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track_fast.py
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track_fast.py
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import os
import numpy as np
import pandas as pd
import networkx as nx
import time as timing
import SimpleITK as sitk
import concurrent.futures
from networkx.algorithms import similarity
def compute_unique_vals(array: np.ndarray, return_counts: bool = False,
return_sorted: bool = True) -> (np.ndarray, np.ndarray):
'''
helper-func to calc unique vals and their frequency in a np.ndarray
using the highly optimized pd.unique()
significantly faster than np.unique() for smaller numbers of unique vals
'''
unique_vals = pd.unique(array.flatten('K'))
if return_sorted:
unique_vals = np.sort(unique_vals)
if return_counts:
occurrences = np.zeros(len(unique_vals), dtype=np.uint16)
for idx, val in enumerate(unique_vals):
occurrences[idx] = np.count_nonzero(array == val)
return unique_vals, occurrences
else:
return unique_vals
def load_img_from_tiff(path2img: str) -> np.ndarray:
"""
helper-func to parallelize loading imgs from tiff-files
"""
img = sitk.ReadImage(path2img)
img_array = sitk.GetArrayFromImage(img)
return img_array
def save_img_as_tiff(img_array: np.ndarray, filename: str, save_dir: str):
"""
helper-func to parallelize saving imgs as tiff-files
"""
img = sitk.GetImageFromArray(img_array.astype("uint16"))
sitk.WriteImage(img, os.path.join(save_dir, filename))
def cell_center_fast(seg_img: np.ndarray, labels: np.ndarray) -> dict:
"""
faster version of cell_center()
speed gained by reusing previously calculated labels
"""
results = {}
for label in labels:
if label != 0:
all_points_z, all_points_x, all_points_y = np.where(seg_img == label)
avg_z = np.round(np.mean(all_points_z))
avg_x = np.round(np.mean(all_points_x))
avg_y = np.round(np.mean(all_points_y))
results[label] = [avg_z, avg_x, avg_y]
return results
def compute_cell_location_fast(seg_img: np.ndarray, all_labels: np.ndarray) \
-> nx.Graph:
"""
faster version of compute_cell_location()
speed gained by reusing previously calculated labels and
by using cell_center_fast()
"""
g = nx.Graph()
centers = cell_center_fast(seg_img, all_labels)
# Compute vertices
for i in all_labels:
if i != 0:
g.add_node(i)
# Compute edges
for i in all_labels:
if i != 0:
for j in all_labels:
if j != 0:
if i != j:
pos1 = centers[i]
pos2 = centers[j]
distance = np.sqrt((pos1[0] - pos2[0])**2 +
(pos1[1] - pos2[1])**2 +
(pos1[2] - pos2[2])**2)
g.add_edge(i, j, weight=distance)
return g
def tracklet_fast(g1: nx.Graph, g2: nx.Graph, seg_img1: np.ndarray, seg_img2: np.ndarray,
maxtrackid: int, time: int, linelist: list, tracksavedir: str,
labels_img1: np.ndarray, labels_img2: np.ndarray) -> (int, list):
"""
faster version of tracklet()
speed gained by parallelizing IO and
by parallelizing some computations
"""
f1 = {}
f2 = {}
dict_associate = {}
new_seg_img2 = np.zeros(seg_img2.shape)
with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
thread1 = executor.submit(cell_center_fast, seg_img1, labels_img1)
thread2 = executor.submit(cell_center_fast, seg_img2, labels_img2)
thread3 = executor.submit(g1.degree, weight='weight')
thread4 = executor.submit(g2.degree, weight='weight')
cellcenter1 = thread1.result()
cellcenter2 = thread2.result()
loc1 = thread3.result()
loc2 = thread4.result()
for ele1 in loc1:
cell = ele1[0]
f1[cell] = [cellcenter1[cell], ele1[1]]
for ele2 in loc2:
cell = ele2[0]
f2[cell] = [cellcenter2[cell], ele2[1]]
for cell in f2.keys():
tmp_center = f2[cell][0]
min_distance = seg_img2.shape[0]**2 + seg_img2.shape[1]**2 + \
seg_img2.shape[2]**2
for ref_cell in f1.keys():
ref_tmp_center = f1[ref_cell][0]
distance = (tmp_center[0] - ref_tmp_center[0])**2 + \
(tmp_center[1] - ref_tmp_center[1])**2 + \
(tmp_center[2] - ref_tmp_center[2])**2
if distance < min_distance:
dict_associate[cell] = ref_cell
min_distance = distance
inverse_dict_ass = {}
for cell in dict_associate:
if dict_associate[cell] in inverse_dict_ass:
inverse_dict_ass[dict_associate[cell]].append(cell)
else:
inverse_dict_ass[dict_associate[cell]] = [cell]
maxtrackid = max(maxtrackid, max(inverse_dict_ass.keys()))
for cell in inverse_dict_ass.keys():
if len(inverse_dict_ass[cell]) > 1:
for cellin2 in inverse_dict_ass[cell]:
maxtrackid = maxtrackid + 1
new_seg_img2[seg_img2 == cellin2] = maxtrackid
string = '{} {} {} {}'.format(maxtrackid, time+1, time+1, cell)
linelist.append(string)
else:
cellin2 = inverse_dict_ass[cell][0]
new_seg_img2[seg_img2 == cellin2] = cell
i = 0
for line in linelist:
i = i + 1
if i == cell:
list_tmp = line.split()
new_string = '{} {} {} {}'.format(list_tmp[0], list_tmp[1],
time+1, list_tmp[3])
linelist[i-1] = new_string
filename1 = 'mask' + '%0*d' % (3, time) + '.tif'
filename2 = 'mask' + '%0*d' % (3, time+1) + '.tif'
with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
thread1 = executor.submit(save_img_as_tiff, seg_img1, filename1, tracksavedir)
thread2 = executor.submit(save_img_as_tiff, new_seg_img2, filename2, tracksavedir)
return maxtrackid, linelist
def track_main_fast(seg_fold: str, track_fold: str):
"""
faster version of track_main()
speed gained by parallelizing IO,
by reusing unique_vals and
by parallelizing some computations
"""
folder1 = track_fold
folder2 = seg_fold
times = len(os.listdir(folder2))
maxtrackid = 0
linelist = []
total_start_time = timing.time()
for time in range(times-1):
print('linking frame {} to previous tracked frames'.format(time+1))
start_time = timing.time()
threshold = 100
if time == 0:
file1 = 'mask000.tif'
img1 = sitk.ReadImage(os.path.join(folder2, file1))
img1 = sitk.GetArrayFromImage(img1)
img1_label, img1_counts = compute_unique_vals(img1, return_counts=True)
for l in range(len(img1_label)):
if img1_counts[l] < threshold:
img1[img1 == img1_label[l]] = 0
labels = compute_unique_vals(img1)
start_label = 0
for label in labels:
img1[img1 == label] = start_label
start_label = start_label + 1
img1 = sitk.GetImageFromArray(img1)
sitk.WriteImage(img1, os.path.join(folder1, file1))
file1 = 'mask' + '%0*d' % (3, time) + '.tif'
file2 = 'mask' + '%0*d' % (3, time+1) + '.tif'
path2file1 = os.path.join(folder1, file1)
path2file2 = os.path.join(folder2, file2)
with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
thread1 = executor.submit(load_img_from_tiff, path2file1)
thread2 = executor.submit(load_img_from_tiff, path2file2)
img1 = thread1.result()
img2 = thread2.result()
if len(compute_unique_vals(img2)) < 2:
img2 = img1
img2_img = sitk.GetImageFromArray(img2)
sitk.WriteImage(img2_img, os.path.join(folder2, file2))
img2_label_counts = np.array(compute_unique_vals(img2, return_counts=True)).T
i = 0
adjusted_labels_img2 = False
for label in img2_label_counts[:, 0]:
if img2_label_counts[i, 1] < threshold:
img2[img2 == label] = 0
adjusted_labels_img2 = True
i = i + 1
if adjusted_labels_img2:
labels_img2 = compute_unique_vals(img2)
else:
labels_img2 = img2_label_counts[:, 0]
labels_img1 = compute_unique_vals(img1)
with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
thread1 = executor.submit(compute_cell_location_fast, img1, labels_img1)
thread2 = executor.submit(compute_cell_location_fast, img2, labels_img2)
g1 = thread1.result()
g2 = thread2.result()
if time == 0:
for cell in compute_unique_vals(img1):
if cell != 0:
string = '{} {} {} {}'.format(cell, time, time, 0)
linelist.append(string)
maxtrackid = max(cell, maxtrackid)
maxtrackid, linelist = tracklet_fast(g1, g2, img1, img2, maxtrackid,
time, linelist, folder1, labels_img1, labels_img2)
print('--------%s seconds-----------' % (timing.time() - start_time))
filetxt = open(os.path.join(folder1, 'res_track.txt'), 'w')
for line in linelist:
filetxt.write(line)
filetxt.write("\n")
filetxt.close()
print('whole time sequnce running time %s' % (timing.time() - total_start_time))