Added script for pickling a map

.gitignore

@@ -2,6 +2,7 @@ examples/__pycache__
 examples/*.pyc
 examples/*.pgm
 examples/*.png
+examples/*.map
 python/breezyslam/*.pyc
 python/breezyslam/__pycache__
 python/build

examples/log2pkl.py

@@ -0,0 +1,125 @@
+#!/usr/bin/env python
+
+'''
+log2pkl.py : BreezySLAM Python demo.  Reads logfile with odometry and scan data
+             from Paris Mines Tech and pickles the map file for loading by another
+             program
+             
+Copyright (C) 2014 Simon D. Levy
+
+This code is free software: you can redistribute it and/or modify
+it under the terms of the GNU Lesser General Public License as 
+published by the Free Software Foundation, either version 3 of the 
+License, or (at your option) any later version.
+
+This code 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 Lesser General Public License 
+along with this code.  If not, see <http://www.gnu.org/licenses/>.
+'''
+
+# Map size, scale
+MAP_SIZE_PIXELS          = 800
+MAP_SIZE_METERS          =  32
+
+from breezyslam.algorithms import Deterministic_SLAM, RMHC_SLAM
+
+from mines import MinesLaser, Rover, load_data
+from progressbar import ProgressBar
+
+from sys import argv, exit, stdout
+from time import time
+import pickle
+
+def main():
+	    
+    # Bozo filter for input args
+    if len(argv) < 3:
+        print('Usage:   %s <dataset> <use_odometry> <random_seed>' % argv[0])
+        print('Example: %s exp2 1 9999' % argv[0])
+        exit(1)
+    
+    # Grab input args
+    dataset = argv[1]
+    use_odometry  =  True if int(argv[2]) else False
+    seed =  int(argv[3]) if len(argv) > 3 else 0
+    
+	# Load the data from the file, ignoring timestamps
+    _, lidars, odometries = load_data('.', dataset)
+    
+    # Build a robot model if we want odometry
+    robot = Rover() if use_odometry else None
+        
+    # Create a CoreSLAM object with laser params and optional robot object
+    slam = RMHC_SLAM(MinesLaser(), MAP_SIZE_PIXELS, MAP_SIZE_METERS, random_seed=seed) \
+           if seed \
+           else Deterministic_SLAM(MinesLaser(), MAP_SIZE_PIXELS, MAP_SIZE_METERS)
+           
+    # Report what we're doing
+    nscans = len(lidars)
+    print('Processing %d scans with%s odometry / with%s particle filter...' % \
+        (nscans, \
+         '' if use_odometry else 'out', '' if seed else 'out'))
+    progbar = ProgressBar(0, nscans, 80)
+    
+    # Start with an empty trajectory of positions
+    trajectory = []
+
+    # Start timing
+    start_sec = time()
+    
+    # Loop over scans    
+    for scanno in range(nscans):
+    
+        if use_odometry:
+                  
+            # Convert odometry to velocities
+            velocities = robot.computeVelocities(odometries[scanno])
+                                 
+            # Update SLAM with lidar and velocities
+            slam.update(lidars[scanno], velocities)
+            
+        else:
+        
+            # Update SLAM with lidar alone
+            slam.update(lidars[scanno])
+                    
+        # Get new position
+        x_mm, y_mm, theta_degrees = slam.getpos()    
+        
+        # Add new position to trajectory
+        trajectory.append((x_mm, y_mm))
+        
+        # Tame impatience
+        progbar.updateAmount(scanno)
+        stdout.write('\r%s' % str(progbar))
+        stdout.flush()
+
+    # Report elapsed time
+    elapsed_sec = time() - start_sec
+    print('\n%d scans in %f sec = %f scans / sec' % (nscans, elapsed_sec, nscans/elapsed_sec))
+                    
+                                
+    # Create a byte array to receive the computed maps
+    mapbytes = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
+    
+    # Get final map    
+    slam.getmap(mapbytes)
+
+    # Pickle the map to a file
+    pklname = dataset + '.map'
+    print('Writing map to file ' + pklname)
+    pickle.dump(mapbytes, open(pklname, 'wb'))
+    
+            
+# Helpers ---------------------------------------------------------        
+
+def mm2pix(mm):
+        
+    return int(mm / (MAP_SIZE_METERS * 1000. / MAP_SIZE_PIXELS))  
+    
+                    
+main()