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tsdf_inserter.py
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tsdf_inserter.py
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import numpy as np
import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as patches
import math as math
from matplotlib import pyplot
from matplotlib import cm
from shapely.geometry import LineString
import tsdf
def circular_weighted_average(angles, weights):
summed_weight = 0
summed_y = 0
summed_x =0
for i in range(len(angles)):
summed_y += np.sin(angles[i]) * weights[i]
summed_x += np.cos(angles[i]) * weights[i]
return math.atan2(summed_y,summed_x)
def getRaytracingHelperVariables(observation_origin, observation_ray,t_start, t_end, grid_size_inv):
traversal_start = observation_origin + t_start * observation_ray
traversal_end = observation_origin + t_end * observation_ray
traversal_start_scaled = traversal_start * grid_size_inv
traversal_end_scaled = traversal_end * grid_size_inv
traversal_ray_scaled = traversal_end_scaled - traversal_start_scaled
traversal_ray_scaled_inv =(1. / traversal_ray_scaled[0], 1. / traversal_ray_scaled[1])
grid_index = np.round(traversal_start_scaled)
grid_step = np.sign(traversal_ray_scaled)
t_max = (grid_index - traversal_start_scaled) * traversal_ray_scaled_inv
t_delta = grid_step * traversal_ray_scaled_inv
return grid_index, grid_step, t_max, t_delta
def unit_vector(vector):
return vector / np.linalg.norm(vector)
def toTwoPi(value):
if(value > math.pi):
return toTwoPi(value - 2 * math.pi) #value % math.pi
if(value < - math.pi):
return toTwoPi(value + 2 * math.pi) #value % -math.pi
return value
def angle_between(v1, v2):
v1_normalized = unit_vector(v1)
v2_normalized = unit_vector(v2)
return toTwoPi(angle(v1)-angle(v2))
def angle(v):
v_normalized = unit_vector(v)
return math.atan2(v_normalized[1], v_normalized[0])
def distanceLinePoint(line_p0, line_p1, point):
numerator = np.abs((line_p1[1]-line_p0[1])*point[0] - (line_p1[0]-line_p0[0])*point[1] + line_p1[0]*line_p0[1] - line_p1[1]*line_p0[0])
denominator = np.linalg.norm(line_p1-line_p0)
return numerator/denominator
def gaussian(x, mu=0, sigma=1):
return 1/(math.sqrt(2*math.pi)*sigma**2)*math.e**(-0.5*((x-mu)/sigma**2)**2)
class ScanNormalTSDFRangeInserter:
def __init__(self, use_normals_weight=False, n_normal_samples=8, default_weight=1, use_distance_cell_to_observation_weight=False, use_distance_cell_to_ray_weight=False, use_scale_distance=False, normal_distance_factor=1, max_weight=1000, draw_normals_scan_indices=[0], use_distance_cell_to_hit = False):
self.use_normals_weight = use_normals_weight
self.use_distance_cell_to_observation_weight = use_distance_cell_to_observation_weight
self.sigma_distance_cell_to_observation_weight = 1.0
self.use_distance_cell_to_ray_weight = use_distance_cell_to_ray_weight
self.sigma_distance_cell_to_ray_weight = 0.6
self.n_normal_samples = n_normal_samples
self.default_weight = default_weight
self.normal_distance_factor = normal_distance_factor #0 --> all normals same weight, 1 --> f(0)=1, f(0.1)=0.9 f(0.2)=0.82 independent of distance, inf -->only closest normal counts
self.max_weight = max_weight
self.draw_normals_scan_indices = draw_normals_scan_indices
self.num_inserted_scans = 0
self.use_scale_distance = use_scale_distance
self.use_distance_cell_to_hit = use_distance_cell_to_hit
print(self)
def __str__(self):
return "ScanNormalTSDFRangeInserter \n use_normals_weight %s \n n_normal_samples %s\n default_weight %s\n normal_distance_factor %s\n" % (self.use_normals_weight, self.n_normal_samples, self.default_weight, self.normal_distance_factor)
def updateCell(self, tsdf, cell_index, update_distance, ray_length, update_weight):
if(abs(update_distance)< tsdf.truncation_distance):
updated_weight = min(tsdf.getWeight(cell_index) + update_weight, self.max_weight)
updated_tsdf = (tsdf.getTSDF(cell_index) * tsdf.getWeight(cell_index) + update_distance * update_weight) / (update_weight + tsdf.getWeight(cell_index))
tsdf.setWeight(cell_index, updated_weight)
tsdf.setTSDF(cell_index, updated_tsdf)
#tsdf.setWeight(cell_index, 0.5)
#tsdf.setTSDF(cell_index, 0.5)
def computeNormal(self, sample, neighbors, sample_origin):
normals = []
normal_distances = []
normal_weights = []
for neighbor in neighbors:
sample_to_neighbor = sample - neighbor
origin_to_neighbor = sample_origin - neighbor
origin_to_sample = sample_origin - sample
sample_to_neighbor_rotated = np.array([-sample_to_neighbor[1],sample_to_neighbor[0]])
if(sample_to_neighbor_rotated.dot(origin_to_sample) > 0):
sample_to_neighbor = -sample_to_neighbor
tangent_angle = angle(sample_to_neighbor)
normal_angle = toTwoPi(tangent_angle - math.pi/2)
normals += [normal_angle]
normal_distance = np.linalg.norm(sample-neighbor)
normal_distances += [normal_distance]
normal_weights += [math.e**(-self.normal_distance_factor * normal_distance)]
normals = np.array(normals)
normal_weights = np.array(normal_weights)
normal_mean = circular_weighted_average(normals, normal_weights)
delta = normals-normal_mean
delta_flipped = (normals-normal_mean)-2*math.pi
is_min_delta = np.abs(delta) < np.abs(delta_flipped)
min_deltas = delta*is_min_delta + delta_flipped*(1-is_min_delta)
normal_var = np.average((min_deltas-normal_mean)**2, weights=normal_weights)
normal_weight_sum = np.sum(normal_weights)
return normal_mean, normal_var, normal_weight_sum
def drawScanWithNormals(self, hits, normal_orientations, sensor_origin, normal_weights, normal_variances, normal_angle_to_ray):
fig = plt.figure()
ax = plt.subplot(311)
x_val = [x[0] for x in hits]
y_val = [x[1] for x in hits]
sc = ax.scatter(x_val, y_val, c=normal_weights, marker='x', cmap=cm.jet)
plt.colorbar(sc)
ax.scatter(sensor_origin[0], sensor_origin[1], marker='x')
for idx, normal_orientation in enumerate(normal_orientations):
normal_scale = 0.1
dx = normal_scale*np.cos(normal_orientation)
dy = normal_scale*np.sin(normal_orientation)
ax.arrow(x_val[idx], y_val[idx], dx, dy, fc='k', ec='k', color='b')
ax.set_aspect('equal')
plt.title('Normal Estimation Weights')
'''
ax = plt.subplot(412)
x_val = [x[0] for x in hits]
y_val = [x[1] for x in hits]
sc = ax.scatter(x_val, y_val, c=normal_variances, marker='x', cmap=cm.jet)
plt.colorbar(sc)
ax.scatter(sensor_origin[0], sensor_origin[1], marker='x')
for idx, normal_orientation in enumerate(normal_orientations):
normal_scale = 0.1
dx = normal_scale*np.cos(normal_orientation)
dy = normal_scale*np.sin(normal_orientation)
ax.arrow(x_val[idx], y_val[idx], dx, dy, fc='k', ec='k', color='b')
ax.set_aspect('equal')
plt.title('Normal Estimation Variances')
'''
ax = plt.subplot(312)
x_val = [x[0] for x in hits]
y_val = [x[1] for x in hits]
sc = ax.scatter(x_val, y_val, c=np.cos(normal_angle_to_ray), marker='x', cmap=cm.jet)
plt.colorbar(sc)
ax.scatter(sensor_origin[0], sensor_origin[1], marker='x')
for idx, normal_orientation in enumerate(normal_orientations):
normal_scale = 0.1
dx = normal_scale*np.cos(normal_orientation)
dy = normal_scale*np.sin(normal_orientation)
ax.arrow(x_val[idx], y_val[idx], dx, dy, fc='k', ec='k', color='b')
ax.set_aspect('equal')
plt.title('Angle normal to ray')
ax = plt.subplot(313)
x_val = [x[0] for x in hits]
y_val = [x[1] for x in hits]
combined_weights = np.reciprocal(np.sqrt(np.array(normal_variances))) * (np.square(np.array(normal_weights))) * np.square(np.cos(normal_angle_to_ray))
combined_weights = np.cos(normal_angle_to_ray)
sc = ax.scatter(x_val, y_val, c=combined_weights, marker='x', cmap=cm.jet)
plt.colorbar(sc)
ax.scatter(sensor_origin[0], sensor_origin[1], marker='x')
for idx, normal_orientation in enumerate(normal_orientations):
normal_scale = 0.1
dx = normal_scale*np.cos(normal_orientation)
dy = normal_scale*np.sin(normal_orientation)
ax.arrow(x_val[idx], y_val[idx], dx, dy, fc='k', ec='k', color='b')
ax.set_aspect('equal')
plt.title('Combined weight')
def insertScan(self, tsdf, hits, origin):
origin = np.array(origin)
hits = np.array(hits)
n_hits = len(hits)
normal_orientations = []
normal_orientation_variances = []
normal_estimation_weight_sums = []
normal_estimation_angles_to_ray = []
normal_estimation_angle_to_ray = 0
normal_orientation = 0
for idx, hit in enumerate(hits):
#print('origin',origin)
#print('hit',hit)
hit = np.array(hit)
ray = hit - origin
if self.use_normals_weight or True:
neighbor_indices = np.array(list(range(idx-int(np.floor(self.n_normal_samples/2)), idx)) + list(range(idx+1, idx+int(np.ceil(self.n_normal_samples/2) + 1))))
neighbor_indices = neighbor_indices[neighbor_indices >= 0]
neighbor_indices = neighbor_indices[neighbor_indices < n_hits]
normal_orientation, normal_var, normal_estimation_weight_sum = self.computeNormal(hit, hits[neighbor_indices], origin)
normal_orientations += [normal_orientation]
normal_estimation_weight_sums += [normal_estimation_weight_sum]
normal_orientation_variances += [normal_var]
normal_estimation_angle_to_ray = normal_orientation - angle(-ray)
normal_estimation_angles_to_ray += [normal_estimation_angle_to_ray] #
ray_range = np.linalg.norm(ray)
range_inv = 1.0 / ray_range
t_truncation_distance = tsdf.truncation_distance * range_inv
t_start = 1.0 - t_truncation_distance
t_end = 1.0 + t_truncation_distance
grid_index, grid_step, t_max, t_delta = getRaytracingHelperVariables(origin, ray, t_start,t_end, 1. / tsdf.resolution)
t = 0
while t < 1.0 :
#print('t',t,'t_max',t_max,'t_delta',t_delta)
#print('grid_index',grid_index)
t_next = np.min(t_max)
min_coeff_idx = np.argmin(t_max)
sampling_point = grid_index * tsdf.resolution #origin + (t + t_next)/2 * ray
#print('sampling_point',sampling_point,'t',origin + (t) * ray,'tn',origin + (t_next) * ray)
cell_index = tsdf.getCellIndexAtPosition(sampling_point)
cell_center = tsdf.getPositionAtCellIndex(cell_index)
distance_cell_center_to_origin = np.linalg.norm(cell_center - origin)
distance_cell_center_to_hit = np.linalg.norm(cell_center - hit)
update_weight = 1
update_distance = ray_range - distance_cell_center_to_origin
#use_distance_cell_to_observation_weight
if self.use_normals_weight:
update_weight = np.cos(normal_estimation_angle_to_ray)
if(update_weight < 0):
print('WARNING update_weight=',update_weight)
if self.use_distance_cell_to_observation_weight:
normalized_distance_cell_to_observation = np.abs(ray_range - distance_cell_center_to_origin)/tsdf.resolution
distance_cell_to_observation_weight = gaussian(normalized_distance_cell_to_observation, 0, self.sigma_distance_cell_to_observation_weight)
'''
distance_cell_to_observation_weight = np.abs((tsdf.truncation_distance - np.abs(ray_range - distance_cell_center_to_origin))/tsdf.truncation_distance)
'''
update_weight *= distance_cell_to_observation_weight
if distance_cell_to_observation_weight < 0:
print('WARNING distance_cell_to_observation_weight=',distance_cell_to_observation_weight)
if self.use_distance_cell_to_ray_weight:
distance_cell_to_ray = distanceLinePoint(origin, hit, cell_center)/tsdf.resolution
#distance_cell_to_ray_weight = distance_cell_to_ray
distance_cell_to_ray_weight = gaussian(distance_cell_to_ray, 0, self.sigma_distance_cell_to_ray_weight)
update_weight *= distance_cell_to_ray_weight
if distance_cell_to_ray_weight < 0:
print('WARNING distance_cell_to_ray_weight=',distance_cell_to_ray_weight)
if self.use_scale_distance:
#print(np.array([np.cos(normal_orientation), np.sin(normal_orientation)]))
update_distance = (cell_center - hit).dot(np.array([np.cos(normal_orientation), np.sin(normal_orientation)]))
if self.use_distance_cell_to_hit:
update_distance = distance_cell_center_to_hit
if self.use_distance_cell_to_hit and self.use_scale_distance:
print('CONFIGURATION ERROR')
self.updateCell(tsdf, cell_index, update_distance , ray_range, update_weight)
#print('cell_index', cell_index)
t = t_next
grid_index[min_coeff_idx] += grid_step[min_coeff_idx]
t_max[min_coeff_idx] += t_delta[min_coeff_idx]
if self.use_normals_weight:
if self.num_inserted_scans in self.draw_normals_scan_indices :
self.drawScanWithNormals(hits, normal_orientations, origin, normal_estimation_weight_sums, normal_orientation_variances, normal_estimation_angles_to_ray)
self.draw_normals = False
#print('avg normal error', np.mean(np.abs(normal_orientations)))
pass
self.num_inserted_scans += 1