I have my FLANN index here:
flann_params = dict(algorithm = 1, trees = 4)
matcher = cv2.FlannBasedMatcher(flann_params, {})
And then I add descriptors of training images to it in a loop:
matcher.add([descriptors])
And then I train it:
matcher.train()
Few more related methods:
matcher.clear()
Clears the train descriptor collection
matcher.empty()
Does the same thing ( right?)
But what I really want is,
A) to store the descriptors to disk and simply load them into the matcher and then train it
B) Make the matcher editable: if I delete an image off the disk, it shouldn't be found by the matcher. Maybe something like matcher.clear(index_of_image_deleted)
C) Save the matcher data to disk so that I don't have to train images everytime I run the program. (Maybe save descriptors to disk?)
