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get_marked_frames_combined_with_main_driver_bounds.m
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get_marked_frames_combined_with_main_driver_bounds.m
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clear; clc;
close all %closes all open figures
folder = '';
name ='';
name = [folder name];
vid = [name '.avi'];
vidObjR = VideoReader(vid);
vidObjW = VideoWriter([vid '_write.avi']);
vidObjW.FrameRate = 1;
open(vidObjW);
%%Percent values
percent = 0.4;
%NBPK skp value - base value is 4 -
%skp is the number of frames to look at the cross correlations (i.e. how many frames to plot after an "event"
skp = 4;
% Default useing 85 pixels calculate distance between cells if dis< 85 pixel then flag as neighbor and set to 1
NxtCeldis=85; %85 value works best for cell culture - miight need to altered for wing cells and basal cells
%NxtCeldis=85*2; %pixel; value for wings cells - maybe doubled what the
%default is from cell culture
%NxtCeldis=85*.5; pixel value for basal cells - mayubed halfed or
%quartered what is default for cell culture
%% Needed inputs for processing
frame_rate = vidObjR.FrameRate;
duration = vidObjR.Duration;
total_frames = frame_rate*duration;
fprintf('Total number of frames in the video = %d\n', total_frames);
begin_frames = 1; %Enter starting frame number
reference_frame = begin_frames + 29; %Enter reference frame number:
end_frames = total_frames - 1; %Enter ending frame number --- if you want total amount of frames
%end_frames = 110; %%if want something execpt total vid uncomment and add number
%%%%from the for loop in get_marked_frames_modified
vidObjR.CurrentTime = reference_frame/frame_rate;
im_frame = readFrame(vidObjR);
[x_centroid{1}, y_centroid{1}, mean_pixel_vals{1}, std_pixel_vals{1},images{1}] = compute_metrics(im_frame);
writeVideo(vidObjW,1);
close(vidObjW);
%{
selected_frames = reference_frame;
%selected_frames = end_frames - begin_frames + 1;
count = 1;
vidObjR.CurrentTime = selected_frames/frame_rate;
im_frame = readFrame(vidObjR);
[x_centroid{count}, y_centroid{count}, mean_pixel_vals{count}, std_pixel_vals{count},images{count}] = compute_metrics(im_frame);
% only need one image
currFrame = getframe;
writeVideo(vidObjW,currFrame);
disp('done');
%}
% Extracting position from centroid cells
xdata = x_centroid{1}';
ydata = y_centroid{1}';
posdata = [xdata,ydata];
posfilename = [name 'pos.xlsx'];
xlswrite(posfilename,posdata);
disp('Pos done');
%% Computing per-frame metrics
mins = [];
entropy_calc = [];
[I,indices_list] = get_pixel_indices(im_frame);
[I, new_image] = display_regions(I,indices_list);
count = 1;
for ii = begin_frames:end_frames
vidObjR.CurrentTime = ii/frame_rate;
im_frame = readFrame(vidObjR);
im = rgb2gray(im_frame);
current_image = adapthisteq(im);
[mean_pixel_vals{count}] = get_intensities(current_image,indices_list);
count = count + 1;
end
%%
intmat = zeros (length(mean_pixel_vals),length(mean_pixel_vals{1}));
for count = 1:length(mean_pixel_vals)
intmat(count,:) = mean_pixel_vals{count};
end
%Allocating the range of the matrices for writing to excel
[m,n] = size(intmat);
firstCol = 'B';
lastCol = xlscol(n+1);
cellRange = [firstCol,num2str(1),':',lastCol,num2str(m)];
%Writing the intensity to excel file
intfilename = [name 'int.xlsx'];
xlswrite(intfilename,intmat,cellRange);
disp('Int done');
%%
%Calling PKcorr function
[position,txt,raw] = xlsread(posfilename) ;
[intensity,txt,raw] = xlsread(intfilename) ;
Pkcorr(intensity,position,percent,skp,NxtCeldis,name);
%heatmap, detected events, and returned values and NBPKdata excel file
%NBPKdata produces the individual cell total peak (totpk), nearby peaks
%(NBPK) and the probability a signal was nearby signal (prbNB)
%Pkcorr2(intensity,position,percent,skp);
%plot event triggered average traces of neighbors and
%plot event triggered average traces of that cell and
%comparison between two graph
Pkcorr3(intensity,position,skp);
%intensity graphs, peak graphs, detected events over time
%%
%addation of the hierarchiacal clustering code developed by K. Segars
%Outputs a dendrogram, a histogram, and a Cophenetic correlation coefficinet
%representing the cells that have different signaling phenotypes
%For Cophenetic correlation coefficinet the closer the otput is to 1 the
%better - .90s are what you want and .80s can be tolerated
Hierarchical_Clustering(name, intmat);
%Hierarchical_Clustering_Independent for the independent version of the
%code --- for more control over the defined frames of intrest and etc. Use
%the independent version for the publication graphs because it produces
%graphs and values with a higher correlation coefficient --- could not
%figure out the exact cause of why independent version of code produced
%better values and grahs then the attahed