/
figures.py
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/
figures.py
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# pylint: disable=R0915
import math
import itertools
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
from ..utils import get_task_error, get_pixel_error
def show_results(dic_stats, show=False, save=False, stereo=False):
"""
Visualize error as function of the distance and compare it with target errors based on human height analyses
"""
dir_out = 'docs'
phase = 'test'
x_min = 0
x_max = 38
y_min = 0
y_max = 4.7
xx = np.linspace(0, 60, 100)
excl_clusters = ['all', '50', '>50', 'easy', 'moderate', 'hard']
clusters = tuple([clst for clst in dic_stats[phase]['monoloco'] if clst not in excl_clusters])
yy_gender = get_task_error(xx)
styles = printing_styles(stereo)
for idx_style, (key, style) in enumerate(styles.items()):
plt.figure(idx_style)
plt.grid(linewidth=0.2)
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xlabel("Ground-truth distance [m]")
plt.ylabel("Average localization error [m]")
for idx, method in enumerate(style['methods']):
errs = [dic_stats[phase][method][clst]['mean'] for clst in clusters]
assert errs, "method %s empty" % method
xxs = get_distances(clusters)
plt.plot(xxs, errs, marker=style['mks'][idx], markersize=style['mksizes'][idx], linewidth=style['lws'][idx],
label=style['labels'][idx], linestyle=style['lstyles'][idx], color=style['colors'][idx])
plt.plot(xx, yy_gender, '--', label="Task error", color='lightgreen', linewidth=2.5)
if key == 'stereo':
yy_stereo = get_pixel_error(xx)
plt.plot(xx, yy_stereo, linewidth=1.7, color='k', label='Pixel error')
plt.legend(loc='upper left')
if save:
path_fig = os.path.join(dir_out, 'results_' + key + '.png')
plt.savefig(path_fig)
print("Figure of results " + key + " saved in {}".format(path_fig))
if show:
plt.show()
plt.close()
def show_spread(dic_stats, show=False, save=False):
"""Predicted confidence intervals and task error as a function of ground-truth distance"""
phase = 'test'
dir_out = 'docs'
excl_clusters = ['all', '50', '>50', 'easy', 'moderate', 'hard']
clusters = tuple([clst for clst in dic_stats[phase]['our'] if clst not in excl_clusters])
plt.figure(2)
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 save:
path_fig = os.path.join(dir_out, 'spread.png')
plt.savefig(path_fig)
print("Figure of confidence intervals saved in {}".format(path_fig))
if show:
plt.show()
plt.close()
def show_task_error(show, save):
"""Task error figure"""
plt.figure(3)
dir_out = 'docs'
xx = np.linspace(0.1, 50, 100)
mu_men = 178
mu_women = 165
mu_child_m = 164
mu_child_w = 156
mm_gmm, mm_male, mm_female = calculate_gmm()
mm_young_male = mm_male + (mu_men - mu_child_m) / mu_men
mm_young_female = mm_female + (mu_women - mu_child_w) / mu_women
yy_male = target_error(xx, mm_male)
yy_female = target_error(xx, mm_female)
yy_young_male = target_error(xx, mm_young_male)
yy_young_female = target_error(xx, mm_young_female)
yy_gender = target_error(xx, mm_gmm)
yy_stereo = get_pixel_error(xx)
plt.grid(linewidth=0.3)
plt.plot(xx, yy_young_male, linestyle='dotted', linewidth=2.1, color='b', label='Adult/young male')
plt.plot(xx, yy_young_female, linestyle='dotted', linewidth=2.1, color='darkorange', label='Adult/young female')
plt.plot(xx, yy_gender, '--', color='lightgreen', linewidth=2.8, label='Generic adult (task error)')
plt.plot(xx, yy_female, '-.', linewidth=1.7, color='darkorange', label='Adult female')
plt.plot(xx, yy_male, '-.', linewidth=1.7, color='b', label='Adult male')
plt.plot(xx, yy_stereo, linewidth=1.7, color='k', label='Pixel error')
plt.xlim(np.min(xx), np.max(xx))
plt.xlabel("Ground-truth distance from the camera $d_{gt}$ [m]")
plt.ylabel("Localization error $\hat{e}$ due to human height variation [m]") # pylint: disable=W1401
plt.legend(loc=(0.01, 0.55)) # Location from 0 to 1 from lower left
if save:
path_fig = os.path.join(dir_out, 'task_error.png')
plt.savefig(path_fig)
print("Figure of task error saved in {}".format(path_fig))
if show:
plt.show()
plt.close()
def show_method(save):
""" method figure"""
dir_out = 'docs'
std_1 = 0.75
fig = plt.figure(1)
ax = fig.add_subplot(1, 1, 1)
ell_3 = Ellipse((0, 2), width=std_1 * 2, height=0.3, angle=-90, color='b', fill=False, linewidth=2.5)
ell_4 = Ellipse((0, 2), width=std_1 * 3, height=0.3, angle=-90, color='r', fill=False,
linestyle='dashed', linewidth=2.5)
ax.add_patch(ell_4)
ax.add_patch(ell_3)
plt.plot(0, 2, marker='o', color='skyblue', markersize=9)
plt.plot([0, 3], [0, 4], 'k--')
plt.plot([0, -3], [0, 4], 'k--')
plt.xlim(-3, 3)
plt.ylim(0, 3.5)
plt.xticks([])
plt.yticks([])
plt.xlabel('X [m]')
plt.ylabel('Z [m]')
if save:
path_fig = os.path.join(dir_out, 'output_method.png')
plt.savefig(path_fig)
print("Figure of method saved in {}".format(path_fig))
def target_error(xx, mm):
return mm * xx
def calculate_gmm():
dist_gmm, dist_male, dist_female = height_distributions()
# get_percentile(dist_gmm)
mu_gmm = np.mean(dist_gmm)
mm_gmm = np.mean(np.abs(1 - mu_gmm / dist_gmm))
mm_male = np.mean(np.abs(1 - np.mean(dist_male) / dist_male))
mm_female = np.mean(np.abs(1 - np.mean(dist_female) / dist_female))
print("Mean of GMM distribution: {:.4f}".format(mu_gmm))
print("coefficient for gmm: {:.4f}".format(mm_gmm))
print("coefficient for men: {:.4f}".format(mm_male))
print("coefficient for women: {:.4f}".format(mm_female))
return mm_gmm, mm_male, mm_female
def get_confidence(xx, zz, std):
theta = math.atan2(zz, xx)
delta_x = std * math.cos(theta)
delta_z = std * math.sin(theta)
return (xx - delta_x, xx + delta_x), (zz - delta_z, zz + delta_z)
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
def height_distributions():
mu_men = 178
std_men = 7
mu_women = 165
std_women = 7
dist_men = np.random.normal(mu_men, std_men, int(1e7))
dist_women = np.random.normal(mu_women, std_women, int(1e7))
dist_gmm = np.concatenate((dist_men, dist_women))
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 # pylint: disable=C0415
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))
def printing_styles(stereo):
style = {'mono': {"labels": ['Mono3D', 'Geometric Baseline', 'MonoDepth', 'Our MonoLoco', '3DOP (stereo)'],
"methods": ['m3d_merged', 'geometric_merged', 'monodepth_merged', 'monoloco_merged',
'3dop_merged'],
"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']}}
if stereo:
style['stereo'] = {"labels": ['3DOP', 'Pose Baseline', 'ReiD Baseline', 'Our MonoLoco (monocular)',
'Our Stereo Baseline'],
"methods": ['3dop_merged', 'pose_merged', 'reid_merged', 'monoloco_merged',
'ml_stereo_merged'],
"mks": ['o', '^', 'p', 's', 's'],
"mksizes": [6, 6, 6, 4, 6], "lws": [1.5, 1.5, 1.5, 1.2, 1.5],
"colors": ['darkorange', 'lightblue', 'red', 'b', 'b'],
"lstyles": ['solid', 'solid', 'solid', 'dashed', 'solid']}
return style