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POpyplot.cm instance is producing different results for same values but different data type
 

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  1. POpyplot.cm instance is producing different results for same values but different data type
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    <p>This question is in continuation of the solution provided by <a href="https://stackoverflow.com/users/380231/tcaswell">tcaswell</a> (answer #2) for my question: <a href="https://stackoverflow.com/questions/14869321/is-there-a-way-to-convert-pyplot-imshow-object-to-numpy-array">Is there a way to convert pyplot.imshow() object to numpy array?</a> </p> <p>Consider the following python code:</p> <pre><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 ) </code></pre> <p>I am using a small array to save space, but this is what is seen even when large arrays are used.</p> <p>The results:</p> <pre><code>&gt;&gt;&gt; a array([ 30., 129.], dtype=float32) &gt;&gt;&gt; b array([ 30, 129]) &gt;&gt;&gt; a_mapped_data array([[ 0.5, 0. , 0. , 1. ], [ 0.5, 0. , 0. , 1. ]]) &gt;&gt;&gt; b_mapped_data array([[ 0. , 0. , 1. , 1. ], [ 0.5028463 , 1. , 0.46489564, 1. ]]) </code></pre> <p>I don't seem to understand the behavior here. Even though the values are same, <code>cm.get_map()</code> instance is producing different results for <code>numpy.int32</code> and <code>numpy.float32</code> data types. Is there something wrong with the code above? Please help out with this. I need to plot 2D arrays of type numpy.float.</p> <p>Thank you</p> <p>I am using python 2.7.3 32bit on Windows7 x64 Home Basic </p> <hr> <p><strong>EDIT :</strong> <strong><em>Solution for those who are facing the same problem as I did</em></strong></p> <p>The script below performs a color map on the input data and the map is converted to image as is, without using <code>pylab.imshow</code> or <code>pylab.pcolor</code> and without any scales or borders. I thank everyone contributed and helped me understand on how it can be done.</p> <pre><code>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() </code></pre> <hr> <p><strong>EDIT :</strong> Return type <code>cm.get_cmap</code> instance is of RGBA format but OpenCV by default operates on BGR format. Hence before displaying any image obtained by converting return values of <code>cm.get_cmap()</code> instance as in the above code, convert it to BGR format ( <em>The ALPHA channel is anyway stripped by default in opencv before the image is displayed so dont bother to convert it into BGRA unless necessary</em> ). The code below gives a better explanation:</p> <pre><code>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() </code></pre>
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