def create_uv_tree(obj, quantity, uv_center, mean = [0.0, 0.0], variance = [0.005, 0.005], balancing_factor = 0.0, build_type = 'CURVE', interpolation_resolution = 0):
# Generate scattered points
x = [uv_center[0]]
y = [uv_center[1]]
x = np.concatenate((x, np.random.normal(uv_center[0], variance[0], quantity) + mean[0]))
y = np.concatenate((y, np.random.normal(uv_center[1], variance[1], quantity) + mean[1]))
points = np.dstack((x, y))
np.clip(points, 0., 1., out = points)
# all items are stored in the first list-item of the output
points = points[0]
# Run mstree
root_point = mstree.mstree(points, balancing_factor)
# Convert node positions from uv to 3d
nodes = mstree.tree_to_list(root_point)
interpolated_nodes = []
for node in nodes:
parent = node.parent
interpolated_nodes.append(node)
if parent is None:
continue
points = interpolate(parent.pos, node.pos, interpolation_resolution)[1:-1]
print(len(points))
parent.children.remove(node)
active_parent = parent
for point in points:
new_node = mstree.Node(active_parent, np.array(point), -1)
interpolated_nodes.append(new_node)
active_parent = new_node
node.parent = active_parent
active_parent.children.append(node)
node_uv_points = [mathutils.Vector((node.pos[0], node.pos[1])) for node in interpolated_nodes]
print(len(node_uv_points))
node_3d_points = pam.mapUVPointTo3d(obj, node_uv_points)
print(len(node_3d_points))
for i, node in enumerate(interpolated_nodes):
node.pos = node_3d_points[i]
if build_type == 'CURVE':
curve_obj = mst_blender.buildTreeCurve(root_point)
curve_obj.data.bevel_depth = 0.002
elif build_type == 'MESH':
mesh_obj = mst_blender.buildTreeMesh(root_point)
def create_uv_tree(obj,
quantity,
uv_center,
mean=[0.0, 0.0],
variance=[0.005, 0.005],
balancing_factor=0.0,
build_type='CURVE',
interpolation_resolution=0):
# Generate scattered points
x = [uv_center[0]]
y = [uv_center[1]]
x = np.concatenate(
(x, np.random.normal(uv_center[0], variance[0], quantity) + mean[0]))
y = np.concatenate(
(y, np.random.normal(uv_center[1], variance[1], quantity) + mean[1]))
points = np.dstack((x, y))
np.clip(points, 0., 1., out=points)
# all items are stored in the first list-item of the output
points = points[0]
# Run mstree
root_point = mstree.mstree(points, balancing_factor)
# Convert node positions from uv to 3d
nodes = mstree.tree_to_list(root_point)
interpolated_nodes = []
for node in nodes:
parent = node.parent
interpolated_nodes.append(node)
if parent is None:
continue
points = interpolate(parent.pos, node.pos,
interpolation_resolution)[1:-1]
print(len(points))
parent.children.remove(node)
active_parent = parent
for point in points:
new_node = mstree.Node(active_parent, np.array(point), -1)
interpolated_nodes.append(new_node)
active_parent = new_node
node.parent = active_parent
active_parent.children.append(node)
node_uv_points = [
mathutils.Vector((node.pos[0], node.pos[1]))
for node in interpolated_nodes
]
print(len(node_uv_points))
node_3d_points = pam.mapUVPointTo3d(obj, node_uv_points)
print(len(node_3d_points))
for i, node in enumerate(interpolated_nodes):
node.pos = node_3d_points[i]
if build_type == 'CURVE':
curve_obj = mst_blender.buildTreeCurve(root_point)
curve_obj.data.bevel_depth = 0.002
elif build_type == 'MESH':
mesh_obj = mst_blender.buildTreeMesh(root_point)
import mstree
import numpy as np
def drawTree(root_node):
plt.figure()
plt.scatter(points[:, 0], points[:, 1])
drawTreeRecursive(root_node)
plt.show()
def drawTreeRecursive(root_node):
for child in root_node.children:
print('Plotting from point ' + str(root_node.index) + ' to ' +
str(child.index))
plt.plot((root_node.pos[0], child.pos[0]),
(root_node.pos[1], child.pos[1]), '-')
drawTreeRecursive(child)
if __name__ == '__main__':
points = np.random.rand(100, 2) * 10 - 5
points[0] = (0, 0)
# plt.scatter(points[:,0], points[:,1])
# plt.show()
# f = open('testdata.csv', 'r')
# reader = csv.reader(f, delimiter=";", quoting=csv.QUOTE_NONNUMERIC)
# points = np.array([row for row in reader])
# print(points)
root = mstree.mstree(points, threshold=50, balancing_factor=0)
drawTree(root)
import mstree
import numpy as np
import cProfile
import csv
import time
f = open('testdata_large.csv', 'r')
reader = csv.reader(f, delimiter=";", quoting=csv.QUOTE_NONNUMERIC)
points = np.array([row for row in reader])
v = [1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000]
for vv in v:
points = np.random.rand(vv,2) * 10 - 5
points[0] = (0,0)
# cProfile.run('mstree.mstree(points, threshold = 100, balancing_factor = 0.0)')
t1 = time.time()
tree = mstree.mstree(points, balancing_factor = 0.0)
t2 = time.time()
print(vv, '\t', t2-t1)
# mstree.add_quad_diameter(tree)
import matplotlib.pyplot as plt
import mstree
import numpy as np
def drawTree(root_node):
plt.figure()
plt.scatter(points[:,0], points[:,1])
drawTreeRecursive(root_node)
plt.show()
def drawTreeRecursive(root_node):
for child in root_node.children:
print('Plotting from point ' + str(root_node.index) + ' to ' + str(child.index))
plt.plot((root_node.pos[0], child.pos[0]), (root_node.pos[1], child.pos[1]),'-')
drawTreeRecursive(child)
if __name__ == '__main__':
points = np.random.rand(100,2) * 10 - 5
points[0] = (0,0)
# plt.scatter(points[:,0], points[:,1])
# plt.show()
# f = open('testdata.csv', 'r')
# reader = csv.reader(f, delimiter=";", quoting=csv.QUOTE_NONNUMERIC)
# points = np.array([row for row in reader])
# print(points)
root = mstree.mstree(points, threshold = 50, balancing_factor = 0)
drawTree(root)
import mstree
import numpy as np
import cProfile
import csv
import time
f = open('testdata_large.csv', 'r')
reader = csv.reader(f, delimiter=";", quoting=csv.QUOTE_NONNUMERIC)
points = np.array([row for row in reader])
v = [1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000]
for vv in v:
points = np.random.rand(vv, 2) * 10 - 5
points[0] = (0, 0)
# cProfile.run('mstree.mstree(points, threshold = 100, balancing_factor = 0.0)')
t1 = time.time()
tree = mstree.mstree(points, balancing_factor=0.0)
t2 = time.time()
print(vv, '\t', t2 - t1)
# mstree.add_quad_diameter(tree)
