This forum is disabled, please visit https://forum.opencv.org
 | 1 | initial version |
hog.compute() returns a single column (a "vertical" array), while you probably need a single row here
| | 2 | No.2 Revision |
hog.compute() returns a single column (a "vertical" array), while you probably need a single row here
look at your training code, and reshape() it in the same way
| | 3 | No.3 Revision |
hog.compute() returns a single column (a "vertical" array), while you probably need a single row here
look at your training code, and reshape() it in the same way
then, while there is a RAW_OUTPUT flag for the SVM prediction, it only would return the distance to the margin, not a probability, and using that would only make sense in a binary case (single support vector, yours is probably multi-class).
so, while an estimation for a single prediction is difficult, you could still run your classifier on a large (labelled) testset, and make a confudion matrix, to see how good it is in general.
| | 4 | No.4 Revision |
hog.compute() returns a single column (a "vertical" array), while you probably need a single row here
look at your training code, and reshape() it in the same way
then, while there is a RAW_OUTPUT flag for the SVM prediction, it only would return the distance to the margin, not a probability, and using that would only make sense in a binary case (single support vector, yours is probably multi-class).
so, while an estimation for a single prediction is difficult, you could still run your classifier on a large (labelled) testset, and make a confudion confusion matrix, to see how good it is in general.
