Per cercare di migliorare i risultati di pose estimation di alcuni Aruco tag ho provato la rete neurale a questo link https://github.com/herohuyongtao/deeptag-pytorch
Il progetto non funziona ma bastano un paio di correzione e si adatta il codice a Python attuale
In blu la stima della pose estimation con OpenCv pura ed in rosso DeepTag
I risultati risultano evidenti
== configurazioene dei parametri di DeepTag
{
"is_video":0,
"filepath":"qui/20250929.jpg",
"family": "aruco",
"hamming_dist": 8,
"codebook": "",
"cameraMatrix": [2520.70396, 0, 1919.89817, 0, 2518.23371, 1022.39117, 0, 0, 1],
"distCoeffs": [ -0.39480147, 0.22634207, 0.00189933, 0.00101961, -0.08608824],
"marker_size": 0.25
}
===================================
Deeptag non comprende altre famiglie di Aruco Tag diverse da 5x5...io invece ho usato 4x4_250
per questo motivo ho modificato il file ./codebook/arucocodebook.txt
0,1,0,1,1,0,1,0,1,0,0,1,1,0,0,1,0
1,0,0,0,0,1,1,1,1,1,0,0,1,1,0,1,0
2,0,0,1,1,0,0,1,1,0,0,1,0,1,1,0,1
3,1,0,0,1,1,0,0,1,0,1,0,0,0,1,1,0
4,0,1,0,1,0,1,0,0,1,0,0,1,1,1,1,0
5,0,1,1,1,1,0,0,1,1,1,0,0,1,1,0,1
6,1,0,0,1,1,1,1,0,0,0,1,0,1,1,1,0
7,1,1,0,0,0,1,0,0,1,1,1,1,0,0,1,0
8,1,1,1,1,1,1,1,0,1,1,0,1,1,0,1,0
9,1,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0
10,1,1,1,1,1,0,0,1,1,0,0,1,0,0,0,1
11,0,0,0,1,0,0,0,1,1,0,1,0,0,1,1,1
12,0,0,0,0,1,1,1,0,1,0,1,1,0,1,1,1
13,0,0,1,0,1,0,1,0,0,0,0,0,1,1,1,1
14,0,0,1,0,0,1,0,0,1,0,1,1,0,0,0,1
15,0,0,1,0,0,1,1,0,0,0,1,1,1,1,1,0
16,0,1,0,0,0,1,1,0,0,1,1,0,0,1,0,1
17,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0
18,0,1,1,0,1,1,0,0,0,1,0,1,1,1,1,0
19,0,1,1,1,0,1,1,0,1,0,1,0,1,1,1,1
20,1,0,0,0,0,1,1,0,1,0,0,0,1,0,1,1
21,1,0,1,1,0,0,0,0,0,0,1,0,1,0,1,1
22,1,1,0,0,1,1,0,0,1,1,0,1,0,1,0,1
23,1,1,0,1,1,1,0,1,1,0,0,0,0,0,1,0
24,1,1,1,1,1,1,1,0,0,1,0,0,0,1,1,1
25,1,0,0,1,0,1,0,0,0,1,1,1,0,0,0,1
26,1,0,1,0,1,1,0,0,1,1,1,0,0,1,0,0
27,1,0,1,0,0,1,0,1,0,1,0,1,0,1,0,0
28,0,0,1,0,0,0,0,1,0,0,1,0,0,0,1,1
29,0,0,1,1,0,1,0,0,0,1,1,0,1,1,1,1
30,0,1,0,0,0,1,0,0,0,0,0,1,0,1,0,1
31,0,1,0,1,0,1,1,1,1,0,1,1,0,0,1,0
32,1,0,0,1,1,1,1,0,1,1,0,0,1,1,1,1
33,1,1,1,1,0,0,0,0,1,1,0,0,1,0,1,1
34,0,0,0,0,1,0,0,0,1,0,1,0,1,1,1,0
35,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1
36,0,0,0,1,1,0,0,0,0,1,1,1,0,1,0,1
37,0,0,0,0,0,1,0,0,1,1,1,1,1,1,1,1
38,0,0,0,0,1,1,0,1,1,1,1,1,0,1,1,0
39,0,0,0,1,1,1,0,0,0,1,0,1,1,0,1,0
40,0,0,0,1,0,1,1,1,0,0,0,1,1,0,0,0
41,0,0,1,0,1,0,1,0,0,0,1,0,1,0,0,0
42,0,0,1,1,0,0,1,0,1,0,0,0,1,1,0,0
43,0,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0
44,0,0,1,0,0,1,0,0,1,1,1,0,1,0,0,0
45,0,0,1,0,1,1,1,0,1,1,1,0,1,0,1,1
46,0,0,1,0,1,1,0,1,0,0,1,1,1,1,1,1
47,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0
48,0,1,0,1,0,0,0,0,0,0,1,0,1,1,1,0
49,0,1,0,1,0,0,0,0,0,0,0,1,0,0,1,1
50,0,1,0,1,0,0,0,1,1,0,0,1,0,1,0,0
51,0,1,0,1,0,1,0,1,0,1,1,0,1,0,0,0
52,0,1,0,1,1,1,0,1,0,1,0,0,0,0,0,1
53,0,1,0,1,1,1,1,1,1,0,0,1,0,1,1,1
54,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,1
55,0,1,1,0,1,0,0,0,0,1,1,0,0,1,1,1
56,0,1,1,0,0,0,0,1,0,0,1,0,0,1,0,0
57,0,1,1,0,0,0,0,1,1,1,1,0,1,0,0,1
58,0,1,1,0,1,0,1,1,0,0,0,1,0,0,1,0
59,0,1,1,0,1,1,1,1,1,1,1,0,0,1,0,1
60,0,1,1,0,0,1,1,1,1,1,0,1,1,1,1,1
61,0,1,1,1,1,1,1,0,0,0,0,1,1,0,1,1
62,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0
63,1,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0
64,1,0,0,0,1,0,1,1,1,0,1,0,0,0,1,0
65,1,0,0,1,0,0,1,1,0,1,1,1,1,0,1,0
66,1,0,0,0,0,1,0,0,0,1,1,0,1,1,0,0
67,1,0,0,0,0,1,0,1,0,0,1,0,1,0,1,0
68,1,0,0,0,0,1,0,1,1,0,0,1,1,1,0,0
69,1,0,0,1,1,1,0,0,1,0,0,0,1,0,0,1
70,1,0,0,1,1,1,1,1,1,0,1,0,0,0,0,1
71,1,0,1,1,1,0,1,1,0,1,1,1,1,1,0,0
72,1,0,1,1,1,1,0,0,0,0,0,0,0,1,0,0
73,1,0,1,1,0,1,1,0,0,1,0,1,1,0,1,1
74,1,0,1,1,1,1,1,1,1,1,0,0,1,0,0,0
75,1,0,1,1,0,1,1,1,1,0,1,0,1,0,1,1
76,1,1,0,0,1,0,1,0,0,0,0,1,1,1,1,1
77,1,1,0,0,1,0,0,1,0,1,1,0,0,0,1,0
78,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0
79,1,1,0,1,0,0,1,1,1,1,0,1,0,1,0,1
80,1,1,0,0,1,1,0,0,1,0,0,1,1,0,0,0
81,1,1,0,0,0,1,1,1,1,0,1,0,0,0,0,0
82,1,1,0,0,0,1,0,1,0,0,1,1,0,1,1,1
83,1,1,1,0,1,0,0,1,0,1,0,1,1,1,0,1
84,1,1,1,1,1,0,0,1,0,0,1,0,0,1,0,1
85,1,1,1,1,1,0,1,1,1,0,1,1,1,0,1,1
86,1,1,1,0,1,1,1,0,0,0,1,0,1,0,1,0
87,1,1,1,1,0,1,1,1,0,1,0,0,1,1,0,1
88,0,0,1,1,0,1,0,1,0,1,1,1,0,1,0,1
89,1,0,0,0,1,0,1,0,1,0,1,0,1,1,0,1
90,0,1,1,1,0,1,1,0,0,0,0,1,0,1,1,1
91,0,0,0,0,1,0,1,0,1,1,0,0,1,1,1,1
92,0,0,0,0,0,1,1,0,0,1,0,0,1,0,1,1
93,0,0,1,0,1,1,0,1,1,1,0,0,0,0,0,1
94,0,1,0,0,1,0,0,1,1,1,0,1,1,0,0,0
95,0,1,0,0,0,0,1,1,1,1,1,1,0,1,0,0
96,0,1,0,0,1,1,1,1,0,0,1,1,0,1,1,0
97,0,1,0,0,1,1,1,1,1,1,0,1,0,0,1,1
98,0,1,1,0,1,0,0,1,1,1,1,0,0,1,0,0
99,0,1,1,1,0,0,0,0,1,1,0,0,0,1,1,1
100,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0
101,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,0
102,1,1,1,0,1,1,0,1,0,1,0,0,1,1,1,1
103,1,1,1,1,1,1,0,0,1,1,1,0,0,1,1,1
104,1,1,1,1,1,1,1,0,1,0,1,0,0,1,1,0
105,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1
106,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1
107,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0
108,0,0,0,0,1,0,1,0,1,0,0,0,0,1,1,0
109,0,0,0,0,0,0,1,0,0,1,1,0,1,1,1,1
110,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0
111,0,0,0,0,0,0,0,0,1,0,0,1,0,1,1,1
112,0,0,0,0,1,0,0,0,0,0,1,1,0,1,1,1
113,0,0,0,0,1,0,1,0,0,0,1,1,0,0,0,1
114,0,0,0,0,1,0,0,1,1,1,0,0,0,1,1,0
115,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,1
116,0,0,0,0,1,0,0,1,1,1,1,1,1,0,1,1
117,0,0,0,0,1,0,1,1,0,1,0,1,1,0,0,0
118,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0
119,0,0,0,1,1,0,0,0,0,0,1,0,1,1,0,1
120,0,0,0,1,0,0,0,0,0,1,1,1,1,0,0,0
121,0,0,0,1,0,0,0,0,0,1,1,1,0,0,1,1
122,0,0,0,1,0,0,1,0,0,1,1,1,0,1,0,0
123,0,0,0,1,0,0,1,0,1,0,1,1,0,0,0,1
124,0,0,0,1,1,0,1,0,1,1,1,1,1,0,0,1
125,0,0,0,1,0,0,1,1,0,0,0,0,0,1,1,0
126,0,0,0,0,1,1,0,0,0,0,0,0,1,1,1,0
127,0,0,0,0,1,1,0,0,1,1,1,1,0,0,0,1
128,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,1
129,0,0,0,0,1,1,0,0,1,0,0,1,1,1,1,1
130,0,0,0,0,1,1,1,0,1,1,1,1,0,0,1,0
131,0,0,0,0,1,1,1,0,1,1,1,1,1,1,0,1
132,0,0,0,0,0,1,1,1,0,1,0,0,1,1,0,0
133,0,0,0,0,1,1,1,1,1,0,1,0,0,1,0,0
134,0,0,0,0,0,1,1,1,0,0,1,0,1,1,1,1
135,0,0,0,0,0,1,0,1,1,0,1,1,0,1,0,1
136,0,0,0,0,1,1,1,1,1,0,0,1,0,0,0,1
137,0,0,0,0,0,1,1,1,1,1,0,1,1,0,1,1
138,0,0,0,1,1,1,1,0,1,1,1,0,0,1,0,0
139,0,0,0,1,0,1,0,0,0,0,1,1,1,0,0,1
140,0,0,0,1,1,1,0,1,1,0,0,0,0,0,0,0
141,0,0,0,1,0,1,0,1,1,1,0,0,1,0,0,0
142,0,0,0,1,1,1,1,1,1,0,0,0,1,0,1,1
143,0,0,0,1,0,1,0,1,1,0,1,1,1,0,1,0
144,0,0,0,1,1,1,0,1,1,0,1,1,0,0,0,1
145,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0
146,0,0,1,0,1,0,0,0,1,1,1,0,1,0,0,1
147,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0
148,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,1
149,0,0,1,0,1,0,1,0,1,1,1,1,0,0,0,0
150,0,0,1,0,0,0,1,0,1,1,1,1,0,1,1,1
151,0,0,1,0,1,0,0,1,0,1,0,0,0,0,0,0
152,0,0,1,0,0,0,0,1,0,1,0,0,0,1,1,0
153,0,0,1,0,1,0,0,1,1,0,1,1,1,0,0,1
154,0,0,1,0,1,0,1,1,1,0,0,1,1,1,0,0
155,0,0,1,0,1,0,1,1,1,0,1,1,0,0,1,0
156,0,0,1,1,1,0,0,0,1,1,0,0,1,0,1,0
157,0,0,1,1,1,0,0,0,0,0,1,0,1,1,1,0
158,0,0,1,1,0,0,0,0,0,0,0,0,0,1,1,1
159,0,0,1,1,1,0,0,0,1,1,1,0,0,1,1,1
160,0,0,1,1,1,0,1,0,0,1,0,0,1,0,0,1
161,0,0,1,1,1,0,1,0,0,1,1,0,0,1,0,1
162,0,0,1,1,0,0,1,0,0,1,0,1,1,1,0,1
163,0,0,1,1,1,0,1,1,1,0,0,0,1,0,0,0
164,0,0,1,1,1,0,0,1,0,0,0,1,1,1,0,1
165,0,0,1,1,1,0,1,1,1,1,0,1,0,0,1,1
166,0,0,1,0,0,1,1,0,0,1,0,0,0,1,1,1
167,0,0,1,0,0,1,1,1,1,0,0,0,0,0,0,0
168,0,0,1,0,1,1,1,1,1,0,1,0,1,0,1,0
169,0,0,1,0,1,1,0,1,0,0,0,1,0,1,0,0
170,0,0,1,0,0,1,0,1,1,1,0,1,1,1,1,0
171,0,0,1,0,0,1,0,1,0,1,0,1,0,0,1,1
172,0,0,1,0,1,1,1,1,0,1,1,1,0,1,1,1
173,0,0,1,1,0,1,0,0,0,1,0,0,1,0,0,0
174,0,0,1,1,1,1,0,0,1,0,1,0,1,0,0,0
175,0,0,1,1,1,1,0,0,0,1,0,0,0,0,0,1
176,0,0,1,1,0,1,0,0,0,0,0,0,1,1,0,1
177,0,0,1,1,0,1,0,0,1,1,1,1,1,0,1,1
178,0,0,1,1,0,1,1,0,1,0,0,1,1,0,1,0
179,0,0,1,1,1,1,0,1,1,1,1,0,0,0,0,0
180,0,0,1,1,0,1,0,1,0,1,1,0,1,0,1,0
181,0,0,1,1,1,1,0,1,0,0,0,0,1,0,0,1
182,0,0,1,1,1,1,0,1,1,1,1,0,1,1,0,1
183,0,0,1,1,1,1,1,1,1,1,0,0,0,1,0,0
184,0,0,1,1,1,1,1,1,0,1,1,0,1,1,0,0
185,0,0,1,1,0,1,1,1,1,1,0,0,1,1,1,0
186,0,0,1,1,1,1,0,1,0,1,0,1,1,1,0,0
187,0,0,1,1,1,1,0,1,0,1,1,1,0,1,1,0
188,0,0,1,1,0,1,1,1,1,0,1,1,0,0,0,0
189,0,0,1,1,1,1,1,1,0,0,0,1,0,1,1,1
190,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1
191,0,1,0,0,1,0,0,0,1,1,1,0,0,1,0,1
192,0,1,0,0,0,0,1,0,0,1,1,0,1,0,0,0
193,0,1,0,0,1,0,1,0,0,0,1,0,1,1,0,1
194,0,1,0,0,0,0,0,1,0,1,1,0,0,0,0,0
195,0,1,0,0,1,0,0,1,0,1,0,1,0,0,0,1
196,0,1,0,0,0,0,0,1,1,1,0,1,1,1,0,1
197,0,1,0,0,1,0,1,1,1,1,0,1,1,1,1,1
198,0,1,0,1,1,0,0,0,0,1,0,0,1,1,1,1
199,0,1,0,1,1,0,1,0,0,1,0,0,1,0,0,0
200,0,1,0,1,1,0,0,0,0,0,0,1,0,1,1,0
201,0,1,0,1,0,0,0,0,0,1,0,1,1,1,0,1
202,0,1,0,1,1,0,1,0,1,1,1,1,1,0,1,0
203,0,1,0,1,1,0,1,0,1,0,1,1,0,1,0,1
204,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,1
205,0,1,0,1,1,0,1,1,1,0,0,0,1,0,1,0
206,0,1,0,1,1,0,0,1,0,0,0,1,1,0,0,1
207,0,1,0,1,0,0,0,1,0,0,1,1,0,1,0,1
208,0,1,0,0,1,1,0,0,0,1,1,0,1,0,0,1
209,0,1,0,0,0,1,1,0,1,1,0,0,0,0,0,1
210,0,1,0,0,1,1,1,0,0,0,0,0,1,0,1,1
211,0,1,0,0,0,1,0,0,0,1,0,1,1,1,1,1
212,0,1,0,0,1,1,1,0,0,1,0,1,1,0,0,1
213,0,1,0,0,1,1,0,1,1,0,0,0,0,0,1,1
214,0,1,0,0,1,1,0,1,0,1,1,1,1,1,0,1
215,0,1,0,0,0,1,1,1,1,1,0,1,1,0,0,0
216,0,1,0,0,0,1,1,1,0,1,1,1,0,0,1,1
217,0,1,0,1,1,1,0,0,1,0,0,0,0,1,0,1
218,0,1,0,1,1,1,1,0,0,1,0,0,0,1,0,0
219,0,1,0,1,0,1,1,0,0,0,1,0,1,0,1,1
220,0,1,0,1,1,1,0,0,1,0,1,1,1,0,1,1
221,0,1,0,1,0,1,0,1,1,1,0,0,0,0,1,1
222,0,1,0,1,1,1,1,1,0,1,1,0,1,1,1,0
223,0,1,0,1,1,1,1,1,1,1,1,0,1,0,1,1
224,0,1,0,1,1,1,0,1,0,0,0,1,0,0,1,0
225,0,1,0,1,0,1,0,1,0,1,0,1,1,1,1,0
226,0,1,1,0,0,0,1,0,0,1,1,1,0,0,0,0
227,0,1,1,0,0,0,1,0,0,0,0,1,0,1,0,1
228,0,1,1,0,0,0,0,1,1,1,0,0,0,0,1,0
229,0,1,1,0,1,0,1,1,0,0,1,0,0,0,0,0
230,0,1,1,0,0,0,1,1,0,1,0,0,0,1,0,1
231,0,1,1,0,1,0,1,1,0,1,0,1,1,1,0,0
232,0,1,1,0,1,0,1,1,0,1,0,1,1,0,1,1
233,0,1,1,1,1,0,0,0,0,0,0,0,1,1,0,0
234,0,1,1,1,1,0,1,0,1,1,0,0,1,1,1,1
235,0,1,1,1,1,0,0,0,0,1,1,1,1,1,1,1
236,0,1,1,1,1,0,0,1,1,0,0,0,0,0,0,0
237,0,1,1,1,0,0,0,1,1,1,1,0,0,1,0,1
238,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,0
239,0,1,1,1,1,0,0,1,1,0,1,1,0,1,1,0
240,0,1,1,1,0,0,0,1,1,1,0,1,0,0,1,1
241,0,1,1,1,1,0,1,1,0,0,1,1,0,0,1,1
242,0,1,1,0,0,1,0,0,0,1,1,0,1,0,1,0
243,0,1,1,0,0,1,1,0,1,0,1,0,1,0,0,0
244,0,1,1,0,1,1,1,0,1,0,1,0,0,1,1,1
245,0,1,1,0,1,1,1,0,1,0,0,1,0,0,0,1
246,0,1,1,0,0,1,0,1,0,0,1,0,0,0,1,0
247,0,1,1,0,1,1,0,1,1,1,0,0,1,0,1,1
248,0,1,1,0,0,1,1,1,1,0,0,0,1,1,0,1
249,0,1,1,0,1,1,0,1,0,0,1,1,0,0,0,1
per crearsi il file in modo autonomo si puo' usare il codice
import cv2
import json
import numpy as np
aruco_dict = cv2.aruco.getPredefinedDictionary(cv2.aruco.DICT_4X4_250)
codebook = {"family": "DICT_4X4_250", "markers": []}
for marker_id in range(250):
# Generate marker image (for verification)
marker_img = cv2.aruco.generateImageMarker(aruco_dict, marker_id, 200)
# Get binary pattern (4x4)
bits = aruco_dict.bytesList[marker_id][0]
bits = np.unpackbits(bits).reshape((4, 4)).astype(int).tolist()
codebook["markers"].append({
"id": int(marker_id),
"bits": bits
})
# Save to JSON
with open("aruco_4x4_250.json", "w") as f:
json.dump(codebook, f, indent=2)
===================================
#!/bin/bash
# Run DeepTag detection for all JPGs in ./qui
CONFIG="config_image.json"
FOLDER="./qui"
# Check if folder exists
if [ ! -d "$FOLDER" ]; then
echo "Error: folder '$FOLDER' not found!"
exit 1
fi
# Loop over all .jpg and .JPG files in folder
for img in "$FOLDER"/*.jpg "$FOLDER"/*.JPG; do
# Skip if no files found (glob didn't expand)
[ -e "$img" ] || continue
echo "Processing $img ..."
python test_deeptag.py --config "$CONFIG" --nome "$img"
# Optional: small delay to avoid CPU overload
# sleep 0.2
done
echo "✅ All images processed."
===================================
import cv2
from deeptag_model_setting import load_deeptag_models
from marker_dict_setting import load_marker_codebook
from stag_decode.detection_engine import DetectionEngine
import json
import os
import numpy as np
import sys
import csv
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='./config_video.json', help='path of configuration file for camera params and marker size')
parser.add_argument('--CPU', action='store_true', help='use CPU only')
parser.add_argument('--nome', type=str, help='nomefile')
args = parser.parse_args()
config_filename = args.config
device = 'cpu' if args.CPU else None
# Load configuration
try:
config_dict = json.load(open(config_filename, 'r'))
cameraMatrix = config_dict['cameraMatrix']
distCoeffs = config_dict['distCoeffs']
tag_real_size_in_meter = config_dict['marker_size']
is_video = config_dict['is_video'] != 0
filename = config_dict['filepath']
tag_family = config_dict['family']
codebook_filename = (
config_dict['codebook']
if len(config_dict['codebook'])
else os.path.join('codebook', tag_family + '_codebook.txt')
)
hamming_dist = config_dict['hamming_dist']
except Exception as e:
print(f'Cannot load config: {config_filename} ({e})')
sys.exit(1)
if args.nome:
filename = args.nome
# Load models
try:
model_detector, model_decoder, device, tag_type, grid_size_cand_list = load_deeptag_models(tag_family, device)
except Exception as e:
print(f'Cannot load models: {e}')
sys.exit(1)
# Load marker library
try:
codebook = load_marker_codebook(codebook_filename, tag_type)
except Exception as e:
print(f'Cannot load codebook: {codebook_filename} ({e})')
sys.exit(1)
# Initialize detection engine
stag_image_processor = DetectionEngine(
model_detector, model_decoder, device, tag_type, grid_size_cand_list,
stg2_iter_num=2,
min_center_score=0.2, min_corner_score=0.2,
batch_size_stg2=4,
hamming_dist=hamming_dist,
cameraMatrix=cameraMatrix, distCoeffs=distCoeffs, codebook=codebook,
tag_real_size_in_meter_dict={-1: tag_real_size_in_meter}
)
# Read image
image = cv2.imread(filename)
if image is None:
print('Cannot read image.')
sys.exit(1)
decoded_tags = stag_image_processor.process(image, detect_scale=None)
# Prepare CSV
output_file = "risultato_tags.csv"
file_exists = os.path.exists(output_file)
date_str = filename[6:14] if len(filename) >= 14 else "unknown"
with open(output_file, 'a', newline='') as f:
writer = csv.writer(f)
#if not file_exists:
#writer.writerow(["other_id", "date", "dx", "dy", "dz", "distance_m"])
# Extract tvec for tag ID = 31
ref_tag = next((tag for tag in decoded_tags if tag.get('tag_id') == 11 and 'tvecs' in tag), None)
if ref_tag is None:
print("Tag ID 31 not found in this image.")
sys.exit(0)
ref_tvec = np.array(ref_tag['tvecs']).reshape(3)
# Compute relative distances to all other tags
for tag in decoded_tags:
if 'tvecs' in tag and tag['tag_id'] != 31:
other_tvec = np.array(tag['tvecs']).reshape(3)
diff = other_tvec - ref_tvec
distance = np.linalg.norm(diff)
writer.writerow([
tag['tag_id'], date_str,round(distance, 3)
])
#print(f"ID31 → ID{tag['tag_id']} : dx={diff[0]:.3f}, dy={diff[1]:.3f}, dz={diff[2]:.3f}, dist={distance:.3f}")
'''
overlay = image.copy()
for tag in decoded_tags:
tag_id = tag.get("tag_id", -1)
# Take first 4 keypoints as tag corners
corners = np.array(tag["keypoints_in_images"][:4], dtype=float)
corners = np.clip(corners, 0, [image.shape[1]-1, image.shape[0]-1]).astype(int)
# Compute center
center = np.mean(corners, axis=0).astype(int)
# Draw green outline
cv2.polylines(overlay, [corners], True, (0, 255, 0), 2, cv2.LINE_AA)
# Draw black rectangle behind text
text = f"ID:{tag_id}"
(text_w, text_h), _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2)
cv2.rectangle(
overlay,
(center[0] - text_w // 2 - 4, center[1] - text_h // 2 - 10),
(center[0] + text_w // 2 + 4, center[1] + text_h // 2),
(0, 0, 0),
-1,
)
# Draw yellow tag ID
cv2.putText(
overlay, text,
(center[0] - text_w // 2, center[1] + text_h // 2 - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2, cv2.LINE_AA
)
# Blend overlay
disp = cv2.addWeighted(image, 0.7, overlay, 0.3, 0)
cv2.imshow("Detected Tags", disp)
print("Press any key to close...")
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
sys.exit(0)
Nessun commento:
Posta un commento