plot depth error percentiles

svo_analysis/src/svo_analysis/analyse_depth.py

@@ -0,0 +1,68 @@
+#!/usr/bin/python
+
+import os
+import yaml
+import argparse
+import numpy as np
+import matplotlib.pyplot as plt
+import svo_analysis.tum_benchmark_tools.associate as associate
+import vikit_py.transformations as transformations
+import vikit_py.align_trajectory as align_trajectory
+from matplotlib import rc
+
+rc('font',**{'family':'serif','serif':['Cardo']})
+rc('text', usetex=True)
+
+def precision_plot(ax, errors, max_error, color, linestyle):
+    number_of_error_ranges = 500
+    error_step = max_error / number_of_error_ranges
+    precision_array = np.zeros(number_of_error_ranges)
+    error_array = np.zeros(number_of_error_ranges)
+    n_measurements = len(errors)
+    for error_ind in range(0, number_of_error_ranges):
+        precision_array[error_ind] = np.sum(errors < error_step*error_ind) / float(n_measurements) * 100.0
+        error_array[error_ind] = error_step*error_ind
+    plot_obj, = ax.plot(error_array, precision_array, label='error', linestyle=linestyle, color=color)
+    return plot_obj
+    
+def analyse_depth(results_dir):
+    D = np.loadtxt(os.path.join(results_dir, 'depth_error.txt'), delimiter=' ')
+    D = D[D[:,0] > 1,:]
+
+    fig = plt.figure(figsize=(6,5))
+    ax = fig.add_subplot(111, xlabel='depth error [m]', ylabel='Precision [\%]')
+    errors = np.abs(D[:,1])
+    precision_plot(ax, errors, 0.05, 'r', '-')    
+    fig.tight_layout()
+    fig.savefig(os.path.join(results_dir,'depth_error.pdf'), bbox_inches="tight")
+    
+def analyse_depth_over_time(results_dir):
+    D = np.loadtxt(os.path.join(results_dir, 'depth_error.txt'), delimiter=' ')
+    D = D[D[:,0] > 1,:]
+
+    fig = plt.figure(figsize=(6,5))
+    ax = fig.add_subplot(111, xlabel='Measurement', ylabel='Scale-Drift')
+    
+    idxs = np.unique(D[:,0])
+    percentile_20 = np.zeros(len(idxs))
+    percentile_50 = np.zeros(len(idxs))
+    percentile_80 = np.zeros(len(idxs))
+    for i in range(len(idxs)):
+        errors = np.abs(D[D[:,0]==idxs[i],1])
+        percentile_20[i] = np.percentile(errors, 20)
+        percentile_50[i] = np.percentile(errors, 50)
+        percentile_80[i] = np.percentile(errors, 80)
+
+    base_plot, = ax.plot(idxs, percentile_50, linewidth=2)
+    ax.fill_between(idxs, percentile_20, percentile_80, alpha=0.25, color=base_plot.get_color())
+    fig.tight_layout()
+    fig.savefig(os.path.join(results_dir,'scale_drift.pdf'), bbox_inches="tight")
+    
+    
+        
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Analyse depth estimate')
+    parser.add_argument('results_dir', help='folder with the results')
+    args = parser.parse_args()
+    analyse_depth(args.results_dir)
+    analyse_depth_over_time(args.results_dir)