Changed shortpath.py and created new example.

- Added a new possible output to shortpath.py in order to debug the
graph generation.
- New example using this step-wise output from shortpath.py to create a
.gif of the graph creation.

pc2graph/example_batch.py

@@ -29,6 +29,7 @@
 from downsampling import (downsample_cloud, upsample_cloud)
 import os
 
+
 if __name__ == '__main__':
 
     # Declaring list of files to process and output folder.
@@ -70,7 +71,7 @@
 
         # Maximum distance between points. If distance > threshold, neighboring
         # point is not added to graph.
-        nbrs_threshold = 0.08
+        nbrs_threshold = 0.15
 
         # When initial growth process is broken (gap in the cloud) and no
         # other point can be added, incease threshold to include missing points.

pc2graph/example_plot_gif.py

@@ -0,0 +1,93 @@
+# Copyright (c) 2017, Matheus Boni Vicari, TLSeparation Project
+# All rights reserved.
+#
+#
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+#
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+#
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+__author__ = "Matheus Boni Vicari"
+__copyright__ = "Copyright 2017"
+__credits__ = ["Matheus Boni Vicari"]
+__license__ = "GPL3"
+__version__ = "0.1"
+__maintainer__ = "Matheus Boni Vicari"
+__email__ = "[email protected]"
+__status__ = "Development"
+
+
+from downsampling import downsample_cloud
+from shortpath import array_to_graph
+import numpy as np
+import glob
+import os
+import mayavi.mlab as mlab
+
+
+if __name__ == '__main__':
+
+    # Setting up where to save the data.
+    out_folder = '../data/test_gif/'
+    out_file = 'test'
+    layer_delay = 2
+
+    # Setting up parameters to generate the graph.
+    point_cloud = np.loadtxt('../data/point_cloud_example.txt')
+    downsample_dist = 0.05
+    kpairs = 3
+    knn = 100
+    nbr_dist_threshold = downsample_dist * 1.5
+    nbr_dist_threshold_step = 0.03
+    downsample_cloud = downsample_cloud(point_cloud, downsample_dist)
+
+    # Obtaining base id (lowest point in the cloud).
+    base_id = np.argmin(downsample_cloud[:, 2])
+
+    # Generating graph with option to return the step_register as True.
+    G, step_register = array_to_graph(downsample_cloud, base_id, kpairs,
+                                      knn, nbr_dist_threshold,
+                                      nbr_dist_threshold_step,
+                                      return_step=True)
+
+
+    # Starts the iterative plotting. This will require the Mayavi package.
+    iteration = 0
+    mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
+    mlab.points3d(downsample_cloud[:, 0], downsample_cloud[:, 1],
+                  downsample_cloud[:, 2], color=(0.3, 0.3, 0.3), mode='point')
+    for s in np.unique(step_register):
+        mask = step_register == s
+        mlab.points3d(downsample_cloud[mask, 0], downsample_cloud[mask, 1],
+                      downsample_cloud[mask, 2], color=(1, 0, 0), mode='point')
+        mlab.savefig(out_folder + '%s.png' % iteration)
+        iteration += 1
+    mlab.close()
+
+    # Generating the list of step images to join in a gif later.
+    file_list = glob.glob('%s*.png' % out_folder)
+    file_list = [os.path.basename(f) for f in file_list]
+    list.sort(file_list, key=lambda x: int(os.path.basename(x).split('.png')[0]))
+    with open('%simage_list.txt' % out_folder, 'w') as file:
+        for item in file_list:
+            file.write("%s\n" % item)
+
+    # Changing directory and using ImageMagik to join all images into a gif.
+    # Then, delets all unnecessary files.In the end, returns to initial
+    # directory.
+    cwd = os.getcwd()
+    os.chdir(out_folder)
+    os.system('convert -loop 0 -delay %s @image_list.txt %s.gif' % (layer_delay, out_file))
+    os.remove('image_list.txt')
+    for f in file_list:
+        os.remove(f)
+    os.chdir(cwd)
+

pc2graph/shortpath.py

@@ -30,7 +30,8 @@
 
 
 def array_to_graph(arr, base_id, kpairs, knn, nbrs_threshold,
-                   nbrs_threshold_step, graph_threshold=np.inf):
+                   nbrs_threshold_step, graph_threshold=np.inf,
+                   return_step=False):
 
     """
     Converts a numpy.array of points coordinates into a Weighted BiDirectional
@@ -61,12 +62,18 @@ def array_to_graph(arr, base_id, kpairs, knn, nbrs_threshold,
     graph_threshold : float
         Maximum distance between pairs of nodes (edge distance) accepted in
         the graph generation.
+    return_step : bool
+        Option to select if function should output the step register, which
+        can be used to debug the creationg of graph 'G'.
 
     Returns
     -------
     G : networkx graph
         Graph containing all points in 'arr' as nodes.
-
+    step_register : array
+        1D array with the same number of entries as 'arr'. Stores the step
+        number of which each point in 'arr' was added to 'G'.
+        
     """
 
     # Initializing graph.
@@ -88,9 +95,18 @@ def array_to_graph(arr, base_id, kpairs, knn, nbrs_threshold,
     # and all ids already processed (processed_idx).
     current_idx = [base_id]
     processed_idx = [base_id]
+
+    # Setting up the register of at which step each point was added to the
+    # graph.
+    step_register = np.full(arr.shape[0], np.nan)
+    current_step = 0
+    step_register[base_id] = current_step
 
     # Looping while there are still indices (idx) left to process.
     while idx.shape[0] > 0:
+
+        # Increasing a single step count.
+        current_step += 1
 
         # If current_idx is a list containing several indices.
         if len(current_idx) > 0:
@@ -191,8 +207,15 @@ def array_to_graph(arr, base_id, kpairs, knn, nbrs_threshold,
 
         # Generating list of remaining proints to process.
         idx = idx_base[np.in1d(idx_base, processed_idx, invert=True)]
-
-    return G
+
+        # Adding new nodes to the step register.
+        current_idx = np.array(current_idx).astype(int)
+        step_register[current_idx] = current_step
+
+    if return_step is True:
+        return G, step_register
+    else:
+        return G
 
 
 def extract_path_info(G, base_id, return_path=True):