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step2 tet tracking #38

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Jul 27, 2024
Merged

step2 tet tracking #38

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84 changes: 57 additions & 27 deletions step2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,9 @@
from skimage.morphology import skeletonize
from scipy.stats import mode
from functions.SR_240222_cal_allob import cal_allob
from functions.OAM_231216_bina import binar
from functions.SR_240222_cal_celldata import cal_celldata
import matplotlib.pyplot as plt

# Define thresholds and parameters
thresh_percent = 0.015
Expand Down Expand Up @@ -57,17 +59,17 @@

# Remove shock-induced timepoints
for start, end in [shock_period]:
for i in range(start, end + 1):
for i in range(start-1, end):
tet_masks[i] = None

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

# Tracking all detections
if start != 0:
if start != -1:
rang = range(start, len(tet_masks))
I2 = tet_masks[start]
A = np.zeros_like(tet_masks[start])
Expand All @@ -84,21 +86,22 @@
rang2 = rang
ccel = 1

while xx != 0:
while xx != -1:
k = 0
for im_no in rang2:
# im_no = 72
I2 = tet_masks[im_no] if ccel == 1 else TETC[1][im_no]
if I2 is None:
continue
if im_no == min(rang2):
ind1 = np.unique(I2)[1:] # Exclude background
I3 = I2 == ind1[0]
I3A = I3
I3 = (I2 == ind1[0])
I3A = I3.copy()
else:
I3A = IS6
I3A = IS6.copy()

I3A = skeletonize(I3A > 0)
I2A = I2
I3A = skeletonize(binar(I3A))
I2A = I2.copy()
I3B = I3A.astype(np.uint16) * I2A.astype(np.uint16)
ind = mode(I3B[I3B != 0])[0]

Expand All @@ -111,8 +114,8 @@
TETC[1][im_no_1] = tet_masks[im_no_1]
break
else:
TETC[0][im_no] = I3B
TETC[1][im_no] = I2A
TETC[0][im_no] = I3B.copy()
TETC[1][im_no] = I2A.copy()
continue
elif ind == 0 and ccel != 1:
k += 1
Expand All @@ -124,11 +127,18 @@
k = 0
pix = np.where(I2A == ind)
pix0 = np.where(I2A != ind)

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

I2A[pix] = ccel
I2A[pix0] = 0
IS6 = I2A

IS6 = I2A.copy()
I22 = np.zeros_like(I2)

pix1 = np.where(IS6 == ccel)

I2[pix1] = 0
pix2 = np.unique(I2)
pix2 = pix2[1:] # Exclude background
Expand All @@ -137,7 +147,7 @@
for ity, p2 in enumerate(pix2):
pix4 = np.where(I2 == p2)
I22[pix4] = ity + 1
TETC[0][im_no] = IS6
TETC[0][im_no] = IS6.copy()
else:
if len(pix2) > 0:
for ity, p2 in enumerate(pix2):
Expand All @@ -149,9 +159,9 @@
IS61[pix] = ccel
TETC[0][im_no] = IS61.astype(np.uint16)

TETC[1][im_no] = I22
TETC[1][im_no] = I22.copy()

xx = 0
xx = -1
for i in rang:
if TETC[1][i] is not None and np.sum(TETC[1][i]) > 0:
xx = i
Expand All @@ -165,13 +175,29 @@
# 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)

# Removing artifacts - cells that appear once and cells that disappear thresh % of the time or more
all_obj = cal_allob(ccel, TETC, rang)
cell_data = cal_celldata(all_obj, ccel)
def cal_allob1(ccel, TETC, rang):
# Initialize the all_obj array with zeros
all_obj = np.zeros((ccel, len(TETC[1])))

for iv in range(ccel): # Adjusted to 1-based index
for its in rang:
if TETC[0][its] is not None and np.sum(TETC[0][its]) > 0: # Check if the array is not None and not empty
all_obj[iv, its] = np.sum(TETC[0][its] == iv + 1) # Adjusted for 1-based index logic
else:
all_obj[iv, its] = -1

return all_obj

all_obj = cal_allob1(ccel, TETC, rang)
x_scale = 2
y_scale = 250
plt.imshow(all_obj, extent=[0, x_scale, 0, y_scale], aspect='auto')
cell_data = cal_celldata(all_obj, ccel) ## double check values

k = 1
cell_artifacts = []
Expand All @@ -182,7 +208,7 @@

all_ccel = list(range(1, ccel + 1))

if cell_artifacts:
if len(cell_artifacts) > 0:
cell_artifacts = list(set(cell_artifacts))
for iv in cell_artifacts:
for its in rang3:
Expand All @@ -199,7 +225,8 @@

# Correcting the SpoSeg track masks or filling the empty spaces between the first and last appearance
# Removing artifacts
all_obj1 = cal_allob(len(good_cells), TETC, list(range(len(TETC[0]))))
all_obj1 = cal_allob1(len(good_cells), TETC, rang)
# plt.imshow(all_obj1, extent=[0, x_scale, 0, y_scale], aspect='auto')
cell_data1 = cal_celldata(all_obj1, len(good_cells))

for iv in range(len(good_cells)):
Expand All @@ -214,23 +241,23 @@
TETmasks = [TETC[0][i] for i in range(len(TETC[0]))]

# Calculate the size of tetrads

def cal_allob1(ccel, TETC, rang):
def cal_allob2(ccel, TETC, rang):
# Initialize the all_obj array with zeros
all_obj = np.zeros((ccel, len(TETC)))

for iv in range(0, ccel): # Adjusted to 1-based index
for iv in range(ccel): # Adjusted to 1-based index
for its in rang:
if TETC[its] is not None: # Check if the array is not None and not empty
all_obj[iv, its] = np.sum(TETC[its] == iv) # Adjusted for 1-based index logic
if TETC[its] is not None and np.sum(TETC[its]) > 0: # Check if the array is not None and not empty
all_obj[iv, its] = np.sum(TETC[its] == iv + 1) # Adjusted for 1-based index logic
else:
all_obj[iv, its] = -1

return all_obj

TET_obj = len(good_cells)
all_obj_final = cal_allob1(TET_obj, TETmasks, list(range(len(TETmasks))))
TET_Size = all_obj_final
all_obj_final = cal_allob2(TET_obj, TETmasks, list(range(len(TETmasks))))
plt.imshow(all_obj1, extent=[0, x_scale, 0, y_scale], aspect='auto')
TET_Size = all_obj_final.copy()

# Calculate first detection and last detection of tetrads
TET_exists = np.zeros((2, TET_obj), dtype=int)
Expand All @@ -250,6 +277,9 @@ def replace_none_with_empty_array(data):

TETmasks = replace_none_with_empty_array(TETmasks)

sio.savemat(os.path.join(sav_path, "art_py.mat"), {
'all_ob_py': all_ob
})

# Save results
sio.savemat(sav_path + '_TET_Track.mat', {
Expand Down
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