def generate_networkx_graphs(rand, num_examples, num_nodes_min_max, theta):
"""Generate graphs for training.
Args:
rand: A random seed (np.RandomState instance).
num_examples: Total number of graphs to generate.
num_nodes_min_max: A 2-tuple with the [lower, upper) number of nodes per
graph. The number of nodes for a graph is uniformly sampled within this
range.
theta: (optional) A `float` threshold parameters for the geographic
threshold graph's threshold. Default= the number of nodes.
Returns:
input_graphs: The list of input graphs.
target_graphs: The list of output graphs.
graphs: The list of generated graphs.
"""
input_graphs = []
target_graphs = []
graphs = []
for _ in range(num_examples):
graph = generate_graph(rand, num_nodes_min_max, theta=theta)[0]
graph = add_shortest_path(rand, graph)
input_graph, target_graph = graph_to_input_target(graph)
input_graphs.append(input_graph)
target_graphs.append(target_graph)
graphs.append(graph)
return input_graphs, target_graphs, graphs
def generate_graphs(img_path, solution_path, region, j, placeholder=False):
img = imageio.imread(img_path)
img_solution = imageio.imread(solution_path)
img = np.array(img)
img_solution = np.array(img_solution)
img_subsample = img[::2, ::2]
img_solution_subsample = img_solution[::2, ::2]
images = slice_blocks(img_subsample, 128, 128)
#images = add_arrays(images, slice_blocks(img, 128, 128))
images_solutions = slice_blocks(img_solution_subsample, 128, 128)
images_solutions = add_arrays(images_solutions,
slice_blocks(img_solution, 128, 128))
# if placeholder == True:
# index = j * 4096 - 1
# j2 = j + 1
# else:
#
# j2 = 1
graphs = []
# iterace radky a sloupce
for i in range(len(images)):
index = 0 * 4096 - 1
graph = nx.Graph()
for row in range(0, 128):
for column in range(0, 128):
index += 1
node_solution = 0
value = images[i][row][column] / 255
if images_solutions[i][row][column] == 255:
node_solution = 1
# pridani uzlu
graph.add_node(index, value=value, solution=node_solution)
# pridani hran
add_edges_to_graph(index, graph, img, row, column, region,
images_solutions[i])
graphs.append(graph)
return graphs
def generate_networkx_graphs(graphcache,all_db,rand,num_examples,dataset):
"""Generate graphs for training.
Args:
rand: A random seed (np.RandomState instance).
num_examples: Total number of graphs to generate.
dataset: 'train', 'val', 'test'
Returns:
input_graphs: The list of input graphs.
target_graphs: The list of output graphs.
graphs: The list of generated graphs.
"""
input_graphs = []
target_graphs = []
graphs = []
totnum = len(all_db[dataset])
if totnum==num_examples:
selids = np.arange(totnum)
else:
selids = rand.choice(totnum,num_examples)
for ni in range(num_examples):
#print(all_db[dataset][selids[ni]])
if dataset == 'test':
tt = True
da = False
elif dataset == 'val':
tt = False
da = False
else:
tt = False
da = True
graph = generate_graph(graphcache,all_db[dataset][selids[ni]],rand,data_aug=da,test=tt)
#graph = add_shortest_path(rand, graph)
input_graph, target_graph = graph_to_input_target(graph)
input_graphs.append(input_graph)
target_graphs.append(target_graph)
graphs.append(graph)
return input_graphs, target_graphs, graphs, selids