python 归一化_python-OpenCV之normalize（归一化 ）函数详解
归一化就是要把需要处理的数据经过处理后(通过某种算法)限制在你需要的一定范围内。
归一化的目的，是使得没有可比性的数据变得具有可比性，同时又保持相比较的两个数据之间的相对关系，如大小关系；或是为了作图，原来很难在一张图上作出来，归一化后就可以很方便的给出图上的相对位置等。
接下来主要讲述python_opencv中的normalize()函数，其原型为：
def normalize(src, dst, alpha=None, beta=None, norm_type=None, dtype=None, mask=None): # real signature unknown; restored from __doc__
"""
normalize(src, dst[, alpha[, beta[, norm_type[, dtype[, mask]]]]]) -> dst
. @brief Normalizes the norm or value range of an array.
.
. The function cv::normalize normalizes scale and shift the input array elements so that
. \f[\| \texttt{dst} \| _{L_p}= \texttt{alpha}\f]
. (where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that
. \f[\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\f]
.
. when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be
. normalized. This means that the norm or min-n-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 norm and Mat::convertTo.
.
. In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this,
. the range transformation for sparse matrices is not allowed since it can shift the zero level.
.
. Possible usage with some positive example data:
. @code{.cpp}
. vector positiveData = { 2.0, 8.0, 10.0 };
. vector normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax;
.
. // 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)
. normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1);
.
. // Norm to unit vector: ||positiveData|| = 1.0
. // 2.0 0.15
. // 8.0 0.62
. // 10.0 0.77
. normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_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)
. normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_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)
. normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX);
. @endcode
.
. @param src input array.
. @param dst output array of the same size as src .
. @param alpha norm value to normalize to or the lower range boundary in case of the range
. normalization.
. @param beta upper range boundary in case of the range normalization; it is not used for the norm
. normalization.
. @param norm_type normalization type (see cv::NormTypes).
. @param dtype when negative, the output array has the same type as src; otherwise, it has the same
. number of channels as src and the depth =CV_MAT_DEPTH(dtype).
. @param mask optional operation mask.
. @sa norm, Mat::convertTo, SparseMat::convertTo
"""
pass
可能有些同学看不懂注释，没关系，我们通过几个例子来详细阐述该函数是如何实现归一化的。
在做图像处理的过程中，我们有时会对图像增强，增加其对比度，在增强之后就会利用该函数进行取值范围归一化。
cv2.normalize(img,img,0,255,cv2.NORM_MINMAX)
注：把所有的值缩放到【0，255】范围内，具体的操作方式可参考https://blog.csdn.net/lanmeng_smile/article/details/49903865