def plot_old_new_clusters(filenr, limb1, limb2, path="C:/Users/Cassandra/Documents/GitHub/Body_measurement/Python/Data/Test 4 - Normal"):
cloud1 = np.loadtxt(str(path + '/' + str(filenr) + '_' + limb1 + '.txt'), delimiter=',')
cloud2 = np.loadtxt(str(path + '/' + str(filenr) + '_' + limb2 + '.txt'), delimiter=',')
A = np.column_stack((cloud1[:,0],cloud1[:,1],cloud1[:,2]))
B = np.column_stack((cloud2[:,0],cloud2[:,1],cloud2[:,2]))
A_center = cloud_linalg.get_cloud_center(A)
B_center = cloud_linalg.get_cloud_center(B)
full_cloud, new_cluster_centers, n_clusters_, labels = cloud_meanshift.perform_meanshift(A, B)
colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
fig = plt.figure()
ax = fig.gca(projection='3d')
for k, col in zip(range(n_clusters_), colors):
my_members = labels == k
cluster_center = new_cluster_centers[k]
ax.plot(full_cloud[my_members, 0], full_cloud[my_members, 1], full_cloud[my_members, 2], '.')
ax.plot([cluster_center[0]], [cluster_center[1]], [cluster_center[2]],'o', markersize=14)
fig2 = plt.figure()
ax2 = fig2.gca(projection='3d')
ax2.plot(A[:,0], A[:,1], A[:,2], '.')
ax2.plot([A_center[0]], [A_center[1]], [A_center[2]],'o', markersize=14)
ax2.plot(B[:,0], B[:,1], B[:,2], '.')
ax2.plot([B_center[0]], [B_center[1]], [B_center[2]],'o', markersize=14)
plt.show()
def load_cloud(nr, cl1, cl2, path="C:/Users/Cassandra/Documents/GitHub/Body_measurement/Python/Data/Test 4 - Normal"):
cloud1 = np.loadtxt(str(path + '/' + str(nr) + '_' + cl1 + '.txt'), delimiter=',')
cloud2 = np.loadtxt(str(path + '/' + str(nr) + '_' + cl2 + '.txt'), delimiter=',')
A = np.column_stack((cloud1[:,0],cloud1[:,1],cloud1[:,2]))
B = np.column_stack((cloud2[:,0],cloud2[:,1],cloud2[:,2]))
#find cluster centers
A_center = cloud_linalg.get_cloud_center(A)
B_center = cloud_linalg.get_cloud_center(B)
full_cloud, new_cluster_centers = perform_meanshift(A, B)
return [A_center, B_center], assign_to_cluster(full_cloud, new_cluster_centers), new_cluster_centers
def plot_old_new_cluster_centers(filenr, limb1, limb2, path="C:/Users/Cassandra/Documents/GitHub/Body_measurement/Python/Data/Test 4 - Normal"):
cloud1 = np.loadtxt(str(path + '/' + str(filenr) + '_' + limb1 + '.txt'), delimiter=',')
cloud2 = np.loadtxt(str(path + '/' + str(filenr) + '_' + limb2 + '.txt'), delimiter=',')
A = np.column_stack((cloud1[:,0],cloud1[:,1],cloud1[:,2]))
B = np.column_stack((cloud2[:,0],cloud2[:,1],cloud2[:,2]))
A_center = cloud_linalg.get_cloud_center(A)
B_center = cloud_linalg.get_cloud_center(B)
full_cloud, new_cluster_centers, n_clusters_, labels = cloud_meanshift.perform_meanshift(A, B)
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot(full_cloud[:,0], full_cloud[:,1], full_cloud[:,2], '.')
ax.plot([A_center[0]], [A_center[1]], [A_center[2]],'o', markersize=14)
ax.plot([B_center[0]], [B_center[1]], [B_center[2]],'o', markersize=14)
ax.plot([new_cluster_centers[0][0]], [new_cluster_centers[0][1]], [new_cluster_centers[0][2]],'o', markersize=14)
ax.plot([new_cluster_centers[1][0]], [new_cluster_centers[1][1]], [new_cluster_centers[1][2]],'o', markersize=14)
plt.show()