How to visualize a matrix of categories as an RGB image?

Question

I am using neural network to do semantic segmentation(human parsing), something like taking a photo of people as input and the neural network tells that every pixel is most likely to be head, leg, background or some other parts of human. The algorithm runs smoothly and giving a numpy.ndarray as output . The shape of the array is (1,23,600,400), where 600*400 is the resolution of the input image and 23 is the number of categories. The 3d matrix looks like a 23-layer stacked 2d matrices, where each layer using a matrix of float to tell the possibility that each pixel is of that category.

To visualize the matrix like the following figure, I used numpy.argmax to squash the 3d matrix into a 2d matrix that holds the index of the most possible category. But I don't have any idea how to proceed to get the visualization I want.

EDIT

Actually, I can do it in a trivial way. That is, use a for loop to traverse through every pixel and assign a color to it to get a image. However, this is not a vectorized coding, since numpy has built-in way to speed up matrix manipulation. And I need to save CPU cycles for real time segmentation.

Answer 1

这很容易。您只需要一个lookup table即可将23个标签映射为唯一的颜色。最简单的方法是创建一个23 x 3的numpy数组，每行存储对应标签的RGB值：

import numpy as np
import matplotlib.pyplot as plt
lut = np.random.rand(23, 3)   # using random mapping - but you can do better
lb = np.argmax(prediction, axis=1)  # converting probabilities to discrete labels
rgb = lut[lb[0, ...], :]  # this is all it takes to do the mapping.
plt.imshow(rgb)
plt.show()

或者，如果您只对用于显示的色图感兴趣，则可以使用plt.imshow的cmap参数，但这将需要将lut转换为“色图” ：

from matplotlib.colors import LinearSegmentedColormap
cmap = LinearSegmentedColormap.from_list('new_map', lut, N=23)
plt.imshow(lb[0, ...], cmap=cmap)
plt.show()