Normalizes the norm or value range of an array

dst = cv.normalize(src)
dst = cv.normalize(..., 'OptionName', optionValue, ...)

Input

• src input array.

Output

• dst output array of the same size as src. See DType option.

Options

• Alpha norm value to normalize to or the lower range boundary in case of the range normalization. default 1
• Beta upper range boundary in case of the range normalization; it is not used for the norm normalization. default 0
• NormType normalization type, default 'L2'. One of:
  • Inf
  • L1
  • L2
  • MinMax
• DType when negative, the output array has the same type as src; otherwise, it has the same number of channels as src and the specified depth (a numeric class name: 'uint8', 'double', etc...). default -1
• Mask optional operation mask. Not set by default.
• Dest initial array used for output. This can initialize dst when Mask is used. Not set by default.

The functions cv.normalize scale and shift the input array elements so that:

||dst||_Lp = alpha

(where p=Inf, 1 or 2) when NormType='Inf', 'L1', or 'L2', respectively; or so that:

min(dst) = alpha, max(dst) = beta

when NormType='MinMax' (for dense arrays only). The optional Mask specifies a sub-array to be normalized. This means that the norm or min-max are calculated over the sub-array, and then this sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or min-max but modify the whole array, you can use cv.norm and Mat::convertTo.

Example

Possible usage with some positive example data:

positiveData = [2.0, 8.0, 10.0];

% Norm to probability (total count)
% sum(numbers) = 20.0
% 2.0      0.1     (2.0/20.0)
% 8.0      0.4     (8.0/20.0)
% 10.0     0.5     (10.0/20.0)
normalizedData_l1 = cv.normalize(positiveData, 'NormType','L1');

% Norm to unit vector: ||positiveData|| = 1.0
% 2.0      0.15
% 8.0      0.62
% 10.0     0.77
normalizedData_l2 = cv.normalize(positiveData, 'NormType','L2');

% Norm to max element
% 2.0      0.2     (2.0/10.0)
% 8.0      0.8     (8.0/10.0)
% 10.0     1.0     (10.0/10.0)
normalizedData_inf = cv.normalize(positiveData, 'NormType','Inf');

% Norm to range [0.0;1.0]
% 2.0      0.0     (shift to left border)
% 8.0      0.75    (6.0/8.0)
% 10.0     1.0     (shift to right border)
normalizedData_minmax = cv.normalize(positiveData, 'NormType','MinMax');