Hello Kegan,
If you don't provide any raw data is hard to come up with a specific solution, but the code structure could be something like this:
- Let's say you start with the videos saved as individual image sequences (.jpg) in a folder. One folder per experiment, 400 images per folder.
- Read the images in the folder.
inputfoler='C/Docs/Experiment/Video_1/'
dir_images=dir(fullfile(inputfolder,'*.jpg')); % dir_images will contain a structure with all the .jpg images in your folder
3. Initialize the variables you want to study, select the ROI and loop through the images
MAX=zeros(size(dir_images,1),1);
MIN=MAX;STD=MAX;MEAN=MAX;
I=imread(fullfile(dir_images(i).folder,dir_images(i).name)); % Read the first image to use it for extracting the ROI
ROI=imrect;
l=floor(ROI.getPosition);
py=l(1); px=l(2); ly=l(3); lx=l(4) % Postion of the ROI rectangle
ROI=I(px:px+lx,py:py+ly,:);
%imshow(ROI) % Show ROI image
MAX(1)=max(ROI(:)); MIN(1)=min(ROI(:)); MEAN(1)=mean(ROI(:)); STD(1)=std(ROI(:));
for i=2:size(dir_images,1) % Loop through the images
I=imread(fullfile(dir_images(i).folder,dir_images(i).name));
ROI=I(px:px+lx,py:py+ly,:);
% Whatever process or object recognition code would go here
MAX(1)=max(ROI(:)); MIN(1)=min(ROI(:)); MEAN(1)=mean(ROI(:)); STD(1)=std(ROI(:));
end
% Plot your variables MAX, MIN, MEAN ,STD
plot(MAX)
You can continue from here by adding the object recognition, mask, filter, ...
Hope this helps
