refactor article figures
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lorenzo
parent
10874caea0
commit
bbe040d4a2
@ -1,5 +1,4 @@
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# pylint: skip-file
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import argparse
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@ -96,8 +95,6 @@ def cli():
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def main():
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args = cli()
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from .visuals.paper import paper
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paper()
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if args.command == 'predict':
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if args.webcam:
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from .visuals.webcam import webcam
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@ -156,8 +153,8 @@ def main():
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training = Trainer(joints=args.joints)
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_ = training.evaluate(load=True, model=args.model, debug=False)
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# else:
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# raise ValueError("Main subparser not recognized or not provided")
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else:
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raise ValueError("Main subparser not recognized or not provided")
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if __name__ == '__main__':
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271
monoloco/visuals/figures.py
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271
monoloco/visuals/figures.py
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# pylint: disable=R0915
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import math
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import itertools
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.patches import Ellipse
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from ..utils import get_task_error
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def show_results(dic_stats, show=False):
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"""
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Visualize error as function of the distance and compare it with target errors based on human height analyses
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"""
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dir_out = 'docs'
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phase = 'test'
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x_min = 0
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x_max = 38
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xx = np.linspace(0, 60, 100)
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excl_clusters = ['all', '50', '>50', 'easy', 'moderate', 'hard']
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clusters = tuple([clst for clst in dic_stats[phase]['our'] if clst not in excl_clusters])
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yy_gender = get_task_error(xx)
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yy_gps = np.linspace(5., 5., xx.shape[0])
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plt.figure(0)
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plt.grid(linewidth=0.2)
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plt.xlabel("Distance [meters]")
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plt.ylabel("Average localization error [m]")
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plt.xlim(x_min, x_max)
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labels = ['Mono3D', 'Geometric Baseline', 'MonoDepth', 'Our MonoLoco', '3DOP (stereo)']
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mks = ['*', '^', 'p', 's', 'o']
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mksizes = [6, 6, 6, 6, 6]
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lws = [1.5, 1.5, 1.5, 2.2, 1.6]
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colors = ['r', 'deepskyblue', 'grey', 'b', 'darkorange']
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lstyles = ['solid', 'solid', 'solid', 'solid', 'dashdot']
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plt.plot(xx, yy_gps, '-', label="GPS Error", color='y')
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for idx, method in enumerate(['m3d_merged', 'geom_merged', 'md_merged', 'our_merged', '3dop_merged']):
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errs = [dic_stats[phase][method][clst]['mean'] for clst in clusters]
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xxs = get_distances(clusters)
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plt.plot(xxs, errs, marker=mks[idx], markersize=mksizes[idx], linewidth=lws[idx], label=labels[idx],
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linestyle=lstyles[idx], color=colors[idx])
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plt.plot(xx, yy_gender, '--', label="Task error", color='lightgreen', linewidth=2.5)
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plt.legend(loc='upper left')
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if show:
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plt.show()
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else:
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plt.savefig(os.path.join(dir_out, 'results.png'))
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plt.close()
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def show_spread(dic_stats, show=False):
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"""Predicted confidence intervals and task error as a function of ground-truth distance"""
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phase = 'test'
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dir_out = 'docs'
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excl_clusters = ['all', '50', '>50', 'easy', 'moderate', 'hard']
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clusters = tuple([clst for clst in dic_stats[phase]['our'] if clst not in excl_clusters])
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plt.figure(1)
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fig, ax = plt.subplots(2, sharex=True)
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plt.xlabel("Distance [m]")
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plt.ylabel("Aleatoric uncertainty [m]")
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ar = 0.5 # Change aspect ratio of ellipses
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scale = 1.5 # Factor to scale ellipses
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rec_c = 0 # Center of the rectangle
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plots_line = True
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bbs = np.array([dic_stats[phase]['our'][key]['std_ale'] for key in clusters])
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xxs = get_distances(clusters)
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yys = get_task_error(np.array(xxs))
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ax[1].plot(xxs, bbs, marker='s', color='b', label="Spread b")
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ax[1].plot(xxs, yys, '--', color='lightgreen', label="Task error", linewidth=2.5)
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yys_up = [rec_c + ar/2 * scale * yy for yy in yys]
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bbs_up = [rec_c + ar/2 * scale * bb for bb in bbs]
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yys_down = [rec_c - ar/2 * scale * yy for yy in yys]
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bbs_down = [rec_c - ar/2 * scale * bb for bb in bbs]
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if plots_line:
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ax[0].plot(xxs, yys_up, '--', color='lightgreen', markersize=5, linewidth=1.4)
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ax[0].plot(xxs, yys_down, '--', color='lightgreen', markersize=5, linewidth=1.4)
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ax[0].plot(xxs, bbs_up, marker='s', color='b', markersize=5, linewidth=0.7)
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ax[0].plot(xxs, bbs_down, marker='s', color='b', markersize=5, linewidth=0.7)
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for idx, xx in enumerate(xxs):
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te = Ellipse((xx, rec_c), width=yys[idx]*ar*scale, height=scale, angle=90, color='lightgreen', fill=True)
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bi = Ellipse((xx, rec_c), width=bbs[idx]*ar*scale, height=scale, angle=90, color='b', linewidth=1.8,
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fill=False)
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ax[0].add_patch(te)
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ax[0].add_patch(bi)
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fig.subplots_adjust(hspace=0.1)
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plt.setp([aa.get_yticklabels() for aa in fig.axes[:-1]], visible=False)
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plt.legend()
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if show:
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plt.show()
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else:
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plt.savefig(os.path.join(dir_out, 'spread_bi.png'))
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plt.close()
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def show_method():
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""" method figure"""
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std_1 = 0.75
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fig = plt.figure(1)
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ax = fig.add_subplot(1, 1, 1)
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ell_3 = Ellipse((0, 2), width=std_1 * 2, height=0.3, angle=-90, color='b', fill=False, linewidth=2.5)
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ell_4 = Ellipse((0, 2), width=std_1 * 3, height=0.3, angle=-90, color='r', fill=False,
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linestyle='dashed', linewidth=2.5)
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ax.add_patch(ell_4)
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ax.add_patch(ell_3)
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plt.plot(0, 2, marker='o', color='skyblue', markersize=9)
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plt.plot([0, 3], [0, 4], 'k--')
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plt.plot([0, -3], [0, 4], 'k--')
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plt.xlim(-3, 3)
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plt.ylim(0, 3.5)
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plt.xticks([])
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plt.yticks([])
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plt.xlabel('X [m]')
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plt.ylabel('Z [m]')
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plt.savefig(os.path.join('docs', 'output_method.png'))
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def show_task_error():
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"""Task error figure"""
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plt.figure(2)
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xx = np.linspace(0, 40, 100)
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mu_men = 178
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mu_women = 165
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mu_child_m = 164
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mu_child_w = 156
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mm_gmm, mm_male, mm_female = calculate_gmm()
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mm_young_male = mm_male + (mu_men - mu_child_m) / mu_men
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mm_young_female = mm_female + (mu_women - mu_child_w) / mu_women
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yy_male = target_error(xx, mm_male)
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yy_female = target_error(xx, mm_female)
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yy_young_male = target_error(xx, mm_young_male)
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yy_young_female = target_error(xx, mm_young_female)
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yy_gender = target_error(xx, mm_gmm)
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yy_gps = np.linspace(5., 5., xx.shape[0])
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plt.grid(linewidth=0.3)
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plt.plot(xx, yy_gps, color='y', label='GPS')
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plt.plot(xx, yy_young_male, linestyle='dotted', linewidth=2.1, color='b', label='Adult/young male')
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plt.plot(xx, yy_young_female, linestyle='dotted', linewidth=2.1, color='darkorange', label='Adult/young female')
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plt.plot(xx, yy_gender, '--', color='lightgreen', linewidth=2.8, label='Generic adult (task error)')
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plt.plot(xx, yy_female, '-.', linewidth=1.7, color='darkorange', label='Adult female')
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plt.plot(xx, yy_male, '-.', linewidth=1.7, color='b', label='Adult male')
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plt.xlim(np.min(xx), np.max(xx))
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plt.xlabel("Ground-truth distance from the camera $d_{gt}$ [m]")
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plt.ylabel("Localization error $\hat{e}$ due to human height variation [m]")
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plt.legend(loc=(0.01, 0.55)) # Location from 0 to 1 from lower left
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plt.savefig(os.path.join('docs', 'task_error.png'))
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def target_error(xx, mm):
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return mm * xx
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def calculate_gmm():
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dist_gmm, dist_male, dist_female = height_distributions()
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# get_percentile(dist_gmm)
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mu_gmm = np.mean(dist_gmm)
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mm_gmm = np.mean(np.abs(1 - mu_gmm / dist_gmm))
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mm_male = np.mean(np.abs(1 - np.mean(dist_male) / dist_male))
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mm_female = np.mean(np.abs(1 - np.mean(dist_female) / dist_female))
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print("Mean of GMM distribution: {:.4f}".format(mu_gmm))
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print("coefficient for gmm: {:.4f}".format(mm_gmm))
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print("coefficient for men: {:.4f}".format(mm_male))
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print("coefficient for women: {:.4f}".format(mm_female))
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return mm_gmm, mm_male, mm_female
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def get_confidence(xx, zz, std):
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theta = math.atan2(zz, xx)
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delta_x = std * math.cos(theta)
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delta_z = std * math.sin(theta)
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return (xx - delta_x, xx + delta_x), (zz - delta_z, zz + delta_z)
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def get_distances(clusters):
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"""Extract distances as intermediate values between 2 clusters"""
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clusters_ext = list(clusters)
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clusters_ext.insert(0, str(0))
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distances = []
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for idx, _ in enumerate(clusters_ext[:-1]):
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clst_0 = float(clusters_ext[idx])
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clst_1 = float(clusters_ext[idx + 1])
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distances.append((clst_1 - clst_0) / 2 + clst_0)
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return tuple(distances)
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def get_confidence_points(confidences, distances, errors):
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confidence_points = []
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distance_points = []
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for idx, dd in enumerate(distances):
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conf_perc = confidences[idx]
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confidence_points.append(errors[idx] + conf_perc)
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confidence_points.append(errors[idx] - conf_perc)
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distance_points.append(dd)
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distance_points.append(dd)
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return distance_points, confidence_points
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def height_distributions():
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mu_men = 178
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std_men = 7
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mu_women = 165
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std_women = 7
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dist_men = np.random.normal(mu_men, std_men, int(1e7))
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dist_women = np.random.normal(mu_women, std_women, int(1e7))
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dist_gmm = np.concatenate((dist_men, dist_women))
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return dist_gmm, dist_men, dist_women
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def expandgrid(*itrs):
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mm = 0
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combinations = list(itertools.product(*itrs))
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for h_i, h_gt in combinations:
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mm += abs(float(1 - h_i / h_gt))
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mm /= len(combinations)
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return combinations
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def plot_dist(dist_gmm, dist_men, dist_women):
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try:
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import seaborn as sns
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sns.distplot(dist_men, hist=False, rug=False, label="Men")
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sns.distplot(dist_women, hist=False, rug=False, label="Women")
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sns.distplot(dist_gmm, hist=False, rug=False, label="GMM")
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plt.xlabel("X [cm]")
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plt.ylabel("Height distributions of men and women")
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plt.legend()
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plt.show()
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plt.close()
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except ImportError:
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print("Import Seaborn first")
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def get_percentile(dist_gmm):
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dd_gt = 1000
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mu_gmm = np.mean(dist_gmm)
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dist_d = dd_gt * mu_gmm / dist_gmm
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perc_d, _ = np.nanpercentile(dist_d, [18.5, 81.5]) # Laplace bi => 63%
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perc_d2, _ = np.nanpercentile(dist_d, [23, 77])
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mu_d = np.mean(dist_d)
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# mm_bi = (mu_d - perc_d) / mu_d
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# mm_test = (mu_d - perc_d2) / mu_d
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# mad_d = np.mean(np.abs(dist_d - mu_d))
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@ -1,148 +0,0 @@
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import math
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import os
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import itertools
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.patches import Ellipse
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def paper():
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"""Print paper figures"""
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method = False
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task_error = True
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# Pull figure
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if method:
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std_1 = 0.75
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fig = plt.figure(1)
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ax = fig.add_subplot(1, 1, 1)
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ell_3 = Ellipse((0, 2), width=std_1 * 2, height=0.3, angle=-90, color='b', fill=False, linewidth=2.5)
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ell_4 = Ellipse((0, 2), width=std_1 * 3, height=0.3, angle=-90, color='r', fill=False,
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linestyle='dashed', linewidth=2.5)
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ax.add_patch(ell_4)
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ax.add_patch(ell_3)
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plt.plot(0, 2, marker='o', color='skyblue', markersize=9)
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plt.plot([0, 3], [0, 4], 'k--')
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plt.plot([0, -3], [0, 4], 'k--')
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plt.xlim(-3, 3)
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plt.ylim(0, 3.5)
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plt.xticks([])
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plt.yticks([])
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plt.xlabel('X [m]')
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plt.ylabel('Z [m]')
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plt.savefig(os.path.join('docs', 'output_method.png'))
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# Task error figure
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if task_error:
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plt.figure(2)
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xx = np.linspace(0, 40, 100)
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mu_men = 178
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mu_women = 165
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mu_child_m = 164
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mu_child_w = 156
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mm_gmm, mm_male, mm_female = gmm()
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mm_young_male = mm_male + (mu_men - mu_child_m) / mu_men
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mm_young_female = mm_female + (mu_women - mu_child_w) / mu_women
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yy_male = target_error(xx, mm_male)
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yy_female = target_error(xx, mm_female)
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yy_young_male = target_error(xx, mm_young_male)
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yy_young_female = target_error(xx, mm_young_female)
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yy_gender = target_error(xx, mm_gmm)
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yy_gps = np.linspace(5., 5., xx.shape[0])
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plt.grid(linewidth=0.3)
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plt.plot(xx, yy_gps, color='y', label='GPS')
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plt.plot(xx, yy_young_male, linestyle='dotted', linewidth=2.1, color='b', label='Adult/young male')
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plt.plot(xx, yy_young_female, linestyle='dotted', linewidth=2.1, color='darkorange', label='Adult/young female')
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plt.plot(xx, yy_gender, '--', color='lightgreen', linewidth=2.8, label='Generic adult (task error)')
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plt.plot(xx, yy_female, '-.', linewidth=1.7, color='darkorange', label='Adult female')
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plt.plot(xx, yy_male, '-.', linewidth=1.7, color='b', label='Adult male')
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plt.xlim(np.min(xx), np.max(xx))
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plt.xlabel("Ground-truth distance from the camera $d_{gt}$ [m]")
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plt.ylabel("Localization error $\hat{e}$ due to human height variation [m]")
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plt.legend(loc=(0.01, 0.55)) # Location from 0 to 1 from lower left
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plt.savefig(os.path.join('docs', 'task_error.png'))
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def target_error(xx, mm):
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return mm * xx
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def gmm():
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dist_gmm, dist_male, dist_female = height_distributions()
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# get_percentile(dist_gmm)
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mu_gmm = np.mean(dist_gmm)
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mm_gmm = np.mean(np.abs(1 - mu_gmm / dist_gmm))
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mm_male = np.mean(np.abs(1 - np.mean(dist_male) / dist_male))
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mm_female = np.mean(np.abs(1 - np.mean(dist_female) / dist_female))
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print("Mean of GMM distribution: {:.4f}".format(mu_gmm))
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print("coefficient for gmm: {:.4f}".format(mm_gmm))
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print("coefficient for men: {:.4f}".format(mm_male))
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print("coefficient for women: {:.4f}".format(mm_female))
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return mm_gmm, mm_male, mm_female
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def get_confidence(xx, zz, std):
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theta = math.atan2(zz, xx)
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delta_x = std * math.cos(theta)
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delta_z = std * math.sin(theta)
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return (xx - delta_x, xx + delta_x), (zz - delta_z, zz + delta_z)
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def height_distributions():
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mu_men = 178
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std_men = 7
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mu_women = 165
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std_women = 7
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dist_men = np.random.normal(mu_men, std_men, int(1e7))
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dist_women = np.random.normal(mu_women, std_women, int(1e7))
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dist_gmm = np.concatenate((dist_men, dist_women))
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return dist_gmm, dist_men, dist_women
|
||||
|
||||
|
||||
def expandgrid(*itrs):
|
||||
mm = 0
|
||||
combinations = list(itertools.product(*itrs))
|
||||
|
||||
for h_i, h_gt in combinations:
|
||||
mm += abs(float(1 - h_i / h_gt))
|
||||
|
||||
mm /= len(combinations)
|
||||
|
||||
return combinations
|
||||
|
||||
|
||||
def plot_dist(dist_gmm, dist_men, dist_women):
|
||||
try:
|
||||
import seaborn as sns
|
||||
sns.distplot(dist_men, hist=False, rug=False, label="Men")
|
||||
sns.distplot(dist_women, hist=False, rug=False, label="Women")
|
||||
sns.distplot(dist_gmm, hist=False, rug=False, label="GMM")
|
||||
plt.xlabel("X [cm]")
|
||||
plt.ylabel("Height distributions of men and women")
|
||||
plt.legend()
|
||||
plt.show()
|
||||
plt.close()
|
||||
except ImportError:
|
||||
print("Import Seaborn first")
|
||||
|
||||
|
||||
def get_percentile(dist_gmm):
|
||||
dd_gt = 1000
|
||||
mu_gmm = np.mean(dist_gmm)
|
||||
dist_d = dd_gt * mu_gmm / dist_gmm
|
||||
perc_d, _ = np.nanpercentile(dist_d, [18.5, 81.5]) # Laplace bi => 63%
|
||||
perc_d2, _ = np.nanpercentile(dist_d, [23, 77])
|
||||
mu_d = np.mean(dist_d)
|
||||
# mm_bi = (mu_d - perc_d) / mu_d
|
||||
# mm_test = (mu_d - perc_d2) / mu_d
|
||||
# mad_d = np.mean(np.abs(dist_d - mu_d))
|
||||
@ -1,128 +0,0 @@
|
||||
# pylint: disable=R0915
|
||||
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.patches import Ellipse
|
||||
|
||||
from ..utils import get_task_error
|
||||
|
||||
|
||||
def print_results(dic_stats, show=False):
|
||||
|
||||
"""
|
||||
Visualize error as function of the distance on the test set and compare it with target errors based on human
|
||||
height analyses. We consider:
|
||||
Position error in meters due to a height variation of 7 cm (Standard deviation already knowing the sex)
|
||||
Position error not knowing the gender (13cm as average difference --> 7.5cm of error to add)
|
||||
"""
|
||||
|
||||
# ALE figure
|
||||
dir_out = 'docs'
|
||||
phase = 'test'
|
||||
x_min = 0
|
||||
x_max = 38
|
||||
xx = np.linspace(0, 60, 100)
|
||||
excl_clusters = ['all', '50', '>50', 'easy', 'moderate', 'hard']
|
||||
clusters = tuple([clst for clst in dic_stats[phase]['our'] if clst not in excl_clusters])
|
||||
yy_gender = get_task_error(xx)
|
||||
yy_gps = np.linspace(5., 5., xx.shape[0])
|
||||
|
||||
plt.figure(0)
|
||||
plt.grid(linewidth=0.2)
|
||||
plt.xlabel("Distance [meters]")
|
||||
plt.ylabel("Average localization error [m]")
|
||||
plt.xlim(x_min, x_max)
|
||||
labels = ['Mono3D', 'Geometric Baseline', 'MonoDepth', 'Our MonoLoco', '3DOP (stereo)']
|
||||
mks = ['*', '^', 'p', 's', 'o']
|
||||
mksizes = [6, 6, 6, 6, 6]
|
||||
lws = [1.5, 1.5, 1.5, 2.2, 1.6]
|
||||
colors = ['r', 'deepskyblue', 'grey', 'b', 'darkorange']
|
||||
lstyles = ['solid', 'solid', 'solid', 'solid', 'dashdot']
|
||||
|
||||
plt.plot(xx, yy_gps, '-', label="GPS Error", color='y')
|
||||
for idx, method in enumerate(['m3d_merged', 'geom_merged', 'md_merged', 'our_merged', '3dop_merged']):
|
||||
errs = [dic_stats[phase][method][clst]['mean'] for clst in clusters]
|
||||
xxs = get_distances(clusters)
|
||||
|
||||
plt.plot(xxs, errs, marker=mks[idx], markersize=mksizes[idx], linewidth=lws[idx], label=labels[idx],
|
||||
linestyle=lstyles[idx], color=colors[idx])
|
||||
plt.plot(xx, yy_gender, '--', label="Task error", color='lightgreen', linewidth=2.5)
|
||||
plt.legend(loc='upper left')
|
||||
if show:
|
||||
plt.show()
|
||||
else:
|
||||
plt.savefig(os.path.join(dir_out, 'results.png'))
|
||||
plt.close()
|
||||
|
||||
# SPREAD b Figure
|
||||
plt.figure(1)
|
||||
fig, ax = plt.subplots(2, sharex=True)
|
||||
plt.xlabel("Distance [m]")
|
||||
plt.ylabel("Aleatoric uncertainty [m]")
|
||||
ar = 0.5 # Change aspect ratio of ellipses
|
||||
scale = 1.5 # Factor to scale ellipses
|
||||
rec_c = 0 # Center of the rectangle
|
||||
plots_line = True
|
||||
|
||||
bbs = np.array([dic_stats[phase]['our'][key]['std_ale'] for key in clusters])
|
||||
xxs = get_distances(clusters)
|
||||
yys = get_task_error(np.array(xxs))
|
||||
ax[1].plot(xxs, bbs, marker='s', color='b', label="Spread b")
|
||||
ax[1].plot(xxs, yys, '--', color='lightgreen', label="Task error", linewidth=2.5)
|
||||
yys_up = [rec_c + ar/2 * scale * yy for yy in yys]
|
||||
bbs_up = [rec_c + ar/2 * scale * bb for bb in bbs]
|
||||
yys_down = [rec_c - ar/2 * scale * yy for yy in yys]
|
||||
bbs_down = [rec_c - ar/2 * scale * bb for bb in bbs]
|
||||
|
||||
if plots_line:
|
||||
ax[0].plot(xxs, yys_up, '--', color='lightgreen', markersize=5, linewidth=1.4)
|
||||
ax[0].plot(xxs, yys_down, '--', color='lightgreen', markersize=5, linewidth=1.4)
|
||||
ax[0].plot(xxs, bbs_up, marker='s', color='b', markersize=5, linewidth=0.7)
|
||||
ax[0].plot(xxs, bbs_down, marker='s', color='b', markersize=5, linewidth=0.7)
|
||||
|
||||
for idx, xx in enumerate(xxs):
|
||||
te = Ellipse((xx, rec_c), width=yys[idx]*ar*scale, height=scale, angle=90, color='lightgreen', fill=True)
|
||||
bi = Ellipse((xx, rec_c), width=bbs[idx]*ar*scale, height=scale, angle=90, color='b', linewidth=1.8,
|
||||
fill=False)
|
||||
|
||||
ax[0].add_patch(te)
|
||||
ax[0].add_patch(bi)
|
||||
|
||||
fig.subplots_adjust(hspace=0.1)
|
||||
plt.setp([aa.get_yticklabels() for aa in fig.axes[:-1]], visible=False)
|
||||
plt.legend()
|
||||
if show:
|
||||
plt.show()
|
||||
else:
|
||||
plt.savefig(os.path.join(dir_out, 'spread_bi.png'))
|
||||
plt.close()
|
||||
|
||||
|
||||
def get_distances(clusters):
|
||||
"""Extract distances as intermediate values between 2 clusters"""
|
||||
|
||||
clusters_ext = list(clusters)
|
||||
clusters_ext.insert(0, str(0))
|
||||
distances = []
|
||||
for idx, _ in enumerate(clusters_ext[:-1]):
|
||||
clst_0 = float(clusters_ext[idx])
|
||||
clst_1 = float(clusters_ext[idx + 1])
|
||||
distances.append((clst_1 - clst_0) / 2 + clst_0)
|
||||
return tuple(distances)
|
||||
|
||||
|
||||
def get_confidence_points(confidences, distances, errors):
|
||||
|
||||
confidence_points = []
|
||||
distance_points = []
|
||||
for idx, dd in enumerate(distances):
|
||||
|
||||
conf_perc = confidences[idx]
|
||||
confidence_points.append(errors[idx] + conf_perc)
|
||||
confidence_points.append(errors[idx] - conf_perc)
|
||||
distance_points.append(dd)
|
||||
distance_points.append(dd)
|
||||
|
||||
return distance_points, confidence_points
|
||||
Loading…
Reference in New Issue
Block a user