ENSEEIHT/projet-probleme-inverse-3D
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feat: idx

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gdamms 2023-01-11 16:26:06 +01:00
parent 25b851a1bf
commit 902550225e
1 changed files with 38 additions and 28 deletions
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66
fvi.py
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@ -1,7 +1,7 @@
import numpy as np
def fast_voxel_intersect(start, end, origin, step) -> tuple[list, list]:
def fast_voxel_intersect(start, end, origin, step, shape) -> tuple[list, list, list]:
"""Compute the voxels intersected by a line segment.
Args:
@ -13,6 +13,7 @@ def fast_voxel_intersect(start, end, origin, step) -> tuple[list, list]:
Returns:
list: list of intersection points
list: list of intersected voxels
list: list of voxels idx
"""
# Convert to numpy arrays
@ -28,17 +29,18 @@ def fast_voxel_intersect(start, end, origin, step) -> tuple[list, list]:
# Initialize list of intersected voxels
intersections = []
voxels = []
voxels_idx = []
# Compute direction of line segment
direction = end - start
global_distance = np.linalg.norm(direction, axis=0)
if global_distance == 0:
return intersections
return intersections, voxels, voxels_idx
direction = direction / global_distance
non_zero_direction = (direction != 0).argmax()
# Compute the sign of the direction
direction_signs = np.sign(direction)
is_positive = direction_signs > 0
is_negative = direction_signs < 0
# Initialize current position to start
@ -48,7 +50,7 @@ def fast_voxel_intersect(start, end, origin, step) -> tuple[list, list]:
while True:
# Compute the distance to the next boundaries
next_boundaries = np.divide(position + step * direction_signs, step)
distances = (is_positive * np.floor(next_boundaries) +
distances = ((1 - is_negative) * np.floor(next_boundaries) +
is_negative * np.ceil(next_boundaries)) * step - position
# Determine the nearest boundary to be reached
@ -57,7 +59,7 @@ def fast_voxel_intersect(start, end, origin, step) -> tuple[list, list]:
clothest_boundary_distance = boundary_distances[clothest_boundary]
# Check if we are done
distance_to_end = abs((end[0] - position[0]) / direction[0])
distance_to_end = abs((end[non_zero_direction] - position[non_zero_direction]) / direction[non_zero_direction])
if clothest_boundary_distance > distance_to_end:
break
@ -69,22 +71,25 @@ def fast_voxel_intersect(start, end, origin, step) -> tuple[list, list]:
position[clothest_boundary] / step[clothest_boundary]) * step[clothest_boundary]
# Get corresponding voxel
boundary_reached_negativly = np.zeros(start.shape, dtype=bool)
boundary_reached_negativly[clothest_boundary] = is_negative[clothest_boundary]
voxel = np.floor(position) - boundary_reached_negativly * step
on_boundary = np.mod(position, step) == 0
voxel = np.floor(position) - is_negative * on_boundary * step
# Add voxel to list
idx = np.floor_divide(voxel, step).astype(int)
if np.any(idx < 0) or np.any(idx >= shape):
continue
intersections.append(position + origin)
voxels.append(voxel + origin)
voxels_idx.append(idx)
return intersections, voxels
return intersections, voxels, voxels_idx
if __name__ == '__main__':
import matplotlib.pyplot as plt
def update_figure():
positions, voxels = fast_voxel_intersect(start, end, origin, step)
positions, voxels, voxels_idx = fast_voxel_intersect(start, end, origin, step, shape)
plt.clf()
@ -93,6 +98,15 @@ if __name__ == '__main__':
plt.fill([voxel[0], voxel[0] + step[0], voxel[0] + step[0], voxel[0]],
[voxel[1], voxel[1], voxel[1] + step[1], voxel[1] + step[1]],
color='#e25', alpha=0.5)
for voxel_id in voxels_idx:
plt.fill([
origin[0] + voxel_id[0] * step[0], origin[0] + (voxel_id[0] + 1) * step[0],
origin[0] + (voxel_id[0] + 1) * step[0], origin[0] + voxel_id[0] * step[0]
], [
origin[1] + voxel_id[1] * step[1], origin[1] + voxel_id[1] * step[1],
origin[1] + (voxel_id[1] + 1) * step[1], origin[1] + (voxel_id[1] + 1) * step[1]
], color='#2e3', alpha=0.5)
# Plot line segment
plt.plot([start[0], end[0]], [start[1], end[1]], 'k-')
@ -107,35 +121,31 @@ if __name__ == '__main__':
plt.axis('equal')
plt.xlim((-10, 10))
plt.ylim((-10, 10))
xmin, xmax = plt.xlim()
ymin, ymax = plt.ylim()
plt.xticks(np.arange(xmin + origin[0],
xmax + origin[0] + step[0], step[0]))
plt.yticks(np.arange(ymin + origin[1],
ymax + origin[1] + step[1], step[1]))
plt.xticks(origin[0] + step[0] * np.arange(shape[0] + 1))
plt.yticks(origin[1] + step[1] * np.arange(shape[1] + 1))
plt.grid()
plt.draw()
def onclick(event):
def onkey(event):
global start, end
# if event.button == 1:
# start = np.array([event.xdata, event.ydata])
# elif event.button == 3:
# end = np.array([event.xdata, event.ydata])
start = np.random.rand(2) * 10 - 5
end = np.random.rand(2) * 10 - 5
update_figure()
if event.key == ' ':
start = np.random.rand(2) * 20 - 10
end = np.random.rand(2) * 20 - 10
update_figure()
# Define voxel grid
origin = np.array([.1, -.3])
origin = np.array([-5., -5.])
step = np.array([1.0, 1.0])
shape = (10, 10)
# Define segment
start = np.random.rand(2) * 10 - 5
end = np.random.rand(2) * 10 - 5
# start = np.random.rand(2) * 20 - 10
# end = np.random.rand(2) * 20 - 10
start = np.array([0., 0.])
end = np.array([4., -4.])
# Plot
fig = plt.figure()
fig.canvas.mpl_connect('button_press_event', onclick)
fig.canvas.mpl_connect('key_press_event', onkey)
update_figure()
plt.show()