Convert X,Y floating point coordinates to binary matrix and then perform a Hough line transform

Question

Is it possible to compute a hough line transform of array of xy floating points, similar to this matlab code in python?

BW=full(sparse(x,y,true));

the data looks like

Answer 1

Your example in MATLAB only works on integer (x,y) coordinates.

For example

% I use a 10x10 identity matrix to simulate a line of points
% And scale the resulting x, y coordinates to be floating point 
[X, Y] = find(eye(10));
X = X * 0.1;
Y = Y * 0.1;
A = full(sparse(X, Y, true));

Throws the error

Error using sparse. Index into matrix must be an integer.

If you want to convert floating point coordinates into a binary matrix, the only way I know of is to decimate your space.

% Precision of the decimated grid
scale = .01;

% Scale the X, Y values to be integers greater than 1
row_indices = round((Y - min(Y))/scale) + 1;    
col_indices = round((X - min(X))/scale) + 1;

% row values also need to be flipped 
% i.e. y = 0 should be the maximum row in the matrix to maintain the same orientation of the coordinate system
row_indices = max(row_indices) -  row_indices + 1;

% Create matrix using your method
A = full(sparse(row_indices, col_indices, true));

% Each row and column in A corresponds to the value in these range vectors
xrange = min(X):scale:max(X);
yrange = max(Y):-scale:min(Y);

To test whether these transformations produced the desired result. I plotted the matrix.

figure; 
subplot(1,2,1); imagesc(A);
xticks(1:20:100); xticklabels(xrange(1:20:end));
yticks(1:20:100); yticklabels(yrange(1:20:end));
subplot(1,2,2); plot(X, Y, 'ko');

And it's looking good.

A similar approach should be easy to implement using numpy.