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step3 nan correction
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@huygensCortex
huygensCortex committed Jul 31, 2024
1 parent 4a39806 commit d83349e
Showing 1 changed file with 48 additions and 121 deletions.
169 changes: 48 additions & 121 deletions step3.py
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@author: samarth
"""
import os
import math
import numpy as np
import scipy.io as sio
from skimage.io import imread
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# Define thresholds and parameters
pos = 'Pos0_2'
path = f'/Users/samarth/Documents/MATLAB/Full_Life_Cycle_tracking/Python_Track_Test/MAT/'
path = f'/Users/samarth/Documents/MATLAB/Full_Life_Cycle_tracking/Python_Track_Test/Pos32_1_B/'
# path = f'/Users/samarth/Documents/MATLAB/Full_Life_Cycle_tracking/Python_Track_Test/MAT/'
sav_path = '/Users/samarth/Documents/MATLAB/Full_Life_Cycle_tracking/saved_res/py_res/'
shock_period = [122, 134]

# Load image file names
file_names = [f for f in os.listdir(path) if f.endswith('_Ph3_000_cp_masks.tif')]
file_numbers = [int(f.split('img_')[1].split('_Ph3_000_cp_masks.tif')[0]) for f in file_names]
file_names = [f for f in os.listdir(path) if f.endswith('_Ph3_000_MAT16_masks.tif')]
file_numbers = [int(f.split('img_')[1].split('_Ph3_000_MAT16_masks.tif')[0]) for f in file_names]

sorted_indices = np.argsort(file_numbers)
sorted_numbers = np.array(file_numbers)[sorted_indices]
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# Remove shock-induced timepoints
mat_masks_original = mat_masks.copy()
# for start, end in [shock_period]:
# for i in range(start, end + 1):
# mat_masks[i] = None
for start, end in [shock_period]:
for i in range(start-1, end):
mat_masks[i] = None

start = -1
for its in range(len(mat_masks)):
# if mat_masks[its] is not None and np.sum(mat_masks[its]) > 0:
if np.sum(mat_masks[its]) > 0:
if mat_masks[its] is not None and np.sum(mat_masks[its]) > 0:
start = its
break

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while xx != -1:
for im_no in rang2:
#I2 = mat_masks[im_no] if ccel == 1 else MATC[1][im_no]

if ccel == 1:
I2 = mat_masks[im_no]
else:
I2 = MATC[1][im_no]

if I2 is None:
if I2 is None or I2.size == 0:
continue


# plt.figure()
# plt.imshow(np.uint16(I2), cmap='gray')
# plt.title('I2')
# plt.show()

# if(im_no < 280):
# print(im_no, min(rang2))

if im_no == min(rang2):
#TODO!
ind1 = np.unique(I2)[1:] # Exclude background
I3 = (I2 == ind1[0])
I3A = I3.copy()
# plt.figure()
# plt.imshow(np.uint16(I3A), cmap='gray')
# plt.title('I3A')
# plt.show()
else:
I3A = IS6.copy()
# plt.figure()
# plt.imshow(np.uint16(I3A), cmap='gray')
# plt.title('I3A')
# plt.show()
I3A = np.copy(IS6)



I3A = skeletonize(I3A)
I3A = skeletonize(I3A > 0)
I2A = np.copy(I2)
I3B = np.uint16(I3A) * np.uint16(I2A)


# plt.figure()
# plt.imshow(np.uint16(I3A), cmap='gray')
# plt.title('I3A')
# plt.show()


# plt.figure()
# plt.imshow(np.uint16(I3B), cmap='gray')
# plt.title('I3B')
# plt.show()

I3B = I3A.astype(np.uint16) * I2A.astype(np.uint16)

ind = mode(I3B[I3B != 0])[0]

# =============================================================================
# if I3B[I3B != 0].size == 0:
# if ccel == 1:
# MATC[0][im_no] = I3B
# MATC[1][im_no] = I2A
# continue
#
# ind = np.argmax(np.bincount(I3B[I3B != 0]))
# =============================================================================


if ind == 0 and ccel == 1:
MATC[0][im_no] = I3B.copy()
MATC[1][im_no] = I2A.copy()
if (ind == 0 or math.isnan(ind)) and ccel == 1:
MATC[0][im_no] = I3B
MATC[1][im_no] = I2A
continue
elif ind == 0 and ccel != 1:
elif (ind == 0 or math.isnan(ind)) and ccel != 1:
continue

pix = np.where(I2A == ind)
pix0 = np.where(I2A != ind)

# pix = np.flatnonzero(I2A == ind)
# pix0 = np.flatnonzero(I2A != ind)

# I2A.flat[pix] = ccel
# I2A.flat[pix0] = 0
# IS6 = I2A.copy()
# I22 = np.zeros_like(I2)
# pix1 = np.flatnonzero(IS6 == ccel)
# I2.flat[pix1] = 0
# pix2 = np.unique(I2)
# pix2 = pix2[1:]

I2A[pix] = ccel
I2A[pix0] = 0
IS6 = I2A.copy()

"""
plt.figure()
plt.imshow(np.uint16(IS6), cmap='gray')
plt.title('IS6')
plt.show()
"""

IS6 = np.copy(I2A)

I22 = np.zeros_like(I2)
pix1 = np.where(IS6 == ccel)
I2[pix1] = 0

# #TODO!
pix2 = np.unique(I2)[1:] # Exclude background
pix2 = np.unique(I2)
pix2 = pix2[1:] # Exclude background

if ccel == 1:
for ity in range(len(pix2)):
pix4 = np.where(I2 == pix2[ity])
# for ity in range(len(pix2)):
# pix4 = np.where(I2 == pix2[ity])
# I22[pix4] = ity + 1'
for ity, p2 in enumerate(pix2):
pix4 = np.where(I2 == p2)
I22[pix4] = ity + 1
MATC[0][im_no] = IS6.copy()
MATC[0][im_no] = np.copy(IS6)
else:
if pix2.size > 0:
for ity in pix2:
pix4 = np.where(I2 == ity)
I22[pix4] = ity
if len(pix2) > 0:
# for ity in range(len(pix2)):
# pix4 = np.where(I2 == pix2[ity])
# I22[pix4] = ity + 1
for ity, p2 in enumerate(pix2):
pix4 = np.where(I2 == p2)
I22[pix4] = ity + 1
else:
I22 = I2
IS61 = (MATC[0][im_no]).copy()
I22 = I2.copy()
IS61 = np.copy(MATC[0][im_no])
IS61[pix] = ccel
MATC[0][im_no] = np.uint16(IS61.copy())

MATC[1][im_no] = I22.copy()
# if ccel == 1:
# for ity in range(len(pix2)):
# pix4 = np.flatnonzero(I2 == pix2[ity])
# I22.flat[pix4] = ity + 1 # Adjust for 0-based indexing in Python
# MATC[0][im_no - 1] = IS6.copy()
# else:
# if len(pix2) > 0:
# for ity in range(len(pix2)):
# pix4 = np.flatnonzero(I2 == pix2[ity])
# I22.flat[pix4] = ity + 1 # Adjust for 0-based indexing in Python
# else:
# I22 = I2
# IS61 = (MATC[0][im_no - 1]).copy()
# IS61.flat[pix] = ccel
# MATC[0][im_no - 1] = (IS61.copy()).astype(np.uint16)
# MATC[1][im_no - 1] = I22.copy()


# plt.figure()
# plt.imshow(np.uint16(IS6), cmap='gray')
# plt.title('IS6')
# plt.show()

MATC[0][im_no] = IS61.astype(np.uint16)

MATC[1][im_no] = np.copy(I22)

xx = -1
for i in rang:
if MATC[1][i] is not None and np.sum(MATC[1][i]) > 0:
if MATC[1][i] is not None and MATC[1][i].size > 0 and np.sum(MATC[1][i]) > 0:
xx = i
break
ccel += 1
rang2 = range(xx, len(mat_masks))
print(xx)

print(xx + 1)

ccel -= 1 # number of cells tracked


# Removing the shock-induced points from rang
rang3 = list(rang)
for start, end in [shock_period]:
for i in range(start, end + 1):
for i in range(start-1, end):
if i in rang3:
rang3.remove(i)

# Correction Code
all_obj = cal_allob(ccel, MATC, rang)
cell_data = cal_celldata(all_obj, ccel)

sio.savemat('st3_allob.mat', {
"all_obj_py": all_obj
})

for iv in range(ccel):
if np.any(all_obj[iv, min(rang):shock_period[-1]] > 0):
if all_obj[iv, shock_period[-1] + 1] != 0:
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