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 | 1 | initial version |
What do you mean? The docu says: "or so that min(dist) = alpha and max(dist) = beta when normType=NORM_MINMAX (for dense arrays only)"
| | 2 | No.2 Revision |
What do you mean? The docu says: "or so that min(dist) = alpha and max(dist) = beta when normType=NORM_MINMAX (for dense arrays only)"
As it only scales and shifts, the transformation should be (if a and b are you lower and upper bound in the input)
dst = (x-a)/(b-a)*(beta-alpha) + alpha
(but that should be easy to verify in the code)
| | 3 | No.3 Revision |
What do you mean? The docu says: "or so that min(dist) = alpha and max(dist) = beta when normType=NORM_MINMAX (for dense arrays only)"
As it only scales and shifts, the transformation should be (if a and b are you lower and upper bound in the input)
dst = (x-a)/(b-a)*(beta-alpha) + alpha
(but that should be easy to verify in the code)
// the code is here: https://github.com/Itseez/opencv/blob/master/modules/core/src/convert.cpp#L6076
