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time_align.py
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time_align.py
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import numpy as np
import matplotlib.pyplot as plt
import os
import matplotlib.pyplot as plt
import knn
import Icp2d
from scipy.interpolate import UnivariateSpline
import numpy as np
import numpy as np
import filter
import util
def time_knn(best_scale, best_shift, data_sync, data_ref):
shifted_time = best_scale*np.array(list(data_sync)) + best_shift
argmins, argmins1, d2_sorted = knn.knn(np.expand_dims(list(data_ref), axis = 1), np.expand_dims(shifted_time, axis = 1))
sync_dict = {}
for i in range(len(argmins)):
sync_dict[list(data_ref)[argmins[i]]] = int(list(data_sync)[argmins1[i]])
return sync_dict
def time_all(data_ref, data_sync, save_dir = None, sync = True, name = '', window = 1, dilation = 1, search_div = 6.0, init_sync = []):
best_shift_array = []
best_scale_array = []
sync_dict_array = []
for i in range(len(data_sync)):
scale_array_init = [1]#[0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2.0, 2.25,2.5,2.75,3.0, 3.25, 3.5]
shift_array_init = list(range(-int(len(list(data_sync[i].keys()))/search_div), int(len(list(data_sync[i].keys()))/search_div)))
if len(init_sync) != 0:
shift_array_init = init_sync
best_shift, best_scale, sync_dict = time_align_corr_multi(data_ref, data_sync[i], save_dir = save_dir, name = str(i) + '_' + name, sync = sync, scale_search = scale_array_init, shift_search = shift_array_init, trial_name = 'init', window = window, dilation = dilation)
best_shift_array.append(best_shift)
best_scale_array.append(best_scale)
sync_dict_array.append(sync_dict)
return best_shift_array, best_scale_array, sync_dict_array
def union_and_pad(x1, y1, x2, y2):
x_union = np.union1d(x1, x2) # Find the union of times from x1 and x2
# Pad y1 based on its last or first value to match the union time range
y1_padded = np.interp(x_union, x1, y1, left=y1[0], right=y1[-1])
# Pad y2 based on its last or first value to match the union time range
y2_padded = np.interp(x_union, x2, y2, left=y2[0], right=y2[-1])
return x_union, y1_padded, y2_padded
def time_align_corr(data_ref, data_sync, save_dir = None, name = None, end = True, window = 1, dilation = 1, thresh_mag = 0.0, sync = True, trial_name = 'init', scale_search = [1], shift_search = [0]):
if os.path.isdir(save_dir + '/search_time') == False:
os.mkdir(save_dir + '/search_time')
if os.path.isdir(save_dir + '/search_time/' + name) == False:
os.mkdir(save_dir + '/search_time/' + name)
data_interp_ref = get_track(data_ref, 2*dilation)
data_interp_sync = get_track(data_sync, 2*dilation)
ref_vel_array = []
ref_vel_dict = {}
fig1, ax1 = plt.subplots(1, 1)
fig2, ax2 = plt.subplots(1, 1)
fig3, ax3 = plt.subplots(1, 1)
#print(data_ref)
############################
ref_mean_array = []
sync_mean_array = []
for track in data_interp_ref.keys():
ref_time = data_interp_ref[track]['frame']
ref_points = data_interp_ref[track]['points']
B_set = set(data_interp_ref[track]['frame_actual'])
indices = [i for i, x in enumerate(ref_time) if x in B_set]
ref_vel = np.transpose(np.array(ref_points))
for st in range(len(ref_time)):
ref_mean_array.append(ref_vel[:, st])
for track in data_interp_sync.keys():
sync_time = data_interp_sync[track]['frame']
sync_points = data_interp_sync[track]['points']
B_set = set(data_interp_sync[track]['frame_actual'])
indices = [i for i, x in enumerate(sync_time) if x in B_set]
sync_vel = np.transpose(np.array(sync_points))
for st in range(len(sync_time)):
sync_mean_array.append(sync_vel[:, st])
ref_mean = np.mean(ref_mean_array, axis = 0)
sync_mean = np.mean(sync_mean_array, axis = 0)
############################
for track in data_interp_ref.keys():
ref_time = data_interp_ref[track]['frame']
ref_points = data_interp_ref[track]['points']
if len(ref_points) < 2*dilation + 1:
continue
B_set = set(data_interp_ref[track]['frame_actual'])
indices = [i for i, x in enumerate(ref_time) if x in B_set]
ref_vel = np.transpose(np.array(ref_points))
ax1.scatter(ref_time, np.linalg.norm(np.array(ref_vel), axis = 0).tolist(), c = 'b')
ax1.set_yscale("linear")
ref_vel_array.append(ref_vel[:, indices])
#print(ref_vel.shape, " r ef vel")
for st in range(len(ref_time)):
if ref_time[st] in ref_vel_dict:
ref_vel_dict[ref_time[st]].append(ref_vel[:, st] - ref_mean)
else:
ref_vel_dict[ref_time[st]] = [ref_vel[:, st] - ref_mean]
ref_vel_dict_avg = {}
#print(ref_vel_dict)
for f in ref_vel_dict.keys():
ref_vel_dict_avg[f] = np.linalg.norm(np.mean(np.array(ref_vel_dict[f]), axis = 0))
ref_time = np.array(list(ref_vel_dict_avg.keys()))
ref_vel = np.array(list(ref_vel_dict_avg.values()))
ref_average = filter.window_average(ref_vel, window = window)
#############################################################
sync_vel_dict = {}
###################################
sync_vel_array = []
sync_vel_dict_array = []
for track in data_interp_sync.keys():
sync_frame = data_interp_sync[track]['frame']
sync_points = data_interp_sync[track]['points']
sync_time = sync_frame
if len(sync_points) < 2*dilation + 1:
continue
B_set = set(data_interp_sync[track]['frame_actual'])
indices = [i for i, x in enumerate(sync_time) if x in B_set]
sync_vel = np.transpose(np.array(sync_points))#torch.squeeze(util.central_diff(torch.unsqueeze(torch.transpose(torch.from_numpy(np.array(sync_points)), 0 , 1), dim = 1).double(), time = 1, dilation = dilation))
sync_vel_array.append(sync_vel[:, indices])
sync_vel_dict_array.append((sync_time, np.linalg.norm(np.array(sync_vel), axis = 0).tolist()))
#ax1.plot(sync_time, np.linalg.norm(np.array(sync_vel), axis = 0).tolist(), c = 'r')
ax1.scatter(sync_time, np.linalg.norm(np.array(sync_vel), axis = 0).tolist(), c = 'r')
ax1.set_yscale("linear")
for st in range(len(sync_time)):
if sync_time[st] in sync_vel_dict:
sync_vel_dict[sync_time[st]].append(sync_vel[:, st] - sync_mean)
else:
sync_vel_dict[sync_time[st]] = [sync_vel[:, st] - sync_mean]
sync_vel_dict_avg = {}
for f in sync_vel_dict.keys():
sync_vel_dict_avg[f] = np.linalg.norm(np.mean(np.array(sync_vel_dict[f]), axis = 0))
ax2.scatter(list(sync_vel_dict_avg.keys()), list(sync_vel_dict_avg.values()), c = 'r')
ax2.set_yscale("linear")
ax2.scatter(list(ref_vel_dict_avg.keys()), list(ref_vel_dict_avg.values()), c = 'b')
ax2.set_yscale("linear")
ax3.scatter(np.array(list(ref_vel_dict_avg.keys())), np.array(list(ref_vel_dict_avg.values())))
ax3.scatter(np.array(list(sync_vel_dict_avg.keys())), np.array(list(sync_vel_dict_avg.values())))
best_sync_time = None
best_sync_average = None
best_ref_time = None
best_ref_average = None
best_scale = 1.0
best_shift = 0.0
error_array = []
shift_array = []
shift_val = shift_search
scale_val = scale_search
if sync == False:
shift_val = [0]
scale_val = [1]
best_correlation = -np.inf
sync_vel_array = np.array(list(sync_vel_dict_avg.values()))# = util.normalize_data_median(np.array(list(sync_vel_dict_avg.values())))
ref_average = ref_average#util.normalize_data_median(ref_average)
if save_dir is not None:
fig, ax1 = plt.subplots(1, 1)
ax1.set_yscale("linear")
#ax1.plot(list(sync_vel_dict_avg.keys()), list(sync_vel_dict_avg.values()), c = 'r')
#ax1.plot(ref_time, ref_average, c = 'b')
ax1.scatter(list(sync_vel_dict_avg.keys()), list(sync_vel_array), c = 'r')
ax1.scatter(ref_time, ref_average, c = 'b')
ax1.set_title("init")
if name is not None:
fig.savefig(save_dir + '/search_time/' + name + '/' + 'init_' + trial_name + '_' + str(name) + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
else:
fig.savefig(save_dir + '/search_time/' + name + '/' + 'init_' + trial_name + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
fig1, ax2 = plt.subplots(1, 1)
ax2.set_yscale("linear")
#ax2.plot(shift_array, error_array, c = 'r')
ax2.scatter(shift_array, error_array, c = 'r')
ax2.set_title(str(best_correlation))
plt.close('all')
for scale in scale_val:#list(np.linspace(0.5, 2, 5)) + [1.0]:
shift_array_loop = []
error_array_loop = []
correlation_array_loop = []
for shift in shift_val:#list(range(-int(sync_time_init.shape[0]/64), int(sync_time_init.shape[0]/64))):
sync_time = scale*np.array(list(sync_vel_dict_avg.keys())) + shift
sync_average = sync_vel_array
sync_index = np.where(sync_average > thresh_mag)[0]
argmins, argmins1, d2_sorted = knn.knn(np.expand_dims(ref_time, axis = 1), np.expand_dims(sync_time, axis = 1))
x_union, y1_padded, y2_padded = union_and_pad(ref_time, ref_average, sync_time, sync_average)
correlation = np.corrcoef(y1_padded, y2_padded, rowvar = True)[0,1]
mean_error = np.mean(np.abs(y1_padded - y2_padded))
#correlation = correlation_signed
correlation_array_loop.append(correlation)
error_array.append(mean_error)
shift_array.append(shift)
if best_correlation < correlation:
best_scale = scale
best_shift = shift
best_correlation = correlation
best_sync_time = x_union#sync_time
best_sync_average = y2_padded
best_ref_time = x_union
best_ref_average = y1_padded
shift_array_loop.append(shift)
error_array_loop.append(mean_error)
####################
if save_dir is not None:
fig1, ax1 = plt.subplots(1, 1)
#ax1.plot(shift_array_loop, error_array_loop, c = 'r')
ax1.scatter(shift_array_loop, error_array_loop, c = 'r')
ax1.set_yscale("linear")
fig2, ax2 = plt.subplots(1, 1)
#ax2.plot(shift_array_loop, correlation_array_loop, c = 'r')
ax2.scatter(shift_array_loop, correlation_array_loop, c = 'r')
ax2.set_yscale("linear")
if name is not None:
#fig1.savefig(save_dir + '/search_time/' + name + '/' + 'error_' + str(scale) + '_' + str(name) + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
fig1.savefig(save_dir + '/search_time/' + name + '/' + 'error_' + name + '_' + str(best_shift) + '_' + '.png')
else:
#fig1.savefig(save_dir + '/search_time/' + name + '/' + 'error_' + str(scale) + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
fig1.savefig(save_dir + '/search_time/' + name + '/' + 'error_' + name + '_' + str(best_shift) + '_' + '.png')
if name is not None:
#fig2.savefig(save_dir + '/search_time/' + name + '/' + 'cor_' + str(scale) + '_' + str(name) + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
fig2.savefig(save_dir + '/search_time/' + name + '/' + 'cor_' + name + '_' + str(best_shift) + '_' + '.png')
else:
#fig2.savefig(save_dir + '/search_time/' + name + '/' + 'cor_' + str(scale) + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
fig2.savefig(save_dir + '/search_time/' + name + '/' + 'cor_' + name + '_' + str(best_shift) + '_' + '.png')
plt.close('all')
if save_dir is not None:
fig, ax1 = plt.subplots(1, 1)
ax1.set_yscale("linear")
#ax1.plot(best_sync_time, best_sync_average, c = 'r')
#ax1.plot(best_ref_time, best_ref_average, c = 'b')
ax1.scatter(best_sync_time, best_sync_average, c = 'r')
ax1.scatter(best_ref_time, best_ref_average, c = 'b')
ax1.set_title(str(best_correlation))
if name is not None:
fig.savefig(save_dir + '/search_time/' + name + '/' + 'best_' + trial_name + '_' + str(name) + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
else:
fig.savefig(save_dir + '/search_time/' + name + '/' + 'best_average_' + trial_name + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
fig1, ax2 = plt.subplots(1, 1)
ax2.set_yscale("linear")
#ax2.plot(shift_array, error_array, c = 'r')
ax2.scatter(shift_array, error_array, c = 'r')
ax2.set_title(str(best_correlation))
plt.close('all')
shifted_time = best_scale*np.array(list(data_sync.keys())) + best_shift
argmins, argmins1, d2_sorted = knn.knn(np.expand_dims(list(data_ref.keys()), axis = 1), np.expand_dims(shifted_time, axis = 1))
sync_dict = {}
for i in range(len(argmins)):
sync_dict[list(data_ref.keys())[argmins[i]]] = int(list(data_sync.keys())[argmins1[i]])
#stop
return best_shift, best_scale, sync_dict
def get_track(data, min_size = 2):
print(data)
mean_array = []
track_dict = {}
for fr in list(data.keys()):
print(data[fr], " THE DATA")
for tr in list(data[fr].keys()):
mean_array.append(data[fr][tr][0:2])
if tr in track_dict:
track_dict[tr].append({'frame': fr, 'coord': data[fr][tr][0:2]})
else:
track_dict[tr] = [{'frame': fr, 'coord': data[fr][tr][0:2]}]
mean_center = np.mean(mean_array, axis = 0)
points_dict = {}
for t in track_dict.keys():
points_array = []
frame_array = []
frame_actual_array = []
points_actual_array = []
for i in range(len(track_dict[t])):
points_array.append(track_dict[t][i]['coord'] - mean_center)
frame_array.append(track_dict[t][i]['frame'])
points_actual_array.append(track_dict[t][i]['coord'] - mean_center)
frame_actual_array.append(track_dict[t][i]['frame'])
points_dict[t] = {'points': points_array, "frame": frame_array, 'points_actual': points_actual_array, 'frame_actual': frame_actual_array}
return points_dict
def time_align_corr_multi(data_ref, data_sync, save_dir = None, name = None, end = True, window = 1, dilation = 1, thresh_mag = 0.0, sync = True, trial_name = 'init', scale_search = [1], shift_search = [0]):
if os.path.isdir(save_dir + '/search_time') == False:
os.mkdir(save_dir + '/search_time')
if os.path.isdir(save_dir + '/search_time/' + name) == False:
os.mkdir(save_dir + '/search_time/' + name)
#print(data_ref)
#print(data_sync)
#data_interp_ref = data_ref#Icp2d.interpolate(data_ref, 2*dilation)
#data_interp_sync = data_sync#Icp2d.interpolate(data_sync, 2*dilation)
#print(data_ref.keys(), len(list(data_ref.keys())), " DATA REF")
data_interp_ref = Icp2d.get_track(data_ref)
data_interp_sync = Icp2d.get_track(data_sync)
#print(data_interp_ref.keys(), " DATA interp REF")
#stop
ref_vel_array = []
ref_vel_dict = {}
fig1, ax1 = plt.subplots(1, 1)
fig2, ax2 = plt.subplots(1, 1)
fig3, ax3 = plt.subplots(1, 1)
#print(data_ref)
############################
ref_mean_array = []
sync_mean_array = []
for track in data_interp_ref.keys():
ref_time = data_interp_ref[track]['frame']
ref_points = data_interp_ref[track]['points']
B_set = set(data_interp_ref[track]['frame_actual'])
indices = [i for i, x in enumerate(ref_time) if x in B_set]
ref_vel = np.transpose(np.array(ref_points))
for st in range(len(ref_time)):
ref_mean_array.append(ref_vel[:, st])
for track in data_interp_sync.keys():
sync_time = data_interp_sync[track]['frame']
sync_points = data_interp_sync[track]['points']
B_set = set(data_interp_sync[track]['frame_actual'])
indices = [i for i, x in enumerate(sync_time) if x in B_set]
sync_vel = np.transpose(np.array(sync_points))
for st in range(len(sync_time)):
sync_mean_array.append(sync_vel[:, st])
ref_mean = np.mean(ref_mean_array, axis = 0)
sync_mean = np.mean(sync_mean_array, axis = 0)
############################
#print(ref_mean, sync_mean, " ref sync")
#stop
ref_time_array = []
for track in data_interp_ref.keys():
ref_time = data_interp_ref[track]['frame']
ref_points = data_interp_ref[track]['points']
if len(ref_points) < 2*dilation + 1:
continue
B_set = set(data_interp_ref[track]['frame_actual'])
indices = [i for i, x in enumerate(ref_time) if x in B_set]
ref_vel = np.transpose(np.array(ref_points))
#ax1.scatter(ref_time, np.linalg.norm(np.array(ref_vel), axis = 0).tolist(), c = 'b')
#ax1.set_yscale("linear")
#print(ref_vel.shape, " r ef vel")
for st in range(len(ref_time)):
ref_time_array.append(ref_time[st])
ref_vel_array.append(np.linalg.norm(ref_vel[:, st] - ref_mean))
if ref_time[st] in ref_vel_dict:
ref_vel_dict[ref_time[st]].append(ref_vel[:, st] - ref_mean)
else:
ref_vel_dict[ref_time[st]] = [ref_vel[:, st] - ref_mean]
sync_vel_dict = {}
sync_vel_array = []
sync_time_array = []
sync_vel_dict_array = []
for track in data_interp_sync.keys():
sync_frame = data_interp_sync[track]['frame']
sync_points = data_interp_sync[track]['points']
sync_time = sync_frame
if len(sync_points) < 2*dilation + 1:
continue
B_set = set(data_interp_sync[track]['frame_actual'])
indices = [i for i, x in enumerate(sync_time) if x in B_set]
sync_vel = np.transpose(np.array(sync_points))
sync_vel_dict_array.append((sync_time, np.linalg.norm(np.array(sync_vel), axis = 0).tolist()))
for st in range(len(sync_time)):
sync_vel_array.append(np.linalg.norm(sync_vel[:, st] - sync_mean))
sync_time_array.append(sync_time[st])
if sync_time[st] in sync_vel_dict:
sync_vel_dict[sync_time[st]].append(sync_vel[:, st] - sync_mean)
else:
sync_vel_dict[sync_time[st]] = [sync_vel[:, st] - sync_mean]
arg_sort = np.argsort(sync_time_array)
sync_time_array = list(np.array(sync_time_array)[arg_sort])
sync_vel_array = list(np.array(sync_vel_array)[arg_sort])
best_sync_time = None
best_sync_average = None
best_ref_time = None
best_ref_average = None
best_scale = 1.0
best_shift = 0.0
error_array = []
shift_array = []
shift_val = shift_search
scale_val = scale_search
if sync == False:
shift_val = [0]
scale_val = [1]
best_chamfer_dist = np.inf
sync_vel_array = util.normalize_data_median(sync_vel_array)
ref_vel_array = util.normalize_data_median(ref_vel_array)
if save_dir is not None:
fig, ax1 = plt.subplots(1, 1)
ax1.set_yscale("linear")
#ax1.plot(list(sync_vel_dict_avg.keys()), list(sync_vel_dict_avg.values()), c = 'r')
#ax1.plot(ref_time, ref_average, c = 'b')
ax1.scatter(sync_time_array, sync_vel_array, c = 'r')
ax1.scatter(ref_time_array, ref_vel_array, c = 'b')
ax1.set_title("init")
if name is not None:
fig.savefig(save_dir + '/search_time/' + name + '/' + 'init_' + trial_name + '_' + str(name) + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
else:
fig.savefig(save_dir + '/search_time/' + name + '/' + 'init_' + trial_name + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
fig1, ax2 = plt.subplots(1, 1)
ax2.set_yscale("linear")
#ax2.plot(shift_array, error_array, c = 'r')
ax2.scatter(shift_array, error_array, c = 'r')
ax2.set_title(str(best_chamfer_dist))
plt.close('all')
#A = np.transpose(np.stack((ref_time_array, ref_vel_array)))
#B = np.transpose(np.stack((np.array(sync_time_array), np.array(sync_vel_array))))
#A = util.remove_outliers_array(A, eps=10)
#B = util.remove_outliers_array(B, eps=10)
#ref_time_array = A[:, 0]
#ref_vel_array = A[:, 1]
#sync_time_array = B[:, 0]
#sync_vel_array = B[:, 1]
for scale in scale_val:#list(np.linspace(0.5, 2, 5)) + [1.0]:
shift_array_loop = []
error_array_loop = []
chamfer_array_loop = []
correlation_array_loop = []
for shift in shift_val:#list(range(-int(sync_time_init.shape[0]/64), int(sync_time_init.shape[0]/64))):
sync_time = scale*np.array(sync_time_array) + shift
sync_vel = np.array(sync_vel_array)
#sync_average = filter.window_average(sync_vel/scale, window = window)
#sync_index = np.where(sync_average > thresh_mag)[0]
#argmins, argmins1, d2_sorted = knn.knn(np.expand_dims(ref_time, axis = 1), np.expand_dims(sync_time, axis = 1))
#util.match_3d_plotly_input2d_farthest_point(ref_time_array, ref_vel_array, sync_time, sync_vel)
#print(ref_vel_array)
#print(ref_time_array)
#x_union, y1_padded, y2_padded = union_and_pad(ref_time_array, ref_vel_array, sync_time, sync_vel)
#print(x_union, " x union")
#print(y1_padded, " y1 padded ")
#print(y2_padded, " y2_padded")
#correlation = np.corrcoef(sync_average[argmins1], ref_average[argmins], rowvar = True)[0,1]
#mean_error = np.mean(np.abs(sync_average[argmins1] - ref_average[argmins]))
#A = np.transpose(np.stack((x_union, y1_padded)))
#B = np.transpose(np.stack((x_union, y2_padded)))
A = np.transpose(np.stack((np.array(ref_time_array), ref_vel_array)))
B = np.transpose(np.stack((sync_time, sync_vel)))
#print(A.shape, "A SHAPE")
#stop
chamfer_dist, A_, B_ = util.hungarian_match_distance(A, B)
#chamfer_dist = util.chamfer_distance(A, B, metric='l2', direction='bi')
#print(chamfer_dist, " CHAMFER DIST")
#correlation = np.corrcoef(y1_padded, y2_padded, rowvar = True)[0,1]
#mean_error = np.mean(np.abs(y1_padded - y2_padded))
#correlation = correlation_signed
#error_array.append(mean_error)
shift_array.append(shift)
if best_chamfer_dist > chamfer_dist:
best_scale = scale
best_shift = shift
best_chamfer_dist = chamfer_dist
A_array = util.dictionary_to_array(A_)
B_array = util.dictionary_to_array(B_)
best_sync_time = list(np.array(B_array)[:, 0])
best_sync_average = list(np.array(B_array)[:, 1])
best_ref_time = list(np.array(A_array)[:, 0])
best_ref_average = list(np.array(A_array)[:, 1])
'''
best_sync_time = sync_time
best_sync_average = sync_vel
best_ref_time = ref_time_array
best_ref_average = ref_vel_array
'''
#best_sync_time = x_union#sync_time
#best_sync_average = y2_padded
#best_ref_time = x_union
#best_ref_average = y1_padded
chamfer_array_loop.append(chamfer_dist)
shift_array_loop.append(shift)
#error_array_loop.append(mean_error)
#correlation_array_loop.append(correlation)
#print(chamfer_array_loop)
#print(shift, "HELLOOOOO")
####################
if save_dir is not None and sync is True:
fig1, ax1 = plt.subplots(1, 1)
#ax1.plot(shift_array_loop, error_array_loop, c = 'r')
'''
ax1.scatter(shift_array_loop, error_array_loop, c = 'r')
ax1.set_yscale("linear")
fig2, ax2 = plt.subplots(1, 1)
#ax2.plot(shift_array_loop, correlation_array_loop, c = 'r')
ax2.scatter(shift_array_loop, correlation_array_loop, c = 'r')
ax2.set_yscale("linear")
'''
fig0, ax0 = plt.subplots(1, 1)
#ax2.plot(shift_array_loop, correlation_array_loop, c = 'r')
ax0.scatter(shift_array_loop, chamfer_array_loop, c = 'r')
ax0.set_yscale("linear")
'''
if name is not None:
#fig1.savefig(save_dir + '/search_time/' + name + '/' + 'error_' + str(scale) + '_' + str(name) + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
fig1.savefig(save_dir + '/search_time/' + name + '/' + 'error_' + name + '_' + str(best_shift) + '_' + '.png')
else:
#fig1.savefig(save_dir + '/search_time/' + name + '/' + 'error_' + str(scale) + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
fig1.savefig(save_dir + '/search_time/' + name + '/' + 'error_' + name + '_' + str(best_shift) + '_' + '.png')
if name is not None:
#fig2.savefig(save_dir + '/search_time/' + name + '/' + 'cor_' + str(scale) + '_' + str(name) + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
fig2.savefig(save_dir + '/search_time/' + name + '/' + 'cor_' + name + '_' + str(best_shift) + '_' + '.png')
else:
#fig2.savefig(save_dir + '/search_time/' + name + '/' + 'cor_' + str(scale) + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
fig2.savefig(save_dir + '/search_time/' + name + '/' + 'cor_' + name + '_' + str(best_shift) + '_' + '.png')
'''
if name is not None:
#fig2.savefig(save_dir + '/search_time/' + name + '/' + 'cor_' + str(scale) + '_' + str(name) + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
fig0.savefig(save_dir + '/search_time/' + name + '/' + 'chamfer_' + name + '_' + str(best_shift) + '_' + '.png')
else:
#fig2.savefig(save_dir + '/search_time/' + name + '/' + 'cor_' + str(scale) + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
fig0.savefig(save_dir + '/search_time/' + name + '/' + 'chamfer_' + name + '_' + str(best_shift) + '_' + '.png')
plt.close('all')
#stop
if save_dir is not None and sync is True:
fig, ax1 = plt.subplots(1, 1)
ax1.set_yscale("linear")
#ax1.plot(best_sync_time, best_sync_average, c = 'r')
#ax1.plot(best_ref_time, best_ref_average, c = 'b')
ax1.scatter(best_sync_time, best_sync_average, c = 'r')
ax1.scatter(best_ref_time, best_ref_average, c = 'b')
ax1.set_title(str(best_chamfer_dist))
if name is not None:
fig.savefig(save_dir + '/search_time/' + name + '/' + 'best_' + trial_name + '_' + str(name) + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
else:
fig.savefig(save_dir + '/search_time/' + name + '/' + 'best_average_' + trial_name + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
#fig1, ax2 = plt.subplots(1, 1)
#ax2.set_yscale("linear")
#ax2.plot(shift_array, error_array, c = 'r')
#ax2.scatter(shift_array, error_array, c = 'r')
#ax2.set_title(str(best_chamfer_dist))
'''
if name is not None:
fig1.savefig(save_dir + '/search_time/' + name + '/' + 'error_' + str(name) + '_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
else:
fig1.savefig(save_dir + '/search_time/' + name + '/' + 'error_' + str(best_shift) + '_' + str(best_scale) +'_' + '.png')
'''
plt.close('all')
shifted_time = best_scale*np.array(list(data_sync.keys())) + best_shift
argmins, argmins1, d2_sorted = knn.knn(np.expand_dims(list(data_ref.keys()), axis = 1), np.expand_dims(shifted_time, axis = 1))
sync_dict = {}
for i in range(len(argmins)):
sync_dict[list(data_ref.keys())[argmins[i]]] = int(list(data_sync.keys())[argmins1[i]])
#stop
return best_shift, best_scale, sync_dict