identifying shapes in image

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Any help with identifying the shapes in this image. they are incorrectly identified due to their rotaion. this code works great on identifying shapes in other pictures but this one it fails.

%% ========================================================================
%  IMAGE PREPROCESSING + WATERSHED SEGMENTATION + RBC SHAPE CLASSIFICATION
%  ========================================================================
% --- Select Image ---
[filename, pathname] = uigetfile( ...
    {'*.jpg;*.jpeg;*.png;*.tif;*.tiff;*.bmp;*.gif', ...
    'Image Files (*.jpg, *.jpeg, *.png, *.tif, *.tiff, *.bmp, *.gif)'; ...
    '*.*','All Files (*.*)'}, ...
    'Select an image');
if isequal(filename,0)
    disp('No file selected.');
    return;
end
imgPath = fullfile(pathname, filename);
img = imread(imgPath);
% Detect grayscale or RGB
isGray = is_image_grayscale(img);
figure('Name', filename, 'NumberTitle', 'off');
%% ========================================================================
%  PATH A — GRAYSCALE IMAGE
% ========================================================================
if isGray
    % Original
    subplot(3,2,1); imshow(img); title('Loaded (Grayscale)');
    
    % Ensure grayscale
    GRAY = im2gray(img);
    subplot(3,2,2); imshow(GRAY); title('Grayscale Image');
    % Dilation (line)
    se_line = strel("line",10,0);
    dil1 = imdilate(GRAY, se_line);
    subplot(3,2,3); imshow(dil1); title('1st Dilation');
    % Dilation (disk)
    se_disk2 = strel("disk",2);
    dil2 = imdilate(dil1, se_disk2);
    subplot(3,2,4); imshow(dil2); title('2nd Dilation');
    % Erode
    se_disk1 = strel("disk",1);
    erode = imerode(dil2, se_disk1);
    subplot(3,2,5); imshow(erode); title('Erosion');
    % Binary
    BW = imbinarize(erode, 25/255);
    
    subplot(3,2,6); imshow(BW); title('Binary (Threshold 25)');
    pause(2);
    % Invert
    BW_inv = ~BW;
    subplot(3,2,6); imshow(BW_inv); title('Inverted Binary');
    
    
    % ------------------ Watershed ------------------
    figure; 
    D = -bwdist(BW);
    subplot(3,2,1); imshow(D, []); title('Distance Transform');
    mask = imextendedmin(D,2);
    D2 = imimposemin(D,mask);
    L = watershed(D2);
    L(BW) = 0;
    subplot(3,2,2); imshow(label2rgb(L,'jet','w','shuffle'));
    title('Watershed Segmentation');
    subplot(3,2,3); imshow(label2rgb(L,'jet',[0.5 0.5 0.5]));
    title('Watershed Transform');
    %% ========================================================================
    %  PATH B — RGB IMAGE
    % ========================================================================
else
    subplot(3,2,1); imshow(img); title('Loaded (Colour)');
    % HSV saturation adjustment
    HSV = rgb2hsv(img);
    S = HSV(:,:,2);
    gamma = 3;
    S_tweaked = S.^gamma;
    subplot(3,2,2); imshow(S_tweaked, []); title('Saturation (Gamma=3)');
    % Convert to grayscale
    GRAY = im2gray(S_tweaked);
    subplot(3,2,3); imshow(GRAY); title('Grayscale');
    % Morphological closing
    se_close = strel("disk",10);
    close_img = imclose(GRAY, se_close);
    subplot(3,2,4); imshow(close_img); title('Close');
    % Opening
    se_open = strel("disk",3);
    open_img = imopen(close_img, se_open);
    subplot(3,2,5); imshow(open_img); title('Open');
    % Binary image
    BW = imbinarize(open_img);
    subplot(3,2,6); imshow(BW); title('Binary');
    pause(2);
    BW_inv = ~BW;
    subplot(3,2,6); imshow(BW_inv); title('Inverted Binary');
    % ------------------ Watershed ------------------
    figure;
    D = -bwdist(~BW);
    subplot(3,2,1); imshow(D,[]); title('Distance Transform');
    mask = imextendedmin(D,2);
    D2 = imimposemin(D,mask);
    L = watershed(D2);
    L(~BW) = 0;
    subplot(3,2,2); imshow(label2rgb(L,'jet','w','shuffle'));
    title('Watershed Segmentation');
    subplot(3,2,3); imshow(label2rgb(L,'jet',[0.5 0.5 0.5]));
    title('Watershed Transform');
end
%% ========================================================================
%  SHAPE CLASSIFICATION (CIRCLE / TRIANGLE / SQUARE / RECTANGLE)
% ========================================================================
figure; imshow(img); hold on;
labels = unique(L);
labels(labels==0) = [];   % remove background
for k = 1:length(labels)
    regionMask = (L == labels(k));
    % Stats
    stats = regionprops(regionMask,'Area','Perimeter','BoundingBox','Centroid');
    A = stats.Area;
    P = stats.Perimeter;
    BB = stats.BoundingBox;
    C = stats.Centroid;
    % RBC classification
    shape = classifyPrimitive(A, P, BB);
    % Put label on image
    text(C(1), C(2), shape, ...
        'Color', 'white', ...
        'FontSize', 12, ...
        'FontWeight', 'bold', ...
        'HorizontalAlignment', 'center');
end
hold off;
%% ========================================================================
%  FUNCTIONS
% ========================================================================
function tf = is_image_grayscale(I)
if ndims(I)==2
    tf = true;
elseif ndims(I)==3 && size(I,3)==3
    tf = isequal(I(:,:,1),I(:,:,2)) && isequal(I(:,:,1),I(:,:,3));
else
    tf = false;
end
end
% --- RBC Primitive Classifier ---
function label = classifyPrimitive(area, perimeter, bbox)
w = bbox(3);
h = bbox(4);
%% Circle metric from RBC paper (should be ~1)
circle_metric = (perimeter^2) / (4*pi*area);
if abs(circle_metric - 1) < 0.158
    label = 'Circle';
    return;
end
%% Triangle metric: area approx half bounding box
bbox_area = w*h;
area_ratio = area / bbox_area;
if area_ratio > 0.30 && area_ratio < 0.65
    label = 'Triangle';
    return;
end
%% Square vs Rectangle
aspect_ratio = w/h;
if abs(aspect_ratio - 1) < 0.60
    label = 'Square';
else
    label = 'Rectangle';
end