The pyplot.cm instance produces different results for the same values, but a different data type

This question is a continuation of tcaswell's solution (answer No. 2) for my question: is there a way to convert the pyplot.imshow () object to a numpy array?

Consider the following python code:

import pylab
import numpy as np

a = np.array( ( 30, 129 ) , dtype = np.float32 )
b = np.array( ( 30, 129 ) , dtype = np.int32 )
my_cm = pylab.cm.get_cmap('jet')
a_mapped_data = my_cm( a )
b_mapped_data = my_cm( b )

I use a small array to save space, but this is what is visible even when using large arrays.

Results:

>>> a
array([  30.,  129.], dtype=float32)

>>> b
array([ 30, 129])

>>> a_mapped_data
array([[ 0.5,  0. ,  0. ,  1. ],
       [ 0.5,  0. ,  0. ,  1. ]])

>>> b_mapped_data
array([[ 0.        ,  0.        ,  1.        ,  1.        ],
       [ 0.5028463 ,  1.        ,  0.46489564,  1.        ]])

I don't seem to understand this behavior. Although the values ​​are the same, the instance cm.get_map()produces different results for the data types numpy.int32and numpy.float32. Is there something wrong with the code above? Please help with this. I need to build 2D arrays like numpy.float.

thank

I am using python 2.7.3 32bit on Windows7 x64 Home Basic

EDIT: , , ,

script , , pylab.imshow pylab.pcolor - . , .

import pylab
import numpy as np

a = np.random.random( (512, 512) )*100
# a is a 2D array of random data not in the range of 0.0 to 1.0

# normalize the data
normed_a = ( a - a.min() )/( a.max() - a.min() )

# now create an instance of pylab.cm.get_cmap()
my_cm = pylab.cm.get_cmap('jet_r')

# create the map
mapped_a = my_cm( normed_a )

# to display the map, opencv is being used
# import opencv
import cv2 as cv

# convert mapped data to 8 bit unsigned int
mapped_au8 = (255 * mapped_a).astype('uint8')

# show the image
cv.imshow( 'a', mapped_au8 )
cv.waitKey( 0 )
cv.destroyAllWindows()

EDIT: cm.get_cmap RGBA, OpenCV BGR. , , cm.get_cmap(), , BGR ( ALPHA opencv , , BGRA ). :

mapped_au8 = (255 * mapped_a).astype('uint8')

#convert mapped_au8 into BGR fromat before display
mapped_u8 = cv.cvtColor( mapped_au8, cv.COLOR_RGBA2BGR )

# show the image
cv.imshow( 'a', mapped_au8 )
cv.waitKey( 0 )
cv.destroyAllWindows()