Comments (15)
Olá professor @miguelriemoliveira
Estou a seguir os passos deste link para detetar os marcadores Aruko no Matlab:
https://www.mathworks.com/matlabcentral/answers/372403-how-to-use-aruco-marker-with-the-opencv-support-package-of-matlab-and-the-computer-vision-system-too
mas estou a ter problemas com o compilador que o matlab usa.
Este é o warning que me dá no Matlab:
E o compilador que está predefinido no sistema é o que eles pedem penso eu, como se pode ver:
Tem alguma solução para este problema?
cumprimentos
Filipe
from optimization.
Já consegui resolver!
Filipe
from optimization.
Fixe!
from optimization.
Já temos imagens a detetar os aruco?
from optimization.
Ainda não.
from optimization.
Não estou a conseguir usar o exemplo que eles dão. E não arranjo muitos exemplos em matlab.
from optimization.
Podemos tentar falar hoje. Se tiver tempo de manhã vou procurá-lo.
from optimization.
Sim, vou estar pelo LAR
from optimization.
Acho que já descobri o problema mas não o consegui resolver.
Acho que o problema é que quando eu instalei o OpenCV ele não estabeleceu as ligações com o matlab, e dessa forma os includes para utilizar as funções relativas aos marcadores Aruco não estão ativos.
from optimization.
Olá Filipe. Eu não vou ter tempo esta semana. Sugiro que pegue noutro assunto (o RADLOCC?) e depois na segunda falamos.
Abraço,
Miguel
from optimization.
Está bem professor, vou então ver outra coisa.
Filipe
from optimization.
Olá professor @miguelriemoliveira
A que horas vai estar pelo seu gabinete?
Filipe
from optimization.
from optimization.
Olá professor @miguelriemoliveira
Já são detetáveis os marcadores aruco em python, onde são retornados os ids dos marcadores e as coordenadas na imagens dos pontos dos cantos dos marcadores:
Filipe
from optimization.
OK, parece bem.
from optimization.
Related Issues (17)
- Implementar otimização N pontos - exemplo matlab HOT 5
- Implement a python optimization routine HOT 3
- Create a comparison table for the lar meeting HOT 5
- implement an optimization routine with image HOT 9
- Represent 3D view of the position of the cameras and draw the cameras HOT 6
- Realize the calibration of 3 cameras at the same time HOT 3
- New optimization with the intrinsic matrix variable HOT 5
- Study the process SFM ( structure from multiple views ) HOT 4
- Use RADOCC to a camera-laser calibration HOT 1
- Validate the method of calculating transformations between cameras and markers HOT 2
- Cost function for the new process of calibration with Aruco markers HOT 5
- Initial estimation of the position of cameras and Aruco markers HOT 19
- Calculate and represent the error between ground truth and initial estimation HOT 1
- Find the way to set plots in python HOT 3
- Prove the method of calculation of the transformations between cameras and arucos. Dataset has real known dimensions. Calculate the error between ground truth and initial estimation/Optimization result HOT 17
- Add cameras to ATLASCAR2 to prove and relate the method to it
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from optimization.