The idea is that I want to thin a large 2D buffer of x,y,z points to a given
resolution by dividing the data into equal sized "cubes" (i.e. resolution is
number of cubes along each axis) and averaging the points inside each cube
(if any).
* # Fill up buffer data for demonstration purposes with initial buffer of
size 10,000,000 to reduce to 1,000,000
size = 10000000
buffer = np.ndarray(shape=(size,3), dtype=np.float)
# fill it up
buffer[:, 0] = np.random.ranf(size)
buffer[:, 1] = np.random.ranf(size)
buffer[:, 2] = np.random.ranf(size)
# Create result buffer to size of cubed resolution (i.e. 100 ^ 3 =
1,000,000)
resolution = 100
thinned_buffer = np.ndarray(shape=(resolution ** 3,3), dtype=np.float)
# Trying to convert the following into C to speed it up
x_buffer = buffer[:, 0]
y_buffer = buffer[:, 1]
z_buffer = buffer[:, 2]
min_x = x_buffer.min()
max_x = x_buffer.max()
min_y = y_buffer.min()
max_y = y_buffer.max()
min_z = z_buffer.min()
max_z = z_buffer.max()
z_block = (max_z - min_z) / resolution
x_block = (max_x - min_x) / resolution
y_block = (max_y - min_y) / resolution
current_idx = 0
x_idx = min_x
while x_idx < max_x:
y_idx = min_y
while y_idx < max_y:
z_idx = min_z
while z_idx < max_z:
inside_block_points = np.where((x_buffer >= x_idx) &
(x_buffer <=
x_idx + x_block) &
(y_buffer >=
y_idx) &
(y_buffer <=
y_idx + y_block) &
(z_buffer >=
z_idx) &
(z_buffer <=
z_idx + z_block))
if inside_block_points[0].size > 0:
mean_point = buffer[inside_block_points[0]].mean(axis=0)
thinned_buffer[current_idx] = mean_point
current_idx += 1
z_idx += z_block
y_idx += y_block
x_idx += x_block
return thin_buffer
*
--
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