def draw(self):
fig = visualizeData(
self._position_data,
[graph.points for graph in self._clusters] + [self._nodes_left],
reduce(add, [graph.edges for graph in self._clusters]), True)
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3, ))
cv2.imshow('win', data)
cv2.waitKey(10)
plt.close(fig)
def draw(self, saveFile=None, drawEdges=True, time=10):
fig = visualizeData(self._position_data,
[graph.points
for graph in self._clusters] + [self._nodes_left],
reduce(add,
[graph.edges for graph in self._clusters]),
True,
drawEdges=drawEdges)
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3, ))
if saveFile is not None:
cv2.imwrite(saveFile, data)
cv2.imshow('win', data)
cv2.waitKey(time)
plt.close(fig)
from Code.Graph import Graph
from Code.Data.DistanceMatrix import getDistanceMatrix
from Code.Data.LoadData import readData
from Code.drawGraph import visualizeData
from functools import reduce
from operator import add
position_data = readData(path="../objects.data")
matrix = getDistanceMatrix(position_data)
all_nodes = len(position_data)
g = Graph(list(range(all_nodes)), matrix)
edges_cut = []
# cutting max edges
for i in range(9):
edges_cut.append(g.get_max_edge())
g.remove_edge([edges_cut[i]])
print(g.compute_edges_length())
graphs = []
visualizeData(position_data, [g.points], g.edges)
# building clusters from disconnected graphs
for i in range(10):
points = g.get_first_connected_subgraph()
g.removePoints(points)
graphs.append(Graph(points, matrix))
visualizeData(position_data, [graphs[i].points], graphs[i].edges)
visualizeData(position_data, [graph.points for graph in graphs],
reduce(add, [graph.edges for graph in graphs]))
def visualize(self, position_data):
visualizeData(position_data,
[graph.points for graph in self._clusters],
reduce(add, [graph.edges for graph in self._clusters]))