How to build OneHot Decoder in python

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I have encoded my images(masks) with dimensions (img_width x img_height x 1) with OneHotEncoder in this way:

import numpy as np

def OneHotEncoding(im,n_classes):
  one_hot = np.zeros((im.shape[0], im.shape[1], n_classes),dtype=np.uint8)
  for i, unique_value in enumerate(np.unique(im)):
    one_hot[:, :, i][im == unique_value] = 1
  return one_hot

After doing some data manipulation with deep learning, softmax activation function will result in probabilities instead of 0 and 1 values, so in my Decoder I wanted to implement the following approach:

  1. Threshold the output to obtain 0 or 1 only.
  2. Multiply each channel with weight equal to the channel index.
  3. take the max between labels along channels axis.
import numpy as np

arr = np.array([
    [[0.1,0.2,0,5],[0.2,0.4,0.7],[0.3,0.5,0.8]],
    [[0.3,0.6,0  ],[0.4,0.9,0.1],[0  ,0  ,0.2]],
    [[0.7,0.1,0.1],[0,6,0.1,0.1],[0.6,0.6,0.3]],
    [[0.6,0.2,0.3],[0.4,0.5,0.3],[0.1,0.2,0.7]]
])

# print(arr.dtype,arr.shape)

def oneHotDecoder(img):
    # Thresholding
    img[img<0.5]=0
    img[img>=0.5]=1
    # weigts of the labels
    img = [i*img[:,:,i] for i in range(img.shape[2])]
    # take the max label
    img = np.amax(img,axis=2)
    print(img.shape)
    return img

arr2 = oneHotDecoder(arr)
print(arr2)

My questions is:

  1. How to git rid of the error: line 15, in oneHotDecoder img[img<0.5]=0 TypeError: '<' not supported between instances of 'list' and 'float'
  2. Is there any other issues in my implementaion that you suggest to improve?

Thanks in advance.

one-hot-encoding decoding numpy python
1 Answers

You have typos with commas and dots with some of your items (e.g. your first list should be [0.1, 0.2, 0.5] instead of [0.1, 0.2, 0, 5]).

The fixed list is:

l = [
      [[0.1,0.2,0.5],[0.2,0.4,0.7],[0.3,0.5,0.8]],
      [[0.3,0.6,0  ],[0.4,0.9,0.1],[0  ,0  ,0.2]],
      [[0.7,0.1,0.1],[0.6,0.1,0.1],[0.6,0.6,0.3]],
      [[0.6,0.2,0.3],[0.4,0.5,0.3],[0.1,0.2,0.7]]
]

Then you could do:

np.array(l)  # np.dstack(l) would work as well

Which would yield:

array([[[0.1, 0.2, 0.5],
        [0.2, 0.4, 0.7],
        [0.3, 0.5, 0.8]],
       [[0.3, 0.6, 0. ],
        [0.4, 0.9, 0.1],
        [0. , 0. , 0.2]],
       [[0.7, 0.1, 0.1],
        [0.6, 0.1, 0.1],
        [0.6, 0.6, 0.3]],
       [[0.6, 0.2, 0.3],
        [0.4, 0.5, 0.3],
        [0.1, 0.2, 0.7]]])
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