Mercurial > repos > imgteam > points_association_nn

comparison points_association_nn.py @ 0:04e692ee53a8 draft

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"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/points_association_nn/ commit db4c2a87a21f32e5d12d11e68f32773bfc06fcfd"
author imgteam
date Thu, 22 Jul 2021 22:29:47 +0000
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children b30aa285ac0a
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-1:000000000000 0:04e692ee53a8
1 """
2 Copyright 2021 Biomedical Computer Vision Group, Heidelberg University.
3 Author: Qi Gao (qi.gao@bioquant.uni-heidelberg.de)
4
5 Distributed under the MIT license.
6 See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
7
8 """
9
10 import argparse
11
12 import numpy as np
13 import openpyxl # noqa: F401
14 import pandas as pd
15 import skimage.util
16
17
18 def disk_mask(imsz, ir, ic, nbpx):
19 ys, xs = np.ogrid[-nbpx:nbpx + 1, -nbpx:nbpx + 1]
20 se = xs ** 2 + ys ** 2 <= nbpx ** 2
21 mask = np.zeros(imsz, dtype=int)
22 if ir - nbpx < 0 or ic - nbpx < 0 or ir + nbpx + 1 > imsz[0] or ic + nbpx + 1 > imsz[1]:
23 mask = skimage.util.pad(mask, nbpx)
24 mask[ir:ir + 2 * nbpx + 1, ic:ic + 2 * nbpx + 1] = se
25 mask = skimage.util.crop(mask, nbpx)
26 else:
27 mask[ir - nbpx:ir + nbpx + 1, ic - nbpx:ic + nbpx + 1] = se
28 return mask
29
30
31 def find_nn(cim, icy, icx, nim, nbpx):
32 mask = disk_mask(cim.shape, icy, icx, nbpx)
33 iys_nim, ixs_nim = np.where(nim * mask)
34 if iys_nim.size == 0:
35 return np.NaN, np.NaN
36
37 d2 = (icy - iys_nim) ** 2 + (icx - ixs_nim) ** 2
38 I1 = np.argsort(d2)
39 iy_nim = iys_nim[I1[0]]
40 ix_nim = ixs_nim[I1[0]]
41
42 mask = disk_mask(cim.shape, iy_nim, ix_nim, nbpx)
43 iys_cim, ixs_cim = np.where(cim * mask)
44 d2 = (iy_nim - iys_cim) ** 2 + (ix_nim - ixs_cim) ** 2
45 I2 = np.argsort(d2)
46 if not iys_cim[I2[0]] == icy or not ixs_cim[I2[0]] == icx:
47 return np.NaN, np.NaN
48
49 return iy_nim, ix_nim
50
51
52 def points_linking(fn_in, fn_out, nbpx=6, th=25, minlen=50):
53 data = pd.read_csv(fn_in, delimiter="\t")
54 all_data = np.array(data)
55 assert all_data.shape[1] in [3, 4], 'unknow collum(s) in input data!'
56
57 coords = all_data[:, :3].astype('int64')
58
59 frame_1st = np.min(coords[:, 0])
60 frame_end = np.max(coords[:, 0])
61 assert set([i for i in range(frame_1st, frame_end + 1)]).issubset(set(coords[:, 0].tolist())), "spots missing at some time point!"
62
63 nSlices = frame_end
64 stack_h = np.max(coords[:, 2]) + nbpx
65 stack_w = np.max(coords[:, 1]) + nbpx
66 stack = np.zeros((stack_h, stack_w, nSlices), dtype='int8')
67 stack_r = np.zeros((stack_h, stack_w, nSlices), dtype='float64')
68
69 for i in range(all_data.shape[0]):
70 iyxz = tuple(coords[i, ::-1] - 1)
71 stack[iyxz] = 1
72 stack_r[iyxz] = all_data[i, -1]
73
74 tracks_all = np.array([], dtype=float).reshape(0, nSlices, 4)
75 maxv = np.max(stack_r)
76 br_max = maxv
77 idx_max = np.argmax(stack_r)
78 while 1:
79 iyxz = np.unravel_index(idx_max, stack.shape)
80
81 spot_br = np.empty((nSlices, 1))
82 track = np.empty((nSlices, 3))
83 for i in range(nSlices):
84 spot_br[i] = np.NaN
85 track[i, :] = np.array((np.NaN, np.NaN, np.NaN))
86
87 spot_br[iyxz[2]] = maxv
88 track[iyxz[2], :] = np.array(iyxz[::-1]) + 1
89
90 # forward
91 icy = iyxz[0]
92 icx = iyxz[1]
93 for inz in range(iyxz[2] + 1, nSlices):
94 iny, inx = find_nn(stack[:, :, inz - 1], icy, icx, stack[:, :, inz], nbpx)
95 if np.isnan(iny) and not inz == nSlices - 1:
96 iny, inx = find_nn(stack[:, :, inz - 1], icy, icx, stack[:, :, inz + 1], nbpx)
97 if np.isnan(iny):
98 break
99 else:
100 iny = icy
101 inx = icx
102 stack[iny, inx, inz] = 1
103 stack_r[iny, inx, inz] = stack_r[iny, inx, inz - 1]
104 elif np.isnan(iny) and inz == nSlices - 1:
105 break
106
107 track[inz, :] = np.array((inz, inx, iny)) + 1
108 spot_br[inz] = stack_r[iny, inx, inz]
109 icy = iny
110 icx = inx
111
112 # backward
113 icy = iyxz[0]
114 icx = iyxz[1]
115 for inz in range(iyxz[2] - 1, -1, -1):
116 iny, inx = find_nn(stack[:, :, inz + 1], icy, icx, stack[:, :, inz], nbpx)
117 if np.isnan(iny) and not inz == 0:
118 iny, inx = find_nn(stack[:, :, inz + 1], icy, icx, stack[:, :, inz - 1], nbpx)
119 if np.isnan(iny):
120 break
121 else:
122 iny = icy
123 inx = icx
124 stack[iny, inx, inz] = 1
125 stack_r[iny, inx, inz] = stack_r[iny, inx, inz + 1]
126 elif np.isnan(iny) and inz == 0:
127 break
128
129 track[inz, :] = np.array((inz, inx, iny)) + 1
130 spot_br[inz] = stack_r[iny, inx, inz]
131 icy = iny
132 icx = inx
133
134 for iz in range(nSlices):
135 if not np.isnan(track[iz, 0]):
136 stack[track[iz, 2].astype(int) - 1, track[iz, 1].astype(int) - 1, iz] = 0
137 stack_r[track[iz, 2].astype(int) - 1, track[iz, 1].astype(int) - 1, iz] = 0
138
139 # discard short trajectories
140 if np.count_nonzero(~np.isnan(spot_br)) > minlen * (frame_end - frame_1st) / 100:
141 tmp = np.concatenate((track, spot_br), axis=1)
142 tracks_all = np.concatenate((tracks_all, tmp.reshape(1, -1, 4)), axis=0)
143
144 maxv = np.max(stack_r)
145 idx_max = np.argmax(stack_r)
146 if maxv < th * br_max / 100:
147 break
148
149 with pd.ExcelWriter(fn_out, engine="openpyxl") as writer:
150 for i in range(tracks_all.shape[0]):
151 df = pd.DataFrame()
152 df['FRAME'] = tracks_all[i, :, 0]
153 df['POS_X'] = tracks_all[i, :, 1]
154 df['POS_Y'] = tracks_all[i, :, 2]
155 df['INTENSITY'] = tracks_all[i, :, 3]
156 df.to_excel(writer, sheet_name='spot%s' % (i + 1), index=False, float_format='%.2f')
157 writer.save()
158
159
160 if __name__ == "__main__":
161 parser = argparse.ArgumentParser(description="Association of points in consecutive frames using the nearest neighbor algorithm")
162 parser.add_argument("fn_in", help="Name of input file (tsv tabular)")
163 parser.add_argument("fn_out", help="Name of output file (xlsx)")
164 parser.add_argument("nbpx", type=int, help="Neighborhood size in pixel")
165 parser.add_argument("thres", type=float, help="Percentage of the global maximal intensity for thresholding some event")
166 parser.add_argument("minlen", type=float, help="Minimum length of tracks (percentage of senquence length)")
167 args = parser.parse_args()
168 points_linking(args.fn_in, args.fn_out, args.nbpx, args.thres, args.minlen)