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map_segmentation_demo_all.py
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map_segmentation_demo_all.py
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import argparse
from GridMapDecompose import map_handling as mh
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('img_file', help='Path to the map')
parser.add_argument('threshold_type', help='Threshold type')
args = parser.parse_args()
test_map = mh.GridMapHandling()
test_map.load_map_flat_file(args.img_file)
test_map.threshold_map(args.threshold_type)
test_map.fill_gaps(1)
test_map.build_graphs()
test_map.label_map()
test_map.evaluate_segments()
visualisation = {"adjacency matrix": False,
"nodes": False,
"edges": False,
"corners and walls": False,
"corners and walls mbb": True,
"labeled map": True,
"graph and mbb": True}
test_map.show(visualisation)
# test_map.save()
# visualize = [False, False, False, True, True, True, True]
#
# if visualize[0]:
# ################################
# fig, ax = plt.subplots(nrows=1, ncols=1, sharex=True, sharey=True)
# im2 = ax.imshow(test_map.graph.adjacency_matrix)
# divider = make_axes_locatable(ax)
# cax = divider.append_axes("right", size="5%", pad=0.05)
# plt.colorbar(im2, cax=cax)
# if visualize[1]:
# ################################
# fig1, ax1 = plt.subplots(nrows=1, ncols=1, sharex=True, sharey=True)
# min_score = np.nanmin(test_map.graph.adjacency_matrix)
# max_score = np.nanmax(test_map.graph.adjacency_matrix)
# norm = plt.Normalize(min_score, max_score)
# cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.jet)
# cmap.set_array([])
# ax1.imshow(test_map.skeleton, cmap="nipy_spectral")
# for it in range(0, test_map.graph.adjacency_matrix.shape[0]):
# if not test_map.graph.node_labels[it]:
# ax1.plot(test_map.graph.nodes[it]['coordinates'][1], test_map.graph.nodes[it]['coordinates'][0], 'g+')
# else:
# ax1.plot(test_map.graph.nodes[it]['coordinates'][1], test_map.graph.nodes[it]['coordinates'][0], 'r.')
# for it in range(0, test_map.graph.adjacency_matrix.shape[0]):
# for jt in range(it, test_map.graph.adjacency_matrix.shape[0]):
# if not np.isnan(test_map.graph.adjacency_matrix[it, jt]):
# a = test_map.graph.nodes[it]
# b = test_map.graph.nodes[jt]
# a = a['coordinates']
# b = b['coordinates']
# y = [a[0], b[0]]
# x = [a[1], b[1]]
# im = ax1.plot(x, y, color=cm.jet(norm(test_map.graph.adjacency_matrix[it, jt])))
# fig1.subplots_adjust(right=0.8)
# cbar_ax = fig1.add_axes([0.85, 0.15, 0.05, 0.7])
# fig1.colorbar(cmap, cax=cbar_ax)
# ax1.axis('off')
# fig1.tight_layout()
# if visualize[2]:
# #############################
# fig2, ax2 = plt.subplots(nrows=1, ncols=1, sharex=True, sharey=True)
# min_score = np.nanmin(test_map.graph.walls)
# max_score = np.nanmax(test_map.graph.walls)
# norm = plt.Normalize(min_score, max_score)
# cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.jet)
# cmap.set_array([])
# ax2.imshow(test_map.skeleton, cmap="nipy_spectral")
# for it in range(0, test_map.graph.walls.shape[0]):
# for jt in range(it, test_map.graph.walls.shape[0]):
# if not test_map.graph.walls[it, jt] == 0.0:
# a = test_map.graph.nodes[it]
# b = test_map.graph.nodes[jt]
# a = a['coordinates']
# b = b['coordinates']
# y = [a[0], b[0]]
# x = [a[1], b[1]]
# im = ax2.plot(x, y, color=cm.jet(norm(test_map.graph.walls[it, jt])))
# fig2.subplots_adjust(right=0.8)
# cbar_ax = fig2.add_axes([0.85, 0.15, 0.05, 0.7])
# fig2.colorbar(cmap, cax=cbar_ax)
# fig2.tight_layout()
# ax2.axis('off')
#
# if visualize[3]:
# #############################
# fig3, ax3 = plt.subplots(nrows=1, ncols=1, sharex=True, sharey=True)
# ax3.imshow(test_map.skeleton, cmap="nipy_spectral")
# for sg in test_map.graph.C:
# coord = []
# for node_id in sg.node:
# a = test_map.graph.nodes[node_id]
# coord.append(a['coordinates'])
# coord = np.array(coord)
# ax3.plot(coord[:, 1], coord[:, 0], 's', markerfacecolor='none')
# for sg in test_map.graph.W:
# coord = []
# for node_id in sg.node:
# a = test_map.graph.nodes[node_id]
# coord.append(a['coordinates'])
# coord = np.array(coord)
#
# ax3.plot(coord[:, 1], coord[:, 0], '.')
#
# fig3.tight_layout()
# ax3.axis('off')
#
# if visualize[4]:
# #############################
# fig4, ax4 = plt.subplots(nrows=1, ncols=1, sharex=True, sharey=True)
# ax4.imshow(test_map.binary_map, cmap="nipy_spectral")
# for local_segment, local_segment_type in zip(test_map.segments, test_map.segment_type):
# if local_segment_type is 'w':
# ax4.plot(local_segment.minimal_bounding_box[:, 1], local_segment.minimal_bounding_box[:, 0], 'g')
# if local_segment_type is 'f':
# ax4.plot(local_segment.minimal_bounding_box[:, 1], local_segment.minimal_bounding_box[:, 0], 'r')
#
# ax4.axis('off')
#
# if visualize[5]:
# #############################
# fig5, ax5 = plt.subplots(nrows=1, ncols=1, sharex=True, sharey=True)
# ax5.imshow(test_map.labeled_map, cmap="nipy_spectral")
#
# ax5.axis('off')
#
# if visualize[6]:
# #############################
# fig6, ax6 = plt.subplots(nrows=1, ncols=1, sharex=True, sharey=True)
# ax6.imshow(test_map.binary_map, cmap="nipy_spectral")
# for local_segment, local_segment_type in zip(test_map.segments, test_map.segment_type):
# if local_segment_type is 'w':
# ax6.plot(local_segment.minimal_bounding_box[:, 1], local_segment.minimal_bounding_box[:, 0], 'g')
# if local_segment_type is 'f':
# ax6.plot(local_segment.minimal_bounding_box[:, 1], local_segment.minimal_bounding_box[:, 0], 'r')
#
# # quickly find edges
# LU_adjacency_matrix = np.triu(test_map.adjacency_matrix_segments)
# edges = np.column_stack(np.nonzero(LU_adjacency_matrix))
# for edge in edges:
# x = (test_map.segments[edge[0] - 1].center[1], test_map.segments[edge[1] - 1].center[1])
# y = (test_map.segments[edge[0] - 1].center[0], test_map.segments[edge[1] - 1].center[0])
# ax6.plot(x, y, 'b')
#
# ax6.axis('off')
#
# plt.show()