I am attempting to find the governing equation for the Position(x,y,t) and Orientation θ(x,y,t) of these pellets, which collectively behave as a fluid. To perform flow analysis and find governing equations for these pellets (which will be accordingly moved across by a conveyor belt): PELLETS
I first attempted to do a Langrangian analysis of this problem, which required that I find every pellet and its position (Xi) and Angle/orientation relative to the horizontal (θi). However so I did it, I used MATLAB and python modules such as OpenCV, I was not able to segment all the pellets to even make this a possibility.
CODE I USED:
clear
function [BW,maskedRGBImage] = mymask(RGB)
%createMask Threshold RGB image using auto-generated code from colorThresholder app.
% [BW,MASKEDRGBIMAGE] = createMask(RGB) thresholds image RGB using
% auto-generated code from the colorThresholder app. The colorspace and
% range for each channel of the colorspace were set within the app. The
% segmentation mask is returned in BW, and a composite of the mask and
% original RGB images is returned in maskedRGBImage.
% Auto-generated by colorThresholder app on 01-Apr-2024
%------------------------------------------------------
% Convert RGB image to chosen color space
I = rgb2hsv(RGB);
% Define thresholds for channel 1 based on histogram settings
channel1Min = 0.009;
channel1Max = 0.112;
% Define thresholds for channel 2 based on histogram settings
channel2Min = 0.103;
channel2Max = 0.275;
% Define thresholds for channel 3 based on histogram settings
channel3Min = 0.347;
channel3Max = 0.687;
% Create mask based on chosen histogram thresholds
sliderBW = (I(:,:,1) >= channel1Min ) & (I(:,:,1) <= channel1Max) & ...
(I(:,:,2) >= channel2Min ) & (I(:,:,2) <= channel2Max) & ...
(I(:,:,3) >= channel3Min ) & (I(:,:,3) <= channel3Max);
BW = sliderBW;
% Initialize output masked image based on input image.
maskedRGBImage = RGB;
% Set background pixels where BW is false to zero.
maskedRGBImage(repmat(~BW,[1 1 3])) = 0;
end
inpict = imread('test.png');
mk = mymask(inpict); % create a mask using global thresholds
mk = bwareaopen(mk,100); % get rid of a thousand tiny specks
S = regionprops(mk,'area');
CT = hsv(numel(S));
CT(2:2:end,:) = flipud(CT(2:2:end,:));
% the result is one giant conglomerate blob
% and several smaller conglomerate blobs
alpha = 0.8;
outpict = labeloverlay(inpict,bwlabel(mk), ...
'transparency',1-alpha,'colormap',CT);
imshow(outpict,'border','tight')
RESULT IMAGE FOR THE LANGRANGIAN ATTEMPT USING MATLAB: ATTEMPT
The python attempt at segmentation was even worse
Attempting to track all pellets as a function of time is simply too difficult for this problem. How can I use Eulerian analysis to find governing equations or field parameters for this problem?
