format and refactor the SFM BAL example

python/gtsam/examples/SFMExample_bal.py

@@ -7,49 +7,58 @@
   See LICENSE for the license information
 
   Solve a structure-from-motion problem from a "Bundle Adjustment in the Large" file
-  Author: Frank Dellaert (Python: Akshay Krishnan, John Lambert)
+  Author: Frank Dellaert (Python: Akshay Krishnan, John Lambert, Varun Agrawal)
 """
 
 import argparse
 import logging
 import sys
 
-import matplotlib.pyplot as plt
-import numpy as np
-
 import gtsam
-from gtsam import (
+from gtsam import (GeneralSFMFactorCal3Bundler,
-    GeneralSFMFactorCal3Bundler,
+                   PriorFactorPinholeCameraCal3Bundler, PriorFactorPoint3,
-    PinholeCameraCal3Bundler,
+                   readBal)
-    PriorFactorPinholeCameraCal3Bundler,
+from gtsam.symbol_shorthand import C, P
-    readBal,
+from gtsam.utils.plot import plot_3d_points, plot_trajectory
-    symbol_shorthand
-)
 
-C = symbol_shorthand.C
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-P = symbol_shorthand.P
 
 
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+def plot(scene_data: gtsam.SfmData, result: gtsam.Values):
+    """Plot the trajectory."""
+    plot_vals = gtsam.Values()
+    for cam_idx in range(scene_data.number_cameras()):
+        plot_vals.insert(C(cam_idx),
+                         result.atPinholeCameraCal3Bundler(C(cam_idx)).pose())
+    for t_idx in range(scene_data.number_tracks()):
+        plot_vals.insert(P(t_idx), result.atPoint3(P(t_idx)))
 
-def run(args):
+    plot_3d_points(0, plot_vals, linespec="g.")
+    plot_trajectory(0, plot_vals, show=True)
+
+
+def run(args: argparse.Namespace):
     """ Run LM optimization with BAL input data and report resulting error """
-    input_file = gtsam.findExampleDataFile(args.input_file)
+    if args.input_file:
+        input_file = args.input_file
+    else:
+        input_file = gtsam.findExampleDataFile("dubrovnik-3-7-pre")
 
     # Load the SfM data from file
     scene_data = readBal(input_file)
-    logging.info(f"read {scene_data.number_tracks()} tracks on {scene_data.number_cameras()} cameras\n")
+    logging.info("read %d tracks on %d cameras\n", scene_data.number_tracks(),
+                 scene_data.number_cameras())
 
     # Create a factor graph
     graph = gtsam.NonlinearFactorGraph()
 
     # We share *one* noiseModel between all projection factors
-    noise = gtsam.noiseModel.Isotropic.Sigma(2, 1.0) # one pixel in u and v
+    noise = gtsam.noiseModel.Isotropic.Sigma(2, 1.0)  # one pixel in u and v
 
     # Add measurements to the factor graph
     j = 0
     for t_idx in range(scene_data.number_tracks()):
-        track = scene_data.track(t_idx) # SfmTrack
+        track = scene_data.track(t_idx)  # SfmTrack
         # retrieve the SfmMeasurement objects
         for m_idx in range(track.number_measurements()):
             # i represents the camera index, and uv is the 2d measurement
@@ -60,20 +69,18 @@ def run(args):
 
     # Add a prior on pose x1. This indirectly specifies where the origin is.
     graph.push_back(
-        gtsam.PriorFactorPinholeCameraCal3Bundler(
+        PriorFactorPinholeCameraCal3Bundler(
-            C(0), scene_data.camera(0), gtsam.noiseModel.Isotropic.Sigma(9, 0.1)
+            C(0), scene_data.camera(0),
-        )
+            gtsam.noiseModel.Isotropic.Sigma(9, 0.1)))
-    )
     # Also add a prior on the position of the first landmark to fix the scale
     graph.push_back(
-        gtsam.PriorFactorPoint3(
+        PriorFactorPoint3(P(0),
-            P(0), scene_data.track(0).point3(), gtsam.noiseModel.Isotropic.Sigma(3, 0.1)
+                          scene_data.track(0).point3(),
-        )
+                          gtsam.noiseModel.Isotropic.Sigma(3, 0.1)))
-    )
 
     # Create initial estimate
     initial = gtsam.Values()
-    
+
     i = 0
     # add each PinholeCameraCal3Bundler
     for cam_idx in range(scene_data.number_cameras()):
@@ -81,12 +88,10 @@ def run(args):
         initial.insert(C(i), camera)
         i += 1
 
-    j = 0
     # add each SfmTrack
     for t_idx in range(scene_data.number_tracks()):
-        track = scene_data.track(t_idx)  
+        track = scene_data.track(t_idx)
-        initial.insert(P(j), track.point3())
+        initial.insert(P(t_idx), track.point3())
-        j += 1
 
     # Optimize the graph and print results
     try:
@@ -94,25 +99,31 @@ def run(args):
         params.setVerbosityLM("ERROR")
         lm = gtsam.LevenbergMarquardtOptimizer(graph, initial, params)
         result = lm.optimize()
-    except Exception as e:
+    except RuntimeError:
         logging.exception("LM Optimization failed")
         return
+
     # Error drops from ~2764.22 to ~0.046
-    logging.info(f"final error: {graph.error(result)}")
+    logging.info("initial error: %f", graph.error(initial))
+    logging.info("final error: %f", graph.error(result))
+
+    plot(scene_data, result)
+
+
+def main():
+    """Main runner."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-i',
+                        '--input_file',
+                        type=str,
+                        default="",
+                        help="""Read SFM data from the specified BAL file.
+        The data format is described here: https://grail.cs.washington.edu/projects/bal/.
+        BAL files contain (nrPoses, nrPoints, nrObservations), followed by (i,j,u,v) tuples,
+        then (wx,wy,wz,tx,ty,tz,f,k1,k1) as Bundler camera calibrations w/ Rodrigues vector
+        and (x,y,z) 3d point initializations.""")
+    run(parser.parse_args())
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
+    main()
-    parser.add_argument(
-        '-i',
-        '--input_file',
-        type=str,
-        default="dubrovnik-3-7-pre",
-        help='Read SFM data from the specified BAL file'
-        'The data format is described here: https://grail.cs.washington.edu/projects/bal/.'
-        'BAL files contain (nrPoses, nrPoints, nrObservations), followed by (i,j,u,v) tuples, '
-        'then (wx,wy,wz,tx,ty,tz,f,k1,k1) as Bundler camera calibrations w/ Rodrigues vector'
-        'and (x,y,z) 3d point initializations.'
-    )
-    run(parser.parse_args())
-