创建 COCO注释图像的数据集，您需要根据对象的类型将二进制掩码转换为多边形或未压缩的游程编码表示。
pycocotools库具有对压缩 RLE 进行编码和解码的函数，但没有用于多边形和未压缩 RLE 的函数。
我可以使用 skimage 的测量库来 generate polygons of masks ，但我不确定如何创建未压缩的 RLE。
我可以使用 this RLE encoder从图像创建 RLE 的 a 表示，但我不确定 COCO 期望的格式。 COCO 只是提到他们使用“自定义运行长度编码 (RLE) 方案”
例如，
ground_truth_binary_mask = np.array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
fortran_ground_truth_binary_mask = np.asfortranarray(ground_truth_binary_mask)
rle(fortran_ground_truth_binary_mask)
输出:
(array([26, 36, 46, 56, 61]), array([3, 3, 3, 3, 1]))
这是 coco RLE 的样子:
{
"segmentation": {
"counts": [
272,
2,
4,
4,
4,
4,
2,
9,
1,
2,
16,
43,
143,
24,
5,
8,
16,
44,
141,
25,
8,
5,
17,
44,
140,
26,
10,
2,
17,
45,
129,
4,
5,
27,
24,
5,
1,
45,
127,
38,
23,
52,
125,
40,
22,
53,
123,
43,
20,
54,
122,
46,
18,
54,
121,
54,
12,
53,
119,
57,
11,
53,
117,
59,
13,
51,
117,
59,
13,
51,
117,
60,
11,
52,
117,
60,
10,
52,
118,
60,
9,
53,
118,
61,
8,
52,
119,
62,
7,
52,
119,
64,
1,
2,
2,
51,
120,
120,
120,
101,
139,
98,
142,
96,
144,
93,
147,
90,
150,
87,
153,
85,
155,
82,
158,
76,
164,
66,
174,
61,
179,
57,
183,
54,
186,
52,
188,
49,
191,
47,
193,
21,
8,
16,
195,
20,
13,
8,
199,
18,
222,
17,
223,
16,
224,
16,
224,
15,
225,
15,
225,
15,
225,
15,
225,
15,
225,
15,
225,
15,
225,
15,
225,
15,
225,
14,
226,
14,
226,
14,
39,
1,
186,
14,
39,
3,
184,
14,
39,
4,
183,
13,
40,
6,
181,
14,
39,
7,
180,
14,
39,
9,
178,
14,
39,
10,
177,
14,
39,
11,
176,
14,
38,
14,
174,
14,
36,
19,
171,
15,
33,
32,
160,
16,
30,
35,
159,
18,
26,
38,
158,
19,
23,
41,
157,
20,
19,
45,
156,
21,
15,
48,
156,
22,
10,
53,
155,
23,
9,
54,
154,
23,
8,
55,
154,
24,
7,
56,
153,
24,
6,
57,
153,
25,
5,
57,
153,
25,
5,
58,
152,
25,
4,
59,
152,
26,
3,
59,
152,
26,
3,
59,
152,
27,
1,
60,
152,
27,
1,
60,
152,
86,
154,
80,
160,
79,
161,
42,
8,
29,
161,
41,
11,
22,
2,
3,
161,
40,
13,
18,
5,
3,
161,
40,
15,
2,
5,
8,
7,
2,
161,
40,
24,
6,
170,
35,
30,
4,
171,
34,
206,
34,
41,
1,
164,
34,
39,
3,
164,
34,
37,
5,
164,
34,
35,
10,
161,
36,
1,
3,
28,
17,
155,
41,
27,
16,
156,
41,
26,
17,
156,
41,
26,
16,
157,
27,
4,
10,
25,
16,
158,
27,
6,
8,
11,
2,
12,
6,
2,
7,
159,
27,
7,
14,
3,
4,
19,
6,
160,
26,
8,
22,
18,
5,
161,
26,
8,
22,
18,
4,
162,
26,
8,
23,
15,
4,
164,
23,
11,
23,
11,
7,
165,
19,
17,
22,
9,
6,
167,
19,
22,
18,
8,
3,
170,
18,
25,
16,
7,
1,
173,
17,
28,
15,
180,
17,
30,
12,
181,
16,
34,
6,
184,
15,
225,
14,
226,
13,
227,
12,
228,
11,
229,
10,
230,
9,
231,
9,
231,
9,
231,
9,
231,
8,
232,
8,
232,
8,
232,
8,
232,
8,
232,
8,
232,
7,
233,
7,
233,
7,
233,
7,
233,
8,
232,
8,
232,
8,
232,
9,
231,
9,
231,
9,
231,
10,
230,
10,
230,
11,
229,
13,
227,
14,
226,
16,
224,
17,
223,
19,
221,
23,
217,
31,
3,
5,
201,
39,
201,
39,
201,
39,
201,
39,
201,
39,
201,
40,
200,
40,
200,
41,
199,
41,
199,
41,
199,
22,
8,
12,
198,
22,
12,
8,
198,
22,
14,
6,
198,
22,
15,
6,
197,
22,
16,
5,
197,
22,
17,
5,
196,
22,
18,
4,
196,
22,
19,
4,
195,
22,
19,
5,
194,
22,
20,
4,
194,
25,
21,
1,
193,
27,
213,
29,
211,
30,
210,
35,
6,
6,
193,
49,
191,
50,
190,
50,
190,
51,
189,
51,
189,
52,
188,
53,
187,
53,
187,
54,
186,
54,
186,
54,
186,
55,
185,
55,
185,
55,
185,
55,
185,
55,
185,
55,
185,
55,
185,
55,
185,
55,
185,
55,
185,
55,
185,
55,
185,
55,
185,
55,
185,
55,
185,
28,
1,
26,
185,
23,
11,
21,
185,
20,
17,
17,
186,
18,
21,
15,
186,
16,
23,
14,
187,
14,
25,
14,
187,
14,
26,
12,
188,
14,
28,
10,
188,
14,
226,
14,
226,
16,
224,
17,
223,
19,
221,
20,
220,
22,
218,
24,
18,
3,
12,
3,
180,
25,
10,
1,
4,
6,
10,
6,
178,
28,
7,
12,
8,
8,
177,
49,
3,
12,
176,
65,
175,
67,
173,
69,
171,
53,
3,
14,
170,
37,
20,
9,
4,
1,
169,
36,
21,
8,
175,
35,
22,
7,
176,
34,
23,
7,
176,
34,
23,
6,
177,
35,
22,
6,
177,
35,
22,
8,
175,
35,
23,
9,
173,
35,
205,
36,
204,
39,
201,
43,
197,
48,
36,
1,
155,
48,
35,
3,
154,
49,
33,
5,
154,
48,
32,
6,
155,
49,
27,
10,
155,
51,
24,
11,
154,
54,
21,
11,
155,
56,
19,
11,
155,
56,
18,
11,
156,
56,
17,
11,
157,
56,
16,
12,
157,
56,
14,
13,
159,
56,
12,
13,
160,
61,
5,
14,
162,
78,
165,
75,
167,
73,
168,
72,
170,
70,
171,
69,
173,
67,
176,
64,
179,
61,
182,
58,
183,
57,
185,
54,
187,
53,
188,
51,
191,
49,
192,
47,
195,
45,
196,
43,
198,
42,
199,
40,
201,
38,
203,
36,
205,
34,
207,
32,
210,
28,
213,
26,
216,
22,
221,
16,
228,
8,
10250
],
"size": [
240,
320
]
}
}
最佳答案
有关格式的信息可在此处获得:https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/mask.py
RLE is a simple yet efficient format for storing binary masks. RLE first divides a vector (or vectorized image) into a series of piecewise constant regions and then for each piece simply stores the length of that piece. For example, given M=[0 0 1 1 1 0 1] the RLE counts would be [2 3 1 1], or for M=[1 1 1 1 1 1 0] the counts would be [0 6 1] (note that the odd counts are always the numbers of zeros).
import numpy as np
from itertools import groupby
def binary_mask_to_rle(binary_mask):
rle = {'counts': [], 'size': list(binary_mask.shape)}
counts = rle.get('counts')
for i, (value, elements) in enumerate(groupby(binary_mask.ravel(order='F'))):
if i == 0 and value == 1:
counts.append(0)
counts.append(len(list(elements)))
return rle
test_list_1 = np.array([0, 0, 1, 1, 1, 0, 1])
test_list_2 = np.array([1, 1, 1, 1, 1, 1, 0])
print(binary_mask_to_rle(test_list_1))
print(binary_mask_to_rle(test_list_2))
输出:
{'counts': [2, 3, 1, 1], 'size': [7]}
{'counts': [0, 6, 1], 'size': [7]}
您可以使用 mask.frPyObjects(rle, size_x, size_y) 对 RLE 进行编码，然后执行所有常用的 mask 操作。
import json
import numpy as np
from pycocotools import mask
from skimage import measure
ground_truth_binary_mask = np.array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
fortran_ground_truth_binary_mask = np.asfortranarray(ground_truth_binary_mask)
将掩码编码为 RLE:
rle = binary_mask_to_rle(fortran_ground_truth_binary_mask)
print(rle)
输出:
{'counts': [6, 1, 40, 4, 5, 4, 5, 4, 21], 'size': [9, 10]}
压缩RLE，然后解码:
compressed_rle = mask.frPyObjects(rle, rle.get('size')[0], rle.get('size')[1])
mask.decode(compressed_rle)
输出:
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
关于python - 使用未压缩的 RLE 为 COCO 数据集编码 numpy 数组，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/49494337/