diff --git a/scripts/matching_eval_plot.py b/scripts/matching_eval_plot.py index 75b79ec..7e130ec 100644 --- a/scripts/matching_eval_plot.py +++ b/scripts/matching_eval_plot.py @@ -7,6 +7,8 @@ import seaborn as sns import argparse import os +import sklearn.metrics +import random parser = argparse.ArgumentParser(description='Plot matching evaluation results') parser.add_argument('results_path', help='matching results file, source bag file, or working directory') @@ -76,10 +78,77 @@ ax3.set_zlim([0, maxz]) ax3.view_init(elev=100, azim=270) - ax4.set_title('Distribution of matches over changes in radial distance') - ax4.set_ylabel('Number of matches') + # ax4.set_title('Distribution of matches over changes in radial distance') + # ax4.set_ylabel('Number of matches') + # ax4.set_xlabel('Delta radial distance') + # ax4.hist(good_radial_distances[:, 0].ravel(), color='c') + + # match_dict = [[] for i in range(10)] + # for row in good_radial_distances: + # r = int(min(row[0], 0.499999) / 0.05) + # match_dict[r].append((row[1], 0)) + # for row in bad_radial_distances: + # r = int(min(row[0], 0.499999) / 0.05) + # match_dict[r].append((row[1], 1)) + # si_bins = [0] * 10 + # for r in range(len(match_dict)): + # match_dict[r] = sorted(match_dict[r], key=lambda x: x[0]) + # if len(match_dict[r]) <= 1: + # si_bins[r] = 1 + # continue + # good_nn = 0 + # for i in range(len(match_dict[r])): + # if i == 0: + # if match_dict[r][i][1] == match_dict[r][i + 1]: + # good_nn += 1 + # elif i == len(match_dict[r]) - 1: + # if match_dict[r][i][1] == match_dict[r][i - 1]: + # good_nn += 1 + # else: + # if match_dict[r][i][0] - match_dict[r][i - 1][0] > match_dict[r][i + 1][0] - match_dict[r][i][0]: + # if match_dict[r][i + 1][1] == match_dict[r][i][1]: + # good_nn += 1 + # else: + # if match_dict[r][i][1] == match_dict[r][i - 1][1]: + # good_nn += 1 + # si_bins[r] = good_nn / float(len(match_dict[r])) + # ax4.plot([i for i in range(0, len(match_dict))], [si_bins[i] for i in range(0, len(match_dict))]) + + ax4.set_title('Match separability over changes in radial distance') + ax4.set_ylabel('Silhouette coefficient') ax4.set_xlabel('Delta radial distance') - ax4.hist(good_radial_distances[:, 0].ravel(), color='c') + num_bins = 50 + num_samples = 5000 + sr = [0 for i in range(num_bins)] + X_good = [np.array([]) for i in range(num_bins)] + labels_good = [np.array([]) for i in range(num_bins)] + X_bad = [np.array([]) for i in range(num_bins)] + labels_bad = [np.array([]) for i in range(num_bins)] + valid_good = [False for i in range(num_bins)] + valid_bad = [False for i in range(num_bins)] + valid = [False for i in range(num_bins)] + for row in good_radial_distances: + r = int(min(row[0], 0.499999) / (0.5 / num_bins)) + X_good[r] = np.append(X_good[r], row[1]) + labels_good[r] = np.append(labels_good[r], 0) + valid_good[r] = True + for row in bad_radial_distances: + r = int(min(row[0], 0.499999) / (0.5 / num_bins)) + X_bad[r] = np.append(X_bad[r], row[1]) + labels_bad[r] = np.append(labels_bad[r], 1) + valid_bad[r] = True + for i in range(num_bins): + valid[i] = valid_good[i] and valid_bad[i] + if not valid[i]: + continue + idx_good = np.arange(len(X_good[i])) + idx_bad = np.arange(len(X_bad[i])) + if len(X_good[i]) > num_samples: + idx_good = np.random.choice(np.arange(len(X_good[i])), num_samples, replace=False) + if len(X_bad[i]) > num_samples: + idx_bad = np.random.choice(np.arange(len(X_bad[i])), num_samples, replace=False) + sr[i] = sklearn.metrics.silhouette_score(np.concatenate((X_good[i][idx_good], X_bad[i][idx_bad])).reshape(-1, 1), np.concatenate((labels_good[i][idx_good], labels_bad[i][idx_bad])), metric = 'l1') + ax4.plot([i * 0.5 / num_bins + 0.5 / num_bins / 2 for i in range(0, len(sr)) if valid[i]], [sr[i] for i in range(0, len(sr)) if valid[i]]) df = pd.DataFrame({'Delta radial distance': ['{}-{}'.format(r * 0.05, (r + 1) * 0.05) for r in (np.minimum(np.hstack((good_radial_distances[:, 0], bad_radial_distances[:, 0])), 0.499999) / 0.05).astype(int)], 'Descriptor distance': np.hstack((good_radial_distances[:, 1], bad_radial_distances[:, 1])), 'Match': ['Good' for i in range(len(good_radial_distances))] + ['Bad' for i in range(len(bad_radial_distances))]}) sns.violinplot(x="Delta radial distance", y="Descriptor distance", hue="Match", data=df, split=True, ax=ax5, palette="Set2", inner="quart") @@ -142,6 +211,7 @@ ax2.set_prop_cycle(color=[cm(1. * i / len(detdesclist)) for i in range(len(detdesclist))]) ax3.set_prop_cycle(color=[cm(1. * i / len(detdesclist)) for i in range(len(detdesclist))]) ax4.set_prop_cycle(color=[cm(1. * i / len(detdesclist)) for i in range(len(detdesclist))]) + ax6.set_prop_cycle(color=[cm(1. * i / len(detdesclist)) for i in range(len(detdesclist))]) handles = [] for detdesc in detdesclist: @@ -203,6 +273,49 @@ ax5.legend(handles=handles[0:], labels=labels[0:], fontsize='small') ax5.set_title('Distribution of good and bad matches over descriptor distances') + ax6.set_title('Match separability over changes in radial distance') + ax6.set_ylabel('Silhouette coefficient') + ax6.set_xlabel('Delta radial distance') + handles = [] + for detdesc in detdesclist: + num_bins = 50 + num_samples = 1000 + sr = [0 for i in range(num_bins)] + X_good = [np.array([]) for i in range(num_bins)] + labels_good = [np.array([]) for i in range(num_bins)] + X_bad = [np.array([]) for i in range(num_bins)] + labels_bad = [np.array([]) for i in range(num_bins)] + valid_good = [False for i in range(num_bins)] + valid_bad = [False for i in range(num_bins)] + valid = [False for i in range(num_bins)] + for row in good_radial_distances[detdesc]: + r = int(min(row[0], 0.499999) / (0.5 / num_bins)) + X_good[r] = np.append(X_good[r], row[1]) + labels_good[r] = np.append(labels_good[r], 0) + valid_good[r] = True + for row in bad_radial_distances[detdesc]: + r = int(min(row[0], 0.499999) / (0.5 / num_bins)) + X_bad[r] = np.append(X_bad[r], row[1]) + labels_bad[r] = np.append(labels_bad[r], 1) + valid_bad[r] = True + for i in range(num_bins): + valid[i] = valid_good[i] and valid_bad[i] + if not valid[i]: + continue + idx_good = np.arange(len(X_good[i])) + idx_bad = np.arange(len(X_bad[i])) + if len(X_good[i]) > num_samples: + idx_good = np.random.choice(np.arange(len(X_good[i])), num_samples, replace=False) + if len(X_bad[i]) > num_samples: + idx_bad = np.random.choice(np.arange(len(X_bad[i])), num_samples, replace=False) + sr[i] = sklearn.metrics.silhouette_score(np.concatenate((X_good[i][idx_good], X_bad[i][idx_bad])).reshape(-1, 1), np.concatenate((labels_good[i][idx_good], labels_bad[i][idx_bad])), metric = 'l1') + # sr[i] = sklearn.metrics.davies_bouldin_score(X[i].reshape(-1, 1), labels[i]) + color = next(ax6._get_lines.prop_cycler)['color'] + h, = ax6.plot([i * 0.5 / num_bins + 0.5 / num_bins / 2 for i in range(0, len(sr)) if valid[i]], [sr[i] for i in range(0, len(sr)) if valid[i]], color=color) + handles.append(h) + l1 = ax6.legend(handles, ['{}+{}'.format(det, desc) for det, desc in detdesclist], loc=1, title='Detector+Descriptor', fontsize='small') + ax6.add_artist(l1) + plt.show() else: