g = graph.read_graph(EXISTING_GRAPH_FNAME)
gc = graph.GraphContainer(g)
g = g.clone()
graph.densify(g, SEGMENT_LENGTH)
tile_data = {
'region': REGION,
'rect': r,
'search_rect': r.add_tol(-WINDOW_SIZE // 2),
'cache': tiles.cache,
'starting_locations': [],
}
#path = model_utils.Path(gc, tile_data, g=g)
path = model_utils.Path(None, tile_data, g=g)
for vertex in g.vertices:
vertex.edge_pos = None
path.prepend_search_vertex(vertex)
big_ims = tile_data['cache'].get(tile_data['region'],
tile_data['rect'])
m6_old = tf_util.apply_conv(session,
m,
big_ims['input'],
scale=4,
channels=64)
m6_new = tf_util.apply_conv(session,
m,
big_ims['input1'],
scale=4,
channels=64)
#m6_mask = (m6_old < 0.1).astype('float32')
m6_mask = m6_old > 0.4
pos2_point = next_positions[0].point()
pos2_pos = next_positions[0]
start_loc = [{
'point': pos1_point,
'edge_pos': pos1_pos,
}, {
'point': pos2_point,
'edge_pos': pos2_pos,
}]
path = model_utils.Path(tile_data['gc'], tile_data, start_loc=start_loc)
else:
g = graph.read_graph(EXISTING_GRAPH_FNAME)
r = g.bounds()
tile_data = {
'region': REGION,
'rect': r.add_tol(WINDOW_SIZE/2),
'search_rect': r,
'cache': cache,
'starting_locations': [],
}
path = model_utils.Path(None, tile_data, g=g)
for vertex in g.vertices:
path.prepend_search_vertex(vertex)
compute_targets = SAVE_EXAMPLES or FOLLOW_TARGETS
result = eval([path], m, session, save=SAVE_EXAMPLES, compute_targets=compute_targets, follow_targets=FOLLOW_TARGETS)
print result
path.graph.save(output_fname)