I'm trying to filter a point cloud with numpy.
i convert everything to a numpy array.
verts = np.asarray (points.get_vertices (2)). reshape (h, w, 3)
now I would e.g. like to only view values in a certain xmin, xmax range. or also xmin, xmax, ymin, ymax.
I tried the following for the x filter
verts = np.asarray(points.get_vertices(2)).reshape(h, w, 3)
texcoords = np.asarray(points.get_texture_coordinates(2))
xmin = -0.25
xmax = 0.25
ymin = 0.0
ymax = 1.0
inidx = np.all(np.logical_and(xmin <= verts, verts <= xmax), axis=0)
inbox = verts[inidx]
print(verts.length, inbox.length)
but already here I get an error message
IndexError: boolean index did not match indexed array along dimension 1; dimension is 212 but corresponding boolean dimension is 3
By separating your different components with np.split:
>>> x, y, z = np.split(verts, 3, axis=-1)
You can combine conditions with the multiplication operator based on x, and y:
>>> mask = (xmin <= x)*(x <= xmax)*(ymin <= y)*(y <= ymax)
Then mask your verts array:
>>> verts_masked = verts[mask[..., 0]]
tensor([[ 0.1221, 0.2402, 0.7808],
[ 0.1274, 0.1203, -0.9398],
[ 0.0789, 0.8018, -0.7915],
[ 0.1515, 0.3616, -0.2061],
[ 0.2166, 0.3970, 0.4706],
[ 0.2421, 0.0457, 0.1082],
[ 0.0480, 0.9252, 0.2259],
[ 0.2145, 0.5752, -0.3701],
[-0.2099, 0.4220, 0.2342],
[ 0.0949, 0.9467, -0.4768],
[ 0.0746, 0.2131, -0.1160],
[-0.2072, 0.4472, -0.3754],
[-0.0994, 0.8972, -0.7704],
[ 0.2424, 0.3210, -0.2291],
[ 0.1093, 0.2599, -0.2868],
[-0.2482, 0.6001, -0.3283]])
Additionally, if you are looking to mask texcoords with mask as well:
>>> textcoords_masked = textcoords[mask[..., 0]]
To sort the resulting arrays based on z you can use np.argsort:
>>> indices = np.argsort(verts_masked[:,-1])
Then get the filtered-sorted arrays for verts and textcoords as verts_masked[indices] and textcoords_masked[indices] respectively.