I = ROI.*h;
Below is an arbitrary hand-drawn Intensity profile of a line in an image:
The task is to draw the line. The profile can be approximated to an arc of a circle or ellipse.
This I am doing for camera calibration. Since I do not have the actual industrial camera, I am trying to simulate the correction needed for calibration.
The question can be rephrased as I want pixel values which will follow a plot similar to the above. I want to do this using program (Preferably using opencv) and not manually enter these values because I have thousands of pixels in the line.
An algorithm/pseudo code will suffice. Also please note that I do not have any actual Intensity profile, otherwise I would have read those values.
When will you encounter such situation ?
Suppose you take a picture (assuming complete white) from a Camera, your object being placed on table, and camera just above it in vertical direction. The light coming on the center of the picture vertically downward from the camera will be stronger in intensity as compared to the light reflecting at the edges. You measure pixel values across any line in the Image, you will find intensity curve like shown above. Since I dont have camera for the time being, I want to emulate this situation. How to achieve this?
This is not exactly image processing, rather image generation... but anyways.
Since you want an arc, we still need three points on that arc, lets take the first, middle and last point (key characteristics in my opinion):
N = 100; % number of pixels
x1 = 1;
x2 = floor(N/2);
x3 = N;
y1 = 242;
y2 = 255;
y3 = 242;
and now draw a circle arc that contains these points.
This problem is already discussed here for matlab: http://www.mathworks.nl/matlabcentral/newsreader/view_thread/297070
x21 = x2-x1; y21 = y2-y1;
x31 = x3-x1; y31 = y3-y1;
h21 = x21^2+y21^2; h31 = x31^2+y31^2;
d = 2*(x21*y31-x31*y21);
a = x1+(h21*y31-h31*y21)/d; % circle center x
b = y1-(h21*x31-h31*x21)/d; % circle center y
r = sqrt(h21*h31*((x3-x2)^2+(y3-y2)^2))/abs(d); % circle radius
If you assume the middle value is always larger (and thus it's the upper part of the circle you'll have to plot), you can draw this with:
x = x1:x3;
y = b+sqrt(r^2-(x-a).^ 2);
plot(x,y);
you can adjust the visible window with
xlim([1 N]);
ylim([200 260]);
which gives me the following result: