# infer the total number of classes along with the spatial dimensions
# of the mask image via the shape of the output array
(numClasses, height, width) = output.shape[1:4]

# our output class ID map will be num_classes x height x width in
# size, so we take the argmax to find the class label with the
# largest probability for each and every (x, y)-coordinate in the
# image
classMap = np.argmax(output[0], axis=0)

# given the class ID map, we can map each of the class IDs to its
# corresponding color
mask = COLORS[classMap]