-
Notifications
You must be signed in to change notification settings - Fork 1
/
jaccard.py
254 lines (203 loc) · 8.51 KB
/
jaccard.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
from __future__ import absolute_import, division
import cv2
import numpy as np
from skimage.morphology import disk
__all__ = ['batched_jaccard', 'batched_f_measure']
def batched_jaccard(y_true, y_pred, average_over_objects=True, nb_objects=None):
""" Batch jaccard similarity for multiple instance segmentation.
Jaccard similarity over two subsets of binary elements $A$ and $B$:
$$
\mathcal{J} = \\frac{A \\cap B}{A \\cup B}
$$
# Arguments
y_true: Numpy Array. Array of shape (B x H x W) and type integer giving the
ground truth of the object instance segmentation.
y_pred: Numpy Array. Array of shape (B x H x W) and type integer giving the
prediction of the object segmentation.
average_over_objects: Boolean. Weather or not to average the jaccard over
all the objects in the sequence. Default True.
nb_objects: Integer. Number of objects in the ground truth mask. If
`None` the value will be infered from `y_true`. Setting this value
will speed up the computation.
# Returns
ndarray: Returns an array of shape (B) with the average jaccard for
all instances at each frame if `average_over_objects=True`. If
`average_over_objects=False` returns an array of shape (B x nObj)
with nObj being the number of objects on `y_true`.
"""
y_true = np.asarray(y_true, dtype=np.int)
y_pred = np.asarray(y_pred, dtype=np.int)
if y_true.ndim != 3:
raise ValueError('y_true array must have 3 dimensions.')
if y_pred.ndim != 3:
raise ValueError('y_pred array must have 3 dimensions.')
if y_true.shape != y_pred.shape:
raise ValueError('y_true and y_pred must have the same shape')
if nb_objects is None:
objects_ids = np.unique(y_true[(y_true < 255) & (y_true > 0)])
nb_objects = len(objects_ids)
else:
objects_ids = [i + 1 for i in range(nb_objects)]
objects_ids = np.asarray(objects_ids, dtype=np.int)
if nb_objects == 0:
raise ValueError('Number of objects in y_true should be higher than 0.')
nb_frames = len(y_true)
jaccard = np.empty((nb_frames, nb_objects), dtype=np.float)
for i, obj_id in enumerate(objects_ids):
mask_true, mask_pred = y_true == obj_id, y_pred == obj_id
union = (mask_true | mask_pred).sum(axis=(1, 2))
intersection = (mask_true & mask_pred).sum(axis=(1, 2))
for j in range(nb_frames):
if np.isclose(union[j], 0):
jaccard[j, i] = 1.
else:
jaccard[j, i] = intersection[j] / union[j]
if average_over_objects:
jaccard = jaccard.mean(axis=1)
return jaccard
def _seg2bmap(seg, width=None, height=None):
"""
From a segmentation, compute a binary boundary map with 1 pixel wide
boundaries. The boundary pixels are offset by 1/2 pixel towards the
origin from the actual segment boundary.
# Arguments
seg: Segments labeled from 1..k.
width: Width of desired bmap <= seg.shape[1]
height: Height of desired bmap <= seg.shape[0]
# Returns
bmap (ndarray): Binary boundary map.
David Martin <[email protected]>
January 2003
"""
seg = seg.astype(np.bool)
seg[seg > 0] = 1
assert np.atleast_3d(seg).shape[2] == 1
width = seg.shape[1] if width is None else width
height = seg.shape[0] if height is None else height
h, w = seg.shape[:2]
ar1 = float(width) / float(height)
ar2 = float(w) / float(h)
assert not (width > w | height > h | abs(ar1 - ar2) >
0.01), "Can't convert %dx%d seg to %dx%d bmap." % (w, h, width,
height)
e = np.zeros_like(seg)
s = np.zeros_like(seg)
se = np.zeros_like(seg)
e[:, :-1] = seg[:, 1:]
s[:-1, :] = seg[1:, :]
se[:-1, :-1] = seg[1:, 1:]
b = seg ^ e | seg ^ s | seg ^ se
b[-1, :] = seg[-1, :] ^ e[-1, :]
b[:, -1] = seg[:, -1] ^ s[:, -1]
b[-1, -1] = 0
if w == width and h == height:
bmap = b
else:
bmap = np.zeros((height, width))
for x in range(w):
for y in range(h):
if b[y, x]:
j = 1 + math.floor((y - 1) + height / h)
i = 1 + math.floor((x - 1) + width / h)
bmap[j, i] = 1
return bmap
def f_measure(true_mask, pred_mask, bound_th=0.008):
"""F-measure for two 2D masks.
# Arguments
true_mask: Numpy Array, Binary array of shape (H x W) representing the
ground truth mask.
pred_mask: Numpy Array. Binary array of shape (H x W) representing the
predicted mask.
bound_th: Float. Optional parameter to compute the F-measure. Default is
0.008.
# Returns
float: F-measure.
"""
true_mask = np.asarray(true_mask, dtype=np.bool)
pred_mask = np.asarray(pred_mask, dtype=np.bool)
assert true_mask.shape == pred_mask.shape
bound_pix = bound_th if bound_th >= 1 else (np.ceil(
bound_th * np.linalg.norm(true_mask.shape)))
fg_boundary = _seg2bmap(pred_mask)
gt_boundary = _seg2bmap(true_mask)
fg_dil = cv2.dilate(
fg_boundary.astype(np.uint8),
disk(bound_pix).astype(np.uint8))
gt_dil = cv2.dilate(
gt_boundary.astype(np.uint8),
disk(bound_pix).astype(np.uint8))
# Get the intersection
gt_match = gt_boundary * fg_dil
fg_match = fg_boundary * gt_dil
# Area of the intersection
n_fg = np.sum(fg_boundary)
n_gt = np.sum(gt_boundary)
# Compute precision and recall
if n_fg == 0 and n_gt > 0:
precision = 1
recall = 0
elif n_fg > 0 and n_gt == 0:
precision = 0
recall = 1
elif n_fg == 0 and n_gt == 0:
precision = 1
recall = 1
else:
precision = np.sum(fg_match) / float(n_fg)
recall = np.sum(gt_match) / float(n_gt)
# Compute F measure
if precision + recall == 0:
F = 0
else:
F = 2 * precision * recall / (precision + recall)
return F
def batched_f_measure(y_true,
y_pred,
average_over_objects=True,
nb_objects=None,
bound_th=0.008):
""" Batch F-measure for multiple instance segmentation.
# Arguments
y_true: Numpy Array. Array of shape (B x H x W) and type integer giving
the ground truth of the object instance segmentation.
y_pred: Numpy Array. Array of shape (B x H x W) and type integer giving
the
prediction of the object segmentation.
average_over_objects: Boolean. Weather or not to average the F-measure
over all the objects in the sequence. Default True.
nb_objects: Integer. Number of objects in the ground truth mask. If
`None` the value will be infered from `y_true`. Setting this value
will speed up the computation.
# Returns
ndarray: Returns an array of shape (B) with the average F-measure for
all instances at each frame if `average_over_objects=True`. If
`average_over_objects=False` returns an array of shape (B x nObj)
with nObj being the number of objects on `y_true`.
"""
y_true = np.asarray(y_true, dtype=np.int)
y_pred = np.asarray(y_pred, dtype=np.int)
if y_true.ndim != 3:
raise ValueError('y_true array must have 3 dimensions.')
if y_pred.ndim != 3:
raise ValueError('y_pred array must have 3 dimensions.')
if y_true.shape != y_pred.shape:
raise ValueError('y_true and y_pred must have the same shape')
if nb_objects is None:
objects_ids = np.unique(y_true[(y_true < 255) & (y_true > 0)])
nb_objects = len(objects_ids)
else:
objects_ids = [i + 1 for i in range(nb_objects)]
objects_ids = np.asarray(objects_ids, dtype=np.int)
if nb_objects == 0:
raise ValueError('Number of objects in y_true should be higher than 0.')
nb_frames = len(y_true)
f_measure_result = np.empty((nb_frames, nb_objects), dtype=np.float)
for i, obj_id in enumerate(objects_ids):
for frame_id in range(nb_frames):
gt_mask = y_true[frame_id, :, :] == obj_id
pred_mask = y_pred[frame_id, :, :] == obj_id
f_measure_result[frame_id, i] = f_measure(
gt_mask, pred_mask, bound_th=bound_th)
if average_over_objects:
f_measure_result = f_measure_result.mean(axis=1)
return f_measure_result