Hello !
One possibility :
function [grad_x, grad_y] = compute_gradients(dem)
diff_x = diff(dem, 1, 'c');
diff_y = diff(dem, 1, 'r');
grad_x = zeros(dem);
grad_x(:, 2 : $-1) = (diff_x(:,1:$-1) + diff_x(:,2:$)) / 2;
grad_x(:, 1) = diff_x(:,1);
grad_x(:, $) = diff_x(:,$);
grad_y = zeros(dem);
grad_y(2 : $-1,:) = (diff_y(1:$-1,:) + diff_y(2:$,:)) / 2;
grad_y(1,:) = diff_y(1,:);
grad_y($,:) = diff_y($,:);
endfunction
Alain
From: users <[email protected]> On Behalf Of Lester Anderson
Sent: vendredi 5 avril 2024 12:26
To: International users mailing list for Scilab. <[email protected]>
Subject: [Scilab-users] Compute gradients of grid data
Hello,
I am looking to create a hillshade function, but issues arise defining the the
X and Y gradients.
The test code so far:
function [grad_x, grad_y] = compute_gradients(dem)
grad_x = diff(dem, 1, 'c');
grad_y = diff(dem, 1, 'r');
endfunction
// Create a test DEM grid
dem=[1.23 1.45 1.67 1.89 2.10;...
1.54 1.73 1.92 2.11 2.30;...
1.85 2.01 2.17 2.33 2.49;...
2.16 2.29 2.42 2.55 2.68];
// Compute gradient
[grad_x, grad_y] = compute_gradients(dem);
// Display the results
disp('Gradient in x-direction:');
disp(grad_x);
disp('Gradient in y-direction:');
disp(grad_y);
function slope = compute_slope(grad_x, grad_y)
// Compute magnitude of gradient vector
magnitude = sqrt(grad_x.^2 + grad_y.^2);
// Compute slope using arctangent
slope = atan(magnitude);
endfunction
slope = compute_slope(grad_x, grad_y);
disp(slope)
How to make the grad_x and grad_y matrices the same size?
One route may be central differences as a method:
function [grad_x, grad_y] = calculate_gradient(Z)
[m, n] = size(Z);
grad_x = zeros(m, n);
grad_y = zeros(m, n);
for i = 2:(m-1)
for j = 2:(n-1)
grad_x(i, j) = (Z(i+1, j) - Z(i-1, j)) / 2;
grad_y(i, j) = (Z(i, j+1) - Z(i, j-1)) / 2;
end
end
endfunction
Any suggestions how to gradients for grid data.
Thanks
Lester
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