def plot_results(times_A, times_B, signal_A, signal_B,
convoluted_signals, time_offset, block=True):
fig = plt.figure()
title_position = 1.05
matplotlib.rcParams.update({'font.size': 20})
# fig.suptitle("Time Alignment", fontsize='24')
a1 = plt.subplot(1, 3, 1)
a1.get_xaxis().get_major_formatter().set_useOffset(False)
plt.ylabel('angular velocity norm [rad]')
plt.xlabel('time [s]')
a1.set_title(
"Before Time Alignment", y=title_position)
plt.hold("on")
min_time = min(np.amin(times_A), np.amin(times_B))
times_A_zeroed = times_A - min_time
times_B_zeroed = times_B - min_time
plt.plot(times_A_zeroed, signal_A, c='r')
plt.plot(times_B_zeroed, signal_B, c='b')
times_A_shifted = times_A + time_offset
a3 = plt.subplot(1, 3, 2)
a3.get_xaxis().get_major_formatter().set_useOffset(False)
plt.ylabel('correlation')
plt.xlabel('sample idx offset')
a3.set_title(
"Correlation Result \n[Ideally has a single dominant peak.]",
y=title_position)
plt.hold("on")
plt.plot(np.arange(-len(signal_A) + 1, len(signal_B)), convoluted_signals)
a2 = plt.subplot(1, 3, 3)
a2.get_xaxis().get_major_formatter().set_useOffset(False)
plt.ylabel('angular velocity norm [rad]')
plt.xlabel('time [s]')
a2.set_title(
"After Time Alignment", y=title_position)
plt.hold("on")
min_time = min(np.amin(times_A_shifted), np.amin(times_B))
times_A_shifted_zeroed = times_A_shifted - min_time
times_B_zeroed = times_B - min_time
plt.plot(times_A_shifted_zeroed, signal_A, c='r')
plt.plot(times_B_zeroed, signal_B, c='b')
plt.subplots_adjust(left=0.04, right=0.99, top=0.8, bottom=0.15)
if plt.get_backend() == 'TkAgg':
mng = plt.get_current_fig_manager()
max_size = mng.window.maxsize()
max_size = (max_size[0], max_size[1] * 0.45)
mng.resize(*max_size)
plt.show(block=block)
def plot_angular_velocities(title,
angular_velocities,
angular_velocities_filtered,
block=True):
fig = plt.figure()
title_position = 1.05
fig.suptitle(title, fontsize='24')
a1 = plt.subplot(1, 2, 1)
a1.set_title(
"Angular Velocities Before Filtering \nvx [red], vy [green], vz [blue]",
y=title_position)
plt.plot(angular_velocities[:, 0], c='r')
plt.plot(angular_velocities[:, 1], c='g')
plt.plot(angular_velocities[:, 2], c='b')
a2 = plt.subplot(1, 2, 2)
a2.set_title(
"Angular Velocities After Filtering \nvx [red], vy [green], vz [blue]", y=title_position)
plt.plot(angular_velocities_filtered[:, 0], c='r')
plt.plot(angular_velocities_filtered[:, 1], c='g')
plt.plot(angular_velocities_filtered[:, 2], c='b')
plt.subplots_adjust(left=0.025, right=0.975, top=0.8, bottom=0.05)
if plt.get_backend() == 'TkAgg':
mng = plt.get_current_fig_manager()
max_size = mng.window.maxsize()
max_size = (max_size[0], max_size[1] * 0.45)
mng.resize(*max_size)
plt.show(block=block)
def update_fig(fig=None):
"""
Update a matplotlib figure with a Qt4 backend
@param fig: pylab figure
"""
if fig and "canvas" in dir(fig) and fig.canvas:
fig.canvas.draw()
if "Qt4" in pylab.get_backend():
QtGui.qApp.postEvent(fig.canvas,
QtGui.QResizeEvent(fig.canvas.size(),
fig.canvas.size()))
if not main_loop:
QtCore.QCoreApplication.processEvents()
def update_fig(fig=None):
"""
Update a matplotlib figure with a Qt4 backend
@param fig: pylab figure
"""
if fig and "canvas" in dir(fig) and fig.canvas:
fig.canvas.draw()
if "Qt4" in pylab.get_backend():
QtGui.qApp.postEvent(
fig.canvas,
QtGui.QResizeEvent(fig.canvas.size(), fig.canvas.size()))
if not main_loop:
QtCore.QCoreApplication.processEvents()
def maximize_fig(fig=None):
"""
Try to set the figure fullscreen
"""
if fig and "canvas" in dir(fig) and fig.canvas:
if "Qt4" in pylab.get_backend():
fig.canvas.setWindowState(QtCore.Qt.WindowMaximized)
else:
mng = pylab.get_current_fig_manager()
# attempt to maximize the figure ... lost hopes.
win_shape = (1920, 1080)
event = Event(*win_shape)
try:
mng.resize(event)
except TypeError:
mng.resize(*win_shape)
update_fig(fig)
def maximize_fig(fig=None):
"""
Try to set the figure fullscreen
"""
if fig and "canvas" in dir(fig) and fig.canvas:
if "Qt4" in pylab.get_backend():
fig.canvas.setWindowState(QtCore.Qt.WindowMaximized)
else:
mng = pylab.get_current_fig_manager()
# attempt to maximize the figure ... lost hopes.
win_shape = (1920, 1080)
event = Event(*win_shape)
try:
mng.resize(event)
except TypeError:
mng.resize(*win_shape)
update_fig(fig)
def plot_time_stamped_poses(title,
time_stamped_poses_A,
time_stamped_poses_B,
block=True):
fig = plt.figure()
title_position = 1.05
fig.suptitle(title + " [A = top, B = bottom]", fontsize='24')
a1 = plt.subplot(2, 2, 1)
a1.set_title(
"Orientation \nx [red], y [green], z [blue], w [cyan]",
y=title_position)
plt.plot(time_stamped_poses_A[:, 4], c='r')
plt.plot(time_stamped_poses_A[:, 5], c='g')
plt.plot(time_stamped_poses_A[:, 6], c='b')
plt.plot(time_stamped_poses_A[:, 7], c='c')
a2 = plt.subplot(2, 2, 2)
a2.set_title(
"Position (eye coordinate frame) \nx [red], y [green], z [blue]", y=title_position)
plt.plot(time_stamped_poses_A[:, 1], c='r')
plt.plot(time_stamped_poses_A[:, 2], c='g')
plt.plot(time_stamped_poses_A[:, 3], c='b')
a3 = plt.subplot(2, 2, 3)
plt.plot(time_stamped_poses_B[:, 4], c='r')
plt.plot(time_stamped_poses_B[:, 5], c='g')
plt.plot(time_stamped_poses_B[:, 6], c='b')
plt.plot(time_stamped_poses_B[:, 7], c='c')
a4 = plt.subplot(2, 2, 4)
plt.plot(time_stamped_poses_B[:, 1], c='r')
plt.plot(time_stamped_poses_B[:, 2], c='g')
plt.plot(time_stamped_poses_B[:, 3], c='b')
plt.subplots_adjust(left=0.025, right=0.975, top=0.8, bottom=0.05)
if plt.get_backend() == 'TkAgg':
mng = plt.get_current_fig_manager()
max_size = mng.window.maxsize()
max_size = (max_size[0], max_size[1] * 0.45)
mng.resize(*max_size)
plt.show(block=block)
import matplotlib.pylab as plt
from matplotlib.gridspec import GridSpec
from matplotlib import use, is_interactive
use('TkAgg')
# plt.ion()
print('Using', plt.get_backend(), 'as graphics backend.')
print('Is interactive:', is_interactive())
import random
import tensorflow as tf
import tensorflow_hub as hub
import numpy as np
import pandas as pd
from time import time
from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score, f1_score, confusion_matrix, \
precision_score, recall_score, accuracy_score
from sklearn.model_selection import train_test_split
from tensorflow.keras.initializers import constant
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from pathlib import Path
from tensorflow.keras.applications.densenet import DenseNet121
from hyperopt import hp
from hyperopt.pyll.stochastic import sample
from hyperopt import tpe
from hyperopt import Trials
from hyperopt import fmin
import csv
from datetime import datetime
import pickle
def plot_input_data(quaternions_A,
quaternions_B,
quaternions_A_interp,
quaternions_B_interp,
angular_velocity_norms_A,
angular_velocity_norms_B,
angular_velocity_norms_A_filtered,
angular_velocity_norms_B_filtered,
block=True):
quat_A = np.array([quat.q for quat in quaternions_A])
quat_A_interp = np.array([quat.q for quat in quaternions_A_interp])
quat_B = np.array([quat.q for quat in quaternions_B])
quat_B_interp = np.array([quat.q for quat in quaternions_B_interp])
fig = plt.figure()
title_position = 1.05
fig.suptitle("Input Data [A = top, B = bottom]", fontsize='24')
a1 = plt.subplot(2, 4, 1)
a1.set_title(
"Input quaternions \nx [red], y [green], z [blue], w [cyan]",
y=title_position)
plt.plot(quat_A[:, 0], c='r')
plt.plot(quat_A[:, 1], c='g')
plt.plot(quat_A[:, 2], c='b')
plt.plot(quat_A[:, 3], c='c')
a2 = plt.subplot(2, 4, 2)
a2.set_title(
"Interpolated quaternions \nx [red], y [green], z [blue], w [cyan]",
y=title_position)
plt.plot(quat_A_interp[:, 0], c='r')
plt.plot(quat_A_interp[:, 1], c='g')
plt.plot(quat_A_interp[:, 2], c='b')
plt.plot(quat_A_interp[:, 3], c='c')
a3 = plt.subplot(2, 4, 3)
a3.set_title("Norm of angular velocity", y=title_position)
plt.plot(angular_velocity_norms_A, c='r')
a4 = plt.subplot(2, 4, 4)
a4.set_title(
"Norm of angular velocity \n[median filter]", y=title_position)
plt.plot(angular_velocity_norms_A_filtered, c='r')
plt.subplot(2, 4, 5)
plt.plot(quat_B[:, 0], c='r')
plt.plot(quat_B[:, 1], c='g')
plt.plot(quat_B[:, 2], c='b')
plt.plot(quat_B[:, 3], c='c')
plt.subplot(2, 4, 6)
plt.plot(quat_B_interp[:, 0], c='r')
plt.plot(quat_B_interp[:, 1], c='g')
plt.plot(quat_B_interp[:, 2], c='b')
plt.plot(quat_B_interp[:, 3], c='c')
plt.subplot(2, 4, 7)
plt.plot(angular_velocity_norms_B, c='b')
plt.subplot(2, 4, 8)
plt.plot(angular_velocity_norms_B_filtered, c='b')
plt.subplots_adjust(left=0.025, right=0.975, top=0.8, bottom=0.05)
if plt.get_backend() == 'TkAgg':
mng = plt.get_current_fig_manager()
max_size = mng.window.maxsize()
max_size = (max_size[0], max_size[1] * 0.45)
mng.resize(*max_size)
plt.show(block=block)