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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 | |