plotter = MeshPlotter(mesh, figsize=(12, 9))
plotter.draw_lines(lines)
plotter.draw_faces()
callback = partial(callback,
plotter=plotter,
filepath=THERE,
export=EXPORT_PNG)
# ==========================================================================
# Set up K-Means algorithm
# ==========================================================================
faces = make_faces(str_mesh, vector_tag, weight=False)
clusters = furthest_init(NUM, faces, callback=None)
sel_clusters = clusters[-1]
all_clusters = k_means(sel_clusters,
faces,
ITERS,
MERGESPLIT,
callback=None)
final_clusters = all_clusters[-1]
callback(1, clusters=final_clusters)
# ==========================================================================
# Visualization
# ==========================================================================
plotter.update(pause=0.5)
# Set up K-Means algorithm
# ==========================================================================
# define loss file
errors = []
# NUM = 5 # number of clusters
TEST_RANGE = 100
ITERS = 30 # number of iterations
MERGESPLIT = False # merge split in k means. True is good for this example, but not for knitcandela!
for num in range(1, TEST_RANGE):
print('num clusters: ', num)
faces = make_faces(str_mesh, vector_tag, weight=False)
clusters = furthest_init(num, faces)
sel_clusters = clusters[-1]
all_clusters = k_means(sel_clusters, faces, ITERS, MERGESPLIT)
final_clusters = all_clusters[-1]
error = 0.0
for idx, cluster in final_clusters.items():
proxy = normalize_vector(cluster.proxy)
fkeys = cluster.faces_keys
for fkey in fkeys:
vector = str_mesh.cMesh.get_face_attribute(fkey, vector_tag)
vector = normalize_vector(vector)
ali_vec = ut.align_vector(vector, proxy)
difference = cg.subtract_vectors(ali_vec, proxy)