from pathlib import Path
import matplotlib as mpl
import matplotlib.pylab as plt
import numpy as np
from cell_analysis_tools.io import load_sdt_file
mpl.rcParams["figure.dpi"] = 300
[docs]
def sum_pool_3d(im, bin_size, stride=1, pad_value=0, debug=False):
"""
Sums the pixel intensity by a given kernel, reducing output image dimensions.
Parameters
----------
im : ndarray
3d array containig FLIM data (x,y,t).
bin_size : int
number of pixels to select before and after, bin of 3 is a 7x7 kernel
stride : int, optional
number of pixels to advance when doing raster calculations. The default is 1.
pad_value : int, optional
Value to pad the edges with if kernel needs it. The default is 0.
debug : bool , optional
Show debugging output. The default is False.
Returns
-------
im_sum_pool : ndarray
3d ndarray that holds a summed version of the image resized image.
"""
if stride != 1:
print("strides larger than 1 not yet implemented")
kernel_length = bin_size * 2 + 1 # 7 kernel length
# convolve with surrounding pixels
## Make sure image is right size or pad
im_rows, im_cols, im_timebins = im.shape # collapse and get 2d shape
# COMPUTE PADDING PIXELS
remainder_n_row_pixels = (im_rows / stride) % kernel_length
remainder_n_col_pixels = (im_cols / stride) % kernel_length
if remainder_n_row_pixels != 0:
need_n_row_pixels = kernel_length - remainder_n_row_pixels
else:
need_n_row_pixels = 0
if remainder_n_col_pixels != 0:
need_n_col_pixels = kernel_length - remainder_n_col_pixels
else:
need_n_col_pixels = 0
# pad image at the end of rows and cols
rows_before = 0
rows_after = int(need_n_row_pixels)
cols_before = 0
cols_after = int(need_n_col_pixels)
padded_sdt = np.pad(
im, # this takes in width(cols) and height(rows)
(
(rows_before, rows_after),
(cols_before, cols_after),
(0, 0), # dont pad 3rd dim
),
"constant",
constant_values=pad_value,
)
new_im_rows, new_im_cols, _ = padded_sdt.shape
# new image placeholder
im_sum_pool = np.zeros(
(
int(new_im_rows / kernel_length),
int(new_im_cols / kernel_length),
im_timebins,
)
)
new_n_rows, new_n_cols, _new_n_timebins = im_sum_pool.shape
# bin image
for row in np.arange(new_n_rows):
for col in np.arange(new_n_cols):
pass
sdt_block = padded_sdt[
row * kernel_length : row * kernel_length + kernel_length,
col * kernel_length : col * kernel_length + kernel_length,
:,
]
im_sum_pool[row, col, :] = np.sum(sdt_block, axis=(0, 1))
if debug:
plt.title(f"kernel_shape: {kernel_length}x{kernel_length}")
plt.imshow(im_sum_pool.sum(axis=2))
plt.show()
return im_sum_pool
if __name__ == "__main__":
### sum_pool TEST
HERE = Path(__file__).resolve().parent
data = HERE.parent / r"example_data\t_cell\Tcells-001.sdt".replace("\\", "/")
# load SDT
im = load_sdt_file(data)
for ch in np.arange(im.shape[0]):
plt.title(f"channel: {ch}")
plt.imshow(im[ch, ...].sum(axis=2))
plt.show()
# select channel with data
im = im[1, ...]
# run with debug on
sum_pool_3d(im, bin_size=2, debug=True)