Rebuild trinity visualizer from bare SDL and OpenGL to using Raylib. Added Code for serial parsing on linux. Current functionality reads incoming quaternion packet data coming in over serial and displays the values and also visualizes with a cube, connect and disconnect is implemented. Essentially rebuild the functionality of the old version with added linux support.

2026-09-13 19:44:26 +02:00
commit 62abf4d5d6
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+/// @ref gtx_matrix_factorisation
+
+namespace glm
+{
+	template <length_t C, length_t R, typename T, qualifier Q>
+	GLM_FUNC_QUALIFIER mat<C, R, T, Q> flipud(mat<C, R, T, Q> const& in)
+	{
+		mat<R, C, T, Q> tin = transpose(in);
+		tin = fliplr(tin);
+		mat<C, R, T, Q> out = transpose(tin);
+
+		return out;
+	}
+
+	template <length_t C, length_t R, typename T, qualifier Q>
+	GLM_FUNC_QUALIFIER mat<C, R, T, Q> fliplr(mat<C, R, T, Q> const& in)
+	{
+		mat<C, R, T, Q> out;
+		for (length_t i = 0; i < C; i++)
+		{
+			out[i] = in[(C - i) - 1];
+		}
+
+		return out;
+	}
+
+	template <length_t C, length_t R, typename T, qualifier Q>
+	GLM_FUNC_QUALIFIER void qr_decompose(mat<C, R, T, Q> const& in, mat<(C < R ? C : R), R, T, Q>& q, mat<C, (C < R ? C : R), T, Q>& r)
+	{
+		// Uses modified Gram-Schmidt method
+		// Source: https://en.wikipedia.org/wiki/Gram%E2%80%93Schmidt_process
+		// And https://en.wikipedia.org/wiki/QR_decomposition
+
+		//For all the linearly independs columns of the input...
+		// (there can be no more linearly independents columns than there are rows.)
+		for (length_t i = 0; i < (C < R ? C : R); i++)
+		{
+			//Copy in Q the input's i-th column.
+			q[i] = in[i];
+
+			//j = [0,i[
+			// Make that column orthogonal to all the previous ones by substracting to it the non-orthogonal projection of all the previous columns.
+			// Also: Fill the zero elements of R
+			for (length_t j = 0; j < i; j++)
+			{
+				q[i] -= dot(q[i], q[j])*q[j];
+				r[j][i] = 0;
+			}
+
+			//Now, Q i-th column is orthogonal to all the previous columns. Normalize it.
+			q[i] = normalize(q[i]);
+
+			//j = [i,C[
+			//Finally, compute the corresponding coefficients of R by computing the projection of the resulting column on the other columns of the input.
+			for (length_t j = i; j < C; j++)
+			{
+				r[j][i] = dot(in[j], q[i]);
+			}
+		}
+	}
+
+	template <length_t C, length_t R, typename T, qualifier Q>
+	GLM_FUNC_QUALIFIER void rq_decompose(mat<C, R, T, Q> const& in, mat<(C < R ? C : R), R, T, Q>& r, mat<C, (C < R ? C : R), T, Q>& q)
+	{
+		// From https://en.wikipedia.org/wiki/QR_decomposition:
+		// The RQ decomposition transforms a matrix A into the product of an upper triangular matrix R (also known as right-triangular) and an orthogonal matrix Q. The only difference from QR decomposition is the order of these matrices.
+		// QR decomposition is Gram-Schmidt orthogonalization of columns of A, started from the first column.
+		// RQ decomposition is Gram-Schmidt orthogonalization of rows of A, started from the last row.
+
+		mat<R, C, T, Q> tin = transpose(in);
+		tin = fliplr(tin);
+
+		mat<R, (C < R ? C : R), T, Q> tr;
+		mat<(C < R ? C : R), C, T, Q> tq;
+		qr_decompose(tin, tq, tr);
+
+		tr = fliplr(tr);
+		r = transpose(tr);
+		r = fliplr(r);
+
+		tq = fliplr(tq);
+		q = transpose(tq);
+	}
+} //namespace glm