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I’ve got an image read into numpy with quite a few pixels in my resulting array.
I calculated a lookup table with 256 values. Now I want to do the following:
for i in image.rows: for j in image.cols: mapped_image[i,j] = lut[image[i,j]] Yep, that’s basically what a lut does.
Only problem is: I want to do it efficient and calling that loop in python will have me waiting for some seconds for it to finish.
I know of numpy.vectorize(), it’s simply a convenience function that calls the same python code.
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Indexing in NumPy is a reasonably fast operation.
Because the Numpy array is densely packed in memory due to its homogeneous type, it also frees the memory faster. So overall a task executed in Numpy is around 5 to 100 times faster than the standard python list, which is a significant leap in terms of speed.
NumPy arrays are efficient data structures for working with data in Python, and machine learning models like those in the scikit-learn library, and deep learning models like those in the Keras library, expect input data in the format of NumPy arrays and make predictions in the format of NumPy arrays.
You can search an array for a certain value, and return the indexes that get a match. To search an array, use the where() method.
You can just use image to index into lut if lut is 1D.
Here's a starter on indexing in NumPy:
http://www.scipy.org/Tentative_NumPy_Tutorial#head-864862d3f2bb4c32f04260fac61eb4ef34788c4c
In [54]: lut = np.arange(10) * 10 In [55]: img = np.random.randint(0,9,size=(3,3)) In [56]: lut Out[56]: array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90]) In [57]: img Out[57]: array([[2, 2, 4], [1, 3, 0], [4, 3, 1]]) In [58]: lut[img] Out[58]: array([[20, 20, 40], [10, 30, 0], [40, 30, 10]]) Mind also the indexing starts at 0
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