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import numpy as np
def colorize_label_map(label_map):
unique_labels = np.unique(label_map)
n = unique_labels.max() + 1
# Random colormap: shape (n_labels, 3), dtype=uint8
np.random.seed(42) # for reproducibility
colormap = np.random.randint(0, 255, size=(n, 3), dtype=np.uint8, )
colormap[0] = [0, 0, 0] # make background black if label 0
# Create color image
h, w = label_map.shape
color_image = np.zeros((h, w, 3), dtype=np.uint8)
for label in unique_labels:
color_image[label_map == label] = colormap[label]
return color_image
def colorize_mask_2d(mask_prediction):
empty_channel = np.zeros_like(mask_prediction[:,:,0])
color_mask = np.stack([mask_prediction[:,:,1], empty_channel, mask_prediction[:,:,0]], axis=-1) * 255
return color_mask
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