Function normalize

Synopse

Normalize the pixels values between the specified range.

g = normalize(f, range)
- g: Output: ndmage, same type as f. If range==[0,255], dtype is set to 'uint8'
- f: Input: ndimage.
- range: Input: vector with two elements, minimum and maximum values in the output image, respectively.

Description

Normalize the input image f. The minimum value of f is assigned to the minimum desired value and the maximum value of f, to the maximum desired value. The minimum and maximum desired values are given by the parameter range. The data type of the normalized image is the same data type of input image, unless the range is [0,255], in which case the dtype is set to 'uint8'.



In [1]:

    
import numpy as np

def normalize(f, range=[0,255]):

    f = np.asarray(f)
    range = np.asarray(range)
    if f.dtype.char in ['D', 'F']:
        raise Exception('error: cannot normalize complex data')
    faux = np.ravel(f).astype(float)
    minimum = faux.min()
    maximum = faux.max()
    lower = range[0]
    upper = range[1]
    if upper == lower:
        g = np.ones(f.shape) * maximum
    if minimum == maximum:
        g = np.ones(f.shape) * (upper + lower) / 2.
    else:
        g = (faux-minimum)*(upper-lower) / (maximum-minimum) + lower
    g = g.reshape(f.shape)

    if f.dtype == np.uint8:
        if upper > 255: 
            raise Exception('normalize: warning, upper valuer larger than 255. Cannot fit in uint8 image')
    if lower == 0 and upper == 255:
        g = g.astype(np.uint8)
    else:
        g = g.astype(f.dtype) # set data type of result the same as the input image
    return g

Examples



In [1]:

    
testing = (__name__ == "__main__")

if testing:
    import numpy as np
    import sys,os
    ia898path = os.path.abspath('../../')
    if ia898path not in sys.path:
        sys.path.append(ia898path)
    import ia898.src as ia

Example 1



In [2]:

    
if testing:
    f = np.array([100., 500., 1000.])
    g1 = ia.normalize(f, [0,255])
    print(g1)









    



[  0 113 255]



In [3]:

    
if testing:
    g2 = ia.normalize(f, [-1,1])
    print(g2)









    



[-1.         -0.11111111  1.        ]



In [4]:

    
if testing:
    g3 = ia.normalize(f, [0,1])
    print(g3)









    



[ 0.          0.44444444  1.        ]



In [5]:

    
if testing:
    #
    f = np.array([-100., 0., 100.])
    g4 = ia.normalize(f, [0,255])
    print(g4)
    g5 = ia.normalize(f, [-1,1])
    print(g5)
    g6 = ia.normalize(f, [-0.5,0.5])
    print(g6)
    #
    f = np.arange(10).astype('uint8')
    g7 = ia.normalize(f)
    print(g7)
    #
    f = np.array([1,1,1])
    g8 = ia.normalize(f)
    print(g8)









    



[  0 127 255]
[-1.  0.  1.]
[-0.5  0.   0.5]
[  0  28  56  85 113 141 170 198 226 255]
[127 127 127]

Equation

$$ g = f|_{gmin}^{gmax}$$

$$ g(p) = \frac{g_{max} - g_{min}}{f_{max}-f_{min}} (f(p) - f_{min}) + g_{min} $$

References

Wikipedia - Normalization)

Contributions

Marcos Fernandes, course IA368S, 1st semester 2013



In [7]:

    
if testing:
    print('testing normalize')
    print(repr(ia.normalize(np.array([-100., 0., 100.]), [0,255])) == repr(np.array([   0. ,  127.5,  255. ])))









    



testing normalize
True



In [ ]: